ethicalabs/Research-Intent-Collab huggingface dataset:
Do Authentic Leadership And Organizational Citizenship Behavior Affect The Relationship Between Talent Management And Job Performance Among Teachers Of Private Sector Universities, Pakistan

The purpose of the research was to determine how talent management, authentic leadership, organizational citizenship behavior, and job performance are connected to one another. This research also looked at the link between talent management and job performance and the mediating roles that authentic leadership and organizational citizenship behavior play. Using the Organizational Citizenship Behaviour scale (Podsakoff, MacKenzie, Moorman, &amp; Fetter, 1990), the Authentic Leadership scale (Walumbwa, Avolio, Gardner, Wernsing, &amp; Peterson, 2008), the Job Performance scale (Goodman and Svyantek, 1999), and the talent management scale (Human Capital Institute, 2008), data were gathered from 539 teachers working in private institutions in the regions of Khyber Pakhtunkhwa, Balochestan, Punjab, and Sindh. According to the findings, there was a positive correlation between TM and AL (r =.496), TM and OCB (r =.391), and [1]TM and JP (r =.235). Additionally, a strong positive correlation between OCB and JP (r =.274) and AL and JP (r =.357) was also verified by the data. The inclusion of AL and OCB as mediators resulted in a drop in the regression coefficient of TM's impact on JP from 0.43 to 0.18. Therefore, the connection between TM and JP was partly mediated by AL and OCB.
Enhancing multi-class neurodegenerative disease classification using deep learning and explainable local interpretable model-agnostic explanations

IntroductionAlzheimer's disease (AD) and Parkinson's disease (PD) are two of the most prevalent neurodegenerative disorders, necessitating accurate diagnostic approaches for early detection and effective management.MethodsThis study introduces two deep learning architectures, the Residual-based Attention Convolutional Neural Network (RbACNN) and the Inverted Residual-based Attention Convolutional Neural Network (IRbACNN), designed to enhance medical image classification for AD and PD diagnosis. By integrating self-attention mechanisms, these models improve feature extraction, enhance interpretability, and address the limitations of traditional deep learning methods. Additionally, explainable AI (XAI) techniques are incorporated to provide model transparency and improve clinical trust in automated diagnoses. Preprocessing steps such as histogram equalization and batch creation are applied to optimize image quality and balance the dataset.ResultsThe proposed models achieved an outstanding classification accuracy of 99.92%.DiscussionThe results demonstrate that these architectures, in combination with XAI, facilitate early and precise diagnosis, thereby contributing to reducing the global burden of neurodegenerative diseases.
A Discrete-Event and Monte Carlo-Based Simulation Model for Multi-Server Call Center Queueing Systems

This study presents the implementation and performance evaluation of a multi-server queueing system model for call center operations using discrete-event simulation combined with Monte Carlo analysis. The objective is to analyze system performance under varying numbers of service agents to identify the optimal configuration that balances service efficiency and customer satisfaction. The model assumes that customer arrivals follow a Poisson distribution, while service times are exponentially distributed to represent realistic call handling behavior. Simulation experiments were conducted over eight-hour operational periods with server counts ranging from one to eight, each replicated 500 times for statistical robustness. Performance indicators such as average waiting time, server utilization, and Service Level Agreement (SLA) compliance were analyzed to measure system efficiency. Results show that increasing the number of servers significantly reduces average waiting time and enhances service level compliance. Configurations with five or more servers achieved average waiting times close to zero and over 99% compliance with the SLA, while maintaining moderate server utilization levels between 70% and 80%. These findings demonstrate that integrating discrete-event simulation with Monte Carlo methods provides an effective and reliable framework for evaluating service system performance, optimizing resource allocation, and supporting decision-making in call center management.
Optimizing Teleradiology in the UAE: Risks, Patient Safety, and System Improvements for Telehealth Quality

