Science topics: Handling (Psychology)
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Handling (Psychology) - Science topic

Handling (Psychology) is a physical manipulation of animals and humans to induce a behavioral or other psychological reaction. In experimental psychology, the animal is handled to induce a stress situation or to study the effects of "gentling" or "mothering".
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I appreciate if you can comment your thoughts to improve this article be be more handy checklist for researchers looking to plan a conference
Conference Planning Checklist
I have been in the conference planning business for over 25 years and I know how to plan and organize successful conferences. However, I never stop using my conference planning checklist for conference I organize.
Because;
- It’s the easiest way to create a common language with conference stakeholders and partners to work in close coordination,
- The simplest way to share tasks and responsibilities with conference stakeholders and partners,
- “I don't want to keep my head busy with the question of whether I'm missing a task.
- Allows me to focus on the "How can I do it better?" question instead of the "What should I do?" question,
- As I update my checklist with each conference I organize, it transforms my knowledge and experience into an easy-to-use roadmap,
Did you know that event planning and management software also works like a checklist and guides you step by step with a "must have" and "nice to have" approach? For example, regardless of the complexity of your conferences, by using MeetingHand Online Event Management Software, one of the widely used event management platforms in the market, you can plan and run every step of your events in a seamless way by benefiting from the experience of many events organized by it.
There are many different checklists to help you plan your conferences and events, taking into account criteria such as timeline, topic, or event type. But the most important thing here is to agree on a traceable workflow with your business partners.
Considering that each conference has unique planning and management concerns and needed to be handled with a broad perspective, I wanted to share with you my academic and scientific conference planning checklist, which is one of the most comprehensive types of events, so that you can create your custom checklist that might suit your needs best.
I'm sharing my checklist here as a general guideline, but you can always email me for more details explaining your specific requirements or mentioning your interests.
Build your conference identity
As success always lies in the details, start with analyzing and defining the essentials of your conference. At this point, my to-do list includes these steps:
- Clarify the aims and objectives of your conference,
- Form an organizing committee,
- Create a master plan with a timeline,
- Choose an online collaboration and communication platform,
- Build your conference management team,
- Choose a conference venue/destination and set the conference dates,
- Fulfill legal permits and procedures which are necessary to hold the conference,
- Set start dates and deadlines for registrations and abstract submissions.
- Prepare the necessary information packages and documents for your potential participants.
Make a financial forecast for your conference
Accurate financial forecasting will help you better focus on planning and executing the conference goals. For seamless financial forecasting, my checklist includes the followings:
- Get at least 3 quotations from third-party suppliers for services such as venue, food, & beverages, technical equipment, travel, accommodation, additional staff, insurance, etc.
- Define the registration types, and fees and decide if you wish to offer advantageous registration options, such as early bird discounts to increase attendance,
- Prepare written sponsorship proposals including sponsorship categories, benefits, and fees,
- Prepare a cashflow table for the conference expenditures,
- Define the free or paid services that will be provided to participants,
- Create a conference budget and keep it updated,
- Choose the payment processing methods; offline, online (gateway), etc.
- Deposit conference payments in your account and pay the suppliers,
- Keep your conference records, contracts, expenses, and revenue details,
- Always update your budget with actual expenditures and revenues.
Develop a conference program
Start finding an answer to the "Why should they attend this conference?" question as “it’s the key to convincing the target audience to attend your conference”. Then follow these steps:
- Set up the initial "Conference Program at a Glance"
- Decide on the conference theme, topics, and presentation types,
- Recruit your conference speakers,
- Plan the schedule of the social activities,
- Finalize and announce the detailed conference program,
- Create the Book of Abstracts / Conference Proceedings,
Promote your conference
In fact, the whole success story is about how to encourage your audience to attend your conference, the marketing tactics you will apply to re-engage your previous attendees, and ultimately how to make your conference stand out.
Follow these steps to stand your conference out;
- Build a conference brand identity,
- Plan the advertising and promotional activities of the conference,
- Prepare visual materials for the advertisement and promotion of your conference,
- Purchase a web page domain and create social media accounts,
- Create a stunning conference webpage including the biographies and images of your invited speakers, and/or with relevant details, links, and logos of your sponsors, etc.,
- Review lists of your past conferences to contact potential contributors, authors, partners, and sponsors,
- Plan the timing of the materials you intend to share at regular intervals, such as your announcements, posts, press releases, and reminders, and decide upon their contents,
- Publish your conference web page and share the link on your social media accounts,
- Personally lead your promotional activities for the conference,
- Publish the first announcement or the invitation for the conference,
- Send an invitation e-mail to your target audience as a "Call for Abstracts",
- Send registration or participation invitation e-mails to your target audience,
- Announce and promote your invited speakers and social activity programs of your conference on your website and in your social media accounts,
- Remind abstract submission deadline,
- Remind registration and payment deadlines,
- Periodically update/inform your audience about the important activities of the conference.
Setup your online conference management system
The success of a conference largely depends on how seamlessly you collect and manage the registrations, abstract submissions, and payments, and how harmoniously you can coordinate your team, reviewers, partners, etc.
In fact, how to run a conference is a serious issue that every event planner must decide at the very beginning of the planning process. For this reason, the organizers prefer to use conference management software where they can manage all processes and bring all parties together on a common ground.
To find out your software requirement and set up your conference in it follow these steps:
- Determine what participant information you need to collect during online registration,
- Determine the format in which you will collect the abstracts and which information about the authors you need,
- Determine the evaluation method of abstracts and how to schedule presentations in the conference program,
- Consider the additional services you’ll offer during your conference; contents, fees, and terms,
- Define solutions and features’ requirements of conference management software that enables you easily manage the whole process of registration, submission, scientific program and more
- Research the market and select an online conference planning and management tool,
- Set up your registration, payment, and booking forms
- Set up your abstract submission form,
- Set up your abstract evaluation system by defining your abstract evaluation criteria, and adding reviewers
- Set up and customize your management process,
- Build a communication and follow-up plan with your participants, authors, and partners,
- Publish online conference registration and abstract submission forms,
- Track and manage collected registrations and abstracts,
- Assign abstract submissions to reviewers and follow the evaluation process,
- Notify the authors of the abstract evaluation results and remind them about the payment deadline,
- Confirm the conference attendance status of your participants and place the accepted abstracts in the conference program and their presenter details.
Arrange and coordinate your suppliers
Who is the right supplier?
- The supplier who gives you the best possible advice and focuses on your conference goals,
- The supplier who you can trust that he would provide you the solution that will not keep you busy and complete the work on time.
Select your business partners and suppliers by following these steps:
- Choose a venue that fits your conference requirements such as number of meeting rooms, capacities, breakout areas, and check their policy about providing technical equipment, food and beverage,
- Plan the food and beverages to be served and,
- Identify your visual and technical equipment needs in detail,
- Determine the necessary services for the social activities within the scope of your conference,
- Identify your travel and housing needs,
- Determine the services to be provided to the invited speakers including the fees and conditions,
- Make agreements with a the supplier you have selected including all the possible details such as payments, penalties, amount updates, insurances, etc.
- Determine and purchase the participation kit items you will distribute to your participants,
- Determine the decoration and visual material needs of the conference venue, have your designer make the designs, and coordinate when and how the installation will be done,
- Review logistics services and determine the exact amount to be served,
- Prepare guidelines for onsite registration and identify and supply the tools to be used at the registration desk,
- Check the workflow, job descriptions and instructions with your team one more time, and rehearse the event flow with your team onsite,
- Prepare speech notes such as welcome, introduction of the speakers, thanks and closing speech,
- Set up onsite registration desk and measure the registration time of an average participant,
- Plan the presentations of the speakers, test the entire systems including computers, speakers, projectors, and connections, rehearse the presentations to be projected on the screen and set up a presentation management desk,
- Build an operational team of staff, vendors, students and volunteers.
Manage the conference onsite
The conference start date is the most exciting time for every event planner when months of preparations and plans will be implemented and rewarded.
For the success of your conference, you must reinforce your well-planned workflow and instructions with effective communication. A seamless communication will enable your team to work together as a single body,
To make things work as planned, do the followings:
- Inform all stakeholders and your team members about the duties and timelines,
- Assign tasks and give instructions,
- Rehearse and see how it works,
- Check logistics, decorations, visuals, equipment, exhibitors, etc.
- Open the gates, give badges, accept new registrations, and collect the presentations,
- Start the conference with a Welcome Speech
- Share conference images and news on social media channels,
- Compile and report details of the daily activities, including registrations, payments, documents, etc.
- Periodically check the process and do daily evaluation sprints,
- Check the services offered by the suppliers every day and agree on the figures,
- Share the final figures and highlights of the conference, on the closing session, social media, and the conference web page.
Conclude the conference
Completing and concluding a conference is not just about closing the budget and packing the technical and visual materials in the conference venue. It's also about doing public relations and keeping in touch with your attendees and partners for next year's conference.
You can do even better by using my checklist:
- Gather all data, documents, feedbacks, and suggestions from the team, committees, venue, and suppliers,
- Follow up with everyone attending the conference,
- Send thank you messages and collect feedbacks,
- Analyze the data and feedback of the conference,
- Prepare a conference report covering attendance and presentation numbers, conference goals, budget applications, satisfaction level, recommendations, etc.,
- Organize an after-action-review meeting with the organizing committee,
- Archive conference data, important documents, and reports.
I wish you and your team successful conference planning!
And I’ll be more than happy to get your comments
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We want to hold EI academic conference, may I ask if we can cooperate?
Looking forward to hearing from you, you can also contact me directly via WhatsApp at +86 178 8346 2633
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Question about SPSS Process Model 4 which tests mediation
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Happens all the time and can be caused by several things. Most common is some sort of misspecification (maybe the mediator isn't that relevant in this particular context). Could also simply be a power issue if the mediating effect is harder to detect (low sample). Or it could be caused by distressors/suppressors, where another variable in your model can "diminish" the indirect effects too
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Hello everyone,
I am currently conducting data analysis for a project using an existing large survey dataset. I am particularly interested in certain variables that are measured by 3–4 items in the dataset. Before proceeding with the analysis, I performed basic statistical tests, including a reliability test (Cronbach’s α), average variance extracted (AVE), and confirmatory factor analysis (CFA). However, the results were unsatisfactory—specifically, Cronbach’s α is below 0.5, and AVE is below 0.3.
To address potential issues, I applied the listwise deletion approach to handle missing data and re-ran the analysis, but the results remained problematic. Upon reviewing previous studies that used this dataset, I noticed that most did not report reliability measures such as Cronbach’s α, AVE, or CFA. Instead, they selected specific items to operationalize their constructs of interest.
Given this challenge, I would greatly appreciate any suggestions on how to handle the issue of low reliability, particularly when working with secondary datasets.
Thank you in advance for your insights!
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First, I would try to find out why Cronbach's alpha is low. Usually, one major reason is multidimensionality of the items (i.e., the items measure more than one factor/dimension). You already conducted a CFA. The CFA should tell you whether the items measure one or several factors (i.e., whether a single factor model fits well and whether the items have decent standardized loadings). Cronbach's alpha is a measure for unidimensional items that are tau-equivalent (have equal unstandardized loadings). Alpha is not appropriate for multidimensional measures.
Also, listwise deletion is not recommended due to its restrictive assumptions (missing completely at random data) and the potentially massive loss of data when retaining only complete cases. A better way to handle missing data is by full information maximum likelihood or multiple imputation.
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How relevant is the ancient Turkish proverb: "The trees voted for the axe again, because the axe was crafty and had convinced them that it was one of them, because it had the wooden handle."
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Dear Doctor
[A World of Wisdom in Turkish Proverbs
“Two captains will sink the ship!”
“Listen a hundred times; ponder a thousand times; speak only once.”
“A wise man remembers his friends at all times; a fool, only when he needs them.”
“Patience is bitter, but it bears sweet fruit.”
“A lion sleeps in the heart of every brave man.”
“A kind word warms a man through three winters.”
“Study from new books but with old teachers.”
“Beauty passes; wisdom remains.”
“A cup of coffee commits one to forty years of friendship.”
“A good companion shortens the longest road.”
“Thorns and roses grow on the same tree.”
“He that conceals his grief finds no remedy for it.”
“A knife wound heals, but a tongue wound festers.”
“A thousand friends are too few, but one enemy is too many.”
“No matter how far you have gone on the wrong road, turn back.”
“Patience is the key to paradise.”
“The fool dreams of wealth; the wise man dreams of happiness.”
“Whoever gossips to you will gossip about you!”
“The green twig is easily bent.”]
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Does it matter? In this 21st century, teachers must be well-equipped with digital skills to handle students with advanced technological knowledge.
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Considerando que a educação é um professo, os professores devem se atualizar constantemente. A tecnologia faz parte do processo educacional e tornar as práticas e metologias cada vez mais atraentes aos alunos torna-se uma das atribuições do professor, voltada para uma aprendizagem significativa. A interação, a inclusão, as competências digitais facilitam e proporcionam que o ensino hibrido seja eficiente e de boa qualidade.
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This question explores how Agile teams can handle technical debt (quick fixes that may cause future issues) while delivering fast, without compromising long-term product quality. It seeks practical strategies and ways to measure success.
