ethicalabs/Research-Intent-Collab huggingface dataset:
Fostering Social Harmony: A Peace-building Approach to Conflict Resolution and Enhanced Social Skills

Education is the best means of resolving every conflict that arises in human life. Despite the frequent occurrence of conflict and violence among students, it is still regarded as a norm, posing significant danger if left unaddressed. This acceptance can contribute to the development of a deteriorating culture, perpetuating hostility, intolerance, intergenerational violence, and diminishing social character values, particularly within student communities. This study formulates a peace-building-based conflict resolution learning model to instill the values of students' social skills, which is carried out on students through the non-equivalent control group design. Data were collected and processed using experimental procedures. Data analysis techniques were analyzed using design models processed through VOSviewer design software and the SEM (Structural Equation Model) method. Structural Equation Modeling, better known as SEM, has several other names, such as covariance structure analysis), analysis of latent variables, analysis of confirmatory factors and analysis of linear structure relationships. The results of the research show that the peace-building model of conflict resolution learning is effective in improving students' social skills, this is evidenced by the increased students' understanding of conflict resolution and violence that they experience directly or indirectly. Increased collaboration between students in finding solutions to the cases studied shows a better understanding of differences of opinion among students. Increased solidarity in solving problems within the group. Based on this, peacebuilding-based conflict resolution learning is sufficient to make a good contribution to cultivating the values of social skills that can be applied in students' daily lives.
International Criminal Tribunals and Human Rights Law: Adherence and Contextualization

Given their mandate to prosecute persons responsible for the most atrocious of human rights violations, International Criminal Tribunals (ICTs) are generally hailed as welcome enforcers of international human rights law: a new instrument in the toolkit of human rights protectors. However, ICTs investigate, detain, prosecute and try individuals, which are all activities that necessarily entail the potential infringement of human rights. However, unlike States, ICTs are not party to human rights treaties, as a result of which it is unclear whether they are bound by international human rights law. Similarly, the differences between States and ICTs may warrant a modification in the way in which ICTs interpret and apply human rights norms, as compared to States, given the specific context in which they operate. The central question of this dissertation is how ICTs should interpret and apply human rights norms in their procedural practice. The first part of the dissertation investigates whether and to what extent international human rights law applies to the ICTs. The second part of the thesis analyzes the ICTs’ interpretation and application of three specific human rights: the right to privacy, the right to liberty, and the right to be tried without undue delay, as compared to the way in which these rights are interpreted and applied in international human rights law. The final Part offers conclusions and recommends ways in which the ICTs’ interpretative practice of human rights norms can be improved.
A Novel Algorithm for Identifying Generalized Pustular Psoriasis Flares Using Natural Language Processing and Outpatient Electronic Health Records (Preprint)

BACKGROUND                         Generalized pustular psoriasis (GPP) is a systemic, neutrophilic, inflammatory disease characterized by chronic symptoms and periods of flaring, which can be potentially life-threatening. The rarity of GPP and inconsistent documentation complicate real-world identification of GPP flares.                                                                       OBJECTIVE                         We developed an algorithm to identify outpatient encounters indicative of GPP flares using structured data and unstructured clinical notes from electronic health records (EHRs).                                                                       METHODS                         De-identified patient-level EHR data (2017–2024) from six specialty dermatology networks in the USA were accessed. Eligible outpatient encounters had (1) an International Classification of Diseases, 10th Revision, Clinical Modification diagnosis code for GPP, (2) a dermatologist-defined diagnosis status of “inadequately controlled” or “worsening” disease (gold standard indicator of flare), and (3) available clinical notes. Natural language processing (NLP) of clinical notes identified the most common terms (eg, clinical manifestations, treatment) by flare status.                                                                       RESULTS                         Multivariable logistic regression models and leave pair-out cross-validation selected models yielding the highest area under the receiver-operating characteristic plot (AUROC) (discriminative ability for flares) from 1,354 encounters (679 patients: 81% female, 84% Caucasian, mean age 58 years). The final model combined NLP terms and EHR variables and yielded a higher discriminative ability (AUROC=0.72; sensitivity=0.68; specificity=0.66) than either NLP terms or EHR data alone.                                                                       CONCLUSIONS                         Although not fully discriminative, the model could be used in future to identify GPP flares from EHR data.                                                                       CLINICALTRIAL                         N/A
Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challenges

