ethicalabs/Research-Intent-Collab huggingface dataset:
The Emergence of Standards regarding the Right of Access to Water and Sanitation

Despite continuing uncertainty over the precise legal status of the putative human right(s) of access to water and sanitation in international law, and also within the domestic legal frameworks of many national jurisdictions, the elaboration continues apace of a rich montage of water services standards by a diverse cast of formal and informal global, regional, State and transnational actors. In addition to emerging standards regarding the physical safety and adequacy of water supplied for domestic purposes, notably including the WHO Guidelines for Drinking-Water Quality, standards are also being adopted by bodies such as the International Organisation for Standardisation (ISO), which set down more general service quality guidelines for utilities providing domestic water and sanitation services. Also, certain institutions providing finance for major water services projects, such as multilateral development banks (MDBs), are developing sophisticated standards for cost recovery which seek to adopt elements of a human rights-based approach by taking account of the affordability of water and sanitation services and providing safeguards for poor and vulnerable people, including restrictions on service disconnection for non-payment of charges. At every level of decision-making regarding water and sanitation services, standards of governance informed by the practice of human rights, including standards concerned with transparency, participation, reviewability and accountability, have become pervasive.
Contextual Model-Based Collaborative Filtering for Recommender Systems

Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore that user preferences can change according to context, resulting in recommendations that do not fit user interests. Context-aware models have been proposed to address this issue, but these models have problems of their own. The ever-increasing speed at which data are generated presents a scalability challenge for single-model approaches. Moreover, the complexity of these models prevents small players from adapting and implementing contextual models that meet their needs. This thesis addresses these issues by proposing the (CF)2 architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering (CF) models. CF has been available for decades, and its methods and benefits have been extensively discussed and implemented. Moreover, the use of context as filtering criteria for local learning addresses the scalability issues caused by the use of large datasets. Therefore, the proposed architecture enables the creation of contextual recommendations using several models instead of one, with each model representing a context. In addition, the architecture is implemented and evaluated in two case studies. Results show that contextual models trained with a small fraction of the data resulted in similar or better accuracy compared to CF models trained with the total dataset. Moreover, experiments indicate that local learning using contextual information outperforms random selection in accuracy and in training time.
Assessment and Conservation of Forest Biodiversity in the Western Ghats of Karnataka, India. 1. General Introduction and Forest Land Cover and Land Use Changes (1977-1997)

PPE volumes 6 and 7 are parts of a project report published in 1999 in collaboration with the Karnataka Forest Department on the assessment and conservation of forest biodiversity in the Western Ghats of Karnataka. After introducing the project objectives and the study area, this volume deals more specifically with forest land-cover and land-use changes over a 20-year period (1977-1997), assessed from vegetation maps and satellite images. The study revealed that forest areas were converted to anthropogeneic cover types at an annual rate of 0.63%. Forest loss was mitigated in areas under state protection (reserve forest), while degraded or fragmented forests lost more area than dense, undisturbed ones. Conservation priorities and recommendations for forest management are discussed in the second volume (PPE 7).
Computational Simulation of Geometric Orientation Codes: Addressing Isotropic Assumptions in Quantum Error Correction

Fault-tolerant quantum computing requires robust error correction traditionally met using planar topologies that assume noise is perfectly isotropic. However, recent astrophysical measurements suggest the universe possesses fundamental anisotropic properties. This paper challenges the isotropic assumption in quantum error correction by proposing geometric orientation codes. This novel architecture utilizes 3D spatial orientation as the primary redundancy degree of freedom. It explicitly counteracts highly directional noise by aligning physical qubits along distinct spatial axes. To evaluate this paradigm, we executed extensive Python-based Monte Carlo simulations modeling a highly anisotropic quantum noise channel. We compared the logical error suppression capabilities of an N=4 tetrahedral orientation code against a standard d=3 planar surface code. The simulation swept physical error rates from 1e-4 to 1e-1 across anisotropy bias levels ranging from 1:1 to 100:1. At a 100:1 noise bias and a physical error rate of 0.01, the N=4 orientation code achieved a logical error rate of 5.0e-5. By replacing 2D planar grids with 3D tetrahedral unit cells, the physical-to-logical qubit overhead ratio can be drastically reduced. We recognize this compares a specialized spatial repetition code against a universal planar code. We propose inducing artificial 100:1 noise biases via magnetic gradients to physically test these architectures. This establishes geometric orientation codes as potentially viable architectural blueprints for next-generation quantum processors.
Emerging Technologies in Critical Infrastructure Protection and Resilience-By-Design: the Case of Cyber Ranges

