ethicalabs/Research-Intent-Collab huggingface dataset:
Genetic testing and insurance implications: Surveying the US general population about discrimination concerns and knowledge of the Genetic Information Nondiscrimination Act (GINA)

AbstractGlobally, due to public concerns of genetic discrimination, some countries and insurance industries have adopted policies restricting insurer use of genetic information, such as the US Genetic Information Nondiscrimination Act (GINA). This study reports on a combined analysis of two surveys assessing public knowledge of GINA and concerns of genetic discrimination in a diverse US sample (N = 1616). We focus on whether occupation, genetic testing history, and insurance status are correlated with knowledge of GINA or concerns of discrimination. While bivariate analysis identified some populations with higher subjective/objective knowledge and concern relative to counterparts, multivariable regression identified very few significant associations with outcomes of interest. Overall, this study highlights a lack of awareness and understanding of GINA, even among subpopulations hypothesized to have greater knowledge of the law. These findings have implications for the broader debate around insurer use of genetic information.
VT-KGNN: A Valid Time Knowledge Graph Neural Network

Temporal Knowledge Graphs, a form of a graph-structured knowledge bases, gained increased interest in both academia and industry in the last years. Most Knowledge Graphs suffer from in completeness, i.e. from facts which are valid in the real world that are not represented in the Knowledge Graph at hand. A set of methods to counterthis problem are Knowledge Graph Embeddings, which learn vector embeddings for the knowledge graph elements in a way that the underlying structure of the Knowledge Graphis preserved and utilize the learned embeddings to predict missing facts. Knowledge Graph Embeddings show good results but allow only transductive link prediction. In addition their integration of prior information of the entities as well as of temporal information is insufficient and they suffer from scalability problems for large graphs.In this thesis we propose the temporal knowledge graph neural network VT-KGNN. The model overcomes the limitations of Knowledge Graph Embeddings by using a multi relational adaption of local message passing graph neural networks as encoder and a Knowledge Graph Embedding score function as decoder. VT-KGNN especially focuses on the integration of temporal facts, more concretely on facts annotated with a valid time.It does not rely on snapshots but utilizes the temporal information by directly encoding it as a feature used in the Graph Neural Network. The model considers the start andend time of a fact individually, as well as in combination in the form of its duration. In addition it can utilize temporal relationships between facts that share entities.VT-KGNN is able to outperform existing state-of-the-art Knowledge Graph Embedding models for a dataset that allows harnessing the respective temporal patterns. In a dataset without such temporal patterns the performance is comparable to the state-of-the-art Knowledge Graph Embedding models.
Navigáció a változásban: a monetáris politika fejlődése és a központi banki digitális valuták (CBDC-k) ígérete Ghánában [Navigating Change: The Evolution of Monetary Policy and the Promise of Central Bank Digital Currencies (CBDCs) in Ghana]

Kulcsszavak: Központi banki digitális valuták; digitális gazdaság; Gazdasági fejlődés; Ghána; Pénzügyi befogadás Keywords: Central Bank digital currencies; digital economy; Economic development; Ghana; Financial inclusion JEL osztályozási kódok: E5, E50, E52, E58, G20, G28 Absztrakt Ez a tanulmány a monetáris politika történelmi evolúciójával foglalkozik, gyökereit az ősi civilizációktól a Federal Reserve System egyesült államokbeli megalakulásáig vezetve. Feltárja a mérföldkövek, például a Bretton Woods-i rendszer hatását a globális valutákra és a kereskedelmi hiányokra, kiemelve a monetáris elmélet és a valós világ gazdasági gyakorlata közötti dinamikus kölcsönhatást. Ezenkívül megvizsgálja Ghána kezdeményezéseit monetáris politikai környezetének korszerűsítésére vonatkozóan, különös tekintettel a központi banki digitális valutákra (CBDC). A tanulmány azokat a kihívásokat tárgyalja, amelyekkel Ghána szembesül a CBDC-k megvalósítása során, mint például a robusztus technológiai infrastruktúra szükségessége, a kiberbiztonsági kockázatokkal kapcsolatos aggodalmak és a külső tényezők, például az internetszolgáltatás megszakadásának hatásai. Ghána digitális valuta bevezetésére tett erőfeszítéseinek elemzésével és e kihívások kezelésével ez a tanulmány rávilágít a digitális valutarendszerre való átállás bonyolultságára és a feltörekvő gazdaságok gazdasági növekedésére gyakorolt ​​lehetséges előnyökre. This paper examines the historical evolution of monetary policy, tracing its roots from ancient civilizations to the establishment of the Federal Reserve System in the United States. It explores the impact of milestones such as the Bretton Woods system on global currencies and trade deficits, highlighting the dynamic interplay between monetary theory and real-world economic practice. It also examines Ghana's initiatives to modernize its monetary policy environment, with particular focus on Central Bank Digital Currencies (CBDCs). The paper discusses the challenges facing Ghana in implementing CBDCs, such as the need for robust technology infrastructure, concerns about cyber security risks and the effects of external factors such as internet service outages. By analyzing Ghana's efforts to introduce a digital currency and addressing these challenges, this paper highlights the complexities of transitioning to a digital currency system and the potential benefits for economic growth in emerging economies.
Construction and Research on the "Generative Inquiry" Teaching Model for Python Programming Course under the Empowerment of Artificial Intelligence Generated Content

