ethicalabs/Research-Intent-Collab huggingface dataset:
Review of Artificial Intelligence Methods for Faults Monitoring, Diagnosis, and Prognosis in Hydroelectric Synchronous Generators

This scientific article aims to provide a comprehensive review of fault monitoring, diagnosis, and prognosis methods based on Artificial Intelligence (AI) for Hydroelectric Generator Units (HGUs). It presents a compilation of research studies that have utilized AI models for fault monitoring, diagnosis, and prognosis in HGUs. Additionally, it outlines the process for building an AI model in the context of fault management in HGUs and discusses the advantages and disadvantages associated with AI methods in this domain. Furthermore, the article examines the research prospects and trends of AI models for fault management in HGUs. By synthesizing existing literature and highlighting future directions, this article serves as a valuable resource for researchers and practitioners seeking to leverage AI techniques for effective fault management in HGUs.
Epidemiological modelling in infectious diseases: stages and classification

Modelling in infectious diseases has recently been an important field due to avian influenza, swine influenza, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome-coronavirus (MERS-CoV), novel coronavirus (nCoV) and many other diseases. Epidemiological models are usually defined as mathematical and/or logical demonstrations of epidemiology of diseases and the related process. Concerning animal disease management, ‘models’ can be defined more widely in that they contain a range of statistical/mathematical tools regarding other aspects of the disease in addition to its spreading. Modelling might be useful when experimental or field studies are impossible or not practical or in retrospective analyzing of previous epidemics in order to search alternative control strategies. The aim of this study was to examine some of the modelling methods and determine what mathematical modelling meant in infectious diseases, its purpose of use, to classify the steps followed during modelling period and models used in the field of animal health.
A Hybrid Hierarchical Framework Toward Security Effectiveness for Critical Infrastructure Protection and Resiliency: A Hospital Case Study

Abstract                   A successful Denial of Service attack on a CI can indirectly have devastating and irreversible effects to those that depend on its services. Furthermore, recent disruptions have raised concerns regarding the resiliency, security effectiveness and emergency preparedness of CIs and dependent resources. To address the persistent challenge of protecting CIs and maintaining the essential services they provide, this research offers emergency management personnel a conceptual framework to evaluate security effectiveness and estimate the cascading effects that may result from inadequate security measures. We combine the philosophy of multi-dimensional modeling, with the statistical engine of Bayesian Belief Networks to provide proactive, scenario-based interdependency analysis for CI protection and resiliency. The findings of this research resulted in a multi-dimensional approach that enables a heightened awareness of one’s risk-posture by highlighting the existence (strength) or absence (weakness) of relevant security factors. Through stakeholder risk-assessment, preemptive implementation of threat mitigation plans for dependent resources are permissible. Specifically, we provide this proof of concept, “what-if” analysis tool to assist in the reduction of vulnerabilities. To illustrate the conceptual framework, we provide a Healthcare and Public Health sector case study that evaluates the impact to a hospital patient given a successful DoS attack on a CI.
A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis

Following the identification of Coronavirus Disease 2019 (COVID-19) in Wuhan, China in December 2019, AI researchers have teamed up with a health specialist to combat the virus. This study explores the medical and non-medical areas of COVID-19 that AI has impacted: the prevalence of the AI technologies adopted across all stages of the pandemic, the collaboration networks of global AI researchers, and the open issues.  21,219 papers from ACM Digital, Science Direct and Google Scholar were examined. Adherence to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and utilizing the PICO (population, intervention, comparison, and outcome) paradigm, guided the inclusion of researches in the review. Tables and graphs were utilized to display the results. Analysis revealed that AI has impacted 4 molecular, 4 clinical, and 7 societal areas of COVID-19. Deep Learning among other AI technologies was traced to all aspects of the pandemic. 2173 authors and co-authors were traced to these achievements, while 32 of the most connected 51 authors were affiliated with institutions in China, 18 to the United States, and 1 to Europe. The open issues identified had to do with the quality of datasets, AI model deployment, and privacy issues.  This study demonstrates how AI may be utilized for COVID-19 diagnosis, prediction, medication and vaccine identification, prognosis, and contact person monitoring. This investigation began at the beginning of the epidemic and continued until the first batch of vaccinations received approval. The study provided collaboration opportunities for AI researchers and revealed open issues that will spike further research toward preparing the world for any future pandemic   Keywords: Artificial Intelligence, COVID-19, Diagnosis, Prognosis, Contact Tracing
Nesnelerin İnterneti (IoT) ve Kablosuz Algılayıcı Ağların Güvenliğine Yapılan Saldırıların Tespit Edilmesi ve Önlenmesi : Detection and Prevention of Attacks on the Internet of Things (IoT) and Wireless Sensor Networks

