ethicalabs/Research-Intent-Collab huggingface dataset:
CHAPTER 13. The European Union's military engagement in the fight against smuggling at sea and the role of the United Nations Security Council.

Abstract: In order to fight against migrant smugglers in the Mediterranean, the European Union has established Operation Sophia, empowered to adopt measures involving the use of force. The United Nations Security Council, exercising its powers in the field of the maintenance of international peace and security, has authorised the (partial) implementation of the Operation in an atypical manner: the smuggling of migrants off the coast of Libya has not been explicitly qualified as a threat to international peace and the formula «all necessary measures» has been replaced by the formula «all means commensurate to the specific circumstances». The prudent approach taken by the Security Council in the determination of both the circumstances justifying the authorisation to use force and the extent of the authorised coercive powers can be explained by the scenario the Council is faced with: the repression of an illicit activity which takes place at sea and involves migrants, thus requiring a balance between the law of the sea, the human rights law and collective security.
Survival Prediction in Pancreatic Cancer: Insights and Limitations from Clinico-Genomic Integration

This repository contains the complete code, data, and results for a research project on pancreatic cancer tumor grade prediction. Somatic mutation data and clinical features from TCGA (PAAD cohort) and ICGC (PACA-AU, PACA-CA, PAEN-AU, PAEN-IT cohorts) were integrated to predict tumor grade (high vs low) in approximately 800 patients (75% train / 25% test). The pipeline includes: raw data preparation from MAF files, feature selection with MICE imputation, Cox Proportional Hazard analysis, nomogram building, SMOTE balancing, Kaplan-Meier survival plots, paper figure generation, and machine learning classification (10 models &times; 5 survival time points). Two gene mutation encodings are compared throughout: Mutation Impact (Low/High) and Mutation Status (Absent/Present). The repository is organised into 9 step-by-step folders (Steps 0&ndash;8), each with its own README. Please read MASTER_README.md first for full instructions.
Digital Privacy vs. State Surveillance: Balancing Fundamental Rights in Cyber Investigations

Escalating cybercrime pressure and ubiquitous digitization have driven governments to expand surveillance and investigative powers, often faster than privacy safeguards evolve. This article examines how democratic legal systems can balance digital privacy and state surveillance during cyber investigations without undermining due process or the integrity of evidence. Using a mixed-methods study—semi-structured interviews with practitioners (n = 27) and an organizational survey (n = 256) conducted in 2024–2025—we test three hypotheses: (H1) clear warrant standards and judicial oversight correlate with faster, court-sustained access to e-evidence; (H2) time-bound preservation orders and standardized provider attestations reduce evidentiary exclusion; and (H3) narrow, necessary, and proportionate bulk-capable powers are more likely to withstand constitutional and human-rights review. We triangulate our primary dataset with recent doctrinal baselines: Carpenter v. United States (CSLI warrant rule), Riley v. California (cell-phone search warrants), ECtHR’s Big Brother Watch (bulk interception safeguards), the EU’s E-Evidence framework, the UK’s Investigatory Powers (Amendment) Act 2024, India’s Digital Personal Data Protection Act 2023, and India’s draft Telecom Interception Rules 2024. Results suggest that predictable warrant standards, prompt preservation, and cryptographically signed provider logs materially improve prosecutorial outcomes without measurable harm to investigative timelines. We propose a policy blueprint—the Four Anchors—combining (1) necessity/proportionality tests, (2) standardized e-evidence integrity, (3) expedited but reviewable cross-border access, and (4) transparent oversight. These measures align with evolving constitutional jurisprudence and multilateral instruments, enabling effective cyber enforcement while preserving fundamental rights.
Can’t Buy Me Health-Care Access: Qualitative Experiences of U.S.-Born Latinx Adults’ Health Insurance Coverage and Health-Care Use Post ACA

