ethicalabs/Research-Intent-Collab huggingface dataset:
Protection of minority rights: System of guarantees in the Covenant of the League of Nations

The relevance of this paper stems from the fact that today, as we are approaching the 100 year anniversary of the creation of the League of Nations, a new vision is increasingly required in assessment of its legacy through the prism of modern international law. Further study in this field is necessary considering that the history of the League of Nations still remains a source of legal and political experience for the modern United Nations. The paper analyzes the stages of creation of a system for guarantees protecting the rights of national minorities within the framework of the League of Nations. Although this system was clearly very politicized in its nature, its novelty and integration of new elements of better quality for protection of minority rights cannot be denied. The primary goal of the paper is to reveal the reasons for the ultimate weakening of the League of Nations procedure for addressing petitions concerning violations of minority rights. In order to achieve this goal, the author uses the methods of objective, historical, and scientific analysis. As a result of the analysis, the conclusion has been made that the basic characteristics of the process for addressing violations of minority rights were established in the first three years after the League of Nations had been organized. Furthermore, the key trends undermining this process became apparent in less than a year following the establishment of the League of Nations. The paper sums up that the flaws of the process included the lack of experience and understanding among the members of the “committees of the three” in the field of minority rights, as well as the limited time allotted for the council's deliberation sessions. These factors lead to the deliberation on individual petitions dragging for years and also contributed to the gradual rise to greater prominence of the minority section established within the secretariat of the League of Nations. The results of this study can serve as a foundation for further scientific analysis of issues related to the protection of minority rights and human rights in general.
The Establishment of Risk Communication for China-Made Foods : An Investigation of Food Safety, Consumer Perception of Food-Related Risks and Social Trust Based on the Toxic Chinese Dumpling Scare in Japan

大阪大學中國文化論壇 討論文件
A comprehensive exploration and interpretability of Android malware with explainable deep learning techniques

This study introduces an innovative approach to tackle evolving Android malware threats using explainable artificial intelligence (XAI) methods combined with deep learning techniques. The framework enhances detection accuracy and provides interpretable insights into the model's decision-making process. The research utilizes the CICInvesAndMal2019 dataset for training with Deep Neural Network (DNN) techniques. It incorporates Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) XAI techniques to refine the model's features and understand its predictions. This framework uses permissions and intents as static features from Android apps. The proposed framework reduces the execution time, reducing model loss to an impressively lower value of 0.26, and exhibits a commendable accuracy of 97.86%.
Optimizing differential privacy in a federated learning framework: strategies for dynamic clipping and privacy allocation

Abstract                Differential privacy has garnered significant attention for its ability to protect the privacy of parameters uploaded in federated learning. Two key factors in differential privacy, threshold clipping and the privacy budget, determine the amount of noise added to the data to achieve privacy protection. However, traditional differential privacy approaches do not account for the unique dynamic characteristics of the federated learning process, often resulting in trade-offs between privacy protection and model performance. To address this limitation, this study proposes a federated learning framework that combines dynamic threshold clipping and adaptive privacy budgeting. By adjusting these parameters in response to the changes during training, our method applies privacy protection more precisely without compromising model performance. The framework effectively balances privacy preservation and model accuracy by dynamically adapting to the training process and data distribution. Experimental results demonstrate that our approach significantly outperforms traditional differential privacy-based federated learning algorithms.
A rapid evaluation of vanadium oxide and manganese oxide as battery materials with a micro-electrochemistry technique

Abstract   The electrochemical behavior of vanadium oxide V 2 O 5  and bismuth doped manganese oxide Bi–MnO 2  as battery materials was studied with a cavity microelectrode (CME). This technique, which allows working on a very few amount of material, appears to be suitable when high scan rates are used. Thus, it is possible to perform many hundreds cycles in a short time. The performances of pure vanadium oxide as lithium insertion material in inorganic media, examined with a CME appear to be excellent whereas poor results were obtained with Bi doped MnO 2 , for which the electrochemical mechanism proceeds via dissolution.
Nuclear physical properties of ferritic-martensitic steel EK-181 under conditions of long-term neutron irradiation in fast breeder BN-600 and fusion DEMO-RF reactors

