ethicalabs/Research-Intent-Collab huggingface dataset:
Landslide-effects on food security, the nutritional status of children under five years and the human right to adequate food in Uganda

Landslides predispose individuals from low-resource communities to risk factors that may lead to poor nutritional health. A mixed methods cohort study was conducted to assess landslides and seasonality effects on food security, the nutritional status of small children, and the right to adequate food among households affected by the major 2010 and 2018 landslides in rural Uganda. The landslide-affected community was the study arm while a nearby community was the control arm. Using three-stages of simple randomization, 422 households including 392 children were assessed in May-August (food-plenty season) 2019. Six months later, in January-March (food-poor season) 2020, 388 households and 366 children were re-assessed. Food insecurity and child anthropometry were measured by standardized scoring questionnaires and WHO-standards, respectively. Four focus group discussions with 36 adults aged 18-55 and key informant interviews with 10 purposively sampled persons of authority, explored food insecurity and the right to adequate food issues. Overall, stunting (reduced height) prevalence during the food-poor season was 42.6% vs 37.7% in the food plenty season. The landslide-affected households experienced significantly more malnutrition and food insecurity than the controls and the effect was higher during the food-poor season. The landslide-affected households were significantly more likely to adopt food insecurity coping strategies compared to the control and in both seasons. Coping strategies were more pronounced during the food-poor season. Comprehension and awareness of the human right principles and state obligations regarding the right to adequate food were low among the study participants. This study indicates that landslides and seasonality contribute to food insecurity, child malnutrition and non-realization of the right to adequate food in rural Uganda.
The Reactions of Employees Toward the Implementation of Human Resources Information Systems (HRIS) as a Planned Change Program: A Case Study in Malaysia

The main purpose of this research is to examine the main factors that contributed to the acceptance of employees toward the new HRIS implemented in the Malaysian Airlines System (MAS). The emphasis is on data collection based on employee perception. The study did not attempt to establish cause and effect analysis, but focused exclusively on determining the relationship between 10 selected variables and employees' acceptance of a planned change program. The findings may help managers to direct efforts toward the variables that influence employees' acceptance, which will increase probability of employee participation, which, in turn, will lead to the success of the implementation process of the change program. A questionnaire was developed and administered with 250 employees. The findings clearly indicated that top management involvement and organizational commitment appeared as the two largest coefficients for the impact on the acceptance level of employees toward the planned change effort. Based on the fi...
Supplemental Material - Effects of the Affordable Care Act Insurance Expansions on Health Care Coverage, Access, and Health Status of 50–64-Year-Old Adults: Evidence From the First Six Years

Supplemental Material for Effects of the Affordable Care Act Insurance Expansions on Health Care Coverage, Access, and Health Status of 50–64-Year-Old Adults: Evidence From the First Six Years by Redwan Bin Abdul Baten and George L. Wehby in Journal of Applied Gerontology.
The Caring-Disability Creation Process model: a new way of combining ‘Care’ in nursing and ‘Rehabilitation’ for better quality of services and patient safety

In an effort to continuously improve the quality of the services provided to persons in rehabilitation and patient safety, this article presents a model for the development of a Caring-Disability Creation Process that combines two elements that, in the past, have often been separated: care in nursing and rehabilitation. This new model and its underlying concepts introduce to a comprehensive approach in the recognition of the added value of caring by the contribution of rehabilitation nurses making an optimal development of the individual's potential and achieving successful social participation.
A Systematic Review of Reproducibility Research in Natural Language Processing

Against the background of what has been termed a reproducibility crisis in science, the NLP field is becoming increasingly interested in, and conscientious about, the reproducibility of its results. The past few years have seen an impressive range of new initiatives, events and active research in the area. However, the field is far from reaching a consensus about how reproducibility should be defined, measured and addressed, with diversity of views currently increasing rather than converging. With this focused contribution, we aim to provide a wide-angle, and as near as possible complete, snapshot of current work on reproducibility in NLP, delineating differences and similarities, and providing pointers to common denominators.
Contingent Sovereignty, Territorial Integrity and the Sanctity of Borders

��� This article looks at emergent challenges to the sanctity of international borders. It first provides a brief discussion of international law on the issues of uti possidetis and territorial integrity. It then examines challenges to these ideas that have emerged in recent years through the notion of contingent sovereignty and its relation to earlier calls for humanitarian intervention and current discussions around reform of the United Nations. In contrast the other side of the coin is the notion of earned sovereignty, where new states can enjoy transitional paths to independence or secession. The former has enjoyed much more international support, but both ways of rethinking the notion of sovereignty have important territorial implications. The article concludes by raising questions about this relation and the question of borders more generally.
Artificial Intelligence Is Reshaping Healthcare amid COVID-19: A Review in the Context of Diagnosis &amp; Prognosis

