ethicalabs/Research-Intent-Collab huggingface dataset:
Right to health, essential medicines, and lawsuits for access to medicines – A scoping study

Despite countries' efforts to ensure access to essential medicines, some people do not have their needs met, and often resort to the Judiciary to get access to the medicines they need. This phenomenon, known as "judicialization of access to medicines", has aroused the academia's interest in law, health and social fields. In this context, this scoping study investigates, through qualitative thematic analysis, the approach to judicialization of access to medicines (normative or social) and its possible impacts (positive or negative) described in articles published in scientific journals indexed in the main health databases prior to July 2012. 65 of 384 papers met the inclusion criteria of focusing on lawsuits for access to medicines or judicialization of access to medicines as a phenomenon; empiric studies, review articles or theoretical discussions, written in English, Portuguese or Spanish; most of them were about Brazil, Colombia and England. Results show that judicialization is a complex phenomenon that involves technical-scientific, legal and social aspects. The judicialization impacts mentioned have changed over time. In the late 1990s and early 2000s the emphasis of positive impacts predominated both on the normative and social approaches, having as main reference the movements that claimed from the States the guarantee of access to HIV/AIDS treatment. In the mid-2000s, however, there was an emphasis of the negative effects of judicial intervention, when lawsuits for access to medicines became a problem in some countries. Few studies used the social approach to judicialization. For this reason, there is not enough information about whether lawsuits for access to medicines are related to a real recognition of the right to health as an exercise of citizenship. Such aspects need to be further studied.
The Relationship between Leadership and Communication Skills of Nurse Managers and the Organizational Citizenship Behaviors of Medical-Surgical Nurses and Nursing Assistants

This quantitative research study, with a correlation design, used an online survey to determine if a positive correlation existed between the leadership and communication skills of nurse managers and the organizational citizenship behaviors of the nurses and nursing assistants who work on the medical-surgical nursing units of a community hospital in Northern New Jersey. The predictor variables were the employee’s perceptions of their nurse manager leadership and communication skills and the self-reported organizational citizenship behaviors of the registered nurses and nursing assistants. Results demonstrated a moderately positive, statistically significant correlation, r .35 and p value .000, between the leadership and communication skills of the nurse manager and the self-reported organizational citizenship behaviors of the registered nurses and nursing assistants. A leadership implication from this study is that nurse managers who have effective leadership and communication skills have a positive impact on the organizational citizenship behaviors of the nursing team.
A Topological Data Analysis Framework for Automated Equity Trading using Persistent Homology, Betti Numbers, and Laplacian Diffusion

Financial markets exhibit highly non-linear, complex dynamics that resist effective characterization by conventional time-series methods. This study introduces and evaluates a fully automated equity trading system grounded in topological data analysis (TDA), a branch of mathematics that quantifies the shape and connectivity of high-dimensional data. The system constructs a sliding-window embedding of S&amp;amp;P 500 price data into point clouds, applies the Vietoris-Rips filtration to produce a nested sequence of simplicial complexes, and computes persistent homology to extract Betti numbers-specifically β 0 (connected components) and β 1 (independent loops)-as proxies for market regime and complexity. A graph Laplacian operator is then applied to the market correlation network to diffuse these topological signals and suppress noise before they are supplied to a Ridge regression model that generates directional trading signals. Backtested on daily equity data spanning January 2021 through early 2026, the Topo + Ridge strategy achieved a cumulative return of approximately 80%, substantially outperforming passive benchmark investment during the same interval. Periods of elevated Betti-0 persistence sum were found to correlate strongly with market stress and high volatility, validating the topological regime-detection hypothesis. These results suggest that persistent homology provides a geometrically meaningful and statistically informative characterization of equity market structure that can be exploited by supervised regression models to generate alpha.
Implementing Site Reliability Engineering (SRE) in Legacy Retail Infrastructure

During digital transformation, retail companies with legacy IT infrastructures struggle to maintain service dependability, scalability, and agility. Many mainframes, on-premise applications, batch processing processes, and monolithic codebases were not designed for today's dynamic operational contexts. Google-developed Site Reliability Engineering (SRE) approaches including Service Level Objectives (SLOs), automation, and blameless postmortems can bridge the gap between outdated systems and modern operational excellence. This article proposes gradual adoption, cultural change, and measurable service reliability improvements for legacy retail environments adopting SRE. A concentrated SRE rollout helped a national retail chain reduce toil and improve mean time to detect (MTTD), mean time to resolve (MTTR), and MTTR. The model shows that incremental SRE adoption can modernize legacy systems and prepare them for future innovation without comprehensive re-architecture.
De novo protein design by inversion of the                     <scp>AlphaFold</scp>                     structure prediction network