Abstract                   Resilience-by-design is emerging as a key practice in developing and enhancing the resilience of critical infrastructure systems. Cybersecurity is a key concern for critical infrastructure operator and therefore an important issue to address in order to enhance resilience. One important tool in this regard is cyber ranges. The present article lays out their use in resilience enhancement, using academic and industry sources, while compiling the emerging technologies and paradigms that are making cyber ranges more effective than before. The research is the result of a practical knowledge transfer project between two countries with the goal of creating a federated energy cyber range.
Constraining Climate Model Projections of Regional Precipitation Change

&amp;lt;p class=&amp;quot;Abstract&amp;quot;&amp;gt;As communities prepare for the impacts of climate change, policy makers and stakeholders increasingly require locally-resolved projections of future climate. Statistical downscaling uses low-resolution outputs from climate models and historical observations to both enhance the spatial resolution and correct for systematic biases. By examining the downscaled rainfall over land, we show that although bias corrections are effective in reducing biases in the current climate, they do not reduce the intermodel spread in future rainfall projections. This failure stems from the strong dependence of future rainfall change upon the current climatological rainfall patterns. Even after bias corrections are applied, the downscaled projections of precipitation change retain this dependence upon their native climatology. However, we show that this dependence can be exploited; even very simple methods to sub-set models according to their ability to resolve the observed rainfall climatology can substantially reduce the intermodel spread in rainfall projections.&amp;lt;/p&amp;gt;
Leveraging mRNA Platform Technology to Accelerate Development of Vaccines for Some Emerging and Neglected Tropical Diseases Through Local Vaccine Production

The mRNA vaccine technology platform may enable rapid response to some emerging infectious diseases (EIDs), as demonstrated through the COVID-19 pandemic. Beyond the role it could play in future EID response, mRNA technology also could have an important role in accelerating the development of, and access to, vaccines for some neglected tropical diseases (NTDs), which occur mainly in impoverished regions of the world. Despite their significant disease burden, few vaccines against NTDs have been developed, in part because of the uncertain market and return on investment. In addition, the probability of technical and regulatory success is considered to be low for developing vaccines against multicellular parasites, or organisms that have sophisticated mechanisms for evading immunological surveillance, such as many of the NTD pathogens. The global 2021-2030 road map for neglected tropical diseases sets ambitious targets for the eradication, elimination, and control of NTDs. For some, effective interventions exist but are underutilized. For others, vaccines need to be developed or their use expanded to meet global targets on control and elimination. This article discusses the application of the mRNA technology platform to the development of vaccines for NTDs as well as EIDs, highlights the challenges in bringing these products to the market, and indicates potential areas which could be explored, including leveraging investment for vaccines with a more profitable market potential and enabling local manufacturing in regions where NTDs are endemic. Such regional production could include collaborations with the mRNA vaccine technology transfer hubs that are being established with the support of WHO and COVAX partners.
CRISPR-mediated engineering of mesenchymal stromal/stem cells: a summary of recent progress in immunological applications for regenerative medicine and cancer therapy

Mesenchymal stromal/stem cells (MSCs) have introduced as a cornerstone of regenerative medicine, owing to their immunomodulatory properties and therapeutic potential in autoimmune and inflammatory disorders. Although, their clinical application is often restricted due to immune rejection and heterogeneity in immunoregulatory responses. The advent of Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9 (CRISPR/Cas9) technology has revolutionized MSC engineering, enabling precise genetic modifications to enhance their immunological efficacy. This review explores how CRISPR-mediated editing of MSCs can mitigate immunogenicity, amplify anti-inflammatory functions, and repurpose MSCs for targeted immunotherapy. Key strategies include knockout of β2-microglobulin to evade T-cell recognition, augmentation of anti-inflammatory mediators like interleukin (IL)-10 and TNF-alpha stimulated gene/protein 6 (TSG-6), and disruption of pro-inflammatory pathways such as toll-like receptor 4 (TLR4)/NF-κB. In addition, CRISPR-engineered MSCs demonstrate promise in reshaping tumor microenvironments and combating bacterial infections through enhanced innate immunity. Despite challenges including off-target effects and delivery optimization, CRISPR-tailored MSCs represent a transformative approach to overcoming immunological barriers, paving the way for universal, off-the-shelf therapies in rheumatoid arthritis, cancer, and beyond.
Indigenous Peoples' Rights and Customary Land in East African Legal Frameworks: Decolonial Reflections