Intrahepatic cholangiocarcinoma (ICC) is a primary liver malignancy with increasing global incidence and mortality rates. The 5-year overall survival rate for patients with ICC is approximately 9%. Surgical resection currently represents the only curative treatment option. However, due to the high aggressiveness, insidious onset, and atypical clinical presentation of ICC, many patients either miss the optimal surgical window or experience early postoperative recurrence and metastasis. This poses significant challenges for hepatobiliary surgeons worldwide. Artificial intelligence (AI), as a prominent driver of technological advancement, offers promising new avenues for managing ICC. By leveraging powerful machine learning and deep learning algorithms, AI has demonstrated promising outcomes in ICC diagnosis, particularly in differentiating it from hepatocellular carcinoma, and in predicting critical prognostic factors such as early recurrence, lymph node metastasis, and microvascular invasion. These innovations can support clinical decision-making and ultimately improve patient outcomes. Future efforts should prioritize robust clinical studies evaluating the effectiveness of AI in ICC management.
Assessing Climate Vulnerability and Its Linkages to Adaptation Strategies and Farm Resilience in Rice Farming Systems

Rice farming systems in swamp lowland ecosystems are highly vulnerable to climate change due to their dependence on hydrological conditions and limited adaptive resources. This study aims to examine the interaction between vulnerability, adaptation strategies, and farmer resilience to strengthen the sustainability of swamp-based rice farming. Using a mixed-methods approach that integrates Vulnerability and Capacity Assessment, SWOT analysis, and Structural Equation Modeling with data from 80 farmers selected through stratified random sampling, the research evaluates vulnerability components, identifies context-specific adaptive strategies, and tests causal relationships among key variables. The findings show that farmers face high vulnerability driven by strong exposure and sensitivity, while adaptive capacity remains limited. Although farmers possess experiential knowledge and social capital, technological gaps, low climate literacy, and financial constraints reduce adaptive readiness. The SEM results indicate that farmer characteristics significantly shape adaptation strategies, and both factors play a critical role in determining resilience. Overall, the study demonstrates that resilience emerges from the interaction between biophysical pressures and socioeconomic constraints, highlighting the importance of strengthening knowledge, technology access, and institutional support to enhance adaptive capacity and ensure the long-term sustainability of rice farming in vulnerable swamp ecosystems.
Identifying Incarceration Status in the Electronic Health Record Using Natural Language Processing in Emergency Department Settings

ABSTRACT                                    Background                   Incarceration is a highly prevalent social determinant of health associated with high rates of morbidity and mortality and racialized health inequities. Despite this, incarceration status is largely invisible to health services research due to poor electronic health record capture within clinical settings. Our primary objective is to develop and assess natural language processing (NLP) techniques for identifying incarceration status from clinical notes to improve clinical sciences and delivery of care for millions of individuals impacted by incarceration.                                                     Methods                   We annotated 1,000 unstructured clinical notes randomly selected from the emergency department for incarceration history. Of these annotated notes, 80% were used to train the Longformer-based and RoBERTa NLP models. The remaining 20% served as the test set. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1 score and Shapley values.                                                     Results                   Of annotated notes, 55.9% contained evidence for incarceration history by manual annotation. ICD-10 code identification demonstrated accuracy of 46.1%, sensitivity of 4.8%, specificity of 99.1%, precision of 87.1%, and F1 score of 0.09. RoBERTa NLP demonstrated an accuracy of 77.0%, sensitivity of 78.6%, specificity of 73.3%, precision of 80.0%, and F1 score of 0.79. Longformer NLP demonstrated an accuracy of 91.5%, sensitivity of 94.6%, specificity of 87.5%, precision of 90.6%, and F1 score of 0.93.                                                     Conclusion                   The Longformer-based NLP model was effective in identifying patients’ exposure to incarceration and has potential to help address health disparities by enabling use of electronic health records to study quality of care for this patient population and identify potential areas for improvement.
Beyond coverage: a qualitative study exploring the perceived impact of Gabon’s health insurance plan on access to and quality of prenatal care

