ethicalabs/Research-Intent-Collab huggingface dataset:
Generic Dijkstra for optical networks

We present the generic Dijkstra shortest path algorithm: an efficient algorithm for finding a shortest path in an optical network, both in a wavelength-division multiplexed network, and an elastic optical network (EON). The proposed algorithm is an enabler of real-time softwarized control of large-scale networks, and is not limited to optical networks. The Dijkstra algorithm is a generalization of the breadth-first search, and we generalize the Dijkstra algorithm further to resolve the continuity and contiguity constraints of the frequency slot units required in EONs. Specifically, we generalize the notion of a label, change what we iterate with, and reformulate the edge relaxation so that vertices are revisited, loops avoided, and worse labels discarded. We also used the typical constriction during edge relaxation to take care of the signal modulation constraints. The algorithm can be used with various spectrum allocation policies. We motivate and discuss the algorithm design, and provide our free, reliable, and generic implementation using the Boost Graph Library. We carried out 85000 simulation runs for realistic and random networks (Gabriel graphs) of 75 vertices with about a billion shortest-path searches, and found that the proposed algorithm outperforms considerably three other competing optimal algorithms that are frequently used.
Bridging Nutritional and Environmental Sustainability Within Planetary Boundaries in Food Life Cycle Assessments: SWOT Review and Development of the Planet Health Conformity Index

To promote sustainable food choices, it is essential to provide easily understandable information that integrates health, environmental impacts and planetary boundaries. For this purpose, the Planet Health Conformity Index (PHC) was developed and tested. Current labels, such as the Nutri-Score for health and the Eco-Score for environmental impacts, provide separate information, which may result in consumers receiving conflicting messages. The PHC combines these dimensions into a single label, aligning with consumer demand for clearer guidance and fostering sustainable food consumption and development. Methods: The PHC assesses 18 nutrients and five environmental impacts—Global Warming Potential (GWP), cropland use, freshwater use, nitrogen application (N-min) and phosphorus application (P-min)—within the framework of planetary boundaries. Six different algorithm designs, varying in capping and weighting, were tested on 125 food products from the German market. The analysis compared mass-, energy- and multi-nutrient-based functional units. Results: Under mass- and energy-based units, many products meet planetary boundaries. However, incorporating nutrient profiles often leads to exceeding these boundaries (exceedance rate PHC: GWP: 38% of products transgressed the boundary, cropland use: 41%, freshwater use: 27%, N-min: 34%, P-min: 71%). Accordingly, the PHC contextualizes nutritional strengths and weaknesses environmentally. Moreover, it disaggregates the Planetary Health Diet (PHD) at the nutrient level, facilitating adaptation to individual nutritional needs. Conclusions: Traditional food Life Cycle Assessments should include nutrients in the functional unit and consider planetary boundaries to enable more accurate food comparisons. The PHC presented here takes these aspects into account. In addition, its dual-factor approach, integrating health and environmental metrics, ensures broad applicability. Thus, the PHC Index can be applied not only to single food items but also to recipes, dishes, menus and entire diets.
Emerging Trends in Business Ethics, Corporate Social Responsibility, and Sustainability

The second chapter explores the evolution of the concepts of business ethics, corporate social responsibility (CSR), and corporate sustainability (CS). It highlights the main advantages and limits of these approaches. The chapter points out how changes in the field of business ethics and CSR lead to the progressive affirmation of CS. This is considered a new business approach in guiding the life of an organisation. CS is analysed with three components: (1) people, (2) profit, and (3) planet. Finally, the work recommends areas for further discussion and research.
Efficacy of constitutional support to enhance access to essential medicines as a human right to health in the Eastern Mediterranean Region

Access to essential medicines is an element of the international agreements on the human right to health. This review summarizes the current situation concerning access to medicines in the Eastern Mediterranean Region (EMR) by examining the policies, constitutional provisions and other legal instruments of the Member States. The constitutions of 18 out of 22 EMR countries enshrine health as a human right (8 countries have a duty statement, 5 have a programmatic statement and 5 specify entitlement); only 4 EMR countries do not enshrine health as a human right in a clause in their constitution. More than half the countries (i.e. 12) have an official national medicines policy, 4 have a draft policy and 6 have no national medicines policy. A total of 11 countries operate an essential medicines list. Realization of this right to health necessitates that duty bearers take all necessary legislative measures to respect, protect and fulfil this right.
Semantic-based Detection of Segment Outliers and Unusual Events for Wireless Sensor Networks

