ethicalabs/Research-Intent-Collab huggingface dataset:
Nurse perceptions of practice environment, quality of care and patient safety across four hospital levels within the public health sector of South Africa

AbstractImproving the practice environment, quality of care and patient safety are global health priorities. In South Africa, quality of care and patient safety are among the top goals of the National Department of Health; nevertheless, empirical data regarding the condition of the nursing practice environment, quality of care and patient safety in public hospitals is lacking.AimThis study examined nurses’ perceptions of the practice environment, quality of care and patient safety across four hospital levels (central, tertiary, provincial and district) within the public health sector of South Africa.MethodsThis was a cross-sectional survey design. We used multi-phase sampling to recruit all categories of nursing staff from central (n = 408), tertiary (n = 254), provincial (n = 401) and district (n = 244 [largen = 81; mediumn = 83 and smalln = 80]) public hospitals in all nine provinces of South Africa. After ethical approval, a self-reported questionnaire with subscales on the practice environment, quality of care and patient safety was administered. Data was collected from April 2021 to June 2022, with a response rate of 43.1%. ANOVA type Hierarchical Linear Modelling (HLM) was used to present the differences in nurses’ perceptions across four hospital levels.ResultsNurses rated the overall practice environment as poor (M = 2.46; SD = 0.65), especially with regard to the subscales of nurse participation in hospital affairs (M = 2.22; SD = 0.76), staffing and resource adequacy (M = 2.23; SD = 0.80), and nurse leadership, management, and support of nurses (M = 2.39; SD = 0.81). One-fifth (19.59%;n = 248) of nurses rated the overall grade of patient safety in their units as poor or failing, and more than one third (38.45%;n = 486) reported that the quality of care delivered to patient was fair or poor. Statistical and practical significant results indicated that central hospitals most often presented more positive perceptions of the practice environment, quality of care and patient safety, while small district hospitals often presented the most negative. The practice environment was most highly correlated with quality of care and patient safety outcomes.ConclusionThere is a need to strengthen compliance with existing policies that enhance quality of care and patient safety. This includes the need to create positive practice environments in all public hospitals, but with an increased focus on smaller hospital settings.
Mesozoic dykes and sills from Uruguay: Sr – Nd isotope and trace element geochemistry

Abstract   The Mesozoic mafic intrusions in Uruguay comprise dykes and sills grouped as the Cuaro Formation. They are mainly distributed along the southern portion of the Parana basin, and they are considered part of the Parana Magmatic Province. They crop out as typically grey moderately altered dykes and sills, characterized by glomero-porphyritic textures, with clusters of plagioclase and occasional clinopyroxene, set in a fine-grained groundmass composed by labradorite, augite and titaniferous magnetite. We present new lithogeochemical results particularly regarding Sr – Nd isotopes to discuss petrogenetic processes. All samples have high  87 Sr/ 86 Sr (0.71160–0.70781) and low  143 Nd/ 144 Nd ratios (0.512274–0.512585), with epsilon Nd (0)  between −4.37 and −7.1. T DM  model ages, calculated for 130 Ma, are composed of approximately 1.41–1.61 Ga, except for one dyke with 1.29 Ga. The isotopic data allow their classification as derived from the Gramado magma-type. Trace element geochemistry and isotopic data indicate that the primary magma would be a product of an enriched mantle source with a strong crustal signature, probably due to inherited subduction components and/or assimilation processes.
Negative sampling strategies for contrastive self-supervised learning of graph representations

Abstract   Contrastive learning has become a successful approach for learning powerful text and image representations in a self-supervised manner. Contrastive frameworks learn to distinguish between representations coming from augmentations of the same data point (positive pairs) and those of other (negative) examples. Recent studies aim at extending methods from contrastive learning to graph data. In this work, we propose a general framework for learning node representations in a self supervised manner called Graph Constrastive Learning (GraphCL). It learns node embeddings by maximizing the similarity between the nodes representations of two randomly perturbed versions of the same graph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. We investigate different standard and new negative sampling strategies as well as a comparison without negative sampling approach. We demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks in both transductive and inductive learning setups.
EXAMINING THE RELATIONSHIP BETWEEN MANAGERS&#039; TOXIC LEADERSHIP BEHAVIORS AND EMPLOYEES&#039; ORGANIZATIONAL COMMITMENT AND ORGANIZATIONAL HAPPINESS

