ethicalabs/Research-Intent-Collab huggingface dataset:
Association between nurses' burnout, hospital patient safety climate and quality of nursing care

Aim: To evaluate the associations between nurse burnout, the hospital patient safety climate, the patient safety grade, and adverse events. Design: Cross-sectional. Methods: 117 nurses completed the Copenhagen Burnout Inventory and the Hospital Survey on patient safety culture. Pearson correlation and linear regression analysis was conducted to assess associations between variables. Results: Higher level of burnout significantly met with lower grade of patient safety, overall perception of patient safety, higher frequency of adverse events recorded, and medication errors. The overall perception of safety was positively related to teamwork within hospital units and non-punitive responses to error. The frequency of recorded events was significantly negatively associated with hospital management support and supervisors’ activities, and positively with feedback. Medication errors correlated positively with organizational learning and continuous safety improvement and negatively with staffing. Significant relationships have been identified between management support, non-punitive responses to error, teamwork within hospital units, and selected adverse events. Conclusion: Enhancement of the patient safety climate and nurses’ mental health are important patient safety improvements in healthcare organisations.
Indigenous Peoples’ Rights in International Law: The Ogiek Decision by the African Court of Human and Peoples’ Rights

The Inherent Rights of Indigenous Peoples in International Law
A comparative analysis and guide to virtual reality robotic surgical simulators

AbstractBackgroundSince the US Food and Drug Administration approved robotically assisted surgical devices for human surgery in 2000, the number of surgeries utilizing this innovative technology has risen. In 2015, approximately 650 000 robot‐assisted procedures were performed worldwide. Surgeons must be properly trained to safely transition to using such innovative technology. Multiple virtual reality robotic simulators are now commercially available for educational and training purposes. There is a need for comparative evaluations of these simulators to aid users in selecting an appropriate device for their purposes.MethodsWe conducted a comparison of the design and capabilities of all dedicated simulators of the da Vinci robot – the da Vinci Skills Simulator (dVSS), dV‐Trainer (dVT), Robotic Skills Simulators (RoSS) and the RobotiX Mentor. This paper provides the base specifications of the hardware and software, with an emphasis on the training capabilities of each system.ResultsEach simulator contains a large number of training exercises for skills development: dVSS n = 40, dVT n = 65, RoSS n = 52, RobotiX Mentor n = 31. All four offer 3D visual images but use different display technologies. The dVSS leverages the real robotic surgical console to provide visualization, hand controls and foot pedals. The dVT, RoSS and RobotiX Mentor created simulated versions of all of these control systems. Each includes systems management services that allow instructors to collect, export and analyze the scores of students using the simulators.ConclusionsThis study provides comparative information on the four simulators' functional capabilities. Each device offers unique advantages and capabilities for training robotic surgeons. Each has been the subject of validation experiments, which have been published in the literature. But those do not provide specific details on the capabilities of the simulators, which are necessary for an understanding sufficient to select the one best suited for an organization's needs. This article provides comparative information to assist with that type of selection.
A computational foray into the mechanism and catalysis of the adduct formation reaction of guanine with crotonaldehyde

Crotonaldehyde, a common environmental pollutant and product of endogenous lipid peroxidation, reacts with guanine to form DNA adducts with pronounced genotoxicity and mutagenicity. Here, we explore the molecular mechanism of this adduct formation using double‐hybrid density functional theory methods. The reaction can be envisaged to occur in a two‐step fashion via an aza‐Michael addition leading to an intermediate ring‐open adduct followed by a cyclization reaction giving the mutagenic ring‐closed adduct. We find that (i) a 1,2‐type addition is favored over a 1,4‐type addition for the aza‐Michael addition, and (ii) an initial tautomerization of the guanine moiety in the resulting ring‐open adduct significantly reduces the barrier toward cyclization compared to the direct cyclization of the ring‐open adduct in its keto‐form. Overall, the aza‐Michael addition is found to be rate‐determining. We further find that participation of a catalytic water molecule significantly reduces the energy barriers of both the addition and cyclization reaction. © 2018 Wiley Periodicals, Inc.
METAGENOMIC ANALYSIS OF DUST MICROBIOME IN THE HOMES OF ASTHMATIC CHILDREN