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Agile teams balance technical debt and rapid delivery by:
1. Treating debt as backlog items: Prioritizing and addressing it within sprints.
2. Integrating quality into each sprint: Refactoring, code reviews, and automated testing are core.
3. Maintaining a strict "Definition of Done": Enforcing quality standards with each delivery.
4. Ensuring transparency: Communicating debt's impact to stakeholders.
5. Employing automation: Using tools to monitor and manage debt efficiently.
This proactive approach allows for quick iterations while maintaining long-term product health.
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One major limitation of machine translation is its reliance on direct word-to-word translation, which often fails when dealing with legal terms that require contextual adaptation.
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According to my experience, MT alghoritms should be changed in order to streamline MT translation processes. Some scholars posit that corpus consultation can be integrated in MT processes. I tried myself to integrate MT output with corpus evidence and it worked satisfactorily. However, it was a very time consuming activity. For this reason, some scholars have suggested APE (Automated Post-Editing) systems which automatically correct MT output (do Carmo et al. 2020). Such APE systems could learn from parallel corpora (Chatterjee et al. 2017; Negri et al. 2018).
References:
Chatterjee, Rajen, Gebremedhen Gebremelak, Matteo Negri, and Marco Turchi. 2017. Online automatic post-editing for MT in a multi-domain translation environment. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics:
Volume 1, Long Papers, 525–535. Valencia, Spain.
do Carmo, Félix, Dimitar Shterionov, Joss Moorkens, Joachim Wagner, Murhaf Hossari, Eric Paquin, Dag Schmidtke, Declan Groves, and Andy Way. 2020. A review of the state-of-the-art in automatic post-editing. Machine Translation 35: 101–143.
Escribe, Marie, and Mitkov Ruslan. 2023. Applying Incremental Learning to Post-editing Systems: Towards Online Adaptation for Automatic Post-editing Models. In Jun Pan and Sara Laviosa (Eds), Corpora and Translation Education, Advances and Challenges. Singapore: Springer Nature Singapore,35-62.
Giampieri, Patrizia. 2023. Legal Machine translation Explained. Newcastle Upon Thyne: Cambridge Scholars Publishing.
Giampieri, Patrizia. 2024. AI and the BoLC: streamlining legal translation. Comparative Legilinguistics, 58: 67-90.
Negri, Matteo, Marco Turchi, Nicola Bertoldi, and Marcello Federico. 2018a. Online neural automatic post-editing for neural machine translation. In Proceedings of the 5th Italian Conference on Computational Linguistics (CLIC-IT 2018), 288–293. Torino, Italy.
Negri, Matteo, Marco Turchi, Rajen Chatterjee, and Nicola Bertoldi. 2018b. eSCAPE: A large-scale synthetic corpus for automatic post-editing. In Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018), 24–30. Miyazaki, Japan.
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Most of students of research methodology gets confused as to meaning of varimax rotation and its purpose in handling data for factor analysis. Can it be explained in a easy visual manner
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VARIMAX is an orthogonal factor rotation method. It leads to uncorrelated factors. This is usually not plausible because factors in the social sciences tend to be at least somewhat correlated. Oblique rotation methods that allow factors to be correlated (e.g., OBLIMIN, PROMAX) are more recommended. See
Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift's electric factor analysis machine. Understanding statistics: Statistical issues in psychology, education, and the social sciences, 2(1), 13-43.
Here is a visual explanation:
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Your representatives are responsible for customer care. A qualified customer care representative should possess the following characteristics and skills:
  • A helpful nature
  • Friendliness and empathy
  • Active listening
  • Quick decision-making
  • Problem-solving
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On net, calls or through mail, all staffs are able to address the problem of due to his or her role is to care & clear customer query truly
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Has anyone ever worked on Sobolev spaces using the Dirac bra ket notation? If so, references would be appreciated. In particular, I am interested in the Logarithmic Sobolev inequality and/or more specifically the Logarithmic Schrödinger Equation (LogSE). To the best of my knowledge, the LogSE has never been fully written in terms of Dirac bra ket notation, because Dirac bra ket cannot directly handle nonlinear operators. This is a somewhat obscure question because Mathematicians rarely use Dirac bra ket and Physicists are often compromised if not placated by the rigorous mathematical details of nonlinear differential equations.
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This is not answer to my question, rather,an explanation into its
background.
Google AI tells me that in Dirac bracket notation, the nonlinear Schrödinger equation (NLSE) is expressed as:
iħ ∂|ψ⟩/∂t = (H + V(|ψ⟩))|ψ⟩,
where |ψ⟩ represents the quantum state, H is the Hamiltonian operator, V is the nonlinear potential, and ħ is the reduced Planck constant.
Dirac Notation:
Dirac notation, also known as bra-ket notation, is a mathematical formalism used to represent quantum states (kets: |ψ⟩) and their duals (bras: ⟨ψ|).
Equation Breakdown:
· iħ ∂|ψ⟩/∂t: This term represents the time evolution of the quantum state |ψ⟩, where i is the imaginary unit, ħ is the reduced Planck constant, and ∂/∂t is the partial derivative with respect to time.
· H: This is the Hamiltonian operator, which represents the total energy of the system.
· V(|ψ⟩): This term represents the nonlinear potential, which depends on the quantum state |ψ⟩ itself.
ð V(||ψ⟩) = constant*⟨ψ|ψ⟩ = for NLS.
ð V(||ψ⟩) = constant*log(⟨ψ|ψ⟩ ) for LogSE.
However, there must be wrong. Apart from the linearity constraint, there is an important associativity rule in Dirac bra ket:
which does not hold for nonlinear operators.
Hence my question. I know the LogSE has been treated in Sobolev
space but is there a way of extending Dirac bra ket to express the
time evolution of the LogSE?
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Dear researchers,
I have a plan to conduct a meta-analysis of a construct that has five dimensions. Do you think that would be possible? How can I handle the issue that each study considers only one dimension of the construct? Please recommend some references that I can use.
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Thanks for your comment. If possible, please share any resources or articles that I can use.
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The majority of professors I have worked with, and colleagues as well do not use LaTeX, but rather Word for document preparation. I am not sure whether it's purely cultural, or is it possible that researchers globally also find Word easier to handle than LaTeX.
Do you use Word or LaTeX? If you use LaTeX, how do you use it in your research?
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It depends on the field of science. E.g., in fields with a lot of formulas (e.g., astronomy), LaTeX is widely used. Also those who are used to do programming do not have problems with LaTeX. Others prefer Word because it is easier to handle. However, with modern LaTeX editors, the usage has become easier.
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Dear all,
I am currently conducting a meta-analysis using the CMA software and have encountered some SEM studies. I have selected the "Correlation and sample size" option in CMA, and I am entering the path coefficients into the correlation field, while using the study's sample size as the number of participants.
I would greatly appreciate any advice or clarification on whether this approach is correct. Should path coefficients be treated as correlations in this context, or is there a better way to handle SEM data in meta-analysis? Additionally, I was wondering if anyone could point me to any articles that provide detailed guidance on how to enter different types of effect sizes in CMA?
Thank you so much for your time and help!
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I am currently conducting a study using a photothrombotic stroke model in C57BL6 mice and measuring motor function outcome following strokes to determine if a pharmacological treatment can help improve their motor recovery. To measure motor recovery, I am using the tapered beam task and the grid walk task. Both of these tasks measure the number of errors that the mice make during trials. One thing that I've noticed is that a handful of the mice in the placebo group (no pharmacological treatment, just saline) are unable to complete the tasks on the first day of behavior due to the severity of the injury and the lack of treatment.
As such, I'm wondering if there is a standard way to handle missing data that is a result of severe injuries and is important for accurately reflecting differences between my groups. The methods that I can think of would either be filling with the mean for the group, filling with the highest number of errors of the group (e.g. the worst recorded score was 93 errors in the placebo group, presumably the mice unable to complete the task have more severe strokes and should receive the max number of errors observed), or multiple imputation using the MICE package in R. My understanding is that multiple imputation is the standard for filling in data that is not missing at random, but I want to ensure that is true in this scenario as well. Any citations (especially those specific to animal models) to support these methods would be greatly appreciated as well.
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A few questions: 1. are the mice "pre-trained to an established baseline" prior to injury? 2. If so, are there any "qualitative scores" of ambulation? 3. are the mice able to do any part of the task, or simply fail immediately? 4. finally, do they recover in the subsequent days, post injury i.e. repeated task on day 2, 3,4...?
If the mice are trained well before the insult, to a zero or close to zero error standard, any animal that can't perform would be maximal error (which could be set as the max time for the task, ie 60sec to traverse the beam is the length of the task). Piloting, as suggested, may also be helpful if you pilot the number of errors on day 1 for untreated controls finding the upper threshold to set a true failure limit. The scoring should have a wide enough range to accommodate for total failure and complete success. Bracketing the score may be in order as well, (0-2 foot slips is perfect vs 3-10 foot slips are mildly impaired...etc). In this manner the repeated measures ANOVA Group (mean score) X Day stats would show deficit and improvement significance. You can assign each bracket a score number.
It appears by your question you only have a few missing day 1 data points in the non-treated injured mice. So long as the injury is standard across all groups and repeatable it should be fine to set "failure limits." Furthermore, we don't want to eliminate mice that fail to perform but spontaneously recover eventually. Titrating the injury too much can also have unintended consequences, ending up with no significant treatment effects.
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Hi, What could be the possible reasons for my Sf-9 insect cell culture not showing proper growth, despite using fresh Corning fetal bovine serum (FBS) that was heat-inactivated for 30 minutes at 56°C before being aliquoted? What factors should I consider to troubleshoot this issue, such as media preparation, cell handling, or other potential stressors that might be affecting the cell growth?
Thanks,
Muhammad
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Hey, Your cells might not have been stored properly, We had a similar issue, ordered a new cell line, and everything started working.
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I am conducting a study on the spatial distribution of pteridophytes across the specific area. My dataset includes grid-based sampling with quadrats, but I have encountered two challenges:
1. Some grid data points are missing due to gaps in field surveys.
2. The number of quadrats per grid is uneven, ranging from 5 to 50 depending on species occurrences.
How should I address these issues to ensure robust statistical analysis?
Specially;
What imputation methods or approaches would work best for missing grid data?
Should I normalize or adjust data to account for uneven sampling intensity? If so, how?
Insights or references to similar studies dealing with these challenges would be greatly appreciated?
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You can either estimate the missing values (imputation) or delete data to make it even. Or use a statistical test that takes uneven data into account (leave-as-is), usually non-parametric :)
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Objective: Finding the optimal solution for an optimization problem involving bilinear constraints.
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Constraint handling is not necessarily an integral component of a specific metaheuristic but rather a general technique that can be incorporated into any algorithm, including metaheuristics, to address constraints effectively. There are various strategies to handle non-linear equality constraints, such as penalty functions, repair methods, decoders, or multi-objective approaches, which can be adapted based on the problem context and the chosen algorithm.
For a deeper understanding of how to handle constraints in optimization, I recommend reviewing the following papers:
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Do you have empirical experience concerning using LAM tools to plan complicated problem-solving processes or context-dependent activity scenarios?
How strong is their predictive power?
Please, no chat-box answers!
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Transformative Actions of MELA
1. Moderate-Large:
- Events: Implementation of continuous evaluation practices that promote constant feedback between coaches and athletes.
- Changes: Improvement in dialogic communication, which can lead to increased trust and team cohesion.
-Expected Results: Anticipated more consistent sports performance and greater satisfaction among athletes.
2. Medium-Large:
Events : Creation of training workshops for coaches focused on communication skills.
Changes: Adaptation of training styles that incorporate the athlete's voice in decision-making.
Expected Results: Development of self-determination skills in athletes, which could enhance their individual and collective performance.
3. Massive-Large:
- Events: establishment of an inter-institutional network to share best practices in sports training.
- Changes: integration of technologies that facilitate the evaluation and tracking of athletes' progress.
- Expected results: significant increase in the competitive level of sports at the national level, promoting a more human-centered approach to training.
4. Extra-Large:
-Events: Implementation of national sports policies that prioritize the comprehensive training of the athlete.
- Changes: Cultural transformation within sports, where effective communication is valued as a fundamental pillar.
- Expected results: generation of a paradigm shift that not only improves sports performance but also fosters the personal and social development of athletes.
Future considerations for 2025-2026
- Recent research suggests that as these models are implemented, an evolution in sports dynamics is expected, where transparency and viability become guiding principles.
- It is anticipated that MELA can facilitate greater inclusion and active participation of athletes in their training processes, which is crucial for the sustainable development of sports.
In conclusion, a MELA from moderate-large to extra-large can generate multiple transformative actions that significantly impact both sports performance and the integral development of athletes. The key will be how these strategies are implemented and adapted to meet the specific needs of the Cuban context.
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Challenge that we faced and felt lost:
The choice of search terms plays a critical role in the quality of bibliometric analysis. Variability in terminology and the use of synonyms, abbreviations, or alternative spellings across different publications can lead to inconsistent results. We as team were often struggling with the trade-offs between broadening the search to include various keywords and narrowing it to ensure relevance to the research question. This included several back and forth work to simplify the same....