Abstract                   De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence‐structure space. AlphaFold2 (AF2), a state‐of‐the‐art neural network architecture, achieved remarkable accuracy in predicting protein structures from amino acid sequences. This raises the question whether AF2 has learned the principles of protein folding sufficiently for de novo design. Here, we sought to answer this question by inverting the AF2 network, using the prediction weight set and a loss function to bias the generated sequences to adopt a target fold. Initial design trials resulted in de novo designs with an overrepresentation of hydrophobic residues on the protein surface compared to their natural protein family, requiring additional surface optimization. In silico validation of the designs showed protein structures with the correct fold, a hydrophilic surface and a densely packed hydrophobic core. In vitro validation showed that 7 out of 39 designs were folded and stable in solution with high melting temperatures. In summary, our design workflow solely based on AF2 does not seem to fully capture basic principles of de novo protein design, as observed in the protein surface's hydrophobic vs. hydrophilic patterning. However, with minimal post‐design intervention, these pipelines generated viable sequences as assessed experimental characterization. Thus, such pipelines show the potential to contribute to solving outstanding challenges in de novo protein design.
The drug resistance index as an indicator for the impact of antimicrobial stewardship program implementation

To assess the changes in the drug resistance index (DRI), a composite measure that combines the level of resistance of individual pathogens with the extent of antimicrobial drugs consumption in a multidisciplinary hospital, after implementation of the antimicrobial stewardship program (AMSP) and during COVID-19 pandemic.
Support Recovery in Mixture Models with Sparse Parameters

Mixture models are widely used to fit complex and multimodal datasets. In this paper we study mixtures with high dimensional sparse latent parameter vectors and consider the problem of support recovery of those vectors. While parameter learning in mixture models is well-studied, the sparsity constraint remains relatively unexplored. Sparsity of parameter vectors is a natural constraint in variety of settings, and support recovery is a major step towards parameter estimation. We provide efficient algorithms for support recovery that have a logarithmic sample complexity dependence on the dimensionality of the latent space. Our algorithms are quite general, namely they are applicable to 1) mixtures of many different canonical distributions including Uniform, Poisson, Laplace, Gaussians, etc. 2) Mixtures of linear regressions and linear classifiers with Gaussian covariates under different assumptions on the unknown parameters. In most of these settings, our results are the first guarantees on the problem while in the rest, our results provide improvements on existing works.
Multiresolution quantum chemistry in multiwavelet bases: time-dependent density functional theory with asymptotically corrected potentials in local density and generalized gradient approximations

A multiresolution solver for fully numerical linear response calculations of excitation states via the time-dependent Hartree–Fock and density functional theory (TD-HF/DFT) is presented. The linear response method Yanai et al. previously reported [J. Chem. Phys., submitted] was limited to the Tamm–Dancoff approximation and could only use the Hartree–Fock exchange and the local-spin density approximation (LSDA) with a crude asymptotic correction. The present development enables us to perform full TD-HF/DFT calculations employing generalized gradient approximation (GGA) exchange-correlation potentials as well as hybrid ones. The linear response of TD-HF/DFT is computed by means of iteratively solving the coupled integral equations with the Green's functions. In this study, Tozer and Handy's asymptotic correction (AC) is applied to existing DFT exchange-correlations, and is found numerically stable and efficient. Furthermore, the new hybrid exchange-correlation functional CAM-B3LYP, which was recently propos...
Business ethics and corporate social responsibility – a case study

A key nexus that characterizes the contemporary corporate landscape is the strong connection between business ethics and corporate social responsibility. This article explores the dynamic relationship between these two ideas, showing how closely related and supportive they are of one another. Corporate social responsibility efforts are built on a foundation of business ethics, which encompasses moral principles and values. On the other hand, corporate social responsibility strengthens and improves ethical standards within corporations. The ethical underpinning of corporate social responsibility is examined in this article, focusing on how moral principles direct ethical business behavior. It emphasizes the stakeholder-centric methodology shared by corporate social responsibility and business ethics, emphasizing their shared dedication to fairness, openness, and responsibility. Transparency and accountability emerge as crucial components to build confidence and credibility in ethical behavior and corporate social responsibility initiatives. Additionally, ethical conundrums within CSR activities are investigated, demonstrating how businesses navigate complex ethical issues when addressing societal and environmental challenges. The commitment to sustainability and societal well-being made by corporate social responsibility and the long-term ethical perspective inherent in business ethics are in perfect harmony. Finally, it is clarified how effectively ethical conduct and corporate social responsibility work together to enhance a company's reputation and inspire trust. Understanding the profound relationship between business ethics and corporate social responsibility is crucial for organizations seeking to thrive when moral responsibility is paramount as society increasingly holds businesses accountable for their social and environmental impact.
Convergence analysis of a discontinuous Galerkin/Strang splitting approximation for the Vlasov--Poisson equations