Kućna prašina predstavlja složen mikrobni ekosustav s mogućim utjecajem na respiratorno zdravlje. Ovo istraživanje, provedeno u sklopu EDIAQI projekta, koristi metagenomske metode za usporedbu mikrobioma prašine u domovima djece s astmom i bez nje, naglašavajući utjecaj unutarnjeg okoliša na respiratorno zdravlje. Analize su otkrile različite mikrobne obrasce između dviju skupina, ukazujući na potencijalne mikrobne pokazatelje astmatskog statusa. Bakterijske i gljivične zajednice pokazale su razlike u prisutnosti pojedinih taksa, pri čemu su rodovi Staphylococcus i Malassezia glavni pokazatelji varijacija. Istraživanje dodatno potvrđuje postojeće dokaze da su mikrobne zajednice zatvorenih prostora oblikovane različitim utjecajima, bakterije ponajviše ovise o unutarnjim čimbenicima poput prisutnosti i aktivnosti ljudi, dok su gljivice više pod utjecajem vanjskih okolišnih uvjeta. Ovo je prvo istraživanje takve vrste u Hrvatskoj i pruža vrijednu osnovu za buduća istraživanja respiratornog zdravlja i unutarnjeg okoliša.
Compressed Sensing and Sparse Recovery

The central mathematical tool for algorithm analysis and development is the concentration of measure for random matrices. This chapter is motivated to provide applications examples for the theory developed in Part I. We emphasize the central role of random matrices.
Welfare state dissatisfaction and support for major welfare reform: Towards means‐tested welfare or a universal basic income?

AbstractThis research note investigates how people combine their views on two radically opposing welfare reforms: a universal basic income and a fully means‐tested welfare state. Using data from the 2016–2017 European Social Survey, we found that support for transformative welfare reform is rooted in perceptions of the performance of the current system. The preferred direction of reform, however, strongly depends on the specific aspects of the welfare state people are happy or unhappy with. At the country‐level, we show that underperforming welfare states—in terms of higher poverty rates and lower social spending—increase popular demand for transformative welfare reform, in either direction. These findings are of crucial importance for ongoing debates about the future of the welfare state.
Central bank digital currency as a new means of payment: An experimental approach

Many Central banks around the world are considering the introduction of a Central Bank Digital Currency (CBDC) as a new means of payment. One of the reasons for introducing a CBDC is a change in payment behavior towards an increasing use of electronic forms of payment. This paper examines the introduction of a CBDC as a new means of payment. We conduct a controlled laboratory experiment to assess how adoption costs and anonymity affect the demand for CBDC compared to established means of payment such as cash and deposits. We use a 2x2 treatment design in which CBDCs differ in adoption costs and anonymity. We find that adoption costs play an important role in the decision to use CBDC as a new means of payment and that anonymity plays a role in the allocation of experimental money between different means of payment.
Expected policy gradients for reinforcement learning

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadratic critics and then extend it to a universal analytical method, covering a broad class of actors and critics, including Gaussian, exponential families, and policies with bounded support. For Gaussian policies, we introduce an exploration method that uses covariance proportional to eH, where H is the scaled Hessian of the critic with respect to the actions. For discrete action spaces, we derive a variant of EPG based on softmax policies. We also establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. Furthermore, we prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and with little computational overhead. Finally, we provide an extensive experimental evaluation of EPG and show that it outperforms existing approaches on multiple challenging control domains.
Analysis of methods for assessing and managing cyber risks and information security

