ethicalabs/Research-Intent-Collab huggingface dataset:
Training efficacy of robotic duct-to-mucosa pancreaticojejunostomy simulation using silicone models for surgical fellows

In the era of minimally invasive surgery (MIS), robotic pancreatoduodenectomy (PD) is actively performed, and clinical fellows need to thoroughly prepare for MIS-PD during the training process. Although pancreaticojejunostomy (PJ) is a difficult anastomosis that requires repeated practice, there are obstacles preventing its practice that concerns patient safety and limited time in the actual operating room. This study evaluated the efficacy of simulation-based training of robotic duct-to-mucosa PJ using pancreatic and intestinal silicone models using a scoring system.Three pancreatobiliary clinical fellows who had never performed a real robotic PJ participated in this study. Each trainee, who was well acquainted with master's video created by a senor surgeon, performed the robotic PJ procedures 9 times, and 3 independent pancreatobiliary surgeons assessed the videos and analyzed the scores using a blind method.The mean robotic PJ times for the 3 trainees were 42.8 and 29.1 minutes for the first and 9th videos, respectively. The mean score was 13.8 (range, 6-17) for the first video and 17.7 (range, 15-19) for the 9th video. When comparing earlier and later attempts, the PJ time decreased significantly (2,201.67 seconds vs. 2,045.50 seconds, P = 0.007), whereas test scores increased significantly (total score 14.22 vs. 16.89, P = 0.011).This robotic education system will help pancreatobiliary trainees overcome the learning curves efficiently and quickly without raising ethical concerns associated with animal models or direct practice with human subjects. This will be of practical assistance to trainees preparing for MIS-PD.
Gender and Climate Change in Latin America: An Analysis of Vulnerability, Adaptation and Resilience Based on Household Surveys

AbstractThis paper analyses gender differences in vulnerability and resilience to shocks, including climate change and climate variability, for Peru, Brazil and Mexico, which together account for more than half the population in Latin America. Vulnerability and resilience indicators are measured by a combination of the level of household incomes per capita and the degree of diversification of these incomes. Thus, households which simultaneously have incomes which are below the national poverty line and which are poorly diversified (Diversification Index below 0.5) are classified as highly vulnerable, whereas households which have highly diversified incomes above the poverty line are classified as highly resilient. The analysis shows that female headed households in all three countries tend to be less vulnerable and more resilient than male headed households, despite the fact that the former usually have lower education levels. Copyright © 2016 John Wiley &amp; Sons, Ltd.
International and Ethiopian Laws on Sustainable Development Goal  6 and the Right to Water and Sanitation

Sustainable development goal 6 is planned to ensuring water and sanitation by 2030.  This article investigates the international law of sustainable development goal 6 that  promote the right to water and sanitation and its implementation under Ethiopian law.  The article investigates the question how does the international law of sustainable  development goal 6 promote the right to water and sanitation and how does the  Ethiopian law helps to implement it? To investigate this question, the article  employed qualitative research methodology.
VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models

Text-to-image diffusion models (T2I) have demonstrated unprecedented capabilities in creating realistic and aesthetic images. On the contrary, text-to-video diffusion models (T2V) still lag far behind in frame quality and text alignment, owing to insufficient quality and quantity of training videos. In this paper, we introduce VideoElevator, a training-free and plug-and-play method, which elevates the performance of T2V using superior capabilities of T2I. Different from conventional T2V sampling (i.e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating. Specifically, temporal motion refining uses encapsulated T2V to enhance temporal consistency, followed by inverting to the noise distribution required by T2I. Then, spatial quality elevating harnesses inflated T2I to directly predict less noisy latent, adding more photo-realistic details. We have conducted experiments in extensive prompts under the combination of various T2V and T2I. The results show that VideoElevator not only improves the performance of T2V baselines with foundational T2I, but also facilitates stylistic video synthesis with personalized T2I. Our code is available at https://github.com/YBYBZhang/VideoElevator.
Understanding the diffusion and adoption of digital finance innovation in emerging economies: M-Pesa money mobile transfer service in Kenya

The growth of digital financial innovations in emerging economies varies across countries with Kenya’s mobile money transactions leading in Africa. Empirical evidence is lacking to describe from a ...
Thermal and neutron-physical features of the nuclear reactor for a power pulsation plant for space applications

We have explored the possibility of creating small-size reactors with a high power output with the provision of thermal stability and nuclear safety under standard operating conditions and in emergency situations. The neutron-physical features of such a reactor have been considered and variants of its designs preserving the main principles and approaches of nuclear rocket engine technology are presented.
Application cases of remote sensing-integrated crop model to simulate and predict crop yield with satellite images