Environmental scientists have increasingly been deploying wireless sensor networks to capture valuable data that measures and records precise information about our environment. One of the major challenges associated with wireless sensor networks is the quality of the data and more specifically the detection of segment outliers and unusual events. Most previous research has focused on detecting outliers that are errors that are caused by unreliable sensors and sensor nodes. However, there is an urgent need for the development of new tools capable of identifying, tagging and visualizing erroneous segment outliers and unusual events from sensor data streams. In this paper, we present a SOUE Detector (Segment Outlier and Unusual Event-Detector) system for wireless sensor networks that combines statistical analyses using Dynamic Time Warping (DTW) with domain expert knowledge (captured via an ontology and semantic inferencing rules). The resulting Web portal enables scientist to efficiently search across a collection of wireless sensor data streams and identify, retrieve and display segment outliers (both erroneous and genuine) within the data streams. In this paper, we firstly describe the detection algorithms, the implementation details and the functionality of the SOUE Detector system. Secondly we evaluate our approach using data that comprises sensor observations collected from a sensor network deployed in the Springbrook National Park in Queensland, Australia. The experimental results show that the SOUE-Detector can efficiently detect segment outliers and unusual events with high levels of precision and recall.
AI for Translation and Interpreting. A Roadmap for Users and Policy Makers.

With this reflection paper by the Special Interest Group on AI in Translation and Interpreting (hereafter T&amp;I) of the European Language Council (www.celelc.org), 25 scholars from 19 universities across 14 countries call for a more informed use of AI for T&amp;I purposes. As specialists in the field of multilingual communication, the authors, while acknowledging the potential of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs), point to a number of concerns to be considered, and risks and liabilities that occur when these concerns are not duly addressed. Their aim is to raise policy makers’ and the general public’s awareness of how LLMs ‘communicate’, what they can and cannot do, and in which communication contexts their quality and trustworthiness fall short of what is needed, and professional help from duly trained translators and interpreters as well as from T&amp;I scholars should be called in.
Analog sparse approximation for compressed sensing recovery

Non-smooth convex optimization programs such as L1 minimization produce state-of-the-art results in many signal and image processing applications. Despite the progress in algorithms to solve these programs, they are still too computationally expensive for many real-time applications. Using recent results describing dynamical systems that efficiently solve these types of programs, we demonstrate through simulation that custom analog ICs implementations of this system could potentially perform compressed sensing recovery for real time applications approaching 500 kHz. Furthermore, we show that this architecture can implement several other optimization programs of recent interest, including Smoothly Clipped Absolute Deviations and group L1 minimization.
Structure and Epigenetic Regulation of Chromatin Fibers

In eukaryotes, genomic DNA is hierarchically packaged by histones into chromatin on several levels to fit inside the nucleus. As a central-level structure between nucleosomal arrays and higher-order chromatin organizations, the 30-nm chromatin fiber and its dynamics play a crucial role in gene regulation. However, despite considerable efforts over the past three decades, the fundamental structure and its dynamic regulation of chromatin fibers still remain as a big challenge in molecular biology. Here, we mainly summarize the most recent progress in elucidating the structure of the 30-nm chromatin fiber in vitro and epigenetic regulation of chromatin fibers by chromatin factors, particularly histone variants. In addition, we also discuss recent studies in unraveling the three-dimensional organization of chromatin fibers in situ by genomic approaches and electron microscopy.
Uncertainty quantification for plant disease detection using Bayesian deep learning

Abstract   Climate change is having an enormous impact on crop production in Latin America and the Caribbean. This problem not only concerns the volume of crop production but also the quality and safety of the food industry. Several research studies have proposed deep learning for plant disease detection. However, there is little information about the confidence of the prediction on unseen samples. Therefore, uncertainty in models of plant disease detection is required for effective crop management. In particular, uncertainty arising from sample selection bias makes it difficult to scale automatic plant disease detection systems to production. In this paper, we develop a probabilistic programming approach for plant disease detection using state-of-the-art Bayesian deep learning techniques and the uncertainty as a misclassification measurement. The results show that Bayesian inference achieves classification performance that is comparable to the standard optimization procedures for fine-tuning deep learning models. At the same time, the proposed method approximates the posterior density for the plant disease detection problem and quantify the uncertainty of the predictions for out-of-sample instances.
Statistical mechanical approaches to phase transitions in hydrophobic colloids. II. Effects of particle size

Abstract   The Monte Carlo Method, the cell model and first order perturbation theory are used to determine excess pressures and the disorder-order phase transition in hydrophobic colloids. Particular regard is paid to the effect of particle size on the calculated properties.
Iota Reliability Concept of the Second Generation. Measures for Content Analysis Done by Humans or Artificial Intelligences