This study aims to investigate the relationship between managers&#039; toxic leadership behaviors and employees&#039; organizational commitment and organizational happiness. The research was designed using a quantitative research method and a correlational survey model. The population of the study consists of employees working under the Youth and Sports Provincial Directorate as of the year 2023, and the sample includes 285 employees selected through random sampling from this population. The data of the study were collected through scale instruments administered to participants via the internet. The scales used in the study include a personal information form, toxic leadership scale, organizational commitment scale, and organizational happiness scale. The data obtained from the research were analyzed using the SPSS 25 software package. The data analysis involved the use of descriptive statistics such as mean and standard deviation, t-test, ANOVA, Pearson correlation, and linear regression analyses. Based on the data collected from the research, it was found that employees&#039; perceptions of toxic leadership were low, perceptions of organizational commitment were moderate, and perceptions of organizational happiness were high. Toxic leadership, organizational commitment, and organizational happiness perceptions were not found to differ significantly based on gender, age, and tenure variables. According to the research findings, there was a low negative relationship between employees&#039; perceptions of toxic leadership and organizational commitment, a moderate negative relationship between employees&#039; perceptions of toxic leadership and organizational happiness, and a moderate positive relationship between employees&#039; perceptions of organizational happiness and organizational commitment. The sub-dimensions of the toxic leadership scale, namely narcissism and selfishness, were found to be significant predictors of organizational commitment, and employees&#039; perceptions of toxic leadership accounted for approximately 11% of the total variance. Additionally, the sub-dimensions of the toxic leadership scale, namely negative mood, narcissism, and selfishness, were found to be significant predictors of organizational happiness, and employees&#039; perceptions of toxic leadership accounted for approximately 26% of the total variance.
On pairwise distances and median score of three genomes under DCJ

In comparative genomics, the rearrangement distance between two genomes (equal the minimal number of genome rearrangements required to transform them into a single genome) is often used for measuring their evolutionary remoteness. Generalization of this measure to three genomes is known as the median score (while a resulting genome is called median genome). In contrast to the rearrangement distance between two genomes which can be computed in linear time, computing the median score for three genomes is NP-hard. This inspires a quest for simpler and faster approximations for the median score, the most natural of which appears to be the halved sum of pairwise distances which in fact represents a lower bound for the median score.   In this work, we study relationship and interplay of pairwise distances between three genomes and their median score under the model of Double-Cut-and-Join (DCJ) rearrangements. Most remarkably we show that while a rearrangement may change the sum of pairwise distances by at most 2 (and thus change the lower bound by at most 1), even the most powerful rearrangements in this respect that increase the lower bound by 1 (by moving one genome farther away from each of the other two genomes), which we call strong, do not necessarily affect the median score. This observation implies that the two measures are not as well-correlated as one's intuition may suggest.   We further prove that the median score attains the lower bound exactly on the triples of genomes that can be obtained from a single genome with strong rearrangements. While the sum of pairwise distances with the factor 2/3 represents an upper bound for the median score, its tightness remains unclear. Nonetheless, we show that the difference of the median score and its lower bound is not bounded by a constant.
Research on Computer Vision-Based Object Detection and Classification

Computer vision techniques become particularly important in agriculture applications due to their fast response, high accuracy and strong adaptability. Two of the most demanding and widely studied applications relate to object detection and classification. The task is challenging due to variations in product quality differences under certain complicate circumstances influenced by nature and human. Research in these fields has resulted in a wealth of processing and analysis methods. In this paper, we explicitly explore current advances in the field of object detecting and categorizing based on computer vision, and a comparison of these methods is given.
Addressing single object tracking in satellite imagery through prompt-engineered solutions

