ethicalabs/Research-Intent-Collab huggingface dataset:
Affirmation of Principles and Improved Corporate Governance in Serbia - Business Ethics and Corporate Social Responsibility

The aim of this chapter is to examine whether corporate governance in Serbia is based on affirmation of responsible and ethical conduct. The concept of corporate governance refers to the system by which companies are managed and controlled in order to generate long term economic value for its shareholders, while respecting the interests of stakeholders and society as a whole. Companies’ responsibility towards their stakeholders is recognized as a principle of good corporate governance. In this chapter, we present results of the poll on the managers’ attitudes towards business ethics and corporate social responsibility in Serbian business environment. We found out that managers are uniformed in the belief that companies have responsibilities towards their stakeholders: employees, business partners, suppliers, customers, community and environment. In addition, they are of the opinion that ethical behaviour and business success could go along, and also that immoral conduct is not justified in business. Nevertheless, most of examined managers see current business environment in Serbia as an uncompromising struggle. We can conclude that while managers’ attitudes towards business form a solid basis for the affirmation of principles and improved corporate governance, the perception of business environment as an uncompromising struggle indicate that current business practice in Serbia, in fact, hinder ethical and responsible conduct and reflect its opposite.
PRODUCTION OF LITERATURE ABOUT HUMAN SEXUAL AND REPRODUCTIVE RIGHTS IN HEALTH CARE AND ITS RELATIONSHIP WITH NURSING

Resumo: Os direitos humanos são atributos que todas as pessoas têm, sem distinção de idade, raça, nacionalidade, gênero, classe social ou forma de pensar. Dentre os vinculados à saúde temos os sexuais e os reprodutivos que são temas presentes na assistência de enfermagem. OBJETIVO: conhecer a produção bibliográfica acerca do assunto e analisar a prática da enfermagem na promoção desses direitos a partir da bibliografia levantada. MÉTODOS: Trata-se de um estudo qualitativo de natureza exploratória do tipo pesquisa bibliográfica. RESULTADOS: O levantamento ocorreu nas bases de dados da BIREME. Foram encontrados 114 artigos de 2004 a 2008. Após o refinamento que consistiu na retirada de artigos repetidos em base de dados diferentes e afinidade com o tema proposto e conseqüentemente sua relevância para o estudo, o número total de artigos selecionados para entrarem no processo de análise consistiu em 46. CONCLUSÃO: Os resultados mostram uma incipiente produção nesta área pela enfermagem. Refletindo assim, não só a necessidade de aprofundamento do tema pelos profissionais de saúde como também sua posterior publicação como fruto científico. Descritores: Direitos sexuais e reprodutivos, Enfermagem, Pesquisa.
The Development of Resilience Management Guidelines to Protect Critical Infrastructures in Europe

The capability to be resilient in the face of crises and disasters is a topic of highest political concern in Europe especially as far as critical infrastructures and urban environments are concerned. Critical infrastructures are systems or part of systems essential for the maintenance of vital societal functions, the disruption or destruction of which would have a significant impact on the well-being of people. Examples of them are transportation services, energy infrastructures, water and wastewater systems, health and emergency services, financial services, communication infrastructures, etc. The symposium focuses on the experience of four different projects funded under the Horizon 2020 Programme: DARWIN, RESILIENS, RESOLUTE, SMR. The projects are all dealing with the application of resilience engineering, community resilience and urban resilience concepts to concrete examples of crises and situations of emergency. Such principles are translated into guidelines covering different resilience abilities that the organizations managing critical infrastructure should possess.
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to adress challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.
From Images to Insights: The Role of AI in Radiology Report Generation

Radiology reports are important for diagnosing and managing a variety of health conditions, but manually crafting them is always laborious and often tiresome for radiologists. Machine learning has made significant advances and is beginning to show promise to help automate this process in recent years by enabling the production of reports from a clinical image, such as a chest X-ray or CT scan. This paper provides a review of the state of machine learning in Radiology Report Generation. We focus on the ability to use Convolutional Neural Networks (CNNs) to understand image data as well as transformer-based models to produce text data. In addition, we will examine the newer multimodal models which produce output combining image and text for potentially more accurate and contextually aware reports. Furthermore, our review highlights important observations, particularly the growing use of Deep Learning models and large medical datasets (e.g., MIMIC-CXR, CheXpert). Although there has been advancement, challenges remain such as clinically inaccurate results, lack of explainability, and inadequate variety of data. In summary, we conclude that although machine learning has a strong potential to significantly reduce work for radiologist and add consistent reporting, substantial additional work needs to be done to bring these systems to greater trust and reliability for real-world clinical settings.
The Protection of the Interests of Future Generations in the 10-Year-Old Hungarian Constitution, With Special Reference to the Right to a Healthy Environment and Other Environmental Issues

