Handling (Psychology) - Science topic

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

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

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.

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.”]

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.

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.

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.

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:

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

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, also known as bra-ket notation, is a mathematical formalism used to represent quantum states (kets: |ψ⟩) and their duals (bras: ⟨ψ|).

· 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?

Prof James Hurley

Thanks for your comment. If possible, please share any resources or articles that I can use.

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.

Hi, have a look on these courses on CMA:

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.

Hey, Your cells might not have been stored properly, We had a similar issue, ordered a new cell line, and everything started working.

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 :)

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:

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.

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

I have the book Statistical methods in medical research by Armitage and Berry

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.

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

2. Transparency and Accountability

3. Enhanced Security

4. Speed and Efficiency

5. Tokenization of Assets

6. Innovative Financial Products

7. Global Inclusion

8. Regulatory Compliance

9. Supply Chain Finance

Challenges and Considerations

While the impact of blockchain on modern finance is largely positive, it also comes with challenges, including:

Conclusion

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.

No because it is not maximum likelihood. WLSMV uses pairwise deletion as described in the following document:

Gert Lang Mplus uses pairwise deletion with WLSMV. See p. 5 of the document below:

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.

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.

Keep away from it if possible. Otherwise read this https://nj.gov/health/eoh/rtkweb/documents/fs/1530.pdf

You are most welcome

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:

Bapuji Maringanti

Nolberto Munier

Roberto Villarreal

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.

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.

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 .

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

, 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.

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.

ReadyAPI

Rest Assured

Malzemenin özelliği değişmiş olur.

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.

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!

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?

· 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.

· During loading, ensure that the containers are securely fastened to prevent movement or tipping.

· Use appropriate lifting equipment to handle heavy containers safely.

· 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).

· 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.

· Choose transportation routes carefully.

· Avoid densely populated areas, sensitive ecosystems, and water bodies.

· Consider road conditions, traffic, and potential hazards.

· Regularly monitor the condition of the containers during transit.

· Maintain communication with the transport team and provide updates on the shipment’s progress.

· 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

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

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

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:

Md Foysal Ahmed Wasswa Shafik

Hussain Nizam

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.

Manys thanks, Hussain Nizam !

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.

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.

Abhishek Pandey why not, how would you like to proceed?

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.

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.

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

Thank you very much for your kind advice, I'll try again without EDTA.

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.

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".

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.

Malcolm Nobre

any tips for the sub-cultivation step?? :)

Can Bayesian regression be used when there is a multicollinearity problem?

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.

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 .

Hello Chloé Chevalier

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.

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

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.

I suppose deathbed conversions or confessions are one way.

https://cran.r-project.org/web/packages/rcdk/index.html without any doubts.

The depedency to java is quite annoying but clearly better than the rest.

I am trying to go through this process as well. I have tried your protocol Matthew and it gets hard in the xylene process.

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.

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

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.

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.

Thanks a lot

Vered

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.

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:

PFC 2D/3D by Itasca (https://www.itascacg.com/software/pfc)

Becker3d (https://www.becker3d.com/)

OpenSource options include:

LAMMPS (https://www.lammps.org)

MFIX-DEM (https://mfix.netl.doe.gov/products/mfix/)

LIGGGTHS (https://www.cfdem.com/liggghtsr-open-source-discrete-element-method-particle-simulation-code)

Granoo (https://www.granoo.org/)

A list of open source options can be found here: (https://www.simonwenkel.com/lists/software/list-of-opensource-DEM-software.html)

Your efforts are appreciated

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.

Thank you, i was able to detect the problem

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!