ethicalabs/Research-Intent-Collab huggingface dataset:
Green HRIS: Innovating Human Resource Information Systems to Promote Sustainable HR Practices in the ESG ((Environmental, Social, and Governance)

The purpose of this study is to analyze the strategic role of the Green Human Resource Information System (Green HRIS) in bridging the transformation of human resource management with the demands of Environmental, Social, and Governance (ESG) principles, specifically examining how Green HRIS contributes to sustainable HR practices, organizational performance, and digital HR transformation. Using a Systematic Literature Review (SLR) approach, this study identifies, evaluates, and synthesizes prior research by conducting a structured search across major academic databases—Scopus, Web of Science, Springer, Elsevier, and Wiley—for publications from 2020 to 2025 that address green HRM, HR digitalization, sustainable HR practices, and ESG integration. The review process includes screening titles, abstracts, and full texts, extracting key data, and categorizing findings into environmental, social, and governance dimensions. The results demonstrate that Green HRIS strengthens ESG implementation by reducing paper usage, lowering carbon emissions, and promoting sustainable HR practices such as digital recruitment and e-learning, while also improving governance through enhanced transparency, accountability, regulatory compliance, and real-time reporting. Empirical evidence indicates that Green HRIS fosters employee engagement, organizational innovation, and the development of green competitive advantages. Practically, the study highlights how organizations, policymakers, and HR managers can utilize Green HRIS to optimize digital transformation and meet ESG requirements, thereby reinforcing legitimacy and long-term competitiveness within the green economy. This research offers originality as one of the first systematic reviews addressing Green HRIS in the ESG era, integrating theories such as the Resource-Based View, Technology Acceptance Model, and organizational sustainability theory, while also mapping trends, best practices, and gaps for future research.
The Effects of Transformational Leadership and Emotional Intelligence of Managers on Organizational Citizenship Behaviors of Employees

In today’s turbulent environment, organizations needed leaders who were not only emotionally intelligent but those who had ability to imbibe such values in their followers that were helpful both for service climate and followers themselves. Existing literature suggested that Transformational Leadership (TL) and leader’s Emotional Intelligence (EI) were likely to enhance Organizational Citizenship Behavior (OCB) in followers and significant predictors of OCBs. The purpose of this study was to examine the effects of TL and EI of managers on the dimensions of OCB employees display in manufacturing industry. A survey questionnaire was used to collect data from a sample of 60 dyads of employees and their supervisors (i.e., 120 respondents) in one large manufacturing company. SPSS was used for analyzing the data. In this study, separate regression analyses were conducted to examine the effects of TL and EI of managers on the dimensions of OCB. The results indicated that EI of managers had significant positive effects on OCB dimensions of altruism, courtesy, conscientiousness, and civic virtue, but TL had no effects on the dimensions of OCB.
Biodiversity Conservation and Sustainable Development in Hefei Dashushan National Forest Park—An Empirical Analysis Based on Ten-Year Ecological Monitoring Data

Based on ecological monitoring data from 2015 to 2025, this study systematically analyzes the conservation outcomes of Dashushan National Forest Park in avian community restoration, vegetation succession, and enhancement of ecosystem services, proposing an urban “close-to-nature” forest conservation paradigm.
LightViz: Autonomous Light-field Surveying and Mapping for Distributed Light Pollution Monitoring

Existing technologies for distributed light-field mapping and light pollution monitoring (LPM) rely on either remote satellite imagery or manual light surveying with single-point sensors such as SQMs (sky quality meters). These modalities offer low-resolution data that are not informative for dense light-field mapping, pollutant factor identification, or sustainable policy implementation. In this work, we propose LightViz -- an interactive software interface to survey, simulate, and visualize light pollution maps in real-time. As opposed to manual error-prone methods, LightViz (i) automates the light-field data collection and mapping processes; (ii) provides a platform to simulate various light sources and intensity attenuation models; and (iii) facilitates effective policy identification for conservation. To validate the end-to-end computational pipeline, we design a distributed light-field sensor suit, collect data on Florida coasts, and visualize the distributed light-field maps. In particular, we perform a case study at St. Johns County in Florida, which has a two-decade conservation program for lighting ordinances. The experimental results demonstrate that LightViz can offer high-resolution light-field mapping and provide interactive features to simulate and formulate community policies for light pollution mitigation. We also propose a mathematical formulation for light footprint evaluation, which we integrated into LightViz for targeted LPM in vulnerable communities. A test-case of the LightViz software release is available at: https://github.com/uf-robopi/LightViz.
Speech as a Multimodal Digital Phenotype for Multi-Task LLM-based Mental Health Prediction