A rigorous convergence analysis of the Strang splitting algorithm with a discontinuous Galerkin approximation in space for the Vlasov--Poisson equations is provided. It is shown that under suitable assumptions the error is of order $\mathcal{O}(τ^2+h^q +h^q / τ)$, where $τ$ is the size of a time step, $h$ is the cell size, and $q$ the order of the discontinuous Galerkin approximation. In order to investigate the recurrence phenomena for approximations of higher order as well as to compare the algorithm with numerical results already available in the literature a number of numerical simulations are performed.
Getting Scrappy in the Classroom During COVID-19

This teaching reflection, co-authored by two librarians and three instructors, offers a case study in collaborative assignment design and argues for the value of both collaboration as an instructional model and digital exhibitions as open educational resources. It explores how the transition to remote curating, learning, and teaching prompted by COVID-19 occasioned changes in how we curated exhibitions, on the one hand, and developed learning opportunities for students, on the other hand. Focused on a digital exhibition of nineteenth-century scrapbooks and the integration of scrapbooking—as a hands-on activity and a topic of scholarly inquiry—into three courses across two disciplines (English and art history), it also provides a model of how librarians and instructors might collaborate on assignment and coursedevelopment and scaffold such collaboration into assignment and course design. The reflection includes assignments and rubrics as well as examples of students’ work. It concludes with a series of recommendations for librarians and instructors who wish to collaborate.
EXPERIMENTAL OPTIMIZATION OF A CONTINUOUS-FLOW REACTIVE DISTILLATION REACTOR FOR BIODIESEL PRODUCTION

A comprehensive study of biodiesel preparation from canola oil was performed on a continuous-flow reactive  distillation (RD) reactor system. Optimization of six process variables was studied experimentally and analyzed statistically  on the overall performance of the RD reactor system. These variables include the feed methanol to triglycerides molar ratio,  reaction time, reboiler temperature, catalyst concentration, methanol circulation mode, and catalyst formulation. An  experimental design was used in the experiments, and statistical multiple response regression models were employed for  process optimization. Under the operating conditions explored, product yields ranged from 41.5% to 94.9%, productivity  ranged from 16 to 55.8 kmol/m3·h (5.6 to 19.5 m3/m3·h), and soap formation varied from 4.44 to 29.1 mol/100 mol (0.19 to  1.27%wt.). For different optimization criteria, the following optimum variable ranges were found: feed molar ratio from  3.65:1 to 4.50:1, reaction time from 3.76 to 5.56 min, reboiler temperature from 100°C to 130°C, and catalyst concentration  from 0.13 to 0.24 mol/mol. Although the process variables individually affected the system performance to a certain extent,  the interactive effect of the process variable combinations affected the system efficiency more significantly. When maximized,  the product yields and productivity were 98.8% and 55.6 kmol/m3·h (18.5 m3/m3·h), respectively. However, when soap  formation was minimized, the yield and productivity were 72% and 9.3 kmol/m3·h (3.1 m3/m3·h), respectively. It is  recommended that the optimization of the RD reactor system be based on the maximization of product yield and reactor  productivity.
The Role of Tanzanian Academic Libraries in Promoting Scholarly Communication through Open Access: A Literature Review