Latinx persons have lower levels of health insurance coverage than other racial and ethnic groups even after passage of the Patient Protection and Affordable Care Act (ACA). Using 182 interviews from the American Voices Project, this study examines how U.S.-born Latinx adults experience health-care coverage and health-care use. Interview data demonstrate that health-care access is insufficient to ensure full health-care use. Health-care use costs are so high that they are insurmountable for Latinx Americans. Wealth and liquid assets constrain and are constrained by health-care use. Family members become a safety net. This study can inform policies and programs aiming to improve equity in Latinx individuals’ health-care access by centering the importance of reducing economic costs of health-care use.
Questioning the right to privacy and data protection in light of biometric surveillance

A aceleração tecnológica do capitalismo digital tem como um de seus elementos a ênfase na coleta de dados ― em especial, dados pessoais ― como matéria-prima para a operação de algoritmos computacionais. Em meio a tal fenômeno, percebe-se uma crescente coleta de informações referentes a características corporais humanas por meio de tecnologias biométricas como o reconhecimento facial. Neste contexto,&nbsp; o presente artigo busca analisar o fenômeno da digitalização do corpo humano, pelo referencial teórico dos estudos de vigilância e da biopolítica. Observa-se uma objetificação do corpo pelas tecnologias biométricas, fenômeno este que serve aos interesses de Estado, como a segurança pública, bem como à lógica do mercado de comodificação de informações pessoais. Com este percurso teórico, passa-se a uma breve análise de dois casos judiciais que envolvem o metrô de São Paulo, que consistiram no uso intrusivo de reconhecimento facial de seus usuários para fins publicitários e de segurança pública. O exame dos casos possibilita a demonstração da necessidade de instrumentos jurídicos de proteção aos dados pessoais, bem como os limites de uma tutela puramente jurídica fundada na privacidade.
Superconvergent Discontinuous Galerkin Method for the Scalar Teukolsky Equation on Hyperboloidal Domains: Efficient Waveform and Self-Force Computation

The long-time evolution of extreme mass-ratio inspiral systems requires minimal phase and dispersion errors to accurately compute far-field waveforms, while high accuracy is essential near the smaller black hole (modeled as a Dirac delta distribution) for self-force computations. Spectrally accurate methods, such as nodal discontinuous Galerkin (DG) methods, are well suited for these tasks. Their numerical errors typically decrease as $\propto (Δx)^{N+1}$, where $Δx$ is the subdomain size and $N$ is the polynomial degree of the approximation. However, certain DG schemes exhibit superconvergence, where truncation, phase, and dispersion errors can decrease as fast as $\propto (Δx)^{2N+1}$. Superconvergent numerical solvers are, by construction, extremely efficient and accurate. We theoretically demonstrate that our DG scheme for the scalar Teukolsky equation with a distributional source is superconvergent, and this property is retained when combined with the hyperboloidal layer compactification technique. This ensures that waveforms, total energy and angular-momentum fluxes, and self-force computations benefit from superconvergence. We empirically verify this behavior across a family of hyperboloidal layer compactifications with varying degrees of smoothness. Additionally, we show that self-force quantities for circular orbits, computed at the point particle's location, also exhibit a certain degree of superconvergence. Our results underscore the potential benefits of numerical superconvergence for efficient and accurate gravitational waveform simulations based on DG methods.
Human-Centric AI Governance: An Adaptive Public International Law Framework for Ethical and Inclusive AI Regulation in Public Health

Abstract                   Artificial Intelligence (AI) is transforming public health, presenting both opportunities and ethical and legal challenges. This study adopts an interdisciplinary approach, integrating legal doctrinal analysis, public health ethics, AI governance scholarship and a scoping review of international legal instruments to evaluate and operationalize three core pillars: ethical accountability, regulatory adaptability and transparency. Through a scoping review of treaties, regional regulations and policy frameworks, the study maps jurisdictional gaps and proposes an adaptive public law framework that addresses critical shortcomings in existing AI governance models, such as the WHO’s limited enforceability and the GDPR’s rigid data-sharing rules. The framework introduces scalable, region-specific regulations to enhance interoperability while respecting local governance needs. Its human-centric design, modular regulation and accountability mechanisms ensure adaptability across diverse legal, cultural and health system contexts. Informed by case studies and a thematic synthesis of global best practices, this framework offers policymakers and practitioners a structured yet flexible approach to balancing AI-driven innovation with ethical imperatives, promoting equitable public health outcomes.
Observed and Projected Future Changes in Climate and Extremes in a Himalayan Watershed Based on CMIP6 Model Outputs

