ethicalabs/Research-Intent-Collab huggingface dataset:
Towards Assessing Spread in Sets of Software Architecture Designs

Several approaches have recently used automated techniques to generate architecture design alternatives by means of optimization techniques. These approaches aim at improving an initial architecture with respect to quality aspects, such as performance, reliability, or maintainability. In this context, each optimization experiment usually produces a different set of architecture alternatives that is characterized by specific settings. As a consequence, the designer is left with the task of comparing such sets to identify the settings that lead to better solution sets for the problem. To assess the quality of solution sets, multi-objective optimization commonly relies on quality indicators. Among these, the quality indicator for the maximum spread estimates the diversity of the generated alternatives, providing a measure of how much of the solution space has been explored. However, the maximum spread indicator is computed only on the objective space and does not consider architectural information (e.g., components structure, design decisions) from the architectural space. In this paper, we propose a quality indicator for the spread that assesses the diversity of alternatives by taking into account architectural features. To compute the spread, we rely on a notion of distance between alternatives according to the way they were generated during the optimization. We demonstrate how our architectural quality indicator can be applied to a dataset from the literature.
Supplementary Table S2 from Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome-wide Expression Data: A Validation Study

&lt;p&gt;Supplementary Table S2.&lt;/p&gt;
A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment

Abstract   Fault tree analysis has been widely utilized as a tool for nuclear power plant probabilistic safety assessment. This analysis can be completed only if all basic events of the system fault tree have their quantitative failure rates or failure probabilities. However, it is difficult to obtain those failure data due to insufficient data, environment changing or new components. This study proposes a fuzzy-based reliability approach to evaluate basic events of system fault trees whose failure precise probability distributions of their lifetime to failures are not available. It applies the concept of failure possibilities to qualitatively evaluate basic events and the concept of fuzzy sets to quantitatively represent the corresponding failure possibilities. To demonstrate the feasibility and the effectiveness of the proposed approach, the actual basic event failure probabilities collected from the operational experiences of the David–Besse design of the Babcock and Wilcox reactor protection system fault tree are used to benchmark the failure probabilities generated by the proposed approach. The results confirm that the proposed fuzzy-based reliability approach arises as a suitable alternative for the conventional probabilistic reliability approach when basic events do not have the corresponding quantitative historical failure data for determining their reliability characteristics. Hence, it overcomes the limitation of the conventional fault tree analysis for nuclear power plant probabilistic safety assessment.
Allpix<sup>2</sup> — silicon detector Monte Carlo simulations for particle physics and beyond

Abstract                Allpix2 is a versatile, open-source simulation framework for silicon pixel detectors. Its goal is to ease the implementation of detailed simulations for both single sensors and more complex setups with multiple detectors. While originally created for silicon detectors in high-energy physics, it is capable of simulating a wide range of detector types for various application scenarios, through its interface to Geant4 to describe the interaction of particles with matter, and the different algorithms for charge transport and digitization. The simulation chain is arranged with the help of intuitive configuration files and an extensible system of modules, which implement the individual simulation steps. Detailed electric field maps imported from TCAD simulations can be used to precisely model the drift behavior of the charge carriers, bringing a new level of realism to the Monte Carlo simulation of particle detectors.                Recently, Allpix2 has seen major improvements to its core framework to take full advantage of multi- and many-core processor architectures for simulating events fully parallel. Furthermore, new physics models such as charge carrier recombination in silicon have been introduced, further extending the application range. This contribution provides an overview of the framework and its components, highlighting the versatility and recent developments.
Effectiveness of early-warning systems and disaster risk reduction for rural food security under hydro-meteorological hazards in Sub-Saharan Africa: systematic review