In educational settings, analyzing textual data via content analysis is a popular research method. The data is a valuable source of information as it offers deep insights into learning and learning outcomes. In practice, it can be used to improve classroom diagnostics and instruction. Nowadays, technology such as learning analytics can be used for the same cause. For both purposes, reliable research instruments are needed. Content analysis, often the measure of choice, is required to meet quality criteria such as objectivity, reliability and validity. However, some of the reliability measures most frequently used have lately been discussed controversially, indicating that there is room for improvement. The first generation of the Iota concept caters to the idea of improved reliability measures for content analysis done by humans or artificial intelligences. In this book, the authors introduce a refined measure: The Iota concept of the second generation. In contrast to pre-existing measures, second generation Iota can for example a) provide insights into the reliability of every single category of a scale and how a coding scheme may produce bias, b) provide rules of thumb for evaluating content analysis and c) provide possibilities for data replication and error-corrected data. This book is structured as a guide for researchers that want to learn more about the mechanics and details of the Iota concept or use it as the reliability measure of choice in their research.
Reward Collapse in Aligning Large Language Models

The extraordinary capabilities of large language models (LLMs) such as ChatGPT and GPT-4 are in part unleashed by aligning them with reward models that are trained on human preferences represented as rankings of responses to prompts. In this paper, we document the phenomenon of reward collapse, an empirical observation where the prevailing ranking-based approach results in an identical reward distribution for diverse prompts during the terminal phase of training. This outcome is undesirable as open-ended prompts like “write a short story about your best friend” should yield a continuous range of rewards for their completions, while specific prompts like “what is the capital city of New Zealand” should generate either high or low rewards. Our theoretical investigation reveals that reward collapse is primarily due to the insufficiency of the ranking-based objective function to incorporate prompt-related information during optimization. Then we derive closed-form expressions for the reward distribution associated with a set of utility functions in an asymptotic setting. Based on the reward distributions for different utility functions, we introduce a prompt-aware optimization scheme that provably admits a prompt-dependent reward distribution within the interpolating regime. Our experimental results suggest that our proposed prompt-aware utility functions significantly alleviate reward collapse during the training of reward models.
How can sustainable agricultural systems promote food security in a changing climate?

A theoretical framework of the link between climate change, rural development, sustainable agriculture, poverty, and food security is presented. Some options to respond to climate change are described. Current knowledge and potential effects on agricultural productivity is discussed. Necessary conditions for successful adaptation includes secured property rights to land, institutions that make market access possible and credit possibilities. The options of mitigation and enhanced adaptive capacity and the requirements for their implementation are discussed.
Discovery of Repetitive Optical Variation Patterns from the Accretion Disk During the 2015 Outbursts of the Black Hole X-Ray Binary V404 Cyg

We report on multi-color optical photometry in the 2015 outbursts of V404 Cygni, an X-ray transient.  This system showed optical oscillations on timescales of 100 sec to 2.5 hours in these outbursts, and they were correlated with the simultaneous X-ray variations.  On the basis of the $\sim$1-min optical delays against X-rays and multi-wavelength SED analyses, the optical variability in this system was likely caused by X-ray reprocessing at the outer accretion disk.  We also find that some repetitive optical oscillations can occur at mass accretion rates $>$10 times lower than previously thought.  Although the mechanism of this kind of variations is still unclear, we suggest that the lack of sustained mass accretion, which would be induced in long-period systems, may be a key condition.
The Wittenberg Framework (Part V) Case Studies in Cross-Cultural AI Governance, Bioethics, and Algorithmic Regulation

Reader’s note: New readers are encouraged to begin with Part 0 , Chapter 4 (“Architecture of the Series and Reader Orientation ”), which provides a brief overview and orientation to the Framework. Links to Preprints are listed in Part 0, Chapter 5 (“Publication and Citation Record”)  Part V presents three structurally contrasting case studies that demonstrate how the Wittenberg Governance Framework operates when applied to concrete, cross-cultural governance problems. Building on the conceptual architecture introduced in Parts I–IV — Moral Order (MO), Relational Order (RO), Regulatory Orientation (RegO), and Meta-Contextual Complexity (MCC) — this Part shows how deep normative divergence, relational architectures, and meta-contextual activation shape institutional behaviour across different cultural contexts.  The case studies cover:(1) emotion-recognition systems in EU–China comparison,(2) algorithmic welfare infrastructures in the United States versus the European Union, and(3) genetic screening regimes in East Asia versus Europe.  Each case reveals characteristic drift mechanisms, irrationality loops, and structural incompatibilities that remain invisible in conventional metric-driven, rights-based, or compliance-oriented analyses. The Framework not only explains why similar technologies lead to systematically different governance outcomes, but also clarifies how moral and relational deep structures generate predictable institutional patterns.  Part V thus serves as the analytical bridge between the theory-building of Parts I–IV and the methodological formalisation of Part VI, where issues of reliability, reproducibility, and inter-analyst calibration will be systematically addressed.  A thematically related but independent structural study on interpersonal and professional conflict dynamics is available here: https://doi.org/10.5281/zenodo.18085191. This study is not part of the core series, but illustrates a domain-specific application of a configurational perspective.  Version 1.0 (2025-12-10): Initial version  Version 2 (2026-03-15): Structural and editorial revision of Part V. Clarified the methodological role of case studies, refined axis mappings and composite-type definitions across all three case studies, and improved the comparative findings section. Added literature-positioning sections and strengthened the link to the methodological discussion developed in Part VI.
Supplementary Table 10 from Sample Size Estimations Based on Human Microbiome Temporal Stability Over 6 Months: A Shallow Shotgun Metagenome Sequencing Analysis

