ethicalabs/Research-Intent-Collab huggingface dataset:
Revolutionizing Supply Chains for Optimized Demand Planning, Inventory Management, and Logistics

AI-driven demand forecasting applications play a pivotal role in elevating forecast accuracy by scrutinizing historical demand and supply data. Machine learning models excel at uncovering latent patterns within historical demand data, resulting in reduced holding costs and the establishment of optimal inventory levels. These models can furnish detailed, region-specific demand insights, facilitating the customization of fulfillment processes based on region-specific requirements. The application of AI and machine learning extends across various facets of Supply Chain optimization, including demand forecasting, inventory management, and logistics automation. This technology aids in minimizing losses, cutting costs, and achieving more accurate forecasts of customer preferences. This chapter expresses the diverse aspects of AI and ML Solutions for Supply Chains for Optimized Demand Planning, Inventory Management and Logistics in the Modern Era.
Corporate social responsibility and business ethics as strategic drivers of employee motivation: An integrative analytical study

Purpose: This study examines the multidimensional impact of Corporate Social Responsibility (CSR) and business ethics on employee motivation within contemporary organizational environments. It addresses how CSR initiatives and ethical conduct influence internal organizational dynamics, including employee attitudes, engagement, and commitment, beyond traditional financial performance metrics. Method: The research adopts a systematic literature review and analytical research design. It integrates classical motivational theories—such as Maslow’s hierarchy of needs and Herzberg’s two-factor theory—with a structured synthesis of peer-reviewed academic sources, international reports (e.g., ILO, Deloitte), and contemporary empirical findings from global studies. Secondary quantitative data from previous research were comparatively analyzed to identify consistent motivational trends. Findings: The analysis reveals that CSR initiatives significantly enhance intrinsic motivation by fostering a sense of purpose and organizational identification. Statistics indicate that CSR-active organizations report 20–25% higher job satisfaction and 22% higher engagement rates than industry averages. Furthermore, ethical environments contribute to trust and psychological safety, with ethical leadership reducing workplace conflicts by 30%. Integrated CSR and ethics strategies demonstrate a synergistic effect on employee retention and productivity. Conclusion/Contribution: The study concludes that CSR and business ethics are strategic drivers essential for sustainable competitive advantage and resilient internal cultures. It contributes to organizational behavior literature by bridging theoretical frameworks with empirical global data. Practical recommendations include integrating CSR into human resource practices and fostering ethical leadership at all levels to enhance both employee well-being and organizational legitimacy.
Profiling and algorithms used for automated decision-making: the Influence of GDPR

The current study, in the form of a systematic literature review, considers the specific implications of using algorithmic calculations to make assumptions based on patterns in big data and the related decision-making process. The question is how algorithmic decision-making meaningfully differs from other forms of big data analysis and use, and what implications does that have for both business ethics and related privacy laws. Specifically, in the European Union, the measure of legality is according to the General Data Protection Regulation (GDPR), which is designed to protect the privacy of all EU citizens. However, there is a concern that the law is insufficient to protect against the more invasive nature of algorithmic configurations and to protect individual from the application of incorrect assumptions, that may result from their use. Thus, what possible revision of the GDPR is needed should be considered as it transmits to the primary research question, which is related to how profiling and algorithms are being used for automated decision-making, and how the national law or GDPR should be used to respond to those threats
DASI First Results: A Measurement of the Cosmic Microwave Background Angular Power Spectrum

8 pages, 1 figure, minor changes in response to referee comments
Using AI to generate visual art: Do individual differences in creativity predict AI-assisted art quality? PhD Candidate Researcher

As artificial intelligence (AI) advances in the realm of generative art, a critical question emerges: does human creativity matter? That is, do more-creative people produce more-creative AI-assisted artwork? To explore this, we conducted an online, pre-registered study in which we measured individual differences in creativity through two divergent-thinking tasks: The Alternate Uses Task and the Divergent Associations Task. Separately, participants produced creative wordsets for a hypothetical AI-art generator, which we then input into DALL-E to generate images. A group of trained raters independently assessed these images for creativity. Results revealed that both DAT performance and semantic diversity of the wordsets positively associated with the creativity of the AI-assisted images, suggesting that individuals with stronger divergent-thinking skills, and those who generated more-creative wordsets, tended to inspire more-creative AI-assisted artwork. Mediation analyses supported this conclusion by demonstrating a significant pathway between individual creative ability and AI-art creativity, mediated by semantic diversity. However, while our models yielded significant results, the effect sizes were modest, suggesting that the relationship between individual creative ability and AI-assisted creative outputs is relatively small. Taken together, these results suggest that while individual creativity appears to contribute to the quality of AI-assisted artwork, its influence may be relatively limited.
Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI

As artificial intelligence becomes more powerful and a ubiquitous presence in daily life, it is imperative to understand and manage the impact of AI systems on our lives and decisions. Modern ML systems often change user behavior (e.g. personalized recommender systems learn user preferences to deliver recommendations that change online behavior). An externality of behavior change is preference change. This article argues for the establishment of a multidisciplinary endeavor focused on understanding how AI systems change preference: Preference Science. We operationalize preference to incorporate concepts from various disciplines, outlining the importance of meta-preferences and preference-change preferences, and proposing a preliminary framework for how preferences change. We draw a distinction between preference change, permissible preference change, and outright preference manipulation. A diversity of disciplines contribute unique insights to this framework.
Generative Adversarial Privacy

We present a data-driven framework called generative adversarial privacy (GAP). Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary. We show that for appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries. We also evaluate GAP's performance on the GENKI face database.
Anthrax in Humans, Animals, and the Environment and the One Health Strategies for Anthrax Control

Anthrax is a notorious disease of public health importance caused by Bacillus anthracis. The causative agent can also be used as a biological weapon. Spores of these bacteria can sustain extreme environmental conditions and remain viable in soil for decades. Domestic and wild ruminants are highly susceptible to this pathogen, which usually presents as a peracute to acute disease. In humans, cutaneous anthrax is frequent but pulmonary and enteric anthrax are more serious. Humans, animals, and the environment are all involved, making anthrax a perfect target for a One Health approach. The environment plays a key role in disease transmission. At a time when the One Health concept is not mere slogans, collaborative efforts of medical professionals, veterinarians, and environmental scientists will be valuable for the prevention and control of this disease. In this review, we discussed the transmission dynamics of anthrax in the environment, animals, and humans, as well as One Health strategies to control and prevent anthrax.
Harnessing citizen science in preventive health research, policy and practice

Addressing complex problems in chronic disease prevention requires purposeful engagement with actors that can effect change, including the public. Citizen science approaches, which actively involve members of the public (known as “citizen scientists”) as collaborators in the research process are increasingly being recognized for their potential application in chronic disease prevention. This thesis aimed to explore how citizen science can support community engagement in research and decision making in preventive health, and where the opportunities and challenges lie for supporting wider uptake of citizen science in research, policy and practice. My thesis took a pragmatic constructivist approach, involving four studies. First, I present a published scoping review which explored how citizen science approaches have been applied and/or evaluated in prevention. I then present a rapid review and narrative synthesis examining how citizen science aligns with similar approaches to participatory research in public health. Next, I present a published mixed-methods study which explored policy and practice stakeholders’ perceptions of and experiences with citizen science, and their needs to utilise these approaches in their work. Finally, I report a published in-depth case study investigation exploring how and why citizen science has been utilised by two Australian government organisations in environmental and healthy ageing policy. Through this thesis, I demonstrate there is growing interest and untapped potential to harness citizen science in research, policy and practice in preventive health to support more innovative, meaningful and impactful community engagement processes and outcomes. However, capitalising on this potential and momentum requires ongoing development, distinction, and legitimation of citizen science approaches in preventive health.
Using Secondary Health Data in Research

The use of data in medical research that was originally collected for different purposes, known as secondary data, is an effective way to conduct reliable and cost-effective studies so as to progress knowledge in medicine. A number of serious practical, ethical and legal issues and concerns about this process exist, however. Ensuring a high level of data quality is imperative to produce reliable results, and researchers may face accessibility problems. Projects designed to alleviate these issues are underway, however, lowering the cost and increasing the access to secondary data even further. Although secondary data is de-identified to protect the confidentiality, ethical problems of individual rights versus the benefit of society persist, leading some to call for a new ‘macroethics’ surrounding data use. Legislation to this end has been introduced in many countries, but issues relating to the exemptions it offers and its interpretability remain. To ensure that the use of secondary data in medical research can continue to accelerate the pace of development in medicine, a global effort involving technological and ethical standardization needs to be developed.&#x0D;
Nurse Managers’ Leadership, Patient Safety, and Quality of Care: A Systematic Review