This article examines Indigenous Peoples' Rights and Customary Land in East African Legal Frameworks: Decolonial Reflections with a focused emphasis on Republic of Congo within the field of Medicine. It is structured as a intervention study that organises the problem, the strongest verified scholarship, and the main analytical implications in a concise publication-ready format. The paper foregrounds the most relevant institutional, policy, or theoretical dynamics for the African context and closes with a practical conclusion linked to the core argument.
MULTIVARIATE BIOSTATISTICAL ANALYSIS OF HEALTH INSURANCE COVERAGE AND ACCESS TO HEALTHCARE SERVICES IN GHANA USING COMPLEX SURVEY DATA

Access to healthcare is a fundamental human right, yet in Ghana, disparities persist due to uneven health insurance coverage and socioeconomic inequities. This study is crucial as it addresses a significant policy gap by analyzing how demographic and structural factors influence healthcare access, using robust multivariate biostatistical tools on complex survey data from 2020-2024. The primary objective was to examine how health insurance type, income, education, employment, gender, and location affect access to healthcare services. Employing Generalized Linear Mixed Models, chi-square tests, ANOVA, and correlation analyses on a nationally representative sample of 3,000 respondents, the study found that public insurance covered 40.0% of the population, while 33.3% remained uninsured. The chi-square test revealed a significant association between insurance type and healthcare utilization (χ² = 198.42, p< 0.001), with private insurance holders accessing care more frequently. ANOVA results confirmed that income significantly affects insurance status (F = 87.19, p< 0.001), and education showed a positive gradient with healthcare access-from 55% (primary) to 85% (tertiary). Urban residents accessed healthcare at a rate of 78.0% compared to 49.0% for rural residents (t = 19.42, p< 0.001), and females utilized services more than males (t = 6.37, p< 0.01). The correlation matrix showed strong associations between insurance and education (r = 0.50), income (r = 0.45), and healthcare visits (r = 0.40). The overall regression model explained 61% of the variance in healthcare access (R² = 0.61), identifying education (β = 0.34), insurance (β = 0.29), and income (β = 0.27) as the strongest predictors. These findings imply that achieving equitable healthcare requires integrated policies that combine economic support, education, and infrastructure, particularly for rural and low-income populations. It is recommended that Ghana adopt targeted subsidies, mobile clinics, and gender-sensitive outreach programs to bridge the coverage and access gaps.
Generative AI for visualization: State of the art and future directions

Generative AI (GenAI) has witnessed remarkable progress in recent years and demonstrated impressive performance in various generation tasks in different domains such as computer vision and computational design. Many researchers have attempted to integrate GenAI into visualization framework, leveraging the superior generative capacity for different operations. Concurrently, recent major breakthroughs in GenAI like diffusion model and large language model have also drastically increase the potential of GenAI4VIS. From a technical perspective, this paper looks back on previous visualization studies leveraging GenAI and discusses the challenges and opportunities for future research. Specifically, we cover the applications of different types of GenAI methods including sequence, tabular, spatial and graph generation techniques for different tasks of visualization which we summarize into four major stages: data enhancement, visual mapping generation, stylization and interaction. For each specific visualization sub-task, we illustrate the typical data and concrete GenAI algorithms, aiming to provide in-depth understanding of the state-of-the-art GenAI4VIS techniques and their limitations. Furthermore, based on the survey, we discuss three major aspects of challenges and research opportunities including evaluation, dataset, and the gap between end-to-end GenAI and generative algorithms. By summarizing different generation algorithms, their current applications and limitations, this paper endeavors to provide useful insights for future GenAI4VIS research.
Systematic literature review on the effects of occupational safety and health (OSH) interventions at the workplace.

Objectives The aim of this review was to assess the evidence that occupational safety and health (OSH) legislative and regulatory policy could improve the working environment in terms of reduced levels of industrial injuries and fatalities, musculoskeletal disorders, worker complaints, sick leave, and adverse occupational exposures. Methods A systematic literature review covering the years 1966‒2017 (February) was undertaken to capture both published and gray literature studies of OSH work environment interventions with quantitative measures of intervention effects. Studies that met specified in-and exclusion criteria went through an assessment of methodological quality. Included studies were grouped into five thematic domains: (i) introduction of OHS legislation, (ii) inspection/enforcement activity, (iii) training, such as improving knowledge, (iv), campaigns, and (v) introduction of technical device, such as mechanical lifting aids. The evidence synthesis was based on meta-analysis and a modified Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Results The search for peer-reviewed literature identified 14 743 journal articles of which 45 fulfilled the inclusion criteria and were eligible for meta-analysis. We identified 5181 articles and reports in the gray literature, of which 16 were evaluated qualitatively. There was moderately strong evidence for improvement by OHS legislation and inspections with respect to injuries and compliance. Conclusions This review indicates that legislative and regulatory policy may reduce injuries and fatalities and improve compliance with OHS regulation. A major research gap was identified with respect to the effects of OSH regulation targeting psychological and musculoskeletal disorders.
Կենտրոնական բանկի թվային արժույթներ. վճարային համակարգերի փոխակերպվող ապագան / CENTRAL BANK DIGITAL CURRENCIES: A TRANSFORMED FUTURE OF PAYMENT SYSTEMS