Global trends to increase the threats to information and cybersecurity, increasing the level of vulnerability of information and telecommunications systems (ITS) necessitate the development and implementation of new standards and regulations on information security, the introduction of new technologies and best practices in information security. The main approach to information and cybersecurity in ITS is the Risk-Based Protection Strategy. The main task of information risk management (IR) is to identify and assess objectively the most significant risks for the company's business, as well as the need to use risk controls to increase the efficiency and profitability of the company's economic activities. It is believed that quality risk management allows you to use the optimal efficiency and cost of risk control and information protection measures, adequate to the current goals and objectives of the company's business. The paper presents results of solving the current problem of finding optimal methods for assessing the risks of information and cybersecurity. Criteria for selecting the best methods of risk assessment are proposed. The analysis of known methods of risk assessment for compliance with these criteria is performed. Proposals have been formulated to create promising methods for risk assessment, their application to modern information security management systems, especially those designed for critical infrastructure, will most effectively address the problems of information and cybersecurity, as well as privacy.
Cognitive Effort in the Two-Step Task: An Active Inference Drift-Diffusion Model Approach

High-level theories rooted in the Bayesian Brain Hypothesis often frame cognitive effort as the cost of resolving the conflict between habits and optimal policies. In parallel, evidence accumulator models (EAMs) provide a mechanistic account of how effort arises from competition between the subjective values of available options. Although EAMs have been combined with frameworks like Reinforcement Learning to bridge the gap between high-level theories and process-level mechanisms, relatively less attention has been paid to their implications for a unified notion of cognitive effort. Here, we combine Active Inference (AIF) with the Drift-Diffusion Model (DDM) to investigate whether the resulting AIF-DDM can simultaneously account for effort arising from both habit violation and value discriminability. To our knowledge, this is the first time AIF has been combined with an EAM. We tested the AIF-DDM on a behavioral dataset from the two-step task and compared its predictions to an information-theoretic definition of cognitive effort based on AIF. The model's predictions successfully accounted for second-stage reaction times but failed to capture the dynamics of the first stage. We argue the latter discrepancy likely stems from the experimental design rather than a fundamental flaw in the model's assumptions about cognitive effort. Accordingly, we propose several modifications of the two-step task to better measure and isolate cognitive effort. Finally, we found that integrating the DDM significantly improved parameter recovery, which could help future studies to obtain more reliable parameter estimates.
Multi-Modal Attribute Prompting for Vision-Language Models

Accepted for Publication in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
A class of quasilinear second order partial differential equations which describe spherical or pseudospherical surfaces

Second order partial differential equations which describe spherical surfaces (ss) or pseudospherical surfaces (pss) are considered. These equations are equivalent to the structure equations of a metric with Gaussian curvature $K = 1$ or $K = -1$, respectively, and they can be seen as the compatibility condition of an associated su(2)-valued or sl(2, R)-valued linear problem, also referred to as a zero curvature representation. Under certain assumptions we give a complete and explicit classiffcation of equations of the form $z_{tt} = A(z, z_x , z_t) z_{xx} + B(z, z_x , z_t ) z_{xt} + C(z, z_x , z_t)$ describing pss or ss, in terms of some arbitrary differentiable functions. Several examples of such equations are provided by choosing the arbitrary functions. In particular, well known equations which describe pseudospherical surfaces, such as the short pulse and the constant astigmatism equations, as well as their generalizations and their spherical analogues are included in the paper.
LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models

Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable when individual-level mobility and contacts mirror real behavior, while aggregates match real-world statistics like case counts or travel flows. However, collecting such fine-grained data at scale is impractical, leaving researchers with only macro-level data. LLMSynthor addresses this by turning a pretrained LLM into a macro-aware simulator that generates realistic micro-records consistent with target macro-statistics. It iteratively builds synthetic datasets: in each step, the LLM generates batches of records to minimize discrepancies between synthetic and target aggregates. Treating the LLM as a nonparametric copula allows the model to capture realistic joint dependencies among variables. To improve efficiency, LLM Proposal Sampling guides the LLM to propose targeted record batches, specifying variable ranges and counts, to efficiently correct discrepancies while preserving realism grounded in the model's priors. Evaluations across domains (mobility, e-commerce, population) show that LLMSynthor achieves strong realism, statistical fidelity, and practical utility, making it broadly applicable to economics, social science, and urban studies.
Cosmological neutrino N-body simulations of dark matter halo

