ethicalabs/Research-Intent-Collab huggingface dataset:
PROTECTION OF THE RIGHTS OF MINORS FROM SEXUAL VIOLENCE: IMPLEMENTATION OF THE PROVISIONS OF ARTICLE 132 OF THE CRIMINAL CODE OF THE RUSSIAN FEDERATION IN JUDICIAL PRACTICE

В статье рассматриваются вопросы общественной опасности и противоправности преступле- ний против половой неприкосновенности несовершеннолетних. Защита прав несовершеннолетних от насиль- ственных действий сексуального характера и сексуального насилия в современных условиях предполагает соз- дание комплексных мер противодействия подобным преступным посягательствам. Основополагающим прин- ципом является половая неприкосновенность и морально-нравственная защищенность несовершеннолетнего от противоправных преступных посягательств, как в целом, так и со стороны членов его семьи.                                                                                             The article discusses issues of public danger and illegality of crimes against the sexual integrity of minors. Protecting the rights of minors from sexual assault and sexual violence in modern conditions involves the creation of comprehensive measures to counter such criminal attacks. The fundamental principle is the sexual integrity and moral protection of minors from unlawful criminal attacks, both in general and from members of their family.
Ab initio statistical mechanics of the ferroelectric phase transition in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">PbTiO</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>

An effective Hamiltonian for the ferroelectric transition in PbTiO3 is constructed from first-principles density-functional-theory total-energy and linear-response calculations through the use of a localized, symmetrized basis set of ``lattice Wannier functions.'' Explicit parametrization of the polar lattice Wannier functions is used for subspace projection, addressing the issues of LO-TO splitting and coupling to the complementary subspace. In contrast with ferroelectric BaTiO3 and KNbO3, we find significant involvement of the Pb atom in the lattice instability. Monte Carlo simulations for this Hamiltonian show a first-order cubic-tetragonal transition at 660 K. Resulting temperature dependence of spontaneous polarization, c/a ratio and unit-cell volume near the transition are in good agreement with experiment. Comparison of Monte Carlo results with mean field theory analysis shows that both strain and fluctuations are necessary to produce the first order character of this transition.
Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer

A reliable and efficient representation of multivariate time series is crucial in various downstream machine learning tasks. In multivariate time series forecasting, each variable depends on its historical values and there are inter-dependencies among variables as well. Models have to be designed to capture both intra- and inter-relationships among the time series. To move towards this goal, we propose the Time Series Attention Transformer (TSAT) for multivariate time series representation learning. Using TSAT, we represent both temporal information and inter-dependencies of multivariate time series in terms of edge-enhanced dynamic graphs. The intra-series correlations are represented by nodes in a dynamic graph; a self-attention mechanism is modified to capture the inter-series correlations by using the super-empirical mode decomposition (SMD) module. We applied the embedded dynamic graphs to times series forecasting problems, including two real-world datasets and two benchmark datasets. Extensive experiments show that TSAT clearly outerperforms six state-of-the-art baseline methods in various forecasting horizons. We further visualize the embedded dynamic graphs to illustrate the graph representation power of TSAT. We share our code at https://github.com/RadiantResearch/TSAT.
Development of Computerized Epidemiological Models of Infectious Diseases

The use of multistate computer models in the epidemiology of infectious diseases is presented and the conditions for simulation games are discussed. The theoretical basis of the deterministic multistated models is sketched. A general purpose interactive software package for continuous simulation of such models was implemented and successfully tested.
Emotion in Reinforcement Learning Agents and Robots: A Survey

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human-robot interaction (HRI) community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling (AM) researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses 1) from what underlying dimensions (e.g., homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, 2) what types of emotions have been derived from these dimensions, and 3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.
The wicked problem of humanitarian logistics and disaster relief aid