With the rapid advancement of generative artificial intelligence technology, the intelligent transformation of education continues to deepen. To address long-standing issues in the Python programming course, such as significant cognitive disparities among students and difficulties in teaching abstract concepts, this study constructs an AIGC-empowered "Generative Inquiry" teaching model based on constructivist theory. This model positions students as the main actors and AIGC as a cognitive partner, systematically reconstructing teaching objectives, the roles of teachers and students, teaching processes, and evaluation systems through a "generate-validate-iterate" inquiry process, thereby providing a comprehensive solution to overcome the limitations of traditional teaching. This research not only offers an innovative practical paradigm for integrating intelligent technology into disciplinary teaching but also opens new pathways for cultivating students' computational thinking and innovative abilities. Subsequent studies will involve interdisciplinary validation and empirical research on the model's effectiveness.
Frankfurt school’s critical theory and a critique of video games as popular culture products

Frankfurt School’s critical theory provides a framework to study mass media. In Dialectic of Enlightenment, Adorno and Horkheimer, two of the most prominent theorists of the School, coined the term “culture industry” to discuss the state of the entertainment sector. However, the critical theorists of the Frankfurt School were either dead or they simply did not analyze the case of video games when the medium became an important agent both in social and economic terms. The main argument of this paper is that, as an inseparable part of the entertainment sector of today’s capitalist societies, video games are eligible for an analysis as products of the culture industry. It is argued in the study that the fantasy worlds of video games divert people’s attention away from the problems of capitalist societies. Further, the commodities are claimed to be getting increasingly standardized and are aimed at providing easy pleasures to consumers. Besides, a discussion of video games is carried out in accordance with the view that the video games of today seem to cater more to casual gamers than “serious” gamers, transpiring a dichotomy akin to that of high culture versus low culture.
Cancer systems biology in the genome sequencing era: Part 1, dissecting and modeling of tumor clones and their networks

Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones.
The MAT/TOCO Measurement of the Angular Power Spectrum of the Cosmic Microwave Background at 30 and 40 GHz

We present a measurement of the angular spectrum of the cosmic microwave background from l = 26 to 225 from the 30 and 40 GHz channels of the MAT/TOCO experiment based on two seasons of observations. At comparable frequencies, the data extend to a lower l than the recent Very Small Array and DASI results. After accounting for known foreground emission in a self-consistent analysis, a rise from the Sachs-Wolfe plateau to a peak of δTl ≈ 80 μK near l ≈ 200 is observed.
A Systematic Literature Review on Optimizing Freight Logistics in Multimodal Transportation: Emerging Trends and Potential Research Directions

The objective of this paper is to examine the development of multimodal transportation system to optimize freight logistics and emphasize its role in global trade and sustainability. The paper identifies key themes, recent advancements, and research gaps through bibliometric and thematic analysis of 178 research articles published over 45 years, in multimodal transportation. The analysis highlights seven major themes including planning, strategy, environmental impacts, and adoption of digital technologies such as artificial intelligence, blockchain, IoT. There is a rise in green logistics trends such as adoption of alternative fuels or electric vehicles which highlights the growing commitment to reduce environmental footprint. The growing interest in integrating technologies have led to considerable improvements in developed countries, enhancing efficiency and transparency. However, there are critical gaps in implementation in developing countries such as SAARC and BRICS (excluding China and Russia) face obstacles due to infrastructure, regulatory barriers, and economic conditions. Through a comprehensive analysis of the existing literature and research agenda, this study intends to direct the future research towards building a sustainable, resilient and efficient multimodal freight logistics by reducing lead times and operational costs.
Ethics, Corporate Social Responsibility, Sustainability, and  Governance in International Business