Sub-Saharan Africa (SSA) faces escalating hydro-meteorological hazards that disrupt rural food systems, intensify hunger, and undermine livelihoods. This systematic review synthesizes evidence from 2010 to August 2025 on the effectiveness of Early-Warning Systems (EWS) and Disaster Risk Reduction (DRR) interventions in protecting rural food security. A PRISMA-style screening identified 320 records, with 29 duplicates removed, and 212 full-text articles assessed for eligibility, with data extracted independently by two reviewers. Sources include peer-reviewed studies, program evaluations, regional assessments, and institutional reports from FEWS-NET, ICPAC, IPCC, FAO, WFP, WHO, and CREWS. The review addresses three questions: (i) How effective are EWS in hazard detection, dissemination, and anticipatory action?; (ii) What are the impacts of DRR measures including social protection, risk financing, nature-based solutions, water management, and community-based approaches on food security?; and (iii) Which governance, institutional, and socio-economic factors facilitate or constrain adoption? Findings indicate EWS and DRR reduce vulnerability to droughts and floods when systems are reliable, people-centered, and linked to pre-arranged finance and delivery mechanisms. Integrated approaches combining early warnings with anticipatory cash, climate-smart land and water management, and inclusive social protection are most effective. Constraints include fragmented governance, underfunded hydromet-infrastructure, weak last-mile communication, insurance basis risk, and inequities excluding pastoralists, women, and remote communities. Emerging technologies-satellite monitoring, AI, IoT, drones, and digital advisories enhance forecasting if co-produced, ethically governed, and sustainably financed. Strengthening legal-frameworks, regional interoperability, shock-responsive social protection, and harmonized monitoring aligned with Sendai Target C and SDG-2 is essential for resilient, equitable, adaptive food systems in SSA.
Optimization of logistics train allowance scenario in order to balance logistics transportation in Pantura line

The large dimensions and extra weight frequently encountered on the Pantura line create a need for policymakers to introduce allowances for logistics transportation services using government-subsidized funds, particularly directed at railway logistics services transporting goods on this route. This study focuses on optimizing subsidy allocations to KALOG (the Indonesian Train Logistics Company) by examining three key factors: logistic railway service expenses, the distance between industrial centers and the nearest KALOG facilities, and truck operational costs. These elements are instrumental in enhancing KALOG's profitability while reducing issues related to overdimensioning and overloading along the Pantura line. Centered in Jakarta, which also serves as the nation’s logistics hub, the railway network extends to major cities including Tangerang, Bandung, Semarang, Yogyakarta, and Surabaya, offering substantial potential to improve the efficiency and reliability of rail freight in Indonesia. The study uses an optimization simulation to determine the optimal percentage for subsidy allocation, aiming to maximize profitability and, in turn, benefit both KALOG’s capital accumulation and Indonesia’s broader economic infrastructure. Numerical estimates generated in the simulation indicate that reallocating subsidies toward rail transport rather than highway logistics can lower operational costs significantly and reduce traffic congestion and road damage on highways in the Pantura region. Additionally, by adjusting for increased demand in logistics railway services through measures such as adding more wagons or improving infrastructure, the subsidy allocation model enhances rail transport's economic viability. The results provide a clear picture of the impact that a targeted optimization strategy can have on both the performance and competitiveness of logistics companies – particularly in the rail sector. Through continuous innovation, adapting to market changes, and prioritizing high-quality services, logistics companies not only enhance their competitive edge but also contribute to long-term, sustainable growth, benefiting Indonesia's logistics sector and national development
The Securitization of Conflict-Related Sexual Violence Against Women and the Myth of Protection in War : A (Feminist) Critical Discourse Analysis of the United Nations Security Council's Resolutions on Women, Peace and Security from 2000-2009

The inclusion of women in security at the highest institutional level the United Nations Security Council through its Resolution 1325, which passed unanimously in 2000, has been hailed as a monumental achievement for feminists. However, scholars have argued that the feminist agenda got lost in the (masculine) Security Council, as gendered stereotypes are reinforced through the Council’s resolutions, with the potential utilization of those stereotypes to justify continued militarism. This is discussed through a (feminist) critical discourse analysis of the Council’s resolutions on Women, Peace and Security from 2000-2009, to discern how the discursive victimization and feminization of women led to the securitization of conflict-related sexual violence (CRSV). Securitizing CRSV, recognized as a symptom of war that disproportionately affect women, works to challenge the myth the wars are fought to protect women. However, the securitization of CRSV at the highest institutional level simultaneously grants legitimacy, through association with the Council, to actors using the discourse of protecting women to enter military action.
On Fairness of Systemic Risk Measures

In our previous paper, "A Unified Approach to Systemic Risk Measures via Acceptance Set" (\textit{Mathematical Finance, 2018}), we have introduced a general class of systemic risk measures that allow for random allocations to individual banks before aggregation of their risks. In the present paper, we prove the dual representation of a particular subclass of such systemic risk measures and the existence and uniqueness of the optimal allocation related to them. We also introduce an associated utility maximization problem which has the same optimal solution as the systemic risk measure. In addition, the optimizer in the dual formulation provides a \textit{risk allocation} which is fair from the point of view of the individual financial institutions. The case with exponential utilities which allows for explicit computation is treated in details.
From Models to Medicine: Understanding Stem Cell Differentiation in Regenerative Therapy