So how did we handle the same, as team lead, it was my responsible to brainstorm regarding this concern!!!
We picked it up this way: To mitigate this challenge, systematic development of search strategies is essential. Using controlled vocabularies like MeSH (Medical Subject Headings) for health sciences or keywords from a standardized thesaurus can help ensure consistency in capturing relevant articles (This is very crucial to appraise the time trend and evolution of the vocabulary of the same disease , eg: decay versus dental caries presently. Additionally, combining various search terms and using Boolean operators can help refine search results while minimizing omissions.
Citation: Boulton, A., & Hughes, G. (2016). Bibliometric Analysis: Challenges and Opportunities. Journal of Research Evaluation, 25(1), 102-110.
NOW I REQUEST THE EXPERTS IN THIS DOMAIN TO ANSWER THIS BASED ON THEIR EXPERIENCE?
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Dear Anitha Again, I would recommend a scientific paper to you, concerning your questions/interests.
Ng, J. Y., Liu, H., Masood, M., Syed, N., Ayala, A. P., Sabé, M., … Moher, D. (2024). Guidance for the Reporting of Bibliometric Analyses: A Scoping Review. https://doi.org/10.17605/OSF.IO/WYP63 I hope, that this will be helpful for your work. Best regards Anne-Katharina
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Logistic regression can handle small datasets by using shrinkage methods such as penalized maximum likelihood or Lasso. These techniques reduce regression coefficients, improving model stability and preventing overfitting, which is common in small sample sizes (Steyerberg et al., 2000).
Steyerberg, E., Eijkemans, M., Harrell, F. and Habbema, J. (2000) ‘Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets’, Statistics in medicine, 19(8), pp. 1059-1079.
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I have the book Statistical methods in medical research by Armitage and Berry
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I am exploring the intersection of pragmatics and artificial intelligence, focusing on how AI can handle the complexity of speech acts, such as requests, promises, or commands, in multilingual contexts. The challenge lies in AI systems accurately interpreting the intended meaning behind speech acts, which often involves context, cultural norms, and implicit understanding—factors not easily reducible to simple linguistic rules. Additionally, generating appropriate speech acts in a target language requires a nuanced understanding of the pragmatic rules of that language. I am interested in approaches, models, or algorithms that enhance AI's ability to manage these aspects of communication, particularly in translation systems and virtual assistants. How can AI improve in recognizing and generating pragmatically accurate responses across different languages and cultural contexts? Any case studies, research, or practical examples would be greatly appreciated.
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الأمر متوقف على قاعدة البيانات النصية الواردة ، والتي يجب أن تكون بيانات لغوية ضخمة، كما أن تقنية التعلم العميق جعلت إمكانية فهم اللغة في سياقاتها المختلفة ممكنة إلى حد بعيد، يماثل استعمال البشر للغاتهم في مجتمعاتهم. يكفي أن نحرج برمجيات اللغة. بالسياقات الخاصة ليتعلم البرنامج خوارزميات جديدة تمكنه من تقدم باهر في فهم وتأويل الأقوال اللغوية... أظن حتى برنامج الذكاء الاصطناعي يعرف مشكلته في إدراك أبعاد الكلام البشري، ويرى أن التعلم الآلي العميق سيحسن من أدائه لاحقا.
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1.
How does blockchain technology ensure the security of transactions and avoid the risks of hacker attacks and data leaks?
2.
Is the current regulatory framework sufficient to cope with the new challenges brought by blockchain and cryptocurrency? How can the US government formulate effective policies?
3.
Can the scalability problem of blockchain be solved when handling high transaction volumes? Can existing technology support large-scale commercial applications?
4.
Does the automatic execution of smart contracts mean that legal responsibilities are unclear? How to hold people accountable when disputes arise in contracts?
5.
Will the transparency of blockchain technology affect user privacy? Is the storage of personal data on the blockchain safe?
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Blockchain technology has had a significant impact on modern finance, transforming various aspects of how financial transactions are conducted, assets are managed, and trust is established. Below are key areas where blockchain technology has made a substantial impact:
1. Decentralization and Trust
  • Trustless System: Blockchain enables transactions to occur without intermediaries by using consensus mechanisms. Participants can trust the validity of transactions through cryptographic proofs rather than relying on third parties.
  • Disintermediation: Financial institutions, such as banks and clearinghouses, can be bypassed, reducing costs, transaction times, and the risk of fraud.
2. Transparency and Accountability
  • Immutable Ledger: All transactions are recorded on a public or private ledger that is transparent and cannot be altered. This increases accountability as all participants can view transaction histories.
  • Auditing: Blockchain provides an auditable trail, making it easier for regulators and auditors to track transactions and ensure compliance.
3. Enhanced Security
  • Cryptographic Security: Transactions are secured through cryptographic algorithms, reducing the risk of hacks and fraud. The decentralized nature of blockchain also makes it more resilient against attacks compared to traditional systems.
  • Smart Contracts: Self-executing contracts with the terms of the agreement directly written into code minimize the risk of human error and enhance security.
4. Speed and Efficiency
  • Faster Transactions: Blockchain can significantly reduce transaction times, especially for cross-border payments, which can take days in traditional systems, to just minutes or seconds.
  • Real-Time Settlement: Transactions can be settled in real-time, improving cash flow and operational efficiencies for businesses.
5. Tokenization of Assets
  • Digital Assets: Traditional assets, such as real estate, stocks, and bonds, can be tokenized, making them more liquid and accessible to a broader range of investors.
  • Fractional Ownership: Tokenization allows for fractional ownership of high-value assets, enabling more people to invest in assets that were previously out of reach.
6. Innovative Financial Products
  • Decentralized Finance (DeFi): The rise of DeFi applications has led to the creation of new financial products, including decentralized lending, borrowing, and trading, which exist outside traditional banking systems.
  • Stablecoins: Cryptocurrencies pegged to stable assets (like fiat currencies) provide a means of reducing volatility in cryptocurrency trading and can facilitate transactions.
7. Global Inclusion
  • Access to Financial Services: Blockchain can provide financial services to unbanked or underbanked populations, enabling access to banking, loans, and payment systems without needing traditional infrastructure.
  • Microfinance: Blockchain can facilitate microloans and peer-to-peer lending services, fostering entrepreneurship in emerging markets.
8. Regulatory Compliance
  • KYC/AML Improvements: Blockchain can streamline Know Your Customer (KYC) and Anti-Money Laundering (AML) processes by providing secure and verifiable identity data.
  • RegTech Innovations: Use of blockchain for compliance tracking and reporting can enhance the efficiency of regulatory oversight.
9. Supply Chain Finance
  • Transparent Supply Chains: Blockchain can provide transparency in supply chains, allowing financial institutions to assess risks better and offer financing solutions based on actual supply chain data.
  • Provenance Tracking: Improved tracking of assets from source to destination can reduce fraud and associated costs.
Challenges and Considerations
While the impact of blockchain on modern finance is largely positive, it also comes with challenges, including:
  • Regulatory Uncertainty: Regulatory frameworks for blockchain and cryptocurrencies are still developing, leading to uncertainties for businesses and investors.
  • Scalability: Some blockchain networks face scalability issues, limiting their ability to handle high transaction volumes.
  • Energy Consumption: Certain consensus mechanisms (like Proof of Work) require significant energy, raising environmental concerns.
  • Interoperability: Lack of standards and protocols can hinder blockchain systems' ability to communicate and integrate with each other and existing financial systems.
Conclusion
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Hello everyone :)
For my bachelor‘s thesis, I‘m conducting a linear mixed models analysis using R. Since LMMs aren't part of our statistics course until the master‘s program, I'm honestly a bit at loss about the intricacies of the lme4() command.
My missing data is MCAR since it‘s due to a mistake in programming the study. Put simply, due to missing one letter in the randomization, four out of eight experimental groups haven‘t been shown 5 items each for one of 12 Texts. Their other data is complete. According to https://rpsychologist.com/lmm-slope-missingness MCAR data is largely ignorable.
My supervisor suggested just using the data as is. But what exactly would lme4() do in that case?
Or would I be better advised reading into multiple imputation (for example using the mice package - which I‘ve seen recommended elsewhere)?
Many thanks to everyone who can give their input!
(I know it can be quite frustrating talking complete newbies. I‘m honestly looking for a sensible starting point where I can dig more deeply without wasting too much time trying to understand dead ends to my problem.)
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It depends if the data missing are predictors or outcomes, but in your case it seems you lost both missing predictors and outcomes for some trials. In this case the issue is really imbalance. Multilevel models and by extension lme4 handles this well and will correctly estimate the model (though with some loss of precision for some effects because of the lost trials).
More generally if only outcomes are missing the estimates of multilevel models are OK under MCAR or MAR (the latter basically assumes the predictors in the model account for missingness). There can still be reasons to use multiple imputation - mainly for missing predictors - this is often more to preserve power by not dropping too many cases.
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Hello,
I am wondering whether I can use FIML to handle missing data with WLSMV in mplus?
Thank u!
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No because it is not maximum likelihood. WLSMV uses pairwise deletion as described in the following document:
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Dear all,
I am conducting CFA and SEM with WLSMV, which is best option for the ordered-categorical data. However, I am wondering how does WLSMV handle the missing data in Mplus? Can I use FIML or multiple imputation with WLSMV in Mplus to handle the missing data? or Does this estimator only uses pairwise deletion method as a default option in Mplus?
P.S. I am only asking this question under the Mplus context, not other softwares.
Best,
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Gert Lang Mplus uses pairwise deletion with WLSMV. See p. 5 of the document below:
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Hello everyone,
I'm planning to develop cancer model using CCl4, i have been search to disposal and detoxified method to handling tools, waste, and animal used for this research. But, unfortunately I don't really find that I looks for to detoxified tools and animal handling care. The dispose and detoxified method from literature, MSDS, and some goverment procedure I got was not mentioned how to detoxified CCl4, It just mentioned to use absorbant for any spill of CCl4. For animal I suggested it need to treat like hazardous waste for 24 hours after injection of CCl4.
Does anyone can share experience for handing the animal waste that using CCl4? and how to detoxified tools that have been use/contact with CCl4? or it just can use large amount of soap water to rinse the tools?
thankyou for everyone that have been answer
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You're asking about
‘Disposal and detoxification methods for the treatment of instruments , waste and animals used for this study.
Disposal of animal waste using CCl4?’
The carbon tetrachloride content of animal tissues is so low that there are no specific disposal and detoxification methods. Besides, tetrachloromethane is metabolised in animal organism....
***
Carbon tetrachloride is a highly dangerous substance (2nd class of hazard), it has narcotic effect, affects the central nervous system, liver, kidneys, has local irritating effect on the skin of hands, mucous membranes of eyes, upper respiratory tract, has cumulative properties. It can enter the human body by inhalation, through the skin, through the digestive organs. Maximum single maximum permissible concentration in the atmospheric air of residential areas is 4.0 mg/m, average daily concentration is 0.7 mg/m. In the water of water bodies of economic-potable and cultural-domestic water use the approximate permissible level is 0.006 mg/m.
Disposal and burial of toxic industrial waste is carried out at special engineering facilities - toxic industrial waste disposal sites.
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what are the parts of a conceptual framework in a thesis writing titled Lived experiences among teachers handling multigrade classes
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The conceptual framework is your synthesis of different concepts found in the relevant literature. Your conceptual framework depends on your research goal. In your case, the goal is to understand experiences of teachers in multi-grade glasses. The conceptual framework should include concepts about subjective experience (what is it, how can it be studied), multi-grade classes, other class types, and how class type relates to teacher experience. Your conceptual framework should also address concepts related to the context of your thesis, which is not explicit in the goal.
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..
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Keep away from it if possible. Otherwise read this https://nj.gov/health/eoh/rtkweb/documents/fs/1530.pdf
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how correlations between criteria are handled in sensitivity analysis of MCDM model?
Plz anyone suggest me regarding it.
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You are most welcome
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Are there existing novel algorithms or techniques for handling imbalance data?
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Yes, Fatimah Binta Abdullahi there are several novel algorithms and techniques for handling imbalanced datasets in machine learning. Imbalanced datasets arise when certain classes in the dataset are underrepresented compared to others, which can lead to biased models that fail to effectively learn from minority classes. Here are some techniques and approaches that have been developed or gained popularity:
  1. Resampling Techniques:Oversampling: Generating synthetic samples for the minority class using methods like SMOTE (Synthetic Minority Over-sampling Technique) or ADASYN (Adaptive Synthetic Sampling). Undersampling: Reducing the number of samples from the majority class to balance the dataset. Hybrid Methods: Combining both oversampling and undersampling techniques to achieve a balanced dataset.Cost-sensitive Learning:Assigning different misclassification costs to classes, thereby penalizing the model more when it misclassifies minority class instances. Cost-sensitive loss functions can be integrated into algorithms like SVMs or neural networks.
  2. Cost-sensitive Learning:Assigning different misclassification costs to classes, thereby penalizing the model more when it misclassifies minority class instances. Cost-sensitive loss functions can be integrated into algorithms like SVMs or neural networks.
  3. Ensemble Methods:Balanced Random Forests: Modifying the standard random forest algorithm to address class imbalance by undersampling the majority class in each bootstrapped sample. EasyEnsemble and BalanceCascade: Creating multiple balanced subsets of the data and training separate classifiers on these to improve minority class predictions.