The study of massive neutrinos and their interactions is a critical aspect of contemporary cosmology. Recent advances in parallel computation and high-performance computing provide new opportunities for accurately constraining Large-Scale Structures (LSS). In this paper, we introduce the TianNu cosmological N-body simulation during the co-evolution of massive neutrino and cold dark matter components via the CUBEP$^3$M code running on the supercomputer Tianhe-2 and TianNu's connected works. We start by analyzing $2.537\times10^7$ dark halos from the scientific data of TianNu simulation, and compare their angular momentum with the matched halos from neutrino-free TianZero, revealing a dependence of angular momentum modulus on neutrino injection at scales below 50 Mpc and around 10 Mpc.
On mass conservation for ice sheets

A new continuum-mechanical formulation is proposed which encompasses all material processes within and surrounding an ice sheet. Using this formulation, the balance of mass and free-surface relations for ice sheets are derived and elaborated upon. The resulting three-dimensional mass-balance relation is then integrated vertically to produce an ice-sheet quasi-mass-balance relation in the horizontal plane, and is demonstrated to reduce velocity errors in regions with high magnitude surface gradients. An analytic velocity satisfying the corrected mass-balance relation is formulated as a means to verify future numerical ice-sheet models.
Adaptive Compressed Sensing for Support Recovery of Structured Sparse Sets

This paper investigates the problem of recovering the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the fundamental limits of accurately recovering such sets through compressive measurements, while simultaneously providing adaptive support recovery protocols that perform near optimally for these classes. We show that by adaptively designing the sensing matrix we can attain significant performance gains over non-adaptive protocols. These gains arise from the fact that adaptive sensing can: (i) better mitigate the effects of noise, and (ii) better capitalize on the structure of the support sets.
AI-DRIVEN CYBERSECURITY, IOT NETWORKING, AND RESILIENCE STRATEGIES FOR INDUSTRIAL CONTROL SYSTEMS: A SYSTEMATIC REVIEW FOR U.S. CRITICAL INFRASTRUCTURE PROTECTION

This study investigates how AI-driven cybersecurity, IoT networking maturity, and resilience strategies jointly enhance the protection of industrial control systems within U.S. critical infrastructure. The problem driving this research is the accelerating convergence of ICS, IoT, and cloud architectures, which expands cyber-attack surfaces while many operators still lack integrated AI-enabled detection and resilience frameworks. The purpose of the study is to evaluate, through quantitative evidence, how AI-based intrusion and anomaly detection, secure IoT networking, and resilience engineering contribute to perceived critical-infrastructure protection effectiveness. Employing a quantitative, cross-sectional, case-based research design, the study collected data from 210 practitioners representing energy, water, transportation, and manufacturing ICS environments. Using multi-item Likert five-point scales, the study measured six key variables: AI-driven cybersecurity capability, IoT networking maturity, ICS resilience strategies, governance maturity, behavioral compliance, and perceived protection effectiveness. Descriptive statistics, Pearson correlations, and multiple regression modeling were used to test the proposed conceptual relationships. Findings show robust adoption of AI-driven security (mean = 3.82) and resilience strategies (mean = 3.74), with all constructs demonstrating high reliability (α = .87–.92). Regression analysis revealed that AI capability strongly predicts resilience (β = 0.38, p &lt; .001) and, together with resilience, significantly improves perceived protection (β = 0.34 and β = 0.37, p &lt; .001). IoT networking maturity also positively affects AI adoption (β = 0.23, p &lt; .001). The study concludes that AI adoption is most effective when embedded in mature IoT security architectures and formal resilience programs. Implications highlight the need for integrated AI–IoT–resilience strategies as core pillars of national critical-infrastructure protection.
Dispute settlement system under the World Trade Organization and borderlines of applicable law