Dynamics of primary radiation damage, nuclear transmutations, and radioactivity accumulation (activation) and decrease (cooling) during long-term (up to 50 years) irradiation in the neutron spectra of the fast breeder BN-600 and fusion DEMO-RF reactors are calculated using the ACDAM computational system for EK-181 (Fe-12 Cr-W-V-Ta-B) ferritic-martensitic steel. In the BN-600 reactor, at short irradiation times (up to 560 days), the EK-181 steel meets the “low-activation“ criterion, but this criterion is not met for long-term irradiation (more than 30 years). In case of the DEMO-RF reactor and long-term irradiation (up to 50 years), the EK-181 steel meets the low-activation criterion.
Navigating the Complexities of Global Climate Change Litigation: China's Emerging Role in International Environmental Law

As climate change accelerates, the need for an effective legal framework to address its impacts becomes increasingly urgent. In China, while environmental laws have evolved, the term "climate litigation" has not yet firmly established itself within the legal system. Traditional environmental approaches, focusing on pollution control and ecological damage compensation, are insufficient to address the complex, long-term challenges posed by climate change. The localization of climate litigation is crucial for creating a comprehensive legal response that aligns with China’s unique socio-economic and environmental conditions. This paper argues for the development of a climate litigation framework that incorporates both mitigation and adaptation dimensions. Mitigation-focused litigation addresses emissions reduction, while adaptation-focused litigation focuses on protecting vulnerable populations from climate impacts such as extreme weather events. Additionally, by learning from international practices, particularly Hong Kong’s robust Environmental, Social, and Governance (ESG) policies, China can develop a more transparent and accountable legal system. The proposed framework includes preventive relief systems, multi-source legal pathways, and specialized climate courts to handle complex climate cases. By integrating domestic and international legal practices, China can establish a pioneering climate litigation system that enhances its role in global climate governance and provides a model for other nations.
PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment

Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because candidate generation is combinatorial and stability is only resolved after costly energy evaluations. Here we introduce PackFlow, a flow matching framework for molecular crystal structure prediction (CSP) that generates heavy-atom crystal proposals by jointly sampling Cartesian coordinates and unit-cell lattice parameters given a molecular graph. This lattice-aware generation interfaces directly with downstream relaxation and lattice-energy ranking, positioning PackFlow as a scalable proposal engine within standard CSP pipelines. To explicitly steer generation toward physically favourable regions, we propose physics alignment, a reinforcement learning post-training stage that uses machine-learned interatomic potential energies and forces as stability proxies. Physics alignment improves physical validity without altering inference-time sampling. We validate PackFlow's performance against heuristic baselines through two distinct evaluations. First, on a broad unseen set of molecular systems, we demonstrate superior candidate generation capability, with proposals exhibiting greater structural similarity to experimental polymorphs. Second, we assess the full end-to-end workflow on two unseen CSP blind-test case studies, including relaxation and lattice-energy analysis. In both settings, PackFlow outperforms heuristics-based methods by concentrating probability mass in low-energy basins, yielding candidates that relax into lower-energy minima and offering a practical route to amortize the relax-and-rank bottleneck.
Airway management in paediatric emergencies outside of an intensive care setting: a quality improvement project using Lean/Six Sigma methodology

Use of a Lean/Six Sigma methodology in a quality improvement project to reduce variation and improve safety in airway management outside of the intensive care environment in a tertiary paediatric hospital.
Advancing sexual and reproductive health and rights (SRHR) of sexual and gender minorities in Gert Sibande district, Mpumalanga: a rapid ethographic assessment

The study reported on in this document builds on research conducted in the second year of the AIDS Foundation of South Africa (AFSA) three-year programme (2014-2017), Making Sexual and Reproductive Rights Real. The programme is intended to strengthen SRHR and community-based HIV/AIDS interventions, and to build the evidence base for effective regional responses. In line with recommendations for further research from earlier studies forming part of the AFSA three-year programme, (see Morison, Moolman & Reddy, 2015; Morison & Lynch, 2016a), this study examined (a) healthcare workers' perceptions and treatment of sexual and gender minorities; (b) the perceptions and experiences of LGBTI persons around accessing quality SRH services; (c) the range of LGBTI-specific SRH services required as part of quality SRHR; and (d) effective practices and lessons from organisations and public-private partnerships towards addressing LGBTI SRHR. The current study approached these research priorities with a deepened focus on a rural National Health Insurance (NHI) pilot area in the province of Mpumalanga, South Africa.
Metagenomics and Microbiome Analysis for Nutritional Insights