Preventing respiratory failure is crucial in a large proportion of COVID-19 patients infected with SARS-CoV-2 virus pneumonia termed as Novel Coronavirus Pneumonia (NCP). Rapid diagnosis and detection of high-risk patients for effective interventions have been shown to be troublesome. Using a large, computed tomography (CT) database, we developed an artificial intelligence (AI) parameter to diagnose NCP and distinguish it from other kinds of pneumonia and traditional controls. The literature was studied and analyzed from diverse assets which include Scopus, Nature medicine, IEEE, Google scholar, Wiley Library, and PubMed. The search terms used were ‘COVID-19’, ‘AI’, ‘diagnosis’, and ‘prognosis’. To strengthen the overall performance of AI in COVID-19 diagnosis and prognosis, we segregated several components to perceive threats and opportunities, as well as their inter-dependencies that affect the healthcare sector. This paper seeks to pick out the crucial fulfillment of factors for AI with inside the healthcare sector in the Indian context. Using critical literature review and experts’ opinion, a total of 11 factors affecting COVID-19 diagnosis and prognosis were detected, and we eventually used an interpretive structural model (ISM) to build a framework of interrelationships among the identified factors. Finally, the matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis resulted the driving and dependence powers of these identified factors. Our analysis will help healthcare stakeholders to realize the requirements for successful implementation of AI.
The role of social media in education: designing a novel digital learning environment

The essence of the concepts of pedagogical support, professional self-determination, professional orientation. The analysis of pedagogical experience on pedagogical support of professional self-determination of pupils is carried out. The program of the elective course "Professional self-determination", the plans-summaries of lessons on pedagogical support of professional self-determination of schoolchildren. Organized, conducted and analyzed experimental work on the basis of MBOU "SOSH № 15" Vladimir.
Venture Capital and Startup Agglomeration Professor of Finance and Entrepreneurship David L. and Elsie M. Dodd Professor of Finance

The paper studies venture capital’s (VC) role in the geographic clustering of high-growth startups. We exploit a rule change that disproportionately impacted U.S. regions that historically lacked VC financing via a restriction of banks to invest in the asset class. A one-standard-deviation increase in VCs’ exposure to the rule led to a 20% decline in fund size and a 10% decrease in the likelihood of raising a follow-on fund. Startups were not wholly cushioned: financing and valuations declined. Startups also moved out of impacted states after the rule change, likely exacerbating existing geographic disparity in entrepreneurship.
ScholarOne - Impact of Leadership Behavior on Organizational Change Management in Public Universities of Ethiopia: A Study of Leadership Influences and Change Processes

The study aimed to examine how leadership behavior affects organizational change management process within public universities in Ethiopia. It utilized a mixed research approach, combining quantitative and qualitative methods. The research design employed was descriptive and explanatory. The sample size consisted of 581 academic staff and academic leaders who were selected using systematic random sampling techniques. This ensured that a representative sample was obtained from the target population. Data collection involved the use of questionnaires, interviews, and focus group discussions (FGDs). Descriptive and inferential statistics were used to analyze the collected data. The study findings revealed that a positive and significant relationship was found between autocratic, democratic, laissez-faire, and transactional leadership behaviors and organizational change management. However, there was a negative and significant relationship between transformational leadership behavior and organizational change management. Additionally, the study identified significant differences in the perceptions of leadership behaviors and organizational change management between academic staff and academic leaders. Based on the study findings, the authors recommended that leaders in public universities customize and apply the identified positive leadership behaviors (autocratic, democratic, laissez-faire, and transactional) to effectively manage organizational change.
Robot-Assisted Suturing in Surgical Simulation: A Comparative Analysis Between NVIDIA’s Simulator and an Open Platform

Virtual simulations have served as important tools for designing and testing robotic frameworks in various domains. Increasingly, they have been used to train and experiment in surgical robotics applications. Having seen the success of an open simulation environment for robot-assisted surgical suturing in the Asynchronous Multi-Body Framework (AMBF), we sought to reproduce the surgical suturing environment from AMBF in NVIDIA Isaac Sim, a simulator with impressive visual fidelity and GPU-accelerated computation capabilities. We present a virtual scene for robotic suturing comprising two robotic manipulators, an endoscope, two surgical phantoms and a needle attached to a thread. The thread is modeled using three different techniques. This work also provides interactive manipulation of the virtual scene via haptic input devices using the Robot Operating System interface. Although Isaac Sim possesses impressive and realistic visuals, AMBF outperforms in speed by up to 15 frames per second, depending on the task. AMBF also responded faster than Isaac Sim by approximately 0.1 s when using a haptic input device. In this context, our objective is to provide a comprehensive overview of the capabilities and limitations of NVIDIA Isaac Sim in the surgical robotics challenge relative to AMBF.
Deep Learning for Automated Sewer Defect Detection: Benchmarking YOLO and RT-DETR on the Istanbul Dataset

