ethicalabs/Research-Intent-Collab huggingface dataset:
No Fairness without Awareness. Applied Research into Equity in Higher Education. Inaugural Address upon Appointment as Professor of Learning Technology & Analytics

In his inaugural address, Theo C. Bakker, PhD, discusses equity in higher education and the role of data analysis in promoting social justice. From his Research Group for Learning Technology & Analytics at The Hague University of Applied Sciences, Bakker conducts research on equity across different student groups, using predictive models and study data to identify structural inequalities. He emphasizes that equity can only be resolved by first consciously examining the education system. No Fairness without Awareness.   Learning Analytics and bias – Learning analytics focuses on measuring and analyzing student data to improve education. Bakker distinguishes between levels, such as student and institutional analytics, and focuses on inclusion analytics, which examines equity. Bias-systematic prejudices in data can reinforce prejudices in algorithms and thus cause inequality of opportunity. The research method uses the 4/5 criterion, in which fairness in outcomes is measured by examining whether opportunities for the protected group are at least 80% of those for the privileged group.   Research approach – Bakker uses machine learning to predict dropout rates after the first year of study, then examines differences between groups of students, such as vocational and pre-university students. He follows three steps: (1) Prepare data and build models: Data is split and cleaned to create accurate prediction models. (2) Analyzing variables: The influence of characteristics on the results is assessed for different groups. (3) Calculating fairness: The 4/5 criterion is applied to metrics such as accuracy and statistical equality to identify bias and inequalities.  Results, recommendations, and follow-up research – The study shows that many study programmes lack equal opportunities, particularly for men and vocational students, who are more likely to drop out. Bakker recommends including sensitive characteristics such as migration background in analyses based on informed consent. He also advocates greater policy flexibility, inspired by measures taken during the coronavirus crisis that improved study outcomes.  Vision for the future – Bakker emphasizes that not all inequality stems from discrimination and calls for data-informed interventions to promote social justice in education. His method is made available through open access so that other institutions can also apply it and systematically and consciously investigate equity.
Data Science and Machine Learning Integrated Implementation Patterns for Cavernous Knowledge Discovery from COVID-19 Data

Pandemic is a well-known term for the year 2020  It's essentially a disease that spreads across a region or the entire planet  The entire planet appears powerless, and a jerk triggered by a virus outbreak has halted  On 11 March 2020, WHO announced Corona Virus disease 2019 (COVID-19) a pandemic  The outbreak or epidemic of the virus differs widely from one nation to another  Society is the secret to solving the pandemic  Fever is one of the common, easily detectable symptoms of COVID-19  The COVID 19 in India is one of the most widespread pandemics caused by extreme acute corona viral syndrome2 (SARS-CoV-2) in coronvirus disease 2019 (COVID-19)  On 30 January 2020, the original case of COVID-19 in India, arising in China, was registered  India currently has the highest number of confirmed cases in Asia and the second largest number after the United States of America in the world, with a combined number of confirmed cases exceeding the thresholds of 100,000 on 19 May and 1 000,000 confirmed cases on 17 July 2020  The largest one-day increase in COVID-19 cases of 78,761 cases was observed in Indian countries on 29 August 2020, surpassing the previous record in US cases of 77,368 on 17 July 2020  Nowadays, data science tasks are not limited to traditional data analysis with limited attributes and records  In current scenarios, the real-time datasets are huge with enormous attributes and such datasets are very complex to evaluate using classical data analysis tools  For example, the datasets of the medical domain integrate several attributes in which the information of symptoms, diagnosis, travel history, health parameters, and many others are evaluated  To deal with such types of datasets assorted database query tools and programming languages are used  © Published under licence by IOP Publishing Ltd
Applying Natural Language Processing Toolkits to Electronic Health Records &amp;ndash; An Experience Report

A natural language challenge devised by Informatics for Integrating Biology and the Bedside (i2b2) was to analyze free-text health data to construct a multi-class, multi-label classification system focused on obesity and its co-morbidities. This report presents a case study in which a natural language processing (NLP) toolkit, called NLTK, was used in the challenge. This report provides a brief review of NLP in the context of EHR applications, briefly surveys and contrasts some existing NLP toolkits, and reports on our experiences with the i2b2 case study. Our efforts uncovered issues including the lack of human annotated physician notes for use as NLP training data, differences between conventional free-text and medical notes, and potential hardware and software limitations affecting future projects.
MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models