  4. Anomaly Detection Techniques:Treating the minority class as anomalies and applying anomaly detection algorithms can also be effective in some cases. Models like Isolation Forests or One-Class SVMs can be utilized.
  5. Evaluation Metrics:Adopting appropriate evaluation metrics such as F1-score, precision-recall curves, AUC-ROC, and the Matthews correlation coefficient, which are more informative than accuracy in the context of imbalanced datasets.
  6. Generative Adversarial Networks (GANs):Using GANs to generate synthetic samples of the minority class, thus creating a more balanced training dataset. This approach has gained traction in recent years for its ability to produce realistic data samples.
  7. Meta-Learning Approaches:Employing meta-learning frameworks that adjust the learning process based on the performance of the model on different classes, dynamically focusing on harder-to-predict minority class instances.
  8. Deep Learning Strategies:Implementing techniques such as focal loss (which focuses more on hard-to-classify examples) in neural networks to counterclass imbalance issues.
  9. Transfer Learning:Utilizing transfer learning from models trained on larger, balanced datasets in related tasks to improve performance on tasks with imbalanced datasets.
  10. Graph-Based Approaches:Applying graph-based methods to encode relationships and similarities in the dataset. Techniques such as Graph Neural Networks (GNNs) can be beneficial in certain contexts, especially when data can be represented as graphs.
  11. Research in this area is ongoing, with new algorithms and techniques continually being developed to better handle the challenges of imbalanced datasets. It's often useful to experiment with a combination of these approaches tailored to the specifics of your dataset and task at hand.
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Earth is our only home. we have taken our lives to brink by thoughtless green, blue, white revolutions. We are shy to define the limits based on our resources and ability of nature to handle human waste recycling. I feel the optimum number should be equal to the number arable acres of land..one acre per head? We must allow earth to recover every year,
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I believe that we define the optimum human population as the number of people that the Earth can support without running out of essential resources like food, water, and energy. It's about having enough resources for everyone to live a good quality life, without harming the environment or causing shortages. Think of it like having a party: if too many guests come, there won’t be enough food, space, or drinks for everyone. Similarly, if the population grows too big, we might not have enough resources for all. The goal is to balance the number of people with the Earth’s ability to provide for us sustainably.
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The more I search, the more contrasting answers I get, thus kindly explain.
Generally, Mice are preferred over rats because of the easiness of handling.
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I agree genetic relationship is a key issue, but I think there is more important taxonomic detail available today than just the raw genetic similarity. So the Last Common Ancestor of humans and rats (or mice - or rabbits for that matter) was probably a stem member of euarchontoglires, about 90 million years ago. In other words, mice, rabbits and rats are roughly equally related to humans (so choosing between them might be based more on other characteristics such as sociality, cost etc.).
By contrast, say for dogs, the LCA with humans belongs to the boreoeutheria, with an LCA time perhaps 10-20My earlier, so dogs are somewhat less well matched; and in the other direction, the LCA for old world monkeys and humans would be the stem group of the catarrhini, about 35 Mya, and for chimps maybe around 6Mya (making chimps and monkeys by far the most related, but leading to correspondingly greater ethical issues).
By the way, it's worth noting that there are two sources of indefiniteness in the dates. One is lack of knowledge, heavily affecting the older dates (maybe 10My or so), subject to improvement as more fossils are discovered and better dating is applied. But for the more recent dates, the greater difficulty is that taxonomies on the fine scale are actually networks rather than trees, so that even the definition of LCA is a bit fuzzy. For chimps, for example, a reasonable value could be anywhere between about 4Mya (last exchange of genetic information) and 7Mya (when the trees fully merge). This source of indefiniteness is unlikely to disappear.
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Hi all,
I'm working on a project where I have two longitudinal outcomes-hippocampus volume and Alzheimer biomarker levels-but these were measured at different time points for each subject. I'm trying to build a joint model that can handle these different time scales. I've explored using brms for joint modeling with multivariate outcomes, but I'm unsure how to properly handle the fact that the time points differ between the two outcomes. Is there a way to do this directly in brms, or should I be looking at a different package? Any advice on how to structure the data, which package to use, or specific coding strategies would be greatly appreciated!
Thanks in advance!
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I would start by modelling each separately first. So that you will have a good idea what is going on.
The mixed longitudinal model has some sort of indicator that identifies the first, second, third measurement occasion. For that occasion you should also have a 'continuous' measurement of time such as the time since the start of the trial or the age in days. It is this second variable that goes into the model. HO effective this will be will depend on the alignment of the measurements.
I should add that I have never done this, but I have fitted multivariate (several responses) models .
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please share your opinion about early marriage in your country
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I want to agree with Hupkens's view. In my culture there is no definition of what can be called "early marriage" because when two people have reached an age of being able to take decisions (after puberty stage which is above 16 years in South Africa), and they decide to be together in marriage they are avoiding a dangerous situation (producing bastards). In short, here in RSA once you are 16 years old and you agree with your partner you can take your Identity Cards, have two witnesses and get married. The common issue that makes people marry later is the ability to provide for the family. It is a fact that when you get married you are expected to have your own house, children, money, and so forth...so all these are factors that determine what you call "early marriage" but for prevention of producing children outside wedlock I do argue that this concept of early marriage must be deleted from people's vocabulary
Blessed regards
Mohammed X
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Hello, dear RG community.
Personally, I have found Xarray to be excruciatingly slow, especially for big datasets and nonstandard operations (like a custom filtering function). The only suggestion how to speed up the things that I have found on the Internet is to use numpy. When I adjusted my code accordingly (i.e., used numpy), I laughed so hard because I had to convert almost every single piece of Xarray-based code to a numpy-based code. Still, the remnants of the Xarray-based code kept slowing me down. I went ahead and wrote a crazy piece of code combining Dask and Xarray and numpy and, finally, increased the speed to some acceptable magnitude. That was such a pain.
Pandas, of course, are essentially the same speed-wise. And I couldn't find anything else to handle named arrays in Python other than Xarray or Pandas (I work with multidimensional arrays, so I need Xarray anyway).
I read the docs for Xarray. The authors say the reason for Xarray is to be able to work with multidimensional arrays. I can't fully comprehend that. Why not just add this functionality to Pandas? I could understand if they started such big of a project for some big idea, but just add multidimensional functionality that should've better been added to Pandas to spare users time learning two different data bases seems like not a good justification to me. To say nothing that Xarray has ended up being as slow as Pandas.
I think that a good justification for starting a new data base project for Python is to make it really fast first and foremost. I think a new data base project that will follow numpy example must be started: when the code base is written in lightning-fast C/C++ and then Python wrappers are added on top of that.
I am wondering if anybody is aware of such an effort. If so, when should we expect the release?
Thank you in advance.
Ivan
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Brahma Reddy Katam
, your answer doesn't improve on Gershinen Shanding's answer and it doesn't directly answer my question. I know about Dask - I mentioned it in my question. I was, specifically, asking about C-based data set handler for python. Gershinen Shanding provided metrics and concluded that either DuckDB or Polars are the way to go. I checked Polars: it is based on a low level programming language and can handle multidimensional arrays. That's why I accepted Gershinen Shanding's answer.
You, in turn, just briefly listed some options for datasets handling in Python. There is no value in your answer as it is given right now.
And I suspect, you just created it in ChatGPT.
I'll give you some time to say something in your defense. If you don't have to say anything, I will report your answer as a spam.
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How can SIEM solutions better handle and analyze unstructured data, such as logs from IoT devices and social media, to detect advanced threats?
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SIEM systems can effectively handle and analyze unstructured data from IoT devices and social media by using advanced technologies such as AI and machine learning. These technologies allow SIEM systems to process large amounts of diverse data types, extracting valuable patterns and insights. For example, AI-based data analytics can consolidate and contextualize data, providing in-depth insights and coherent risk scores. Machine learning and deep learning algorithms can analyze behavior over time, identifying anomalies and potential threats more effectively.
Furthermore, integrating big data analytics tools with SIEM systems improves their ability to handle and interpret unstructured data. This enhancement leads to improved threat detection and response capabilities.
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In my work environment, I need to design and build Restful APIs, and generate Documentations in Swagger format. Postman really falls short here, it is not 1:1 comparable in my use case.
Currently I am using APIDog since it handles everything well and the UI feels much more modern than SoapUI.
Any input?
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ReadyAPI
Rest Assured
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I'm having a bubbling error while splicing 100/350 um optical fiber (core/cladding) on ​​the Fujikura FSM100P+. I have tried some ways such as changing Prefuse power and Prefuse time but to no avail. Is there any way to handle this error? I put specific images in the file below.
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Malzemenin özelliği değişmiş olur.
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I'm planning to use fuming sulfuric acid (oleum with 20% free SO3) in an experiment with a reflux setup. Given its high toxicity, I'm concerned about safely handling the gases that will be released from the condenser. Should I release these gases directly into a fume hood, or would it be better to neutralize them before release? Any advice on best practices for handling oleum (20% free SO3) in this context would be greatly appreciated.
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Oleum, also known as fuming sulfuric acid, its handling is crucial, especially when dealing with its gas release. Let’s break down the precautions and safety measures you should take when handling this.
What is Oleum?
dissolved sulfur trioxide (SO₃). In the case of 20% oleum, it contains 20% SO₃ and 80% H₂SO₄ by weight. It is important to note that oleum is extremely corrosive, reactive, and can release toxic fumes.
Personal Protective Equipment (PPE)
Slip on those sturdy gloves to shield your hands from oleum’s corrosive touch. Protect those peepers! Oleum doesn’t play nice with eyes. Channel your inner scientist! A lab coat keeps your attire safe from accidental splashes. Breathe easy! Oleum fumes are no joke. Use appropriate respiratory gear.
Safety gear isn’t just fashionable. It’s lifesaving!
What are the Handling Procedures?
Always work in a well-ventilated area. If possible, use a chemical fume hood for added safety. Avoid direct contact with skin, eyes, and clothing. Oleum is corrosive and can cause burns. If accidental contact occurs, rinse thoroughly with water. Seek medical attention promptly if needed. Do not inhale oleum mist, vapor, or spray. In case of ingestion, Again, seek immediate medical assistance. Don’t delay! Oleum reacts aggressively with water. Keep them apart to avoid any unwanted chemical reactions.
Safety protocols are like dance steps. Follow them gracefully, and you’ll
avoid tripping!
How to Storage and Transfer?
Oleum is typically stored in specialized containers like portable tanks, rail cars, or tank trucks. When transferring oleum, use appropriate equipment made of compatible materials, such as stainless steel. Avoid materials that react with sulfuric acid. Ensure proper ventilation during transfer operations to prevent the buildup of potentially harmful fumes. Safety first!
What to do during an Emergency?
Be aware of emergency procedures in case of spills, leaks, or accidental releases. Have spill kits readily available. These kits should include absorbents, personal protective equipment (PPE), and neutralizing agents like sodium bicarbonate. Train personnel on emergency response protocols. They should know how to handle spills, evacuate if necessary, and provide first aid.
What are the Engineering Controls Procedures?
Choose equipment made of materials resistant to oleum corrosion. Stainless steel, for instance, is a trusty companion in this acidic dance. Imagine your equipment as a superhero suit. It needs to withstand the acid villain!
Install fume scrubbers or demisters. These nifty contraptions capture any released oleum fumes. Think of them as the bouncers at an exclusive fume party. Keeping the unwanted guests out!
Keep an eagle eye on storage tanks and piping. Use remote leak detection systems. They’re like silent sentinels, alerting you if anything goes awry.
Remember, leaks are like secrets—they’re best caught early!
What are the transportation safety procedures for handling oleum during its journey from one place to another?
1. Container Selection
· Use containers specifically designed for transporting oleum.
· These containers should be made of materials resistant to oleum corrosion. Stainless steel or specialized plastic containers are common choices.
2. Loading and Securing
· During loading, ensure that the containers are securely fastened to prevent movement or tipping.
· Use appropriate lifting equipment to handle heavy containers safely.
3. Labeling and Documentation
· Clearly label the containers with the proper identification, including the chemical name (oleum), hazard warnings, and emergency contact information.
· Maintain accurate documentation, including shipping manifests and safety data sheets (SDS).
4. Emergency Preparedness
· Be aware of emergency procedures specific to oleum transportation.
· Have spill response kits readily accessible in case of leaks or spills during transit.
· Train personnel involved in transportation on proper emergency protocols.
5. Route Planning
· Choose transportation routes carefully.
· Avoid densely populated areas, sensitive ecosystems, and water bodies.
· Consider road conditions, traffic, and potential hazards.
6. Monitoring and Communication
· Regularly monitor the condition of the containers during transit.
· Maintain communication with the transport team and provide updates on the shipment’s progress.
7. Unloading and Storage
· Upon arrival, handle unloading with care.
· Follow proper procedures for transferring oleum from the transport containers to storage tanks.
· Store oleum in a well-ventilated area away from incompatible materials.
Take note, safe transportation of oleum is crucial not only for the environment but also for the well-being of everyone involved.