Introduction:                     Teleradiology has become an integral component of modern health care, enabling remote access to radiological expertise but introducing new challenges related to communication, technology, and legal accountability. This study evaluates radiologists’ perceptions of malpractice risks in teleradiology within the United Arab Emirates (UAE) and explores their implications for patient safety.                                                           Methods:                     A descriptive cross-sectional survey was administered to radiologists across public and private health care institutions in the UAE between August and November 2024. The survey examined participants’ demographic profiles, teleradiology experience, awareness of malpractice claims, communication practices, and perceived challenges. Quantitative data were analyzed using descriptive and inferential statistics to identify risk patterns and contributing factors.                                                           Results:                     Teleradiology was widely adopted among respondents, reflecting its essential role in current radiology practice. However, respondents identified several malpractice-related risks, most notably limited patient information, inconsistent communication protocols, and technical disruptions affecting image quality and transmission reliability. Many radiologists expressed uncertainty regarding the legal frameworks governing teleradiology and highlighted the need for clear national policies and institutional guidance.                                                           Conclusion:                     While teleradiology enhances accessibility and continuity of care, its safe implementation requires stronger governance, reliable technological infrastructure, and effective interdisciplinary communication. The study underscores the need for unified legal standards and targeted training to strengthen patient safety and minimize malpractice exposure.
3d printing and additive manufacturing - a mapping of invention patents in brazil

Among the technologies of Industry 4.0, one of the most emerging is 3D printing due to its multiple applications and sustainability trends, such as the use of new materials. In this context, this article aimed to analyze invention patents in Brazil. To do so, the methodology consists of classifying 115 applications in the INPI (National Institute of Industrial Property) database involving the title "3D printing" and "additive manufacturing" and their imports. The results showed that applications have increased in recent years, as has the concern for the development of alternative materials that reduce the environmental impact of production processes. Even so, 62% of the applications fall into category B (processing and transportation operations). One fact that has not changed is that most applications are from non-resident companies, while Brazilian applications are from universities, indicating that there are opportunities for partnerships for the joint development of innovations in this area.
Analysis of longitudinal Patient Reported Outcomes data with Classical Test Theory and rasch-based methods: an application on health-related quality of life in breast cancer patients

Patient Reported Outcomes (PRO) can be analyzed by several approaches, most commonly based on the Classical Test Theory (CTT) or on the Rasch model. The most adequate strategy to analyse longitudinal PRO data, taking into account the latent characteristic of what PRO are intended to measure as well as the specificity of longitudinal designs with repeated measurements remains to be identified. This study compares two methods of analysis of longitudinal PRO data based on CTT or on Rasch model approaches. Health-related quality of life data of 100 breast cancer patients allocated in three groups was evaluated at three time points. Both methods led to similar conclusions regarding tests of time and group effects and seem to be adequate for the analysis of longitudinal PRO data.
Bayesian and deep generative modelling for image registration, with a focus on uncertainty quantification &amp; similarity learning

Image registration is the process of aligning images into a common coordinate system where discrete pixel locations represent the same semantic information. Beside object detection and image segmentation, image registration is the backbone of many image analysis pipelines. However, practical use of automated image registration is limited by accuracy, which remains unsatisfactory except in case of relatively simple problems, e.g. rigid image registration, where only a single linear transformation applied to an image. In this thesis we present new methods to improve on the existing algorithms for non-rigid image registration, which models local differences between images. Firstly, we formulate a Bayesian model for image registration that overcomes the existing barriers to uncertainty quantification when using a dense, high-dimensional, and diffeomorphic transformation parametrisation, and use stochastic gradient Markov chain Monte Carlo to quantify image registration uncertainty on large, three-dimensional images. Secondly, we develop a variational Bayesian method for diffeomorphic, non-rigid registration of medical images. This model learns in an unsupervised way a data-specific similarity metric for mono-modal atlas-based image registration, i.e. when all the images in the dataset are aligned to a single target image. The similarity metric is parametrised as a neural network and leads to more accurate results than traditional similarity metrics which are used to initialise the model, e.g. sum of squared differences and local cross-correlation. The proposed approach has little to no impact on image registration speed and transformation smoothness. Finally, we formulate unsupervised similarity learning as a maximum likelihood estimation problem, with the similarity metric parametrised as an energy-based model. This formulation simplifies the model and enables us to make use of larger datasets in order to further improve unsupervised image registration accuracy also in case of pairwise image registration, i.e. when using any image in the dataset as the target image.
Supplementary Table S6 from Stability and Variability of the Human Fecal Microbiome over 2 Years in the Multiethnic Cohort Study: A Metagenomic Analysis

&lt;p&gt;Supplementary Table S6 shows Intraclass Correlation Coefficient Scores at the Pathway Level&lt;/p&gt;
How political and social activism lead to a human rights approach to food and nutritional security in Brazil