Purpose – – Some 40 years ago Horst Rittel and Melvin Webber published a seminal paper in which they discussed the “wicked problems” facing those who sought to develop solutions to urban planning challenges. Their work recognised that many of the decisions faced by modern management are multi‐faceted, and involve a plethora of stakeholders each with a diverse view of what good might look like. The aim of this paper is to consider how the ensuing rich vein of literature relating to the management of such problems might be applied to the logistic challenges of preparing for and responding to a disaster. Design/methodology/approach – – This paper first examines the issues, dilemmas and decisions facing the humanitarian logistician, as a key component of the preparation and response to a disaster, and concludes that they fall firmly into the ambit of a wicked problem. The paper then reviews the literature that proposes methods for management of such problems, and applies it to the humanitarian logistics field. Findings – – The paper concludes that further research is needed to understand the ways in which the three primary approaches of employing authoritative, competitive and collaborative strategies might be best evaluated and employed. In doing so, it recognises that it is essential to engage with the broader disaster management and humanitarian logistic communities in order to help operationalise this theoretical approach. Originality/value – – While the concept of a wicked problem and the associated broad spectrum of literature has developed over a considerable period of time, this has not previously been applied to the challenge of humanitarian logistics which, it is argued, meets all the criteria to be considered as a truly wicked problem.
Jurisdictions of the Dispute Settlement Body of the World Trade Organization and international investment arbitration: overlap or complementarity?

The development of international economic relations has led to the emergence of independent dispute resolution mechanisms such as the Dispute Settlement Body of the World Trade Organization (DSB WTO) and international investment arbitration. However, in recent years there has been an increasing number of disputes with similar factual circumstances that were referred simultaneously to the DSB WTO and to international investment arbitration. As a result, procedural and substantive overlaps between the two fora have emerged. Whether this trend has a positive or negative effect is still an open question. On the one hand, the fact that the resolution of an international dispute may be considered by several international judicial bodies gives a disputing party the possibility of choosing a specialized forum, which makes the final outcome more efficient. On the other hand, the existence of several dispute resolution bodies that consider disputes arising from similar factual backgrounds further exacerbates the fragmentation of international economic law. This article is devoted to an analysis of jurisdictional aspects of the DSB WTO and international investment arbitration in the context of possible overlaps in both their procedural and substantive parts. The analysis reveals that the doctrines of res judicata and systematic interpretation do not provide effective remedies to prevent and overcome the overlaps between the DSB WTO and international investment arbitration. The authors believe that the problem of overlapping jurisdictions of the DSB WTO and international investment arbitration can be solved if a prior decision by one forum is evaluated by the other forum as factual evidence in the dispute and if an interpretation made by one forum is taken into account by the other forum in its decision-making.
Unraveling role of artificial intelligence capability through digital twin practices, lean six sigma and total quality management for smart manufacturing performance

Purpose                     This paper aims to examine the effect of Artificial Intelligence Capabilities (AIC) on Smart Manufacturing Performance (SMP) and, specifically, industry 4.0. It seeks to find out the mediating roles of Lean Six Sigma (LSS), Digital Twin Practices (DTP) and Total Quality Management (TQM) based on the Dynamic Capabilities View (DCV).                                                           Design/methodology/approach                     To gather information, the study took an empirical approach (survey) of 344 employees of medium to large Ready-Made Garment (RMG) firms in Bangladesh. In testing the direct and indirect effects of AIC on SMP, emphasis was put on testing the three mediators using Partial Least Squares-Structural Equation Modeling.                                                           Findings                     The findings indicate that AIC has a lot of influence on SMP either directly or indirectly via the mediating variables of DTP, LSS and TQM. Such operational structures can be used to convert the intelligence of AI as data into quantifiable promotions in terms of responsiveness, quality and flexibility in processes.                                                           Research limitations/implications                     The participants of the study are restricted to the RMG sector in Bangladesh, and this factor can limit generalizability. It might be possible to conduct such research in different industries, locations and contexts.                                                           Originality/value                     In this paper, the DCV is expanded to demonstrate how, when integrated into well-designed systems of improvement, AI can be viewed as a dynamic organizational capability rather than just one of the tools. It offers a process-oriented description of the role of digital transformation in manufacturing excellence in emerging economies.
Additional file 1 of Acupuncture for combat post-traumatic stress disorder: trial development and methodological approach for a randomized controlled clinical trial

Additional file 1. Ethical Approval Document 1 of 2. IRB Approval Letter.
The CLIMATE CHANGE IMPACT ON THE HUMAN RIGHT TO ENVIRONMENTAL CONDITIONS OF A SPECIFIED QUALITY (SAFE, CLEAN, HEALTHY AND SUSTAINABLE ENVIRONMENT):  SOME INTERNATIONAL ENVIRONMENTAL LAW ISSUES

