ethicalabs/Research-Intent-Collab huggingface dataset:
Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment.
A Benchmark Dataset of Endoscopic Images and a Novel Deep Learning Method to Segment Gastric Intestinal Metaplasia Under Linked Color Imaging

ABSTRACT                                        Endoscopy has become a cornerstone in diagnosing gastrointestinal diseases, notably for gastric intestinal metaplasia (GIM)—a condition that significantly elevates the risk of gastric cancer. Variability in medical expertise and patient‐specific complexities render sole reliance on clinicians for GIM diagnosis a process that is both time‐intensive and challenging. There is an urgent clinical need for advanced deep learning methods to assist in clinical decision‐making, particularly in the identification and quantification of GIM areas. Linked Color Imaging (LCI) represents a pioneering advancement in endoscopic technology, offering distinctive color enhancement capabilities. However, the scarcity of GIM datasets acquired using LCI has hindered extensive exploration of tailored deep learning applications. This study directly addresses these dual challenges through two strategic approaches. Firstly, we meticulously assembled a comprehensive and unique dataset—designated as ZD‐LCI‐GIM—comprising 1020 GIM images from 249 patients and 864 non‐GIM images from 303 patients. Each image was acquired via LCI gastroscopy and meticulously annotated by seasoned gastroenterologists—measures that ensure high‐quality data for analysis. Secondly, we introduce VM‐UNetV2—a deep learning model specifically designed for medical image segmentation tasks. This model achieves impressive performance on the ZD‐LCI‐GIM dataset, with a mean Intersection over Union (IoU) of 64.13%, accuracy of 88.79%, F1 score (Dice Similarity Coefficient, DSC) of 78.14%, specificity of 90.96%, and sensitivity of 82.06% for GIM segmentation. Moreover, the model exhibits exceptional efficiency, processing 34.61 frames per second (FPS) when run on an NVIDIA RTX 4090 GPU. In addition, our algorithm demonstrates superior accuracy and competitive efficiency in comprehensive experiments on the public datasets Endoscene, CVC‐ClinicDB, Kvasir, CVC‐ColonDB, and ETIS‐LaribPolypDB. The code for this work is available at                     https://github.com/nobodyplayer1/VM‐UNetV2                     .
The Gig Economy and Labor Market Restructuring: Platform Work, Worker Classification, and the Future of Employment Relations

This study examines the growth, characteristics, and labor market implications of platform mediated gig work across 24 OECD countries from 2015 to 2025. Using administrative data, labor force surveys, and platform transaction records, we document that gig work has grown to represent 4.2% of total employment and 12.8% of labor income for participants. We find substantial heterogeneity in worker experiences: approximately 35% of gig workers use platforms as primary income sources with limited alternative opportunities, while 65% engage in platform work as supplementary income alongside traditional employment or education. Earnings analysis reveals that median hourly compensation after accounting for expenses and unpaid time averages $14.20, approximately 22% below comparable traditional employment, though top decile earners achieve premium wages. Worker classification reforms significantly affect platform labor markets: jurisdictions implementing employee classification requirements experience 18% reductions in platform labor supply but 31% increases in hourly compensation for remaining workers. Social protection gaps remain substantial, with only 23% of gig workers reporting access to employer provided health insurance and 12% participating in retirement savings programs. The findings suggest that gig economy expansion reflects both genuine labor market innovation enabling flexibility and cost shifting from firms to workers that policy intervention may appropriately address.
Recovery of Compressed Sensing Microarray Using Sparse Random Matrices

Due to the uncertainty of elements in the random matrix, the design of composite probes on compressed sensing microarray (CSM) becomes more complexity. In this paper, we proposed a sparse random measurement matrix with ‘0/1’ binary element, and fixed the same amount of elements ‘1’ on each row, to construct the CSM composite probe. There is the same dilution for the mixed solution of target segments to ensure the consistency of gene concentration, so the composite probes which made up of the linear combination of target segments are very simple. Simulation experiment results show that the variation characteristics of the target segment can be accurately recovered by OMP algorithm under N = 96 sequence segments and variation sparsity level K ≤ 12, when M = 48 composite probes are constructed with a sparse random matrix fixed amount of non-zero elements each row.
Fighting impunity: should the international community create an ad hoc criminal tribunal to prosecute violations of international law in Syria?

