ethicalabs/Research-Intent-Collab huggingface dataset:
Racial discrimination in education and EU equality law

This thematic report analyses the legal, institutional and jurisprudential landscape governing racial and ethnic discrimination in education across the European Union. Drawing on contributions from national experts in the European network of legal experts in gender equality and non‑discrimination, it examines the interaction between international human rights treaties, the European Convention on Human Rights, and EU equality law—particularly the Racial Equality Directive. The report maps national legal frameworks, explores domestic and European case law, and assesses enforcement mechanisms including courts, equality bodies, and school inspectorates. It highlights structural challenges such as segregation, evidentiary barriers, and inconsistencies between European and national adjudication, while identifying good practices and pathways for strengthening racial equality in education.
Gross genomic damage measured by DNA image cytometry independently predicts gastric cancer patient survival

DNA aneuploidy reflects gross genomic changes. It can be measured by flow cytometry (FCM-DNA) or image cytometry (ICM-DNA). In gastric cancer, the prevalence of DNA aneuploidy has been reported to range from 27 to 100%, with conflicting associations with clinicopathological variables. The aim of our study was to compare the DNA ploidy status measured using FCM-DNA and ICM-DNA in gastric cancer and to evaluate its association with clinicopathological variables.Cell nuclei were isolated from 221 formalin-fixed, paraffin-embedded gastric cancer samples. DNA ploidy was assessed using FCM-DNA and ICM-DNA.A total of 178 (80.5%) gastric cancer samples were classified as DNA aneuploid using FCM-DNA, compared with 172 (77.8%) gastric cancer samples when using ICM-DNA. Results obtained from both methods were concordant in 183 (82.8%) cases (kappa=0.48). Patients with ICM-DNA diploid gastric cancer survived significantly longer than those with ICM-DNA aneuploid gastric cancer (log rank 10.1, P=0.001). For FCM-DNA data, this difference did not reach statistical significance. The multivariate Cox model showed that ICM-DNA ploidy status predicted patient survival independently of tumour-node-metastasis status.ICM-DNA ploidy status is an independent predictor of survival in gastric cancer patients and may therefore be a more clinically relevant read out of gross genomic damage than FCM-DNA.
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis

Sentiment analysis is often a crowdsourcing task prone to subjective labels given by many annotators. It is not yet fully understood how the annotation bias of each annotator can be modeled correctly with state-of-the-art methods. However, resolving annotator bias precisely and reliably is the key to understand annotators' labeling behavior and to successfully resolve corresponding individual misconceptions and wrongdoings regarding the annotation task. Our contribution is an explanation and improvement for precise neural end-to-end bias modeling and ground truth estimation, which reduces an undesired mismatch in that regard of the existing state-of-the-art. Classification experiments show that it has potential to improve accuracy in cases where each sample is annotated only by one single annotator. We provide the whole source code publicly and release an own domain-specific sentiment dataset containing 10,000 sentences discussing organic food products. These are crawled from social media and are singly labeled by 10 non-expert annotators.
Construction of a lncRNA-mediated ceRNA network and a genomic-clinicopathologic nomogram to predict survival for breast cancer patients

Breast cancer (BC) is the most common cancer among women and a leading cause of cancer-related deaths worldwide. The diagnosis of early patients and the prognosis of advanced patients have not improved over the past several decades. The purpose of the present study was to identify the lncRNA-related genes based on ceRNA network and construct a credible model for prognosis in BC. Based on The Cancer Genome Atlas (TCGA) database, prognosis-related differently expressed genes (DEGs) and a lncRNA-associated ceRNA regulatory network were obtained in BC. The patients were randomly divided into a training group and a testing group. A ceRNA-related prognostic model as well as a nomogram was constructed for further study. A total of 844 DElncRNAs, 206 DEmiRNAs and 3295 DEmRNAs were extracted in BC, and 12 RNAs (HOTAIR, AC055854.1, ST8SIA6-AS1, AC105999.2, hsa-miR-1258, hsa-miR-7705, hsa-miR-3662, hsa-miR-4501, CCNB1, UHRF1, SPC24 and SHCBP1) among them were recognized for the construction of a prognostic risk model. Patients were then assigned to high-risk and low-risk groups according to the risk score. The Kaplan-Meier (K-M) analysis demonstrated that the high-risk group was closely associated with poor prognosis. The predictive nomogram combined with clinical features showed performance in clinical practice. In a nutshell, our ceRNA-related gene model and the nomogram graph are accurate and reliable tools for predicting prognostic outcomes of BC patients, and may make great contributions to modern precise medicine.
Intelligent Compaction Control Based on Fuzzy Neural Network

