ethicalabs/Research-Intent-Collab huggingface dataset:
Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification

The COVID-19 pandemic has threatened global health. Many studies have applied deep convolutional neural networks (CNN) to recognize COVID-19 based on chest 3D computed tomography (CT). Recent works show that no model generalizes well across CT datasets from different countries, and manually designing models for specific datasets requires expertise; thus, neural architecture search (NAS) that aims to search models automatically has become an attractive solution. To reduce the search cost on large 3D CT datasets, most NAS-based works use the weight-sharing (WS) strategy to make all models share weights within a supernet; however, WS inevitably incurs search instability, leading to inaccurate model estimation. In this work, we propose an efficient Evolutionary Multi-objective ARchitecture Search (EMARS) framework. We propose a new objective, namely potential, which can help exploit promising models to indirectly reduce the number of models involved in weights training, thus alleviating search instability. We demonstrate that under objectives of accuracy and potential, EMARS can balance exploitation and exploration, i.e., reducing search time and finding better models. Our searched models are small and perform better than prior works on three public COVID-19 3D CT datasets.
Hybrid Interpretable and Explainable Deep Learning Workflow for Vineyard Wildfire Damage Mapping From                     <scp>UAV</scp>                     ‐                     <scp>RGB</scp>                     Imagery:                     <scp>PCA</scp>                     –k‐Means Calibrated With U‐Net Segmentation (Patras, August 2025)

ABSTRACT                   Wildfires are becoming increasingly frequent and severe in Mediterranean regions, posing growing threats to both natural ecosystems and high‐value agricultural systems such as vineyards. Following the August 2025 wildfire near Patras (Achaia, Western Greece), this study develops a rapid workflow for post‐fire damage assessment in viticultural landscapes using ultra‐high‐resolution UAV RGB imagery (≈1.5 cm GSD). A hybrid interpretable and explainable artificial intelligence (AI) framework was designed to compare traditional and deep learning methods for burn severity mapping. Here, explainability refers to methodological transparency and role separation between interpretable RGB‐based analysis and deep learning segmentation, rather than post hoc inspection of neural network internals. Results demonstrate that the interpretable PCA–k‐means workflow captures vineyard burn severity patterns consistent with U‐Net semantic segmentation, while requiring minimal data and computational cost. The classical approach integrated vegetation indices derived from RGB bands (ExG, NGRDI, GRVI, VARI) with principal component analysis (PCA) and k‐means clustering, providing interpretable severity zonation from minimal data. In parallel, a U‐Net semantic segmentation model was trained to produce pixel‐level delineations of burned and surviving canopy, serving as a high‐fidelity benchmark. PCA–k‐means effectively captured overall burn gradients (PC1 explaining 50.1% of variance), while the U‐Net achieved high spatial accuracy (mIoU = 0.91) and improved boundary precision. The combination of both approaches enabled cross‐validation and calibration of severity thresholds, revealing their complementarity for operational post‐fire analysis. This hybrid AI framework demonstrates that low‐cost UAV RGB imagery, when coupled with interpretable and deep models, can deliver fast, reproducible, and transferable assessments of wildfire impacts in agricultural systems, supporting early recovery decisions and resilience planning under climate‐driven fire regimes.
THE ROLE OF JOB SATISFACTION MEDIATE THE INFLUENCE OF SITUATIONAL LEADERSHIP AND CAREER DEVELOPMENT ON ORGANIZATIONAL CITIZENSHIP BEHAVIOR OF BLU EMPLOYEES BUSINESS CAPITAL MANAGEMENT INSTITUTIONS MARINE AND FISHERIES (LPMUKP)

This study aims to analyze the role of job satisfaction in mediating the influence of situational leadership and career development on organizational citizenship behavior (OCB) of BLU employees of the Marine and Fisheries Business Capital Management Institution (LPMUKP). This research was conducted from September 2019 to March 2020 at BLU LPMUKP. The research method used a quantitative approach, the population in this study were all BLU LPMUKP employees totaling 294 employees consisting of permanent and temporary employees. The sample used in this study were permanent employees of 81 respondents using saturated sampling technique. The data collection techniques using interviews and questionnaire instruments. Data were analyzed using structural equation modeling (SEM) with the Smart PLS 3.2.7 software program. The results of this study indicate that situational leadership and career development have a positive and significant effect on organizational citizenship behavior. Meanwhile, job satisfaction is no more effective in mediating situational leadership and career development on organizational citizenship behavior as without mediation of situational leadership and career development would greatly affect organizational citizenship behavior in BLU LPMUKP.
Land Consolidation as a Cause of Agrarian Conflict in Maluku (2008-2017): The Role of the GPM in Advocating for the Rights of Indigenous Peoples