Within the complex and contemporary Syrian armed conflict, the aim of this thesis will be to understand the possibilities to establish a juridical mechanism in order to prosecute responsible for international violations and crimes committed since the beginning in 2011. This issue will be analysed through an analytical path. Starting from a brief examination of the main events and main actors involved, a classification of the non-international Syrian armed conflict will be provided. Afterwards, through investigations of international crimes by the United Nations Mechanisms, namely, the Commission of Inquiry on the Syrian Arab Republic (COI) and the International, Impartial and Independent Mechanism (IIIM), an evaluation of the contemporary juridical and quasi-juridical instruments will be reported. The Syrian armed conflict case is characterised by the interplay relations among diplomatic, economic, international and political affairs that affect the possibility to fight against impunity through juridical apparatus in order to deal with those responsible for atrocities committed.
The relationship between Security and Health: A New Paradigm for the United Nations Security Council?

Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
A Comprehensive Survey of Graph Neural Networks for Knowledge Graphs

Le graphe des connaissances, un graphe multi-relationnel qui représente des informations factuelles riches parmi des entités de diverses classifications, est progressivement devenu l'un des outils essentiels pour la gestion des connaissances. Cependant, le graphique de connaissances existant présente encore certains problèmes qui constituent des sujets de recherche brûlants ces dernières années. De nombreuses méthodes ont été proposées basées sur diverses techniques de représentation. Graph Neural Network, un cadre qui utilise l'apprentissage en profondeur pour traiter directement les données structurées par graphique, a considérablement fait progresser l'état de l'art au cours des dernières années. Cette étude vise tout d'abord à fournir un aperçu large, complet et complet des technologies basées sur GNN pour résoudre quatre tâches KG différentes, y compris la prédiction des liens, l'alignement des graphes de connaissances, le raisonnement des graphes de connaissances et la classification des nœuds. En outre, nous avons également étudié les applications connexes de l'intelligence artificielle des graphiques de connaissances basés sur des méthodes GNN avancées, telles que les systèmes de recommandation, la réponse aux questions et l'interaction médicamenteuse. Cette revue fournira de nouvelles informations pour une étude plus approfondie de KG et GNN.
On the Heliospheric Modulation of Galactic Cosmic Rays - Thesis

Thesis of Dr. Simon Thomas of the University of Reading.
Philosophy and Ethics in the Age of Artificial Intelligence: Bridging the Gap between Technology and Human Values

The wide deployment of artificial intelligence triggers fundamental changes to social patterns and financial operations and technological applications while generating unknown ethical and philosophical domains. This evaluation examines the AI-normative relationship through an analysis of fundamental thinking principles that change due to machine intelligence including consciousness and moral agency and free will and personhood. This paper evaluates utilitarianism together with deontology and virtue ethics and care ethics to determine their effectiveness and limitations in guiding AI behavior across healthcare and education and finance and defence sectors. The paper devotes extensive study to both the ethical issues stemming from biased algorithms and hidden decision processes and complications arising from ethical differences and dangerous autonomous system operation scenarios. This research shows how inequality appears during global AI implementations to justify ethical frameworks which defend human dignity while being culturally understandable to all groups and socially just. The combination of philosophy with technology practice transforms ethics-by-design into an essential development method that embeds moral reasoning capabilities within artificial intelligence development processes. The research examines superintelligence ethical possibilities and lethal weapons and human-AI unity systems by recommending new governance systems that demand interdisciplinary collaboration. The research defines a innovative model for AI progress which integrates human value-based strategies with regulatory action. This research advances current ethical discussions about AI governance by providing an extensive framework for AI ethical oversight in the twenty-first century.
Error suppression and error correction in adiabatic quantum computation I: techniques and challenges