Interview with Dr. David Schaffer
A review on serverless architectures - function as a service (FaaS) in cloud computing

Emergence of cloud computing as the inevitable IT computing paradigm, the perception of the compute reference model and building of services has evolved into new dimensions. Serverless computing is an execution model in which the cloud service provider dynamically manages the allocation of compute resources of the server. The consumer is billed for the actual volume of resources consumed by them, instead paying for the pre-purchased units of compute capacity. This model evolved as a way to achieve optimum cost, minimum configuration overheads, and increases the application's ability to scale in the cloud. The prospective of the serverless compute model is well conceived by the major cloud service providers and reflected in the adoption of serverless computing paradigm. This review paper presents a comprehensive study on serverless computing architecture and also extends an experimentation of the working principle of serverless computing reference model adapted by AWS Lambda. The various research avenues in serverless computing are identified and presented.
Climate Change and Reproductive Biocomplexity in Fishes: Innovative Management Approaches towards Sustainability of Fisheries and Aquaculture

The ongoing rapid climate change, combined with the disturbance of fish breeding grounds, may impact reproduction by endangering successful breeding and survival, and thus affect the viable sustainability in aquaculture systems as well as in the sea. In this study we focus on the biocomplexity of fish reproduction in response to climate change. Further, we propose adaptive strategies, including technological advancements, using a noninvasive and non-lethal approach, and we outline an assisted reproduction and nutrigenomics approach to mitigating fish reproductive risks posed by climate change. This was done in an effort to monitor fish aquaculture and ensure that, as a livelihood, it may provide a useful source of nutrition for our society.
Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery: From Small Molecules to Biologics

Pharmacokinetic (PK) behavior, which emerges from the underlying processes of absorption, distribution, metabolism, and excretion (ADME), is central to drug discovery and development, dose optimization, and safety assessment. Despite decades of experimental and computational research, early-stage prediction of human PK remains a major challenge, contributing to clinical attrition and inefficiency in pharmaceutical pipelines. Advances in artificial intelligence (AI) and machine learning (ML) have significantly improved ADME predictions, particularly for small molecules. Traditional descriptor-based quantitative structure-activity relationship and classical ML methods offer interpretability and robust performance on standardized datasets. In contrast, graph neural networks, deep learning architectures, and chemical language models facilitate the learning of complex nonlinear structure-property relationships and multitask predictions. Multimodal frameworks further integrate experimental measurements, structural data, and biological contexts, enhancing predictive accuracy under low-data and heterogeneous conditions. Emerging modalities, including peptides, oligonucleotides, and antibody-based therapeutics, pose additional challenges owing to their sequence-dependent stability, conformational flexibility, and mechanistically distinct determinants of ADME and toxicity (ADMET). AI approaches that incorporate sequence-, structure-, and mechanism-aware representations combined with multimodal data integration have demonstrated improved predictability for medium- and large-molecule therapeutics. Recent developments in foundation-model architectures offer unified representations across chemical, biological, and biophysical domains, enabling cross-modality ADMET modeling with enhanced generalization and mechanistic interpretability. In this review, we summarize the evolution of computational ADME- and PK-oriented prediction frameworks from small molecules to complex biologics, highlighting methodological advances, representative studies, and emerging trends in multimodal and foundation-model approaches. We also discuss the limitations and future perspectives of the practical implementation of AI-driven ADMET predictions to support rational drug design and development.
Abstract 4128962: Medicaid Work Requirements and Changes in Insurance Coverage, Employment, Access to Care, and Health Status Overall and Among People with Cardiovascular Disease