The question of applicable law is of essence in discussing access to justice and legal protection under the WTO dispute settlement system because it determines which disputes are suitable for resolving under the WTO framework. The WTO dispute settlement system is primarily oriented towards protection of rights under the covered agreements. One of the central problem in this discussion is the competence of the WTO judicial bodies (panels and the Appellate Body) to resolve disputes concerning rights and obligations that are not regulated by the covered agreements. According to the rulings of the WTO judicial bodies, they only have jurisdiction to examine covered agreements as the only relevant and applicable law in dispute settlement proceedings. Some scholars support a strict conception of the WTO judicial bodies' jurisdiction pertaining only to the WTO law. Some other authors suggest a more flexible approach, given that the WTO is neither 'a closed legal circuit' nor a self-contained regime. As a branch or a sub-system of Public International Law, the WTO has to interact with other branches or subsystems, such as human rights treaties and international environmental protection agreements. Many scholars and experts insist that the WTO bodies must take into account the 'new dimensions of international trade', like environmental protection and human rights. Hence, the rules of free trade cannot be legally and politically excommunicated from general international law. Nevertheless, this assertion must be taken with caution: it would be the only way for ensuring protection of security and predictability of the multilateral trading system under the WTO. Therefore, it is important to establish clear borderlines and situations in which interaction with extrinsic sources of law is necessary.
Childhood maltreatment and mental health: causal links to depression, anxiety, non-fatal self-harm, suicide attempts, and PTSD

Background: This study aims to elucidate the causal relationship between childhood maltreatment (CM) and subsequent mental health outcomes, including major depressive disorder (MDD), anxiety (ANX), post-traumatic stress disorder (PTSD), suicide attempts, and non-fatal self-harm. Utilising Mendelian Randomisation (MR) and genome-wide association studies (GWAS) data from individuals of European descent, this research applies a rigorous analytical methodology to large-scale datasets, overcoming the confounding variables inherent in previous observational studies.Methods: Genetic data were obtained from publicly available GWAS on individuals of European ancestry, focusing on Childhood Maltreatment (CM), Major Depressive Disorder (MDD), Anxiety (ANX), Post-Traumatic Stress Disorder (PTSD), Age at First Episode of Depression, Number of Depression Episodes, Non-fatal self-harm, and Suicide Attempts. Mendelian Randomisation (MR) analyses were conducted to investigate the causal impact of CM on these outcomes. Sensitivity analyses included IVW, MR Egger, WM, and MR-PRESSO. FDR corrections were applied to account for multiple testing. Results were presented as odds ratios (ORs) with confidence intervals (CIs).Results: Significant associations were identified between CM and the likelihood of developing MDD (IVW: OR = 2.28, 95% CI = 1.66-3.14, PFDR < .001), ANX (IVW: OR = 1.01, 95% CI = 1.00-1.02, PFDR =.032), and PTSD (IVW: OR = 2.29, 95% CI = 1.43-3.67, PFDR =.001). CM was also linked to increased non-fatal self-harm (IVW: OR = 1.06, 95% CI = 1.04-1.08, PFDR <.001), higher frequency of depressive episodes (IVW: β=0.31, 95% CI = 0.17-0.46, PFDR <.001), and earlier onset of depression (IVW: β=-0.17, 95% CI = -0.32 to - 0.02, PFDR =.033). No significant association was found between CM and suicide attempts (IVW: OR = 1.09, 95% CI = 0.81-1.45, PFDR =.573).Conclusion: This study provides robust evidence that CM is a significant causal factor for MDD, ANX, PTSD, and non-fatal self-harming behaviours. It is associated with a higher frequency of depressive episodes and earlier onset of depression. These findings highlight the need for early intervention and targeted prevention strategies to address the long-lasting psychological impacts of CM.