ethicalabs/Research-Intent-Collab huggingface dataset:
Non–financial reporting as a communicative tool for corporate social responsibility

The article shows the growing importance of public non–financial reporting as a source of information on the performance of companies in the social, economic, and environmental spheres in the context of the current transformation of the socio–economic environment.  To substantiate the importance of non–financial reporting in the development of corporate social responsibility mechanisms taking into account the interests of stakeholders, an analysis of the main trends in the practical application of non–financial reporting by Russian companies for the period 2013–2024 was conducted.  Based on the analytical data obtained over a twelve–year period, the features of non–financial reporting related to the dynamics of its presentation by companies, the specifics of their industry affiliation, the need to improve its quality and level of information content, and the relevance of the reporting forms used were identified.  The directions of using public non–financial reporting are determined, reflecting its importance as a communicative tool for corporate social responsibility and the need for its effective application for the successful development of corporate social activity.
Privacy in the post General Data Protection Regulation (GDPR) World

This thesis seeks to analyze the motives that correlate to General Data Protection Regulation/ (GDPR 2016/679) one of the toughest privacy and security law in the world which passed by the European Union (EU) and took effect on May 25, 2018. The document’s aspects, and the purpose is to provide useful information on how this journey started and what is the current state. The GDPR BSc Thesis was conducted as a result of high personal interest and has been written to fulfill the graduation requirements of the Bachelor studies degree. The thesis was undertaken during a quite challenging time of period, considering personal situation balancing the different aspects of which I undertook the last years while the EU’s new data protection law affected many people’s lives. My research question was formulated together with my supervisor, Mr. Dimitris Varoutas. I anticipated to address challenges such as “What is the GDPR? Why is this law important and what part/s of it apply to me? How is it affecting the different industries etc.” which are concerns that most European or non- European citizens may have arisen. The purpose is to primarily provide useful information regarding this regulation, how did it all start, some key roles and responsibilities, the penalties and hopefully help you understand the people’s data privacy rights as well as further analyze the impact of this regulation in different sectors up to today. Nonetheless, conducting an extensive investigation has allowed me to answer many of the mentioned identified questions. Please note that nothing on this document constitutes legal advice rather than creating awareness.
Estimating soil carbon sequestration potential and approximating optimal management policies

The impact of a management intervention on the soil organic carbon (SOC) stored in a given volume of soil is moderated by features that determine that soil's sequestration potential under that intervention. To maximize total SOC sequestration cost efficiently, interventions should be targeted to soils with the highest responses and lowest intervention costs. We present a framework for estimating SOC sequestration potentials and approximating efficient management policies. We review relevant sources of measurement uncertainty and formalize policy choice using potential outcomes. An optimal sequestration policy can be approximated by modeling SOC measurements as functions of covariates within each treatment group, using the fitted models to estimate SOC sequestration potential for each plot, and finding the policy that maximizes the average of those estimates. The modeling can use linear regression or other algorithms to learn relationships between features and SOC sequestration potential. We demonstrate this method using data from a study of compost amendments applied to California rangelands. We find that the plots exhibit treatment effects moderated by baseline SOC -- so targeting amendments to plots with lower baseline SOC would increase overall SOC sequestration rates. We evaluate these methods further in simulated field experiments. Refined policy estimates sequestered more SOC than uniform application of the single management policy estimated to have the largest average treatment effect, especially when SOC sequestration potential could be predicted from observed features. We conclude by discussing baseline SOC moderation, observational studies, inference, cost models, and broader policy uncertainties.
Sexual and reproductive health and rights. Lobbying, contestations and compromises - An analysis of the challenges to an agreed language at the UN

