ethicalabs/Research-Intent-Collab huggingface dataset:
Factors Affecting the Acceptance and Impact of Human Resource Information Systems (HRIS): Evidence from HR Professionals in Libyan Companies

This thesis investigates the attitudes of human resource professionals towards human resource information systems (HRIS) in order to better understand their enablers and effects on individuals and organisations. Focusing on factors influencing HRIS acceptance, the study identifies key dimensions relating to HRIS benefits specifically perceived usefulness and user satisfaction, organisational, social, and technical drivers of acceptance, and the consequences of satisfaction with HRIS for organisational behaviour. Drawing mainly on the theory of reasoned action TRA, the technology acceptance model and the DeLoan & McLean success model, a comprehensive multidimensional model is suggested.\ud \ud Data were collected from companies in the Libyan oil, gas and banking sectors and multivariate analysis was used to test the proposed theoretical framework. The findings show that top management support has a significant impact on perceived usefulness and satisfaction with HRIS. Furthermore, information quality, HRIS flexibility, ease of use, and IT staff support have considerable impacts on satisfaction with HRIS via perceived usefulness. Findings also show that social influence and the number of strategic applications also have a significant influence on user satisfaction. User satisfaction has the strongest impact on affective commitment compared to continuance and normative commitment.\ud \ud The study contributes in two ways. In terms of theoretical value, this study extends and develops theory of technology acceptance by relating social interactions, organisational support, and number of HRIS applications in terms of routine and strategic contexts and organisational behaviour. Second, it expands the technology acceptance model to examine and explain the perceptions and attitudes of HR professionals towards HRIS. There is also a practical contribution as Libya is a developing country characterised by a lack of understanding about technology adoption and the impact of technology on human resources activities. The findings inform top management, HR professionals and IT staff about the current practice of HRIS in a Libyan environment highlighting the variables (enablers and barriers) which can enhance or impede individual and organizational performance.
Federated Learning for Dynamic Route Optimization in SAP Transportation Management: Pioneering Sustainable Logistics

In the face of rising environmental concerns and complex global logistics, SAP Transportation Management (TM) is pivotal for planning and executing efficient transportation operations. This research introduces a novel framework leveraging federated learning (FL) to enable dynamic route optimization in SAP TM, ensuring sustainable logistics while preserving data privacy across decentralized fleets. By training models locally on edge devices, the framework optimizes routes in real-time, minimizes carbon emissions, and adapts to disruptions like traffic or weather. A synthetic dataset of 200,000 TM transactions simulates real-world complexities, including multilingual data and disruptions. The analysis achieves 94% route optimization accuracy, reducing emissions by 55% compared to traditional methods. Visualizations, including emission heatmaps and performance graphs, guide sustainable logistics strategies. This approach tackles data privacy, scalability, and SAP integration, offering a transformative solution for ecofriendly transportation management.
An attention-based framework for integrating WSI and genomic data in cancer survival prediction

Cancer survival prediction plays a vital role in enhancing medical decision-making and optimizing patient management. Accurate survival estimation enables healthcare providers to develop personalized treatment plans, improve treatment outcomes, and identify high-risk patients for timely intervention. However, existing methods often rely on single-modality data or suffer from excessive computational complexity, limiting their practical application and the full potential of multimodal integration.To address these challenges, we propose a novel multimodal survival prediction framework that integrates Whole Slide Image (WSI) and genomic data. The framework employs attention mechanisms to model intra-modal and inter-modal correlations, effectively capturing complex dependencies within and between modalities. Additionally, locality-sensitive hashing is applied to optimize the self-attention mechanism, significantly reducing computational costs while maintaining predictive performance, enabling the model to handle large-scale or high-resolution WSI datasets efficiently.Extensive experiments on the TCGA-BLCA dataset validate the effectiveness of the proposed approach. The results demonstrate that integrating WSI and genomic data improves survival prediction accuracy compared to unimodal methods. The optimized self-attention mechanism further enhances model efficiency, allowing for practical implementation on large datasets.The proposed framework provides a robust and efficient solution for cancer survival prediction by leveraging multimodal data integration and optimized attention mechanisms. This study highlights the importance of multimodal learning in medical applications and offers a promising direction for future advancements in AI-driven clinical decision support systems.
Reduced density matrix functional theory at finite temperature. III. Application to the electron gas: Correlation effects

Based on our derivation of finite temperature reduced density matrix functional theory and the discussion of the performance of its first-order functional this work presents several different correlation-energy functionals and applies them to the homogeneous electron gas. The zero temperature limits of the correlation-energy and the momentum distributions are investigated and the magnetic phase diagrams in collinear spin configuration are discussed.
Prediction of the Crystal Morphology of β-HMX using a Generalized Interfacial Structure Analysis Model

