ethicalabs/Research-Intent-Collab huggingface dataset:
The Geneva Convention on Laws of War and the Sudan Armed Conflict

The Geneva Convention contains regulations that govern the conduct of war and the treatment of individuals affected by war. However, despite the existence of this regulation created to guide the conduct of hostilities and protect civilians in international and non-international armed conflict, there are still recorded cases of actions by state and non-state actors that run contrary to the dictates of the convention. Records show that in modern day conflict, indiscriminate attacks and the use of unauthorized weapons are still being launched against civilian population, recruitment of child soldiers in insurrections, the use of weapons with indiscriminate effects, genocide, among others. It is against this backdrop that this paper examined the Sudan conflict and the application of the Geneva Convention on the laws of war. From the findings, a wide range of violation of the dictate of the Geneva Convention in the Sudan conflict was observed. The paper concludes that while the Geneva Convention continues to be relevant today, its provisions are still violated in modern day conflict such as Syria, Yemen, and Myanmar. The international community must continue to uphold the principles of the convention and hold those who violate it accountable.
Changing Data Sources in the Age of Machine Learning for Official Statistics

Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics.   This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.
The Shanghai Multi-Hazard Early Warning System: Addressing the Challenge of Disaster Risk Reduction in an Urban Megalopolis

On 27 June 2008, Mr Hu Jintao, President of China, declared that China needs to develop a sustainable Multi-Hazard Early Warning Systems (MHEWS) with a focus on multi-agency coordination and cooperation at all levels of government, to develop a regional joint response capability, and to build an optimum multi-hazard monitoring and emergency response warning process by actively involving multi-agency participation in the decision-making process. This process should also encourage active public participation in response to climate change. Responding to the President’s direction and with the support of the China Meteorological Administration (CMA), the Shanghai Meteorological Bureau (SMB) enhanced its efforts to establish a comprehensive weather disaster prevention management system under the leadership of the Shanghai Municipal Government (SMG).
Longitudinal Classification of Mental Effort Using Electrodermal Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor

Recent studies show that physiological data can detect changes in mental effort, making way for the development of wearable sensors to monitor mental effort in school, work, and at home. We have yet to explore how such a device would work with a single participant over an extended time duration. We used a longitudinal case study design with ~38 hours of data to explore the efficacy of electrodermal activity, skin temperature, and heart rate for classifying mental effort. We utilized a 2-state Markov switching regression model to understand the efficacy of these physiological measures for predicting self-reported mental effort during logged activities. On average, a model with state-dependent relationships predicted within one unit of reported mental effort (training RMSE = 0.4, testing RMSE = 0.7). This automated sensing of mental effort can have applications in various domains including student engagement detection and cognitive state assessment in drivers, pilots, and caregivers.
Numerical Models in the Epidemiology of Infectious Diseases

The dynamics of infectious disease transmission are subject to fluctuations that are controlled by a number of factors, which must be understood in order to rationally build preventative and control techniques and health policies. In this situation, mathematical modeling can offer helpful insights into patterns of transmission and the identification of parameters to reduce population-wide sickness.
A meta-epidemiological assessment of transparency indicators of infectious disease models

Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.
Regenerative Medicine for Polycystic Ovary Syndrome: Stem Cell-Based Therapies and Brown Adipose Tissue Activation

Polycystic ovary syndrome (PCOS) is a pathological condition prevalent among women of reproductive age: it is associated with varied etiological factors (lifestyle, genetic, environmental…) and characterized by an increased polycystic morphology of the ovaries leading to disturbances in the menstrual cycle and its correlated infertility. Interconnections between PCOS, obesity, and insulin resistance have been recently investigated thoroughly in the scientific community; these findings directed PCOS therapies into unraveling possibilities to target insulin resistance and central adiposity as efficient treatment. On the other hand, brown adipose tissue is known to possess a thermogenic activity that increases lipolysis and directly attenuates fat deposition. Therefore, brown adipose tissue activation lands itself as a potential target for reducing obesity and its induced insulin resistance, subsequently rescuing PCOS phenotypes. In addition, regenerative medicine has proven efficacy in resolving PCOS-associated infertility and its metabolic symptoms. In particular, many stem/progenitor cells have been verified to possess the differentiation capacity into functional brown adipocytes. Thus, throughout this review, we will discuss the different brown adipose tissue activation strategies and stem-cell-based therapies applied to PCOS models and the possible combination of both therapeutic approaches to synergistically act on the activation of brown adipose tissue and attenuate PCOS-correlated infertility and retract the consequences of the metabolic syndrome on the physiological state of patients.
Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis

Artificial intelligence (AI) is an important branch of computer science. With the rapid development of AI, the application in oncology has become increasingly widespread. As a hematologic malignancy characterized by remarkable heterogeneity, lymphoma has long posed significant challenges in both diagnosis and treatment, particularly with regard to its complex classification and difficulties in prognostic evaluation. Breakthroughs in AI technology have provided a new paradigm for precision treatment of tumors. AI can integrate and analyze HE pathology slides and PET/CT images to enhance diagnostic efficiency; meanwhile, in terms of treatment prognosis, AI can identify biomarkers to accurately classify lymphoma subtypes for therapeutic guidance, simultaneously quantify biomarkers to minimize the influence of subjective variability, and predict the patients' prognosis based on the extracted features to assist in the precise treatment of lymphoma. This review aims to provide an overview of AI related to various fields of lymphoma, introducing the core technologies and principles of AI, including deep learning, decision trees, regression models, and so on. We also discuss the clinical applications of AI in lymphoma pathology slides and PET/CT images, systematically analyze the clinical applications of AI in diagnosis, treatment, and prognosis, as well as innovatively summarize the cutting-edge applications of AI in 3D pathology of lymphomas, and finally, we emphasize the development potentials and current challenges of AI in the field of lymphomas to promote precision lymphoma diagnosis and treatment by providing a theoretical foundation.
Appointive Selection and Its Impact on Judicial Independence

Judicial independence is usually understood as an instrumental good, one that is imperative for impartial adjudication. Legal scholars have long argued that this ability of judges to hear cases and controversies impartially, that is solely in respect to fact and law, is necessary to uphold it from possible violations and encroachments by other branches of government. Yet, the extent to which adjudication is independent from outside pressures is far from evident. The holistic line of previous research suggests that judicial bodies are highly responsive to extrajudicial considerations. More recent findings show that selection systems are in part responsible for the politicization of superior courts. This project relates to the growing conception in legal scholarship that the appointive selection system is the most independence-maximizing selection mechanism. We appeal to social identity theory and hypothesize that ingroup bias will increase ideological congruence between nominating bodies and candidates to the judicial bench. To test this hypothesis further, we analyse judicial behaviour of Supreme Council of Justice members retrospectively to the origin of their appointment. The Supreme Council of Justice is a judicial body in Ukraine which examines the legality of judicial behaviour in lower and high courts. Its heterogeneous membership, as well as random assignment of cases to justices, allows for a more rigorous examination of a causal relationship between appointive selection and decision outcomes. We find statistically significant evidence that appointive selection system negatively correlates with measures of judicial independence when the nominating body is a political institution. Results are discussed.
Advantages of using Artificial Intelligence in establishing the diagnosis and prognosis  of dento-maxillary anomalies

Background and objectives. Artificial intelligence (AI) is emerging as a valuable tool in orthodontics, improving the diagnosis and prognosis of dento-maxillary anomalies, large clinical datasets, 3D imaging, and deep learning methods. These systems enhance diagnostic accuracy, standardize cephalometric landmark identification, and strengthen predictive modeling of craniofacial growth and treatment outcomes. This review compiles current evidence on AI’s diagnostic and prognostic roles, outlining its benefits, predictive potential, limitations, and associated risks. Materials and methods. A systematic search of PubMed, Scopus, and Web of Science (2019–2025) was conducted following PRISMA guidelines. Eighteen studies met the inclusion criteria, reporting numerical outcomes related to AI-based diagnosis or prognosis of dento-maxillary anomalies. Results. AI demonstrated high accuracy in automated cephalometric analysis, malocclusion classification, and CBCT segmentation, frequently matching or surpassing clinician performance. Machine learning models showed a promising ability to predict mandibular growth, treatment duration, relapse risk, and root resorption, enabling more personalized data-driven treatment planning. However, limitations remain concerning dataset representativeness, model transparency, and generalizability across populations. Conclusions. AI enhances diagnostic reliability and prognostic accuracy in orthodontics, supporting clinicians in decision-making while reducing subjectivity and variability. Despite these advantages, AI systems must be critically evaluated, ethically deployed, and used as adjuncts, not replacements, for professional expertise.
Quantum Error Mitigation as a Universal Error Reduction Technique: Applications from the NISQ to the Fault-Tolerant Quantum Computing Eras

In the early years of fault-tolerant quantum computing (FTQC), it is expected that the available code distance and the number of magic states will be restricted due to the limited scalability of quantum devices and the insufficient computational power of classical decoding units. Here, we integrate quantum error correction and quantum error mitigation into an efficient FTQC architecture that effectively increases the code distance and T-gate count at the cost of constant sampling overheads in a wide range of quantum computing regimes. For example, while we need 10^{4} to 10^{10} logical operations for demonstrating quantum advantages from optimistic and pessimistic points of view, we show that we can reduce the required number of physical qubits by 80% and 45% in each regime. From another perspective, when the achievable code distance is up to about 11, our scheme allows executing 10^{3} times more logical operations. This scheme will dramatically alleviate the required computational overheads and hasten the arrival of the FTQC era.
Experimental investigation of additively manufactured continuous fiber reinforced composite parts with optimized topology and fiber paths