This review examined relationships between nurse unit managers’ leadership and patient safety and quality of care outcomes in hospital settings. Employing four databases (CINAHL, Embase, PubMed, and Web of Science) and ancestry searches, we identified 14 English-language, peer-reviewed, primary studies that examined such relationships using quantitative analyses. The studies were published between 2012 and 2021, and most used a cross-sectional design. Each study’s quality was assessed using the Mixed-Method Appraisal Tool. In synthesizing results by categorizing outcomes, we found a positive trend in associations between effective nurse unit manager leadership and better outcomes, including fewer patient adverse events, increased nurse error reporting, and higher patient satisfaction and care quality. However, the heterogeneity of leadership measurement approaches and outcomes limited integration of study findings. Future researchers should clearly define study concepts to support interpretation of their findings and should use a suitable theoretical framework to help determine appropriate units of analysis.
Global Connectivity under different Climate and Land-use futures: A Predictive Framework for Biodiversity Conservation

Climate change is a major driver of biodiversity loss, altering species distributions and increasing extinction risk through shifts in suitable habitats and ecological interactions. Reliable predictions of these responses are essential for conservation planning, ecosystem service assessments, and climate adaptation strategies. Species distribution models (SDMs) project future species distributions by relating occurrence data to environmental predictors. Although SDMs increasingly include factors like topography, dispersal, productivity, and land use, climate-only modeling remains common for isolating climate effects. Here, we employ climate-based SDMs to predict species occurrence as climate conditions change, and integrate these results with land-use projections to evaluate how both climate and landscape suitability impact species distribution.Maintaining interconnected landscapes is crucial for supporting ecological processes, including daily animal movements, dispersal from native habitats, gene flow, habitat recolonization, and range shifts driven by climate change. Because ecological processes and movement differ by species and type, determining priority areas for protection requires attention to specific connections, targeted processes, and relevant timescales. This study introduces a global predictive framework for terrestrial vertebrates that integrates changing climate and habitat suitability, and multi-directional connectivity under diverse socioeconomic-climate scenarios (SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5).Using Omniscape’s circuit-theory-based algorithm, we quantified normalized current flow for terrestrial vertebrates across historical (1981–2010) and future (2041–2070, 2071–2100) time periods. Our connectivity maps reveal areas where ecological flows are impeded, intensified, or channeled, enabling us to identify critical pinch-points whose loss could sever essential landscape connections. We aggregated species-level results to create global indicators, such as the Connectivity Change Index (Δ Normalized Current Flow), and identify hotspots where climate suitability may rise but connectivity declines, informing priorities for ecosystem protection (to maintain existing connectivity functions) or restoration (to create alternative routes and alleviate flow constrictions). Furthermore, this approach enables the integration of additional data to target habitats for species of concern.By integrating climate, land use, and connectivity analyses at the global scale, our framework guides evidence-based, adaptive management strategies, highlighting regions where investment in ecological corridors and habitat restoration will most effectively conserve biodiversity amidst ongoing climate and land-use change.
Adaptation and optimality in evolutionary biology: Historical and philosophical perspectives on the interpretations of R.A. Fisher's “Fundamental theorem of natural selection” and the “Formal Darwinism” project

International audience
Adaptive privacy-preserving federated learning

As an emerging training model, federated deep learning has been widely applied in many fields such as speech recognition, image classification and classification of peer-to-peer (P2P) Internet traffics. However, it also entails various security and privacy concerns. In the past years, many researchers have been carried out toward elaborating solutions to alleviate the above challenges via three underlying technologies, i.e., Secure Multi-Party Computation (SMC), Homomorphic Encryption (HE) and Differential Privacy (DP). Compared with SMC and HE, differential privacy is outstanding in terms of efficiency. However, due to the involvement of noise, DP always needs to make a trade-off between security and accuracy. i.e., achieving a strong security requirement has to sacrifice certain accuracy. To seek the optimal balance, we propose APFL, an Adaptive Privacy-preserving Federated Learning framework in this paper. Specifically, in the APFL, we calculate the contribution of each attribute class to the outputs with a layer-wise relevance propagation algorithm. By injecting adaptive noise to data attributes, our APFL significantly reduces the impact of noise on the final results. Moreover, we introduce the Randomized Privacy-preserving Adjustment Technology to further improve the prediction accuracy of the model. We present a formal security analysis to demonstrate the high privacy level of APFL. Besides, extensive experiments show the superior performance of APFL in terms of accuracy, computation and communication overhead.
Business ethics, corporate social responsibility and corporate governance : a review and summary critique