&lt;p&gt;Supplementary Table 10. Number of cases (assuming 1:1 matched case-control study) required to detect an association at a significance level of 0.0001 with 80% power, based on 1, 2, or 3 fecal specimens per subject and median intraclass correlation coefficients estimated from fecal samples collected at 6-months interval. Odds ratio for the top 25% untransformed and transformed relative abundance of species versus the bottom 25% in each prevalence category (i.e., 5%-10%, 10%-25%, 25%-50%, 50%-75%, &gt;75%).&lt;/p&gt;
An overview of the Rome Statute of the International Criminal Court: jurisdiction and complementarity principle and issues in domestic implementation in Nigeria

This paper aims at providing, first, an overview of the Rome statute and the nature and functions of the International Criminal Court; second, an examination of general and specific issues in domestic implementation of the Rome Statute and finally to conclude with some viable options for Nigeria.
Strengthening the Critical Infrastructures in Lithuania: From Protection Towards Resilience

Abstract                   Lithuania’s national security and economic stability rely on the resilience of its critical infrastructures (CIs), which face increasing threats from hybrid warfare and technological complexity. The article analyses Lithuania’s evolving approach to strengthening critical infrastructure (CI), shifting from traditional Critical Infrastructure Protection (CIP) to Critical Infrastructure Resilience (CIR). While CIP focuses on preventing risks and safeguarding assets, CIR emphasizes the ability of systems to anticipate, absorb, and recover from disruptions—especially in the face of hybrid threats and increasing technological complexity. This transition involves proactive policies, integrated risk assessments, and resilience-by-design principles in infrastructure planning. The article points out the foundational role of CI in national security. These systems—ranging from energy and transport to health and ICT—are essential for public safety, economic stability, and societal well-being. Their interdependence with national security sectors underlines the importance of robust protection and resilience frameworks. The article discusses key megatrends impacting CIP in Lithuania, including digital transformation, regulatory shifts, and growing system interconnectivity. These trends necessitate more dynamic and adaptive strategies as infrastructures face both traditional and emerging threats. The author of the article proposes the roadmap that includes seven key steps: adopting a resilience mindset, conducting comprehensive risk assessments, embedding resilience in infrastructure development, enhancing cybersecurity, fostering public–private collaboration, engaging communities, and implementing continuous monitoring and improvement mechanisms. The article concludes by recommending initiatives for further enhancement of the resilience of critical infrastructure.
Climate Change and Public Health in South-South Nigeria's Rural Areas: Engaging Communities in Strategic Mitigation of Malaria, Cholera and other Heat-Related illnesses via Digital Citizen Science"

Climate change is a global phenomenon that has significant impacts on human health, particularly in rural areas where people are more vulnerable to its effects. In Nigeria, the South-South region is one of the most affected and Most Vulnerable Places (MVPs) to climate change, with rising temperatures, changing rainfall patterns, and increased frequency of extreme weather events. These changes have significant implications for public health, particularly in rural areas where access to healthcare is limited. Mitigating malaria, cholera and other heat- related illnesses  in South-South Nigeria requires a multi-faceted approach focusing on prevention, surveillance, and early detection and community engagement.The study explores the potentialities of Digital Citizen Science Observatories as an intervention to enhance community resilience and adaptive capacities. Through Digital Citizen Science, local communities participate in monitoring climate-health links, contributing to data collection, analysis, and interpretation. This approach empowers communities with knowledge and tools for decision-making on climate-related health risks. Findings highlight the promise of Digital Citizen Science in bridging gaps in climate-health data, improving early warning systems, and empowering communities for better health outcomes amidst climate change. The intervention's effectiveness in improving public health outcomes in the context of climate change in South-South Nigeria's rural areas is assessed.