IoT (Internet of Things) ya da diğer adıyla Nesnelerin İnterneti kavramı, internete bağlanan ve diğer cihazlarla iletişimde olan her nesneyi kapsamaktadır. Artık hayatımızın bir parçası haline gelecek otonom araçlar, akıllı buzdolabılar, akıllı çamaşır makineleri, akıllı tost makineleri, akıllı saatler gibi birçok IoT cihazı birbiriyle farklı kablosuz ağ teknolojilerini kullanarak haberleşebilirler. IoT cihazların birçok kritik alanda kullanılmasıyla birlikte IoT güveniğine karşı yapılan saldırılar da artmıştır. Bu saldırılarda IoT katmanlarına yapılarak veri gizliliği, veri bütünlüğü, veri tazeliği, veri erişilebilirliği, kimlik doğrulama gibi kriterler ihlal edilebilmektedir. Bu saldırıları önlemek amacıyla birçok güvenlik çözümü önerilmiştir, fakat sınırlı enerji, kısıtlı batarya süresi, zayıf işlemci gücü ve sınırlı hafıza gibi sınırlamalardan dolayı düşük güçlü IoT cihazlar üzerinde geleneksel güvenlik yöntemlerinin uygulanması mümkün değildir. Bu çalışmada, IoT cihazların güvenliğini tehdit eden saldırılar incelenerek, ağ katmanlarına göre detaylı şekilde sınıflandırılmış ve savunma teknikleri önerilmiştir.
Analysis of Risk Assessment Algorithms in America’s Criminal Justice System

My technical work and my STS research are connected primarily through the idea of machine learning in the justice system, especially the algorithm and impact of risk assessment tools. My technical work focuses on the algorithm behind risk assessment tools on how it can produce biased results, and my research explores the moral issues of risk assessment tools.  My technical work focuses on the algorithm behind the risk assessment tools, and explores different ways to find sources of bias on the algorithms. I use different machine learning algorithms, such as Random forest and Association rule mining, to find any relations in the datasets. For example, I compare the calculated risk assessment scores and other information that was collected on the questionnaire about a defendant such as sex, age, and gender. Using Random forest and Association rule mining, I check if there are any correlations between the attributes. With the results, I was able to find out what highly affects the risk assessment most, such as race. The goal of my technical work is to explore the algorithm and find ways to make it less biased to help produce more accurate results. My STS research also explores risk assessment tools in a similar angle. I focus on the moral issues of using risk assessment tools as it is known to produce biased results. Using Actor Network theory, I explore all actors that are related to the risk assessment tool network and discuss how risk assessment tools have affected them. I specifically look at the case of Wisconsin v Loomis (2016) to discuss the unfairness of risk assessment tools. The goal of my  research is to raise awareness on the negative impact of risk assessment tools and ask for careful use of the tools in the justice system. Working on these two projects simultaneously greatly added value to both. My technical work gave me a better understanding of the algorithms behind risk assessment tools and how machine learning is used in the justice system. It also gave me a better understanding of machine learning itself, on various algorithms that are available. Similarly, the research I conducted for my STS paper helped me learn how algorithms can be used in the justice system and how the outcome can be negatively affected. In summary, working on both my STS research paper and my technical project together this past year has allowed me to explore machine learning from multiple angles and has helped me raise awareness on how I should be careful when designing an algorithm as it can produce negative consequences.
IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization

A comprehensive benchmark is yet to be established in the Image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility; ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark; and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://github.com/scu-zjz/IMDLBenCo.
Piezoelectric Sensors for Real-time Monitoring and Quality Control in Additive Manufacturing