Land consolidation is a land management policy that aims to improve land efficiency and productivity. However, in Maluku, this policy often causes agrarian conflicts due to discrepancies between regulations, government interests, and the rights of indigenous peoples. One notable case is the agrarian conflict in the Aru Islands, where the granting of land concessions to PT Menara Group without the consent of indigenous communities sparked widespread resistance. The Protestant Church of Maluku (GPM) plays an important role in advocating for the rights of indigenous communities through the #SaveAru alliance and various environmental advocacy policies. This study uses a qualitative approach with a case study method to analyze land consolidation policies, socio-economic impacts, and the role of GPM in advocating for the rights of indigenous communities in Maluku. The results of the study show that weak regulations and the neglect of indigenous peoples' rights have contributed to the failure of land consolidation, leading to prolonged agrarian conflicts. Therefore, stricter regulations and the active role of civil society, including religious institutions, are needed to oversee equitable agrarian policies.
A Survey on Generative Diffusion Models

Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount generative models, materialize human ideation into tangible instances across diverse domains, encompassing imagery, text, speech, biology, and healthcare. To provide advanced and comprehensive insights into diffusion, this survey comprehensively elucidates its developmental trajectory and future directions from three distinct angles: the fundamental formulation of diffusion, algorithmic enhancements, and the manifold applications of diffusion. Each layer is meticulously explored to offer a profound comprehension of its evolution. Structured and summarized approaches are presented in https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model.
A call for a positive electoral system for the protection of the rights of voters, minorities and public accountability in Nigeria democracy

One of the cardinal achievements of any nation is the principle of democracy which is the architect of a free and fair election. No nation&nbsp; can move forward where democracy and rule of law are lacking. In Nigeria for example, there have been series of elections dating back&nbsp; to colonial days up to the present time. In order to ensure that there is a positive electoral system for example in the country Nigeria, protection of the rights of the electorates, minorities and public accountability must be watchword of the government. This is because&nbsp; where these are undermined they became attack on rule of law and democracy. This paper aimed at ensuring that there should be&nbsp; adequate protection of the rights of voters. For this purpose, the paper adopted a doctrinal research method. Findings were revealed on the challenge to the protection of voters, minorities to wit; election violence, vote buying, bullying among others. The above necessitated&nbsp; recommendations to the effect that adequate protections should be available to voters, minorities and equally, there should be public&nbsp; accountability in our democratic process. The paper concluded that if all these views and recommendations are put into practice, Nigeria democracy or electoral system will be one of the best which would be emulated by other parts of the world.&nbsp;&nbsp;
Performance Analysis and Reliability Prediction of Multi‐State Service Systems With Multiple Failure Modes of Unreliable Server: An Engineering Perspective

ABSTRACT                   The long‐term reliability of a machining system is essential for ensuring the uninterrupted operation of an automated manufacturing process. For maintaining optimal system performance, it is essential to model and analyze the system's reliability and availability. The current study introduces a unique framework established through evolutionary advancements of the fundamental models, incorporating impatience characteristics of individuals, the notion of unreliable servers, and enhancing its practical significance by considering the working breakdown of the server/repairman. A comparative investigation of the developed model with conventional models is carried out to analyze the overall impact on the reliability and availability characteristics of the machining system through a queueing‐theoretic approach. Further, the differential‐difference equations are implemented to construct the mathematical model, and the Laplace transformation is applied to demonstrate the state probability distribution. Several critical system performance measures, such as mean‐time‐to‐failure (MTTF), machining systems' reliability, and steady‐state availability, are thoroughly investigated to evaluate machining system stability and efficiency. The results demonstrate that the developed model significantly increases system reliability and availability, presenting a notable increase in reliability compared to conventional models under certain service interruption circumstances. To validate the model's applicability in real‐world scenarios, multiple combinations of system parameters are taken into consideration. For straightforward visualization of the impacts of multiple parameters on system reliability and availability, the results are provided, consisting of extensive tables and graphical representations. The proposed research contributes to the queueing literature by addressing the research gaps between theoretical modeling and real‐life applications, highlighting the insights that are essential for system designers, decision‐makers, and researchers aiming to optimize the reliability and availability of complex machining systems.
Reexploring Problematic Social Media Use and Its Relationship with Adolescent Mental Health. Findings from the &ldquo;LifeOnSoMe&rdquo;-Study