International agreements dating back to the landmark International Conference on Population and Development (ICPD) Programme of Action in 1994, and the Beijing Declaration Platform for Action, 1995, have recognized the right to sexual and reproductive health and reproductive rights (SRHR), enabling women to make their own decisions over their bodies. These agreements committed states to provide universal access to sexual and reproductive health (SRH) services, including information and education, access to modern contraceptives, and safe abortion where legal. However, changes in the international political environment are demonstrating challenges in the protection of this agreed language on SRHR at the UN, having an impact on women’s access to SRH services globally. The broadening of the Mexico City Policy attempting to censor the language on SRHR, both domestically and internationally, as well as systematic lobbying of governments at the UN by conservative groups, is having a direct impact on the current global SRHR agenda setting. The aim of this study was to determine whether the efforts of the conservative opposition who seek to roll back the language on SRHR has been replicated in the UN outcome documents and resolutions between 2014 and 2019, before and after the latest reimplementation of the Mexico City Policy. This study presents the first empirical research on the disappearance of the language over time on the basis of a document review. The Health Policy Framework by Walt and Gilson (1994) allowed for an overview of the context in which the lobbying is taking place, map who the actors are that oppose SRHR, describe the process, and analyze the content of the documents. The results demonstrated a disappearance of the language on abortion in the CSW outcome documents, and a changing of the language on comprehensive sexuality education in the CSW and UN resolutions, which saw the removal of sexuality and placed an increased emphasis on the role of families. Furthermore, there was an inability of some states to accept sexual and reproductive health at all. This study has shown that the original agreed language from the ICPD and Beijing commitments are not safe from relentless opposition, and suggests that, going forward, funding for SRHR may need to look at more sustainable sources which are not subject to the international political environment.
The family of topological phases in condensed matter†

Abstract                The discovery of topological insulators and superconductors is an important advance in condensed matter physics. Topological phases reflect global properties of the quantum states in materials, and the boundary states are the characteristic of the materials. Such phases constitute a new branch in condensed matter physics. Here a historic development is briefly introduced, and the known family of phases in condensed matter are summarized.
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health

Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice.   This invited article has undergone anonymous review and is intended as a book chapter for the volume "Frontiers of Statistics and Data Science" edited by Subhashis Ghoshal and Anindya Roy for the International Indian Statistical Association Series on Statistics and Data Science, published by Springer. It covers the material from a short course titled "Artificial Intelligence in Precision and Digital Health" taught by the author Bibhas Chakraborty at the IISA 2022 Conference, December 26-30 2022, at the Indian Institute of Science, Bengaluru.
From Scripts to Platforms-as-Code: The Role of Terraform and Ansible in Declarative Infrastructure Rollouts

Between 2000 and 2020, the evolution of infrastructure provisioning reflected a decisive shift from manual, script-based administration toward fully declarative, code-driven automation enabled by Infrastructure-as-Code (IaC) principles. As enterprises embraced virtualization and later cloud-native architectures, the demand for scalable, reproducible, and policy-governed rollouts grew exponentially. IaC frameworks emerged as the foundation for this transformation, allowing infrastructure to be version-controlled, tested, and deployed through automated pipelines. Tools such as Terraform and Ansible became pivotal in unifying provisioning and configuration, enabling platform teams to define entire environments as reusable modules governed by CI/CD and GitOps workflows. This article contextualizes their role in institutionalizing automation and governance across hybrid and multi-cloud platforms, tracing the lineage from early configuration management systems like CFEngine, Puppet, and Chef to modern self-auditing
Probabilistic Algebraic Analysis of Fault Trees With Priority Dynamic Gates and Repeated Events

This paper focuses on a sub-class of Dynamic Fault Trees (DFTs), called Priority Dynamic Fault Trees (PDFTs), containing only static gates, and Priority Dynamic Gates (Priority-AND, and Functional Dependency) for which a priority relation among the input nodes completely determines the output behavior. We define events as temporal variables, and we show that, by adding to the usual Boolean operators new temporal operators denoted BEFORE and SIMULTANEOUS, it is possible to derive the structure function of the Top Event with any cascade of Priority Dynamic Gates, and repetition of basic events. A set of theorems are provided to express the structure function in a sum-of-product canonical form, where each product represents a set of cut sequences for the system. We finally show through some examples that the canonical form can be exploited to determine directly and algebraically the failure probability of the Top Event of the PDFT without resorting to the corresponding Markov model. The advantage of the approach is that it provides a complete qualitative description of the system, and that any failure distribution can be accommodated.
Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project

This report is part of the Qumphy project (22HLT01 Qumphy) that is funded by the European Union and is dedicated to the development of measures to quantify the uncertainties associated with Machine Learning algorithms applied to medical problems, in particular the analysis and processing of Photoplethysmography (PPG) signals. In this report, a list of six medical problems that are related to PPG signals and serve as Benchmark Problems is given. Suitable Benchmark datasets and their usage are described also.
Statistical inferences for functional data