Sources:
Veolia North America: Sulfur Trioxide and Oleum Technical Information
Fisher Scientific Safety Data Sheet for Fuming Sulfuric Acid
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Sign language is a visual language that uses hand shapes, facial expressions, and body movements to convey meaning. Each country or region typically has its own unique sign language, such as American Sign Language (ASL), British Sign Language (BSL), or Indian Sign Language (ISL). The use of AI models to understand and translate sign language is an emerging field that aims to bridge the communication gap between the deaf community and the hearing world. Here’s an overview of how these AI models work:
Overview
AI models for sign language recognition and translation use a combination of computer vision, natural language processing (NLP), and machine learning techniques. The primary goal is to develop systems that can accurately interpret sign language and convert it into spoken or written language, and vice versa.
Components of a Sign Language AI Model
1. Data Collection and Preprocessing:
Video Data: Collecting large datasets of sign language videos is crucial. These datasets should include diverse signers, variations in signing speed, and different signing environments.
Annotation: Annotating the data with corresponding words or phrases to train the model.
2. Feature Extraction:
Hand and Body Tracking: Using computer vision techniques to detect and track hand shapes, movements, and body posture.
Facial Expression Recognition: Identifying facial expressions that are integral to conveying meaning in sign language.
3. Model Architecture:
Convolutional Neural Networks (CNNs): Often used for processing video frames to recognize hand shapes and movements.
Recurrent Neural Networks (RNNs) / Long Short-Term Memory (LSTM): Useful for capturing temporal dependencies in the sequence of signs.
Transformer Models: Increasingly popular due to their ability to handle long-range dependencies and parallel processing capabilities.
4. Training:
• Training the AI model on the annotated dataset to recognize and interpret sign language accurately.
• Fine-tuning the model using validation data to improve its performance.
5. Translation and Synthesis:
Sign-to-Text/Speech: Converting recognized signs into written or spoken language.
Text/Speech-to-Sign: Generating sign language from spoken or written input using avatars or video synthesis.
Challenges
Variability in Signing: Different individuals may sign differently, and the same sign can have variations based on context.
Complexity of Sign Language: Sign language involves complex grammar, facial expressions, and body movements that are challenging to capture and interpret.
Data Scarcity: There is a limited amount of annotated sign language data available for training AI models.
Applications
Communication Tools: Development of real-time sign language translation apps and devices to assist deaf individuals in communicating with non-signers.
Education: Providing educational tools for learning sign language, improving accessibility in classrooms.
Customer Service: Implementing sign language interpretation in customer service to enhance accessibility.
Future Directions
Improved Accuracy: Enhancing the accuracy of sign language recognition and translation through better models and larger, more diverse datasets.
Multilingual Support: Developing models that can handle multiple sign languages and dialects.
Integration with AR/VR: Leveraging augmented reality (AR) and virtual reality (VR) to create more immersive and interactive sign language learning and communication tools.
The development of AI models for sign language holds great promise for improving accessibility and communication for the deaf and hard-of-hearing communities, fostering inclusivity and understanding in a diverse society.
Existing Sign Language AI Models
1. DeepASL
Description: DeepASL is a deep learning-based system for translating American Sign Language (ASL) into text or speech. It uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to process video frames and capture the temporal dynamics of sign language.
Notable Feature: DeepASL incorporates a sign language dictionary to improve translation accuracy and can handle continuous sign language sequences.
2. Google AI - Hand Tracking
Description: Google has developed a hand-tracking technology that can detect and track 21 key points on a hand in real-time. While not specifically designed for sign language, this technology can be used as a foundation for sign language recognition systems.
Notable Feature: It offers real-time hand tracking using a single camera, which can be integrated into mobile devices and web applications.
3. SignAll
Description: SignAll is a comprehensive sign language translation system that uses multiple cameras to capture hand movements and body posture. It translates ASL into English text and can be used for various applications, including education and customer service.
Notable Feature: SignAll uses a combination of computer vision, machine learning, and NLP to achieve high accuracy in sign language translation.
4. Microsoft Azure Kinect
Description: Microsoft’s Azure Kinect is a depth-sensing camera that can be used to capture detailed hand and body movements. It provides an SDK for developers to build applications that include sign language recognition capabilities.
Notable Feature: The depth-sensing capability of Azure Kinect allows for precise tracking of 3D movements, which is essential for accurate sign language interpretation.
5. Sighthound
Description: Sighthound is a company that develops computer vision software, including models for gesture and sign language recognition. Their software can detect and interpret hand gestures in real-time.
Notable Feature: Sighthound’s software is highly customizable and can be integrated into various platforms and devices.
6. Kinect Sign Language Translator
Description: This was an early project by Microsoft Research that used the Kinect sensor to capture and translate ASL. The project demonstrated the feasibility of using depth-sensing technology for sign language recognition.
Notable Feature: It was one of the first systems to use depth sensors for sign language translation, paving the way for future developments.
7. AI4Bharat - Indian Sign Language
Description: AI4Bharat, an initiative by IIT Madras, has developed models for recognizing Indian Sign Language (ISL). They aim to create an accessible communication platform for the deaf community in India.
Notable Feature: Focuses on regional sign languages, which are often underrepresented in AI research.
Academic and Research Projects
IBM Research: IBM has been involved in developing AI models for sign language recognition and translation, often publishing their findings in academic journals and conferences.
University of Surrey - SLR Dataset: The University of Surrey has created large datasets for Sign Language Recognition (SLR) and developed models that are trained on these datasets.
Online Tools and Apps
SignAll Browser Extension: A browser extension that translates ASL into text in real-time.
ASL Fingerspelling Game: An online game that helps users learn ASL fingerspelling through AI-driven recognition and feedback.
These models and systems demonstrate the progress being made in the field of sign language recognition and translation, and they provide valuable tools for enhancing communication and accessibility for the deaf and hard-of-hearing communities.
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@Shafagat Mahmudova Thank you so much.
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I am working on a project to integrate data from Campus Management Systems and Learning Management Systems into a predictive AI model that forecasts students' academic performance. I want to use Microsoft Copilot for natural language processing and user query handling. What is the best approach to achieve this integration? Should I use open-source predictive AI models (like Scikit-learn, TensorFlow, or PyTorch) and then feed the results into Microsoft Copilot, or should I develop a custom Copilot in Copilot Studio to handle both predictive and generative tasks? Do you have any insights or recommendations on handling the integration?
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Intelligent chatbots, which are in open access on the Internet, are so far not equipped with such advanced generative artificial intelligence technology, on the basis of which it would be possible to create highly complex, multi-faceted, multi-criteria, simulation-based predictive models with the help of which it would be possible to forecast complex processes. Such complex simulation models are built in other computerized information systems, such as Business Intelligence platforms equipped with analytical modules, Big Data Analytics, etc., and also increasingly in generative artificial intelligence technologies, but used for multi-criteria analysis of large sets of data and information.
I would like to invite you to join me in scientific cooperation on this issue,
Warm greetings
Dariusz Prokopowicz
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I'm currently working on a project involving group-based trajectory modelling and am seeking advice on handling multi-level factors within this context. Specifically, I'm interested in understanding the following:
  1. Multi-Level Factors in Trajectory Modelling: How can multi-level factors (e.g., individual-level and group-level variables) be effectively addressed in group-based trajectory modelling? Are there specific methods or best practices recommended for incorporating these factors?
  2. Flexmix Package: I’ve come across the Flexmix package in R, which supports flexible mixture modelling. How can this package be utilised to handle multi-level factors in trajectory modelling? Are there specific advantages or limitations of using Flexmix compared to other methods?
  3. Comparison with Other Approaches: In what scenarios would you recommend using Flexmix over other trajectory modelling approaches like LCMM, TRAJ, or GBTM? How do these methods compare in terms of handling multi-level data and providing accurate trajectory classifications?
  4. Adjusting for Covariates: When identifying initial trajectories (e.g., highly adherent, moderately adherent, low adherent), is it necessary to adjust for covariates such as age, sex, and socioeconomic status (SES)? Or is focusing on adherence levels at each time point sufficient for accurate trajectory identification? What are the best practices for incorporating these covariates into the modelling process?
Any insights, experiences, or references to relevant literature would be greatly appreciated!
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Addressing multi-level factors in group-based trajectory modeling (GBTM) is crucial for accurately capturing the hierarchical structure of the data. This often involves accounting for individual-level and group-level effects, which can significantly influence the trajectory analysis. Here, we’ll explore different methods for addressing these multi-level factors and discuss the role of the flexmix package in R.
Methods for Addressing Multi-Level Factors in GBTM
  1. Hierarchical Linear Models (HLMs) or Multilevel Models:These models explicitly account for the nested structure of the data, allowing for random intercepts and slopes at different levels. Useful when you have nested data (e.g., students within schools, patients within hospitals).
  2. Latent Class Growth Analysis (LCGA):This method identifies distinct trajectory groups without accounting for nested data structures. Useful when the primary interest is in identifying distinct groups of trajectories but less effective for multi-level data.
  3. Growth Mixture Modeling (GMM):Extends LCGA by allowing for within-class variation in growth trajectories. Can incorporate random effects to account for multi-level structures but can be complex and computationally intensive.
  4. Multilevel Growth Mixture Modeling:Combines features of multilevel modeling and GMM to address hierarchical data. Allows for the inclusion of both individual and group-level random effects.
  5. Two-Stage Approaches:First stage involves fitting individual-level growth trajectories. Second stage models the extracted parameters (e.g., intercepts, slopes) as outcomes in a higher-level model.
Role of the flexmix Package in R
The flexmix package in R is a powerful tool for finite mixture modeling, including GBTM. It allows for the specification of various types of mixture models, including those with multi-level data structures.
Key Features of flexmix:
  • Flexibility: Can handle different types of mixture models (e.g., normal, Poisson, binomial).
  • Customization: Users can define their own models and likelihood functions.
  • Integration: Works well with other R packages, enabling complex modeling frameworks.
  • Addressing multi-level factors in GBTM requires careful consideration of the hierarchical structure of the data. Combining tools like flexmix for trajectory identification with multilevel modeling packages can provide a robust framework for analyzing complex data structures. By leveraging the strengths of different methods, you can achieve more accurate and insightful results in your trajectory analysis.
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Recently, I spent a significant amount of time developing a new model, but its accuracy is lower than some existing models. My model's accuracy is above 90%, while some existing models achieve 95-96% accuracy. Is this work still publishable? If so, why? Additionally, how should I handle the recent work and model comparison part?
I would appreciate any insights or guidance on this matter.
Thank you.
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A new deep learning model with an accuracy lower than the existing model can still be publishable if it offers other significant contributions to the field. Factors such as novel architecture, improved computational efficiency, interpretability, robustness to specific types of data, or addressing previously unsolved problems can make the model valuable. The research might also provide insights into the limitations and challenges of current models, offer theoretical advancements, or propose innovative techniques that could be beneficial for future developments. Therefore, while accuracy is an important metric, the overall impact, originality, and potential applications of the research can justify its publication.
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Hello everyone, I am conducting research on Probabilistic Seismic Hazard Assessment (PSHA) and I am looking for software recommendations that can handle PSHA with mainshock and aftershock analysis. Could you please suggest any software tools capable of performing this analysis? I would greatly appreciate your insights and recommendations. Thank you!
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I have a proteomics dataset with missing values. I tried some strategies, but the point is that there are sets completely with missing values.
The last strategy was to apply MissForest in python and it does not handle completely missing value columns.
Any ideas on how to deal with this?
Thanks in advance.
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Manys thanks, Hussain Nizam !
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CHATGPT4
Advances in Natural Language Processing has shown that research questionnaires can handle by CHATGPT4.
Where should results from CHATGPT4! Primary source or Secondary source?
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ChatGPT, including ChatGPT-4, is an AI model that extract and synthesize information based patterns it learnt by employing deep learning algorithms. Basically it can't create new ideas and concepts from what it knows or update based on conducting research or observation.
But, the information it provides can be considered as a primary or secondary source depending on the focus and context of the study. If you use ChatGPT generated information to study the quality and performance of ChatGPT itself, we can consider it as a primary source; Otherwise it is a secondary sources since the information it supplies is not original or firsthand, rather the information it generates is based on patterns it learnt from very large dataset.
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As a teacher, or educator, have you experienced teaching by considering student’s learning trauma? what is your perspective about student’s learning trauma?
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I have tutored many students over the years, and they all needed tutoring because their grades were the worst they could be. During the first couple of sessions, I always had to make time to not only assess where their difficulties were but also to deconstruct this idea they had of themselves that they were either "too stupid" or "would never achieve a better grade." And this idea was usually built on the myth that this one particular subject would forever be their weakness. Without exception, they all had stories of being ridiculed, humiliated, and belittled by their teacher of said subject. Some stories were truly horrifying to hear. Their self-esteem was so broken, and their relation to the subject was negative in every way. I heard "I just hate English" so many times... Some even had physical symptoms, like headaches or stomach issues, before class or our tutoring session. It is sad that many teachers don't work with a more resource-oriented perspective rather than always highlighting deficits. I found a good strategy is not to use their regular class materials for the first couple of sessions and instead to work on their interests and how to include the subject in their lives. For example, if a student loves sports, we talk about that in English, focus on having an enjoyable tutoring session, and then later work on grammar, tenses, etc. In general, I believe that saying "the student is lazy/stupid" is an easy way out instead of doing the investigative work of "where your student is" and how you can "meet them there" (figuratively speaking). Also, many neuroscience studies prove that the brain cannot learn under stress, so yelling at them will never yield long-lasting, positive results.