The remote sensing-integrated crop model (RSCM) was designed to simulate crop growth processes and yield using remote sensing data. The RSCM is based on the radiation use efficiency (RUE) model and employs a within-season calibration procedure recalibrating the daily crop leaf area index (LAI) using satellite images. And it has functions to calculate daily biomass, evapotranspiration (ET), gross primary productivity (GPP), and net primary productivity from the LAI in addition to crop yield. In previous studies, the essential crop growth parameters required in the model, such as RUE, light extinction coefficient, specific leaf area, base temperature, etc., were determined through field experiments. And its performances were validated using various remote sensing data, including proximity sensing data, drone images, and satellite images. Among them, this study presented the application results with satellite images in the RSCM. The target crop is rice (Oryza Sativa), one of the world's major crops, and the study areas range from South Korea to Northeast Asia. Satellite images and meteorological data were used differently depending on the study sites. The types of satellite images used in this study are the RapidEye, the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra/Aqua satellite, and the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) of Communication, Ocean and the Meteorological (COMS) satellite. And gridded data for air temperature and solar radiation was acquired from the Korea Local Analysis and Prediction System (KLAPS) and the European Centre for Medium-Range Weather Forecasts (ECMFW). The primary application of the RSMC is to simulate rice yield, but some results showed crop growth factors such as biomass, LAI, GPP, and ET. In addition, the most recent study performed the early prediction of crop yield by combining deep learning with crop models. Through this study, it is possible to know the future utilization of the RSCM model in the agriculture and satellite application fields.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2022-00165154, "Development of Application Support System for Satellite Information Big Data").
Weather Forecasting Using Artificial Neural Network and Bayesian Network

This paper presents a scheme of weather forecasting using artificial neural network (ANN) and Bayesian network. The study focuses on the data representing central Cebu weather conditions. The parameters used in this study are as follows: mean dew point, minimum temperature, maximum temperature, mean temperature, mean relative humidity, rainfall, average wind speed, prevailing wind direction, and mean cloudiness. The weather data were collected from the PAG-ASA Mactan-Cebu Station located at latitude: 10°19´, longitude: 123°59´ starting from January 2011 to December 2011 and the values available represent daily averages. These data were used for training the multi-layered backpropagation ANN in predicting the weather conditions of the succeeding days. Some outputs from the ANN, such as the humidity, temperature, and amount of rainfall, are fed to the Bayesian network for statistical analysis to forecast the probability of rain. Experiments show that the system achieved 93%–100% accuracy in forecasting weather conditions.
Maximum-likelihood estimation of the cosmic microwave background power spectrum from interferometer observations

A maximum-likelihood method is presented for estimating the power spectrum of anisotropies in the cosmic microwave background (CMB) from interferometer observations. The method calculates flat band-power estimates in separate bins in l-space, together with confidence intervals on the power in each bin. For multifrequency data, the power spectrum of the other foreground components may also be recovered. Advantage is taken of several characteristic properties of interferometer data, which together allow the fast calculation of the likelihood function. The method may be applied to single-field or mosaicked observations, and proper account can be taken of non-coplanar baselines. The method is illustrated by application to simulated data from the Very Small Array.
Disaster Risk Perception and Local Resilience near the ‘Duboko’ Landfill: Challenges of Governance, Management, Trust, and Environmental Communication in Serbia

This study examines residents' perceptions of environmental and disaster risks near the “Duboko” regional landfill in Serbia. It aims to uncover factors that affect public trust and community resilience. This approach is rooted in interdisciplinary perspectives on risk perception, institutional trust, and socio-ecological resilience. A structured, self-administered questionnaire was used to collect data from a stratified sample of 1,180 respondents across nine municipalities in Western Serbia. The statistical analysis involved descriptive statistics, t-tests, ANOVA, Pearson correlation, and multiple linear regression. The regression models indicated that gender, education level, type of settlement, property ownership, and household size were significant predictors across various thematic dimensions. The model addressing risk perception and environmental impact accounted for 5.6% of the variance, while the model regarding institutional trust and transparency explained 7.4% of the variance. Higher perceived risk and lower institutional trust were found among women, individuals with lower levels of education, rural residents, and respondents from smaller households. Furthermore, increased transparency and access to environmental information correlated with enhanced trust and perceived resilience. These results emphasise the complex nature of landfill risk perception and highlight the need for participatory communication, transparent governance, and context-sensitive community involvement to bolster disaster resilience and public health protection in areas impacted by landfills.
Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model

We find that the statistical information in Figure 2 neglect the statistics for tSOS, so we make corrections. Additionally, we change the statistical samples to those where CLIP misidentify, but prompt tuning identify correctly. At the same time, we also revise some of the descriptions. The changes to the supplementary materials will be updated shortly. arXiv admin note: text overlap with arXiv:2307.06948 by other authors
An optimized approach towards bio-capture and carbon dioxide sequestration with microalgae Phormidium valderianum using response surface methodology