The paper studies fuzzy neural network theory and establishes structures of fuzzy neural network for intelligent compaction control. For compaction performance of a roller, fuzzy neural network parameters are self-corrected by learning algorithms of compensatory fuzzy neural network. Fuzzy control rules table that was educed in practice is looked at as training samples of fuzzy neural network. Simulation results show that the fuzzy neural network controller has generalization ability in error bound.
An Accident Investigation: Lessons Learning for Enhancing Safety of Fishing Vessels

In the paper the authors present an accident investigation on a casualty happened in the Gulf of Naples during spring 2000. The environmental and navigation data during casualty are presented as well as capsizing and sinking are described in detail. Moreover the rescue operation following sinking and then the recovery operation of the boat are described. The authors highlight the reasons for sinking as emerged by Court investigation and give an example of a “report on the marine casualty” as requested by IMO MSC/Circ.827 (December 1997). Finally some suggestions are given in order to improve safety on fishing vessels.
The Extent of Constitutionalizing the Environmental Rights as One of the Anchors to Keep a Healthy, Clean Environment: A Difficult Balance between the International Agreements and the Jordanian Constitution’s Restrictions

This study analyzes the extent to which the environmental rights are constitutionalized as one of the anchors to maintain a healthy and clean environment through referring to the international agreements and the Jordanian constitution. This research aims at showing the international frame of the environmental rights, and the environmental rights in the Jordanian constitution. This study followed the analytical approach, analyzing the texts of the international agreements that organizes the environmental rights, also analyzing the extent to which these rules agree with the constitutional principles agreed upon regarding the right of a clean and healthy environment. This study concluded many results and recommendations, most importantly; the need of shedding light over the Jordanians’ right in a healthy environment, enabling its censorship tools adding real protection for the human right in a healthy environment. Dedicating the special procedure rights of protecting the environment to let the audience participation and civil community institutions participate in development decision making through evaluating the environmental effort by providing environmental information about the size of damage caused by the developmental projects, the preventative principal in protecting the environment and the right to have administrative, environmental and legal justice. Putting principals and basis for the national environmental legislations in the Jordanian institution.&#x0D;
Machine Learning for Optimizing Supply Chain, Logistics, and Inventory Management in E-Commerce

The rapid expansion of e-commerce has significantly increased the complexity of supply chain, logistics, and inventory management systems, creating unprecedented challenges for operational efficiency and customer satisfaction. Traditional deterministic and heuristic approaches often fail to accommodate dynamic demand fluctuations, multi-channel distribution networks, and real-time operational uncertainties. Machine learning (ML) has emerged as a transformative tool, offering predictive, prescriptive, and adaptive capabilities to optimize end-to-end supply chain operations. This chapter presents a comprehensive overview of advanced ML techniques—including supervised learning, reinforcement learning, deep learning, and hybrid ensemble models—applied to demand forecasting, inventory optimization, logistics planning, and predictive maintenance. The integration of IoT-enabled data streams, edge intelligence, and automated warehouse systems was discussed, highlighting strategies for real-time decision-making and operational resilience. Case studies demonstrate tangible improvements in inventory accuracy, delivery efficiency, and cost reduction, while emphasizing the potential for multi-objective optimization that balances operational performance with sustainability goals. Key challenges, such as data heterogeneity, model interpretability, and integration with legacy systems, are analyzed to provide a roadmap for future research and implementation. The insights presented underscore the critical role of machine learning in driving intelligent, responsive, and sustainable e-commerce supply chains.
Designing Shared Decision-Making Interventions for Dissemination and Sustainment: Can Implementation Science Help Translate Shared Decision Making Into Routine Practice?

Shared decision making (SDM) is not widely practiced in routine care due to a variety of organizational, provider, patient, and contextual factors. This article explores how implementation science—which encourages attention to the multilevel contextual factors that influence the adoption, implementation, and sustainment of health care practices—can provide useful insights for increasing SDM use in routine practice. We engaged with stakeholders representing different organizations and geographic locations over three phases: 1) multidisciplinary workgroup meeting comprising researchers and clinicians ( n = 11); 2) survey among a purposive sample of 47 patient advocates, clinicians, health care system leaders, funders, policymakers, and researchers; and 3) working session among diverse stakeholders ( n = 30). The workgroup meeting identified priorities for action and research, which included targeting multiple audiences and levels, shifting culture toward valuing and supporting SDM, and considering contextual factors influencing SDM implementation. Survey respondents provided recommendations for increasing adoption, implementation, and maintenance of SDM in practice including providing tools to support SDM, obtaining stakeholders’ involvement, and raising awareness of the importance of SDM. Stakeholders in the working session provided recommendations on the design of a guide for implementation of SDM in clinical settings, strategies to disseminate educational curricula on SDM, and strategies to influence policies to increase SDM use. These specific recommendations serve as a call to action to pursuing specific promising strategies aimed at increasing SDM use in practice and enhance understanding of the perspectives of diverse stakeholders at multiple levels from an implementation science perspective that appear fruitful for further study and application.
Forgetting is Everywhere