The inspection and maintenance of urban sewer infrastructure remain critical challenges for megacities, where conventional manual inspection approaches are labor-intensive, time-consuming, and prone to human error. Although deep learning has been increasingly applied to sewer inspection, the field lacks both a publicly available large-scale dataset and a systematic evaluation of CNN and transformer-based models on real sewer footage. The primary aim of this study is to systematically evaluate and compare state-of-the-art deep learning architectures for automated sewer defect detection using a newly introduced dataset. We present the Istanbul Sewer Defect Dataset (ISWDS), comprising 13,491 expert-annotated images collected from Istanbul’s wastewater network and covering eight defect categories that account for approximately 90% of reported failures. The scientific novelty of this work lies in both the introduction of the ISWDS and the first systematic benchmarking of YOLO (v8/11/12) and RT-DETR (v1/v2) architectures under identical protocols on real sewer inspection footage. Experimental results demonstrate that RT-DETR v2 achieves the best performance (F1: 79.03%, Recall: 81.10%), significantly outperforming the best YOLO variant. While transformer-based architectures excel in detecting partially occluded defects and complex operational conditions, YOLO models provide computational efficiency advantages for resource-constrained deployments. Furthermore, a QGIS-based inspection tool integrating the best-performing models was developed to enable real-time video analysis and automated reporting. Overall, this study highlights the trade-offs between accuracy and efficiency, demonstrating that RT-DETR v2 is most suitable for server-based processing. In contrast, compact YOLO variants are more appropriate for edge deployment.
Climate Change Projections For Central America: A Regional Climate Model Study

Central America has been identified as one of the regions in the world where potential climate change impacts on the environment can be pronounced and is considered a climate change 'hot-spot'. The Intergovernmental Panel on Climate Change Fourth Assessment Report indicates that the region around the western Caribbean and Central America is one where a majority of the climate model simulations indicate rather large changes in temperature and precipitation. This region, however, is one with considerable topographic relief, which implies the existence of large gradients in many critical climate variables. The interactions between the complex topography of Central America and the neighboring oceans give rise to numerous climate zones, horizontally and vertically. Consequently, the region has high biodiversity, it harbors high-value ecosystems and it is important to provide more realistic scenarios to assist in adaptation and mitigation work in the region. In this study, I aim to understand climate change in Central America at spatial scales relevant for impacts assessment.  A regional climate model PRECIS was employed to carry out two experiments: the baseline (present-day) run and the scenario run, both performed at 25-km horizontal resolution. The thorough examination of the model performance showed model's success in capturing the spatial and temporal variability of the key climate variables and its strength in simulating topographically-induced regional climate features. The projected increase in temperature, a large decrease in precipitation in most of Central America, and corresponding hydrological changes under the A2 scenario may have serious negative consequences on water resources, agricultural activities and the ecosystem dynamics in the region.
Mouse Gut Microbiome-Encoded β-Glucuronidases Identified using MAPS, Metagenome Analysis by Protein Structure