Object tracking in satellite videos remains a complex endeavor in remote sensing due to the intricate and dynamic nature of satellite imagery. Existing state-of-the-art trackers in computer vision integrate sophisticated architectures, attention mechanisms, and multi-modal fusion to enhance tracking accuracy across diverse environments. However, the challenges posed by satellite imagery, such as background variations, atmospheric disturbances, and low-resolution object delineation, significantly impede the precision and reliability of traditional Single Object Tracking (SOT) techniques. Our study delves into these challenges and proposes prompt engineering methodologies, leveraging the Segment Anything Model (SAM) and TAPIR (Tracking Any Point with per-frame Initialization and temporal Refinement), to create a training-free point-based tracking method for small-scale objects on satellite videos. Experiments on the VISO dataset validate our strategy, marking a significant advancement in robust tracking solutions tailored for satellite imagery in remote sensing applications.
Towards One Health surveillance of antibiotic resistance, use and residues of antibiotics in France: characterisation and mapping of existing programmes in humans, animals, food and the environment

Abstract                 International organizations are calling for One Health approaches to tackle antibiotic resistance (ABR). In France, the diversity of surveillance programmes makes it difficult to get an overview of the current surveillance system and its level of integration. This study aimed to map and characterise all French surveillance programmes for ABR, antibiotic use (ABU) and antibiotic residues in humans, animals, food and the environment, to identify integration points, gaps and overlaps.                                    A literature review and interviews with 36 programme coordinators were conducted to identify and characterise programmes using a standardized grid (28 variables). Forty-eight programmes were included. They targeted the human (n=35), animal (n=12), food (n=3) and/or the environment (n=1); 35 programmes focused on ABR, 14 on ABU and two on antibiotic residues. Two programmes were cross-sectoral. Among the 35 ABR programmes, 23 collected bacterial isolates. Bacteria most targeted were                   Escherichia coli                   (n=17 programmes),                   Klebsiella pneumoniae                   (n=13), and                   Staphylococcus aureus                   (n=12). ESBL-producing                   E. coli                   was monitored by the majority of ABR programmes in humans, animals and food, and is a good candidate for integrated data analysis. ABU indicators were highly variable. Areas poorly covered were the environmental sector, overseas territories, ABR colonisation in humans and ABU in companion animals.                                  The French surveillance system appears rich and extensive, but with gaps and only few integration points. We believe this mapping will be of high interest to policy makers and surveillance stakeholders, and that our methodology may inspire other countries willing to progress towards One Health surveillance of ABR.
Accident investigation and root cause analysis method

A large number of accidents happened each day in many industries all over the world, which result in injury to people, environment pollution and consequential reputation damage. By investigation into accidents, we can identify root causes of accidents and develop implement corrective actions, which prevent accidents from reoccurring. Accident root cause analysis method is introduced in this paper. By analyzing each causal factor of accidents, root causes are found, and corrective actions are developed due to root causes to prevent accidents from reoccurring. This paper introduces a accident model of root cause analysis and its root cause tree, and apply this method to Luojia “12 23” blowout accident which killed 243 because of hydrothion releasing from blowout in Luojia-16, on 23rd December 2003. Root cause analysis has a good application prospect in accident investigation and analysis in oil well drilling industry.
The Impact of Corporate Social Responsibility Reports on Investors' Decisions: From the Perspective of Accounting Information

In recent years, as Corporate Social Responsibility (CSR) reports have evolved from marginal texts to strategic tools, investors have gradually recognized the limitations of traditional financial indicators. This study focuses on how accounting information in CSR reports penetrates the superficial layer of "responsibility narratives" and actually influences investment decisions. Through literature analysis, case studies, and theoretical reasoning, it answers two core questions: Which accounting data truly trigger investors' nerves? How do these data convey the sustainable development potential of enterprises? The study finds that accounting indicators, such as environmental governance costs and employee welfare expenditures, constitute the anchor points of "responsibility credibility", and the quality of their disclosure directly affects the risk pricing in the capital market. It suggests that enterprises adopt "industry-customized" disclosure strategies, while investors need to establish a "financial-responsibility" dual-dimension analysis framework. The study reveals a new path for the capitalization of non-financial information, providing a new perspective for value assessment in the era of responsibility.
A study on Covid 19's ethical repercussions on business and Corporate Social Responsibility

The purpose of this paper is to investigate various business approaches to the Covid-19 pandemic primarily from the lens of ethics. In addition to this, it intends to provide recommendations to businesses with regard to the role they play in combating the epidemic and the consequences of its spread during these trying times. Which choices should be made by companies in light of the current situation? What moral repercussions are necessary to carefully consider before taking any action? To what extent does the law take into account the complexities of issues pertaining to corporate social responsibility?
Engaging in socially responsible research: exploring scientists' perceived value of online public communication of science