At sufficiently low supersaturations such that the spiral growth mechanism dominates, β-cyclotetramethylenetetranitramine (HMX) grows from acetone into a polyhedron surrounded mainly by the (020) and (011) faces. In order to elucidate the morphology, a generalized form of the interfacial structure analysis model is suggested. In this method, the molecular order parameters of crystals are defined to identify the orientation and conformation of the adsorbed growth unit at the interface. This presents a robust method to calculate the orientational and conformational free energy surfaces that are utilized for the spiral growth model of centrosymmetric growth units with polygonal spiral edges. From the metadynamics simulation using these order parameters as collective variables, the free energy surfaces with respect to the collective variables revealed that high conformational free energy of the chair conformation discouraged preordering of the growth units into crystal-like orientation and conformation. The r...
Provisional measures in international human rights law: the practice of the African Court on Human and Peoples’ Rights

Under exceptional circumstances, international (human rights) courts issue orders on provisional measures preventing a party or parties before them from taking some actions pending the final determination of a case. The main purpose of such orders is to avoid a situation where the final disposition of a matter is pre-emptively rendered fully or partly meaningless by the conduct of a party. Article 27 of the Protocol Establishing the African Court on Human and Peoples’ Rights also envisages the possibility where ‘in cases of extreme gravity and urgency’, the Court may adopt provisional measures to ‘avoid irreparable harm to persons’. The African Court on Human and Peoples’ Rights (Court), relying on this provision, has thus far issued about 50 orders of provisional measures, all of which were against respondent states. This article interrogates the Court’s practice in this regard, with the view to fleshing out its jurisprudential inconsistencies and proposing recommendations to rectify the occasional misapplication of the procedure. Close scrutiny of the Court’s jurisprudence reveals not only glaring discrepancies in approach but also, at times, unnecessary recourse to these measures even when situations do not necessarily warrant their adoption. As evidenced by the backlash from some states, which have openly expressed their refusal to comply with the Court’s orders, the unwarranted use of provisional measures is likely to render the procedure ineffective and may also negatively affect the legitimacy of the Court in the eyes of its creators, the states. Therefore, the Court should fully and strictly adhere to the legal and factual conditions required to adopt provisional measures and always be alive to the intended purpose and nature of provisional measures. The Court particularly needs to adopt a balanced approach without being too liberal or too strict, as this would be overstepping its power or abdicating its responsibility to protect human rights.
Geodesic Retrieval over Learned Manifolds: A Differential Geometry Approach to Knowledge Base Search

Retrieval-Augmented Generation (RAG) systems universally rely on cosine similarity in flat embedding spaces, treating each knowledge fact as an independent point. This fails for chain-structured knowledge where the answer spans a sequence of connected steps; retrieval finds facts near the query but misses intermediate steps along the path. We propose Manifold Retrieval, which constructs a Riemannian manifold over the embedding space using diffusion maps and retrieves along geodesic shortest paths rather than within cosine similarity balls. Our method builds a k-nearest-neighbor graph with locally-scaled Gaussian kernels over 75,867 cybersecurity knowledge facts, computes a 12-dimensional diffusion map embedding, and retrieves facts along Dijkstra geodesics on the resulting manifold graph. We further incorporate Dynamic Time Warping (DTW) shape matching against known procedural chain signatures and Nyström extension for projecting unseen queries onto the manifold at inference time. Evaluated against a FAISS cosine baseline and an LLM-augmented retrieval system, Manifold Retrieval achieves +8% Recall@10 and +22% path completeness over the baseline at 2.4 seconds per query, 5× faster than the LLM-augmented approach with zero regression on Recall@5. The method requires only embeddings and is domain-agnostic: no entity extraction, schema design, or graph construction is needed beyond the embedding model itself.
Learning analytic and educational data mining for learning science and technology

Learning analytics is another trendy choice for technology-enhanced learning. It is not surprising, therefore, that learning analytics is the topic of many scientific papers as a new learning process and tool to improve learning performance. Some of the most common methods used in data mining for learning analytics, include: classification and prediction, clustering, outlier detection, relationship mining, social network analysis, process mining, text mining, distillation of data for human judgment, and discovery with models. The use of analytic learning and data mining in education and learning, generally to store information related to student backgrounds, about their interactions with the learning system and to understand how students interact with learning resources, learning styles, predictions of learning outcomes and their possibilities for completing studies they successfully. With this study teachers and school managers can develop new learning tools how to apply analytic learning to improve and predict student learning success.
The Impact of Human Resource Information System (HRIS) Applications on Organizational Performance (Efficiency and Effectiveness) in Jordanian Private Hospitals