With modern technology development, functional data are being observed frequently in many scientific fields. A popular method for analyzing such functional data is ``smoothing first, then estimation.'' That is, statistical inference such as estimation and hypothesis testing about functional data is conducted based on the substitution of the underlying individual functions by their reconstructions obtained by one smoothing technique or another. However, little is known about this substitution effect on functional data analysis. In this paper this problem is investigated when the local polynomial kernel (LPK) smoothing technique is used for individual function reconstructions. We find that under some mild conditions, the substitution effect can be ignored asymptotically. Based on this, we construct LPK reconstruction-based estimators for the mean, covariance and noise variance functions of a functional data set and derive their asymptotics. We also propose a GCV rule for selecting good bandwidths for the LPK reconstructions. When the mean function also depends on some time-independent covariates, we consider a functional linear model where the mean function is linearly related to the covariates but the covariate effects are functions of time. The LPK reconstruction-based estimators for the covariate effects and the covariance function are also constructed and their asymptotics are derived. Moreover, we propose a $L^2$-norm-based global test statistic for a general hypothesis testing problem about the covariate effects and derive its asymptotic random expression. The effect of the bandwidths selected by the proposed GCV rule on the accuracy of the LPK reconstructions and the mean function estimator is investigated via a simulation study. The proposed methodologies are illustrated via an application to a real functional data set collected in climatology.
Smooth inertial manifolds for neutral differential equations with small delays

In this paper, we study the dynamical behaviors of neutral differential equations with small delays. We first establish the existence and smoothness of the global inertial manifolds for these equations. Then we further prove the smoothness of inertial manifolds with respect to small delays for a certain class of neutral differential equations. The method can be also applied to deal with more general small-delay systems. Finally, we apply our main results to the van der Pol oscillator model with small delay.
Optimal sampling design for global approximation of jump diffusion stochastic differential equations

The paper deals with strong global approximation of stochastic differential equations (SDEs) driven by two independent processes: a nonhomogeneous Poisson process and a Wiener process. We a...
Correlation of the heliospheric current sheet tilts to galactic cosmic ray modulations and solar wind speed

Abstract   The comparison of the heliospheric current sheet tilts from 1971 to 1992 with cosmic ray intensities recorded at Deep River neutron monitor, solar wind speeds and sunspot numbers, has been investigated. Our results have indicated that the inclination of heliospheric neutral sheet is a suitable parameter for studying disturbances in the interplanetary medium. The observed differences in the sensitivity of cosmic ray intensity to changes in the neutral sheet tilt angles before and after the reversal of interplanetary magnetic field polarity have been studied. A much stronger intensity-tilt angle correlation was found when the solar magnetic field in the north polar region was directed inward than it was outward. The slope of the regression line for the 1981–1989 epoch (qA   0). A strong correlation was found between the inclination of neutral sheet and solar wind speeds in the ecliptic plane. Furthermore, an excellent correlation was obtained between sunspot numbers and tilt angles of neutral sheet and it was dependent on the reversal of the Sun's magnetic field. The physical significance of our results is discussed.
Calculation of the Probabilistic Safety Analysis and Reliability by the Fault Trees and Event Trees Methods

One of the main requirements for ensuring a high level of safety and economic efficiency of nuclear power units at all stages of the life cycle - designing new ones, operating existing power units and decommissioning them - is a probabilistic safety analysis of nuclear power units. The most widely used method for probabilistic safety analysis is the fault tree method. NPP power units are a complex system consisting of a large number of units of equipment, systems and units that are interconnected functionally and affect each other. In addition, to increase the adequacy of the developed probabilistic model of a power unit, it is necessary to take into account equipment failures for general reasons and the human factor. The resulting in-depth probabilistic models of power units can contain tens of thousands of fault trees and, as a result, hundreds or more of thousands of minimum sections and require lengthy calculations to obtain acceptable accuracy of the results. This complicates the application of this method, especially when monitoring risk in real time, when it is necessary to promptly make changes to the model and assess the impact of these changes on the current risk. The novelty of the project is the use of a modified modularization method, which significantly accelerates the generation of many minimal sections.
Presenteeism Among Nurses in Switzerland and Portugal and Its Impact on Patient Safety and Quality of Care: Protocol for a Qualitative Study (Preprint)