The present study is inspired by the tenth anniversary of the new Hungarian Constitution, known under the name of Fundamental Law, which was adopted in 2011 and entered into force in 2012. In this study we analyse the ten-year old Fundamental Law and its constitutional practice with regard to the important challenges and tasks of the 21st century, namely how the protection of the interests of future generations and the environment are reflected in it. Particularly important elements of the study are (a) the institutional guarantees of the relevant provisions, such as the provisions relating to the Constitutional Court and the Advocate of Future Generations, (b) the concept of GMO-free agriculture in the Fundamental Law, (c) the theses of the Constitutional Court practice on the prohibition of retrogression and the precautionary principle, (d) new interpretative frameworks and possibilities arising from other values of the Fundamental Law, such as the provisions on Christian culture, (e) the open questions of interpretation of the Fundamental Law on waste and the environmental liability regime, (f) the priority protection of natural resources, which are the common heritage of the nation, and last but not least (g) the particularly forward-looking integration of the interests of future generations in the rules on public finances and national assets.
The Right to Privacy V/S National Security: Examining Media Surveillance Laws in India

This research examines the intricate legal and ethical issues of right to privacy versus national security in the context of media surveillance acts in India. Since surveillance activities with the use of the digital platform have been becoming increasingly prevalent, concerns about the constitutional validity, transparency, and proportionality of state-regulated media monitoring procedures have been arising. The article examines the laws that form the basis of Indian surveillance in the form of the Information Technology Act (2000), the Telegraph Act (1885), the Aadhaar Act, and the forthcoming Personal Data Protection Bill. The laws are examined against Article 21 of the Indian Constitution and the Supreme Court judgment in Justice K.S. Puttaswamy v. Union of India (2017), which established privacy as a constitutional right. The study employs qualitative, doctrinal research method founded on content analysis of legislative documents, case law, policy guidelines, and secondary scholarly literature. Comparative insights are made from foreign surveillance models to contrast India's model against a comparative model. The evidence shows serious legal and ethical issues, specifically the absence of judicial review, transparency gaps, and chilling effects on freedom of the press. The evidence points towards a rebalanced regime of surveillance based on accountability, democratic protection, and constitutional fidelity.
A study on deep learning and explainable &amp; interpretable AI: From general domain to healthcare

Deep learning has witnessed an unprecedented evolution over the past decade, transforming from theoretical concepts into practical applications that permeate numerous domains of human activity. The exponential growth in computational power, availability of large-scale data, and advancements in neural network architectures have collectively facilitated the development of increasingly sophisticated deep learning models (e.g., large language models, LLMs) with performance levels that often surpass human capabilities in specific tasks. Despite these advancements, the deployment of deep learning models for healthcare presents substantial methodological and paradigmatic challenges. The transition from general to healthcare-specific contexts requires addressing fundamental differences in (i) representation learning, (ii) domain knowledge, (iii) data characteristics and (iv) explainability and interpretability. To address these issues, this study aims to systematically investigate the methodological and paradigmatic transition of deep learning and explainable &amp; interpretable AI from general domain to healthcare. Our contributions are in following key areas: (i) we introduce multi-label relations in multi-label supervised contrastive learning (MSCL) and propose a novel contrastive loss function, termed Similarity-Dissimilarity Loss, which dynamically re-weights based on the computed similarity and dissimilarity factors between positive samples and anchors, applying for areas from multi-label classification to automated medical coding; (ii) We propose a Prompting Explicit and Implicit knowledge (PEI) framework for multi-hop question answering (QA) in biomedical domains, which employs CoT prompt-based learning to bridge explicit and implicit knowledge, aligning with human reading comprehension; (iii) we introduce a lexical-based imbalanced data augmentation (LIDA) for mental health moderation, which an easy-to-implement and interpretable DA method that strategically leverages sensitive lexicons by incorporating them into negative samples to transform these instances into positive examples. Through rigorous theoretical analyses and extensive experimental validation across multiple domains, this thesis contributes novel methodologies that enhance the performance, interpretability, and clinical applicability of deep learning methods from general domain to healthcare.
Empirical analysis of the situational variables and their impact on a manager's leadership in influencing employee organizational citizenship behaviors in Korean hotels