This essay examines ethics, corporate social responsibility, sustainability, and governance as key pillars of international business strategy. As firms expand across borders, they face diverse legal systems, cultural expectations, and stakeholder pressures, making ethical conduct and transparent governance increasingly important. Ethical behavior helps strengthen brand trust, reduce reputational risk, and create long-term competitive advantage. Corporate social responsibility initiatives, including community engagement and responsible supply-chain practices, support stronger stakeholder relationships and demonstrate commitment to social well-being. At the same time, sustainability has become a strategic necessity due to climate change, environmental regulation, and resource constraints, pushing firms toward low-carbon and circular business models. Effective governance connects these dimensions by promoting accountability, aligning management incentives with sustainable goals, and ensuring compliance with international standards. The essay argues that companies integrating ethics, CSR, sustainability, and governance into global strategy are better positioned to achieve legitimacy, resilience, and lasting success in international markets.
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks

Spiking Neural Network (SNN), as a brain-inspired approach, is attracting attention due to its potential to produce ultra-high-energy-efficient hardware. Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train an unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to a shallow network with only one learnable layer and cannot achieve satisfactory results when compared with multi-layer SNNs. In this paper, we eased this limitation by: 1)We proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in the Artificial Neural Network (ANN) literature. This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness. 2)We proposed a Pooling-Reshape-Activate (PRA) layer to make the Sp-Inception module stackable. 3)We stacked multiple Sp-Inception modules to construct multi-layer SNNs. Our algorithm outperforms the baseline algorithms on the hand-written digit classification task, and reaches state-of-the-art results on the MNIST dataset among the existing unsupervised SNNs.
Translating (im)personalisation in corporate discourse. A corpus-based analysis of Corporate Social Responsibility reports in English and Italian

Corporate Social Responsibility (CSR) reports constitute a relatively new form of corporate disclosure used by companies to present their values and philosophy with respect to socially relevant themes on which they may have an impact, mainly the environment, the community and employees. Companies thus publish CSR reports to communicate with a variety of stakeholders and provide information about their sustainability initiatives, with the ultimate aim of building, reinforcing, and promoting their corporate image. Personalisation plays an important role in the discursive construction of identity and in the definition of relationships between social actors. The personification of the company – obtained through 1st person plural deixis within corporate reports – is a very powerful rhetorical tool to convey a collective subject which takes responsibility for the actions and results it is giving account of, indicating and enacting a specific relationship with the reader. As a sociopragmatic item, however, it is largely language/culture dependent, and thus represents an interesting locus to observe the impact of translation strategies on the meaning conveyed to the target audience. This paper sets out to analyse how CSR reports translated into English from Italian compare – as regards personalisation – with reports originally produced in English, in order to detect differences in the way corporate identity is construed and conveyed. The study is based on a bilingual corpus which includes translated English reports and their Italian source texts, as well as comparable originals in English and Italian. Corroborating previous research conducted on similar genres, the study shows that (im)personalisation patterns are considerably different in original and translated English CSR reports, largely due to a tendency for the latter to reproduce Italian conventions in this form of specialised discourse
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting

Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions and processing them with a frozen LLM, guided by a textual prompt specifically designed for time-series data. Extensive experiments conducted on multivariate and univariate datasets demonstrate that LLM-Mixer achieves competitive performance, outperforming recent state-of-the-art models across various forecasting horizons. This work highlights the potential of combining multiscale analysis and LLMs for effective and scalable time-series forecasting.
Transforming evidence synthesis: A systematic review of the evolution of automated meta-analysis in the age of AI