Speech is a noninvasive digital phenotype that can offer valuable insights into mental health conditions, but it is often treated as a single modality. In contrast, we propose the treatment of patient speech data as a trimodal multimedia data source for depression detection. This study explores the potential of large language model-based architectures for speech-based depression prediction in a multimodal regime that integrates speech-derived text, acoustic landmarks, and vocal biomarkers. Adolescent depression presents a significant challenge and is often comorbid with multiple disorders, such as suicidal ideation and sleep disturbances. This presents an additional opportunity to integrate multi-task learning (MTL) into our study by simultaneously predicting depression, suicidal ideation, and sleep disturbances using the multimodal formulation. We also propose a longitudinal analysis strategy that models temporal changes across multiple clinical interactions, allowing for a comprehensive understanding of the conditions' progression. Our proposed approach, featuring trimodal, longitudinal MTL is evaluated on the Depression Early Warning dataset. It achieves a balanced accuracy of 70.8%, which is higher than each of the unimodal, single-task, and non-longitudinal methods.
SUCCESS FACTORS EVALUATION OF WATERFALL, AGILE AND LEAN SIX SIGMA PROJECT MANAGEMENT METHODOLOGY USING FUZZY COGNITIVE MAP METHOD

Proje yönetimi metodolojisi bir projeyi yönetmek için temel prensipleri ve süreçleri tanımlayan rehber niteliğindedir. Proje yönetimi metodolojisi seçimi, proje kapsamında nasıl çalıştığınızı ve iletişim kurduğunuzu tanımlar. Bu tezde, üç proje yönetimi metodolojisindeki başarı faktörlerinin çok boyutlu bir görünümünün resmedilmesi amaçlanmıştır. Waterfall, Agile ve Lean Six Sigma proje yönetimi metodolojilerinin başarı faktörlerinin ağırlıklarının ortaya konulması amaçlanmıştır. Çalışmada ilk olarak, üç proje yönetimi metodolojisinin başarı faktörlerini belirlemek için literatür araştırması yapılmıştır. Başarı faktörleri makalelerden, araştırma yazılarından ve gerçek projeler ile ilgili teknik raporlardan tespit edilmiştir. Tespit edilen başarı faktörleri üç üst düzey proje yöneticisinin de yardımı ile teyit edilmiştir. Araştırmanın sonunda on beş başarı faktörü belirlenmiştir. Her proje yönetimi metodolojisi için kriterler arasındaki nedensel ilişkiler üç farklı uzman tarafından belirlenmiştir. Bulanık bilişsel haritalama tekniği ile her bir proje yönetimi metodolojisinin faktör ağırlıkları belirlenmiştir. Faktör ağırlıklarının değerlendirilmesinden sonra, proje yönetimi metodolojilerinin en önemli kriterleri belirlenmiştir.
Workplace verbal abuse, nurse‐reported quality of care and patient safety outcomes among early‐career hospital nurses

To examine the differences in early-career nurses' verbal abuse experiences based on their sociodemographic characteristics, and to investigate the associations of verbal abuse experiences with nurse-reported care quality and patient safety outcomes.Few studies have examined the relationships between early-career nurses' verbal abuse experiences and nurse-reported patient care quality and safety outcomes.Cross-sectional survey data from 799 early-career hospital nurses in the United States were analysed. Items assessed verbal abuse experiences from patients or their families, physicians and other employees. Associations between verbal abuse experiences and nurse-reported care quality and patient safety outcomes were examined using multiple logistic regression analyses.There were significant differences in verbal abuse experiences by age, gender and work unit. Nurses who experienced verbal abuse, regardless of the perpetrator, were less likely to report high-quality care and a favourable safety grade. Nurses who experienced verbal abuse specifically from physicians or other employees were also less likely to feel comfortable reporting safety problems.Managing verbal abuse may be important for improving patient care quality and safety. Future intervention study is needed to reduce verbal abuse.To optimize patient safety, managers should thoroughly monitor verbal abuse and organisations' need to establish clear expectations and ramifications for when verbal abuse occurs.
The Effectiveness of Cognitive-Behavioral Intervention Based on Psychological Capitals on the Experience Avoidance and Suicidal ideations in Female Victims of Domestic Violence