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(1) How to analyze relation between two variables in which data is obtained on the ordinal scale from two different groups and the data sets are asymmetric, where one group has significantly more responses than the other? (2) How does SMART PLS address this issue for unequal numbers of observations between the predictor and criterion variables? If not, what other tools are appropriate? (3) What are some assumptions of this type of analysis? What things are important to consider before starting the data collection?
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Abhishek Pandey why not, how would you like to proceed?
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My data are non stationary seasonal data. I need to know is there any forecasting models can handle non stationary data. and I also want to know STL( Seasonal Trend LOESS) and ETS can handle non stationary data.
Thank you.
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Not at all. First look at the data plot. If there are level changes, use d = 1, otherwise 0. I suppose you have a seasonality so D = 1. Look at the plot of the residuals. If there are no more level changes and deterministic seasonality, you can move to the next step. Look at the autocorrelation and partial autocorrelation. A cut in the correlogram indicates the need for moving averages (MA). A cut in the partial correlogram indicates the need of autoregressions (AR). By changing p (for regular AR), q (for regular MA), P (for seasonal AR), and Q (for seasonal MA), once at the time, you will have a good model in a few steps. Each time, check the residual correlogram and partial correlogram for pikes above 2 or 2.5 the standard error. Don't be too strict. Indeed, looking at a large number of lags is doing multiple statistical tests. It is quite normal that 5% of the pikes are out of the interval +/- 2 standard errors. Sometimes, especially when d + D <= 1, a constant should be added. It is not frequent to use P > 1 or Q > 1. It is better not to have large p and q together (because there is the risk of common roots in the AR and MA operators). Note that often p = P = 0 and q = Q = 1 will do the job. I can check if I have an example in English but my book is in French.
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Why do Long Short-Term Memory (LSTM) networks generally exhibit lower Mean Squared Error (MSE) compared to traditional Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in certain applications?
https://youtu.be/VQDB6uyd_5E In this video, we explore why Long Short-Term Memory (LSTM) networks often achieve lower Mean Squared Error (MSE) compared to traditional Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in specific applications. We delve into the unique architecture of LSTMs, their ability to handle long-range dependencies, and how they mitigate issues like the vanishing gradient problem, leading to improved performance in tasks such as sequence modeling and time series prediction. Topics Covered: 1. Understanding the architecture and mechanisms of LSTMs 2. Comparison of LSTM, RNN, and CNN in terms of MSE performance 3. Handling long-range dependencies and vanishing gradients 4. Applications where LSTMs excel and outperform traditional neural networks Watch this video to discover why LSTMs are favored for certain applications and how they contribute to lower MSE in neural network models! #LSTM #RNN #CNN #NeuralNetworks #DeepLearning #MachineLearning #MeanSquaredError #SequenceModeling #TimeSeriesPrediction #VanishingGradient #AI Don't forget to like, comment, and subscribe for more content on neural networks, deep learning, and machine learning concepts! Let's dive into the world of LSTMs and their impact on model performance. Feedback link: https://maps.app.goo.gl/UBkzhNi7864c9BB1A LinkedIn link for professional queries: https://www.linkedin.com/in/professorrahuljain/ Join my Telegram link for Free PDFs: https://t.me/+xWxqVU1VRRwwMWU9 Connect with me on Facebook: https://www.facebook.com/professorrahuljain/ Watch Videos: Professor Rahul Jain  Link: https://www.youtube.com/@professorrahuljain
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I can bet my money that depends on the task, LSTM are good for text patterns, CNN: images mostly, RNN: time series and text.
However, I want to point a single aspect, for that I will just quote Wikipedia even if that sounds strange:
“March 2022) Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods.”
By the way, nowadays, Wikipedia does peer-review on articles also, so information is getting better.
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In the context of machine learning models for healthcare that predominantly handle discrete data and require high interpretability and simplicity, which approach offers more advantages:
Rough Set Theory or Neutrosophic Logic?
I invite experts to share their insights or experiences regarding the effectiveness, challenges, and suitability of these methodologies in managing uncertainties within health applications.
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I appreciate the resources shared by R.Eugene Veniaminovich Lutsenko.
However, these references seem to focus on a different aspect of healthcare modeling. I'm still interested in gathering insights specifically about the suitability of Rough Set Theory and Neutrosophic Logic for handling discrete data in machine learning healthcare models.
Please feel free to contribute to this discussion if you have expertise in this area. Thank you
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Hello, I want to extract myosin from chicken breast. The operation is as follows: (1) the miced meat was washed with buffer A (0.1 M NaCl, 10 mM phosphate buffer,2 mM MgCl2, 1 mM EDTA, pH 7) for 3 times to obtain myofibrillar protein as a sediment; (2) the sediment was homogenized with Guba-Straub solution (0.3 M KCl, 0.1 M KH2PO4, 50 mM K2HPO4, 1 mM EDTA, 4 mM sodium pyrophosphate, pH 6.5) for 20 min at 4 ℃. However, the solution became so viscous that I couldn't gain the supernatant through centrifugation, even though the speed was up to 13,000 rpm for 12 min (5000 g for 12 min in the literature Food Chemistry 2018,242, 22–28). Could anyone tell me that how to handle this situation? Thank you very much for your nice suggestions.
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Thank you very much for your kind advice, I'll try again without EDTA.
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Hello, I'm a graduate student and my research field is Vehicle motion planning
I'm trying to make a MPC controller for path tracking with carla and the problem is how to update my state variable.
The controller need to update the vehicle's state, such as speed and position, at every step. I'm wondering whether this should be done by calculating x˙=Ax+Bu, or by calculating just the control input and then updating based on the current state of the vehicle obtained from the simulator at each step.
I am curious if it is valid to update the state based on the information received from the simulator and then calculate the control input. If it needs to be updated through calculation, I wonder how to handle parameters such as tire friction coefficient or parameters that change over time.
Any answer can be a great help for me
Thank you
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Dear Lee Heyojea,
To update your vehicle's state variable in a simulator, you typically need to access the simulation environment's API or scripting interface. Here is a general outline of the steps that could help you:
1. First, Identify the specific state variable you want to update for the vehicle in the simulator. This could be any variable like position, velocity, orientation, or any other relevant parameters.
2. Next, using the provided API or scripting interface, retrieve the current state of the vehicle. This may involve querying the simulator for the current values of the state variables.
3. Then modify the state variable according to your requirements. You could set new values for those parameters (Velocity, Position, etc.).
4. Thereafter, update the vehicle's state in the simulator by sending the modified state information back to the simulation environment using the appropriate API calls or commands.
5. Finally, verify that the changes have been applied correctly by observing the updated state of the vehicle in the simulator.
But, don't forget to consult the documentation (manual) or resources provided by the simulator to understand the specific instructions, methods and commands available for updating vehicle state variables.
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When we get conflicting suggestions from reviewers, how should one handle such situations?
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In the case you are referring to, I think that you have the following two choices:
1. Contact the reviewers in order to inform them on the contradiction issue so that they may reconsider their comments,
2. Address both comments in your paper, stating your arguments in order to reach to your conclusion wich will either support one of the two opinions or support a "middle solution".
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I am currently working on measuring the zeta potential of nanosheets, but I've encountered a challenge related to the unknown refractive index (RI) of my samples. Since the exact RI of the nanosheets is not known, is it valid to use the RI of the diluent instead? How critical is the accuracy of the RI in influencing the measurements of zeta potential? Can using an inaccurate RI significantly affect the reliability of the results?
My samples are gel-based (2D nanosheets), and while I am getting valid zeta potential readings, my dynamic light scattering (DLS) measurements are presenting a problem. The system reports a high polydispersity index (PDI) of 1, which is considered to indicate invalid or poor quality DLS data. Can these DLS data still be considered valid?
I am hesitant to apply sonication or centrifugation to improve the homogeneity of the sample, I feel these procedures might damage the structure of the nanosheets. Are there alternative methods or adjustments I can make to ensure more reliable Z potential and DLS readings without compromising the integrity of the nanosheets?
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Salma Younes One further point. The charges on the edges of your plates in comparison to the faces will be very different. There is some literature from the 1930's, I believe, dealing with this concept. This will, again, affect how such particles will move/migrate under an electrical or acoustic field.
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I tried to culture the DU145 cell twice from the beginning (stock from the Nitrogen tank).
The first time I tried EMEM + FBS and the second time I tried RPMI.
both efforts ended up not growing.
I tried a mycoplasma test, small flask, and centrifuge at each time splitting, but still they do not grow!
if you have any idea about any of the steps or anything, Please share it with me.
Thank you in advance.
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any tips for the sub-cultivation step?? :)
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Does Neural Networks handle a multicollinearity?
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Can Bayesian regression be used when there is a multicollinearity problem?
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I`m trying to prepare a solution of water-cmc with the highest content of CMC possible for further processing, but I wanted to know how I can lower the viscosity so it will be easier to handle but still dissolve properly?
Thanks
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As others. have pointed out, you can lower the concentration of NaCMC or add an inert salt like sodium chloride. Adding NaCl only lowers the viscosity if the CMC concentration is low (<≈ 1wt%). At high concentrations, adding salt has little to no effect.
If lowering the CMC concentration is not an option, you could sonicate the solution. This will decrease the molar mass of the polymer thereby lowering the solution viscosity.
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i solve this classical optimization using docplex . The requirement is that I need to solve it with quantum algorithms. For solving QUBO problems, I could use QAOA. But for solving quadratic programming problems with only continuous variables, I don't know which quantum algorithm I could use. Or maybe this problem could be converted to a QUBO problem and then handled by QAOA. I'm not sure. and is it poussible to convert directely docplex problem to quadratic program and after to QUBO for simple ising hamiltonien ?
Could anyone help me?
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Mario Stipčević thank you dear mario i have a complicated problem the model that i put as example is just easy one i know that is very complicated to convert continous variables to binary by discritizing but i will see thank you .
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I've just defrosted some J774A for the first time.
On thawing, the cells are small, round and not at all adherent... Is this normal?
Do I need to add collagen to make them adhere to the support (they're currently in T25)? Is there a more suitable support?
I use DMEM + 10%FBS + 1% Pen/Strep as a medium
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No, you need not add collagen.
J774A, a murine macrophage cell line is semi-adherent i.e. some cells grow in suspension, some loosely attach to the surface and others flatten out and attach to the flask. Please note that they should be passaged by scraping. During subculturing, dislodge cells from the flask substrate with a cell scraper, aspirate and dispense into new flasks. The recommended sub cultivation ratio for J774A cells is 1:3 to 1:6. Replace medium two or three times weekly.
Cells should not be allowed to overgrow and become confluent as this can lead to a loss of the flattened adherent cell characteristic. DMEM + 10%FBS is the complete growth media to culture J774A cells.
Best.
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Tell us about a time that you faced a challenging situation – how did you handle it and what was the outcome?
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learnt this decades ago, then taught it, still strongly influences me in everything I do
Soft systems methodology - Wikipedia
- check it out- practical and powerful
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I encountered a situation where I submitted an article to BMC, but unexpectedly, my preprint was published on ResearchSquare instead. However, I have since withdrawn my article from BMC, and now I need to remove the preprint from ResearchSquare as well, as requested by the new journal. I would appreciate any guidance or advice from individuals who have faced a similar issue on how to handle this situation
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I had submitted a manuscript to a journal (I am keeping it anonymous). I opted to make the preprint available online by mistake and the manuscript preprint appeared in the research square from the date of submission. The manuscript was rejected from the journal after the review process. The review process itself took more than one year. By the time, we (authors) switched to another institution and our affiliations, and our contact mail ids mentioned on the research square preprint became invalid. We noticed that the content of our preprint was copied from the research square to other repositories without any consent from us (authors). We got continuous rejections from other journals due to serious plagiarism when we tried to submit an extended version of our rejected manuscript. We regret to say that the preprint posted on the research square really became a trap that haunted us and spoiled our freedom to extend our research in the same direction and revise the manuscript. From our experience, other journals reject the manuscripts with preprints of previous journal submissions posted on the research square at editorial level itself. The name of the previous journal is clearly written on the research square preprint. As the journals get sufficiently large number of submissions, editors reject such papers at editorial level itself, to avoid further conflicts. A lot of hard work is involved in writing a research paper. It is so painful that authors are not able to submit their research work to a journal of own choice because of the issues caused by the preprint posted on the research square by mistake. We honestly request you to think twice before opting to publish preprints on research square during the manuscript submissions. Research square never removes the preprint content. They just keep the preprint content with a "Withdrawal Notification". The content will still cause problems during plagiarism checks.
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How do humans handle the anxiety of uncertainty about entering eternal salvation? How? Why?
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I suppose deathbed conversions or confessions are one way.
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Which package do you use in R for general chemoinformatics, like handling molecular formulas, computing weights and mass to charge ratios, maybe even handling adducts etc.? Ideally something well maintained and with little dependencies.