The synergy of microbiome analysis and metagenomics has transformed diet-microbe-host relationships, and this chapter delves into how bioinformatics tools and high-throughput sequence technologies are utilized to decipher gut microbiome structure and function and its pivotal role in human nutrition. Investigating microbial diversity, metabolic capacity, and relations between hosts and microbiomes reveals personalized dietary response and pathways underlying nutrient processing, energy homeostasis, and risk factors of chronic disease. This chapter outlines methodological strategies such as sequence analysis of the 16S rRNA gene, shotgun metagenomics, and integration of metabolomics and uses of these in personalized nutrition, biomarker development, and dietary intervention. Obstacles, implications, and future views of microbiome-guided nutrition are presented. This chapter offers an in-depth overview to inform investigators, clinicians, and nutrition experts working to apply microbiome information to precision health and optimal dietary regimes.
Social networks as a tool for science communication and public engagement: focus on Twitter

Social networks have been used to teach and engage people about the importance of science. The integration of social networks in the daily routines of faculties and scientists is strongly recommended to increase their personal brand, improve their skills, enhance their visibility, share and communicate science to society, promote scientific culture, and even as a tool for teaching and learning. Here we review the use of Twitter in science and comment on our previous experience of using this social network as a platform for a Massive Online Open Course (MOOC) in Spain and Latin America. We propose to extend this strategy to a pan-European Microbiology MOOC in the near future.
VCounselor: a psychological intervention chat agent based on a knowledge-enhanced large language model

Conversational artificial intelligence can already independently engage in brief conversations with clients with psychological problems and provide evidence-based psychological interventions. The main objective of this study is to improve the effectiveness and credibility of the large language model in psychological intervention by creating a specialized agent, the VCounselor, to address the limitations observed in popular large language models such as ChatGPT in domain applications. We achieved this goal by proposing a new affective interaction structure and knowledge-enhancement structure. In order to evaluate VCounselor, this study compared the general large language model, the fine-tuned large language model, and VCounselor's knowledge-enhanced large language model. At the same time, the general large language model and the fine-tuned large language model will also be provided with an avatar to compare them as an agent with VCounselor. The comparison results indicated that the affective interaction structure and knowledge-enhancement structure of VCounselor significantly improved the effectiveness and credibility of the psychological intervention, and VCounselor significantly provided positive tendencies for clients' emotions. The conclusion of this study strongly supports that VConselor has a significant advantage in providing psychological support to clients by being able to analyze the patient's problems with relative accuracy and provide professional-level advice that enhances support for clients.
Implementation of Corporate Social Responsibility (CSR) and the Response of the Surrounding Community from the Perspective of Islamic Business Ethics (Case Study of PT. OKI PULP &amp; PAPER MILLS Palembang)

This study aims to analyze the implementation of Corporate Social Responsibility (CSR), determine the response of the surrounding community, and review the implementation of CSR from the perspective of Islamic business ethics at PT. OKI Pulp &amp; Paper Mills Palembang. This study uses a qualitative approach with a case study type, where data collection techniques are carried out through in-depth interviews, direct observation, and documentation, and supported by relevant literature studies. The data obtained are analyzed using qualitative descriptive analysis techniques through the stages of data reduction, data presentation, and drawing conclusions. The results of the study indicate that the company's CSR implementation has been running quite well through various programs covering social, economic, and environmental aspects, and has received a positive response from the community because it provides real benefits. Viewed from the perspective of Islamic business ethics, the implementation of CSR has reflected the values ​​of trust, justice, and social concern, although it still needs to be improved to be more optimal, sustainable, and fully aligned with the principles of Islamic ethics.
Conflict Resolution and Peace Building in Nigeria