This study examined the role of Tanzanian academic libraries in promoting scholarly communication through open access. A desktop documentary review was used to specifically study the status of open access platforms, ways of promoting open access scholarly communication, and factors deterring academic libraries from fostering scholarly communication through open access. The desktop documentary review involved seven university repositories purposely selected from the directory of institution repositories (OpenDOAR). Quantitative and qualitative survey designs on the open access repositories were used. The information associated with the university repositories was gathered from their respective web pages. A documentary review of the studies on open access scholarly communication was conducted to gain insights into the phenomenon under study. The surveyed data were both quantitatively and qualitatively analysed. Quantitative data were analysed using Microsoft Excel, a computer application software, whereas qualitative data were subjected to the content analysis method. The findings indicate that the surveyed repositories were operational and guided by policy, whereas library websites, repositories and digital libraries are used in promoting open access scholarly communication. However, the factors that deter academic libraries from promoting open access scholarly communication are related to financial, infrastructure, human resources or expertise and university management. The study recommends that Tanzanian universities improve planning for open access, commitment, support and engagement in the training and retraining of its practitioners for effective scholarly communication.
Stem cells in regenerative medicine: Unlocking therapeutic potential through stem cell therapy, 3D bioprinting, gene editing, and drug discovery

Stem Cells (SCs) have become potentially instrumental in addressing many human diseases such as cancer, diabetes, age-related diseases and tissue defects. The unique ability of SCs to multiply indefinitely and differentiate into various cell types makes them invaluable in regenerative medicine and treatment. Regenerative medicine is an advancing field that focuses on restoring tissue and organ function in individuals with severe injuries and chronic illnesses. Pluripotent cells, capable of adopting roles from any of the three germ layers, exhibit exceptional versatility and are promising for a wide range of medical conditions. They also offer a solution to limitations posed by animal models in understanding specific disorders. Recent breakthroughs have shown that combining SCs with cutting-edge technologies like 3D bioprinting and 3D culture systems can revolutionize tissue engineering and organ regeneration. 3D bioprinting allows precise construction of complex tissue structures, bringing us closer to recreating functional organs for transplantation. Moreover, the integration of SCs with gene editing techniques presents unprecedented opportunities for precise genetic modification, correcting disease-causing mutations and opening avenues for personalized therapies. In addition, SCs play an important role in drug discovery and testing, serving as valuable models for studying disease mechanisms and screening potential therapeutic biomolecules. This paper provides a comprehensive exploration of SCs, transcription factors, diverse therapeutic applications of these cells as well as their role in the fields of tissue engineering, 3D bioprinting, 3D culture systems, gene editing, disease modeling, and drug discovery and testing.
Adolescence, Social Media and Mental Health

Adolescence is the phase which helps with the transition of an individual into an adult. In this phase, many physical and psychological developments including hormonal changes occur which further makes adolescents more vulnerable to insecurity, mental health disorders and suicide. In recent times, mental health issues have become a major health concern in many countries, developed and developing alike. Social media has become an indispensable aspect of young people's digital interactions, as they use it mostly for entertainment and communication purposes. Consequently, it has the potential to have both positive and negative effects on them. Deterioration in mental health is one of the side effects stemming from social media overuse.
A Study on Data Analysis for Production Schedule Adherence: Root Cause Investigation

Production Schedule Adherence (PSA) plays a significant role in achieving the efficiency of operations and on time delivery of products in the manufacturing industries. Effective scheduling ensures optimum utilization of the available resources, minimization of delays and increase of productivity of the organization. Machine failure, unavailability of raw materials, shortage of manpower, inefficient planning and improper quality control may delay and disrupt the production. This study focuses on identifying the key factors of Production Schedule Adherence using quantitative research. Data from 150 respondents were collected using structured questionnaire on a 5-point Likert scale. Reliability analysis, descriptive statistics, normality test, Spearman’s Rank Correlation, Multiple regression analysis, Pareto analysis and Five Whys analysis were used to analyze the effect of the various factors on PSA. Keywords: Production Schedule Adherence, Manufacturing Efficiency, Machine Breakdown, Preventive Maintenance, Production Planning, Quality Management.
Using Natural Language Processing to Predict Risk in Electronic Health Records

Clinical narratives recording behaviours and emotions of patients are available from EHRs in a forensic psychiatric centre located in Tasmania. This rich data has not been used in risk prediction. Prior work demonstrates natural language processing can be used to identify patient symptoms in these free-text records and can then be used to predict risk. Four dictionaries containing descriptive words of harm were created using the Diagnostic and Statistical Manual of Mental Disorders, the Unified Medical Language System repository, English negative and positive sentiment words, and high-frequency words from the Corpus of Contemporary American English. However, a model based only on these keywords is limited in predictive power. In this study, we introduce an improved NLP approach with a social interaction component to extract additional information about the behavioural and emotional state of patients. These social interactions are subsequently used in a machine-learning model to enhance risk prediction performance.