This article discusses recognition between climate change and human rights at the international level. The analysis shows that despite the UN climate change framework does not adequately address the magnitude of the threat posed by climate change related harm to human rights, domestic, regional or international courts must take account of its provisions in deciding cases. The article argues that the causes for climate cases are diverse, whereby the most often ones are those referring to the competent public authority’s failure to fulﬁl its obligation to regulate limitations of the greenhouse gas (GHG) emissions.  Further identify the links between human rights and environmental protection, were apparent at least from the first international conference on the human environment, held in Stockholm in 1972. More broadly, it demonstrates international environmental agreements, were some aspects of the right to environmental conditions of a specified quality are identify.  This article discusses also theoretical issues of individual environmental rights and the right to environmental safety in Ukraine.  Keywords: climate, human rights, environmental, Ukraine
Interactive Machine Learning from Humans: Knowledge Sharing via Mutual Feedback

People regularly interact with human-in-the-loop learning (HiLL) agents that attempt to adapt to their priorities, tastes, and preferences. Examples of such systems include web search engines, movie recommender systems, text prediction, and even large-language model based chat applications. To be adaptive, these HiLL systems must first learn an accurate model of an individual’s behavior and preferences. The ability to learn such a model depends on the quality of the information the HiLL system is able to elicit from the people with whom it interacts, and how well it is then able to leverage that information. Typically, this information is generated via a loop where an agent or model takes actions or makes suggestions that a person responds to with some feedback, and that response is then used to train future behavior. Henceforth, we will refer to this query-feedback pair as an interaction. We note that the informativeness of a learning interaction is limited by how fully it empowers a person to share their knowledge. We demonstrate that it is possible to improve a person’s teaching performance by providing them with (1) more appropriate modalities for sharing feedback (i.e. interaction types) and (2) insight into the context of the learner they are instructing. Our approach therefore moves towards a model that prioritizes a human teacher’s ability to provide informative feedback. To do this, we first formalize the space of interactions that can be used to learn from human feedback and present four interaction archetypes: Showing, Categorizing, Sorting, and Evaluating. Then, we analyze the effects that these different interaction types may have on learning outcomes via both direct and indirect influences on collected training data. We build on this to contribute a learning approach that enables an algorithmic learner to learn from multiple interaction types based on which would be the most immediately informative. Finally, we develop and evaluate an interaction type-based approach towards bridging the gap between an algorithmic learner and a human teacher’s mental model of that learner.
Found in Translation: semantic approaches for enhancing AI interpretability in face verification

The increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to enhance interpretability and transparency. This study extends previous work by integrating semantic concepts derived from human cognitive processes into XAI frameworks to bridge the comprehension gap between model outputs and human understanding. We propose a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks to generate similarity maps and textual explanations via large language models (LLMs). The methodology was validated through quantitative experiments and user feedback, demonstrating improved interpretability. Results indicate that our semantic-based approach, particularly the most detailed set, offers a more nuanced understanding of model decisions than traditional methods. User studies highlight a preference for our semantic explanations over traditional pixelbased heatmaps, emphasizing the benefits of human-centric interpretability in AI. This work contributes to the ongoing efforts to create XAI frameworks that align AI models behaviour with human cognitive processes, fostering trust and acceptance in critical applications.
[Empowerment and health literacy in medical rehabilitation - recommendations for strengthening patient education].

The recommendations aim to increase patient participation and health literacy by strengthening the role of patient education in medical rehabilitation. Research shows patient education to be effective and efficient; making the implementation of high quality patient education a top priority. To strengthen the role of patient education it is important to address known obstacles, identify potential for improvement, and define future demands for rehabilitative care. Led by the German Society for Medical Rehabilitation (DEGEMED), the Centre for Patient Education at the Würzburg University, and the Institute for Quality Management and Clinical Audit in Medical Rehabilitation (IQEM) an inter- and multidisciplinary panel of 28 experts from research and practice developed recommendations to further patient education in medical rehabilitation. The recommendations address topics such as the implementation of legal requirements under book IX of the German Social Code, SGB 9, structural and organisational prerequisites to promote the importance of patient education in rehabilitation units, the incorporation of quality criteria for patient education in quality assurance, quality management, and certification, as well as networking between medical rehabilitation and other health care sectors.Providers of medical rehabilitation hold the power to strengthen patient education: by implementing patient education programmes that are well-evaluated, manual-based, and standardised, by providing sufficient resources within their institutions, and by placing patient education in the centre of their quality policy, i.e. by nomination of a patient education representative. Stakeholders need to acknowledge these activities by incorporating quality criteria for patient education in clinical audit, and last but not least by adequate funding of medical rehabilitation.
Use  of composite rotations to  correct systematic errors in  NMR quantum computation