A fundamental challenge in developing general learning algorithms is their tendency to forget past knowledge when adapting to new data. Addressing this problem requires a principled understanding of forgetting; yet, despite decades of study, no unified definition has emerged that provides insights into the underlying dynamics of learning. We propose an algorithm- and task-agnostic theory that characterises forgetting as a lack of self-consistency in a learner's predictive distribution, manifesting as a loss of predictive information. Our theory naturally yields a general measure of an algorithm's propensity to forget and demonstrates that exact Bayesian inference allows for adaptation without forgetting. To validate the theory, we design a comprehensive set of experiments that span classification, regression, generative modelling, and reinforcement learning. We empirically demonstrate how forgetting is present across all deep learning settings and plays a significant role in determining learning efficiency. Together, these results establish a principled understanding of forgetting and lay the foundation for analysing and improving the information retention capabilities of general learning algorithms.
The effects of managers’ toxic leadership behaviors on organizational cynicism and employee silence(A research on the public sector)

Günümüz örgütleri varlığını sürdürebilmek, ayakta kalabilmek, ilerlemek ve rekabet edebilmek için yetenekli çalışanlara ihtiyaç duymaktadır. Çünkü örgütler için en önemli faktör olan insan sermayesine olan yatırım kilit rol oynamaktadır. Bu bağlamda bir örgütün başarısı yöneticilerinin ve çalışanlarının başarısına bağlıdır. Yöneticilerin, çalışanların fikir ve önerilerine önem vermeleri ve onların kararlara katılımını sağlamları bir örgütün başarısı açısından büyük önem taşımaktadır. Aksi takdirde örgütler iş verimsizliği, düşük performans ve çalışan tatminsizliği ve sessizliği ile karşı karşıya kalabilmektedirler. Bu çalışmada kamu sektörlerinde toksik liderlik davranışlarının örgütsel sinizm ve çalışan sessizliği üzerine etkilerini incelemek ve bu kavramlar arasındaki ilişkinin saptanması amaçlanmıştır. Bu amaç doğrultusunda; İstanbul (Anadolu yakası) ilinde faaliyet gösteren kamu sektörü çalışanlarına kişisel bilgi formu, toksik liderlik ölçeği, örgütsel sinizm ve çalışan sessizliği ölçeğinden oluşan bir anket formu uygulanmıştır. Anket yöntemiyle elde edilen veriler, korelasyon analizi ve regresyon analizi vasıtasıyla değerlendirilmiştir. Araştırma sonucunda, toksik liderlik, çalışan sessizliği ve örgütsel sinizm arasında anlamlı ilişkiler bulunduğu kabul edilmiştir. Bu noktada örgütlerin başarılı olabilmesi için toksik liderlik davranışlarının örgütsel sinizm ve çalışan sessizliği üzerine çok yönlü önemli etkilerinin olduğunu ve bu kavramların son yıllarda araştırmacıların ilgi odağı haline geldiğini yapılan araştırmalar göstermektedir. Ayrıca bu kavramlar örgütlerde başarı faktörünün önünde ciddi bir engel oluşturabilmektedir.
Observations of TeV gamma ray flares from Markarian 501 with the Telescope Array Prototype

We will report the observations of TeV gamma ray flares from Markarian 501 using Telescope Array Prototype. The observation were carried out continuously from the end of March to the end of July in 1997. The energy spectrum, and the time variation of the gamma ray intensities are shown. The intensity has been changed by the order of magnitude in this period and the possible quasi periodic oscillation of 12.7days were discovered.
Context matters: The meaning of organizational context in managers’ leadership behavior and subordinates’ perceptions of leadership

In the present study we set out to study the meaning of organizational context by investigating the direct and indirect effects of organizational context (indicated by structural factors and working conditions for managers) on managers’ and subordinates’ perceptions of leadership. A sample of 105 managers and 1126 subordinates participated in the study. Data consisted of self-reports in questionnaires (one for managers and one for subordinates) together with organizational data provided by HR and analyzed by means of a multilevel mediation analysis. The results indicate that managers that have better working conditions are also better equipped to engage in positive transformational leadership behaviors. In addition, the results also indicate that when managers put in extra effort to be visible in the organization their followers are more likely to assess them as transformational and trustworthy.
THE ROLE OF ARTIFICIAL INTELLIGENCE IN OCCUPATIONAL SAFETY AND HEALTH: INVESTIGATING THE POTENTIAL APPLICATIONS AND LIMITATIONS OF AI IN IMPROVING WORKPLACE SAFETY AND HEALTH