Adiabatic quantum computation (AQC) is known to possess some intrinsic robustness, though it is likely that some form of error correction will be necessary for large scale computations. Error handling routines developed for circuit-model quantum computation do not transfer easily to the AQC model since these routines typically require high-quality quantum gates, a resource not generally allowed in AQC. There are two main techniques known to suppress errors during an AQC implementation: energy gap protection and dynamical decoupling. Here we show that both these methods are intimately related and can be analyzed within the same formalism. We analyze the effectiveness of such error suppression techniques and identify critical constraints on the performance of error suppression in AQC, suggesting that error suppression by itself is insufficient for large-scale, fault-tolerant AQC and that a form of error correction is needed. We discuss progress towards implementing error correction in AQC and enumerate several key outstanding problems.   This work is a companion paper to "Error suppression and error correction in adiabatic quantum computation II: non-equilibrium dynamics"', which provides a dynamical model perspective on the techniques and limitations of error suppression and error correction in AQC. In this paper we discuss the same results within a quantum information framework, permitting an intuitive discussion of error suppression and correction in encoded AQC.
Gibbs random fields on a lattice: Definitions, existence, uniqueness, and phase transitions (a review of proceedings of the seminar on statistical physics at the faculty of mechanics and mathematics of the Moscow state university in 1962–1994)

The Moscow Grand Seminar on Statistical Physics is one of the first seminars in the world devoted to rigorous methods in statistical physics. The achievements of the seminar can be appreciated in full measure from publications by the leaders of the seminar (R.L. Dobrushin, V.A. Malyshev, R.A. Minlos, Ya.G. Sinai). This paper presents a review (far from complete) of the proceedings of the seminar. The emphasis is made on the fundamental definitions and most important results.
Central bank digital currency (CBDC) : an explorative study on its impact and implications for monetary policy and the banking sector

This thesis focuses on the concept of Central Bank Digital Currencies (CBDC) and the possible implications this could entail for monetary policy, commercial banks, and payment systems. With the declining use of cash and increased market capitalization of cryptocurrencies, central banks face an important decision. They need to consider the possible risks this change poses, and potential actions they could take to mitigate a potential weakening of their monetary authority. A CBDC could be a viable option to moderate this risk, but the potential impacts it can have are unknown. This study aims to understand the possible implementation of a CBDC and the effects this could have on monetary policy, commercial banks, and payment systems. Then, the ideal implementation for Norway is considered based on the knowledge gained throughout the thesis. Through a systematic literature review, this thesis attempts to synthesize the information obtained into an overview of the current state of CBDC research. Therefore, an optimal strategy proved to be a thorough literature search in order to increase the likelihood that all relevant resources were considered. The selection of sources also went through a systematic process to ensure the validity and reliability of the selected literature. The literature highlights two types of CBDC, retail and wholesale. This definition is divided further into token-based and account-based CBDCs. Research on monetary policy suggests a CBDC would be useful at mitigating the zero lower bound (ZLB), but the actual effect of moving the ZLB is unclear. Through a CBDC, an alternative to quantitative easing (QE) is possible, called helicopter money. It enables the direct injection of funds into the public, stimulating the economy. Commercial banks would likely need to increase the interest rate offered on deposit accounts if the CBDC is interest-bearing. Funding this increase can be done in various ways. Although a CBDC increases the ease of transferring funds into a risk-free asset, the findings do not indicate a risk of bank runs, as long as necessary measures are in place. Finally, the payment system can benefit from a CBDC, especially one based on blockchain technology, from lower settlement times and costs, with the added benefit of the security it offers.
Harmonised Standards and Conformity Assessments in the AI Act: Strengthening Independent and Participatory Oversight

The position paper “Harmonised Standards and Conformity Assessments in the AI Act: Strengthening Independent and Participatory Oversight” was developed following the Weizenbaum Forum “An Ecosystem for AI Accountability? Between Evaluation, Audit and Cer-tification” and reflects the Institute’s ongoing interdisciplinary research on technological evaluation and governance of AI systems. In addition to its research-based objectives, this paper serves as a complementary contribution to the Weizenbaum Institute’s official statement on the draft bill for the national implementation of the EU Artificial Intelligence Act (AIA) issued by the Federal Ministry for Digital Transformation and Government Modernisa-tion (BMDS). It thereby underscores its interdisciplinary expertise in shaping responsible AI regulation.
About the connection between the $C_{\ell}$ power spectrum of the Cosmic Microwave Background and the $\Gamma_{m}$ Fourier spectrum of rings on the sky