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The depedency to java is quite annoying but clearly better than the rest.
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Dear All,
I have already seen many Q&As regarding HistoGel handling and tried various changes of the dehydration protocol. Sadly, nothing has worked out so far.
I am currently working with spheroids and use HistoGel for Histology, since it would be very difficult to handle the spheroids otherwise. During dehydration, the gel becomes really hard and makes paraffination almost impossible. While embedding in paraffin, the gel remains like a single unit within the paraffin block and does not really unify. While slicing later, this unit falls sometimes off after transfering on the water, or the slicing does not work at all (paraffin brakes completely). Does anyone know how to avoid HistoGel hardening? I would appreciate any tricks you have up your sleeve. Maybe also not involving HistoGel ;)
Thank you very much!!
Best regards,
Astrid
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I am trying to go through this process as well. I have tried your protocol Matthew and it gets hard in the xylene process.
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Learning to cope with flux, or the dynamic and changing nature of life, society, and systems, can be gleaned from the perspectives of Zhuangzi, Karl Marx, Friedrich Engels, Zygmunt Bauman, Joseph Schumpeter, and Clayton Christensen. Each of these thinkers provides insights that can inform strategies for navigating and adapting to flux:
Zhuangzi:
  1. Embrace Naturalness (Ziran):Lesson: Align yourself with the natural flow of things. Embrace spontaneity and act in accordance with the Dao. Cultivate a mindset of acceptance and adaptability.
  2. Practice Wu Wei (Non-Action):Lesson: Instead of resisting the current, learn to navigate it with minimal resistance. Allow events to unfold naturally, and focus on finding harmony within the unfolding process.
Karl Marx and Friedrich Engels:
  1. Understand Historical Materialism:Lesson: Recognize that societal structures are subject to historical material forces. Understand the inevitability of change and transformation as part of the dialectical process.
  2. Participate in Class Struggle:Lesson: Engage in social and political processes to influence change. Work towards societal transformation through collective action and the resolution of class contradictions.
Zygmunt Bauman:
  1. Adapt to Liquid Modernity:Lesson: Accept that contemporary society is characterized by fluidity and uncertainty. Develop adaptive skills to navigate changing social structures and relationships.
  2. Cultivate Reflexive Individualism:Lesson: Acknowledge the fragmentation of traditional structures and focus on developing individual resilience and reflexivity in the face of constant change.
Joseph Schumpeter:
  1. Embrace Creative Destruction:Lesson: Recognize that innovation and change are inherent in economic systems. Embrace the creative destruction process, and be open to new ideas and ways of doing things.
  2. Nurture Entrepreneurial Spirit:Lesson: Foster an entrepreneurial mindset. Be willing to take risks, explore new opportunities, and adapt to the evolving landscape of innovation and business.
Clayton Christensen:
  1. Anticipate Disruptive Innovation:Lesson: Be vigilant and proactive in identifying disruptive trends and innovations. Anticipate and prepare for changes in your industry, and be willing to pivot when necessary.
  2. Strategic Flexibility:Lesson: Develop strategic flexibility. Understand the potential challenges posed by disruptive technologies, and be ready to adapt your strategies and business models accordingly.
Integrative Strategies:
  1. Cultivate a Growth Mindset:Lesson: Develop a mindset that sees challenges as opportunities for growth. Embrace a continuous learning mentality, viewing setbacks as part of the journey toward improvement and adaptability.
  2. Build Resilience:Lesson: Strengthen your resilience to handle uncertainty and setbacks. Develop coping mechanisms, stress management techniques, and a support network to navigate challenging times.
  3. Stay Informed and Agile:Lesson: Stay informed about changes in your field or industry. Be agile in responding to new information, and be willing to adjust your plans or approaches as needed.
  4. Cultivate Collaboration and Networks:Lesson: Foster collaborative relationships and networks. In times of flux, diverse perspectives and collaborative efforts can provide valuable insights and support.
  5. Strive for Balance:Lesson: Balance adaptability with stability. While embracing change, maintain a foundation of principles and values that guide your decision-making and actions.
  6. Cultivate Mindfulness:Lesson: Practice mindfulness to stay present and focused. Developing a sense of awareness and staying connected to the current moment can help navigate flux with greater clarity.
Incorporating lessons from these philosophical and economic perspectives, one can develop a holistic approach to cope with the flux inherent in life and various societal systems. Embracing change, staying adaptable, and fostering a resilient mindset are key elements in navigating the complexities of a dynamic world.
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Linear economics is the dominating model of the current global society, i.e. it is principally directed against the evolutionary laws of nature, which work by natural cyclical flux. We can also observe a global trend towards state-commanded capitalism (statism) everywhere, which inevitably leads to intense conflict and warfare.
From your Tieu-Tieu Le Phung mentioned sources, only Zhuangzi is the exception, but it is not an economic school of thought.
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All things are in flux; the flux is subject to a unifying measure or rational principle. This principle (logos, the hidden harmony behind all change) bound opposites together in a unified tension, which is like that of a lyre, where a stable harmonious sound emerges from the tension of the opposing forces that arise from the bow bound together by the string. Heraclitus
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Forgive me if I may not have been standard in my formulation of the question.
I wish to analyze the effect of residential environmental factors on depression through linear regression. In the conceptualization of the questionnaire, there are many items in the dependent variable. Two of the questions are 1) whether there is a sports facility near the residence and 2) if so, what is the environment of that sports facility.
Now the problem is that question 2 is only asked if the answer to question 1 is "yes". So as it stands, question 2 will have a lot of missing values. However, in my linear regression, I want to study the effect of both question 1 and 2 on depression. In this case, how should I change the way the questionnaire is asked, or the way the model is constructed, so that both questions can be taken into account in the linear regression.
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You will probably have to treat participants who answered the first question with "yes" vs. "no" as separate groups for the purposes of the regression analysis. Only for the "yes" group would it make sense to analyze the second question at all since that question does not apply to the "no" group.
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Kindly give suggestions to handle ordinal categorial dataset using clustering algorithm
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Луценко Е.В., Подсистема агломеративной когнитивной кластеризации классов системы «Эйдос» ("Эйдос-кластер"). Пат. № 2012610135 РФ. Заяв. № 2011617962 РФ 26.10.2011. Опубл. От 10.01.2012. – Режим доступа: http://lc.kubagro.ru/aidos/2012610135.jpg, 3,125 у.п.л.
Луценко Е.В. Метод когнитивной кластеризации или кластеризация на основе знаний (кластеризация в системно-когнитивном анализе и интеллектуальной системе «Эйдос») / Е.В. Луценко, В.Е. Коржаков // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2011. – №07(071). С. 528 – 576. – Шифр Информрегистра: 0421100012\0253, IDA [article ID]: 0711107040. – Режим доступа: http://ej.kubagro.ru/2011/07/pdf/40.pdf, 3,062 у.п.л.
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Hello fellow researchers,
Nearly two years ago, I submitted a manuscript to the Journal of Psycholinguistic Research. Throughout my extensive experience with academic journals, I have never encountered such poor handling and a lack of responsiveness. Are any of you familiar with this journal and its handling procedures? Do you have any suggestions?
Thank you for your assistance,
Vered
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Thanks a lot
Vered
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How do we handle the legal dilemma of victims being injured to the extent of not being able to advocate for their own justice? An example could be victims receiving brain damage as the result of violent crimes and not being able to advocate for their own justice until the statutes of limitations have passed.
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When victims are unable to advocate for their own justice due to severe injuries, legal systems often appoint legal guardians, family members, or legal representatives to act on their behalf. Courts can appoint advocates, attorneys, or guardians ad litem to ensure the victim's interests are represented in legal proceedings. Additionally, legal frameworks may have provisions for victim advocacy groups, social workers, or other support systems to provide assistance and ensure the victim's rights and interests are upheld in the legal process.
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DEM software used to analyze bulk solids flow in various bulk handling equipment/systems.
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It all depends if you want to pay for it or to use an open source solution:
Under paid license, the following two are good enough:
OpenSource options include:
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Good morning,
for my research project, I am using school meal data selection. I would like to investigate the children's food selection patterns using multiple time series using the K-means method. Given the remit of the study, in my data, I have missing data due to data collection during school and bank holidays, weekend generating breaks in food selection values. When you investigate a phenomenon on a daily scale, how do you manage these kinds of missing values? Do you change the temporal scale for example month rather than day, keeping breaks in graphics, or perform an imputation?
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Your efforts are appreciated
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Hello,
I need to estimate a generalized linear mixed model (GLMM).
When using R, I need to choose a specific fixed effect (one of the independent variables in the model) to have a random slope according to a random variable such as subject ID.
But when using SPSS, I can't choose a specific fixed effect to have a random slope.
I asked chatGPT about it and it responded as follows:
The difference you're observing in the specification of random slopes for specific fixed effects between SPSS and R might be related to how the two software packages handle mixed-effects modeling. This difference is not necessarily due to the capabilities of the software but rather the way they present the options to the user. Here's why this might be the case:
SPSS:
In SPSS, the interface for specifying mixed-effects models is designed to be user-friendly and may abstract away some of the technical details. It allows users to specify random slopes and intercepts for subjects using the Repeated option or Mixed Linear Models (MIXED) procedure.
SPSS may automatically set up random slopes for all fixed effects by default, which is a more automated approach, but it doesn't give you explicit control over which fixed effects have random slopes.
R (lme4 package):
R, specifically with the lme4 package, provides more flexibility and control in specifying mixed-effects models. This can be both an advantage and a challenge for users.
In R, you have to explicitly specify which fixed effect(s) should have random slopes by including them in the model formula. This explicit control allows for more customized modeling but can be more complex and requires users to have a good understanding of their data and the modeling process.
1. Can someone please confirm this answer?
2. Is there a way to estimate a mixed-effect logistic regression model in R that would be the same as the estimated model in SPSS?
I would appreciate any clues about this issue! Thanks!
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Beyond that, practically speaking, you might make a small data set available, be specific about what model you are using in SPSS, present the SPSS output, and then I'm sure someone can offer the equivalent model in R.
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6 choice profiles @3 alternatives
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Thank you, i was able to detect the problem
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We are about to start working on testing anti-protozoan agents against E. histolytica. I was curious if anyone had basic handling and culturing protocols they would be willing to share. I have been working on drug discovery targeting various human parasitic infections for 2 decades, but am new to e. histolytica. Have a bit of experience with giardia. Thanks in advance if anyone is willing to share some tested protocols!
Rob
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Hello there, curious researcher friend John Robert Gillespie! This is a fascinating field, and it's great to hear about your extensive experience in drug discovery.
Culturing E. histolytica can be a bit different from other parasites, so it's essential to follow established protocols carefully. Here's a basic guideline for culturing E. histolytica:
**Materials You'll Need:**
- E. histolytica strain (axenic culture)
- TYI-S-33 medium (Trypticase-yeast extract-iron-serum)
- Tissue culture flasks or tubes
- CO2 incubator (5% CO2)
- Sterile pipettes and culture dishes
- Microscope
**Steps:**
1. **Preparation of TYI-S-33 Medium:**
- Prepare the TYI-S-33 medium according to the specified recipe.
- Sterilize the medium by autoclaving.
- Once cooled, add a few drops of penicillin-streptomycin solution to prevent bacterial contamination.
2. **Inoculation:**
- Inoculate the E. histolytica strain into a fresh TYI-S-33 medium.
- Maintain the culture at 37°C in a CO2 incubator.
3. **Subculturing:**
- Subculture the amoebae every 2-3 days to maintain an actively growing culture.
- To subculture, harvest the amoebae by centrifugation, resuspend in fresh medium, and transfer to a new flask or tube.
4. **Monitoring:**
- Regularly monitor the culture for the presence of trophozoites and signs of contamination.
- Use a microscope to check for the typical appearance of E. histolytica trophozoites.
5. **Harvesting:**
- When you need to harvest the amoebae for experiments, allow them to grow to the desired density.
- Harvest the trophozoites by centrifugation and wash them with an appropriate buffer or medium for your experiments.
6. **Storage:**
- If needed, you can freeze E. histolytica trophozoites for long-term storage in liquid nitrogen or ultra-low temperature freezers.
Remember, maintaining a sterile environment is critical to avoid contamination. Also, note that E. histolytica can be potentially pathogenic, so follow appropriate safety protocols when working with this organism.
These are general guidelines, and the specific protocols may vary depending on the strain and the purpose of your experiments. I'd recommend consulting with colleagues or referring to the literature for more detailed protocols tailored to your specific needs. Good luck with your research!
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Hello, I have first cleaned the data by handling all missing values.
1. I have done factor analysis for four variables regarding environmental concern.
2. I used PCA and Varimax
3. I have got only one component with an engine value greater than 1.
4. So the component cannot be obviously rotated
5. I tried changing the method of factor analysis to Maximum likelihood and Direct oblimin as I did not want to limit my data to orthogonal rotation.
6. I got the same results with components count and the chi-square test has a significance of .000
7. The correlation matrix table for all the four variables also have either .000 or <0.001 values.
I am confused as to how to proceed with this.
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With just four variables, there is not much room for extracting more than 1 factor. Nonetheless, the fact that the chi-square test of model fit is significant may indicate that the four variables are not unidimensional. Without seeing your correlation matrix and other details of your data and analysis, it is impossible to say.