Medical Vision-Language Models (Med-VLMs) achieve strong expert-level performance, yet their ability to generate patient-accessible descriptions remains underexplored. With the 21st Century Cures Act now mandating immediate patient access to diagnostic imaging results, evaluating whether Med-VLMs can bridge this Expert-Lay Gap is both urgent and clinically consequential for patient education and shared decision-making. To this end, we introduce MedLayXPlain, the first large-scale multimodal benchmark and evaluation framework for Medical Lay Language Generation (MLLG). MedLayXPlain-122K provides 122,789 region-grounded samples across 8 imaging modalities from 12 publicly available source datasets, each comprising a medical image with paired expert and lay captions anchored in a three-level Unified Medical Language System (UMLS) ontology hierarchy spanning 7 semantic groups, 43 semantic types, and 2,411 medical concepts. Lay captions are constructed via Hierarchical Ontology-Verified Refinement (HOVER), a three-step pipeline combining patient-centric vocabulary mapping, LLM-based constrained rewriting, and cross-model visual verification to enforce semantic equivalence while preventing hallucination. We further introduce MedLayEval, a lightweight 3B evaluator distilled from a 27B verifier that scores expert-lay alignment across five clinically grounded attributes, addressing the poor correlation between standard NLG metrics and clinical judgment. Benchmarking 33 VLMs on MedLayXPlain-122K reveals a systematic Expert-Lay Gap: medical VLMs achieve strong expert captioning but suffer significant lay-register degradation, while general-purpose VLMs produce more accessible language yet lack clinical precision, confirming that neither current paradigm adequately serves patient-facing communication.
AABAC -- Automated Attribute Based Access Control for Genomics Data

The COVID-19 crisis has demonstrated the potential of cutting-edge genomics research. However, privacy of these sensitive pieces of information is an area of significant concern for genomics researchers. The current security models makes it difficult to create flexible and automated data sharing frameworks. These models also increases the complexity of adding or revoking access without contacting the data publisher. In this work, we investigate an automated attribute-based access control (AABAC) model for genomics data over Named Data Networking (NDN). AABAC secures the data itself rather than the storage location or transmission channel, provides automated data invalidation, and automates key retrieval and data validation while maintaining the ability to control access. We show that AABC when combined with NDN provide a secure and flexible combination for work with genomics research.
Investigating Personalized Price Discrimination of Textile-, Electronics- and General Stores in German Online Retail.

Developers of pricing strategies in e-commerce businesses see a wide range of opportunities for deploying online price discrimination techniques given their ability to track consumers’ online identity and behavior. In theory, an increasing use of personal data enables organizations to show every single consumer their own personalized price, which is determined by the consumer’s characteristics, e.g. age, gender, surfing history, or location. This paper aims to explore the existence of online price discrimination activities within the German ecommerce market using a three-method approach. First, inquiring the online retailers via email and investigating their public documents; second, surveying students; and third, using a software crawler to simulate surfing activity. Our results do not provide any evidence of individualized price discrimination, which, we argue, is due to economic and political reasons, not technical reasons.
Endometrial microbiome in reproductive failure: The possibility of metagenomic analysis

With the advent of metagenomics and 16S ribosomal RNA sequencing, growing attention has been dedicated to the endometrial microbiome. Research involving a relatively large cohort of healthy female participants has reported Lactobacillus dominance (LD) in the endometrial microbiome. Multiple studies have also shown that the loss of LD and/or increased microbial diversity within the endometrium are associated with reproductive failure. This phenomenon may stem from the loss of the immunomodulatory effects of Lactobacillus and the rise of proinflammatory responses triggered by pathogenic proliferation. Recent research has employed personalized antibiotic therapy followed by probiotic supplementation, tailored to the endometrial microbial composition of women with repeated implantation failure. The findings suggest that metagenomic analysis of the endometrial microbiome could be a valuable tool in addressing reproductive failure.
Analysis of the Difference between the Liability for Infringement of Artificial Intelligence Products and the Liability for Subsequent Observation Obligations

Currently, artificial intelligence is entering a rapidly developing era. Artificial intelligence technology is growing rapidly. Artificial intelligence technology can already be applied to various fields of social life, and artificial intelligence products with artificial intelligence technology as the core are abundant. Artificial intelligence products have been integrated into the lives of ordinary people. Due to the autonomy of artificial intelligence products, they may work in an unmanned state at certain times, resulting in infringement liability that may cause personal or property damage to others during the operation of artificial intelligence products. There are two ways to pursue accountability: one is to directly pursue the infringement liability of artificial intelligence products, and the specific subject who caused the infringement of artificial intelligence products shall bear the infringement liability; another approach is to pursue the responsibility of the producer and seller of artificial intelligence products for their subsequent observation obligations. The reason is that the producer and seller of artificial intelligence products fail to fulfill their subsequent observation obligations, resulting in the failure to detect defects in the artificial intelligence products in a timely manner and resulting in subsequent observation obligations. The responsible parties are the producers and sellers of artificial intelligence products.
Modulation of galactic and anomalous cosmic rays in the inner heliosphere