Within the ever-evolving landscape of engineering, particularly in the dynamic domain of additive In manufacturing, a pursuit of precision and excellence in production processes takes centre stage. This research , This paper serves to give a comprehensive understanding of piezoelectric sensors, a topic that is both academically engaging and of practical significance, catering to both seasoned experts and those newly venturing into the field. Additive manufacturing, lauded for its groundbreaking potential, underscores the imperative of rigorous quality control. This introduces piezoelectric sensors, devices that may be unfamiliar to many but possess considerable potential. This paper embarks on a methodical journey, commencing with an introductory elucidation of the piezoelectric effect. It then advances to the vital role of piezoelectric sensors in real-time monitoring and quality control, unveiling their potential and relevance for newcomers and seasoned professionals alike. This research, structured systematically from fundamental principles to pragmatic applications, presents findings that are not only academically informative but also represent a substantial stride towards achieving precision and high-quality manufacturing processes in the engineering field.
Weather Prediction based on Wireless Sensor Network and Internet of Things with Analysis using Hybrid SSOA with MA

Abstract         Weather forecasting is a growing area that predicts the weather that will occur in a particular place at a particular time. Weather forecasting is considered to be the most important part of research where many real-time issues arise. These are the requirements for wireless sensor networks to detect the weather. In this article we propose using the Mayfly Algorithm (MA) hybrid algorithm with Shuffled Shepherd Optimization Algorithm (SSOA). Experimental tasks are carried out with a set of climate data. Based on climatic parameters such as temperature, humidity and clouds, the data is divided into heat, wind and rain. From the result obtained, it is predicted that the weather will prove the proposed qualification methods at the level of accuracy. The result shows that the hybridized SSOA and MA method is efficient and accurate in predicting weather conditions. This experimental result is carried out by using Wireless Sensors Network and IoT on agricultural land.
AI Regulation and Capitalist Growth: Balancing Innovation, Ethics, and Global Governance

Artificial Intelligence (AI) is increasingly central to economic growth, promising new efficiencies and markets. This economic significance has sparked debate over AI regulation: do rules and oversight bolster long term growth by building trust and safeguarding the public, or do they constrain innovation and free enterprise? This paper examines the balance between AI regulation and capitalist ideals, focusing on how different approaches to AI data privacy can impact innovation in AI-driven applications. The central question is whether AI regulation enhances or inhibits growth in a capitalist economy. Our analysis synthesizes historical precedents, the current U.S. regulatory landscape, economic projections, legal challenges, and case studies of recent AI policies. We discuss that carefully calibrated AI data privacy regulations-balancing innovation incentives with the public interest can foster sustainable growth by building trust and ensuring responsible data use, while excessive regulation may risk stifling innovation and entrenching incumbents.
How Many Genes Are Needed for a Discriminant Microarray Data Analysis ?

The analysis of the leukemia data from Whitehead/MIT group is a discriminant analysis (also called a supervised learning). Among thousands of genes whose expression levels are measured, not all are needed for discriminant analysis: a gene may either not contribute to the separation of two types of tissues/cancers, or it may be redundant because it is highly correlated with other genes. There are two theoretical frameworks in which variable selection (or gene selection in our case) can be addressed. The first is model selection, and the second is model averaging. We have carried out model selection using Akaike information criterion and Bayesian information criterion with logistic regression (discrimination, prediction, or classification) to determine the number of genes that provide the best model. These model selection criteria set upper limits of 22-25 and 12-13 genes for this data set with 38 samples, and the best model consists of only one (no.4847, zyxin) or two genes. We have also carried out model averaging over the best single-gene logistic predictors using three different weights: maximized likelihood, prediction rate on training set, and equal weight. We have observed that the performance of most of these weighted predictors on the testing set is gradually reduced as more genes are included, but a clear cutoff that separates good and bad prediction performance is not found.
Design And Build Of A Cafe Dashboard Application As Information System Of Sales Performance PT. Maxx Coffee Prima

PT.Max Coffe Prima is one of the companies that engaged in the sale of food and beverages. At this company all sales reporting proces are still carried out using group chat which of course is considered very ineffective and efficient and causes many problems, one of which is a problem with storing report data. Report data does not have a back up so it is easy to sink or disappear from group chats. And to overcome these problems and facilitate the process of reporting sales and giving rewards to sales, a study was made entitled Design And Build Of A Café Dashboard Application As Information System Of Sales Performance PT. Maxx Coffee Prima. By using the Unifield Modeling Language (UML) as the modeling and MySQL as the database and PHP as the programming language.
Assessing personality: effects of the depressive state on trait measurement