How will the introduction of Central Bank Digital Currencies (CBDCs) influence financial stability and the execution of monetary policy? What risks will arise for the banking sector? Will CBDCs become a 'sword of Damocles' for traditional payment methods, or will they serve as an alternative to cryptocurrencies? These are some of the questions that have recently captured the attention of economic analysts. As a digital form of currency regulated by a central bank, CBDCs differ substantially from traditional currencies. Yet, they present a remarkable hybrid — acting as a fusion of fiat and digital currencies, reinforcing the central bank's role in the evolving digital economy. In an era marked by globalization and digitization, the development of innovative payment systems necessitates central banks to modernize monetary and payment systems. This paper seeks to explore the practical and realistic implications of CBDCs in the context of growing digitalization.
Defining safety functions and safety barriers from fault and event trees analysis of major industrial hazards

The prevention of major accident hazard is the main objective of the SEVESO II directive. It relies notably on risk analysis and on the implementation of a safety management system. However, the methodology for risk analysis is not provided by the directive and different approaches are adopted within the European union. The objective of the European project ARAMIS is to build up a new integrated risk assessment method that will be used as a supportive tool to speed up the harmonized implementation of SEVESO II Directive. ARAMIS not only focuses on the identification and evaluation of the risk but also on the vulnerability of the environment by proposing tools to evaluate it and crosses this result with a spatial representation of the accident effect. A last innovative aspect of ARAMIS is to take explicitly the management into account by defining a management index which reduces or increases the global severity of the dreaded scenarios.
Leveraging mobile health applications for biomedical research and citizen science: a scoping review

AbstractObjectiveThis systematic review aims to analyze current capabilities, challenges, and impact of self-directed mobile health (mHealth) research applications such as those based on the ResearchKit platform.Materials and MethodsA systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. English publications were included if: 1) mobile applications were used in the context of large-scale collection of data for biomedical research, and not as medical or behavioral intervention of any kind, and 2) all activities related to participating in research and data collection methods were executed remotely without any face-to-face interaction between researchers and study participants.ResultsThirty-six unique ResearchKit apps were identified. The majority of the apps were used to conduct observational studies on general citizens and generate large datasets for secondary research. Nearly half of the apps were focused on chronic conditions in adults.DiscussionThe ability to generate large biomedical datasets on diverse populations that can be broadly shared and re-used was identified as a promising feature of mHealth research apps. Common challenges were low participation retention, uncertainty regarding how use patterns influence data quality, need for data validation, and privacy concerns.ConclusionResearchKit and other mHealth-based studies are well positioned to enhance development and validation of novel digital biomarkers as well as generate new biomedical knowledge through retrospective studies. However, in order to capitalize on these benefits, mHealth research studies must strive to improve retention rates, implement rigorous data validation strategies, and address emerging privacy and security challenges.
Experimental Results for the Special Session on Real-Parameter Optimization at CEC 2005: A Simple, Continuous EDA

A comprehensive set of experiments was conducted with a continuous EDA on 25 test problems provided in the real-parameter optimization special session. It is expected that the results presented here could be used to gain some deeper understanding of the performance of the EDA as well as facilitate the comparison across different algorithms.
Clustering Multivariate Time Series using Energy Distance

A novel methodology is proposed for clustering multivariate time series data using energy distance defined in Székely and Rizzo (2013). Specifically, a dissimilarity matrix is formed using the energy distance statistic to measure separation between the finite dimensional distributions for the component time series. Once the pairwise dissimilarity matrix is calculated, a hierarchical clustering method is then applied to obtain the dendrogram. This procedure is completely nonparametric as the dissimilarities between stationary distributions are directly calculated without making any model assumptions. In order to justify this procedure, asymptotic properties of the energy distance estimates are derived for general stationary and ergodic time series. The method is illustrated in a simulation study for various component time series that are either linear or nonlinear. Finally the methodology is applied to two examples; one involves GDP of selected countries and the other is population size of various states in the U.S.A. in the years 1900 -1999.
Dominant contributions to lateral distribution functions in ultra-high energy cosmic ray air showers

19 pages, 21 figures