ethicalabs/Research-Intent-Collab huggingface dataset:
Mechanistic understanding of the dehydroxylation reaction of smectites: Insights from reactive force field (ReaxFF) molecular dynamics simulation

Abstract                Smectite, a major barrier material for contaminants in Earth’s critical zone, is a layered aluminosilicate mineral with cis-vacant (cv) and trans-vacant (tv) configurations. High-temperature transformations of smectite are directly related to smectite’s thermal reactions. However, the precise thermal reaction mechanisms and thermally induced structural transitions of cv and tv smectite remain debated. In this study, we systematically investigated the mechanism of thermal reactions of cv and tv smectite models using reactive force field (ReaxFF) molecular dynamics. We explored the deprotonation and the intralayer dehydration steps of cv and tv smectites at 700 and 900 K. The results revealed that the dehydroxylation reaction of cv smectite exhibited more difficulty than tv smectite at 700 K, while demonstrating less difficulty at 900 K. Furthermore, it was found that the dehydroxylated cv and tv smectites evolved to a consistent structure spontaneously. Our findings further confirmed that cv smectite has a higher dehydroxylation temperature than tv smectite and thus provided a theoretical basis for distinguishing cv and tv smectites using thermogravimetric analysis (TGA). Moreover, we gathered TGA data of smectites from different regions, confirming that montmorillonites have a cv structure and almost all smectites from China (over 11 provinces) are cv. This study provides a molecular-level understanding of the thermal reaction mechanisms of smectites and a physical basis for further study and application of smectites.
Root Cause Analysis Combined with Human Factors Engineering Tools for Adverse Events Investigation in Healthcare

An important way to promote patient safety is through the investigation of adverse events (AEs). There are many available methods used in the health area to carry on an investigation and one of the most used within the healthcare area is the Root Cause Analysis (RCA). Though extremely useful, it presents some limitations. A solution to mitigate RCA limitations may be through the combination of tools available in a different method. This study presents a methodology, that combines a set of tools, derived from two methods: RCA and Human Factors Engineering (HFE). It also presents the results of the methodology application in a real case study involving an adverse event caused by a mechanic ventilator. The outcome of the study showed that the combination of HFE tools to RCA could augment the investigation scope.
Review on Deep Learning Algorithms and Benchmark Datasets for Pairwise Global Point Cloud Registration

Point cloud registration is the process of aligning point clouds collected at different locations of the same scene, which transforms the data into a common coordinate system and forms an integrated dataset. It is a fundamental task before the application of point cloud data. Recent years have witnessed the rapid development of various deep-learning-based global registration methods to improve performance. Therefore, it is appropriate to carry out a comprehensive review of the more recent developments in this area. As the developments require access to large benchmark point cloud datasets, the most widely used public datasets are also reviewed. The performance of deep-learning-based registration methods on the benchmark datasets are summarized using the reported performance metrics in the literature. This forms part of a critical discussion of the strengths and weaknesses of the various methods considered in this article, which supports presentation of the main challenges currently faced in typical global point cloud registration tasks that use deep learning methods. Recommendations for potential future studies on this topic are provided.
Supplementary Table 1 from Genome-wide Profiling of Chromatin Signatures Reveals Epigenetic Regulation of MicroRNA Genes in Colorectal Cancer

Supplementary Table 1 from Genome-wide Profiling of Chromatin Signatures Reveals Epigenetic Regulation of MicroRNA Genes in Colorectal Cancer
The Disconnect between’Upstream’Automation and Legal Protection against Automated Decision Making, JOTWELL (April 7, 2022) (reviewing Reuben Binns and Michael Veale, Is that your final decision? Multi-stage profiling, selective effects, and Article 22 of the GDPR, 11 Int'l Data Privacy L. 319 (2021))

Contains fulltext : 288622.pdf (Publisher’s version ) (Open Access)
On the study of crowdsourced labelled data and annotators: beyond noisy labels.