Gut microbial β-glucuronidase (GUS) enzymes play important roles in drug efficacy and toxicity, intestinal carcinogenesis, and mammalian-microbial symbiosis. In 2017, a catalog of gut microbiome-encoded GUS enzymes was described for the Human Microbiome Project stool sample database, revealing 279 unique GUS proteins that were organized into six categories based on active site structural features. Mice are a critical model organism used to steady health and disease, thus we sought to provide an analogous catalog of mouse intestinal microbial GUS proteins – a mouse gut GUSome. Using metagenome analysis by protein structure (MAPS), we examined the ~2.5 million unique proteins from a comprehensive mouse gut metagenome created using several mouse strains, providers, housing locations, and diets. From these data we found 338 unique GUS proteins and characterized the proteins into six categories similar to the human GUSome. GUS enzymes were encoded by the major gut microbial phyla, including Firmicutes (67%), Bacteroidetes (24%), and nearly 9% for which taxonomy could not be assigned. No differences in gut microbial gus gene composition were observed for mice based on location or housing provider. However, mouse of the Swiss-Webster strain exhibited a different phylogeny of gut microbes encoding gus genes and distinct fractions of GUS proteins based on active-site features compared to the seven other strains examined. Additionally, mouse in a low-fat diet maintained gut microbes that encoded 22 additional GUS proteins, not found in mice on a high-fat diet. Biochemical analysis of a low-fat unique GUS enzyme revealed its capability of processing both sulfate and non-sulfated heparin sulfate nonasaccharides containing terminal glucuronides. Our results show GUS in mice and humans differ at the individual protein level and that gus gene composition in the murine GI tract differ in mice on low fat vs. high fat diets. These data provide a basis for understanding the gut microbial GUS enzymes present in laboratory mice commonly used as organisms to model mammalian health and disease.
GREEN LOGISTICS AND SUSTAINABLE TRANSPORTATION: AI-BASED ROUTE OPTIMIZATION, CARBON FOOTPRINT REDUCTION, AND THE FUTURE OF ECO-FRIENDLY SUPPLY CHAINS

The rapid expansion of global supply chains has led to increased carbon emissions and environmental concerns, necessitating the adoption of sustainable logistics solutions. This study explores the role of artificial intelligence (AI) in optimizing transportation routes, minimizing fuel consumption, and reducing the carbon footprint of supply chains. AI-powered route optimization integrates real-time traffic data, weather conditions, and vehicle efficiency to enhance last-mile delivery and freight management. Machine learning algorithms further contribute to predictive maintenance, fleet electrification strategies, and demand forecasting, ensuring operational sustainability. This research also examines green logistics practices, including the use of electric and hydrogen-powered vehicles, multimodal transportation networks, and circular economy models to minimize environmental impact. Blockchain-enabled carbon tracking and AI-driven sustainability metrics offer improved transparency in carbon footprint reporting. Additionally, the study highlights regulatory frameworks and industry initiatives promoting low-emission transportation and smart logistics hubs. Findings suggest that AI-driven logistics solutions can significantly improve efficiency while meeting sustainability goals. However, challenges such as high implementation costs, data privacy concerns, and infrastructure limitations must be addressed. Future research should focus on integrating AI with IoT and blockchain for enhanced traceability and decision-making in sustainable supply chains. The study concludes that AI-powered green logistics can revolutionize transportation, offering a viable path toward carbon-neutral and cost-effective global supply chains.
Genetic Screening and Price Discrimination in Insurance Markets

Basing insurance prices on the results of an imperfect screening test to identify risk types can reduce or increase aggregate discrimination across insureds. We present a powerful and general new framework of analysis to examine this issue, drawing upon recent work which uses decomposable inequality indices to measure vertical and horizontal inequity in taxation. We find that, whilst improved test performance inevitably reduces vertical discrimination (in the average prices faced by different risk types), even very accurate tests can lead to substantial horizontal discrimination (within risk types) and enhanced overall discrimination. These conclusions are shown to be robust to a range of different value judgements about how to aggregate individual discriminatory effects and to be particularly relevant to the case of genetic screening.
Improved QoS at the Edge Using Serverless Computing to Deploy Virtual Network Functions

Multiaccess edge computing (MEC) will strengthen forthcoming 5G networks by improving the Quality of Service (QoS), in particular, reducing latency, increasing data processing rates, and providing real-time information to develop high-value Internet-of-Things (IoT) services. To enable data-intensive network services and support advanced analytics, many network operators have proposed to integrate MEC systems with network function virtualization (NFV) consolidating virtual network functions (VNFs) and edge capabilities on a shared infrastructure. As of yet, this integration is not fully established, with various architectural issues currently open, even at standardization level. For instance, any update to VNFs deployed in a MEC system requires a time-consuming manual effort, which affects the overall infrastructure operations. To address these pitfalls, VNFs can be decomposed into microservices, which maintain their own states and exhibit different resource consumption requirements. This article presents an approach to integration that leverages serverless computing to merge MEC and NFV at the system level and to deploy VNFs on demand, by combining MEC functional blocks with an NFV orchestrator using a Kubernetes cluster. We further investigate whether the resource utilization of a MEC system can be improved by leveraging networked FPGA-enabled MEC servers, through an extension of the edge layer that takes advantage of available programmable hardware. We quantitatively evaluate and demonstrate the improvement of 75% end-to-end latency, 99.96% VNF execution time, 26.9% resource utilization, and 15.8% energy consumption in comparison with traditional baselines of cloud, edge, and serverless-edge test cases for a high-definition real-time video streaming application.