ethicalabs/Research-Intent-Collab huggingface dataset:
Deep learning approach in undergraduate nursing students and their relationship with learning outcomes: A latent profile analysis

Deep learning approach plays a pivotal role in nursing education, equipping students with the critical thinking skills and knowledge necessary to address complex clinical challenges. However, nursing students exhibit diverse approaches to deep learning, affected by individual characteristics, academic environments and teaching methods.This study aims to identify latent profiles of deep learning approach among undergraduate nursing students and analyze the factors influencing these profiles and their association with learning outcomes.A descriptive cross-sectional survey.A total of 891 undergraduate nursing students from two medical universities in China participated in this study between May and July 2024. Data were collected using the Deep Learning Scale and the Learning Outcomes Scale. Latent profile analysis was employed to identify deep learning profiles. One-way analysis of variance and multinomial logistic regression were used to explore influencing factors of different profiles. The Bolck-Croon-Hagenaars (BCH) method was applied to examine differences in learning outcomes across profiles.Four latent profiles of deep learning were identified: "Comprehensive Deep Learners" (27.0 %), "Ability-Oriented Learners" (25.4 %), "Attitude-Driven Learners" (21.7 %) and "Surface Coping Learners" (25.8 %). Gender, grade, preference for the nursing major and participation in flipped classrooms were significant factors influencing profile membership (p < 0.05). "Comprehensive Deep Learners" had the highest learning outcome scores, while "Surface Coping Learners" scored the lowest.Significant heterogeneity exists in deep learning approach among undergraduate nursing students. "Comprehensive Deep Learners" achieved the highest learning outcomes. Nursing education should adopt tailored interventions based on the characteristics of different deep learning profiles to improve students' learning outcomes and comprehensive competencies.
Predictive Power of the Exact Constraints and Appropriate Norms in Density Functional Theory

Ground-state Kohn-Sham density functional theory provides, in principle, the exact ground-state energy and electronic spin-densities of real interacting electrons in a static external potential. In practice, the exact density functional for the exchange-correlation (xc) energy must be approximated in a computationally efficient way. About twenty mathematical properties of the exact xc functional are known. In this work, we review and discuss these known constraints on the xc energy and hole. By analyzing a sequence of increasingly sophisticated density functional approximations (DFAs), we argue that: (1) the satisfaction of more exact constraints and appropriate norms makes a functional more predictive over the immense space of many-electron systems; (2) fitting to bonded systems yields an interpolative DFA that may not extrapolate well to systems unlike those in the fitting set. We discuss how the class of well-described systems has grown along with constraint satisfaction, and the possibilities for future functional development.
Illuminating humanist nature in teaching translation and interpreting studies: Devising an online customisable AI-driven subtitling course

AbstractThe paper sets out to devise an online subtitling course emphasising the human nature of translation. To gain a deeper understanding of audience behaviours and attitudes towards the consumption of subtitles, the article utilises questionnaires and case studies. Results are used to devise a learner-customised and technology-assisted online course about subtitling. The case studies that inspire the course content and design include the short-video platform Douyin (the Chinese equivalent of TikTok) and the comparison of two MOOCs,Working with Translation: Theories and PracticeandConsecutive Interpreting. In summary, the course design for teaching subtitling will be based on the findings of the research questions and elastically adapted to the syllabus. We put forward our course with an emphasis on the practicality of producing subtitles in light of the technological acceleration in the marketplace and, more importantly, the humanistic nature of translation.
Three-body recombination in physical chemistry

Three-body recombination, or ternary association, is a termolecular reaction in which three particles collide, forming a bound state between two, whereas the third escapes freely. Three-body recombination reactions play a significant role in many systems relevant to physics and chemistry. In particular, they are relevant in cold and ultracold chemistry, quantum gases, astrochemistry, atmospheric physics, physical chemistry, and plasma physics. As a result, three-body recombination has been the subject of extensive work during the last 50 years, although primarily from an experimental perspective. Indeed, a general theory for three-body recombination remains elusive despite the available experimental information. Our group recently developed a direct approach based on classical trajectory calculations in hyperspherical coordinates for three-body recombination to amend this situation, leading to a first principle explanation of ion-atom-atom and atom-atom-atom three-body recombination processes. This review aims to summarize our findings on three-body recombination reactions and identify the remaining challenges in the field.
Enhancing Recruitment Efficiency through Human Resource Information System (HRIS). A Case Study of TAMISEMI Headquarters, Dodoma

