ethicalabs/Research-Intent-Collab huggingface dataset:
Ways to a Rise Efficiency of Logistic Provision for International Transport System “Agroexpress” Activity on the Basis of One-Criterion Optimization

Purpose: To develop technology variants to provide for necessary level of cargo traffic for trains “Agroexpress” and mathematical model proposal for these purposes. Methods: System analysis, operation mathematical optimization and linear programming. Results: Work flows on provision of agricultural cargos of the being considered train are analyzed. Mathematical model of provision of agro-production of “Agroexpress” trains in Uzbekistan is developed. With the help of the model, there is a possibility to improve work technology on provision of cargo traffic of “Agroexpress” in sufficient quantity by minimal expenditure criterion. Practical importance: The proposed model creates favorable conditions for increasing foreign cargo traffic of Russia and Uzbekistan. With the help of the model it is possible to improve the technology of work to ensure the cargo traffic in a sufficient number of “Agroexpress” on the criterion of minimum costs.
Predictive Maintenance in the Internet of Things: Survival Analysis and Deep Learning on Time Series Data from a Large Scale Wireless Sensor Network

A major challenge in deploying battery powered sensors for the Internet of Things (IoT) involves making accurate predictions for when the batteries in the sensors will reach the end of their life. Maintenance of large numbers of deployed sensors includes replacing the batteries, or in the case of inexpensive sensors, replacing the sensors themselves. In either case, the goal is to minimize downtime so that there are as few gaps in data transmission as possible. For smaller, consumer oriented deployments, the end user often is responsible for maintenance. However for larger, enterprise scale deployments a central organization is responsible for maintaining the sensors, along with analyzing the data the sensors generate on the client’s behalf. This organization may have many such clients, and therefore many deployments to maintain. Managing the maintenance of batteries in sensor networks can be costly due to a number of factors. In large enterprise networks particularly, the maintainer of the system will need to place bulk orders to the manufacturers ahead of time, well in advance of device failure, rather than waiting for individual sensors to stop reporting. In general, the number of individual sensors in the overall network will increase the expense relative to the per unit cost of replacing a sensor. However, sensors are often very cheap, and may even be disposable. The more important driver is the cost in personnel needing to travel to the locations of sensors in order to replace them. Therefore the number and spread of locations have a much higher impact on overall maintenance cost. As a result, it is of primary concern to the maintainer to be able to accurately gauge how many sensors will reach their end of life, and when. Knowing this information well in advance enables the maintainer to reduce maintenance costs by proactively addressing potential failures and reducing or removing the need for additional site visits. Additionally, accurate predictions allow bulk orders for new sensors to be placed with enough time for the manufacturer to build and ship the sensors. Importantly, if the maintainers of the network act preemptively to replace sensors before failure occurs, they are able to minimize downtime and therefore have greater continuity of sensor data. This project utilizes Deep Learning models in conjunction with Survival Analysis techniques in order to predict the time at which battery death of sensors will occur, based on their readings. Two Deep Learning models are developed and compared against a baseline Cox Proportional Hazards model. The first is a Deep variant of the baseline model, able to predict the probability of survival of a given sensor, at each time point, based on its features. The sequential, time-dependent nature of the sensor data motivates the development of a second, novel Deep Learning model that is able to perform iterative predictions of a sensor's probability of survival as it receives new readings from that sensor.
Las vvctimas como testigos en el Derecho Penal Internacional (i). Especial referencia a los Tribunales Ad Hoc (Victims as Witnesses in the International Criminal Law (i). Special Reference to the Ad Hoc Tribunals)

El presente trabajo tiene como objetivo fundamental el analisis del marco teorico y jurisprudencial del testimonio de las victimas ante los Tribunales Penales Internacionales creados para juzgar los hechos cometidos en los territorios de la ex Yugoslavia y Ruanda. Se analizan sus derechos, obligaciones, la relevancia del testimonio y el valor probatorio de este. Todos estos aspectos son analizados aportando datos obtenidos principalmente de las resoluciones judiciales que sirven para demostrar que, a pesar de que la victima no es parte en el proceso y de las dificultades que tiene para actuar como testigo, ha sido su testimonio la principal prueba que ha servido para fundamentar la culpabilidad de los sujetos que cometieron estos horrendos crimenes. The focus of this work is the theoretical and jurisprudential analysis of the testimony of victims before the International Criminal Tribunals established to address the crimes committed in the territories of the former Yugoslavia and Rwanda. It analyses their rights, obligations, relevance of their testimonies, and the evidentiary value of it. All these aspects are analyzed to provide data primarily obtained from judicial resolutions that serve to demonstrate that, despite the victim not being part of the process and the encountering difficulties acting as witnesses, their testimony has been the primary evidence that has served to substantiate the guilt of those who have committed these terrible crimes.
Natural language processing to identify lupus nephritis phenotype in electronic health records

Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data. We developed four algorithms: a rule-based algorithm using only structured data (baseline algorithm) and three algorithms using different NLP models. The three NLP models are based on regularized logistic regression and use different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components respectively. The baseline algorithm and the best performed NLP algorithm were external validated on a dataset from Vanderbilt University Medical Center (VUMC). Our best performing NLP model incorporating features from both structured data, regular expression concepts, and mapped CUIs improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.62 vs 0.96) datasets compared to the baseline lupus nephritis algorithm.
Teleworking, Work Life Balance and The Right To Disconnect: Can Current And Emerging Law Adequately Protect Teleworkers?

Telework can increase flexibility and autonomy and enhance work life balance. It made it possible for workers to continue to work from home during the pandemic and this ability may prove valuable in the face of climate change disasters such as extreme weather events. However, telework also has disadvantages; it can result in overwork and blurring of boundaries between work and private life with associated detriment to work life balance, and wellbeing. This is compounded by new technologies which extend the possibilities of employer surveillance to all aspects of employees' lives both during and outside of working hours. This paper identifies some common themes in the emergent body of law which establishes a "right to disconnect" and considers them through a human rights lens which prioritises respect and protection of human dignity. It notes that, while there are variants in how the right to disconnect is regulated, commonly: the right is not universal; the law only require employers to have a policy with no specific requirements as to what this policy should contain; often there is a requirement for consultation with worker representatives to establish the content of policy but not always; frequently there is a lack of enforcement of the right and an absence of penalties for contraventions of the right. It is argued that these laws, as currently imagined, do not go far enough in terms of adequately protecting teleworkers right to a private life and to human dignity. It is concluded that improvements in the areas of scope, consultation, and enforcement of the right are all desirable. It is acknowledged that there are competing concerns to be balanced in developing appropriate policies and that a degree of flexibility and negotiation rather than a rigid approach will often be appropriate. However, it is also argued that current laws and policies treat the problem narrowly as a limitation of working time issue and focus on restrictions aimed at preventing employers from actively contacting their teleworking employees outside of working hours. While this is important, it is argued that passive surveillance and after-hours data extraction by employers also represent a threat to privacy and private life which teleworking employees are especially vulnerable to. It is proposed that this neglected issue should also be addressed by rights to disconnect laws and that rather than this being treated as an issue around which flexibility and negotiation are appropriate, law makers and labour unions should instead pursue an absolute prohibition on after hours data extraction.
Ab initio statistical mechanics of structural phase transitions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 1987. ; Includes bibliographies. ; by Karin M. Rabe. ; Ph.D.
Impact of Social Media Use on Adolescent Mental Health: A Scoping Review

Background: Adolescents are an age group highly exposed to social media in their daily lives. The high intensity of social media use has raised concerns about its negative impact on adolescent mental health. Purpose: This review aims to map the scientific evidence related to the impact of social media on mental health in adolescents. Methods: Using the Arksey and O'Malley scoping review framework, this study followed five key stages: formulating the research question, identifying relevant literature, selecting appropriate studies, organizing the data, and reporting the findings. Articles were searched through the PubMed database and the Google Scholar search engine, covering publication years 2021–2025. A total of 80 articles were screened using the PRISMA flowchart and critical appraisal, following the Joanna Briggs Institute (JBI) guidelines, resulting in eight articles meeting the inclusion criteria. Results: Three main themes emerged: intensity of social media use, disturbed sleep quality, and mental health disorders such as stress, anxiety, and depression. Most adolescents use social media for more than three hours per day, with TikTok and WhatsApp being the dominant platforms. High usage correlates with increased sleep disturbances and the risk of depressive symptoms and stress. Conclusion: Social media has a significant impact on adolescent mental health. Digital literacy education and parental supervision are crucial to prevent these negative impacts.
FF-SRL: High Performance GPU-Based Simulation For Surgical Robot Learning