bcam Excelencia Severo Ochoa
TRANSPARENCY, ACCOUNTABILITY, AND CORRUPTION RISK ASSESSMENT: CENTRAL BANK OF NIGERIA

This Transparency, Accountability, and Corruption Risk Assessment examine the Central Bank of Nigeria (CBN)'s governance, transparency gaps, and corruption vulnerabilities under the CBN Act 2007, aiming to assess how operational independence without accountability enables elite capture, procurement abuses, quasi-fiscal interventions, and breaches like excessive Ways and Means financing.​ The study employs institutional economics and principal-agent theory, drawing on data from Auditor-General reports, forensic audits (e.g., Deloitte on invalid FX claims), court filings (e.g., EFCC charges against former Governor Emefiele), CBN disclosures, legislative probes, and media analyses of scandals, including ₦629 billion unrecovered Anchor Borrowers' loans, ₦29.7 billion recycled condemned notes, and over ₦1.4 billion procurement fraud.​ Key findings reveal systemic opacity in audits, Freedom of Information non-compliance, and financial mismanagement. These factors erode public trust, raise inflation, and reduce Foreign direct Investment. Recent reforms, such as exiting interventions and reinstating reports, show progress. However, persistent accountability deficits continue to pose risks. Recommendations urge phased quasi-fiscal exits, mandatory independent audits, digital procurement, and inflation targeting to bolster credibility and curb corruption.
Basic Ideas and Tools for Projection-Based Model Reduction of Parametric Partial Differential Equations

We provide first the functional analysis background required for reduced order modeling and present the underlying concepts of reduced basis model reduction. The projection-based model reduction framework under affinity assumptions, offline-online decomposition and error estimation is introduced. Several tools for geometry parametrizations, such as free form deformation, radial basis function interpolation and inverse distance weighting interpolation are explained. The empirical interpolation method is introduced as a general tool to deal with non-affine parameter dependency and non-linear problems. The discrete and matrix versions of the empirical interpolation are considered as well. Active subspaces properties are discussed to reduce high-dimensional parameter spaces as a pre-processing step. Several examples illustrate the methodologies.
Comparative Analysis Of Biodiversity Conservation Outcomes In Mpas Versus Non-Protected Marine Areas

Marine Protected Areas (MPAs) are universally accepted as fundamental instruments for the protection of marine biodiversity, but their effectiveness is different in ecological and management contexts. This research compares the ecological effects of MPAs to non-protected areas of the sea across various tropical ecoregions. The main objective is to test whether MPAs have greater biodiversity and ecosystem function than ecologically comparable non-MPA sites. This study also explores the extent to which MPA characteristics such as age, protection, and level of enforcement affect conservation effectiveness. Field data were obtained from 16 reef locations (eight MPAs and eight paired non-MPAs) in the Coral Triangle, Western Indian Ocean, Tropical Eastern Pacific, and Caribbean Basin. Underwater visual census, environmental Deoxyribonucleic Acid (DNA) sampling, and remote sensing provided measurements of species richness, fish biomass, trophic composition, coral cover, and functional diversity. Statistical analysis involved Permutational Multivariate Analysis of Variance (PERMANOVA), Non-metric Multidimensional Scaling (NMDS), and generalized linear mixed models. MPAs always maintained greater biodiversity, with average species richness and fish biomass that were significantly greater than those from non-MPAs. Functional richness and dispersion were also greater in MPAs, suggesting greater ecological functions and resilience. Older strictly protected MPAs showed the most robust conservation benefits, whereas partially protected or younger MPAs showed limited gains. MPAs increase biodiversity and ecosystem organization when properly managed. Effectiveness is time since establishment and enforcement quality dependent. These results affirm the necessity of science-informed MPA expansion policies that focus on governance capacity and ecological surveillance.
Evaluation of the Effectiveness of Self-Supervised Feature Representation Based on Contrastive Learning in Image Classification Tasks