Abstract   Our knowledge on how galactic and anomalous cosmic rays are modulated in the inner heliosphere has been dramatically enlarged due to measurements provided by several missions launched in the past ten years. The current paradigma of singly charged anomalous cosmic rays has been confirmed by recent measurements from the SAMPEX and ACE satelite. Ulysses explored the inner heliosphere at polar regions during the last solar minimum period and is heading again to high heliographic latitudes during the time of the conference in July, 2000. The Sun approaches maximum activity when the spacecraft is at high heliographic latitudes giving us for the first time the possibility to explore modulation of cosmic rays in the inner three-dimensional heliosphere during such conditions. Ulysses electron measurements in addition to the 1 AU ICE electron and IMP helium measurements allows us to investigate charge sign dependent modulation over a full 22-year solar magnetic cycle. Implications of these observations for our understanding of different modulation processes in the inner three-dimensional heliosphere are presented.
HiMoRNA and RNA-Chrom integration: Chromatin-Associated LncRNAs in Genome-Wide Epigenetic Regulation

Abstract                 Long non-coding RNAs (lncRNAs) significantly contribute to genome structure and regulation. Many lncRNAs are known to interact with chromatin and in this way to affect gene expression patterns through epigenetic regulation. Still, experimental protocols for lncRNA-chromatin interactions do not provide any insight into the mechanisms of lncRNA-based genome-wide regulation. Here we present an integration of HiMoRNA – a resource containing correlated lncRNA-epigenetic changes in specific genomic locations genome-wide, – and RNA-Chrom, a resource featuring uniformly processed experimental data on RNA-chromatin interactions. Our integration approach allows generating interpretable and experimentally supported hypotheses on the mechanisms of lncRNA epigenetic regulation of gene expression. For this integration we have tailored the interface of HiMoRNA such that for many lncRNAs experimentally detected RNA-chromatin contacts are available from RNA-Chrom for browsing, analysis and downloading. HiMoRNA peaks supported by RNA-Chrom contacts can be explained by external experimental data. We believe that the integration of HiMoRNA and RNA-Chrom is a convenient and valuable approach that can provide experimental and mechanistic insights and greatly facilitate functional annotation of lncRNAs.
Cosmological Simulation for Fuzzy Dark Matter Model

Fuzzy Dark Matter (FDM), motivated by string theory, has recently become a hot candidate for dark matter. The rest mass of FDM is believed to be $\sim 10^{-22}$eV and the corresponding de-Broglie wave length is $\sim 1$kpc. Therefore, the quantum effect of FDM plays an important role in structure formation. In order to study the cosmological structure formation in FDM model, several simulation techniques have been introduced. We review the current status and challenges in the cosmological simulation for the FDM model in this paper.
POST MARKETING DRUG RISK MANAGEMENT SYSTEM IN THAILAND:SITUATION ANALYSIS AND DEVELOPMENT OF RISK ASSESSMENT CRITERIA

Safe use of drug does not mean zero risk, so benefit and risk must be balanced throughout the drug life cycle by using drug risk management (DRM) activities. In Thailand, DRM activities are implemented via different regulatory actions, tools or laws. These activities are responsible by the Drug Safety Advisory subcommittee, under the Drug Committee of the Thai Food and Drug Administration (Thai FDA). The objectives of this research are to identify strengths, weaknesses, threats and gaps of the post-marketing DRM in Thailand and develop risk assessment criteria for decision making of the Drug Safety Advisory subcommittee. The methods used to identify strengths, weaknesses and gaps in the current system were 1) literature review,2) in-depth interview with committee members and 3) validate the result by questionnaires with Likert scale. Study results showed that the three major strengths were 1) suitable composition of the subcommittee members, 2) clear role and responsibilities of the Drug Safety Advisory subcommittee, and 3) using scientific evidences for DRM decision. The two weaknesses were 1) no process for urgent regulatory decision and 2) no guideline or criteria for committee's decision making. Two threats were 1) accusation of released legal measures and 2) challenge of increasing workload with limited resource. The two gaps were 1) lack of cooperation among the Thai FDA and academic/research institutes and 2) the weak signal detection system.The methodology used to develop risk assessment criteria for decision making were 1) literature review to find the initial framework, 2) case review to analyze the previous regulatory recommendations and modified Delphi method to validate the criteria for decision making. The risk assessment criteria were constructed from four different categories, which were in public health implications, regulatory obligations, strength of evidences and public perceptions. Our study revealed that 13 criteria should be used in the DRM decision making and proposed the regulatory recommendations. They mainly include drug or vaccine exposure, frequency of ADRs, health consequence, strength of evidences, and factors likely to cause public anxiety. This research can result in criteria used for DRM decision making for manage drug’s risk in regulatory perspective.
Chromatin Regulator PRC2 Is a Key Regulator of Epigenetic Plasticity in Glioblastoma