AbstractIntroduction: Violence against women and girls is currently one of the most important and specific forms of violence against human rights.Objectives: The aim of this study was to determine the effectiveness of a cognitive-behavioral intervention based on psychological capital on experience avoidance and suicidal ideations in girls victims of domestic violence in Isfahan.Materials & Methods: The research design was quasi-experimental with a pretest-posttest design with a control group. The statistical population consisted of all girls’ victims of domestic violence in Isfahan who were referred to a Counseling Center in Isfahan in 2019. The sample consisted of 30 girls who were selected by purposive sampling method and randomly assigned to one experimental group (cognitive-behavioral intervention based on psychological capital) and a control group (each consisting of 15 people). The research tools included Beck Scale for Suicidal ideations (BSSI) and Acceptance and Action Questionnaire (AAQ-II). Data were analyzed by SPSS.22 software and univariate analysis of covariance. Results: The findings of this study showed that cognitive-behavioral intervention based on psychological capital was effective on experience avoidance (p <0.001) and suicidal ideation (p <0.001) in girls’ victims of domestic violence in Isfahan.Conclusion: It can be concluded that cognitive-behavioral therapy based on psychological capital is an effective treatment to reduce problems related to experience avoidance and suicidal thoughts of girls’ victims of domestic violence and can be used to improve psychological problems of girls’ victims of domestic violence.
Can a virtual reality surgical simulation training provide a self-driven and mentor-free skills learning? Investigation of the practical influence of the performance metrics from the virtual reality robotic surgery simulator on the skill learning and associated cognitive workloads

While it is often claimed that virtual reality (VR) training system can offer self-directed and mentor-free skill learning using the system's performance metrics (PM), no studies have yet provided evidence-based confirmation. This experimental study investigated what extent to which trainees achieved their self-learning with a current VR simulator and whether additional mentoring improved skill learning, skill transfer and cognitive workloads in robotic surgery simulation training.Thirty-two surgical trainees were randomly assigned to either the Control-Group (CG) or Experiment-Group (EG). While the CG participants reviewed the PM at their discretion, the EG participants had explanations about PM and instructions on how to improve scores. Each subject completed a 5-week training using four simulation tasks. Pre- and post-training data were collected using both a simulator and robot. Peri-training data were collected after each session. Skill learning, time spent on PM (TPM), and cognitive workloads were compared between groups.After the simulation training, CG showed substantially lower simulation task scores (82.9 ± 6.0) compared with EG (93.2 ± 4.8). Both groups demonstrated improved physical model tasks performance with the actual robot, but the EG had a greater improvement in two tasks. The EG exhibited lower global mental workload/distress, higher engagement, and a better understanding regarding using PM to improve performance. The EG's TPM was initially long but substantially shortened as the group became familiar with PM.Our study demonstrated that the current VR simulator offered limited self-skill learning and additional mentoring still played an important role in improving the robotic surgery simulation training.
Course Learning Recommendation System Using Neural Collaborative Filtering

The proliferation of e-learning platforms has created a need for sophisticated course recommendation systems. This paper presents an innovative online course recommendation system using Neural Collaborative Filtering (NCF), a deep learning technique designed to surpass traditional methods in accuracy and personalization. Our system employs a hybrid NCF architecture, integrating matrix factorization with multi-layer perceptron to capture complex user-course interactions. The proposed NCF-based recommendation system aims to address key challenges in the e-learning domain, such as diverse user preferences, varying course content, and evolving learning patterns. By leveraging the power of neural networks, our approach seeks to provide more relevant and personalized course suggestions to learners. Our research contributes to the intersection of deep learning and educational technology, offering new insights into how advanced machine learning techniques can be applied to improve online learning experiences. The proposed system has the potential to enhance the quality of course recommendations, leading to more effective learning pathways for users. This work has important implications for e-learning platforms, educational institutions, and lifelong learners navigating the vast landscape of online courses. By improving the match between learners and courses, we aim to increase engagement, completion rates, and overall satisfaction in online education. Future work will explore the long-term impact of such personalized recommendations on learning outcomes and skill development.
Strategizing global health governance: unpacking opportunities and challenges for least developed nations within the WHO pandemic treaty framework

Exploring the intricacies of the proposed WHO pandemic treaty, this paper underscores its potential benefits and challenges for Least Developed Nations (LDNs) in the global health landscape. While the treaty could elevate LDNs’ access to vital resources, fortify health systems, and amplify their voice in global health governance, tangible challenges in safeguarding equitable access, protecting sovereignty, and ensuring compliance are illuminated. Concluding with targeted recommendations, the paper advocates for treaty revisions that assure resource access, safeguard LDNs’ autonomy, and foster capacity-building. In essence, the paper emphasizes the imperative of genuinely empowering LDNs, crafting a pandemic treaty that establishes a more equitable, resilient, and inclusive global health future.
A Hybrid Approach to Recommender Systems : CONTENT ENHANCED COLLABORATIVE FILTERING

Recommender systems help shape the way the internet is used by leading users directly to the content which will interest them most. Traditionally, collaborative recommender systems based purely on user ratings have been proven to be effective. This report focuses specifically on film recommender systems. It investigates how the film content parameters Actor, Director and Genre can be used to further enhance the accuracy of predictions made by a purely collaborative approach, specifically with regards to the set of films chosen when performing the prediction calculations. The initial results showed that relying solely on content in this selection led to poorer predictions due to a lack of ratings. However, the investigation finds that using a hybrid approach between the two selection techniques with a bias for content solved this problem as well as increasing the overall prediction accuracy by over 11%.
The Implication of Land Use Land Cover Change on Biodiversity Conservation: An Overview from Protected Areas in Ethiopia