Traditional deep learning-based image classification methods rely on large-scale labeled data. In practical scenarios such as medical imaging and agricultural monitoring, obtaining labeled data requires substantial manpower and time, becoming a bottleneck for deployment. Similarly, in the textile industry, the automated inspection of fabric defects and high-precision fiber texture identification often face similar challenges due to the scarcity of highquality annotated data. Self-supervised learning offers a solution. This study evaluates the effectiveness of contrastive learning-based selfsupervised feature representation in image classification, focusing on feature extraction from unlabeled data. An efficient contrastive learning framework was constructed and evaluated on CIFAR-10, ImageNet-1K, and CUB-200-2011. Based on the ResNet architecture, combined with the InfoNCE loss and data augmentation, a two-stage training strategy of self-supervised pre-training followed by supervised fine-tuning was adopted. Model performance was assessed using classification accuracy and F1 score. Results show that the proposed method outperforms traditional supervised learning, especially in low-label regimes and as a robust initialization strategy across datasets. The model demonstrates strong generalization in low-sample settings and adaptability to different data distributions. This study clarifies the role of contrastive learning in feature representation for image classification and provides support for applying self-supervised learning in domains with limited annotations, such as medical image analysis and agricultural monitoring. It also offers a transferable framework for related computer vision tasks.
Critical Protoplanetary Core Masses in Protoplanetary Disks and the Formation of Short‐Period Giant Planets

24 pages (including 9 figures), LaTeX, uses emulateapj.sty, to be published in ApJ, also available at http://www.ucolick.org/~ct/home.html
Neural-Like Dynamics in a Phase-Locked Loop System with Delayed Feedback

We proposed a model of neural dynamics based on a phase-locked loop system with delayed feedback. The model can demonstrate chaotic oscillation modes, in which switching between qualitatively different types of oscillations that are characteristic of neural activity takes place.
Winners and Losers in Health Insurance: Access and Type of Coverage for Women in Same-Sex and Opposite-Sex Partnerships

Using data from the American Community Survey, 2009 (N=580,754), we compared rates of health insurance coverage and types of coverage used between women in same-sex and opposite-sex partnerships. This large, national dataset also allowed us to investigate regional variation in insurance coverage for women in same-sex partnerships by comparing "gay-tolerant" states versus other states. Multivariate analyses revealed that women in same-sex partnerships consistently had lower rates of health insurance coverage than married women in opposite-sex partnerships, but always more than unmarried women in opposite-sex partnerships. We also found that state-level variation in gay tolerance did not contribute to the access or type of coverage used by women in same-sex partnerships.
Improving Evidence-Synthesis for School-Based Obesity Prevention Interventions. Comment on Ginell et al. Unreliable Findings Due to Miscalculations and Errors. Comment on “Nally et al. The Effectiveness of School-Based Interventions on Obesity-Related Behaviours in Primary School Children: A Systematic Review and Meta-Analysis of Randomised Controlled Trials. Children 2021, 8, 489”

As researchers with substantial experience in the child obesity field [...]
SpatialScene2Vec: A self-supervised contrastive representation learning method for spatial scene similarity evaluation