Abstract                                        Hydroclimatic extremes are intensifying across the eastern Himalaya, yet watershed-scale evidence for adaptation planning remains limited. This study evaluates historical (1980–2020) and projected (2021–95) temperature and precipitation extremes in the Dudhkoshi watershed, Nepal, using daily data from nine precipitation and two temperature stations. Eleven precipitation and twelve temperature indices were computed using ClimPACT2 at annual and seasonal scales. Future changes were evaluated for near- (2021–45), mid- (2046–70), and far-future (2071–95) periods under shared socioeconomic pathway (SSP) 2-4.5 and SSP5-8.5 scenarios using a selected multi–global climate model (GCM) ensemble. GCM outputs were bias-corrected using a Bernoulli–Weibull distribution transformation for precipitation and nonparametric quantile mapping with robust empirical quantiles (tricub) for temperature. Observations indicate significant warming in the Middle Mountain (MM) and High Mountain regions, characterized by increasing hot days, declining cold days, and a rise in consecutive dry days (+0.68 days yr                     −1                     ), alongside reductions in wet-day totals and heavy-rainfall indices at multiple stations. Projections indicate continuous warming across all periods, strongest under SSP5-8.5. By the late century, annual maximum and minimum temperatures in the MM are projected to increase by up to 3.34° and 4.58°C, respectively, alongside an overall increase in precipitation. Monsoon and postmonsoon heavy extremes will most likely intensify mostly in MM (Rx1day + 0.44 mm yr                     −1                     ; Rx5day + 1.69 mm yr                     −1                     ), with premonsoon drying under SSP2-4.5 and amplified winter precipitation in the High Himalaya under SSP5-8.5. Ultimately, the MM region emerges as the most climate-sensitive zone due to compounding warming and hydroclimatic shifts, highlighting the need for physiography- and season-specific adaptation strategies to enhance resilience in Himalayan watersheds.
Quality of Reporting of Key Methodological Items of Randomized Controlled Trials in Clinical Ophthalmic Journals

To evaluate the reporting quality of key methodological items in randomized controlled trials (RCTs) in four general clinical ophthalmology journals.The reporting of 11 key methodological items in RCTs published in American Journal of Ophthalmology, Archives of Ophthalmology, British Journal of Ophthalmology and Ophthalmology in the year 2005 was assessed.Sixty-seven eligible RCTs were assessed and the mean number of items reported was 6.3 per RCT. No significant difference in the mean number of items reported was found between the four journals (P=0.20). The most frequently reported item was ethics approval and informed consent (97.0%), followed by masking status (85.1%), description of withdrawals (76.1%), adverse events (73.1%), and intention-to-treat analysis (71.6%). Details on sequence generation, randomization restriction, allocation concealment, allocation implementation, patient flow diagrams, and sample size calculation were reported in <50% of the RCTs assessed. Both sample size and page length of the RCTs correlated with the number of methodological items reported (P=0.024 and P=0.008, respectively).Similar to other specialties, rooms for improvement exist in the reporting of key methodological items of RCTs in clinical ophthalmic journals. Stricter adoption of the CONSORT statement might enhance the reporting quality of RCTs in ophthalmic journals.
Autonomous quantum error correction and quantum computation

In this work, we present a general theoretical framework for the study of autonomously corrected quantum devices. First, we identify a necessary and sufficient revised version of the Knill-Laflamme conditions for the existence of an engineered Lindbladian providing protection against at most $c$ consecutive errors of natural dissipation, giving rise to an effective logical decoherence rate suppressed to order $c$. Moreover, we demonstrate that such engineered dissipation can be combined with generalized realizations of error-transparent Hamiltonians (ETH) in order to perform a quantum computation in the logical space while maintaining the same degree of suppression of decoherence. Finally, we introduce a formalism predicting with precision the emergent dynamics in the logical code space resulting from the interplay of natural, engineered dissipations sources and the generalized ETH.
Implementing Antibiotic Practice Guidelines through Computer-Assisted Decision Support: Clinical and Financial Outcomes