The purpose of this study is to identify situational variables that may influence the effect of a manager's leadership on subordinate organizational citizenship behavior (OCB) in the Korean hotel industry; Data were collected from 107 managers and their 487 subordinates across a wide variety of different departments in the deluxe business hotels in Korea; Thirteen hypotheses were tested using a partial correlation coefficient test in multiple regression to examine the correlation between thirteen situational variables and five employee OCB dimensions. The test generally showed that selected characteristics of a manager, task, and organization either substitute or enhance a manager's leadership in influencing employee OCB by having either positive or negative correlation with selected OCB dimensions; The findings of this study suggested that employee OCB can be maximized when the positive situational variables operate in concert with a manager's leadership. The findings also suggested that a manager's leadership should be enhanced when a negative situational variable influences employee OCB.
Predictions of the Angular Power Spectrum of Clustered Extragalactic Point Sources at Cosmic Microwave Background Frequencies from Flat and All‐Sky Two‐dimensional Simulations

We present predictions of the angular power spectrum of cosmic microwave background (CMB) fluctuations due to extragalactic point sources (EPSs) by using a method for simulating realistic two-dimensional distributions of clustered EPSs. Both radio- and far-IR-selected source populations are taken into account. To analyze different clustering scenarios, we exploit the angular power spectra of EPSs, P(k), estimated either by data coming from currently available surveys or by means of theoretical predictions. By adopting the source number counts predicted by the Toffolatti et al. evolution model, capable of accounting well for the available data at radio (centimeter) wavelengths, we are able to reproduce current data on the two-point angular correlation functions, w(?), of radio sources. We can confirm that the detection of primordial CMB anisotropies is not hampered by undetected clustered sources at frequencies ?150-200 GHz. On the other hand, our current findings show that at higher frequencies the clustering signal could severely reduce the detectability of intrinsic CMB anisotropies, thus confirming previous theoretical predictions. We also show that unsubtracted EPSs can account for the excess signal at high multipoles detected by recent CMB anisotropy experiments. Moreover, the additional power due to the clustering of sources gives rise to a small but nonnegligible contribution to the same excess signal. As a final result, we also present an example of a currently feasible realistic map of EPSs at 70 GHz, by taking into account data on bright detected sources, as well as the previously quoted model for number counts. Eventually, these simulated sky maps could prove very useful for testing the efficiency of component separation techniques, the capability of new algorithms for the detection of EPSs, and the appearance of non-Gaussian signatures in residue CMB maps, in the presence of sources that are not Poisson-distributed in the sky.
Optimizing the Dutch Adult Diet for 2030 and 2050 for Health and Sustainability, Based on EAT-Lancet Environmental Planetary Boundaries

The current Dutch diet fails to meet existing nutritional guidelines, and exceeds the environmental boundaries set for a sustainable food system. We demonstrated that, if there are no changes to the current diet, the Earth system’s boundaries will be further exhausted. Therefore, it is of importance to decrease the environmental impact of the Dutch diet while meeting relevant nutritional recommendations. A future planet-based diet for the Netherlands was created using Optimeal®, a diet optimization software, the most recent food consumption survey, and environmental impact) data for the Netherlands (Agri-footprint 6 database) for males and females (18–50 years). The environmental data was adjusted to incorporate forecasted improvements in 2030 and 2050 and to align with the scope of the planetary boundaries defined by the EAT-Lancet commission. The planetary boundaries translated into optimization constraints with a system boundary from cradle-to-processing for five environmental indicators (GHG emissions, blue water use, cropland use, phosphorus application, and surplus nitrogen), and the nutritional constraints were based on the Dutch food-based dietary guidelines. Furthermore, acceptability constraints were placed on food group consumption: 33–150% of current intake. The results show the optimized diets that meet the environmental and nutritional constraints for Dutch males and females in 2030 and 2050. The required changes in intake point towards a reduction in meat, eggs, fats and oils, potatoes and tubers, and sugar and confectionery, and an increase in legumes, nuts and seeds, vegetables, fish, and meat replacers. In the optimized diets, the main source of protein is 64-74% derived from plant-based products, instead of animal-based products, which is the case for the current diet. The optimal diets reduce the impacts on biodiversity loss by 55-84%. Although it contains a substantial change in protein source, the diet remains acceptable for the majority of consumers in this study, and fits within the planetary boundaries. These results contribute to the development of future planet-based dietary guidelines for the Netherlands.
Interoperable health information exchange between medication therapy management services and the medical home