Abstract: The main objective of this study was to assess the enhancement of recruitment efficiency by Human Resource Information System (HRIS) at Tawala za Mikoa na Serikali za Mitaa (TAMISEMI) Headquarters, Dodoma.  The specific objective was to evaluate the perceived benefits of HRIS usage in enhancing recruitment decision making at TAMISEMI Headquarters. The study used a descriptive research design and adopted a mixed research approach. Simple stratified sampling method was used to select the sample population. The sample size of the study was 90 respondents. Questionnaires and structured interview were used to collect data which were analyzed using Statistical Package for Social Sciences (SPSS) version 26 for quantitative while thematic was done on qualitative. Findings were presented as frequencies, means and percentages. The results of the study revealed a low integration of HRIS with existing systems and the transfer of existing information to the new system was not accurate, hence the perception of employees on the benefits of HRIS in enhancing recruitment decision making at TAMISEMI was not good. The study concluded that, the use of HRIS has proven to have a significant impact on increasing the efficiency of human resource management in modern organizations.   Keywords: Enhancing, Human Resource, Information System, Perceived benefits, Recruitment efficiency.  Title: Enhancing Recruitment Efficiency through Human Resource Information System (HRIS). A Case Study of TAMISEMI Headquarters, Dodoma  Author: Vincent Kasuga, Haji Ng’elenge, Bahati Golyama  International Journal of Novel Research in Education and Learning  ISSN 2394-9686  Vol. 12, Issue 5, September 2025 - October 2025  Page No: 87-96  Novelty Journals  Website: www.noveltyjournals.com  Published Date: 29-October-2025  DOI: https://doi.org/10.5281/zenodo.17471908  Paper Download Link (Source)  https://www.noveltyjournals.com/upload/paper/Enhancing%20Recruitment%20Efficiency%20through-29102025-2.pdf
Promoting Health Behavior Change in Canada: A Quality Improvement Project Integrating Prevention and Wellness in Frontline Care

Background: The critical need for Health Behaviour Change (HBC) for preventative care has been highlighted by the COVID-19 pandemic. This quality improvement project assessed the effectiveness and acceptability of Fountain of Health HBC tools as a minimal intervention for primary and secondary prevention among Canadian clinicians and their patients. Methods: Clinicians received HBC education and tools (paper tools and app-based) to assist their patients in setting a S.M.A.R.T (Specific, Measurable, Action-oriented, Realistic, Time-limited) goal at baseline and assessed four weeks later. Primary outcome measures were: 1) patient self-report of success at goal attainment; 2) patient self-reported evaluation of change in well-being and health attitudes; and 3) clinician experience and engagement. Results: A total of 2184 clinicians received HBC education. Of these, 759 clinicians registered to participate in the project. 961 patients set S.M.A.R.T. HBC goals using either the paper tools (PT) or the app format. Patient data revealed nearly all patients (89% of PT users and 90% of app users) at least partially succeeded in attaining their goal at a four-week follow-up. Most patients (85% of PT and 80% of app users) also reported improvement in their well-being. A significant difference in health beliefs and attitudes was found in both PT (p < 0.001) and app users (p = 0.003). Conclusions: HBC is challenging. Educating and providing clinicians with HBC tools as a minimal intervention was found to be a successful health prevention strategy to improve patient well-being and health attitudes, and achieve S.M.A.R.T. goals. Results show HBC tools, education, and support for clinicians are both acceptable and effective for HBC in frontline care. These findings are relevant and timely given greater reliance on virtual care in the wake of the COVID-19 pandemic. Further research with rigorous methodology is needed to implement programs to achieve sustainable HBC to promote health on a large scale.
Cyber Resilience: A Review of Critical National Infrastructure and Cyber-Security Protection Measures Applied in the UK and USA

This paper presents cyber resilience as key strand of national security. It establishes the importance of critical national infrastructure protection and the growing vicarious nature of remote, well-planned, and well executed cyber attacks on critical infrastructures. Examples of well-known historical cyber attacks are presented, and the emergence of ‘internet of things’ as a cyber vulnerability issue yet to be tackled is explored. The paper identifies key steps being undertaken by those responsible for detecting, deterring, and disrupting cyber attacks on critical national infrastructure in the United Kingdom and the USA.
A Measurement of the Cosmic Microwave Background Lensing Potential and Power Spectrum from 500 deg<sup>2</sup> of SPTpol Temperature and Polarization Data