&lt;p&gt;This study aimed to investigate the impact of human resource information system (HRIS) on organizational performance in Jordanian private hospitals, through examining if the (HRIS) components (job analysis, recruitment, selection, performance appraisal applications, and communications) have a significant impact on organizational performance (efficiency, effectiveness). The data was collected using a questionnaire instrument. The population of the research included all private hospitals located in Amman city, the number of private hospitals located in Amman were 39 hospitals whereas the sample of the research included (170) employees working in HR departments from the private hospitals. The result of this study showed that there are a positive impact of the HRMS applications  on organizational performance, More specifically, it was found that  and a positive attitudes from employees working in Human resources sections in private hospitals towards all human resource information system applications .The result of the study also indicated that employees working in human resources' sections in private hospitals have positive attitudes towards organizational performance which includes efficiency and effectiveness. The research recommended that hospitals should focus on human resource information system applications in the work environment to achieve positive outcome and maximize organizational performance at all level.&lt;/p&gt;
O papel da tecnologia CDP (Customer Data Platform) na criação de uma visão 360º do cliente

Com a transformação digital e a proliferação dos vários canais de comunicação, é cada vez mais desafiante para as empresas compreender o comportamento e necessidades dos seus clientes, com vista à criação de experiências personalizadas. Se, por um lado, o CRM permite agregar toda a informação acerca das interações do cliente com a empresa, tipicamente ligada aos departamentos de marketing, vendas e apoio ao cliente, o CDP, por outro lado, permite integrar e gerir todo e qualquer comportamento que qualquer utilizador tenha com a marca, independentemente do canal ou identificação que utilize. O presente estudo tem como objetivo investigar de que forma a tecnologia CDP ajuda a centralizar e unificar toda a informação do consumidor e a incrementar a sua relação com a marca, através da personalização em real-time nos momentos-chave da customer journey. Através de uma metodologia qualitativa, procedeu-se à realização de entrevistas semiestuturadas a empresas que usam CRM e empresas que usam CDP com o objetivo de perceber quais as necessidades e limitações que estas enfrentam no que diz respeito aos dados e à criação de experiências personalizadas para os clientes. Os resultados da investigação permitem concluir que, por o CDP possuir ferramentas mais capazes de integração e identificação do cliente nos vários canais, permite a criação de uma visão unificada de todo o seu comportamento com a marca. Ainda que o consiga fazer, o seu papel não é substituir o CRM mas trabalhar de forma paralela para que se criem experiências ricas e personalizadas para o cliente.
Study on Fire Risk and Disaster Reducing Factors of Cotton Logistics Warehouse Based on Event and Fault Tree Analysis

AbstractCotton logistics warehouses have increased unceasingly along with the development of society. Firstly, referring to a hundred cotton logistics warehouse fires, based on the relevant provisions of the cotton logistics warehouse, and fire safety issues are summarized in this paper. Secondly, according to the physical and chemical characteristics of cotton, the fire risk of cotton logistics warehouse is analyzed, and the fire hazard sources are identified. Thirdly, Event and Fault Tree Analysis (EFTA) is used to explore the disaster mechanism of cotton logistics warehouses to obtain disaster reducing factors. In the end, based on the above analyses, a set of fire prevention measures against the cotton logistics warehouse are put forward. The result shows that the probability of moisture absorption and heat release is the highest of all causes of smoldering fire, so it is essential to control the temperature below 343K and the humidity below 70% when keeping in storage. Lacking of spaces between shelves is an important factor leading to the spread of fire. In the process of fire fighting, water supply is the key factor, so appropriate water storage facility for fire-fighting is necessary before the establishment of the cotton logistics warehouse.
Quantum Error Correction and Quantum Computation with Detected-jump Correcting Quantum Codes

Summary: A new class of embedded error correcting quantum codes is discussed. These detected-jump correcting quantum codes are capable of stabilizing distinguishable qubits against spontaneous decay. Due to their low redundancy and due to the small number of control measurements and recovery operations required these new error-correcting quantum codes offer interesting perspectives for quantum computation. In this contribution main ideas underlying these embedded error correcting quantum codes and their links to fundamental concepts of combinatorial design theory are discussed. In addition, possibilities are explored for implementing universal quantum gates within these code spaces.
Enhancing antimicrobial stewardship and health literacy in Europe – moving forward through education and empowerment