Turi Reiten Finserås,1 Gunnhild Johnsen Hjetland,1,2 Børge Sivertsen,1,3 Ian Colman,4,5 Randi Træland Hella,6 Amanda Iselin Olesen Andersen,1 Jens Christoffer Skogen1,2,7 1Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway; 2Centre for Evaluation of Public Health Measures, Norwegian Institute of Public Health, Oslo, Norway; 3Department of Research and Innovation, Helse Fonna HF, Haugesund, Norway; 4School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada; 5Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway; 6Regional Centre for Child and Youth Mental Health and Child Welfare, NORCE, Bergen, Norway; 7Alcohol and Drug Research Western Norway, Stavanger University Hospital, Stavanger, NorwayCorrespondence: Turi Reiten Finserås, Norwegian Institute of Public Health, PO Box 973 Sentrum, Bergen, 5808, Norway, Tel +47 980 79 303, Email Turireiten.Finseras@fhi.noPurpose: Previous approaches used to assess problematic social media use risk inflating prevalence numbers and classifying unproblematic social media use as problematic. The main aim of this study was to take an exploratory view as to how different types of activities, experiences, and motivations on social media are associated with problematic mental health outcomes in adolescents.Patients and Methods: This study is based on a cross-sectional survey of 2023 adolescents (mean age 17.4 years (SD 0.9), 44.4% males) from the year 2020. Exploratory graph analysis and exploratory factor analysis were performed on 28 pre-selected items assessing adolescents’ use of social media, to identify underlying potentially problematic factors associated with social media use. Sets of gender-adjusted multiple linear regression analyses were performed to assess the degree to which social media factors predicted depression, anxiety, well-being, and time spent on social media.Results: Three factors were identified: 1) “subjective overuse”, 2) “social obligations”, and 3) “source of concern”. All three factors showed significant positive associations with mental health problems. The factor “source of concern”, which identifies feelings of being overwhelmed and concerned over social media use, had the strongest association to mental health problems and simultaneously the weakest association to time spent on social media.Conclusion: Three identified factors measuring problematic social media use showed positive associations with mental health problems. This lends support to the notion that problematic social media use is a multidimensional phenomenon and demonstrates the need to move beyond addiction criteria when assessing problematic social media use.Keywords: social networking, youth, depression, anxiety, well-being
A study of relationship between suicidal ideas, depression, anxiety, resiliency, daily stresses and mental health among Tehran university students

AbstractThe current study was conducted to explore the relationship between suicidal ideas, depression, anxiety, resiliency, daily stresses and mental health among university students. 265 students of Tehran University were studied. They were asked to answer the Beck's depression and anxiety, suicidal ideation, mental health, daily stresses and resiliency questioners. Pearson correlation coefficient and stepwise regression analyses were used for the data analysis. Suicidal ideations had a significant and negative relationship with resiliency. Anxiety, depression, mental health and daily stresses had a positive relationship with suicidal ideations. Regression analysis revealed that depression had the most contribution in predicting suicidal ideations while anxiety, mental health, Resiliency, and daily stresses were the next contributing factors, respectively. These variables could predict 21 percent of suicidal ideations altogether. Psychological problems and mental health issues and other factors such as resiliency and daily stresses play an important role in suicidal ideations.
True cosmic microwave background power spectrum estimation