To determine the clinical and financial outcomes of antibiotic practice guidelines implemented through computer-assisted decision support.Descriptive epidemiologic study and financial analysis.520-bed community teaching hospital in Salt Lake City, Utah.All 162 196 patients discharged from LDS Hospital between 1 January 1988 and 31 December 1994.An antibiotic management program that used local clinician-derived consensus guidelines embedded in computer-assisted decision support programs. Prescribing guidelines were developed for inpatient prophylactic, empiric, and therapeutic uses of antibiotics.Measures of antibiotic use included timing of preoperative antibiotic administration and duration of postoperative antibiotic use. Clinical outcomes included rates of adverse drug events, patterns of antimicrobial resistance, mortality, and length of hospital stay. Financial and use outcomes were expressed as yearly expenditures for antibiotics and defined daily doses per 100 occupied bed-days.During the 7-year study period, 63 759 hospitalized patients (39.3%) received antibiotics. The proportion of the hospitalized patients who received antibiotics increased each year, from 31.8% in 1988 to 53.1% in 1994. Use of broad-spectrum antibiotics increased from 24% of all antibiotic use in 1988 to 47% in 1994. The annual Medicare case-mix index increased from 1.7481 in 1988 to 2.0520 in 1993. Total acquisition costs of antibiotics (adjusted for inflation) decreased from 24.8% ($987,547) of the pharmacy drug expenditure budget in 1988 to 12.9% ($612,500) in 1994. Antibiotic costs per treated patient (adjusted for inflation) decreased from $122.66 per patient in 1988 to $51.90 per patient in 1994. Analysis using a standardized method (defined daily doses) to compare antibiotic use showed that antibiotic use decreased by 22.8% overall. Measures of antibiotic use and clinical outcomes improved during the study period. The percentage of patients having surgery who received appropriately timed preoperative antibiotics increased from 40% in 1988 to 99.1% in 1994. The average number of antibiotic doses administered for surgical prophylaxis was reduced from 19 doses in the base year to 5.3 doses in 1994. Antibiotic-associated adverse drug events decreased by 30%. During the study, antimicrobial resistance patterns were stable, and length of stay remained the same. Mortality rates decreased from 3.65% in 1988 to 2.65% in 1994 (P < 0.001).Computer-assisted decision support programs that use local clinician-derived practice guidelines can improve antibiotic use, reduce associated costs, and stabilize the emergence of antibiotic-resistant pathogens.
Optimizing Experimental Calibration in Continuous Variable Quantum Key Distribution to Minimize Excess Noise

Continuous Variable Quantum Key Distribution allows the communication of bit strings between two parties using quantum coherent states and heterodyne detection. This method promises a low-cost implementation and high performance results compared to discrete variable protocols as it uses standard telecommunication technology, such as Software Defined Radio (Universal Software Radio Peripheral), Mach-Zehnder modulators and balanced detectors. It has been proven to be secure against collective and general attacks and its main advantage when using free space channels is that coherent detection is less affected by solar background photons which limits daylight transmission in the discrete-variable counterpart.
Quality Management Model Based on Lean Six Sigma for Reducing Returns of Defective Clothing Articles in SMEs from the Clothing Industry

Even though there is a high demand for clothing articles in the market, they do not meet quality standards. This can be appreciated after the clothes are washed and dried and the study revealed that over 20% of the items were returned each month. This occurs when the acquisition of raw materials is focused exclusively on the price. Thus, there are no requirements for compliance with any technical specifications that would enable the production process to be equipped with quality raw materials. Additionally, the absence of control over the quantity of consumables based on demand generates reprocessed inventory, thus damaging the fabric. Therefore, the proposed model was made through the Lean Six Sigma method to reduce waste and meet client requirements. The results were a decrease in returns by over 16.5% and an increase in productivity by 28.5%.
High parasite prevalence driven by the human-animal-environment interface: A One Health Study in an urban area in southern of Chile