The success of modern business is apparent, but recently there is much concern in the business-and-society literature and in the general press on whether business fulfils its social role responsibly. Business ethics, corporate social responsibility and corporate governance movements have been developed in recent decades as responses to a growing sense of corporate wrongdoing. This paper attempts to explain why the three movements seem yet to have generated little in the form of widely accepted prescriptions for improvement of business behaviour to the satisfaction of the “constituents” of business, i.e. the major stakeholders. Without denying the usefulness of any of the three movements, the paper suggests that there are weaknesses in all three, especially concerning the way they conceive modern business operation. To this end business pluralism, responsive codes of practice and re-examination of the assumptions (conditions) of business operation could be helpful.
Kernearl-Based Lifelong Policy Gradient Reinforcement Learning

Policy gradient methods have been widely used in reinforcement learning (RL), especially thanks to their facility to handle continuous state spaces, strong convergence guarantees, and low-complexity updates. Training of the methods for individual tasks, however, can still be taxing in terms of the learning speed and the sample trajectory collection. Lifelong learning aims to exploit the intrinsic structure shared among a suite of RL tasks, akin to multitask learning, but in an efficient online fashion. In this work, we propose a lifelong RL algorithm based on the kernel method to leverage nonlinear features of the data based on a popular union-of-subspace model. Experimental results on a set of simple related tasks verify the advantage of the proposed strategy, compared to the single-task and the parametric counterparts.
Generation of optimal (de)centralized layouts for urban drainage systems: A graph-theory-based combinatorial multi-objective optimization framework

The suitability and effectiveness of decentralized paradigms in the field of underground conveyance drainage infrastructures for coping with the current and upcoming sustainability and resilience demands has been documented in recent research. However, a generic and holistic framework that can be quickly put in practice is lacking. To this end, this study proposes a scheme for creating and optimizing decentralization scenarios in terms of sewer layout configurations for both flat and steep terrains with the aid of a combinatorial graph-theory-based optimization. Additionally, a novel and generalized formula is derived to determine the degree of centralization coupled with three layout objective functions, thereby eliminating several feasible layout solutions. The results indicate that cherry picking an appropriate sewer structure (topological layout) at the planning stage can largely influence the design construction costs (reduction up to 49% for steep terrain and 25% for flat terrain), as well as the resilience response of the final network. Therefore, as a general rule of thumb, breaking down the centralized network into a decentralized one can reduce construction cost and improve resilience.
AI-Driven Cybersecurity Governance Framework for Critical Infrastructure Protection: Integrating Zero Trust, Digital Resilience, and Institutional Preparedness for Adaptive Cyber Defense

EXECUTIVE SUMMARY  Critical infrastructure sectors—including energy, healthcare, transportation, telecommunications, finance, water systems, and government services—are increasingly dependent on interconnected digital technologies. While these technologies improve efficiency, innovation, and service delivery, they simultaneously expand the attack surface available to cyber adversaries.  This study proposes an AI-Driven Cybersecurity Governance Framework for Critical Infrastructure Protection that integrates Artificial Intelligence (AI), Zero Trust governance, cyber risk scoring, operational resilience, and institutional preparedness into a unified governance architecture. The framework addresses a persistent challenge within cybersecurity practice: the disconnect between technical threat detection and governance-level cyber risk decision-making.  Drawing upon established cybersecurity frameworks including NIST Cybersecurity Framework 2.0, NIST Zero Trust Architecture, ISO/IEC 27001:2022, ENISA guidance, and CISA Cybersecurity Performance Goals, the study develops a seven-layer governance model that supports critical asset protection, AI-assisted threat detection, resilience planning, executive oversight, and continuous improvement.  The proposed Cyber Risk Score (CRS) model further enables organizations to prioritize cyber risks according to threat probability, asset criticality, exposure severity, business impact, and recovery readiness.  The framework contributes to cybersecurity governance research by providing a practical and scalable approach suitable for both developed and developing economies. It emphasizes that effective cybersecurity is no longer solely a technical challenge but a governance, resilience, and institutional preparedness challenge requiring coordinated leadership across technical, operational, and executive domains.  Keywords: Cybersecurity Governance, Artificial Intelligence, Critical Infrastructure Protection, Digital Resilience, Zero Trust, Cyber Risk Management, Institutional Preparedness, Cyber Resilience.