Science topic

Automation - Science topic

Controlled operation of an apparatus, process, or system by mechanical or electronic devices that take the place of human organs of observation, effort, and decision. (From Webster's Collegiate Dictionary, 1993)
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Please give your valuable opinions.....
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Yes, It may be a particle section of library
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How does artificial intelligence answering aesthetic questions result in automated emotions? My answer: maybe artificial intelligence will plausibly obtain emotions through learning aesthetics.
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Dear Alexander Ohnemus , good question but very difficult to answer. One needs to understand the nature of consciousness, artificial or not. One area to start is to get acquainted with the philosophy of mind and the relationship with AI in regard to aesthetics and emotions. In short : your answer needs clarification on why, otherwise it is impossible to even considering in answering your question. KR Rob
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AI is expected to have a significant impact on the job market, influencing the nature of work and creating new opportunities while also automating certain tasks. Here are some ways AI may affect the job market and roles that are at risk of automation. How do you think?
  1. Automation of Routine Tasks
  2. Routine Cognitive Tasks
  3. Transportation and Delivery Services
  4. Manufacturing and Assembly
  5. Customer Support
  6. Data Entry and Analysis
  7. Certain Healthcare Tasks
  8. Financial Services
  9. Retail Jobs
However, it's important to note that while automation may eliminate certain jobs, it can also create new opportunities. Many experts argue that AI will lead to the creation of new roles that require uniquely human skills, such as creativity, emotional intelligence, critical thinking, and complex problem-solving. Additionally, there will be a growing demand for jobs that involve developing, maintaining, and managing AI systems.
To adapt to these changes, workers may need to acquire new skills and engage in lifelong learning to stay relevant in the evolving job market. Policies and initiatives that support retraining and upskilling will be crucial for helping the workforce navigate the transition brought about by AI and automation.
Hope to hear from you! Thanks!
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I have written a few thoughts about this in relation to consultancy - see https://interimconsult.co.uk/news/ai-the-impact-on-consultancy/
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Can artificial intelligence read minds? If so, how does that impact automated language translation? My answer: Artificial intelligence either can already read minds or potentially probably will be able to. Assuming the information in thoughts is translatable regardless of whatever language the person thinks in, the translation between humans may come faster. Humans are very visual creatures and language is fluid(always representing imagery in thoughts including extreme abstractions) thus, artificial intelligence translating one language to another could end the need for human language altogether, or maybe at least extremely facilitate translations.
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Artificial Intelligence (AI) has made remarkable advancements in recent years, but the idea of it being able to read minds seems far-fetched. However, if AI were capable of such a feat, it would undoubtedly revolutionize automated language translation. The impact would be immense and could potentially bridge the gap between different cultures and languages.
If AI could read minds, it would have a profound effect on automated language translation. Currently, translation algorithms rely on input text to generate accurate translations. However, this method often falls short in capturing the true meaning and nuances of a particular language. By being able to read minds, AI could understand the intent behind the words and phrases used by individuals.
This newfound ability would significantly enhance automated language translation by providing more context and accuracy. It would enable AI systems to comprehend idiomatic expressions, cultural references, and even sarcasm – elements that are often lost in current translation methods. As a result, communication between people speaking different languages would become more effective and seamless.
Moreover, if AI could read minds accurately, it could eliminate the need for any intermediary step in communication. People wouldn't have to type or speak their thoughts; instead, they could simply think them while AI translates them instantaneously. This direct mind-to-mind communication would break down barriers like never before and foster understanding among diverse groups of people.
However, despite these potential benefits, there are ethical concerns surrounding mind-reading technology that must be addressed. Privacy is one major issue that arises when discussing AI's ability to read minds. If an individual's thoughts can be accessed without consent or control over who can access them, it raises serious questions about personal autonomy and privacy rights.
In conclusion, while the idea of artificial intelligence reading minds may seem like science fiction at present time; if achieved successfully in the future - it will undoubtedly have a significant impact on automated language translation. The potential benefits include improved accuracy in translations as well as direct mind-to-mind communication. However, ethical considerations must be carefully addressed to ensure privacy and consent are respected.
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Project Management Institute notes that 81% of professionals say that AI is impacting their organizations. That number is likely to increase further in the coming years. Automation holds immense promise. By automating low-value-add tasks, project managers may focus their efforts and energy on tasks that will most dramatically benefit their businesses, allowing them to affect greater change, increasing the possibility of each project reaching its strategic goals. Recent years have seen AI adoption on a larger scale by organizations to ensure successful project completion in several ways such as: -> Generating performance insights -> Supporting the decision-making processes -> Making estimates and predictions -> Optimizing resource scheduling -> Enabling data visualization -> Performing risk analysis
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Generating performance insights in project management using artificial intelligence (AI) 🤖 : Using artificial intelligence (AI) and automation can greatly enhance the ability to generate performance insights in project management. Here are some ways you can leverage these technologies: 1. Data analysis: AI algorithms can help process vast amounts of project data, including project timelines, budgets, resource allocation, and performance metrics. By analyzing this data, AI can identify patterns, predict future outcomes, and highlight areas of improvement. 2. Real-time monitoring: AI-powered tools can monitor project activities in real-time, capturing data on tasks, milestones, and team member performance. This enables project managers to identify bottlenecks or potential risks promptly and take corrective actions. 3. Predictive analytics: AI algorithms can analyze historical project data to predict future project performance and outcomes. This can enable managers to make proactive decisions by identifying potential risks, estimating completion dates, and optimizing resource allocation. 4. Intelligent reporting: Automation can streamline the process of generating project reports by automatically pulling relevant data from multiple sources and generating visually appealing, customized reports. This saves time and ensures accurate and up-to-date insights for decision-making. 5. Resource optimization: AI can help optimize resource allocation by analyzing team member availability, skills, and workload. By identifying optimal resource assignments, managers can ensure efficient utilization of resources and maximize productivity. 6. Risk analysis: AI can identify and assess project risks by analyzing historical data, identifying risk indicators, and providing recommendations for risk mitigation strategies. This helps project managers take proactive measures to address potential risks before they become issues. 7. Stakeholder engagement: AI-powered chatbots and virtual assistants can improve stakeholder communication and engagement by providing instant responses to inquiries, scheduling meetings, and providing project updates. This enhances collaboration and ensures stakeholders are well-informed. It is important to remember that AI and automation should augment human judgment and decision-making rather than replace it completely. Therefore, project managers should interpret the insights generated by these technologies and use them as a basis for informed decision-making.
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Good morning everyone! I've just finished reading Shyon Baumann's paper on "Intellectualization and Art World Development: Film in the United States." This excellent paper includes a substantial section of textual analysis where various film reviews are examined. These reviews are considered a fundamental space for the artistic legitimation of films, which, during the 1960s, increasingly gained artistic value. To achieve this, Baumann focuses on two dimensions: critical devices and lexical enrichment. The paper is a bit dated, and the methodologies used can be traced back to a time when text analysis tools were not as widespread or advanced. On the other hand, they are not as advanced yet. The question is: are you aware of literature/methodologies that could provide insights to extend Baumann's work using modern text analysis technologies?
In particular, following the dimensions analyzed by Baumann:
a) CHANGING LANGUAGE
  • Techniques for the formation of artistic dictionaries that can replace the manual construction of dictionaries for artistic vocabulary (Baumann reviews a series of artistic writings and extracts terms, which are then searched in film reviews). Is it possible to do this automatically?
b) CHANGING CRITICAL DEVICES
  1. Positive and negative commentary -> I believe tools capable of performing sentiment analysis can be successfully applied to this dimension. Are you aware of any similar work?
  2. Director is named -> forming a giant dictionary of directors might work. But what about the rest of the crew who worked on the film? Is there a way to automate the collection of information on people involved in films?
  3. Comparison of directors -> Once point 2, which is more feasible, is done, how to recognize when specific individuals are being discussed? Does any tool exist?
  4. Comparison of films -> Similar to point 3.
  5. Film is interpreted -> How to understand when a film is being interpreted? What dimensions of the text could provide information in this regard? The problem is similar for all the following dimensions:
  6. Merit in failure
  7. Art vs. entertainment
  8. Too easy to enjoy
Expanding methods in the direction of automation would allow observing changes in larger samples of textual sources, deepening our understanding of certain historical events. The data could go more in-depth, providing a significant advantage for those who want to view certain artistic phenomena in the context of collective action.
Thank you in advance!
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I appreciate you raising this insightful question on how to leverage modern text analysis methods to build on Baumann's foundational work examining artistic legitimation. Automated techniques can certainly help scale such textual analysis to larger corpora. However, care must be taken to ensure computational approaches do not lose the nuance of manual qualitative interpretation.
Regarding building artistic dictionaries, word embedding models like word2vec can help uncover semantic relationships and suggest terms related to a seed vocabulary. However, human validation is still important before applying these dictionaries to make inferences.
For sentiment analysis, deep learning approaches like BERT have shown promise, but domain-specific tuning and qualitative checks are key to account for the complex expressions in artistic reviews. Models pre-trained on social media may not transfer well.
To identify creators, named entity recognition using dictionaries, rules, and ML approaches can help. However disambiguation remains challenging, so human-in-the-loop verification is recommended before making claims about individuals.
Overall, I believe the best approach is applying computational methods as a starting point, but having experts qualitatively analyze a sample of results to catch subtleties these tools may miss. If used prudently and in collaboration with scholars like yourself, text mining can uncover exciting new insights at scale. Please feel free to reach out to discuss further.
Wishing you the very best,
#textanalysis #digitalhumanities #mixedmethods
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I was thinking about Teensy 3.5, because it has DAC, but it is discontinued, unfortunately. Raspberry Pi Pico seems interesting, but it has PWMs only, which can be converted to DC voltage signal using PWM-to-Voltage converter.
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They are called evaluation boards, right?
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We are going to use the Heidelberg OCT Spectralis for mouse OCT acquisition. However, we find several papers reporting the use of this instrument for rodents, we are not able to successfully do it. We have two separate devices (1- Heidelberg OCT Spectralis and 2- Heidelberg Hra 2). The Heidelberg OCT Spectralis has two lenses one 30° Standard Objective Lens and an Anterior Segment lens. The Heidelberg Hra 2 has 55° widefield lens. unfortunately, the 55° lens could not be assembled to Heidelberg OCT Spectralis.
As far as we know, given the high dioptre of the mouse eye, we should use the 55° widefield lens. However, using the standard 30° we get a rather acceptable cSLO image, no OCT image is displayed.
Can anyone help solve this problem? We already tried using an additional lens in front of the device lens but still not working, however, maybe the total dioptre of the lens was not enough.
Also, a paper suggests minor software modifications (using Alt+Ctrl+Shift+O in Heidelberg Eye Explorer software) which we could not figure out how that should be done. (Spectral domain optical coherence tomography in mouse models of retinal degeneration. Invest Ophthalmol Vis Sci. 2009 Dec;50(12):5888-95. doi: 10.1167/iovs.09-3724.)
These are some papers about using the Heidelberg OCT Spectralis for rodents:
1- Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images. Trans. Vis. Sci. Tech. 2015;4(4):9. doi: https://doi.org/10.1167/tvst.4.4.9.