Abstract                   Exponential growth in scientific literature has heightened the demand for efficient evidence-based synthesis, driving the rise of the field of automated meta-analysis (AMA) powered by natural language processing and machine learning. This PRISMA systematic review introduces a structured framework for assessing the current state of AMA, based on screening 13,216 papers (2006–2024) and analyzing 61 studies across diverse domains. Findings reveal a predominant focus on automating data processing (52.5%), such as extraction and statistical modeling, while only 16.4% address advanced synthesis stages. Just one study (approximately 2%) explored preliminary full-process automation, highlighting a critical gap that limits AMA’s capacity for comprehensive synthesis. Despite recent breakthroughs in large language models and advanced AI, their integration into statistical modeling and higher-order synthesis, such as heterogeneity assessment and bias evaluation, remains underdeveloped. This has constrained AMA’s potential for fully autonomous meta-analysis (MA). From our dataset spanning medical (67.2%) and non-medical (32.8%) applications, we found that AMA has exhibited distinct implementation patterns and varying degrees of effectiveness in actually improving efficiency, scalability, and reproducibility. While automation has enhanced specific meta-analytic tasks, achieving seamless, end-to-end automation remains an open challenge. As AI systems advance in reasoning and contextual understanding, addressing these gaps is now imperative. Future efforts must focus on bridging automation across all MA stages, refining interpretability, and ensuring methodological robustness to fully realize AMA’s potential for scalable, domain-agnostic synthesis.
Corporate Social Responsibility, Corporate Governance and Business Ethics in Tourism Management: A Business Strategy for Sustainable Organizational Performance

Drawing on research from around the world, this collection of essays explores key challenges, solutions and applications of business ethics, CSR, and corporate governance in the tourism industry.
Antimicrobial Stewardship as a Collective Imperative: Integrating Multidisciplinary Healthcare Expertise to Mitigate Antimicrobial Resistance

Background: Antimicrobial Resistance (AMR) constitutes a critical global health threat, undermining the efficacy of essential medicines and escalating morbidity, mortality, and healthcare costs. Traditional antimicrobial stewardship (AMS) programs have often been siloed within infectious diseases or pharmacy, overlooking the pivotal contributions of the broader healthcare team.  Aim: This narrative review aims to deconstruct the monolithic concept of AMS, reconceptualising it as an integrative, multidisciplinary "team sport." It seeks to synthesise evidence on the specific roles and responsibilities of diverse healthcare professions—including medical microbiology, laboratory science, nursing, respiratory therapy, dentistry, and midwifery—in a cohesive AMS strategy.  Methods: A comprehensive literature search was conducted across PubMed, Scopus, and CINAHL databases (2010-2024), focusing on empirical studies, reviews, and guidelines addressing multidisciplinary involvement in AMS.  Results: Effective AMS is predicated on seamless interprofessional collaboration. Key findings delineate profession-specific interventions: laboratory scientists enable rapid diagnostic stewardship; prescribers adhere to evidence-based guidelines; while nursing, allied health, dental, and midwifery professionals ensure optimal specimen collection, timely antimicrobial administration, patient education, and infection prevention. Successful programs are characterised by clear communication channels, shared accountability, and institutional support.  Conclusion: Combating AMR requires a paradigm shift from fragmented, specialist-driven models to embedded, collective ownership of stewardship principles across all healthcare disciplines. Institutional investment in interdisciplinary education, communication infrastructure, and role-specific resources is essential to operationalise this team-based approach.
Surveillance, Policing and Privacy in the Community of Rights

Abstract not available.
CORPORATE ETHICS AS A BASIS FOR SOCIALLY RESPONSIBLE BUSINESS

The importance of corporate ethics as the foundation of socially responsible business is growing due to rising public expectations, regulatory pressure and the need for sustainable economic development. Ethical business practices help to build trust between stakeholders, ensure long-term stability and strengthen corporate reputation, as well as positively influence the corporate culture of the company itself.  This study aims to analyze the role of corporate ethics in shaping responsible business strategies and its impact on financial and non-financial performance of the company and society.The study uses analytical and comparative methods to examine ethical frameworks, corporate policies and best practices, and to identify key factors that drive ethical decision-making in business. The findings show that companies with strong ethical commitments demonstrate greater employee engagement, customer loyalty, and investor confidence, which leads to sustainable competitive advantages. In addition, adherence to corporate ethics reduces legal risks, increases transparency, and fosters positive relations with the public and regulators. Conversely, unethical behavior leads to reputational damage, financial losses, and reduced market confidence.  The practical significance of the study is to provide strategic recommendations for business on the effective integration of ethical principles into corporate governance and their impact on the corporate culture of the enterprise, as well as employee motivation.  The proposed approaches can help organizations improve risk management, strengthen brand positioning, and align their activities with social and environmental goals of sustainable development. By integrating corporate ethics into their core strategies, businesses can achieve long-term growth while fulfilling their social responsibilities. It has also been proven that companies that are guided by ethical principles and social responsibility not only build a positive image, but also ensure the stability and efficiency of their operations in the face of global market changes.