Robot-Assisted Surgical Systems (RASS) are increas- ingly widespread and researchers aim to introduce au- tomation during operations, supporting a progressive increase in the autonomy of RASS [1]. Given the sig- nificant successes obtained in other robotic applications [2], many works adopt Reinforcement Learning (RL) to support the RASS execution of different tasks, such as the manipulation of deformable tissues [3], [4], and the removal of debris or liquids during operations [5]. Generally, RL training requires numerous task repeti- tions, which are unsafe and impractical in real RASS. Fast and Flexible Surgical Reinforcement Learning (FF- SRL) addresses this by creating a realistic and com- putationally efficient simulator that runs entirely on a single GPU, eliminating bottlenecks associated with data transfer. The goal is to accelerate learning rates, mak- ing experimentation with RL in robotic surgery more accessible. The results indicate a significant reduction in training time for complex tasks, down to a couple of minutes compared to conventional CPU/GPU simula- tors. Such advancements are important in facilitating the development of autonomous and efficient RASS through RL techniques.
A Real Benchmark Swell Noise Dataset for Performing Seismic Data Denoising via Deep Learning

Deep learning (DL) gained great importance in geophysics exploration due to its great generalization capacity, which allows the prediction of complex system states. This ability can be leveraged in various applications in the oil and gas industry, such as seismic denoising. However, the difficulty of applying and exploring the different methods often lies in the lack of open datasets that can be used as a benchmark, given their scarcity, which makes testing and reproducibility of results difficult. This work presents a benchmark dataset of synthetic seismic data with real extracted swell noise and introduces a new evaluation metric that can capture small variations in model results. Additionally, a comparison between two widely known DL-based denoising models is performed on the proposed dataset, which constitutes a benchmark for accelerating the development of new solutions for seismic data denoising.
Draft proposal for a long-term EU-Africa Research and Innovation Flagship Programme on Food Nutrition Security and Sustainable agriculture (FNSSA)

The ERA-NET Cofund LEAP-Agri was launched following the recommendation of the European Union – African Union (AU-EU) High Level Policy Dialogue on STI (HLPD) and its first priority “Food and nutrition security and sustainable agriculture” (FNSSA). The first aim of LEAP-Agri was to fund Research and Innovation projects (R&I) between European and African research institutions. In this frame, 27 research and innovation (R&I) projects were funded with participants from the two continents. LEAP-Agri demonstrated that it is possible to involve, with the support of a top up funded by EU, 24 funding bodies from 18 EU and AU countries, all of them committing national budget. The second main aim of LEAP-Agri was to propose a flagship programme in the light of this cofunded call experience.The LEAP-Agri partners propose that this flagship programme should target agroecology topics addressing sustainable agricultural practices, farm development, management of soil erosion and of water access, promotion of species of interest in changing climate conditions, access to seeds and supporting farmers. The projects should participate to producing knowledge and recommendation to face the common AU-EU challenges of scaling farms for economic viability in order to ensure well-being of animals, farmers and consumers, nutrition and food security, limit greenhouse gas emission and global warming, promote biodiversity and protect soils and natural resources. It has to be written and aligned with the current national and intercontinental AU-EU research strategic agendas, in particular the current R&I FNSSA Roadmap approved by both Unions, and build on the results already obtained in the national and bi-continental R&I programmes for FNSSA. At this turning point for the FNSSA in terms of environmental and societal challenges, no R&I programme can be successful if no attention is paid to the involvement of a wide range of relevant stakeholders, including private actors from farmers to micro, small and medium enterprises (MSMEs) or industry. This involvement has to be ensured from the start, right at the stage of the proposal elaboration, in a co-design process with a multisectoral/multidisciplinary approach.From the experience of LEAP-Agri and other documents, collaborative R&I projects should be cofunded partly by the European Commission (EC) and partly by other bodies (AU body or private donors). National financial commitments for R&I projects should be mandatory for all participants. Special financial features of funding should be devoted to countries of underrepresented regions of AU and EU in the R&I FNSSA projects in order to favour their access to R&I networks. This deliverable D1.6 together with D6.1 and D6.2 gives the overall picture of the outputs of LEAP-Agri as a contribution to the AU-EU Partnership on FNSSA.
Which Metrics Best Capture Protein Structural Change in Molecular Dynamics Simulations? Evaluating Score Combinations and Force Field Effects