2- Tracking Longitudinal Retinal Changes in Experimental Ocular Hypertension Using the cSLO and Spectral Domain-OCT. Invest. Ophthalmol. Vis. Sci. 2010;51(12):6504-6513. doi: https://doi.org/10.1167/iovs.10-5551.
3- Giannakaki-Zimmermann H, Kokona D, Wolf S, Ebneter A, Zinkernagel MS. Optical Coherence Tomography Angiography in Mice: Comparison with Confocal Scanning Laser Microscopy and Fluorescein Angiography. Transl Vis Sci Technol. 2016 Aug 18;5(4):11. doi: 10.1167/tvst.5.4.11. PMID: 27570710; PMCID: PMC4997887.
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Hi Danilo. Unfortunately, no. We could only get some preliminary results using TOPCON using 7line raster mode. however, we could not achieve a full field of the retina.
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Hello,
I’m conducting market research on the management tools used by researchers to automate workflow, visualize processes, and manager budget & supply.
Anyone willing to address few questions in this regard, please connect.
Im interested in researchers all around the world!
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I agree with Dr. Qamar Ul Islam.
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How is automation transforming agriculture? How will it impact jobs on farms and rural economies?
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Automation is significantly transforming agriculture by introducing advanced technologies that improve efficiency, productivity, and sustainability in farming practices. While these innovations offer numerous benefits, they also raise questions about the impact on jobs in agriculture and rural economies. Here's how automation is influencing agriculture and its potential implications for jobs and rural communities:
· Precision Farming: Automation technologies such as GPS-guided tractors, drones, and remote sensors allow for precise planting, fertilizing, and harvesting. This precision reduces input waste and increases yield, improving farm productivity.
· Autonomous Machinery: Autonomous or self-driving machinery, like driverless tractors, can perform tasks with minimal human intervention. These machines can operate 24/7, increasing farm efficiency and reducing labor requirements.
· Robotic Systems: Agricultural robots can handle various tasks, from weeding and pruning to fruit picking. These robots improve accuracy and efficiency, reducing the need for manual labor.
· AI and Data Analytics: Artificial intelligence (AI) and data analytics help farmers make informed decisions about crop management, pest control, and resource allocation. This reduces guesswork and labor-intensive tasks.
· IoT Sensors: Internet of Things (IoT) sensors provide real-time data on soil conditions, weather, and crop health. Farmers can make data-driven decisions, optimizing resource use and reducing the need for constant monitoring.
· Smart Irrigation: Automated irrigation systems adjust water delivery based on real-time data, reducing water waste and human labor.
Impact on Jobs and Rural Economies:
· Reduced Labor Demand: Automation can lead to a reduced demand for manual labor in agriculture, which has traditionally been a significant source of employment in rural areas. This may result in job displacement for farmworkers.
· Shift in Job Roles: Automation creates new job opportunities related to technology, data analysis, robotics maintenance, and AI management. However, these jobs may require different skills than traditional farming tasks.
· Improved Working Conditions: Automation can lead to safer and more comfortable working conditions for agricultural workers, reducing physical strain and exposure to harsh environmental conditions.
· Increased Productivity: By improving efficiency and yield, automation can make farming more economically viable. Increased farm profitability can have positive effects on rural economies by stimulating investments in local businesses.
· Challenges in Transition: The transition to automation may pose challenges for some rural communities, especially if there is a mismatch between the skills of displaced workers and the demands of new, technology-oriented jobs.
· Education and Training: Preparing the rural workforce for the use of automation and technology is crucial. Education and training programs can help rural workers acquire the necessary skills to adapt to changing employment opportunities.
· Innovation and Diversification: Automation can free up farmers' time and resources to diversify their income sources, including agritourism, value-added products, and direct-to-consumer sales.
· Balancing Automation: Striking a balance between automation and the retention of traditional farming practices may be important for preserving cultural and local heritage while embracing new technologies.
In conclusion, while automation is transforming agriculture by increasing efficiency and productivity, it also brings changes to the job landscape in rural areas. The impact on jobs and rural economies will depend on the extent of automation, the preparedness of the workforce, and the ability of rural communities to adapt to evolving opportunities and challenges. Balancing the benefits of automation with the need to address potential job displacement is a critical consideration for the future of rural agriculture.
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The term NDE 4.0 originates from the idea of the fourth industrial revolution, or Industry 4.0. It is defined as the cyber-physical confluence between non-destructive testing and evaluation (NDT&E) and digital technologies including Artificial Intelligence, Digital Twins, Robotics, Advanced Sensing, and Sensor Networks. These ideas have the potential to become a cyber-physical NDE ecosystem (similar to the ongoing transformations in the medical and mobile communication sectors) that reshapes many traditional NDE, structural health monitoring, and prognoses. NDE 4.0 is also about the inspection of new generations of multi-functional components like additively manufactured parts and about fully automated inspection systems integrated into smart factories. Interest in this interdisciplinary field is growing and has the potential to involve all aspects of NDE. This special issue is focused on NDE 4.0, especially from a physics perspective.
Guest editors:
  • Antonello Tamburrino
  • Johannes Vrana
  • Norbert Meyendorf
  • Zheng Liu
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The first paper of this special issue has been published (Written by Norbert G Meyendorf , Nathan Ida, Ripi Singh , and myself):
We are waiting for your paper ...
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In the a molecule I am working on, there is a certain O-H bond. I wanted to study the variation of energy on changing the bond length, for that I did a relax-scan computation on g16. I have generated the output where in the .log file I have 50 different structures, each with their energies.
As my next step, I want to calculate vertical excitation energy for each of the 50 structures. I can do it manually by switching to each structure from the window shown in the attachment and use the GUI to generate a .com file of energy calculation for each of them.
But this process gets too tedious and eventually impossible as the number of structures in my scan increase to >100. Is there any way to automate this using some script? Any help will be appreciated
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You can run simultaneously both relax-scan and td-dft calculations.
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We submitted a manuscript to PLOS ONE and were accepted for publication on July 12, 2023. We paid the article processing charge a few days later, but the article has still not been published (after 40 days). A DOI has been generated but it leads nowhere (10.1371/journal.pone.0289151).
We have tried contacting PLOS ONE through multiple email addresses and have received no response. We also tried to call the phone number listed on our invoice to confirm payment, but were directed to an automated message that told us to send them an email. Due to 'high volume' they expected to get back to us in 7-10 business days.
Has anyone else had delays in publishing with PLOS lately?
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Congrats!
By the way they claim the following: Received April 10th Accepted July 12th Published September 8th, 2023, wonder if the publishing date is correct.
Best regards.
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How can end-to-end workflow automation be achieved for cloud-based machine and deep learning pipelines?
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Here I provided a comprehensive approach to performing E2E automation. Initialize automation by defining the requirements of your ML/DL-based pipeline. Develop models and train large data sets by storing them in secure cloud-based data lakes. Deploy and monitor the real-time insights.
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What is smart agriculture system using IoT in India and how can Internet of Things help farming by automating farming techniques?
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In fact, thanks to it, farmers can see and manage all data and equipment using one device in real-time without going on the field. This smart farming approach improves overall plant productivity, reduces waste, and optimizes electricity, fuel, water, and fertilizer use. IoT smart agriculture products are designed to help monitor crop fields using sensors and by automating irrigation systems. As a result, farmers and associated brands can easily monitor the field conditions from anywhere without any hassle. IoT-based smart agriculture refers to applying IoT technology in the agricultural sector for optimized farming processes. It integrates sensors, actuators, and other smart devices to enable data collection, analysis, and automated decision-making in farming operations. By using IoT sensors to collect environmental and machine metrics, farmers can make informed decisions, and improve just about every aspect of their work – from livestock to crop farming. Digital technologies such as satellite imaging, drones, and sensors can continue to help farmers optimize their use of resources such as water, fertilizers, and pesticides. This can help to increase productivity, reduce costs, and minimize environmental impacts. Increasing control over production leads to better cost management and waste reduction. The ability to trace anomalies in crop growth or livestock health, for instance, helps eliminate the risk of losing yields. Additionally, automation boosts efficiency. Climate-smart agriculture (CSA) is a part of India's and the G20 countries' Sustainable Development Goals (SDGs) vision. The objective of CSA is to optimize a country's agricultural productivity, resilience and emissions in response to climate change using real-time, localized information. With the right IoT technologies, farmers can make better decisions about fertilizer use, harvesting, and livestock health monitoring, reducing post-harvest losses. Additionally, the IoT in agriculture will help farms reduce their energy usage and repair costs. The Future of IoT in agriculture in India will enable growers to grow better crops at lower costs. The husbandry IoT devices can help them identify herd health, prognosticate crop water conditions, and collect environmental and machine criteria. IoT and AI based systems are capable of enhancing input use efficiency on the farm. Smart farming leverages digital technologies to automate agricultural operations in real-time. Deep learning based solutions can solve numerous day to day agricultural problems.
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Can artificial intelligence farming make agriculture more sustainable and how IoT and machine learning are automating agriculture?
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In fact, AI enables farmers to cultivate in a smart and sustainable manner, reaping higher and better yields while using fewer resources. If adopted at scale, AI definitely has the potential to power the next agricultural revolution. For this, farmers, governments and AI experts need to work in tandem to ease AI adoption. By collecting data on plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions. With the help of AI, scientists can identify the best-performing plant varieties and crossbreed them to create even better hybrids. AI can help detect field boundaries and bodies of water to enable sustainable farming practices, improve crop yields, and support India's 1.4 billion people and the rest of the world. AI can be appropriate and efficacious in agriculture sector as it optimizes the resource use and efficiency and solves the scarcity of resources and labor to a large extent. Artificial intelligence can be technological revolution and boom in agriculture to feed the increasing amount of human population in the world. AI systems are helping improve the harvest quality and accuracy, which is known as precision agriculture. AI technology assist's in detecting the diseases in plants, pests, poor plant nutrition, etc. It also allows the farmers to monitor the health of the crops and the soil.PwC numbers estimate that the use of AI can reduce worldwide greenhouse gas (GHG) emissions by 4%. Use of AI for environmental purposes can also contribute up to $5.2 trillion USD to the global economy in 2030. Artificial intelligence (AI) is playing a critical role in the agriculture sector as it helps farmers to increase crop yields, improve crop quality, and reduce production costs. In addition, AI can help farmers to optimize irrigation systems, predict weather patterns, and forecast market prices.IoT and AI based systems are capable of enhancing input use efficiency on the farm. Smart farming leverages digital technologies to automate agricultural operations in real-time. Deep learning based solutions can solve numerous day to day agricultural problems. By collecting IoT data, smart sensors can enable real-time monitoring of “what is happening on the ground.” Farming can be made more efficient by knowing when to harvest, the amount of water used and whether irrigation is needed, soil health, and fertilizer requirements. I oT in agriculture uses robots, drones, remote sensors, and computer imaging combined with continuously progressing machine learning and analytical tools for monitoring crops, surveying, and mapping the fields, and provide data to farmers for rational farm management plans to save both time and money. Using the deep learning and ML techniques, the crop productivity in the agriculture can be improvised. By using the sensor data, the accurate data are predicted by evolving the artificial intelligence, which allows a smart way in farmer decision making. Using the deep learning and ML techniques, the crop productivity in the agriculture can be improvised. By using the sensor data, the accurate data are predicted by evolving the artificial intelligence, which allows a smart way in farmer decision making.