BACKGROUND                         Nurses dispense direct care in a wide variety of settings and are considered the backbone of the health care system. They often work long hours, face emotional stress, and are at a high risk of psychosocial and somatic illnesses. Nurses sometimes fall sick but work regardless, leading to presenteeism and subsequent risks to quality of care and patient safety due to the increased likelihood of patients falling, medication errors, and staff-to-patient disease transmission.                                                                       OBJECTIVE                         This study aims to understand presenteeism among frontline nurses and nurse managers in acute, primary, and long-term health care settings and to contribute to the development of future interventional studies and recommendations.                                                                       METHODS                         A qualitative study based on online focus group discussions will explore the perceptions of, attitudes to, and experiences with presenteeism among frontline nurses and nurse managers. Using a pilot-tested interview guide, 8 focus group discussions will involve nurses working in acute care hospitals, primary care settings, and long-term residential care facilities in Switzerland’s French-speaking region and Portugal’s Center region. The data collected will be examined using a content analysis approach via NVivo 12 QSR International software.                                                                       RESULTS                         The University of Applied Sciences and Arts Western Switzerland’s School of Health Sciences and the Polytechnic of Leiria’s School of Health Sciences in Portugal have both approved funding for the study. The research protocol has been approved by ethics committees in both countries. Study recruitment commenced in February 2021. The results of the data analysis are expected by September 2021.                                                                       CONCLUSIONS                         This present study aims to gain more insight into the dilemmas facing nurses as a result of all causes of presenteeism among frontline nurses and nurse managers in different health care settings. The researchers will prepare manuscripts on the study’s findings, publish them in relevant peer-reviewed journals, exhibit them in poster presentations, and give oral presentations at appropriate academic and nonscientific conferences. Regarding further knowledge transfer, researchers will engage with stakeholders to craft messages focused on the needs of nurses and nurse managers and on disseminating our research findings to deal with the issue of nursing presenteeism.                                                                       INTERNATIONAL REGISTERED REPORT                         PRR1-10.2196/27963
Energy-Based Models for Continual Learning

We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing models, or regularization, EBMs change the underlying training objective to cause less interference with previously learned information. Our proposed version of EBMs for continual learning is simple, efficient, and outperforms baseline methods by a large margin on several benchmarks. Moreover, our proposed contrastive divergence-based training objective can be combined with other continual learning methods, resulting in substantial boosts in their performance. We further show that EBMs are adaptable to a more general continual learning setting where the data distribution changes without the notion of explicitly delineated tasks. These observations point towards EBMs as a useful building block for future continual learning methods.
Projected Changes to Tropical Cyclones in the Southwest Indian Ocean Using High‐Resolution Global Climate Models

Abstract                   Tropical Cyclones (TCs) affecting southeastern Africa are poorly understood, due to limited focused research and challenges in modeling TCs. Higher resolution climate models have potential to provide more reliable projections. In this paper, we analyze the latest global high‐resolution simulations (“HighResMIP”), to evaluate what these models tell us about current and future TCs in the southwest Indian Ocean. TC tracks are identified by two tracking algorithms in 10 Global Climate Models (GCMs) and we compare to observations and reanalysis. GCMs capture TCs in the same region as observations during the austral summer cyclone season. However, TCs are too weak, not reaching equivalent low pressures to observed storms, but are more intense in high‐resolution GCMs (28–55 km) than lower resolution GCMs (≥78 km). We find little improvement in the representation of TCs with increasing resolution, beyond TC intensity. There is no one “best” model for simulating TCs, with notable variations between models in TC climatologies. In experiments of near future climate change (to 2050), there are some consistent signals between models, with nearly all models showing fewer TCs in future. Many models indicate a south‐westward shift in TCs. Only the highest resolution model indicates the Lifetime Maximum Intensity of TCs become more intense. All models indicate more favorable oceanic and atmospheric conditions for stronger and more impactful TCs in the Mozambique Channel, including an increase in the Potential Intensity. This study provides an essential benchmark for future research using forthcoming km‐scale simulations.