Nigeria continues to experience diverse and persistent conflicts, including ethno-religious violence, farmer–herder disputes, separatist agitations, resource-based crises, urban criminality, and violent extremism. ese conflicts have significantly undermined social cohesion, economic development, and political stability across different regions of the country. This paper adopts a qualitative approach based on secondary sources to examine the nature, causes, and dynamics of conflicts in Nigeria, as well as the effectiveness of existing conflict resolution and peacebuilding initiatives. Drawing on the perspectives of conflict transformation and human needs theory, the study analyses how structural inequalities, governance challenges, identity politics, and competition over scarce resources continue to fuel tensions and violence. It further evaluates both state-led and community-based peacebuilding mechanisms, including dialogue initiatives, traditional conflict resolution systems, security interventions, and international peacebuilding models applied in the Nigerian context. The findings reveal that while several initiatives have contributed to reducing tensions in some areas, many efforts remain fragmented, reactive, and insufficiently inclusive. e paper argues that sustainable peace in Nigeria requires a more integrated approach that prioritises inclusive governance, grassroots participation, institutional reforms, and socio- economic development. It therefore recommends strengthening community-based peacebuilding structures, promoting equitable resource management, and enhancing collaboration between government institutions, civil society organisations, and local communities to build long-term peace and stability.
Optimization of User-Based Collaborative Filtering Movie Recommendation System Using Mean-Centering and Overlap Weighting on Cosine Similarity

Rapid technological advancements have changed the way people enjoy entertainment. Viewers can now watch movies online without having to go to the cinema. The rapid development of online movie streaming services has increased the need for systems capable of providing content recommendations based on user preferences. This study aims to optimize a User-Based Collaborative Filtering (UBCF)-based recommendation system using the MovieLens 100K dataset containing 100,000 ratings from 943 users for 1,682 movies. The evaluation was conducted by dividing the data into 80% training data and 20% testing data. The system was optimized through two main approaches: user average normalization (mean-centering) in the KNN method to reduce bias in rating scale differences between users, and the application of overlap weighting on cosine similarity to give greater weight to user pairs with a greater number of shared item ratings. Based on experiments, a value of k = 50 was chosen as the optimal trade-off point in predicting ratings. The experimental results show that KNN with mean centering (KNNWithMeans) consistently outperforms standard KNN. The cosine + KNNWithMeans model produced an RMSE of 0.9701 and an MAE of 0.7567, lower than cosine + KNN (RMSE 1.0377; MAE 0.8226). Further overlap weighting was shown to improve prediction accuracy, with the combination of weighted cosine with α = 1 and KNNWithMeans providing the best performance with an RMSE of 0.9686 and an MAE of 0.7556.
The concepts of humanitarian intervention and responsibility to protect.  Development, continuity and periodization.

The concepts of humanitarian intervention and responsibility to protect are important due to the significance of the consequences of their appli cation in international relations. These concepts formed the basis of NATO’s Bosnian operation, the intervention in Iraq, the Kosovo bombings, the aggression in Libya, etc. In the field of the subject field, specific mili tary interventions and concepts are considered in isolation from each other. In the article humanitarian intervention and responsibility to pro tect are presented in a synthetic unity of evolution and a periodization of their successive development is given.
Quantum Chemistry in Drug Design: Density Function Theory (DFT) and Other Quantum Mechanics (QM)-related Approaches

Drug design and development are expensive and time-consuming processes, which in many cases result in failures during the clinical investigation steps. In order to increase the chances to obtain potential drug candidates, several in silico approaches have emerged in the last years, most of them based on molecular or quantum mechanics theories. These computational strategies have been developed to treat a large dataset of chemical information associated with drug candidates. In this context, quantum chemistry is highlighted since it is based on the Schrödinger equation with mathematic solutions, especially the Born-Oppenheimer approximation. Among the Hartree-Fock-based methods, the Density Functional Theory (DFT) of HohenbergKohn represents an interesting and powerful tool to obtain accurate results for electronic properties of molecules or even solids, which in many cases are corroborated by experimental data. Additionally, DFT-related methods exhibit a moderate time-consuming cost when compared to other ab initio methods. In this chapter, we provide a deep overview focused on the formalism behind DFT, including historical aspects of its development and improvements. Moreover, different examples of the application of DFT in studies involving GABA inhibitors, or catalytic mechanisms of enzymes, such as RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2, and different proteases associated impacting diseases, such as malaria, Chagas disease, human African trypanosomiasis, and others. Moreover, the role of metal ions in catalytic enzymatic mechanisms is also covered, discussing iron-, copper-, and nickel-catalyzed processes. Finally, this chapter comprises several aspects associated with the elucidation of catalytic mechanisms of inhibition, which could be used to develop new potential pharmacological agents.