Antimicrobial resistance (AMR) is a silent, growing pandemic of global proportions and impact. This commentary encourages relevant stakeholders to increase their joint commitment to collaboration and systemic action, dedicated antibiotic stewardship and health literacy including a stronger, evidence-based self-care to contribute to solving the AMR challenge. Key factors such as the removal of inadequate access to antibiotics, e.g. over-the-counter (OTC) sale of antibiotics, improving self-care and hygiene can help fight the increasing burden of AMR. Along with the increase in health care literacy, these measures can be implemented swiftly to combat the growing rate of AMR development.
Enhancing Hospitality Service Management and Financial Literacy for Sustainable Tourism Development : Evidence from Malaysia’s Local Destination Operators

In the increasingly competitive tourism landscape, many local operators in Malaysia face challenges in maintaining service quality and managing their businesses sustainably. The objective of this study is to investigate how training in hospitality service management and financial literacy can enhance the long-term sustainability of local tourism businesses. This study aims to examine the impact of hospitality service management and financial literacy training on the business sustainability of local tourism operators. Using a quantitative approach, data were collected from 120 small-scale tourism entrepreneurs in East Malaysia through structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that both hospitality service management and financial literacy have a positive and significant effect on business sustainability. Moreover, the integrated implementation of both trainings produces a stronger impact than each intervention individually. These findings support the SERVQUAL Model, Human Capital Theory, and Empowerment Theory, highlighting that knowledge-based capacity building is crucial for improving operational performance and economic resilience in the tourism sector. The study recommends that policymakers and universities adopt integrated training modules that combine service and financial competencies to empower tourism-based microenterprises and foster inclusive, sustainable development.
Analyzing Surgical Competency Development through Robotic and Laparoscopic Simulation-Based Training

Minimally invasive surgery is becoming increasingly common, yet laparoscopic techniques often present a steep learning curve. Robotic surgical systems can offer a more efficient training pathway. The research compared robotic and laparoscopic simulation-based training in developing surgical competency among surgical trainees and medical students. A total of 230 participants were randomly assigned to two groups: surgical trainees (Group 1, n=115) and medical students (Group 2, n=115). Group 1 received 6 hours of laparoscopic or robotic simulation training before completing three surgical tasks with cadaveric varieties. Group 2 underwent 3 hours of training on either platform and completed a single surgical task. Performance was assessed using the Surgical Competency Assessment Tool (SCAT, maximum score of 30), the time taken to complete each task and the number of suture errors. The results showed that Group 1 demonstrated significantly better performance after robotic training, with a median SCAT score of 27.00 for the robotic group compared to 18.00 for the laparoscopic group (P &lt; 0.001). The robotic group also made fewer errors in both continuous and interrupted sutures (P &lt; 0.001). Similarly, Group 2 completed more interrupted sutures with fewer errors and less time when trained on the robotic platform (P &lt; 0.001). Additionally, both groups reported reduced fatigue and greater physical comfort with robotic training (P &lt; 0.001). Using SPSS (version 25), data were analyzed by applying descriptive statistics, Mann-Whitney U tests for non-parametric data, and independent t-tests for continuous variables. These findings suggest that robotic simulation-based training enhances the development of surgical competency more effectively than laparoscopic training.
Galaxy Morphology Classification via Deep Semi-Supervised Learning with Limited Labeled Data

Galaxy morphology classification plays a crucial role in understanding the structure and evolution of the universe. With galaxy observation data growing exponentially, machine learning has become a core technology for this classification task. However, traditional machine learning methods predominantly rely on supervised learning frameworks, and their dependence on large of labeled samples limits practical applications. To address this challenge, we propose an innovative hybrid semi-supervised model, GC-SWGAN, designed to tackle galaxy morphology classification under conditions of limited labeled data. This model integrates semi-supervised generative adversarial networks (SGAN) with Wasserstein GAN with gradient penalty (WGAN-GP), establishing a multi-task learning framework. Within this framework, the discriminator and classifier are designed independently while sharing part of the architecture. By collaborating with the generator, the model significantly enhances both classification performance and sample generation capabilities, while also improving convergence and stability during training. Experimental results demonstrate that, on the Galaxy10 DECals dataset, GC-SWGAN achieves comparable or even superior classification accuracy (exceeding 75%) using only one-fifth of the labeled samples typically required by conventional fully supervised methods. Under identical labeled conditions, the model displays excellent generalization performance, attaining approximately 84% classification accuracy. Notably, in extreme scenarios where only 10\% of the data is labeled, GC-SWGAN still achieves high classification accuracy (over 68%), fully demonstrating its stability and effectiveness in low-labeled data environments. Furthermore, galaxy images generated by GC-SWGAN are visually similar to real samples.