An analysis is presented of a recent evolutionary process in Brazil of two political and social movements that eventually joined to further common goals of reducing food and nutritional insecurity, and incorporating human rights into national laws and policy measures. A brief history is provided as background. The promotion of human rights, particularly of economic, social and cultural rights provides an important link between recent political and social activism, and national efforts to reduce food and nutritional insecurity. Concrete results of political and social activism are examined, which include the 1999 National Food and Nutrition Policy, the 1999 Social Pact to Reduce Iron‐Deficiency Anaemia, renewed efforts to incorporate economic, social and cultural rights in the National Human Rights Plan, the establishment of a national food security and nutrition network, and the formation of State food security councils. Specific and generalised lessons are also drawn from the Brazilian experience.
An Analysis of the Execution of Judgments and Follow-up Mechanism Under the African Court on Human and Peoples’ Rights: Lessons from the European Human Rights System

The African Court is a critical forum under the African human rights system. Under the Protocol which establishes the African Court, member states have an obligation to comply with the court’s judgments and must guarantee their execution. However, states rarely comply with the judgments of this court. In contrast, judgments under the European human rights system are largely complied with. This article analyses and evaluates the monitoring and supervision of the execution of judgments of the African Court and the European Court. It analyses the supervision mechanisms employed by the African Union’s organs such as the Assembly of Heads of State and Government, the Executive Council and the African Court. These monitoring and enforcement mechanisms are compared to the European mechanisms employed by the Committee of Ministers, the European Court, and other bodies like the Parliamentary Assembly. The article contends that the monitoring and supervision mechanisms under the European human rights system are detailed, coherent, well established, specialised and effective as compared to the mechanisms in Africa. In that regard, the article proposes lessons that can be adopted to strengthen the African human rights system.
Conflicted Counselors: Retaliation Protections for Attorney-Whistleblowers in an Inconsistent Regulatory Regime

Attorneys, especially in-house counsel, are subject to retaliation by employers in much the same way as traditional whistleblowers, often experiencing retaliation and loss of livelihood for reporting instances of wrongdoing about their clients. Although attorney-whistleblowing undoubtedly invokes ethical concerns, attorneys who “appear and practice” before the Securities and Exchange Commission (“SEC”) are required by federal law to act as internal whistleblowers under the Sarbanes-Oxley Act (“SOX”) and report evidence of material violations of the law within the organizations that they represent. An attorney’s failure to comply with these obligations will result in SEC-imposed civil penalties and disciplinary action. Recent federal case law, however, holds that whistleblowers who report violations internally within their organizations are not eligible for the robust retaliation protections available under the Dodd-Frank Wall Street Reform and Consumer Protection Act (“Dodd-Frank”) and must report to the SEC to be protected. Given that external reporting by attorneys would run contrary to professional ethical rules in a number of states, lawyers currently find themselves caught in a “catch-22” making it exceedingly difficult to comply with the conflicting regulatory regimes to which they are held. This Article will address this emerging problem by considering a question that no court has yet addressed — whether the SOX attorney-reporting rules preempt conflicting state law — and will propose amendments to such rules to clarify when external reporting is appropriate. This Article will also consider a state-based solution to this conflict adopting a modified version of Model Rule 1.13, the ethical rule governing the behavior of attorneys when they represent organizations and are called to act as whistleblowers. This Article will also contribute to the ongoing scholarly discussion of “new governance” approaches to regulation by placing attorney-whistleblowers in this context and considering how their gatekeeping role ensures regulatory compliance within the organizations that they represent.
A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization

Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic notations, with emerging extensions to incorporate multi-modal inputs. To advance the new field of LLM for molecular discovery, this survey provides an up-to-date and forward-looking review of the emerging use of LLMs for two central tasks: molecule generation and molecule optimization. Based on our proposed taxonomy for both problems, we analyze representative techniques in each category, highlighting how LLM capabilities are leveraged across different learning settings. In addition, we include the commonly used datasets and evaluation protocols. We conclude by discussing key challenges and future directions, positioning this survey as a resource for researchers working at the intersection of LLMs and molecular science. A continuously updated reading list is available at https://github.com/REAL-Lab-NU/Awesome-LLM-Centric-Molecular-Discovery.
Adverse selection and categorical discrimination in the health insurance markets: the effects of genetic tests

In this paper, the effects of new methods for risk classification, e.g., genetic tests, on health insurance markets are studied using an insurance model with state contingent utility functions. The analysis focuses on the case of treatment costs higher than the patient's willingness to pay where standard models of asymmetric information are not applicable. In this case, the benefit from signing a fair insurance contract will be positive only if illness probability is low. In contrast to the common perception, additional risk classification under symmetric information can be efficiency enhancing. Under asymmetric information about illness risks, however, there can be complete market failure.
Sovereign digital currencies: Central Banking of the future or echoes from the past?