Dataset associated to this research article
Intricate regulation on epigenetic stability of the subtelomeric heterochromatin and the centromeric chromatin in fission yeast

In eukaryotes, the integrity of chromatin structure and organization is crucial to diverse key cellular processes from development to disease avoidance. To maintain the cell identity through mitotic cell generations, the genome (the genomic DNA sequence) as well as the epigenome (pertaining various forms of epigenetic information carriers, such as histone modifications, nucleosome positioning and the chromatin organization) is inherited with high fidelity. In comparison to the wealth of knowledge on genetic stability, we know much less on what may control the accuracy of epigenetic inheritance. In our recent work in the fission yeast Schizosaccharomyces pombe, by quantifying the epigenetic fidelity of CENP-A/Cnp1 or H3K9me2 nucleosome inheritance through cell divisions, we demonstrated that Ccp1, a homolog of histone chaperone Vps75 in budding yeast, participates in the modulation of centromeric nucleosomal epigenetic stability as well as proper heterochromatin organization. In this essay, we focus on discussing the uniquely high dynamicity of the subtelomeric heterochromatin regions and the complex mechanisms regulating epigenetic stability of centromeric chromatin.
Ammonium Ion Batteries: Material, Electrochemistry and Strategy

AbstractAmmonium‐ion batteries (AIBs) have recently attracted increasing attention in the field of aqueous batteries owing to their high safety and fast diffusion kinetics. The NH4+storage mechanism is quite different from that of spherical metal ions (e.g. Li+, Na+, K+, Mg2+, and Zn2+) because of the formation of hydrogen bonds between NH4+and host materials. Although many materials have been proposed as electrode materials for AIBs, their performances hardly meet the requirement of future electrochemical energy storage devices. It is thus urgent to design and exploit advanced materials for AIBs. This review highlights the state‐of‐the‐art research on AIBs. The insights into the basic configuration, operating mechanism and recent progress of electrode materials and corresponding electrolytes for AIBs have been comprehensively outlined. The electrode materials are classified and compared according to different NH4+storage behaviour in the structure. The challenges, design strategies and perspectives are also discussed for the future development of AIBs.
Electronic Nursing Documentation for Patient Safety, Quality of Nursing Care, and Documentation: A Systematic Review

Electronic nursing documentation (END) is a significant indicator of the quality of patient care and must be completed at the highest standards to ensure the safety, quality of healthcare services, and continuity of care. We will perform a systematic review to evaluate the impact of END on patient safety, quality of care, and documentation. We will use seven online databases (Scopus, ScienceDirect, ProQuest, PubMed, CINAHL, Sage Journals, and Google scholar) to obtain the data.
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools, which is in general time-consuming and requires a large amount of human effort. Therefore, recent works have started exploiting large language models (LLM) to lessen such burden and increase the usability of AutoML frameworks via a natural language interface, allowing non-expert users to build their data-driven solutions. These methods, however, are usually designed only for a particular process in the AI development pipeline and do not efficiently use the inherent capacity of the LLMs. This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML, i.e., from data retrieval to model deployment. AutoML-Agent takes user's task descriptions, facilitates collaboration between specialized LLM agents, and delivers deployment-ready models. Unlike existing work, instead of devising a single plan, we introduce a retrieval-augmented planning strategy to enhance exploration to search for more optimal plans. We also decompose each plan into sub-tasks (e.g., data preprocessing and neural network design) each of which is solved by a specialized agent we build via prompting executing in parallel, making the search process more efficient. Moreover, we propose a multi-stage verification to verify executed results and guide the code generation LLM in implementing successful solutions. Extensive experiments on seven downstream tasks using fourteen datasets show that AutoML-Agent achieves a higher success rate in automating the full AutoML process, yielding systems with good performance throughout the diverse domains.