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I am currently conducting research using a panel data - fixed effect regression model with approximately 15,000 data observations before data cleansing. However, for one of the variables (an independent variable with continuous data type), there are quite a few data points with a value of 0 (around 6,000 observations). These zero values are not missing values.
Can I still proceed with the analysis using panel data - fixed effect?
Are there any specific steps I should take to address this issue?
Thanks.
Note: I would greatly appreciate it if there is any reference literature discussing this issue
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Unless I am missing something, these zeros should cause no problem. For example, one might have a dummy variable for males (1 if male, 0 otherwise) which would have a large number of zeros and not give a second thought.
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Which laptop is recommended for data science and managing large datasets among the following options?
  1. MacBook Pro MPHG3 - 2023 (Apple)
  2. ROG Strix SCAR 18 G834JY-N5049-i9 32GB 1SSD RTX4090 (ASUS)
  3. Legion 7 Pro-i9 32GB 1SSD RTX 4080 (Lenovo)
  4. Raider GE78HX 13VH-i9 32GB 2SSD RTX4080 (MSI)
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  1. this one is also good for large dataset Legion 7 Pro-i9 32GB 1SSD RTX 4080 (Lenovo)
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Discuss how preslaughter handling affects meat quality
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Are these any New topics in this field, which has been discussed in many books and review for decades already?
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I wanted to generate input files through swissparam to run some MD simulations. I could generate input files for 2nm*2nm graphene sheet but I want to generate input files for 5nm*5nm graphene sheet. I created gra.mol2 file using MarvinSketch and uploaded to swissparam. Neither swissparam generate the zip file not does it print any helpful error message? Can anyone please suggest what might be wrong? I am not sure of the upper limit of the number of atoms that swissparam can handle. A 5nm*5nm graphene sheet contains 1098 atoms.
Also, I wanted to generate inputs for styrene oligomer (15 styrene monomers).
I created PS_15.mol2 file using MaterialsStudio then opened that file in MarvinSketch, added explicit hydrogens, and saved it as PS_15.mol2 file. Similar error is received when I try to generate input files for PS_15.mol2.
I have attached gra.mol2 & PS_15.mol2 file and the screenshot of the error messages by swissparam.
Thanking you!
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Not really. Still not sure why swissparam fails.
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Can ChatGPT engage effectively with emotionally charged or intricate subjects, offering insightful answers?
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Chat GPT sources data from the internet- mindlessly. If you’ve had even marginal experience on the internet, you know the limitations that ChatGPT will run into. Leaving aside that it is inhumane- and therefore unable to emotionally relate to content- it can only ever approximate the humanity of an internet user…
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I will write in breif the pitfalls/advantages of each of the ways to handle class imbalance. I am looking for recommendations on how to improve each approach or whether there are any recent developments to manage this issue.
  1. Resampling
I believe this is the most common method in the literature, but, there are many reports on the disadvantages especially with SMOTE. Random undersampling results in the loss of valuable data.
2. Class weighting
I have seen some good results with this method from my own experience.
3. Boosting
Certain algorithms (XGBoost, EasyEnsemble...) perform well.
It would be appreciated if more can be added to this discussion.
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Let me add a few ways other than the above.
Algorithmic approaches
Data augmentation
Threshold adjustment
Feature engineering
or Hybrid models.
Mostly, we have to define the me approach based on data type and the data and/or model architecture.
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"Constrained hexahedral mesh generation" refers to solving the internal high-quality pure hexahedral mesh given the surface quadrilateral mesh.
This issue has been studied for decades, but remains open. Related algorithms include sweep method, octree method, polycube method and so on. In recent years, the research on hexahedral mesh generation based on dual topology is very hot, but I think it cannot handle complex 3D geometry.
How long do you think the hexahedral grid generation algorithm can be perfected and achieve good results in industrial software? And, can AI play a role in this issue?
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The practicality of the hexahedron generation algorithm isn't quite around the corner yet, but it's definitely warming up its corners! Progress is being made, so stay tuned. 🔮🛠️
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In one work, suppose multi-disease (skin cancer, covid-19, Monkeypox, and lung disease) data analysis and disease prediction, I used 5 ML models (KNN, GB, RF, SVM, and Ada boost) after pre-processing steps (data cleaning, encoding, missing value handling, outliers handling, feature engineering, etc.) now while analyzing the model performances I want to use statistical test on our ML models. I found no previous works that work on the same kind of dataset instead most of them worked on similar types of disease (i.e. Lung cancer (3 types) = 11, 12, 13) detection. I want to know, (1) How can I compare my work outcomes with others as they not using similar data? (2) When applying the statistical test will be appropriate? (3) Why should I use it? (4) How can I perform statistical tests for my work?
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it sounds like you need something like a middleware that converts some of the factors you are analyzing to have some sort of a commonality index. So at least there are some basis on why you compare them that way. Specially if like you mentioned there is no existing studies published yet about this approach.
Do some factor analysis to reduce the number of things being measureed to some similar ones for comparison purposes.
Also ensure your instruments reliability suffices' chronbach's alpha and employ divergent and convergent validity tests so that at the end of the day, you are:
1. Measuring the factors that are supposed to be measured
2. Excluding the factors that are not supposed to be measured.
I think applying these principles to your tests could help you create a new approach. Since its a new territory to be explored.
Just my thoughts on this matter. Good luck!
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Hello!
I'm currently working on the LDQ detection for my thesis. I have read so many things about missing values and how to deal with them that unfortunately I am now even more confused than ever before.
I am working with SPSS 26 and I have several categorical as well as continuous variables which are important for my research question.
Initially I followed the procedure Analyze menu > Missing Value Analysis
The range of my n is between 145 and 158 (so not a really huge sample size). In the univariate statistics I get percent missing around 7.6% to 8.2%.
I ran Little's MCAR test which became non-significant with Chi²(15) = 1.390 and p = 1.000
I made a mistake in the construction of my questionnaire because participants were not able to skip questions - looking at the data set most of my missing values is simply because after a certain amount of time people simple dropped out and every question that would have followed was considered "missing". The "initial" sample size without the drop outs was around 350 participants.
I ready that missing data analysis would be important especially to look what type of missing it is, that you're dealing with in your data set.
To me (or my really limited understanding of those things compared to experts) it makes complete sense that I have higher amounts than the recommended 5% of missings where some sort of imputation or replacement of the missing values should be considered because mine are mostly due to drop outs. Also the p = 1.000 at the Little's test is highly irritating for me.
I have read a ton of articles the past few days but even though they all give really good explanations for missing data and multiple imputation and which method is best - I found no answer what I should do in my specific case where the missing data is because people stopped at one point to answer the questionnaire and closed it (I already stated in my limitations that preventing them from skipping questions should be handled in a different way in the future).
Can someone please help me out and give me a recommendation?
I found an article from D. A. Bennett (1999) that is called "How can I deal with missing data in my study?" and gives a cut off of 10% of missing values but gives no information if it is considered for each variable or the overall data set and I found no way to calculate the missing values for the complete data set instead of "just" columns or rows.
I hope I somehow was able to explain my issue with that missing values due to drop out and my confusion how to handle them and especially how to argue in my thesis WHY I handled them the way I did.
I really hope that some expert finds time to give me a recommendation - I'm happy to answer any further questions. I'm simply overwhelmed atm with the amount of information I read and suddenly nothing makes sense anymore.
Thanks everyone in advance!
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I have not yet had time to read that Enders (2023) article in its entirety. But after reading his explanation of the MCAR, MAR, and MNAR missingness mechanisms, I cobbled together this slide, which some readers may find helpful.
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Road Rage connotes aggressive behaviours displayed by motorists while driving, which include verbal insults, yelling and physical threats. What are the best ways to handle road rage situations? Sharing is caring!!!
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The following link includes some of the best ways to deal with road rage:
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Reporting Negative Findings in Preclinical Research
In earlier ethnobotanical studies, preclinical pharmacological research is built.
These studies are accessible online, and it is expected that the principal investigator will analyze them carefully and base the study hypothesis on them.
The results of an experiment will typically be favorable if a researcher uses a detailed and well-organized ethnobotanical study.
The chemical under study will therefore exhibit pharmacological therapeutic action in animal models.
Experimental research, however, is susceptible to unidentified confounding factors that are not taken into account; as a result, the results provided may also be unfavorable.
Unfortunately, students find it difficult to include unfavorable results in their graduation papers because they are afraid of being rejected; therefore they often influence it covertly.
How can we handle issues of this nature?
#research #students
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There are a number of journals specifically for the publication of negative results.
You could also publish it as a preprint on a site like medRxiv, which would save you the process of having your negative results going through peer review and taking on any associated costs of publication.
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I've recently faced a question that I couldn't solve on my own or with colleagues.
I want to know what are PCR biases and how to handle them?
Any answer in welcome.
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PCR bias happens when a pcr can amplify 2 or more products and some products amplify better than other products. So if one amplimer is much longer than the other it may melt slower and anneal quicker so the longer amplimer will amplify less well than the short one that melts quickly and does not reanneal easily. Sometimes it is seen in alleles where both amplimers are the same length but one has a CG at one position and the other has AT at that position and the CG is more stable so the AT allele will amplify better as is often seen in dna sequencing or the intensity of peak sizes in using repeat sequences in mapping studies
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I want to solve imbalanced data issue for classification problem, which technique is probably more effective to solve it?
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Dear All,
The question posed by Viswapriya Elangovan is very complex, and there is a vast literature attempting to provide an answer.
Inès François makes valid points but does not directly address the specific question regarding new techniques to address (and overcome) the imbalanced class problem in Data Science.
On the other hand, Oger Amanuel's response is cryptic and not very helpful: dividing features into classes is a pointless operation, unless Oger meant something else and got confused. If confused with the terminology, what he suggests is redundant.
However, to clarify, it is not easy to determine when one is dealing with the problem of imbalanced classes in Data Mining. Typically, an imbalance index is calculated as the ratio between the elements of the positive class (minority) and the negative class (majority) (there are also other ways to calculate the index, such as using entropy). It is not known at what threshold of the index one can speak of an imbalanced class, but certainly, based on my experience, 85% and 15% do not pose a problem for the majority of machine learning algorithms, and Ines does not bring anything new to the table.
The approaches to overcome the imbalanced class problem (for example, if less than 1% of the data points in the dataset belong to the positive class) fundamentally remain two:
1) Data resampling: random undersampling (with various sub-samplings of the majority class, and then MCC can be used to determine which one to consider), oversampling with the creation of synthetic data, combining under and over.
2) Cost-sensitive: using a cost matrix during the learning phase.
Other techniques are less general and may depend on the nature of the problem and the data being analyzed.
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What ChatGPT can't handle is Association of Art and Science: https://www.academia.edu/88466623/Association_of_art_and_science How can we improve our mastery of the association of art and science?
Part1: Which brings us to Roger Penrose and his theories linking consciousness and quantum mechanics. He does not overtly identify himself as a panpsychist, but his argument that self-awareness and free will begin with quantum events in the brain inevitably links our minds with the cosmos.
Part2: My reading of Ibn Khaldoun leads me to the Following reflection: In a context of decadence of Middle Eastern states, an end of all these sub states is inevitable; a new Açabiyya will be reborn. I rely on a stochastic model that announces the decline of these sub states by a convergent sequence of "Triggers" leading at the birth of a non-tribal Açabiyya with a common denominator: the cohesive force.
Note: The Ukrainian model fits perfectly into this approach Amin Elsaleh
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David Atkinson thank you, I am trying to promote a new standard SPDF which is more poweful than ChatGPT . Bridging between Agile methodology & NFT & Art & ML is our next attainable goal.
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Dear colleagues
I have a questionnaire that measured self-handicapping on a scale that ranges from 1 (SD) to 5 (SA). When I administered the questionnaire to a sample of about 300 first year university students, I noticed that a very high percentage of students choose only either 1 (SD) or 2( D) and that few of them choose (Neutral). Almost no student choose 4 (A) or 5 (SA). How this will affect my data? results? what is my best options to handle this problem?
Thank you
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In principle, I don't think there is a problem here. You administered a questionnaire, which gave you data, with the results being what they are. However, if you sense that there is a problem, I advise you to see whether the problem is not actually in the way the questions are formulated. Otherwise, you should be contented with the results obtained. This is scientific research: results sometimes are not what you expect them to be.
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Hi everyone,
For my studies on the IRR I have binary data and three raters. But on one variable they all score a 0 for all subjects. This means that everything is rated as a 0. But SPSS then gives me the warning that every outcome is the same and that the command stopped. What do I do with it and what do I have to report in my studies?
Hopefully you can help me!
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I would delete that item from the kappa calculation, and then when you report your coefficient, note that one item was excluded due to 100% agreement.
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I would like to prepare a calcium carboxylate. The procedure is overbasing the carboxylic acid with excessive Ca(OH)2 than bubbling CO2 through it to precipitate CaCO3. Can I just throw some pieces of dry ice into the mixture? Gas cylinder is hard to handle.
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Could you not put he solid CO2 into a container and bubble the gas through the calcium hydroxide solution?
Throwing the solid CO2 into the solution may simply freeze it