This paper analyzes a specific type of monetary developments instigated by the digitization of the EU payment environment over the last decade –sovereign digital currencies. Namely, the combination of fast-paced technological advancements and regulatory responses lagging behind, have resulted with a noticeable increase in privately issued digital monies. Free-riding on similarities with innovative money formats (such as e-money), privately issued digital currencies compete with digital payment solutions in retail transactions. The competition is taking place in an increasingly cashless payment environment, in which de facto digital monies constitute the majority of the broad money aggregate. In this respect, the idea of sovereign digital currencies –labeled as “central bank digital currency” (CBDC) appears as an intriguing proposal. With this in mind, this paper gives further insight on the public debate on the promises that privately issued digital currencies hold for the future of central banking and the financial system overall
Rethinking Animal Welfare in Tunisian Animal Husbandry Law in the Context of Environmental Rights

The Tunisian Animal Husbandry Law, enacted in 2005, sets the legal framework for farm animals in Tunisia. In the context of environmental rights, this article critically analyzes the Tunisian animal husbandry law and compares it with animal protection laws in other countries. This paper argues that Tunisian law is inadequate in several areas and concludes that Tunisia needs to adopt a more comprehensive and humane approach to animal welfare. This can be achieved by reforming the Animal Husbandry Law to take into account the latest scientific knowledge about animal rights.
Inference for systems of stochastic differential equations from discretely sampled data: a numerical maximum likelihood approach

zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Predicting Genotype and Survival in Glioma Using Standard Clinical MR Imaging Apparent Diffusion Coefficient Images: A Pilot Study from The Cancer Genome Atlas

Few studies have shown MR imaging features and ADC correlating with molecular markers and survival in patients with glioma. Our purpose was to correlate MR imaging features and ADC with molecular subtyping and survival in adult diffuse gliomas.Presurgical MRIs and ADC maps of 131 patients with diffuse gliomas and available molecular and survival data from The Cancer Genome Atlas were reviewed. MR imaging features, ADC (obtained by ROIs within the lowest ADC area), and mean relative ADC values were evaluated to predict isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion status, MGMT promoter methylation, and overall survival.IDH wild-type gliomas tended to exhibit enhancement, necrosis, and edema; >50% enhancing area (P < .001); absence of a cystic area (P = .013); and lower mean relative ADC (median, 1.1 versus 1.6; P < .001) than IDH-mutant gliomas. By means of a cutoff value of 1.08 for mean relative ADC, IDH-mutant and IDH wild-type gliomas with lower mean relative ADC (<1.08) had poorer survival than those with higher mean relative ADC (median survival time, 24.2 months; 95% CI, 0.0-54.9 months versus 62.0 months; P = .003; and median survival time, 10.4 months; 95% CI, 4.4-16.4 months versus 17.7 months; 95% CI, 11.6-23.7 months; P = .041, respectively), regardless of World Health Organization grade. Median survival of those with IDH-mutant glioma with low mean relative ADC was not significantly different from that in those with IDH wild-type glioma. Other MR imaging features were not statistically significant predictors of survival.IDH wild-type glioma showed lower ADC values, which also correlated with poor survival in both IDH-mutant and IDH wild-type gliomas, irrespective of histologic grade. A subgroup with IDH-mutant gliomas with lower ADC had dismal survival similar to that of those with IDH wild-type gliomas.
A set of new multi- and many-objective test problems for continuous optimization and a comprehensive experimental evaluation

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Architectural Implications of Graph Neural Networks

Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achieved in many graph-related tasks. However, GNN is not as well understood in the system and architecture community as its counterparts such as multi-layer perceptrons and convolutional neural networks. This work tries to introduce the GNN to our community. In contrast to prior work that only presents characterizations of GCNs, our work covers a large portion of the varieties for GNN workloads based on a general GNN description framework. By constructing the models on top of two widely-used libraries, we characterize the GNN computation at inference stage concerning general-purpose and application-specific architectures and hope our work can foster more system and architecture research for GNNs.
FinVerBench: Benchmark Validity and Calibration in Large Language Model Financial Statement Verification

37 pages, 9 figures