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suggest a topics that are related to automation or relays and if you a previous design project that are needed to innovate that would good also
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How can I control the centrifugal speed of 4, 6 or 8 bogie wheels on a locomotive? There will be an encoder on each wheel so the circumferencial speed is known. This changes when a curve is approached in the railway line. In the picture above, the large wheels represent the railway line. One bogie wheel can be adjusted in and out to represent a change in circumference of the wheel. The object is to prevent slip and slide of the bogie wheels.
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What has been missing from the open-source availability of ChatGPT-type artificial intelligence on the Internet? What is missing in order to make it possible to comply with the norms of text publishing law, tax law, copyright law, property law, intellectual value law, to make it fully ethical, practical and effective, and to make it safe and not generate misinformation for Internet users to use this type of technology?
How should an automated system for verifying the authorship of texts and other works be structured and made openly available on the Internet in order to verify whether phrases, fragments of text, phrases, wording, etc. are present in a specific text submitted to the editors of journals or publishers of books and other text-based publications? If so, to what extent and from which source texts did the artificial intelligence extract specific phrases, fragments of text, thus giving a detailed description of the source texts, providing footnotes to sources, bibliographic descriptions of sources, etc., i.e. also as is done by efficient and effective computerised anti-plagiarism systems?
The recent appeal by the creators of ChatGPT-type artificial intelligence technology, the appeal by businessmen and founders and co-founders of start-ups developing artificial intelligence technology about the need to halt the development of this type of technology for at least six months confirms the thesis that something was not thought of when OpenAI made ChatGPT openly available on the Internet, that something was forgotten, that something was missing from the openly available ChatGPT-type artificial intelligence system on the Internet. I have already written about the issue of the potential massive generation of disinformation in my earlier posts and comments on previously formulated questions about ChatGPT technology and posted on my discussion profile of this Research Gate portal. On the other hand, to the issue of information security, the potential development of disinformation in the public space of the Internet, we should also add the issue of the lack of a structured system for the digital marking of "works" created by artificial intelligence, including texts, publications, photographs, films, innovative solutions, patents, artistic works, etc., in order to ensure the security of information. In this regard, it is also necessary to improve the systems for verifying the authorship of texts sent to journal editors, so as to verify that the text has been written in full compliance with copyright law, intellectual property law, the rules of ethics and good journalistic practice, the rules for writing texts as works of intellectual value, the rules for writing and publishing professional, popular science, scientific and other articles. It is necessary to improve the processes of verifying the authorship of texts sent to the editorial offices of magazines and publishing houses of various text publications, including the improvement of the system of text verification by editors and reviewers working in the editorial offices of popular-scientific, trade, scientific, daily and monthly magazines, etc., by creating for their needs anti-plagiarism systems equipped with text analysis algorithms in order to identify which fragments of text, phrases, paragraphs were created not by a human but by an artificial intelligence of the ChatGPT type, and whose authorship these fragments are. An improved anti-plagiarism system of this kind should also include tools for the precise identification of text fragments, phrases, statements, theses, etc. of other authors, i.e. providing full information in the form of bibliographic descriptions of source publications, providing footnotes to sources. An anti-plagiarism system improved in this way should, like ChatGPT, be made available to Internet users in an open access format. In addition, it remains to be seen whether it is also necessary to legally oblige editors of journals and publishers of various types of textual and other publications to use this kind of anti-plagiarism system in verifying the authorship of texts. Arguably, the editors of journals and publishers of books and other types of textual publications will be interested in doing so in order to apply this kind of automated verification system for the resulting publication works. At the very least, those editors of journals and publishers of books and other types of textual publications that recognise themselves and are recognised as reputable will be interested in using this kind of improved system to verify the authorship of texts sent to the editors. Another issue is the identification of technological determinants, including the type of technologies with which it will be possible to appropriately improve the automated verification system for the aforementioned issue of text authorship. Paradoxically, here again, the technology of artificial intelligence comes into play, which can and should prove to be of great help in the aforementioned issue of verification of the aforementioned question of authorship of texts and other works.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How should an automated and open-access online system for verifying the authorship of texts and other works be structured in order to verify whether phrases, text fragments, phrases, wordings, etc. are present in a specific text sent to the editors of journals or publishers of books and other textual publications? If YES, to what extent and from which source texts did the artificial intelligence retrieve specific phrases, fragments of text, thus giving detailed characteristics of the source texts, providing footnotes to sources, bibliographic descriptions of sources, etc., i.e. also as implemented by efficient and effective computerised anti-plagiarism systems?
What was missing from making a ChatGPT-type artificial intelligence system available on the Internet in an open access format? What is missing in order to make it possible to comply with the norms of text publishing law, tax law, copyright law, property law, intellectual property law, to make it fully ethical, practical and effective, and to make it safe and not generate disinformation for Internet users to use this type of technology?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
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Much still needs to be improved systemically so that the use of this type of technology complies with the norms of text publishing law, tax law, copyright law, property law, intellectual value law, so that it is fully ethical, practical and effective, as well as safe and does not generate misinformation among Internet users. It is necessary to regulate the use of various tools based on artificial intelligence so that this use generates positive rather than negative aspects. It is necessary to increase the scale of control over the use of artificial intelligence-based tools available on the Internet so that this use does not generate disinformation, copyright violations, new categories of threats, cyber crime, etc.
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
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In the past month, August 2023, I have tried repeatedly to contact Researchgate.net staff about some papers I have posted here. One paper is posted only as an abstract, but the main text is missing; the other was posted for 3 days (abstract and full text) and then it disappeared from my research record. I wish to find out why?
My repeated attempts to contact Researchgate.net staff to receive answers to these questions have been declined. I received an automated message stating that "access to the 'contact' site has been denied".
I do not understand what the problem is here.
If anyone else has had similar experiences, please let me know.
Thank you very much!
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Indeed very strange. You may try to send an email message to support@researchgate.net.
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To what extent can robots and AI replace human jobs across different sectors? What are the socioeconomic implications of widespread automation? How can governments and industries manage potential job displacement and ensure a smooth transition for the workforce?
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No
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What is the role of automation in agriculture and applications of robotic automation in food processing?
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Automation plays a crucial role in modern agriculture and food processing by increasing efficiency, reducing labor costs, improving product quality, and enabling more sustainable practices. Robotic automation in particular has been applied across various stages of the agricultural and food processing supply chain. Here's an overview of its role and applications:
Role of Automation in Agriculture:
Precision Farming: Automation technologies, such as drones and autonomous tractors, are used for precision agriculture. These technologies help optimize the use of resources like water, fertilizers, and pesticides by applying them only where needed, leading to increased crop yield and reduced environmental impact.
Planting and Harvesting: Robotic systems can plant seeds and transplant seedlings with precision, ensuring uniform spacing and optimal planting depth. Harvesting robots equipped with sensors can selectively pick ripe fruits and vegetables, reducing waste and increasing efficiency.
Weed and Pest Management: Automation aids in detecting and treating weeds and pests using targeted methods. This reduces the need for excessive chemical use and promotes sustainable pest management practices.
Monitoring and Data Collection: Sensors and IoT devices are deployed in fields to monitor soil conditions, weather, and crop health. Automated data collection provides farmers with real-time insights, enabling informed decision-making.
Greenhouse Automation: Greenhouses are increasingly automated with climate control systems, irrigation, and shading mechanisms that optimize growth conditions for crops.
Applications of Robotic Automation in Food Processing:
Sorting and Grading: Robots equipped with cameras and sensors can sort and grade fruits, vegetables, and other products based on size, color, and quality. This ensures consistent product quality and reduces the need for manual labor.
Cutting and Processing: Robotic arms equipped with cutting tools can precisely process and prepare raw materials for further processing, reducing the risk of contamination and improving safety.
Packaging and Palletizing: Robots can automate the packaging of products into containers or bags and palletize them for shipping. This increases efficiency and reduces the risk of repetitive strain injuries for human workers.
Quality Inspection: Vision systems and sensors on robots are used to inspect products for defects, ensuring that only high-quality items are packaged and distributed.
Cooking and Preparation: In commercial kitchens and food production facilities, robots can handle tasks such as mixing ingredients, cooking, and assembly, improving consistency and reducing the need for skilled labor.
Traceability: Automation systems can track and record the movement of ingredients and products throughout the processing and distribution chain, ensuring better traceability for food safety and quality control.
Waste Reduction: Automation can help reduce food waste by accurately portioning ingredients, optimizing cutting processes to minimize waste, and identifying and removing defective products early in the production process.
Hygiene and Safety: Robots can work in environments that might be hazardous or unclean for humans, ensuring food safety and worker well-being.
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What is the effect of artificial intelligence and robotics in agriculture and role of robotics and automation in precision farming?
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The use of AI should pave the way for accurate observation and measurement of growers' needs which, among other things, will help them to limit their environmental impact. Without it, the development of large-scale precision agriculture would not be possible. AI algorithms can analyze the chemical composition of soil samples to determine which nutrients may be lacking. AI can also identify or even predict crop diseases. AI in irrigation is useful for identifying optimal patterns and nutrient application times, while predicting the optimal mix of agronomic products. As the global demand for food continues to rise, innovative solutions are emerging to help optimize operations in agriculture. Enter robots – the new, high-tech workforce in agriculture. These versatile machines can handle a wide range of tasks, from planting and irrigation to pest control and soil analysis. The incorporation of robotics in agriculture improves both productivity and working conditions for farmers and workers. Intelligent systems are becoming the ideal solution to drive precision agriculture. Today, a large number of agricultural operations are already being done autonomously. One of the key ways in which AI is used in robotics is through machine learning. This technique enables robots to learn and perform specific tasks through observing and mimicking human actions. AI gives robots a computer vision that enables them to navigate, detect and determine their reactions accordingly. The main area of application of robots in agriculture today is at the harvesting stage. Emerging applications of robots or drones in agriculture include weed control, cloud seeding, planting seeds, harvesting, environmental monitoring and soil analysis. The application of robotics will help in various field operations for movement, localization, capturing, targeting and moving to the next target using drones for addressing spatial as well as temporal management of crops. Same operation can be used in spraying, weeding as well as harvesting of fruits. By the 2030s, the future of robotics in manufacturing could be completely autonomous, from assembly to quality control, thanks to advancements in AI and the Internet of Things (IoT). Robot maintenance and operation will replace traditional jobs in this industry. Technologies developed in the framework of precision agriculture, including automation and robotics, make it possible to produce with a minimal environmental impact and at the same time all treatments and handling are recorded and can be uploaded in the different data bases that are used for tracing. The robot navigates the field and interprets the assigned task. It helps the farmers by blowing weeds, applying chemicals or harvesting the crops, and includes navigation systems to locate themselves. Therefore, while mechanization eases and reduces hard and repetitive work and relieves labour shortages, digital automation technologies further improve productivity by allowing more precise implementation of agricultural operations and more efficient use of resources and inputs.