Abstract                   Tumor cell plasticity contributes to functional and morphologic heterogeneity. To uncover the underlying mechanisms of this plasticity, we examined glioma stem-like cells (GSC) where we found that the biologic interconversion between GSCs and differentiated non-GSCs is functionally plastic and accompanied by gain or loss of polycomb repressive complex 2 (PRC2), a complex that modifies chromatin structure. PRC2 mediates lysine 27 trimethylation on histone H3 and in GSC it affected pluripotency or development-associated genes (e.g., Nanog, Wnt1, and BMP5) together with alterations in the subcellular localization of EZH2, a catalytic component of PRC2. Intriguingly, exogenous expression of EZH2-dNLS, which lacks nuclear localization sequence, impaired the repression of Nanog expression under differentiation conditions. RNA interference (RNAi)–mediated attenuation or pharmacologic inhibition of EZH2 had little to no effect on apoptosis or bromodeoxyuridine incorporation in GSCs, but it disrupted morphologic interconversion and impaired GSC integration into the brain tissue, thereby improving survival of GSC-bearing mice. Pathologic analysis of human glioma specimens revealed that the number of tumor cells with nuclear EZH2 is larger around tumor vessels and the invasive front, suggesting that nuclear EZH2 may help reprogram tumor cells in close proximity to this microenvironment. Our results indicate that epigenetic regulation by PRC2 is a key mediator of tumor cell plasticity, which is required for the adaptation of glioblastoma cells to their microenvironment. Thus, PRC2-targeted therapy may reduce tumor cell plasticity and tumor heterogeneity, offering a new paradigm for glioma treatment. Cancer Res; 73(14); 4559–70. ©2013 AACR.
Evaluating AI-Powered Assistive Technologies in Inclusive Education: A Systematic Review of Literature on Efficacy and Accessibility

This study aims to This study explores the potential of artificial intelligence (AI) tools to enhance accessibility and foster inclusive education for students with diverse learning needs. It also seeks to identify the challenges and limitations in integrating these technologies into educational practices. A comprehensive search was conducted across multiple academic databases, including EBSCOhost, ERIC, JSTOR, ScienceDirect, and Google Scholar, to assure the inclusion of high-quality studies and facilitate a comprehensive literature selection process. The data collected were processed using advanced data mining techniques, which enabled the systematic extraction of meaningful patterns, trends, and relationships from large and diverse datasets. These techniques included text mining for thematic analysis, as well as clustering and classification methods to group similar data points. The results show that AI integration in inclusive education significantly enhances learning outcomes, accessibility, and personalized support for diverse learners. AI technologies hold significant promise for advancing inclusive education by enhancing personalization, support, and learning outcomes for students with diverse needs. While effective in improving academic performance and engagement, successful integration requires addressing challenges such as resource constraints, ethical considerations, and educator readiness. Realizing AI’s full potential demands inclusive frameworks and collaboration among educators, developers, and policymakers. Key recommendations include educator training, inclusive design, infrastructure development, cross-sector collaboration, and ongoing evaluation to ensure equitable and empowering learning experiences for all.
Autonomous vehicles and Italian legal system

The historical period we are living is characterized by very rapid technological innovations, on many different fields. Our society and our legal system are not always ready to immediately embrace new technologies as they usually need a testing and adaptation period, but also adequate regulation. An important innovation already implemented has been the rides sharing (sharing mobility). It is very relevant in the market, especially in Italy, and has changed the way we perceive cars ownership and use. Self-driving cars are also a very important innovation, not only due to the fact that they significantly increase the number of users or the available mobility services, but also mainly for a significant increase in road safety concerns. Based on that, this paper will analyze how the Italian legal system regulates the protection for victims of traffic accidents caused by autonomous vehicles in terms of civil liability and motor insurance rules.
The right to water and sanitation in post-conflict legal mechanisms : An emerging regime ?

The paper seeks to point out the emergence of a regime on the right to water through the analysis of the different avenues available to its inclusion in post conflict settings. It will illustrate why a human rights based approach is important by discussing its linkage with accountability and the rule of law. It will then focus on some of the features of the right to water that are relevant in post conflict situations. Next the range of modalities for the operationalization of the right to water will be examined. Constitutions and peace agreements have already included socio economic rights such as the right to water. States’ compliance with these rights may be promoted through UN human rights bodies. Further judgments of international courts and reports of truth commissions also illustrate an emerging place for the right to water in post conflict settings. However there are still many obstacles to the inclusion of this right to water in post conflict situations. The linkage between the right to water and peace building processes should be strengthened.