The scores RMSD, TM-score, GDT, lDDT, SphereGrinder, CAD, QCS, FlexE, and MolProbity provide an automated, comprehensive assessment of conformational changes and structural quality of proteins and thus represent a promising tool for routine use in molecular dynamics (MD) simulations, particularly in high-throughput settings. However, it remains unclear to what extent these scores provide redundant information and which scores are most informative for capturing conformational changes. Based on MD simulations of 268 diverse proteins, we demonstrate that the investigated scores are highly correlated and that one global score (or QCS), one local score, and FlexE capture almost 90% of the variance across all scores. Since MD randomness partially explains FlexE’s variability, we argue that a combination of one global and one local score is sufficient for most practical applications. We also investigate the influence of simulation setups and find that the choice of the force field can significantly affect scoring results. In particular, the setup using the Amber ff19sb force field yields systematically different scores than all CHARMM36m-based setups, emphasizing the importance of methodological choices when designing MD experiments and interpreting their results.
Reflection Hazard Analysis and Risk Assessment in HSE Management for Oil and Gas Operations in Kirkuk province:An Analytical Study of the opinions of Employees in Baghdad Oil Refinery and the Barham group for refining petroleum product

The primary objective of this paper is to investigate the Reflection of risk analysis and risk assessment into HSE management for oil and gas operations in Kirkuk province. The study focuses on the Baghdad Oil Refinery for petrochemical and bitumen production and the Barham Group for refining petroleum products, which has significant implications for health and safety management. A descriptive analytical method was used to examine the data and describe the relationships between the variables. Questionnaire forms were used for data collection; 92 forms were distributed, and the study population consisted of 64 employees from the two oil operations who completed and returned the form. The IBM SPSS 26 statistical program was used for statistical analysis and findings. The results revealed a direct and significant relationship between the Reflection of risk analysis and risk assessment in HSE management. The relationship variables of Reflection these two aspects also had a positive impact on the HSE management system. The study offers practical recommendations to address identified challenges in the two operations and suggests implementing the findings across industrial facilities in Kirkuk province. It is the first time these variables have been combined in a study of the Baghdad Oil Refinery and the Barham Group.
Genomic Landscape of Macrolide Resistance in High-Burden Bacterial Pathogens: Implications for Antimicrobial Stewardship in Resource-Limited Settings

Abstract                                    Background                   Antimicrobial resistance (AMR) threatens global health, with macrolide resistance emerging as a critical challenge in high-burden pathogens. Understanding taxon-specific resistance mechanisms is essential for guiding interventions in resource-limited settings where diagnostic and therapeutic resources are constrained.                                                     Methods                   We analyzed 9,706 bacterial genomes from the AMRFinderPlus database to characterize macrolide resistance alleles, their distribution across taxa, and associations with multidrug resistance. Statistical and visualization workflows identified taxon-specific patterns and clinical implications.                                                     Results                                        Macrolide resistance mechanisms dominated (34% of 1,243 alleles), with 23S rRNA mutations prevalent in                     Neisseria gonorrhoeae                     (23S_A2045G: 62%; 23S_A2057G: 58%) and                     Streptococcus pneumoniae                     (23S_A2058G: 48%). Efflux pump variants (acrB_R717L/Q) drove multidrug resistance in                     Escherichia spp                     . (41%) and                     Salmonella spp                     . (33%), correlating with cross-resistance to fluoroquinolones (18%) and tetracyclines (14%). These pathogens disproportionately impact regions like Ghana, where syndromic management and antibiotic misuse amplify resistance.                                                                        Conclusions                   These findings underscore the urgent need for precision surveillance and context-specific stewardship to combat macrolide resistance in high-burden pathogens, particularly in resource-limited settings where diagnostic and therapeutic gaps exacerbate AMR threats. Targeted interventions, including affordable molecular diagnostics and antibiotic access reforms, are critical to mitigating global health disparities driven by resistance.
Importance sampling methods for McKean-Vlasov type stochastic differential equations

Published by RWTH Aachen University, Aachen
Natural Language Processing in Electronic Health Records: Progress, Challenges, and Future Directions

Electronic health record systems transformed healthcare documentation by providing a system for storing and sharing extensive patient data. However, much of this information remains in the form of unstructured text, which limits its utility for computational analysis. Natural Language Processing (NLP) has emerged as a prominent approach to extract and structure information from free-text clinical narratives, offering the potential to unlock valuable insights for clinical care, research, and administration. This paper provides an overview of recent advances in NLP methods applied to EHRs, discusses open problems including data quality, privacy, and generalizability, and highlights potential future directions for the integration of NLP into clinical workflows. The conclusions point to the need for continued development of domain-specific language models, privacy-preserving techniques, and explainable AI methods to fully harness the power of NLP for healthcare transformation.