Aims. The cosmic microwave background (CMB) power spectrum is a powerful cosmological probe as it entails almost the entire statistical information of CMB perturbations. Having access to only one sky, the CMB power spectrum measured by our experiments is only a realization of the true underlying angular power spectrum. We aim to recover the true underlying CMB power spectrum from the one realization that we have without knowing the cosmological parameters. Methods. The sparsity of the CMB power spectrum is rst investigated in two dictionaries; discrete cosine transform (DCT) and wavelet transform (WT). The CMB power spectrum can be recovered with very few coe cients in these two dictionaries and hence is very compressible. Results. We studied the performance of these dictionaries in smoothing a set of simulated power spectra. Based on this, we developed a technique that estimates the true underlying CMB power spectrum from data, i.e., without a need to know the cosmological parameters. Conclusions. This smooth estimated spectrum can be used to simulate CMB maps with similar properties as the true CMB simulations with the correct cosmological parameters. This allows us to perform Monte Carlo simulations in a given project without having to know the cosmological parameters. The developed IDL code, TOUSI, for theoretical power spectrum using sparse estimation, will be released with the next version of ISAP.
Formation Damage Investigation to Determine Root Cause Analysis and Remedial Actions – A Case Study for Kuwait's Exploratory Wells

Abstract                A phased study was undertaken with reservoir conditions core flood simulations and geological diagnostics using core plugs from Kuwait's Exploration wells. Fluid-Fluid-Rock compatibility between operational and reservoir fluids within representative cores selected from tight reservoirs were analyzed. Permeability measurements were undertaken at various stages during the core flood simulations of each phase to determine the level of permeability alteration caused by formation damage. The analysis evaluated formation damage mechanisms that may have caused permeability alterations from operational fluid sequences including drilling mud, displacement fluids, perforation simulations, treatment fluids and drawdown. The first phase was undertaken to understand the incompatibilities between the current drilling mud and the reservoir rocks and reservoir fluids. This phase determined formation-damaging mechanisms caused by the drilling mud and helped gain an understanding of the fluid performance and fluid-rock interactions. The second phase was undertaken to understand any incompatibilities of the operation sequence of drilling mud, displacement fluid, drawdown and the reservoir rocks and reservoir fluids. This phase determined formation damaging mechanisms caused by the applied fluids and subsequent drawdown and helped gain an understanding of the fluid-fluid-rock interactions and the fluid performance, specifically the performance of the introduced displacement fluid. The third phase was undertaken to understand any incompatibilities of the operational sequence of drilling mud, displacement fluid/stimulate perforation, treatment fluid, drawdown and the reservoir rocks and reservoir fluids. This phase determined formation damaging mechanisms caused by the applied fluids and subsequent drawdown and helped gain an understanding of the fluid performances. This phase was beneficial as it was an operation fluid simulation that gave an insight to fluid-fluid-rock interactions. The fourth phase was undertaken to understand any incompatibilities of the treatment fluid and the reservoir rocks and reservoir fluids, specifically the crude oil. This phase indicated that the treatment fluid and crude oil were compatible, and this was determined by the absence of emulsions or sludges formed during the core flood simulations. Introduction of treatment fluid caused unsatisfactory amounts of damage by increasing the permeability indicating wellbore instability implications. Core flood simulation permeability results and the nature of formation damage mechanisms were further explained and enhanced using diagnostic geological methods such as micro-CT (computed tomography), as well as dry and cryogenic scanning electron microscopy with energy dispersive spectrum analysis. The study findings strongly believed that the operational fluids be addressed to reduce damage and improve operational efficiency.
Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation

Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence. However, the Subjective-Logic framework constrains evidence to be non-negative, requiring specific activation functions whose geometric properties can induce activation-dependent learning-freeze behavior: a regime where gradients become extremely small for samples mapped into low-evidence regions. We theoretically characterize this behavior and analyze how different evidential activations influence learning dynamics. Building on this analysis, we design a general family of activation functions and corresponding evidential regularizers that provide an alternative pathway for consistent evidence updates across activation regimes. Extensive experiments on four benchmark classification problems (MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet), two few-shot classification problems, and blind face restoration problem empirically validate the developed theory and demonstrate the effectiveness of the proposed generalized regularized evidential models.
One Health Concept: An Integrative Approach to Combat Zoonotic Diseases at the Human-Animal-Environment Interface

Zoonotic diseases, which are transmitted between humans and animals, constitute a significant proportion of emerging infectious threats. These diseases often arise from complex interactions among humans, animals and the environment. The One Health concept provides a holistic framework to address these interactions by promoting interdisciplinary collaboration. This article reviews the principles of One Health, highlights key zoonotic diseases and discusses global and regional strategies for implementation, emphasizing the need for integrated surveillance, policy harmonization, and community engagement.
Collaborative Filtering Recommender Systems

Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decision- making processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.