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Automated hematology analyzer often miss to count the platelet with abnormal size, particularly macro platelet. So every case should be examined manually to exclude macrothrombocythemia.
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I am not Bio major but I can analyze data. Everywhere there outliers, we shall treat this as outlier.
When analyzers miss counting platelets with abnormal sizes, such as macroplatelets, perform a manual examination to exclude macrothrombocytopenia. Steps to handle outlier:
1. Visual Inspection: Prepare a peripheral blood smear slide and examine it under a microscope. Look for platelets with larger sizes compared to the normal range. Macroplatelets are usually identified by their increased diameter and irregular shapes.
2. Manual Counting: Select an area on the slide where platelets are well-distributed. Count the number of macroplatelets separately from the automated count. Use a counting grid or manual counting methods to ensure accuracy.
3. Calculate Corrected Platelet Count: Once you have obtained the manual count of macroplatelets, calculate the corrected platelet count. Subtract the number of macroplatelets from the total automated platelet count. The corrected platelet count reflects the number of normal-sized platelets.
4. Evaluate Clinical Significance: Assess the clinical significance of macrothrombocytopenia based on the patient's medical history, symptoms, and other laboratory findings. Macrothrombocytopenia can be associated with various conditions, including inherited disorders, immune-mediated disorders, or bone marrow abnormalities. Consult with a hematologist or appropriate specialist for further evaluation and interpretation.
5. Follow-up and Monitoring: If macrothrombocytopenia is confirmed, it is essential to monitor the patient closely. Additional investigations, such as genetic testing or bone marrow examination, may be necessary to determine the underlying cause. Regular follow-up and monitoring of platelet counts can help assess the progression of the condition and guide appropriate management.
For manual differentials, it is better to seek assistance from a qualified laboratory professional or hematologist to ensure accurate results and appropriate management of the patient.
Good luck
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"AI and ML technologies are streamlining and optimizing warehouse operations. Let's discuss their current and potential applications, from inventory optimization to automation. Share your experiences, studies, or questions on this topic."
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AI, ML, and the Evolution of Warehouse Management
The landscape of warehouse management is rapidly shifting with the integration of AI and ML, transforming the once manually-driven environment into a haven of efficiency and predictive capabilities. Beyond the obvious realm of automation and robotics, the real magic unfolds in areas like:
  1. Predictive Analytics: Using historical data to forecast demand, thereby optimizing inventory levels and reducing carrying costs.
  2. Intelligent Route Planning: With AI, warehouse robots can autonomously find the most efficient paths, reducing pick times and improving workforce productivity.
  3. Demand Sensing: Leveraging machine learning to interpret signals from diverse data sources, enabling warehouses to adapt inventory strategies in real-time.
  4. Enhanced Quality Control: Through computer vision, anomalies in products can be detected, ensuring only the best quality items make it through.
  5. Energy Optimization: Implementing AI algorithms to ensure equipment runs at optimal times, thereby reducing energy costs.
  6. Maintenance Predictions: Proactively identifying equipment that might fail soon, thus preventing costly downtimes and ensuring continuity.
  7. Customization and Personalization: AI can assist in customizing orders and packaging, providing a tailored experience for the end consumer.
The above are mere glimpses into the potential of AI and ML in warehouse management. How have you seen these technologies being implemented? Are there upcoming innovations we should keep an eye on? Do you foresee any challenges in integrating such advanced systems into traditional warehouse structures?
Please share your insights, case studies, potential pitfalls, or even intriguing questions. The confluence of minds here can pave the way for future innovations in the field.
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Dear academic community,
I would like to invite you to participate in a survey that focuses on artificial intelligence, technostress, and their impact on productivity. Artificial intelligence (AI) is a field of technology that focuses on simulating human intelligence and automating tasks. AI can have a significant impact on academic productivity in a number of ways, such as automating tasks, helping with data analysis, and providing personalized learning experiences. However, AI can also lead to technostress among academic workers. Technostress is an emotional state that is caused by the use of technology and is characterized by feelings of stress, anxiety, and frustration. Technostress can be caused by a number of factors, such as overload, technological problems, and uncertainty about how to use technology.
Technostress can have a negative impact on academic productivity. Those who experience technostress may be less productive, more likely to make mistakes, and more likely to feel burned out. Technostress can also have a negative impact on the learning experience. Those who experience technostress are less likely to be interested in learning, less likely to participate actively in class, and more likely to feel overwhelmed.
It is important to research the relationship between AI, technostress, and academic productivity in order to understand how to use AI safely and effectively in the academic environment.
The survey is anonymous and voluntary; we will analyze the responses once the survey is concluded. Contact:  E- mail adress:  simon.alzbeta.research@gmail.com
Note: If you have any insights or suggestions regarding the topic, please don't hesitate to contact me via e-mail. After the research is concluded, we would be pleased to send you a summary of the study.
Best regards, 
Author: PhDr. Alžbeta Simon Department of Management,  J. Selye University, Slovakia
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Thank you very much.
Best regards.
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What is the role of automation drones and robotics in agriculture and how drones could be the future of Indian farming?
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Agriculture drones and robots are becoming increasingly popular in the farming vertical as farmers are adopting innovative farming practices. Through agriculture drones and agribots, farmers can now survey and monitor crops, study and analyze soil samples, and spray pesticides and fertilizers on the soil. These are some applications of robotics in agriculture for which Robotnik robots are used: Crop condition identification and corresponding chemical application, spraying or harvesting, as required by the fruit or plant. Robots are being used to pick apples, gather strawberries, harvest lettuce and strip away weeds. They are machines with motors and sensors. They perform operational tasks while intelligently responding to their environment. They are used to plant, harvest, package, and transport crops. They can also detect and avoid obstacles while performing tasks, significantly reducing the chances of human injury or equipment failure.Agricultural drones allow farmers to monitor crop and livestock conditions from the air to keep watch for potential problems and help optimize field management. There are several functions that farmers and other agribusiness owners can use agricultural drone services for, including: Land imaging. In the coming years, drones will provide more in-depth data on crop conditions and become increasingly autonomous. Farmers will be able to receive constant, up-to-date information on their crops and use that data to drive their decisions at speeds that, today, are unheard of. Drones can be used for monitoring the conditions of crops throughout the crop season so that the need-based and timely action can be taken. The quick and appropriate action can prevent yield loss. This technology will eliminate the need to visually inspecting the crops by the farmers. In agriculture drones are also being used in a battle – in that case against yield-robbing pests (insects, weeds, diseases). But drones are also a way to dramatically increase the ability to track the growth of crops and identify nutrient deficiencies. All this information can then guide key farm management decisions. Drones can be used for a wide range of tasks in the agriculture sector, including crop mapping, soil analysis, irrigation, and pest management. Agri-drones can be used to spray chemicals as they have reservoirs, which can be filled with fertilizers and pesticides for spraying on crops in very little time, as compared to traditional methods.
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Which are the sensors can be used in agriculture in IoT and use of sensors in the field of automation and control?
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One can connect IoT-based agriculture sensors, such as temperature and moisture sensors in agriculture for environmental monitoring applications. The sensors can ensure fine dust, high-pressure spray, submersion in water, and extreme temperatures. The technology consists of a sensor or series of sensors that measure the level and moisture content in grains, such as corn. This information is then relayed back to an app on a tablet or computer where it can be viewed by the farmer for analysis. Electrochemical Sensors provide key information required in precision agriculture: pH and soil nutrient levels. Sensor electrodes work by detecting specific ions in the soil. Currently, sensors mounted to specially designed “sleds” help gather, process, and map soil chemical data. In addition to monitoring the plants that are harvested, temperature sensors observe the equipment that gathers these plants. Temperature sensors send out alerts whenever an equipment system requires minor maintenance, is underperforming, or is critically failing.Agriculture through precision agriculture implements IoT through the use of robots, drones, sensors, and computer imaging integrated with analytical tools for getting insights and monitoring the farms. Placement of physical equipment on farms monitors and records data, which is then used to get valuable insights. Sensors play a crucial role by detecting and measuring a variety of parameters such as temperature, pressure, humidity, flow rate, motion, and position. They convert physical signals into electric signals and provide information in real-time to the control system, thereby making production intelligent and automated.
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Would you like to use a completely new generation of ChatGPT-type tool that would be based on those online databases you would choose yourself?
What do you think about such a business concept for an innovative startup: creating a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use only those online databases, knowledge bases, portals and websites that individual Internet users will select themselves?
In your opinion, does it make sense to create a new generation of something similar to ChatGPT, which will use databases built exclusively on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use exclusively those Internet databases, knowledge bases, portals and websites that individual Internet users themselves will select, determine, define?
In my opinion, it makes sense to create a new generation of something similar to ChatGPT, which will use databases built exclusively on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use exclusively those Internet databases, knowledge bases, portals and websites that individual Internet users themselves will select, define, define. This kind of solution, which would allow personalization of the functionality of such generative artificial intelligence systems, would significantly increase its functionality for individual users, Internet users, citizens. In addition, the scale of innovative solutions for practical applications of such personalized intelligent systems for analyzing content and data contained in selected specific Internet resources would increase significantly.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
In your opinion, does it make sense to create a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use only those Internet databases, knowledge bases, portals and websites that individual Internet users themselves will select, specify, define?
What do you think of such a business concept for an innovative startup: the creation of a new generation of something similar to ChatGPT, which will use databases built exclusively on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use exclusively those online databases, knowledge bases, portals and websites that individual Internet users will themselves select?
Would you like to use a completely new generation of ChatGPT-type tool, which would be based on those online databases that you yourself would select?
What do you think about this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Counting on your opinions, on getting to know your personal opinion, on a fair approach to the discussion of scientific issues, I deliberately used the phrase "in your opinion" in the question.
Dariusz Prokopowicz
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Certainly, embracing a new ChatGPT generation based on curated online databases offers exciting potential. The enriched data could enhance accuracy and relevance, fostering more insightful and contextually aware interactions, ultimately delivering an elevated user experience.
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We have been dealing with digitization as a 'new concept' for more than 10 years now. Previously, it was referred to as automation and ERP/MES/CRM. Nowadays, the latest systems are encompassed under the term digitization. It seems to be a fundamental novelty. For years, the terms Data Mining, Process Mining, Artificial Intelligence (AI) and KI have also been part of the discussion. Suddenly, the term AI appears to overshadow everything. How do you see the connections between automation, ERP/MES/CRM, digitization, Data/Process Mining, and KI?
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In the realm of technology, the interconnected web of automation, ERP/MES/CRM systems, digitization, data/process mining, and AI is truly a mesmerizing dance of innovation. Automation streamlines tasks, while ERP/MES/CRM unify operations and customer interactions. Digitization breathes life into information, while data/process mining uncovers hidden gems of knowledge. And at the heart of it all, AI reigns supreme, orchestrating intelligence with flair. This symbiosis unlocks unprecedented efficiency and insights, propelling us into a brighter future. So, let us embrace this harmonious symphony, for in the convergence of these forces, lies the transformative power to revolutionize the world as we know it.
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The widespread automation of jobs could lead to mass unemployment. Preparing for a future with increased leisure time and rethinking economic and societal structures becomes crucial to mitigate the negative impacts.