The influence of the clinically depressed state on personality assessment was evaluated by comparing self-report personality inventories of patients while clinically depressed and at follow-up 1 year later. The authors examined two groups from the National Institute of Mental Health (NIMH)-Clinical Research Branch Collaborative Program on the Psychobiology of Depression: Clinical Studies--patients whose symptoms had completely remitted and those who had not recovered. The clinically depressed state strongly influenced assessment of emotional strength, interpersonal dependency, and extraversion. Assessment of rigidity, level of activity, and dominance did not change after symptomatic recovery.
Regenerative medicine in the retina: from stem cells to cell replacement therapy

Following the fast pace of the growing field of stem cell research, retinal cell replacement is finally emerging as a feasible mean to be explored for clinical application. Although neuroprotective treatments are able to slow the progression of retinal degeneration caused by diseases such as age-related macular degeneration and glaucoma, they are insufficient to fully halt disease progression and unable to recover previously lost vision. Comprehensive, technological and intellectual advances over the past years, including the in vitro differentiation of retinal cells at manufacturing scale from embryonic stem (ES) cell and induced pluripotent stem (iPS) cell cultures, progress within the area of retinal disease modeling, and the first clinical trials have started to shape the way towards addressing this treatment gap and translating retinal cell replacement to the clinic. Here, summarize the most recent advances within retinal cell replacement from both a scientific and clinical perspective, and discuss the remaining challenges towards the delivery of the first retinal cell products.
Interactive Exploration of Cosmological Dark-Matter Simulation Data

The winning entry of the 2015 IEEE Scientific Visualization Contest, this article describes a visualization tool for cosmological data resulting from dark-matter simulations. The proposed system helps users explore all aspects of the data at once and receive more detailed information about structures of interest at any time. Moreover, novel methods for visualizing and interactively exploring dark-matter halo substructures are proposed.
Development of tumor microenvironment-oriented digital pathology methods for whole slide image segmentation and classification

To better serve cancer patients, diagnostic and digital pathology methods focus on more novel targets. One of such targets is the tumor microenvironment. Machine vision-dependent digital pathology methods are still very tumor cell-centric and largely ignore the tumor microenvironment. This work has set the aim to investigate and propose new histopathology image segmentation and classification methods by targeting tumor microenvironment-related histologic tissue components. Firstly, convolutional neural networks were identified as a group of state-of-the-art methods of sufficient capacity to handle multiple histologic object segmentation. Then, the existing tumor cell segmentation method was adapted and extended for lymphocyte segmentation and identification. Next, fibrous collagen was identified as a novel tumor microenvironment-borne target for segmentation in bright-field images of tumorous tissue. To address the collagen fiber segmentation task, a fully convolutional neural network-based approach was developed. Finally, an approach integrating knowledge gained in previous experiments was proposed enabling segmentation of lymphocytes, tumor cell nuclei, stromal cell nuclei, collagen fibers, and major tissue compartments. Additionally, by the engineering of image features, a whole slide image transformation was introduced, enabling the prediction of therapeutic biomarker status for individual breast cancer patients from complete tumor tissue whole-slide images. The proposed methods were comparable to the state-of-the-art methods while at the same time providing special additional features.
Relatıonship Between Occupational Accidents of Industrial Workers and Health Literacy and Workplace Safety Climate

Aim: The aim of this study was to investigate the relationship between occupational accidents of industrial workers, health literacy and workplace safety climate. Methods: This study was conducted with workers working in the industrial                         zone of Gemlık, Bursa between August 2020 and December 2020. These individuals were visited in their workplaces. The volunteers were asked to fill out a sociodemographic data form, the Workplace Safety Climate Survey and the Newest                         Vital Sign Form. Results: Health literacy level was evaluated among the workers with and without days away from work due to occupational accident and the results were found to be significantly higher for those without days away                         from work (p=0.001). According to this assessment, total mean score of the Workplace Safety Climate Survey was calculated as 35.45±10.6 (min.=20, max.=70) for individuals with days away from work due to occupational accident and                         as 48.82±7.81 (min.=27, max.=70) for participants without occupational accident, and statistically there was a significant difference between them (p&lt;0.001). When the relationship between the Newest Vital Sign Scale and Workplace                         Safety Climate Survey was analyzed, it was determined that positive safety climate perception of the participants significantly and positively affected their health literacy mean scores (r=0.294; p=&lt;0.001). Conclusions: This                         study has demonstrated that the health literacy level of employees affects their occupational accident experiences. From this point of view, it may be suggested that employees who work in high risk fields could be chosen from among                         those with higher health literacy levels. In general terms, the importance of regular trainings for the purpose of decreasing accident rates has been once more shown with this study.