Abstract                We present a measurement of the cosmic microwave background lensing potential using 500 deg2 of 150 GHz data from the SPTpol receiver on the South Pole Telescope. The lensing potential is reconstructed with signal-to-noise per mode greater than unity at lensing multipoles L ≲ 250, using a quadratic estimator on a combination of cosmic microwave background temperature and polarization maps. We report measurements of the lensing potential power spectrum in the multipole range of 100 &lt; L &lt; 2000 from sets of temperature-only (T), polarization-only (POL), and minimum-variance (MV) estimators. We measure the lensing amplitude by taking the ratio of the measured spectrum to the expected spectrum from the best-fit Λ cold dark matter model to the Planck 2015 TT + low P + lensing data set. For the minimum-variance estimator, we find                                                                                                                                   A                                                                                       MV                                                                              =                         0.944                         ±                         0.058                         (                         Stat                         .                         )                         ±                         0.025                                                  (                         Sys                         .                         )                         ;                                                                restricting to only polarization data, we find                                                                                                                                   A                                                                                       POL                                                                              =                         0.906                         ±                         0.090                                                  (                         Stat                         .                         )                         ±                         0.040                                                  (                         Sys                         .                         )                                                               . Considering statistical uncertainties alone, this is the most precise polarization-only lensing amplitude constraint to date (10.1σ) and is more precise than our temperature-only constraint. We perform null tests and consistency checks and find no evidence for significant contamination.
Advancing additive manufacturing: <scp>3D</scp>‐printing of hybrid natural fiber sandwich (<scp>Nona/Soy‐PLA</scp>) composites through filament extrusion and its effect on thermomechanical properties

AbstractThe additive manufacturing technique represents a technological advancement post the industrial revolution, enabling the development of intricate products with minimal waste. In the automotive sector, polymer extrusion‐based additive manufacturing is predominantly employed for part customization. Recently, reinforced polymer matrices have been used to enhance structural integrity, primarily utilizing synthetic materials. Due to environmental concerns, some recent studies have explored the incorporation of natural fiber reinforcement in polymer matrices for 3D‐printed products. Most research has focused on filler reinforcement, limiting investigations to a single type of reinforcement. To bridge this gap and enhance composite performance, our research introduces a novel method for 3D printing composites. This involves stacking sequences using two different natural fiber‐reinforced polylactic acid (PLA) filaments, namely Nona and Soy, respectively. Mechanical testing reveals that the stacking sequence significantly influences the mechanical properties of the 3D‐printed composites. The 3D‐printed composite with Soy/PLA skin stacks exhibits the highest tensile strength of 74.82 MPa. Thermal analysis shows minor changes in glass transition temperature (Tg) and a consistent degree of crystallinity (10.01%). Notable differences in thermal expansion are observed due to the stacking sequence. Fatigue analysis demonstrates the durability of hybrid composites, enduring over 42,000 cycles with minimal deformation compared with to pure fiber‐reinforced 3D‐printed composites. Morphological analysis reveals excellent fiber–matrix interaction and bonding between stacks. The observations confirm that the developed material can be potentially used for developing structurally enhanced composites for lightweight applications. Moreover, the proposed methodology can be adapted for different 3D printing matrices, including robotic printing with multiple axes, making it suitable for various industrial sectors.Highlights Utilization of 3D printing technology for the development of agro‐waste fiber products. Hybridizing the 3D‐printed composite enhanced the fatigue life. New additive manufacturing technique for developing structurally enhanced composites.
Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study

Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide. We present a comprehensive assessment of pretraining methodologies, covering five different contrastive and non-contrastive self-supervised learning objectives for ECG foundation models, and investigate their scaling behavior with pretraining dataset sizes up to 11M input samples, exclusively from publicly available sources. Pretraining strategy has a meaningful and consistent impact on downstream performance, with contrastive predictive coding (slightly ahead of JEPA) yielding the most transferable representations across diverse clinical tasks. Scaling pretraining data continues to yield meaningful improvements up to 11M samples for most objectives. We also compare model architectures across all pretraining methodologies and find evidence for a clear superiority of structured state space models compared to transformers and CNN models. We hypothesize that the strong inductive biases of structured state space models, rather than pretraining scale alone, are the primary driver of effective ECG representation learning, with important implications for future foundation model development in this and potentially other physiological signal domains.
Benchmarking of Nanopore R10.4 and R9.4.1 flow cells in single-cell whole-genome amplification and whole-genome shotgun sequencing