Abstract                 Background                 Access to affordable and adequate healthcare in a health system determines the universal health coverage achievement for all residents in a country. Achieving access to healthcare requires the availability of a financing system that ensures access to and provision of adequate care, regardless of the ability to pay. In sub-Saharan Africa, accessibility, use and coverage of prenatal visits are very low and poor, which reduces the quality of care. This paper explored the impact of a social health insurance scheme on the quality of antenatal care in Gabon.                                Methods                 This qualitative study involved the analysis of data collected from semi-structured interviews and non-participant observations to assess the quality of antenatal care. The study elicited perceptions on the demand side (pregnant women) and the supply side (health professionals) in health facilities. Fifteen semi-structured interviews were conducted with pregnant women (aged between 15 and 49) and 5 with health professionals, who each had a seniority of at least 10 years, at different levels of care. Nine non-participant observations were also conducted. Coded transcripts were reviewed and analyzed using the Canadian Institute for Public Administration of Citizen-Centered Services model as an analytical guide.                                Results                 On the demand side, women were generally satisfied with the prenatal services they receive in health facilities. However, complaints were made about the rudeness of some nurses, the high price of the delivery kit (50,000 XAF), and the fact that some essential medicines for maternity are not covered. On the supply side, participants agreed that compulsory health insurance is important in providing antenatal care access to those who need it the most. However, some problems remain. The participants outlined some logistical problems and a lack of medical equipment, including the stock of drugs, disinfectants, and the absence of clean water.                                Conclusion                 Understanding the perceptions of pregnant women and health professionals regarding the quality of antenatal care can help to inform refinements to methods through which the services can be better provided. In addition, the study findings are vital to increasing the use of care, as well as combating high maternal mortality rates. Compulsory health insurance has improved the accessibility and utilization of healthcare services and has contributed to improved quality of care.
Artificial Intelligence in the Diagnosis, Treatment, and Prognosis of Hypopharyngeal Carcinoma: A Scoping Review

Hypopharyngeal carcinoma (HPC) has one of the poorest prognoses among all types of head and neck squamous cell carcinoma (HNSCC). Artificial intelligence (AI) is a scientific field that is in the spotlight, especially in the last decade, and AI has also been widely used in the research field of HPC. This scoping review aimed to describe the improvement of HPC clinical cares brought by AI. Literatures utilizing AI and machine learning in HPC were searched in PubMed, EMBASE, and Web of Science, and 116 articles from 1987 to 2024 were retrieved. After removing duplicate and irrelevant articles, 85 were further selected for detailed review. AI helps analyze large amounts of data from HPC patients and develop models to facilitate clinical practice. The emergence of AI improves the endoscopic, radiologic, and pathologic diagnosis accuracy of HPC and guides personalized treatment and prognosis prediction. However, there are certain unmet challenges that need to be further elucidated, like interpreting the AI algorithms into features that can be observed by humans and promoting the AI models in larger and multi-centered cohorts.
Designing Dashboard for Healthcare and Resource Information for CHUK.

Health data and resource management pose significant challenges to healthcare systems in resource-limited settings, such as Rwanda. At the University Teaching Hospital of Kigali (CHUK), outdated paper-based systems and fragmented data sources contribute to inefficiencies in patient care, delayed decision-making, and suboptimal performance. The lack of a centralized dashboard for real-time data access further exacerbates issues related to resource management and patient safety. Method:To ensure a user-centered design, a structured questionnaire was administered to 162 respondents, including both medical and nonmedical staff at CHUK. Key features such as data visualization, accessibility, and integration with the existing OpenClinic Electronic Medical Records (EMR) system were identified as priorities. The dashboard was developed using Vue.js and Tailwind CSS for the front end, with PHP, Laravel framework for the back end, and MySQL as the database. An iterative development process was used, incorporating stakeholder feedback to continuously refine the prototype and ensure alignment with user needs. Results:The user assessment identified critical needs for the dashboard, with 86% of users recommending key performance indicators such as the total number of patients per department and disaggregation by diagnosis. The findings also demonstrated a significant positive relationship between the dashboard and stakeholders' data management priorities (r=0.921,p=0.000r = 0.921, p = 0.000r=0.921,p=0.000), emphasizing its impact on clinical care quality. Conclusion:In conclusion, the development of a healthcare and resource management dashboard for CHUK represents a transformative opportunity to improve decision-making, reporting accuracy, and overall healthcare delivery. The successful implementation of the dashboard will enhance operational efficiency, and it is recommended that CHUK staff actively support its adoption to maximize its impact.
Sample stability of second-order stochastic differential equations with nonsingular phase diffusion

Almost sure sample stability boundaries are obtained in explicit form for specific linear and nonlinear second-order stochastic differential equations with nonsingular phase diffusion. The results are compared with first- and second-moment stability boundaries.
Pengaruh Green Human Resource Management, Green Transformational Leadership, dan Green Organizational Culture Terhadap Green Employee Behavior pada PT Shinta Konveksi di Boyolali