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AI will also lead to new job markets and new opportunities. To not to become unemployed requires that you will learn new technologies and find yourself in new fields
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AI has the potential to automate and enhance various aspects of our lives, but it is unlikely to completely replace humans in the near future. While AI technology has made significant advancements in areas such as image recognition, natural language processing, and automation, there are several reasons why complete human replacement is unlikely in the near term:
  1. Complexity of human skills
  2. Ethical and social considerations
  3. Contextual understanding
  4. Social acceptance
Instead of outright replacement, the future is more likely to involve collaboration between humans and AI systems, with AI augmenting human capabilities and assisting in various tasks. This collaboration can lead to increased productivity, improved decision-making, and the automation of routine and repetitive tasks, freeing up humans to focus on higher-level activities that require uniquely human skills.
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As of my knowledge, AI cannot completely replace human intelligence. While AI has made significant advancements and demonstrated remarkable capabilities in various domains, it is important to understand the fundamental differences between AI and human intelligence.
AI is designed to perform specific tasks based on patterns and data, often with high accuracy and efficiency. It excels in tasks involving data processing, pattern recognition, decision-making, and automation. AI systems can outperform humans in tasks such as playing complex games, image recognition, language translation, and data analysis.
However, human intelligence is characterized by a broad spectrum of qualities that go beyond pattern recognition and data processing. Human intelligence involves creativity, emotional understanding, moral reasoning, empathy, abstract thinking, adaptability, and consciousness. Humans have the ability to engage in open-ended problem-solving, form emotional connections, possess self-awareness, and demonstrate moral judgment.
While AI can excel in specialized tasks, it lacks the depth and breadth of human intelligence. AI systems are limited to what they are programmed for and operate within the boundaries of their algorithms and data. They lack true consciousness, emotions, and an understanding of human experience.
It is also important to consider the ethical implications of replacing human intelligence with AI. The potential consequences of widespread AI deployment, such as job displacement, data privacy concerns, and the concentration of power in the hands of AI creators, need to be carefully evaluated.
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Hello, I'm trying to find pre labelled 90mm petri dishes to be included in an automation workflow for a new biotech lab. Does anyone know a brand? I can't find one! THX
Nidia
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Binyamin Kerman very kind for your answer, thanks. Exploring all your suggestions!
N
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Hello everyone,
I am currently conducting a stencil printing simulation using ABAQUS. The simulation needs to be performed in 20 different locations on the stencil, requiring a separate simulation for each of these locations. In my case, all components remain fixed, and only the location of the blade changes across these 20 locations. The simulation consists of seven steps. Throughout these 20 simulations, all conditions remain identical from the first step until the fifth step. However, after the fifth step, I change the blade's location in the sixth step and continue the simulation in the seventh step.
Given that the first five steps are the same in all simulations, I would like to explore if there is a way to execute these steps only once and then reuse or restart the results for the remaining 19 simulations. In other words, I aim to find a method that avoids repeating the first five steps in the subsequent simulations. Although I have attempted to utilize the restart option, it did not prove successful due to the blade's location change in the sixth step.
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You're correct that restarting simulations can save considerable computational time and resources if multiple simulations share common steps initially. Abaqus provides a restart feature that allows the restart of analysis from any previously completed step or increment in a preceding analysis.
As you are facing an issue due to the change in the blade's location in the sixth step, you need a solution that allows for the modification of your model (i.e., the blade's location) between the fifth and sixth steps, and yet still enables you to restart the simulation from the sixth step.
Abaqus allows for such model changes in a restart analysis, but there are restrictions on the kind of changes that can be made. Changes like modifications to boundary conditions, load magnitudes, or material properties are allowed in a typical restart analysis. However, changes to the model geometry or topology, like moving the blade in your case, are usually not allowed.
One possible workaround would be to model all 20 blade positions in your initial simulation but activate them in sequence in subsequent simulations. Here's how you might do that:
  1. In your base model, define 20 different blades, one for each location, but only activate the blade for the first location. Define this as a model instance and create a step that activates the first blade.
  2. In your subsequent simulations, you restart from the base model but activate a different blade in each simulation. In Abaqus, this can be done using the Model Change, Activate feature in a step.
Remember, this is just a workaround and may or may not be feasible, depending on the specifics of your simulation. You might also need to modify the workaround based on your requirements.
In conclusion, while Abaqus allows for certain modifications during a restart analysis, the changes you're trying to make might not be compatible with this feature. You might need to resort to alternative modelling techniques to achieve your goal. If you're still facing issues, I recommend contacting Abaqus support or consulting the Abaqus user manual or user community for more specific guidance.
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I have to used IEC-61850 communication protocol used for substation automation through OPAL-RT?
I know its working and i have also verified my results through OPAL-RT but there is some problem i am having in case of its block diagram, that how i will introduce its components of working in my normal block diagram?
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Dear Alok Jain
I apologize for my previous response in which I referred to you as a student. What I meant to say was that your students and yourself were waiting for a response, and I am unsure why you did not receive one. Upon seeing this, I felt compelled to reply.
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Using ChatGPT for Automated Literature Reviews what prompt ?
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Any ethical issues ? So do scholars need to learn how to prompt these generative AI for a better output ?
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Hello all, I have been trying 5x Magmax 96 viral RNA isolation kit (AM1836-5) for extraction of total RNA and DNA samples from swab samples (assuming that the viral titer is too low). We have Magmax Express 96 magnetic particle procedure to automate the extraction procedure. So I prepare the plates as per user guide instructions and run the script using the instrument. Please note that I add a spike in RNA control in all of my samples and add two extraction controls in the plate with the spike in control and nuclease-free water instead of the sample to verify whether the extraction went okay. Following extraction, I run conventional PCR using primers for the spike in RNA control. I do quality check using NanoDrop and Qubit and the readings look okay. However, PCR did not work for any of the samples. No peak even for the spike in control in the tape station and Qiaxcel runs. Although PCR positive control worked perfectly and got the expected band size which likely indicates PCR worked okay. There might be something wrong in the extraction procedure which I can't figure out. I tried this process a couple of times with different concentrations of a spike in control (1 and 2 ul in lysis binding solution along with carrier RNA and buffer and isopropanol). Can you please enlighten me a bit about where might be the issue in the extraction process? Thanks a lot for you time and help!
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Asma Sultana Based on the information you provided, it seems that you have followed the protocol and automation procedure correctly for the Magmax viral RNA isolation using the Magmax Express 96 system. However, you are experiencing issues with PCR amplification of the extracted RNA samples, specifically the spike in RNA control.
There could be several factors contributing to the PCR failure. Here are some potential areas to investigate:
1. Inhibition: It is possible that the extracted RNA contains inhibitors that are affecting the PCR amplification. These inhibitors can come from the sample matrix or be introduced during the extraction process. To address this, you can perform an inhibition control by adding known amounts of the extracted RNA to a PCR reaction that contains a known target. This will help you determine if inhibition is a problem.
2. RNA degradation: RNA is susceptible to degradation, especially in samples with low viral titers. Ensure that all reagents used during the extraction are RNase-free and that the samples are handled carefully to minimize degradation. Additionally, consider performing an RNA integrity assessment, such as using an Agilent Bioanalyzer or running a gel, to verify the quality and integrity of the extracted RNA.
3. Concentration of spike in control: The concentration of the spike in control may influence the efficiency of the extraction process. You mentioned trying different concentrations, but it may be worth optimizing this parameter further to ensure sufficient recovery of the spike in control RNA.
4. Sample quality and viral titer: It's important to consider the quality of the original swab samples and the expected viral titer. If the viral titer is indeed low, it may affect the success of the extraction and subsequent PCR amplification. Confirm the presence of viral RNA in your samples by using alternative detection methods like qPCR or digital PCR, if available.
5. PCR conditions: Double-check the PCR conditions, including primer design, annealing temperature, and cycling parameters. Ensure that the PCR protocol is appropriate for the target you are amplifying.
6. Troubleshooting with technical support: If you have tried the above suggestions and are still experiencing difficulties, consider reaching out to the technical support team of the Magmax viral RNA isolation kit or the instrument manufacturer. They may have specific troubleshooting steps or recommendations tailored to your situation.
Remember that troubleshooting experimental procedures can be a process of elimination, and it may require further optimization and testing to identify the root cause of the issue. Reviewing the protocol, confirming reagent integrity, and exploring potential sources of inhibition or degradation are important steps in diagnosing the problem.
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I need short explanations on this area.
Thank you for your support.
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Automated vehicle penetration testing methods refer to the techniques and approaches used to evaluate the security and vulnerabilities of automated vehicles. These methods involve simulating various attack scenarios to identify potential weaknesses in the vehicle's software, hardware, communication systems, and other components. They typically include activities such as fuzz testing, code analysis, reverse engineering, network sniffing, and penetration testing tools to detect and exploit vulnerabilities. While there isn't a specific formula for conducting automated vehicle penetration testing, it generally follows a systematic and comprehensive approach to assess the security posture of the vehicle and identify potential risks that could be exploited by malicious actors.
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Humans play a critical role in the development of next-generation AI in several ways:
1. AI developers: AI systems are developed by humans, and therefore, AI development is driven by the knowledge, skills, and creativity of the developers. AI developers are responsible for designing, coding, testing, and improving the AI systems.
2. Training AI: Humans also play a crucial role in training AI systems. AI systems are trained using large datasets, and humans are responsible for curating and curating those datasets.
3. Human-in-the-loop AI: Humans can also be included in the AI development process by building "human-in-the-loop" AI systems. Such systems require humans to be involved in critical decision-making processes that cannot be fully automated.
4. Ethical considerations: Human intervention is needed to ensure that AI is developed ethically, with a focus on human values and societal well-being. Humans must ensure that AI systems are transparent, responsible, and aligned with human values.
5. Interpreting AI output: Finally, humans play a critical role in interpreting the output of AI systems and analyzing the decisions made by AI systems. Humans must be involved in critical decision-making processes that involve AI systems and must be able to override AI decisions when needed.
Reference:
1. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
2. Adamson, D. (2019). Human and Machine Intelligence: An Overview. AI Magazine, 40(4), 11-24.
3. Bryson, J. J. (2018). Artificial intelligence and human rights. Philosophy & Technology, 31(4), 589-604.
4. Russell, S. J., & Norvig, P. (2010). Artificial intelligence: a modern approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
5. Yudkowsky, E. (2013). The AI Alignment Problem: Why It’s Hard, and Where to Start. Machine Intelligence Research Institute.
In conclusion, the development of next-generation AI is heavily dependent on humans. Human input is needed at every stage of the AI development process. The collaboration between humans and AI systems leads to more effective systems that can achieve better results. Therefore, the role of humans in the development of AI cannot be overstated.
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Deep Learning cannot crack the consciousness code. There is a hope that the new AI emerging in the coming decades will be different from what is out there today.
Engineers will develop futuristic AI material based on quasi polaritons to harness the act of understanding uncertainty.
Machines with minds capturing multiscalar effects through a holarchical modularity of evolving boundary conditions will be the algorithm of the consciousness code.
Software engineers will be the new AI in the future, so humans will not interfere.
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I am wondering if there is any way to refresh the input data from a dynamic text file in COMSOL for each iteration.