Communicating science to broad publics using the Internet and social media has become a key social priority on EU scientists’ professional agenda to enhance socially responsible research (Ball 2016). To date, little research has analysed scientists’ values and motivations regarding digital science communication practices targeted at non-specialist audiences. Using a semi-structured interview protocol, I examine Spanish female STEM scientists’ engagement in digital science communication practices. The interview data were analysed using a content thematic approach on the software Atlas.TI. Results confirm that the primary motivation for engaging in digital practices is to make science accessible to broad publics. They also view communicating beyond the scientific community as a valuable practice to improve citizens’ scientific literacy, a finding that echoes the deficit model of science communication as described by Trench (2008). In other words, scientists consider initiatives such as giving online talks, participating in podcasts, creating videos, or sharing events and content on social media (Instagram, TikTok, Twitter, LinkedIn) as an opportunity to democratise science. They also perceive them as socially responsible research, and as a way of raising awareness for scientific topics and careers (Loroño-Leturiondo & Davies 2018; Metcalfe 2019). Interviewees also highlighted the benefits of digital practices, especially after the COVID-19 pandemic, due to their international range, sustainability, and accessibility. The implications derived from the findings point to the need to design LSP training that supports skills development in communicating scientific content to non-specialized audiences on the Internet and the deployment of digital tools and resources supporting online science communication.
Is There a g-factor of Genderedness? Using a Continuous Measure of Genderedness to Assess Sex Differences in Personality Traits, Personal Values, Cognitive Ability, School Grades, and Educational Track in Finnish 16-year Olds Finishing Elementary School

Some of the most persistently recurring research questions in the fields of personality and social psychology concern sex differences. Although much progress has been made, no research has addressed the basic question of whether there is one general construct of genderedness that runs through various life domains, or whether genderedness is specific to certain domains. In order to determine whether being gender typical in one way goes together with being gender typical also in other ways, we investigated whether 16-year old girls and boys (N = 4106) finishing Finnish elementary school differ in their personality traits, values, cognitive abilities, academic achievement, and educational track. To do this, we updated the gender diagnosticity approach by employing penalized logistic regression to estimate multivariate sex differences based on both binary and continuous variables. The preregistered analysis show that the magnitude of sex differences varies a lot from domain to domain, that narrow measures, such as grade profiles, can be highly accurate in predicting sex, whereas broad measures, such as general cognitive ability, can be useless, and that the correlations between femininity-masculinity scores based on different domains, despite all being positive, are too weak to suggest the existence of a general factor of genderedness. Our more exploratory analyses show that more focus on gender typicality could offer important insights into the role of gender in shaping people’s lives. We discuss how future research could employ the methods we introduce to further our understanding of sex differences and gender typicality in developmental and educational fields.
Generative Hierarchical Materials Search

Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from the domain expert in the form of high-level instructions can be essential for an automated system to output candidate crystals that are viable for downstream research. In this work, we formulate end-to-end language-to-structure generation as a multi-objective optimization problem, and propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates low-level continuous value crystal structures. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures. During inference, GenMS leverages all three components to conduct a forward tree search over the space of possible structures. Experiments show that GenMS outperforms other alternatives of directly using language models to generate structures both in satisfying user request and in generating low-energy structures. We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future.
Born a Transformer -- Always a Transformer? On the Effect of Pretraining on Architectural Abilities

NeurIPS 2025
Properties of barred galaxies in the MaNGA galaxy survey

We present the initial results of a census of 684 barred galaxies in the MaNGA galaxy survey. This large sample contains galaxies with a wide range of physical properties, and we attempt to link bar properties to key observables for the whole galaxy. We find the length of the bar, when normalised for galaxy size, is correlated with the distance of the galaxy from the star formation main sequence, with more passive galaxies hosting larger-scale bars. Ionised gas is observed along the bars of low-mass galaxies only, and these galaxies are generally star-forming and host short bars. Higher-mass galaxies do not contain Hα emission along their bars, however, but are more likely to host rings or Hα at the centre and ends of the bar. Our results suggest that different physical processes are at play in the formation and evolution of bars in low- and high-mass galaxies.