The rapid advancement of Artificial Intelligence (AI) has created significant opportunities to enhance occupational safety and health (OSH) across diverse industries. This study investigates the potential applications, effectiveness, and limitations of AI technologies in improving workplace safety, employee well-being, and productivity. Employing a mixed-methods design, the research integrates quantitative analyses, including hierarchical regression to examine the impact of specific AI tools, with qualitative insights from thematic analysis of expert and employee perspectives. The findings reveal that predictive analytics, wearable monitoring devices, and automated incident reporting significantly contribute to reducing workplace hazards and enhancing employee health, while other technologies, such as robotic exoskeletons, face adoption barriers due to cost and operational challenges. Additionally, the study highlights ethical considerations, employee acceptance, and training as critical factors influencing the success of AI integration. The results suggest that AI can substantially improve OSH outcomes when strategically implemented within a robust governance framework that addresses both technological and human factors. Practical recommendations include prioritizing high-impact AI technologies, providing comprehensive training, establishing ethical protocols, and continuously monitoring performance to optimize safety and productivity. These findings offer valuable guidance for organizations, policymakers, and OSH practitioners seeking to leverage AI for safer and more efficient workplaces.
Sparse Recovery for Overcomplete Frames: Sensing Matrices and Recovery Guarantees

Signal models formed as linear combinations of few atoms from an over-complete dictionary or few frame vectors from a redundant frame have become central to many applications in high dimensional signal processing and data analysis. A core question is, by exploiting the intrinsic low dimensional structure of the signal, how to design the sensing process and decoder in a way that the number of measurements is essentially close to the complexity of the signal set. This chapter provides a survey of important results in answering this question, with an emphasis on a basis pursuit like convex optimization decoder that admits a wide range of random sensing matrices. The results are quite established in the case signals are sparse in an orthonormal basis, while the case with frame sparse signals is much less explored. In addition to presenting the latest results on recovery guarantee and how few random heavier-tailed measurements fulfill these recovery guarantees, this chapter also aims to provide some insights in proof techniques. We also take the opportunity of this book chapter to publish an interesting result (Theorem 3.10) about a restricted isometry like property related to a frame.
Outreach drawer collections and visual science communication enhance public engagement with native bee diversity in Colombia

Scientific research is often confined to academic circles, limiting public awareness of critical discoveries. This document highlights the transformative impact of science communication through an entomological exhibition accompanied by a visual infographic in a rural museum in La Tatacoa, municipality of Villavieja, Huila, Colombia. Based on an undergraduate research project on native bee diversity in the region, a collaborative initiative led to the creation of a small bee exhibition at the Tatacoa Natural History Museum. Despite initial skepticism regarding the presence of more than 110 bee species in this arid ecosystem, the exhibition effectively captured public interest. Following its publication on social media, the exhibition went viral, significantly increasing awareness of local biodiversity. The initiative also became a valuable educational tool, fostering environmental engagement among local students and inspiring conservation efforts. The subsequent renovation of the museum further enhanced public appreciation of regional biodiversity. This case study underscores the impact of accessible science communication in bridging the gap between academic research and societal awareness, demonstrating that visual and interactive formats can foster greater public engagement with conservation and biodiversity.
Using Force Against the 'Weapons of the Weak': Examining A Chemical-Biological Weapons Usage Criterion for Unilateral Humanitarian Intervention Under the Responsibility to Protect

The civil war in Syria has demonstrated the international community’s inability to act to protect civilians caught in mass atrocities during cases of U.N. Security Council deadlock, with permanent member vetoes presenting a significant legal obstacle to international humanitarian intervention. The Responsibility to Protect (R2P) construct, created in response to the NATO intervention in Kosovo, was originally designed to overcome such Security Council paralysis by transforming the debate from the 'right' of states to intervene to the 'responsibility' of the entire international community to protect civilians, as well as opening the door to legitimate unilateral humanitarian intervention in cases of Security Council deadlock. However, despite the innovations of these 'hard R2P' principles, the R2P framework was weakened during U.N. adoption into a 'soft R2P' that is reflected in the current legal impasse over Security Council authorization for humanitarian intervention.This article examines a chemical and biological weapons (CBW) usage criterion as a condition legitimizing unilateral humanitarian intervention under R2P. A CBW usage criterion for R2P intervention can provide the international community with a framework for legitimate humanitarian intervention to end chemical and biological weapons attacks against civilians and deter such attacks in the future. Such a criterion would increase support for a principle of R2P intervention across realist, institutionalist and cosmopolitan perspectives, strengthening a norm of humanitarian intervention in cases of CBW attack and promoting the international community’s overall ability to protect civilians in war. To provide legitimacy to act against future such atrocities, the international community should thus begin efforts to incorporate into the U.N. framework an R2P principle authorizing intervention in cases of CBW use against civilians.