Medication therapy management (MTM) is an important new service delivered by pharmacists in ambulatory and institutional settings.[1][1],[2][2] MTM includes a comprehensive medication therapy review, the development of a fully reconciled personal medication record (PMR), the creation of a medication
Preserving Privacy and Security: A Comparative Study of Health Data Regulations - GDPR vs. HIPAA

Abstract: The digital revolution has irreversibly transformed the healthcare landscape, revolutionizing the way health data is managed and raising critical questions about its protection. The seamless integration of technology has propelled healthcare into a new era of interconnectedness and data-driven innovation, presenting both unprecedented opportunities and challenges. In this context, this paper conducts a comprehensive comparative study of two pivotal regulatory frameworks: the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). These regulations, enacted on different continents, serve as cornerstones in the protection of health data and offer profound insights into the evolving landscape of data privacy and security in the digital age.The surge in digital health platforms, electronic medical records, and data-sharing networks has ushered in an era where personal health information is more vulnerable and valuable than ever before. The GDPR, a landmark legislation within the European Union, empowers individuals with control over their personal data and sets stringent standards for data protection. On the other side of the Atlantic, HIPAA plays a fundamental role in regulating health data in the United States, emphasizing the confidentiality, integrity, and availability of electronic health information.This study aims to unravel the intricate tapestry of health data protection by juxtaposing the GDPR and HIPAA. By exploring their commonalities and disparities, this analysis offers insights into the multifaceted dimensions of safeguarding health data in an interconnected world. Delving into the legal principles, individual rights, organizational responsibilities, security prerequisites, procedural intricacies, and enforcement mechanisms embedded within these frameworks, this study seeks to inform stakeholders and decision-makers about the critical nuances of health data governance.Employing a comprehensive research approach that draws from primary legal sources, scholarly interpretations, case studies, and comparative analyses, this study enriches our understanding of the intricate interplay between regulation and technology. By shedding light on the regulatory strategies of the GDPR and HIPAA, stakeholders can better navigate the intricate terrain of health data protection and contribute to the development of a privacy-respecting and technologically vibrant healthcare ecosystem.
Statistical mechanics of soft-boson phase transitions

The existence of structure on large (100 Mpc) scales, and limits to anisotropies in the cosmic microwave background radiation (CMBR), have imperiled models of structure formation based solely upon the standard cold dark matter scenario. Novel scenarios, which may be compatible with large scale structure and small CMBR anisotropies, invoke nonlinear fluctuations in the density appearing after recombination, accomplished via the use of late time phase transitions involving ultralow mass scalar bosons. Herein, the statistical mechanics are studied of such phase transitions in several models involving naturally ultralow mass pseudo-Nambu-Goldstone bosons (pNGB's). These models can exhibit several interesting effects at high temperature, which is believed to be the most general possibilities for pNGB's.
Persistent Intersection Homology for the Analysis of Discrete Data

Topological data analysis is becoming increasingly relevant to support the analysis of unstructured data sets. A common assumption in data analysis is that the data set is a sample---not necessarily a uniform one---of some high-dimensional manifold. In such cases, persistent homology can be successfully employed to extract features, remove noise, and compare data sets. The underlying problems in some application domains, however, turn out to represent multiple manifolds with different dimensions. Algebraic topology typically analyzes such problems using intersection homology, an extension of homology that is capable of handling configurations with singularities. In this paper, we describe how the persistent variant of intersection homology can be used to assist data analysis in visualization. We point out potential pitfalls in approximating data sets with singularities and give strategies for resolving them.
Machine Learning Assisted Adjustment Boosts Efficiency of Exact Inference in Randomized Controlled Trials

In this work, we proposed a novel inferential procedure assisted by machine learning based adjustment for randomized control trials. The method was developed under the Rosenbaum's framework of exact tests in randomized experiments with covariate adjustments. Through extensive simulation experiments, we showed the proposed method can robustly control the type I error and can boost the statistical efficiency for a randomized controlled trial (RCT). This advantage was further demonstrated in a real-world example. The simplicity, flexibility, and robustness of the proposed method makes it a competitive candidate as a routine inference procedure for RCTs, especially when nonlinear association or interaction among covariates is expected. Its application may remarkably reduce the required sample size and cost of RCTs, such as phase III clinical trials.