I have attempted to do this in Python, but COMSOL only solves the equation for the first input text file and not for any new files generated. The reason for this is that I have coupled COMSOL with a DEM-based software which feeds the input to COMSOL for each iteration. (same situation for the output to save the results as a text file)
While the connection is established through Python codes, I am unsure if Python can trigger the refresh button for each iteration!!!
Any suggestions would be greatly appreciated.
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To anyone encountering a similar question:
A more straightforward approach to automating the refresh button in your COMSOL model is as follows:
1. Extract the Java code from your COMSOL model: In COMSOL, you can export the model to Java code. Locate the section of the code that corresponds to the refresh button functionality.
2. Identify the relevant section for automation: Review the extracted Java code and identify the specific section that is responsible for triggering the refresh action when the button is clicked.
3. Utilize Python to automate the refresh action: In Python, you can make use of the `model.java` feature provided by COMSOL to interact with the Java code. You can create a Python script that sends the appropriate commands to trigger the refresh action in the COMSOL model.
By combining the capabilities of both Java and Python, you can automate the refresh button functionality in your COMSOL model. This allows you to programmatically initiate the refresh action without manual intervention.
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Hello,
Can anyone suggest me on storing state variable values in an array at different time steps? I'm trying to make computations at TIME(1) = 0.1, 0.6 and 0.9. I am able to compute state variables but I need to make calculations at the end of TIME(1)= 0.9. When I want to call values at T(0.1), T(0.6) and T(0.9), I'm unable to do it. These values are stored in ODB but I would like to automate the process so the parameters are updated for the next steps.
Thanks in advance
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In ABAQUS, state variables can be stored in arrays using the appropriate data type. To define an array of state variables, you need to use the `STATEV` keyword in the material definition. For example, if you have `n` state variables, you can define an array as `STATEV(n)`. To access the values of state variables within ABAQUS, you can use the appropriate variable names associated with the state variables. For example, if the state variable array is named `STATEV`, you can access the individual values using the notation `STATEV(1)`, `STATEV(2)`, and so on, depending on the index of the desired state variable.
To store state variable values at different time steps in an array, you can create an array with sufficient capacity to hold the values at each time step. Initialize the array and update its elements with the computed state variable values at the corresponding time steps. For your specific case, you can create an array with three elements to store the values at T(0.1), T(0.6), and T(0.9). After computing the state variables at each time step, assign the values to the corresponding array elements. Then, when you need to retrieve the values at T(0.1), T(0.6), and T(0.9), you can simply access the array elements at the desired indices. This automation ensures that the parameters are updated for subsequent steps, allowing for efficient calculations and retrieval of the required values.
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Hi folks,
I would like to automate our experiment. The mirror/prism, as well as the photodetector, are mounted on rotating stages (both the detector and stage have separate motors/controllers). I'm attempting to automate this entire experiment by using a graphical user interface. The problem is that once the turning begins, the reflected ray does not precisely land on the exact location on the photodetector and finally does not fall on the sensor area at increasing degrees.
I think the issue is that as the mirror moves, the laser beam hits a different spot on the mirror and thus theta/2*theta relationship is not exactly held. What is the solution?
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Hi Hamza,
In order to understand the problem better, what kind of motors/controllers do you use to control the rotation/translation? What is the accuracy of the incremental step, the bi-directional repeatability and the pitch and yaw movements?
In terms of the optical alignment, do you check the beam pointing mismatch as a result of the incremental rotation/translation? And do you check whether everything goes well when you operate the experiment manually before going for all automatic operation?
It can be that the bi-directional repeatability and the pitch/yaw movement errors of your rotation/translations stages are not good enough to avoid the misalignment of your optical beam when components are rotated/translated. Or else, the optical alignment is not at the optimum and thus you observe misalignment as a result of your rotation/translation. I experienced such issues at the beginning of the development of an instrument, which is a scanning Michelson interferometer.
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Hello everyone, I am working on the elaboration of macroporous materials for bone reconstruction. I mainly characterized the porosity using SEM observation. For the moment I use ImageJ to analyze the porous structure (size, shape, orientation,…) but I haven’t found an efficient method to automate the image processing. I wonder if someone know an accurate automatic and efficient method or software than can make me save time ?
Thank you in advance,
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Recently our lab purchased Porometric by ThermoFisher. In my opinion it is very powerfull tool because pores counting process is fully automated and for good results you just need contrast picture and enter pore size. But I suppose that ImageJ is enough for your research you need only learn from Manual about all options
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I want to automate the measuring of neurite outgrowth in PC12 cells. Is there any programm or fiji plugin that can measure cellbody length ,neurite length and number of neurites in a unstained cells ?
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I tested this program but i couldn´t get it to work https://www.biorxiv.org/content/10.1101/2023.04.27.538564v1
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It cannot be certain that artificial intelligence will completely eliminate the accounting profession, but it can lead to changes in the way of work and the skills required for accountants. Artificial intelligence can help improve the accuracy and efficiency of accounting work, through automation and smart analyzes of financial data.What is your opinion ?
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Dear Bashaer Khudhair Abbas Alkhafaji , nice question and for certain very relevant. Please check https://www.aicpa-cima.com/resources/video/re-inventing-finance-for-a-digital-world I think this white-paper is a very good read and gives a good picture of the impact on the finance function in general and accounting specifically. KR Rob
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In your opinion, is the development of artificial intelligence, which consists, among other things, of new generations of this technology creating ever more perfect generative artificial intelligence solutions, processing ever more data, performing ever more complex work and ever more creatively performing human-ordered tasks, a threat to people's creative and critical thinking?
It used to be that people remembered the tel numbers of people they called frequently. Nowadays, tel no.'s are entered into smartphones and do not need to be remembered. Various online information services are available on smartphones and we are using them more and more. In many countries, taxi drivers are increasingly using GPS navigation and no longer have to pass an exam to know the topography and the names of all the streets in a city. Technology is increasingly relieving people of various tasks and the need to remember a lot of data. On the other hand, threats are emerging in the form of the generation of disinformation on online social media by posting pictures and videos showing 'fictitious facts' created by artificial intelligence. As deepfake is now recognised as one of the greatest threats arising from artificial intelligence applications, so it is urgently necessary to create legal regulations that regulate the proper use of artificial intelligence-based tools, including respect for copyright, when artificial intelligence creates new works, texts, graphics, etc. using various publications taken from Internet resources. How much the development of artificial intelligence and its applications will change labour markets in the future is suggested by the results of predictive and futurological analyses, according to which up to half of human jobs globally could disappear by 2050. On the other hand, surveys of a number of companies and enterprises show that over the next few years, the majority of businesses plan to carry out investment processes involving the implementation of new Industry 4.0 technologies, including artificial intelligence, into their operations. Predictive analyses and futurological visions created on their basis show that with the technological progress, along with the emergence of successive generations of artificial intelligence, more and more perfect artificial intelligence systems will be created in the next few years. In addition to this, a tool is already available on the Internet in the form of an intelligent language model based on generative artificial intelligence, which, by generating answers to questions, creates texts in an automated way, based on knowledge resources taken from a large number of Internet sites, Internet article databases, Internet book libraries, etc. On the other hand, a kind of ChatGPT creativity is not yet applied perfected, because within this creativity, "fictitious facts", i.e. nicely described events that never happened, may be described by ChatGPT in the created texts. Arguably, these imperfections in the next generations of this tool and in other ChatGPT-like intelligent, automated, digital chatbots created by subsequent technology companies will be corrected. When they are corrected, it will then become increasingly common for people to use such tools made available on the Internet by commissioning artificial intelligence to write specific texts, which will be created by the artificial intelligence in an increasingly creative manner and successively making fewer and fewer mistakes in this creative process. Consequently, humans will commission more and more complex tasks for the artificial intelligence to perform and increasingly require the use of creativity, innovation, artistry, etc. Thus, there may be another threat to humanity which may be the abandonment of performing creative activities when these activities can be performed by artificial intelligence. Thus, a new category of threat to humanity may emerge from the technological advances made by artificial intelligence. It may therefore happen in the future that the development of artificial intelligence is a threat to people's creative and critical thinking.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
In your opinion, is the development of artificial intelligence, which consists, among other things, of new generations of this technology creating ever more perfect generative artificial intelligence solutions, processing ever more data, performing ever more complex work and ever more creatively performing human-ordered tasks, a threat to people's creative and critical thinking?
Is the development of artificial intelligence a threat to people's creative and critical thinking?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Hoping to hear your opinions, to get to know your personal opinion, to have an honest approach to discussing scientific issues and not ChatGPT-generated ready-made answers, I deliberately used the phrase "in your opinion" in the question.
The above text is entirely my own work written by me on the basis of my research.
I have not used other sources or automatic text generation systems such as ChatGPT in writing this text.
Copyright by Dariusz Prokopowicz
Best wishes,
Dariusz Prokopowicz
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Definitely no. Artificial intelligence is created by human and follows what human programs it. Human possess the intelligence to invent more and more and not following stupidly what orders put in as in case artificial intelligence. Artificial intelligence can do good but can not innovate any thing. Human with intelligence God grant him/her lead the universe.
Dr.Sundus Fadhil Hantoosh
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The auditing industry is profoundly affected by artificial intelligence (AI). It is transforming how auditors carry out their responsibilities and enhancing the accuracy and efficacy of the auditing process. Tools propelled by artificial intelligence can automate routine tasks such as data extraction, analysis, and report generation, enabling auditors to focus on complex tasks that require human judgement and expertise. AI can also assist auditors in identifying risks and anomalies that may be overlooked by conventional auditing techniques, allowing them to provide clients with more valuable insights. AI can also improve the quality of audits by providing real-time surveillance and analysis of financial data, thereby reducing the risk of fraud and errors. In general, AI is revolutionising the auditing industry by enhancing audit quality, increasing audit efficacy, and decreasing audit costs.
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Thank you very much, Dr. Eshetie Berhan, This is very important information Dr.
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I am currently working in the process of automating our research lab to be able to process a higher number of samples. Our work consists in detecting and quantifying pathogens in mammalian samples (e.g., blood, faeces, tissue, swabs). As such, I am interested in an automated extraction system which produces good DNA/RNA yields to be sent for NGS.
Until now, we have been using Qiagen kits for our manual extractions, so I thought that a machine from the same brand would do for us. However, I've been told that the QIAcube Connect does not really take that much work out of your hands, and that the sample volume obtained at the end of the process with the QIAcube HT is way lower than the one with the manual kit. I have also checked other machines, such as Thermo Scientific's Kingfisher Flex and its kits, but do not know how well they do in comparison with Qiagen's kits.
Based on your experience, which automated extraction system would you recommend? And which brand of kits have you used with it? The system and kits do not need to be from the brands mentioned here (as long as the produce good results).
Thank you very much in advance.
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Laia-M. Pardinilla back when i worked in the diagnostic laboratory, i used CyBio Felix from AnalyticJena. It is also based on magnetic beads technology and it is fully automated, you can modify the program, customize configurations.. and we had two of them, so we could process 192 samples at once, i really liked that one https://www.analytik-jena.com/products/liquid-handling-automation/liquid-handling/flexible-benchtop-liquid-handling/cybio-felix-series/
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RNAComposer is a great web server for large-scale automated modeling of RNA structures up to 500 nt residues. We have a big RNA to be predicted. Anyone has good ideas? many thanks!