This study's overarching goal is to dissect how PT Shinta Konveksi's Boyolali location's green HRM, green transformational leadership, and green organizational culture affect green employee behavior. This study relies on numerical data. All 180 workers of PT Shinta Konveksi made up the study's population. Probability sampling is used as a sample approach in this study. There were 125 participants in this research, and the sampling method utilized was simple random sampling. Primary data is required for this study. This study's primary data came from surveys given out to employees at PT Shinta Konveksi Boyolali. In this study, the Structural Equation Model (SEM) with partial least squares (Smart-PLS) was used for data analysis. This study's findings demonstrate a favorable and statistically significant relationship between Green HRM and Green Employee Behavior. This is supported by the fact that the original sample value was 0.250, the value of t-statistic was 3.609 (higher than 1.96), and the value of p-value was as large as 0.000 (less than 0.05). Employees' Green Behavior is Positively and Significantly Affected by Green Transformational Leadership. The results show that this is true: the original sample value was 0.338, the t-statistic was 4.642 (higher than 1.96), and the p-value was as large as 0.000 (less than 0.05). Green employee behavior is positively and significantly impacted by green organizational culture. The fact that the value of the original sample was 0.425, the value of the t-statistic was 4.114 (higher than 1.96), and the value of the p-value was as large as 0.000 (less than 0.05) establish this.
Phase transition in a stochastic geometry model with applications to statistical mechanics

We study the connected components of the stochastic geometry model on Poisson points which is obtained by connecting points with a probability that depends on their relative position. Equivalently, we investigate the random clusters of the random connection model defined on the points of a Poisson process in d‐dimensional space where the links are added with a particular probability function. We use the thermodynamic relations between free energy, entropy, and internal energy to find the functions of the cluster size distribution in the statistical mechanics of extensive and nonextensive. By comparing these obtained functions with the probability function predicted by Penrose, we provide a suitable approximate probability function. Moreover, we relate this stochastic geometry model to the physics literature by showing how the fluctuations of the thermodynamic quantities of this model correspond to other models when a phase transition (10.1002/mma.6965, 2020) occurs. Also, we obtain the critical point using a new analytical method.
Conceptual design study for heat exhaust management in the ARC fusion pilot plant

The ARC pilot plant conceptual design study has been extended beyond its initial scope [B. N. Sorbom et al., FED 100 (2015) 378] to explore options for managing ~525 MW of fusion power generated in a compact, high field (B_0 = 9.2 T) tokamak that is approximately the size of JET (R_0 = 3.3 m). Taking advantage of ARC's novel design - demountable high temperature superconductor toroidal field (TF) magnets, poloidal magnetic field coils located inside the TF, and vacuum vessel (VV) immersed in molten salt FLiBe blanket - this follow-on study has identified innovative and potentially robust power exhaust management solutions.
Force-field parametrization and molecular dynamics simulations of Congo red

Congo red, a diazo dye widely used in medical diagnosis, is known to form supramolecular systems in solution. Such a supramolecular system may interact with various proteins. In order to examine the nature of such complexes empirical force field parameters for the Congo red molecule were developed. The parametrization of bonding terms closely followed the methodology used in the development of the charmm22 force field, except for the calculation of charges. Point charges were calculated from a fit to a quantum mechanically derived electrostatic potential using the CHELP-BOW method. Obtained parameters were tested in a series of molecular dynamics simulations of both a single molecule and a micelle composed of Congo red molecules. It is shown that newly developed parameters define a stable minimum on the hypersurface of the potential energy and crystal and ab initio geometries and rotational barriers are well reproduced. Furthermore, rotations around C-N bonds are similar to torsional vibrations observed in crystals of diphenyl-diazene, which confirms that the flexibility of the molecule is correct. Comparison of results obtained from micelles molecular dynamics simulations with experimental data shows that the thermal dependence of micelle creation is well reproduced.
African Court on Human and Peoples’ Rights

This chapter examines international courts’ premises as objects of international law through the case of the African Court on Human and Peoples’ Rights. When creating an international court, states become legally obligated to supply its physical premises—a functional and symbolic resource that underpins the court’s legal authority and influence. Drawing on archival evidence, this chapter analyses the African Court’s significant challenges in securing this important resource from political actors within the African Union and Tanzania, the court’s host state. It shows that, with international law and courts, there can be a considerable gap between states’ commitment and compliance, and between legal ambition and political reality. This gap, however, can mobilize court officials to assert their needs for resources and, more generally, the significance of their mandate. Examining international courts’ premises, therefore, can elucidate the tensions between law and politics embedded in international justice specifically and international law more broadly.