We implement an ensemble quantum counting algorithm on three NMR spectrometers with 1 H resonance frequencies of 500, 600 and 750 MHz. At higher frequencies, the results deviate markedly from naive theoretical predictions. These systematic errors can be attributed almost entirely to off-resonance effects, which can be substantially corrected for using fully compensating composite rotation pulse sequences originally developed by Tycko. We also derive an analytic expression for generating such sequences with arbitrary rotation angles.
A Pharmacokinetic/Pharmacodynamic Model to Predict Effective HIV Prophylaxis Dosing Strategies for People Who Inject Drugs

The goal of this work was to evaluate dosing strategies for tenofovir disoproxil fumarate (TDF), tenofovir alafenamide (TAF), and emtricitabine (FTC) for pre-exposure prophylaxis (PrEP) with injection drug use with a pharmacokinetic/pharmacodynamics analysis of concentration data generated from two single-dose clinical studies conducted in healthy women. Population pharmacokinetic models were developed using measured intracellular metabolite, endogenous nucleotide competitors, and extracellular parent drug concentrations. Intracellular metabolite concentrations were normalized to endogenous competitors and compared with an EC90 target for PrEP efficacy. Monte Carlo simulations were used to select effective dose strategies of single agents (TAF, TDF, and FTC) and combinations (TDF + FTC and TAF + FTC). Daily, intermittent, and event-driven dosing regimens at varying dosage amounts were explored. When combined, TDF + FTC and TAF + FTC both provided quick (0.5 hours) and durable (up to 84 and 108 hours, respectively) protection of ≥99% after a single dose. When dosed twice per week, protection remained at 100%. Single-agent regimens provided lower estimates of protection than either combination tested. Here, the application of pharmacokinetic modeling to in vitro target concentrations demonstrates the added utility of including FTC in a successful PrEP regimen. While no TAF-based PrEP data are currently available for comparison, this analysis suggests TAF + FTC could completely protect against percutaneous exposure with as little as two doses per week.
Methods applied in cost-effectiveness models for treatment strategies in type 2 diabetes mellitus and their use in Health Technology Assessments: a systematic review of the literature from 2008 to 2013

AbstractObjective:To identify and compare health-economic models that were developed to evaluate the cost-effectiveness of treatments for type 2 diabetes mellitus (T2DM), and their use within Health Technology Assessments (HTAs).Methods:In total, six commonly used databases were searched for articles published between October 2008 and January 2013, using a protocolized search strategy and inclusion criteria. The websites of HTA organizations in nine countries, and proceedings from five relevant conferences, were also reviewed. The identified new health-economic models were qualitatively assessed using six criteria that were developed based on technical components, and characteristics related to the disease or the treatments being assessed. Finally, the number of times the models were applied within HTA reports, published literature, and/or major conferences was determined.Results:Thirteen new models were identified and reviewed in depth. Most of these were based on identical key data sources, and applied a similar model structure, either using Markov modeling or microsimulation techniques. The UKPDS equations and panel regressions were frequently used to estimate the occurrence of diabetes-related complications and the probability of developing risk factors in the long term. The qualitative assessment demonstrated that the CARDIFF, Sheffield T2DM and ECHO T2DM models seem technically equipped to appropriately assess the long-term health-economic consequences of chronic treatments for patients with T2DM. It was observed that the CORE model is the most widely described in literature and conferences, and the most often applied model within HTA submissions, followed by the CARDIFF and UKPDS models.Conclusion:This research provides an overview of T2DM models that were developed between 2008 and January 2013. The outcomes of the qualitative assessments, combined with frequent use in local reimbursement decisions, prove the applicability of the CORE, CARDIFF and UKPDS models to address decision problems related to the long-term clinical and economic consequences of new and existing T2DM treatments.