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Fortunately, I tried 3dRNA/DNA web server (http://biophy.hust.edu.cn/new/3dRNA/) by providing RNA sequence and 2D dot-bracket notation from the RNAflod (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi). After 2 days running, I got the email with a link of results. So so exciting to see the 3D structure of a 1830 nt RNA in Pymol and ChimeraX : )
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Does anyone use the TC20 automated cell counter to count PBMC?
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Can TC10 counts human platelets accurately?
Thank you !...
Baki
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I am looking for study cases, articles, papers about graphoscopy software. The aim is create solutions using graphic computation.
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Petterson Faria de Souza ImageJ, FIJI, and Python libraries such as OpenCV and scikit-image are among the software alternatives for automating the identification of graphoscopy components. These programs may be used to evaluate and extract information such as line thickness, pressure, and stroke direction from handwriting samples.
In terms of study cases, various research publications on the use of these instruments for graphoscopy analysis have been published. "Automated Analysis of Handwriting for Diagnosis and Monitoring of Parkinson's Disease" by V. R. Chandran and S. V. Subramanian is one example, since it explains the creation of an automated system for analyzing handwriting samples to detect indicators of Parkinson's disease.
If you want to create your own solutions using graphic computation, I recommend first becoming acquainted with one of the software options mentioned above, and then experimenting with various feature extraction techniques and machine learning algorithms to see what works best for your specific application. There are also online tutorials and forums for these programs that can assist you in getting started.
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I was wondering what changes should I add in the MDS protocol to account for the Zn ion in the active site of a metalloprotein.
Also, I would be grateful if there was a bash script available to automate the process.
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Marwa Abd El Kader Zaater There are multiple processes to simulating a metalloprotein-ligand complex in GROMACS. To begin, prepare your system by producing a topology file for the protein-ligand complex that contains the Zn ion characteristics. You can accomplish this with software such as CHARMM or AMBER.
The system must then be solvated with an appropriate solvent and counterions added to neutralize it. GROMACS commands such as "editconf" and "genion" can be used for this purpose.
Once the system is ready, you may perform the normal energy minimization, equilibration, and production MD simulations. You must, however, verify that the force field values you employ are appropriate for modelling metalloproteins.
You can enter the right ion parameters in the topology file to account for the Zn ion in the active site of the metalloprotein. The Zn ion can be represented as a charged species with the appropriate charge value and LJ characteristics.
In terms of process automation, you may create a bash script that performs the processes outlined above. This script may accept input files like as the protein-ligand complex structure file, topology file, and Zn ion parameters and run GROMACS instructions to configure the system and simulate it. The intricacies of the script, however, will be determined by the specifications of your system and simulation configuration.
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I want to integrate the new age technologies like Artificial intelligence and Machine learning with Marketing to automate the complete marketing process , for analytics of customer and market, forecasting, to study the customer behavior. for this which theories I should incorporate in this model? like what should be the theoretical contribution of this study?
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Integrating artificial intelligence and machine learning with marketing can lead to significant advancements in the field, particularly in terms of automation, predictive analytics, and personalization. There are several theories that could be relevant to this integration, including:
  1. Customer Relationship Management (CRM) - CRM theory focuses on building and maintaining strong customer relationships to increase customer retention, loyalty, and profitability. By using AI and machine learning, marketers can gain a better understanding of customer behavior, preferences, and needs, and tailor marketing strategies accordingly.
  2. Predictive Analytics - Predictive analytics involves using statistical techniques and machine learning algorithms to analyze historical data and make predictions about future events. By applying predictive analytics to marketing data, marketers can forecast customer behavior, identify trends and patterns, and develop targeted marketing strategies that are more likely to be effective.
  3. Decision-Making Theory - Decision-making theory explores how individuals and organizations make decisions under conditions of uncertainty. By integrating AI and machine learning with marketing, marketers can reduce uncertainty by providing data-driven insights and recommendations, leading to more informed decision-making.
  4. Personalization Theory - Personalization theory focuses on tailoring marketing messages and experiences to individual customers based on their preferences, behavior, and needs. By leveraging AI and machine learning, marketers can automate the process of personalization, delivering relevant and personalized content to customers at scale.
Theoretical contributions of this study could include expanding our understanding of how AI and machine learning can be applied to marketing, identifying new opportunities for automation and personalization, and improving our ability to forecast and respond to changes in customer behavior and market trends. Additionally, this integration could contribute to the development of new marketing strategies and approaches that are more effective and efficient than traditional methods.
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Various domains have methods for compatibility testing. For example: Software, design, electrical (keyword E-Plan), control systems, network technology,
it does not have to be exclusively domains in the mechatronics field
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There are several drawbacks to using harmonic potential functions for obstacle avoidance:
  1. Local minima
  2. Overshoot
  3. Difficulty in tuning
  4. Limited applicability
Overall, while harmonic potential functions are a popular approach to obstacle avoidance, they have some limitations that should be considered when choosing an algorithm for a particular application.
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For the current moment (Apr.2023) we have got the working cloud service with subscription. It's
contain the functions for list operations (import, export, comparing, cleaning) usually
needed as regular MDM system operations, capable of comparing lists of items like product descriptions from different vendors. Those lists are commonly created manually (without the help of automated systems) and though contain variety of fields and
contained types of information. Those data usually contains a lot of mistakes, typos etc. Two lists with 1 thousand positions each give us 1 million pairs to compare. Our associative index in acouple of minutes helps to determine only really needed pairs to compare (it is tenth of percent
of all positions usually), so the similarity is computed in minutes instead of hours on the basic PC with 8 CPUs and 8 GB RAM
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I have taken the liberty of forwarding a suggestion to another researcher who will contact you for a more in depth discussion of the development of the filters if he chooses to participate in our discussion. I would expect him to contact you soon. However, to answer your question, I would expect the filters to be "callable" as needed by something like an event dispatcher or object request broker, with the capability to adapt the sequence of filtering dynamically to the situation requirement and/or prior results. Eventually resulting in a "library" of filters available for reuse.
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As we know Industry 4.0 is using many new technology including robotics, but are the Industry 4.0 implementation is always necessary using automation? Because automation is heavily discussed in Industry 3.0, especially computerization automation. Or maybe we can ask : using automation are Industry 4.0 or Industry 3.0?
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The new shape of design in the age of Industry 4.0
The modern manufacturing process has evolved over the last decades to the point where there are many interconnected disciplines taking place, often at the same time. With the advent of advanced comms technologies, interconnections are taking on a whole new paradigm for engineering...
We want people to use virtual reality to do real things and connect better to life, and this is where our focus is – [to] create better products you can better serve in real life. To waste less, consume less, spend less energy to create what we need, and make sure that is what we need.”
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Does some one know some examples or tutorial how to use the biomaRt R package to automate the gene name recognition of the Ensembl database?
Best wishes,
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In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite.
Regards,
Shafagat
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  • Recent advances in artificial intelligence (AI) are leading to the emergence of a new class of robot.
  • In the next five years, our households and workplaces will become dependent upon the role of robots.
source: AI and robotics: How will robots help us in the future? | World Economic Forum (weforum.org)
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1. Autonomy: The development and use of AI should ensure that autonomy is respected when decisions are made.
2. Privacy: As AI becomes more powerful and ubiquitous, it is important to protect personal data and ensure that data is not used without consent.
3. Social Responsibility: As AI continues to become more deeply integrated into society, it is important to consider the potential social implications of AI technologies and ensure that they are responsible and ethical.
4. Transparency: As AI systems become increasingly complex, it is important to ensure that the decision-making process and results of AI systems are transparent and explainable.
5. Fairness: AI systems should be designed to eliminate bias and ensure fairness for all users and stakeholders.
6. Accountability: AI systems should be designed to be accountable for their decisions and how they are used.
7. Security: The security of AI-driven systems should be a top priority, as malicious actors may use AI to bypass existing security protocols.
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Would like to connect if you have explored using quantitative methods for intersectionality analysis.
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You could have a look at:
The approach is like CHAID but is is model based and uses random effects not fixed which is protective of finding unimportant unreliable results.
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As AI continues to progress and surpass human capabilities in various areas, many jobs are at risk of being automated and potentially disappearing altogether. Signal processing, which involves the analysis and manipulation of signals such as sound and images, is one area that AI is making significant strides in. With AI's ability to adapt and learn quickly, it may be able to process signals more efficiently and effectively than humans. This could ultimately lead to fewer job opportunities in the field of signal processing, and a shift toward more AI-powered solutions. The impact of automation on the job market is a topic of ongoing debate and concern, and examining the potential effects on specific industries such as signal processing can provide valuable insights into the future of work.
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Please read my paper
An Adaptive Filter to Pick up a Wiener Filter from the Error using MSE with and Without Noise
This is a system that is able to learn.
The paper is a singles and systems.
The topic is AI.
I think the two fields support each other.
Thank you
Ziad
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Is the use of artificial intelligence in agriculture ethical, or does it contribute to the further automation and potential loss of jobs in the industry?
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It is well-established that countries that do their best to be productive have the happiest populations, so, yes, it is ethical. The question has came up repeatedly for new technologies. What suffering does come of it can be traced to poor personal decisions and poor government.
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How can the implementation of artificial intelligence, Big Data Analytics and other Industry 4.0 technologies help in the process of automated generation of marketing innovations applied on online social media sites?
In recent years, the application of new Industry 4.0 technologies in the process of generating marketing innovations applied to online social media portals has been on the rise. For the purpose of improving marketing communication processes, including advertising campaigns conducted on social media portals and promoting specific individuals, brands of companies, institutions, their product offers, services, etc., sentiment analysis of Internet users' activity in social media is conducted, including analysis of changes in social opinion trends, general social awareness of citizens by verifying the content of banners, posts, entries, comments, etc. entered by Internet users in social media using computerised, analytical Big Data Analytics platforms. I have described this issue in my articles following their publication on my profile of this Research Gate portal. I invite you to collaborate with me on team research projects conducted in this area. Currently, an important developmental issue is also the application of Big Data Analytics platforms used to analyse the sentiment of Internet user activity in social media, which uses new technologies of Industry 4.0, including, among others, artificial intelligence, deep learning, machine learning, etc. Besides, the implementation of artificial intelligence, Big Data Analytics and other Industry 4.0 technologies can help in the process of automated generation of marketing innovations applied on online social media portals. An important issue in this topic is the proper construction of a computerised platform for the automated generation of marketing innovations applied on online social media portals, in which the new generations of Artificial Intelligence, Big Data Analytics and other Industry 4.0 technologies are used.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How can the implementation of artificial intelligence, Big Data Analytics and other Industry 4.0 technologies help in the process of automated generation of marketing innovations applied to online social media portals?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
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AI and Big Data analytics have revolutionized the way businesses approach online marketing. By leveraging these technologies, businesses can gain a deeper understanding of their customers and create more effective marketing strategies.