Abstract   Additive manufacturing (AM) of fiber-reinforced polymer composites enables various complex geometries in an out-of-autoclave process, eliminates the need for molds and tooling, and eliminates material waste. Accompanying these improvements is the ability to tailor the placement of fibers (intricate fiber steering) within a part to improve its performance for a given state of loading. AM is inherently well-suited for integration with optimization schemes, as the designer can send a geometrically complex model directly to the 3D printer for fabrication. This study investigates the printability and performance of three benchmark geometries and loading scenarios optimized for stiffness. These designs are achieved via topology (part geometry) and fiber placement optimization of AM continuous carbon fiber reinforced polymer composites. Some of the manufacturing constraints were implemented in the optimizer. Post-processing was, however, necessary to apply other AM constraints before parts could be successfully printed. The optimized designs were manufactured, on a printer built in-house, and tested in special load fixtures while parts’ displacements were recorded using digital image correlation (DIC). The stiffnesses found through experiments agreed with the finite element analysis (FEA). The optimized AM parts displayed up to 100% improvement in specific (per weight) stiffness, translating to a maximum of 50% weight reduction over conventional composite parts with unidirectional laminas. This study demonstrates that bridging topology and fiber path optimization with AM of continuous fiber-reinforced composites enables significant weight reduction that would otherwise be impossible.
A Study on 5G Network Slice Isolation Based on Native Cloud and Edge Computing Tools

5G networks support various advanced applications through network slicing, network function virtualization (NFV), and edge computing, ensuring low latency and service isolation. However, private 5G networks relying on open-source tools still face challenges in maturity and integration with edge/cloud platforms, compromising proper slice isolation. This study investigates resource allocation mechanisms to address this issue, conducting experiments in a hospital scenario with medical video conferencing. The results show that CPU limitations improve the performance of prioritized slices, while memory restrictions have minimal impact. The generated data and scripts have been made publicly available for future research and machine learning applications.
Developments in the Law of Procedure and Evidence at the International Criminal Tribunal for the Former Yugoslavia and the International Criminal Court

The purpose of this article is to analyse developments in the Rules of Procedure and Evidence of the International Criminal Tribunal for the Former Yugoslavia (ICTY). In a recent article, I discussed how the Rules of Procedure and Evidence of the ICTY amount to the first coherent body of principles governing the prosecution of violations of international humanitarian law. The ICTY, preoccupied as it is by the need to expedite proceedings, has developed and changed its Rules, and has interpreted these Rules, in such a way as to give it the utmost freedom and flexibility to admit and assess evidence submitted by parties before it. The ICTY has also amended procedures to enable it to accept evidence in a form normally not permitted in adversarial criminal law jurisdictions. This article will first examine the primary principle applied by the ICTY in dealing with the admissibility and use of evidence before it. This principle is not necessarily manifest on the surface of the Rules and requires an understanding of what motivation lies behind the institution’s frequent amendment of the Rules. It will then analyse the relevant amendments made to the Rules since completion of my last article that are informed by this principle. In doing so, it will be apparent that the need to expedite trials is not a motivation born only of the right of an accused person to a fair and expeditious trial, but also of the political pressures under which the International Tribunal operates. It will examine recent jurisprudence applying the Rules of the ICTY which clarify judicial thinking behind the development and amendments to the Rules and give some insight into their interpretation. Finally, it will examine some of the rules of procedure and evidence of the International Criminal Court and make some comparisons with the experience of the ICTY.
Communicating to enhance trust and manage risk perceptions during cancer cluster investigations: an exploratory study of information sources and formats

Background Cancer is a dreaded disease. When suspected cancer clusters are reported, they garner considerable public attention. Communicating cancer risks transparently without triggering undue alarm during cluster investigations is a significant undertaking for investigators. A failure to manage public anxiety can lead to an erosion of trust in government officials.Aims This exploratory research aimed to examine the effects of information about a cancer cluster investigation–that varied by information source and format–on perceptions of cancer risk and information trustworthiness. We also explored the impact of information with different risk framing on cancer risk perceptions. Lastly, we examined these relationships across various psychosocial and demographic factors.Methods We recruited 172 Canadian adults to participate in an online survey and experiment. In the experiment (2x4 factorial design), participants were randomly assigned to view one of eight vignettes about a cancer cluster in a fictional community (general risk framing). Vignettes differed by information source (government, news) and format (contextual information, personal stories, both, neither). Participants reported cancer concern and information trustworthiness. We also measured concern after presenting all participants with a hypothetical scenario about a cancer cluster in their own neighbourhood (personal risk framing), and again after providing them with reassuring information about the cluster (personal and reassuring risk framing).Results Participants viewing the government-authored vignettes and the vignettes containing contextual information had higher odds of perceiving the information as trustworthy compared to news-authored vignettes (OR 2.08, 95%CI 1.13–3.91) and vignettes containing neither format (OR 2.37, 95%CI 0.99–5.83), respectively. Of the eight vignettes, those authored by a government-source and containing contextual information elicited concern about cancer most frequently during the experiment (general risk framing). After providing reassuring information to participants during the hypothetical scenario (personal and reassuring risk framing), news-authored vignettes containing personal stories elicited the most frequent reports of concern. Associations appeared to vary by education.Discussion Results suggest government officials should provide citizens with information that is reassuring and contains contextual details to alleviate concerns and enhance trust during cancer cluster investigations. These findings, with follow-up research, could have implications for health departments responding to community concerns about cancer.
Lattice density-functional theory for quantum chemistry

7 pages, 3 figures