Introduction:             Medicaid work requirements have been at the forefront of health policy debate. Proponents argue that these policies increase employment, reduce poverty, and improve health. Critics have raised concerns that work requirements lead to loss of coverage and impede access to care.                                   Hypothesis:             We hypothesized that Medicaid work requirement implementation would be associated with decreased insurance coverage and healthcare access without impacting employment.                                   Methods:             We used the Behavioral Risk Factor Surveillance System (a nationally representative survey administered by the CDC) to estimate the impact of Medicaid work requirements on insurance coverage, employment, access to care, and self-reported health. We used a difference-in-differences (DID) approach to compare changes in outcomes in Arkansas (where work requirements were implemented) versus control states. Outcomes were self-reported and included: 1) insurance coverage, 2) employment, 3) presence of a personal healthcare provider, 4) inability to see a doctor due to cost, and 5) self-reported health. The analysis was conducted among all respondents and among respondents with cardiovascular disease, who were hypothesized to be particularly vulnerable to negative sequelae of insurance loss.                                   Results:             The implementation of Medicaid work requirements in Arkansas was associated with decreased insurance coverage compared with control states (adjusted DID estimate -4.3%, CI -8.4%, -0.1%), but no increase in employment rates (-2.2% CI -7.0%, 2.6%). There was a trend toward decreased likelihood of having a personal healthcare provider (-4.7% CI -9.9%, 0.5%). There was no change in inability to see a doctor due to cost or self-reported good health. When this analysis was repeated among participants with cardiovascular disease, work requirement implementation was not associated with changes in any of the outcome variables.                                   Conclusions:             The implementation of Medicaid work requirements in Arkansas was associated with decreased insurance coverage and no change in employment. Our findings are particularly relevant given national policy debate about work requirement expansion and the recent implementation of Medicaid work requirements in Georgia. Despite the null findings among respondents with cardiovascular disease (likely the result of a small sample size), this study has important implications for cardiovascular health given the close association between insurance coverage and cardiovascular outcomes.
Regulating the workplace in industrial Ontario : the origins of occupational health and safety policy, 1880-1914

This thesis examines the development of occupational health and safety policies in Ontario from 1880 to 1914 focusing on the Railway Accidents Act, Ontario Factory Act, Workmen's Compensation for Injuries Act and the Workmen's Compensation Act. Together they formed the basic regulatory framework that endured in Ontario until the 1970s. The rise of industrial capitalism and the growth of the private market in the mid-1880s brought forward the factory system which depended on an unskilled labouring class and the use of power-driven machinery. Thus system of production created many risks for workers including accidents and diseases, and resultant financial problems caused by an inability to work. The role of, and interactions among, workers, employers, and the state were key in determining the particular patterns of policy development. Although health and safety policies provided only minimal protection to workers, while at the same time causing little disruption to business, the policies were more beneficial to workers when labour played an active role in the policy-making process.
Correction: Corrigendum: Fault-tolerant quantum computation with a soft-decision decoder for error correction and detection by teleportation

Scientific Reports 5: Article number: 5771; published online: 20 June 2013; updated: 23 March 2018. The original version of this Article contained a typographical error in the volume number ‘5’ was incorrectly given as ‘4’. This error has now been corrected in the PDF and HTML versions of the Article.
Topological Quantum Computation and Error Correction by Biological Cells

A Topological examination of phospholipid dynamics in the Far from Equilibrium state has demonstrated that metabolically active cells use waste heat to generate spatially patterned membrane flows by forced convection and shear. This paper explains the resemblance between this nonlinear membrane model and Witten Kitaev type Topological Quantum Computation systems, and demonstrates how this self-organising membrane enables biological cells to circumvent the decoherence problem, perform error correction procedures, and produce classical level output as shielded current flow through cytoskeletal protein conduit. Cellular outputs are shown to be Turing compatible as they are determined by computable in principle hydromagnetic fluid flows, and importantly, are Adaptive from an Evolutionary perspective.
A Complex Homographic Best Approximation Problem. Application to Optimized Robin--Schwarz Algorithms, and Optimal Control Problems

zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Veterinary Regenerative Medicine: The Evolving Role of Stem Cell-Based Therapies

Veterinary medicine has witnessed a paradigm shift in recent past with the preference to use mesenchymal stem cells (MSCs) for livestock and companion animal therapeutics. MSCs made remarkable developments in treating complex clinical conditions, otherwise difficult to treat with routine medications such as musculoskeletal injuries, osteoarthritis, tendon and ligament damage, degenerative diseases, gastritis, endometritis, diabetes mellitus, and ocular diseases, offering new hope for veterinary practitioners and animal caregivers. This review is divided into two sections: The first part explores recent advancements in veterinary medicine using MSCs, mainly focusing on their sources, administration methods, therapeutic mechanisms, and clinical applications. Whereas, the second section highlights the emerging trends in advanced therapeutic strategies, including MSC and its derived cell-free therapies, with or without scaffolds, offering less invasive and more targeted treatments. Many studies show that MSCs from different sources exhibit unique regenerative and immunomodulatory properties, enabling more effective and better-quality tissue repair with appreciable inflammation control. However, despite the aforementioned promises, some challenges still remain, including the need for standardized therapy protocols, including the doses and a good number of replicates for long-term clinical trials to ensure safety and efficacy. This review covers a comprehensive overview of the current landscape of stem cell research and explores its translational potential in the latest developments shaping the future of veterinary regenerative medicine, especially using mesenchymal stem cells.