One of the primary benefits of AI and Big Data analytics is their ability to collect and analyze large amounts of data from various sources, including social media, website analytics, and customer feedback. This data can provide valuable insights into customer behavior, preferences, and purchasing patterns. With this information, businesses can create targeted marketing campaigns that are tailored to their customers' needs and interests.
Moreover, AI and Big Data analytics can help businesses personalize their marketing efforts. By analyzing customer data, businesses can create personalized recommendations, offers, and promotions that are more likely to resonate with individual customers. This personalized approach can help businesses build stronger relationships with their customers and increase customer loyalty.
Additionally, AI and Big Data analytics can help businesses optimize their marketing efforts by identifying trends and patterns in customer behavior. For example, by analyzing customer data, businesses can identify the most effective marketing channels, content types, and messaging strategies for their target audience. This information can help businesses refine their marketing strategies and improve their return on investment.
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What is your experience of talking to a Chatboot that acts as a call centre adviser on the hotline of a company, institution whose offer you sometimes or permanently use?
Do you like talking to a Chatbot, which, equipped with artificial intelligence, is a kind of IT robot that acts as an adviser to the call centre of the company or institution whose offer you sometimes or permanently use?
Could the use of Big Data Analytics technology to improve the autonomous, automated improvement of the chatbot system's database of questions and answers, supplemented by new questions and answers added to the database, created by artificial intelligence on the basis of available knowledge on the Internet and improved algorithms based on machine learning technology and improving the content quality and professionalism of call centre advice, help to solve the problems of this type of automated advice?
More and more companies and institutions, in order to optimise costs, as they call it, which usually means mainly reducing the number of employees, are employing Chatbots in their call centre departments as call centre advisors, which, being equipped with artificial intelligence, are a kind of computerised robots that replace humans. The costs of such computerised solutions equipped with smart technology are becoming cheaper and available to more and more companies, enterprises, financial and public institutions. Also, the question of the quality of the answers provided by chatbots to the questions asked or the questions asked in the continuation of the conversation both in terms of content, syntax, logic etc. are constantly being improved. Also, the database of ready-made questions and answers can be continuously expanded. These types of solutions can also be equipped with permanent self-improvement systems to improve the algorithms used by removing errors resulting from incorrect answers given by the chatbot or repeated questions, a kind of looping of the answers given by the chatbot when people interested in obtaining specific information give their answers. People calling the hotline who are interested in obtaining specific information usually act as potential or current customers of product or service offers from specific companies and institutions. On the one hand, they would usually like the phone call not to take too long and to get the information they need or to get specific, factually sound advice. In principle, a company, enterprise or institution that engages chatbots in its call centre departments has the analogous goal of improving the conversations that chatbots have with customers. However, it is often the case that the artificial intelligence involved in the chatbots is of an outdated generation, the algorithms involved using not the latest generation of machine learning technology cause a telephone conversation with a chatbot to take much longer than with the human call centre adviser whom the chatbot has replaced. This is because the outdated machine learning technology involved and previous generations of artificial intelligence ask several to several questions of the potential customer calling the hotline, in order to finally redirect the caller interested in a specific product or service offering or in need of specific advice to an advisor who will provide a factual and professional answer to the caller's queries, as the chatbot was unable to do. The question then arises as to why some companies and institutions are replacing their call centre employees, i.e. call centre advisers, with chatbots, since they have used outdated technology to create them and the resulting solutions generate an embarrassing situation and disgust with this type of telephone conversation instead of helping many potential customers? Is this an attempt to improve the image of a company presented in marketing communications as a modern company, using modern technology as it were, especially since another, competing company also already uses similar technological solutions. This happens more than once. Chatbots, which replace human hotline advisers in call centres, are presented as an example of modernity in the marketing communication of a company or institution, and potential or existing customers are disgusted by this kind of pseudo-advice and often look for the information they need on the website of the company or institution instead of calling the hotline once again. And perhaps this is precisely the point, to redirect a potential customer to the website of a particular company or institution, because this type of communication will be the cheapest for those offering certain products or services. But the cheapest solution for companies and institutions does not always mean the highest level of satisfaction for existing or potential customers. The application of deep learning technologies could help to improve this kind of automated call centre advisors. And, perhaps, the use of Big Data Analytics technology to improve the autonomous, automated improvement of the chatbot system's database of questions and answers, supplemented by new questions and answers added to the database, created by artificial intelligence on the basis of available knowledge on the Internet, and improved algorithms based on machine learning technology and improving the content quality and professionalism of call centre advice could help solve the problems described above.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
Do you like to talk to a Chatbot, which, being equipped with artificial intelligence, is a kind of IT robot fulfilling the advisors' role as a call centre in the call centre of the company, institution whose offer you sometimes or permanently use?
Could the use of Big Data Analytics technology to improve the autonomous, automated improvement of the chatbot system's database of questions and answers, supplemented by new questions and answers added to the database, created by artificial intelligence on the basis of available knowledge on the Internet and improved algorithms based on machine learning technology and improving the content quality and professionalism of call centre advice, help to solve the problems of this type of automated advice?
What do you think about it?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
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As an AI language model, I don't have the capability to experience anything in the same way as humans do, but I can provide some information on the typical experience of interacting with a chatbot at a call center.Chatbots are increasingly being used by companies and institutions as a way to provide customer support and assistance. When calling a helpline or customer service number, a chatbot may greet the caller and prompt them to select options or enter information using voice or text commands. The chatbot may also ask the caller questions to better understand their issue and provide relevant information or solutions.The experience of talking to a chatbot can vary depending on the quality of the chatbot's programming and the complexity of the issue. In some cases, the chatbot may be able to quickly and efficiently provide the information or solution the caller needs, while in other cases the chatbot may struggle to understand the caller's issue or provide relevant assistance. If the chatbot is unable to resolve the issue, the caller may be transferred to a human customer service representative.Overall, the experience of interacting with a chatbot at a call center can be convenient and efficient in some cases, but may be frustrating or ineffective in others. As chatbot technology continues to evolve and improve, it is likely that the quality of these interactions will also improve, making them a more reliable and effective means of customer support.
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How could automation assist blood banks enhance the standard and reliability of their laboratories?
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Corrine Estonactoc Lasmarias Certainly, automation in blood banking can assist minimize the chance of mistakes while also improving the quality and dependability of blood bank laboratories. These are some examples of how automation might help blood banks:
1. Automation may increase the accuracy and speed of laboratory testing, lowering the risk of mistakes and shortening the turnaround time for test findings.
2. Automation can assist to standardize laboratory testing by minimizing variability in the testing process, which improves the dependability of test findings.
3. Improved efficiency: Automation can improve laboratory testing efficiency by eliminating the need for manual activities and allowing laboratory employees to focus on more complicated duties.
4. Automation can enable traceability and reliable record-keeping of test findings, which is critical for tracing blood products and maintaining regulatory compliance.
5. Decreased expenses: Automation can help to minimize the costs associated with manual testing, such as labor and supplies.
Overall, blood banking automation can assist to minimize the risk of mistakes, increase the standard and reliability of laboratory testing, and improve patient safety by guaranteeing that blood products are safe and effective for transfusion.
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State differences in terms of:
  • validity of results (accuracy and specificity)
  • advantages and disadvantages
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Lixie Mae Domingo When identifying various analytes, automated equipment and manual procedures differ in numerous ways. The following are some of the distinctions between the two methods:
1. Validity of Results: The most significant aspects in determining the validity of findings from an automated machine or manual approach are accuracy and specificity of the results.
Automatic Machines: Because of the programmed algorithms and advanced equipment utilized in the process, automated machines are noted for their great accuracy, precision, and repeatability. Moreover, automated equipment can handle a huge number of samples in a short amount of time, reducing the possibility of human mistake in processing the samples. As a result, they are a more reliable way of identifying analytes.
Manual procedures are prone to human error, resulting in less precise outcomes. Inconsistencies in sample processing, measurement, and data analysis might jeopardize the accuracy of the results. Manual approaches, on the other hand, can yield precise results if executed properly, especially when expert operators are involved.
2. Disadvantages and Benefits:
Machines that are automated:
Advantages:
  • .They require little human interaction and can handle a large number of samples quickly.
  • Automatic machines are more accurate, precise, and reproducible.
  • They lessen the possibility of contamination and human mistake.
  • They are more efficient and cost-effective in the long term.
  • Acquiring and maintaining automated machinery is costly.
  • These may necessitate the use of highly qualified staff to operate and maintain the equipment.
  • Certain automated devices may have limited flexibility, making it difficult to tailor the process to unique demands.
Manual Techniques:
  • Advantages:
  • Manual procedures are quite cheap.
  • They need little equipment and may be carried out using standard laboratory equipment.
  • Manual techniques are very adaptable, making them appropriate for research investigations requiring specialized circumstances.
Disadvantages:
  • Manual procedures are prone to human mistake, which affects productivity.
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I feel that, in water resource management activities (specially flood management), most of the developed software tools are not widely or continuously used. The reason may be either the decision makers work independently from project to project or fully /partially automate the required processes unique to the project.
I would like to know your experiences as well as comments on the utilization of the software tools to assist flood management decisions.
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I have been very fortunate to be involved with experimental watersheds, gauging streams and interacting at times with Official stream gauges, installing, measuring flow, data analysis, etc. In the US, the US Geological Survey is the preeminent agency for this work, and the US Forest Service and many Universities, and States set up their own experimental catchments, or specific needs for stream gauging, water balance, flood and drought estimates, and often rainfall and other meteorological associated data. The long term network of water based stations has been relied heavily upon. The guidance provided by USGS based on this information is extremely helpful, but as suggested by other commenters, hydrologic analysis may include other factors. My training, experience did not depend on flood models or software for the most part. It was more data based, observational and included site evaluations, such as for culverts, bridges or other crossings, evaluation of floodplain or flood prone areas for example. To the greatest extent, estimates of flooding had a strong relationship to watershed size, with considerations associated with physiographic area, topography and rainfall amount and frequency data. Presence of accumulations of large wood, signs of excess sediment or bedload, potential of landslides would be some of the considerations in assessing conditions. I would recommend if models are used, to try to validate them first with existing data available. If no data, when you drive your roads and highways, as you come by stream crossings, try to find when they were installed, have they failed or been overtopped/damaged, measure their size, use GIS or topographic maps to determine watershed size, aerial photos for land use, geology and soil maps, if you have time bankfull and floodplain cross section, any local information on past flood(s), etc. If you ever have a major flood, when travel is safe, drive the roads, looking for flood water marks, signs of culvert or bridge overtop with deposited debris, sediment, damage. Flood gauging high water can also be done with crest gauges. If culverts and bridges remain in place for many years or the structure lifetime, the local guidance is probably sufficient.
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Hi everyone,
I'm running shell buckling analysis with a shell with perfect geometry and consider geometric nonlinearities. The riks algorithm is set on automatic incrementation. In many cases, the solver gives out a warning message reporting negative eigenvalues, which means, that the bifurcation load may have been exceeded. However, the algorithm still increases the load proportionality factor and just 'runs over' the bifurcation load. This also happens if I decrease the initial and maximum increment. My solution so far is to check the message file for negative eigenvalues. However, this is inconvenient for automatisation of evaluation. Do you know of any other solution?
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