Conversion of natural habitat to other forms of land use is the main threat to protected areas and biodiversity globally. The continued trend of land use land cover change in protected areas resulted in loss of a large portion of biodiversity, overexploitation by humans, transformation of natural land to human settlement, etc. In Ethiopia, the causes for land use land cover change in many protected areas are farmland expansion, deforestation, unsustainable grazing and settlement expansion, and are leading to loss of biodiversity and negative impacts of ecosystem services. In addition, Ethiopia’s protected areas entertain escalating threats and land cover changes due to human population growth, competing claims from the surrounding communities, incompatible investment, lack of environmental law enforcement, absence of complete plan and timely update for protected areas, etc. These have affected protected areas in the country namely the Bale Mountains National Park, Chocke Mountains, Babile Elephant sanctuary, Abijata Shalla Lakes National Park, Awash National Park and others. The continued land use land cover changes are aggravating ecosystem, soil and water resources degradation in mountainous protected areas while they are leading to biodiversity destruction and loss of forest cover in lowland protected areas. In order to halt and reduce the impact of land cover change on biodiversity conservation, undertaking complete land use planning and continuous monitoring of protected areas was found to be important. Similarly, integrating protected areas into the surrounding landscapes and a broader framework of national plans, promoting income generation means for communities surrounding protected areas, promoting biodiversity conservation directly linked to poverty alleviation, involving local communities and stakeholders in land use planning and sustainable management of protected areas, enhancing sound management in vulnerable mountain protected areas and restoring abandoned lands located in and around protected areas are crucial in the proper land use planning and management of protected areas. In addition, enhancing awareness creation and promoting natural resource information of protected areas and enhancing scientific study on land use land cover change pattern of protected areas are vital to undertake effective land use planning and management of protected areas in Ethiopia.
Molecular Simulation Workflows as Parallel Algorithms: The Execution Engine of Copernicus, a Distributed High-Performance Computing Platform

Computational chemistry and other simulation fields are critically dependent on computing resources, but few problems scale efficiently to the hundreds of thousands of processors available in current supercomputers-particularly for molecular dynamics. This has turned into a bottleneck as new hardware generations primarily provide more processing units rather than making individual units much faster, which simulation applications are addressing by increasingly focusing on sampling with algorithms such as free-energy perturbation, Markov state modeling, metadynamics, or milestoning. All these rely on combining results from multiple simulations into a single observation. They are potentially powerful approaches that aim to predict experimental observables directly, but this comes at the expense of added complexity in selecting sampling strategies and keeping track of dozens to thousands of simulations and their dependencies. Here, we describe how the distributed execution framework Copernicus allows the expression of such algorithms in generic workflows: dataflow programs. Because dataflow algorithms explicitly state dependencies of each constituent part, algorithms only need to be described on conceptual level, after which the execution is maximally parallel. The fully automated execution facilitates the optimization of these algorithms with adaptive sampling, where undersampled regions are automatically detected and targeted without user intervention. We show how several such algorithms can be formulated for computational chemistry problems, and how they are executed efficiently with many loosely coupled simulations using either distributed or parallel resources with Copernicus.
Innovative immunotherapies for prostate cancer: understanding the synergies between checkpoint inhibition, CAR T-cell therapy, and next-generation therapeutic modalities

Immunotherapy has transformed oncology, yet has only been marginally effective in prostate cancer (PCa), which is a malignancy with a low mutational load and a highly immunosuppressive tumor microenvironment (TME). This critical review is a reflection on the changing position of the innovative immunotherapies in PCa that extends beyond the description stage to synthesize the synergies and constraints of immune checkpoint inhibitors (ICIs), chimeric antigen receptor (CAR) T-cell therapy, and next-generation modalities such as bispecific T-cell engagers (BiTEs). We assess the mechanistic reasoning of combination therapies, comprising androgen receptor signaling communicators, PARP communicators, and radioligand therapies, which seek to modulate the immunogenicity of the immune-cold PCa TME. Also, we combine new knowledge to novel resistance pathways, including the newly discovered thrombospondin-1-CD47 axis, in the process of T cell exhaustion through calcineurin-NFAT signaling. Although some preclinical data and initial clinical indicators in biomarker-selected subpopulations are promising, the vast majority of Phase III trials of ICIs in unselected populations with metastatic castration-resistant prostate cancer (mCRPC) have failed. This review reveals that the next generation of PCa immunotherapy would not be sequential monotherapies but rather rationally designed multimodal combinations guided by profound molecular and immune profiling to overcome inherent resistance mechanisms.