Spatial scene similarity plays a crucial role in spatial cognition, as it enables us to understand and compare different spatial scenes and their relationships. However, understanding spatial scenes is a complex task. While existing literature has contributed to spatial scene representation learning, these methods primarily focus on comprehending the spatial relationships among objects, often neglecting their semantic features. Furthermore, there is a lack of scene representation learning methods that can seamlessly handle different types of spatial objects (e.g., points, polylines, and polygons) in a scene. Moreover, since expert knowledge is required for the annotation process of spatial scene understanding, publicly available high-quality annotation data has a limited size which usually leads to suboptimal results. To address these issues, we propose a novel multi-scale spatial scene encoding model called SpatialScene2Vec. SpatialScene2Vec utilizes a point location encoder to seamlessly encode the spatial information of different types of spatial objects. A point feature encoder is employed to encode the semantic features of these objects. A spatial scene embedding is generated by integrating the spatial embeddings and feature embeddings of spatial objects within this scene. Furthermore, to address the limited labeled data problem, we propose a self-supervised learning framework to train the SpatialScene2Vec model in which a contrastive loss is used for spatial scene similarity evaluation. In addition, we introduce a novel spatial scene data augmentation method to generate positive scene augmentations by leveraging the unique characteristics of spatial scenes and random sampling points based on the shapes of polyline/polygon objects within the current spatial scenes. We conduct experiments on real-world datasets for spatial scene retrieval tasks, including vector data types of points, polylines, and polygons. Results show that SpatialScene2Vec outperforms well-established encoding methods such as Space2Vec due to the advantages of the integrated multi-scale representations and the proposed spatial scene data augmentation method, with significant improvements and robustness.
Trust and consequences: Role of community science, perceptions, values, and environmental justice in risk communication

AbstractRisk communication is often viewed as imparting information and perhaps as a two‐way dialogue. Risk communication inadequacies on the part of both “communicator” and “community members” can lead to adverse consequences and amplify environmental justice disparities. The paper suggests a transformational approach where risk communicators must learn to trust community experts and their knowledge base (and act upon it), where risk information imparted by risk communicators addresses what communities are most concerned about (as well as risk from specific chemicals or radionuclides), and where risk information and assessments address underlying issues and disparities, as well as cultural traditions (among others). Providing risk probabilities is no longer sufficient; western science may not be enough, and community and native scientific knowledge is needed. Risk communication (or information transfer) for environmental risks that are ongoing usually applies to low‐income, minority communities—people living in dense inner cities, rural communities, Native American communities—or to people living near a risky facility. Communication within this context requires mutual trust, listening and respect, as well as acceptance of indigenous and community knowledge as equally valuable. Examples are given to illustrate a community perspective.
THE INFLUENCE OF INFORMATION QUALITY ON HUMAN RESOURCE PERFORMANCE THROUGH THE HUMAN RESOURCE INFORMATION SYSTEM (HRIS) AND USER SATISFACTION IN THE REGIONAL GOVERNMENT OF KENDAL REGENCY

Objective: To analyze how information quality affects HRIS in the Kendal Regency Government.   Theoretical Framework: Information quality is defined by the outputs produced by an operational information system. It encompasses the characteristics of the output, such as comprehensibility, accuracy, completeness, and timeliness (DeLone &amp; McLean, 2003). Information quality is defined by how thoroughly and clearly information is presented, enabling it to educate users effectively. For instance, when purchasing a product, the completeness of information serves as a critical foundation for consumers to make informed purchasing decisions (Amarin &amp; Wijaksana, 2021).   Method: This study employs a quantitative research method to examine phenomena and causal relationships by collecting and analyzing quantifiable data, this study's population encompassed all civil servants (PNS) working in Regional Government Organizations (OPD) in Kendal Regency, totaling 6,504 employees as of November 4, 2024. These employees are distributed across 49 OPDs, including The Regional Secretariat, Various Agencies, Departments, Hospitals, Civil Service Police Unit (Satpol PP), Regional House of Representatives Secretariat (DPRD Secretariat), Inspectorate, and Sub-district OPDs and a sample of 400 civil servants is required for this research. The sampling technique used in this study is proportional random sampling.   Results and Discussion: High-quality information significantly and positively impacts HRIS, even though it does not directly affect performance. In addition, superior information quality notably enhances user satisfaction. HRIS, on the other hand, has a strong positive effect on both performance and user satisfaction, while elevated user satisfaction also contributes significantly to improved performance. Moreover, the effect of information quality on performance is realized indirectly through its influence on both HRIS and user satisfaction.   Research Implications: The results and limitations of this study can serve as a source of ideas and input for developing further research.   Originality/Value: This study contributes to To contribute to the body of research and studies on management, particularly those related to personnel management information systems. To provide insights that can be utilized by the Kendal Regency Government regarding personnel management information systems.
Voice-based AI Agents: Filling the Economic Gaps in Digital Health Delivery