Third-generation sequencing can be used in human cancer genomics and epigenomic research. Oxford Nanopore Technologies (ONT) recently released R10.4 flow cell, which claimed an improved read accuracy compared to R9.4.1 flow cell. To evaluate the benefits and defects of R10.4 flow cell for cancer cell profiling on MinION devices, we used the human non-small-cell lung-carcinoma cell line HCC78 to construct libraries for both single-cell whole-genome amplification (scWGA) and whole-genome shotgun sequencing. The R10.4 and R9.4.1 reads were benchmarked in terms of read accuracy, variant detection, modification calling, genome recovery rate and compared with the next generation sequencing (NGS) reads. The results highlighted that the R10.4 outperforms R9.4.1 reads, achieving a higher modal read accuracy of over 99.1%, superior variation detection, lower false-discovery rate (FDR) in methylation calling, and comparable genome recovery rate. To achieve high yields scWGA sequencing in the ONT platform as NGS, we recommended multiple displacement amplification with a modified T7 endonuclease Ⅰ cutting procedure as a promising method. In addition, we provided a possible solution to filter the likely false positive sites among the whole genome region with R10.4 by using scWGA sequencing result as a negative control. Our study is the first benchmark of whole genome single-cell sequencing using ONT R10.4 and R9.4.1 MinION flow cells by clarifying the capacity of genomic and epigenomic profiling within a single flow cell. A promising method for scWGA sequencing together with the methylation calling results can benefit researchers who work on cancer cell genomic and epigenomic profiling using third-generation sequencing.
Mobile Application Development Plans: Simplifying the Housing Search; Creativity and Digital Creations: Resistance to AI-Generated Visual Art

Digital systems can simplify complex cognitive tasks, such as locating housing or creating a work of art. Yet in simplifying such processes, digital systems introduce demanding new problems, such as the drawing of the line between legitimate simulation of human creativity and intellectual property theft.    In a project called Mobile Application Development Plans: Simplifying the Housing Search, my team developed a tool called HoosHomeConnect to help users select housing. To simplify user navigation, the tool collects scattered internet data. HoosHomeConnect is a proposed plan to increase user satisfaction when working with housing data. It currently exists as a wireframe model with planned methods of implementation.    AI models trained on human artists’ work can generate visual art that competes with humans’ work. Tech companies, artists, art publishers, agents, fans, and tech users are competing to determine the legitimacy of AI-generated visual works as art, and whether such works constitute new works of art or mere automated theft of intellectual property.
Demonstration of statistical mechanics phase transitions with arrays of thousands of coherent lasers

Thousands of coherent lasers are phased locked by global and local coupling in order to demonstrate first and second order phase transitions. The measured and calculated phase distribution reveals similar behavior to XY spin model.
Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes

Policy gradient (PG) is a reinforcement learning (RL) approach that optimizes a parameterized policy model for an expected return using gradient ascent. While PG can work well even in non-Markovian environments, it may encounter plateaus or peakiness issues. As another successful RL approach, algorithms based on Monte Carlo Tree Search (MCTS), which include AlphaZero, have obtained groundbreaking results, especially in the game-playing domain. They are also effective when applied to non-Markov decision processes. However, the standard MCTS is a method for decision-time planning, which differs from the online RL setting. In this work, we first introduce Monte Carlo Tree Learning (MCTL), an adaptation of MCTS for online RL setups. We then explore a combined policy approach of PG and MCTL to leverage their strengths. We derive conditions for asymptotic convergence with the results of a two-timescale stochastic approximation and propose an algorithm that satisfies these conditions and converges to a reasonable solution. Our numerical experiments validate the effectiveness of the proposed methods.
ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval

The ViDoRe Benchmark V1 was approaching saturation with top models exceeding 90% nDCG@5, limiting its ability to discern improvements. ViDoRe Benchmark V2 introduces realistic, challenging retrieval scenarios via blind contextual querying, long and cross-document queries, and a hybrid synthetic and human-in-the-loop query generation process. It comprises four diverse, multilingual datasets and provides clear evaluation instructions. Initial results demonstrate substantial room for advancement and highlight insights on model generalization and multilingual capability. This benchmark is designed as a living resource, inviting community contributions to maintain relevance through future evaluations.
Building a Safety Culture: The Role of Leadership and Safety Climate on CPO Production Operators in Palm Oil Companies in South Kalimantan

Workplace accidents in the palm oil industry, particularly involving Crude Palm Oil (CPO) processing operators, have shown an upward trend, manifesting in incidents such as unit collisions, slips, and overturning. These incidents are primarily attributed to inadequate safety behavior among workers. This study aims to investigate the influence of safety leadership on safety behavior, both directly and indirectly through safety climate as a mediating variable. Using a quantitative approach, the study surveyed 223 CPO operators selected through simple random sampling. Data were collected via structured questionnaires and analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS) in SmartPLS 3.0. The findings reveal that: (1) safety leadership significantly influences safety behavior, (2) safety leadership positively affects safety climate, (3) safety climate significantly contributes to safety behavior, and (4) safety climate mediates the relationship between leadership and behavior. These results emphasize the pivotal role of leadership and a supportive safety climate in cultivating a safety culture and promoting compliant behavior among CPO operators in South Kalimantan's palm oil sector.