In this article we present and study a scaling law of the $m\Gamma_m$ CMB Fourier spectrum on rings which allows us (i) to combine spectra corresponding to different colatitude angles (e.g. several detectors at the focal plane of a telescope), and (ii) to recover the $C_l$ power spectrum once the $\Gamma_m$ coefficients have been measured. This recovery is performed numerically below the 1% level for colatitudes $\Theta> 80^\circ$ degrees. In addition, taking advantage of the smoothness of the $C_l$ and of the $\Gamma_m$, we provide analytical expressions which allow to recover one of the spectrum at the 1% level, the other one being known.
Gaps in integration of sexual and reproductive health and rights into climate change research in sub-Saharan Africa: A scoping review

Abstract                 Sub-Saharan Africa is faced with triple challenges of high vulnerability to climate change (CC) impacts, high levels of inequality and poor sexual and reproductive health and rights (SRHR) outcomes. Climate change can worsen SRHR situation for high-risk groups such as women, children, adolescent girls and people living with HIV. This scoping review takes stock of the state of research on the intersection between CC and SRHR in SSA with a view of identifying gaps and opportunities for effective evidence generation and integration in climate actions. The review followed Arksey and O’Malley framework. Data charting was conducted using Population, Exposure, Comparator, Outcome tool in Covidence. Thirty-seven (37) studies were reviewed, 57% were quantitative while 41% and 2% used qualitative and mixed methods respectively. SRHR components investigated include maternal newborn and child health at 43% (n=16), HIV at 19% (n=7), gender-based violence at 11% (n=4), and fertility intentions and outcomes at 11% (n=4). SRHR elements that are under-researched in the context of CC include access to and uptake of contraceptives, post abortion care, harmful practices (child marriages and female genital mutilation), menstrual health, pregnancy losses, bodily autonomy, and (in)fertility. Drought, floods, heat stress and rainfall seasonality have received fair attention in research, yet impacts of climate hazards like tropical cyclones, wildfires and salt-water intrusion are missing in research. There is inadequate research at the intersection of CC and SRHR hindering progress towards climate resilience and attainment of good health and well-being for all. Effective and equitable integration requires that SRHR issues be recognized, and deliberate investments (research, policies, programs, interventions and financing) put in place to address the critical SRHR gaps and climate vulnerabilities to enhance resilience.
Corporate Social Responsibility and Business Ethics: Case Study of TATA Group from India

Corporate Social Responsibility (CSR) has increasingly become a vital component of the global business framework, especially within emerging economies such as India (Maheshwari M et al, 2024). This study delves into the relationship between CSR and business ethics by examining case studies of Indian companies, illustrating how these firms address critical elements of corporate governance and their impact on societal well-being. The analysis of the Tata Group offers an insightful look at how Indian corporations have embraced CSR and ethical practices to improve their brand image, support rural and community development, and foster skill development—all while maintaining long-term business sustainability (Tripathi S. K et al 2024). The study's conclusions highlight the fact that, for Indian businesses, corporate social responsibility (CSR) and business ethics go far beyond simple compliance to become crucial components of strategic business management. In addition to advancing society, businesses that successfully integrate CSR into their operations also strengthen their competitive advantage and long-term sustainability. This case study offers important insights for companies around the world, emphasizing how crucial it is to uphold ethical governance and incorporate social responsibility into corporate objectives.
Food Security and Sustainable Development: Overcoming Poverty through Sustainable Agriculture

This study examined the correlation between food security and sustainable development in the context of poverty alleviation through sustainable agriculture. Employing a qualitative approach, this study evaluated the effectiveness of sustainable agricultural practices in improving food security in poverty-prone areas. The data were collected from various sources, including interviews with farmers and secondary data analysis from government reports and international organizations. The study found that the adoption of sustainable agricultural practices such as organic farming systems, crop rotation, and the use of environmentally friendly technologies had a significant positive impact on agricultural productivity. This, in turn, leads to increased income for farmers and greater food availability, ultimately reducing poverty levels. Additionally, this study reveals that government policies and infrastructure support play a crucial role in facilitating the transition to sustainable agriculture. This study offers new insights into the significance of synergies between food security and sustainable development, and their contribution to poverty alleviation strategies. The results indicate the need for more comprehensive policies and a holistic approach when designing agricultural development programs. These programs should not only focus on production but also on social, economic, and environmental aspects.