The integration of voice-based AI agents in healthcare presents a transformative opportunity to bridge economic and accessibility gaps in digital health delivery. This paper explores the role of large language model (LLM)-powered voice assistants in enhancing preventive care and continuous patient monitoring, particularly in underserved populations. Drawing insights from the development and pilot study of Agent PULSE (Patient Understanding and Liaison Support Engine) -- a collaborative initiative between IBM Research, Cleveland Clinic Foundation, and Morehouse School of Medicine -- we present an economic model demonstrating how AI agents can provide cost-effective healthcare services where human intervention is economically unfeasible. Our pilot study with 33 inflammatory bowel disease patients revealed that 70\% expressed acceptance of AI-driven monitoring, with 37\% preferring it over traditional modalities. Technical challenges, including real-time conversational AI processing, integration with healthcare systems, and privacy compliance, are analyzed alongside policy considerations surrounding regulation, bias mitigation, and patient autonomy. Our findings suggest that AI-driven voice agents not only enhance healthcare scalability and efficiency but also improve patient engagement and accessibility. For healthcare executives, our cost-utility analysis demonstrates huge potential savings for routine monitoring tasks, while technologists can leverage our framework to prioritize improvements yielding the highest patient impact. By addressing current limitations and aligning AI development with ethical and regulatory frameworks, voice-based AI agents can serve as a critical entry point for equitable, sustainable digital healthcare solutions.
Conflict, Conflict Resolution and Peace-Building: The Role of Religion in Mozambique, Nigeria and Cambodia

Confounding the expectations of secularists, religion has a strong – perhaps growing – significance as a key source of identity for millions of people, especially in the developing world. In recent years, religion has made a muted but tangible impact in Western development circles, most commonly reflecting the view that religious hatreds and differences are central to many recent and current conflicts in the developing world. This paper argues that religion can both encourage conflict and build peace, reflecting growing evidence that religious forces can play a constructive role in helping to resolve conflicts. Religious individuals and faith-based organisations, as carriers of religious ideas, can play important roles, not only as a source of conflict but also as a tool for conflict resolution and peace-building, providing early warnings of conflict, good offices once conflict has erupted, and contributing to advocacy, mediation and reconciliation. Brief case studies of religious peacemakers – from Mozam...
A digital twin methodology using real patient data for sample size reduction in Alzheimer’s disease randomized controlled clinical trials

Abstract                                    INTRODUCTION                   Recruitment for Alzheimer’s disease randomized controlled trials (RCTs) is difficult and expensive. To reduce RCT sample sizes, our Digital Twin Trial (DTT) methodology combines an interpretable cognitive decline prediction model with prediction-powered inference.                                                     METHODS                   For DTT participants, our model identifies similar individuals (“Digital Twins”) from a retrospective database and uses their cognitive scores to predict decline. Predictions adjust observed scores, reducing variance within treatment groups. We simulated 18-month DTTs and standard RCTs using mixed effects models of decline in Alzheimer’s Disease Neuroimaging Initiative subjects meeting lecanemab’s Phase 3 inclusion criteria.                                                     RESULTS                                        Predicted and observed change in Clinical Dementia Rating Sum-of-Boxes correlated at                     r                     = 0.4. DTTs required 1,855 subjects versus 2,170 for standard RCTs to detect a simulated 25% decline-slowing drug effect at 0.9 power. DTT Type 1 error was consistent with 0.05.                                                                        DISCUSSION                   DTTs could reduce recruitment and cost burdens. Model interpretability could help clinicians trust individualized prognoses.