As carbon dioxide emissions rise, there's need for alternative strategies, including microorganisms, to capture and mitigate them. The present study investigated on the capability and tolerance of microalgal strain, Phormidium valderianum to capture gaseous CO2 at varying levels (5-30 %). A biomass productivity of 0.0216 ± 0.027 gL-1day-1 and rate of CO2 fixation of 0.035 gL-1day-1 was obtained for 25 % CO2 concentration. From this study, it is evident that higher CO2 levels led to elevated carbohydrate concentration. In addition, protein concentration doubled with the introduction of 25 % CO2. In optimization studies, pH 10, 25 % CO2, and 200 mg/L of Ca(OH)2 concentration was found to be optimal for biomass growth. A higher rate of CO2 fixation of 0.315 gL-1day-1 was achieved at these optimum conditions using response surface methodology. Furthermore, the study demonstrated that microalgae, Phormidium valderianum has the potential to serve as a promising alternative for capturing CO2 emissions.
AI-Enhanced Additive Manufacturing: Intelligent 3d Printing for Complex Designs

Additive manufacturing (AM), widely known as 3D printing, has transformed traditional manufacturing by enabling the production of complex geometries, reducing material waste, and supporting rapid, on-demand fabrication. However, conventional AM processes continue to face limitations such as defect formation, inconsistent part quality, and time-consuming design iterations. The integration of artificial intelligence (AI) into AM referred to as AI-enhanced additive manufacturing (AI-AM) offers a promising solution to these challenges. This review explores the convergence of AI and AM, focusing on how machine learning, computer vision, and generative design improve process reliability, adaptability, and efficiency. By synthesizing current research, it highlights how AI enables real-time monitoring, automated defect detection, and adaptive control of printing parameters, leading to reduced failure rates and higher-quality outputs. Furthermore, AI-driven generative design optimizes part geometry and material usage, allowing for the creation of intricate, performance-optimized components. Case studies from aerospace, automotive, and biomedical sectors demonstrate the tangible benefits and limitations of AI-AM systems. Findings indicate that AI integration enhances every stage of the AM workflow from design to fabrication paving the way for more intelligent, autonomous, and efficient production pipelines. As AI continues to evolve, its synergy with AM is poised to redefine the future of manufacturing, offering unprecedented capabilities in producing customized, functionally complex parts with superior precision and performance.
Benchmarking 80 binary phenotypes from the openSNP dataset using deep learning algorithms and polygenic risk score tools

Abstract                 Genotype-phenotype prediction plays a crucial role in identifying disease-causing single nucleotide polymorphisms and precision medicine. In this manuscript, we benchmark the performance of various machine/deep learning algorithms and polygenic risk score tools on 80 binary phenotypes extracted from the openSNP dataset. After cleaning and extraction, the genotype data for each phenotype is passed to PLINK for quality control, after which it is transformed separately for each of the considered tools/algorithms. To compute polygenic risk scores, we used the quality control measures for the test data and the genome-wide association studies summary statistic file, along with various combinations of clumping and pruning. For the machine learning algorithms, we used p-value thresholding on the training data to select the single nucleotide polymorphisms, and the resulting data was passed to the algorithm. Our results report the average 5-fold Area Under the Curve (AUC) for 29 machine learning algorithms, 80 deep learning algorithms, and 3 polygenic risk scores tools with 675 different clumping and pruning parameters. Machine learning outperformed for 44 phenotypes, while polygenic risk score tools excelled for 36 phenotypes. The results give us valuable insights into which techniques tend to perform better for certain phenotypes compared to more traditional polygenic risk scores tools.
Climate vulnerability, impact scenarios and adaptation strategies for resilient cities

This paper deals with the results of the Research Project “METROPOLIS – Integrated and sustainable methods and technologies for resilience and safety in urban systems”, funded under PONREC 2007-2013. Among all the results of the work, the paper is focused on the development of a model to measure climate vulnerability in urban areas. The application of the model provides the assessment of impacts and the subsequent planning of corrective actions to increase building and urban resilience by means of technological retrofitting. The testing of the model for heat waves and pluvial flooding has been implemented in the urban system of Naples City which has been divided into the physical subsystem and the population subsystem.
IoT- and AI-informed urban air quality models for vehicle pollution monitoring

With the rise of intelligent Internet of Things (IoT) systems in urban environments, new opportunities are emerging to enhance real-time environmental monitoring. While most studies focus either on IoT-based air quality sensing or physics-based modeling in isolation, this work bridges that gap by integrating low-cost sensors and AI-powered video-based traffic analysis with high-resolution urban air quality models. We present a real-world pilot deployment at a road intersection in Barcelona's Eixample district, where the system captures dynamic traffic conditions and environmental variables, processes them at the edge, and feeds real-time data into a high-performance computing (HPC) simulation pipeline. Results are validated against official air quality measurements of nitrogen dioxide (NO2). Compared to traditional models that rely on static emission inventories, the IoT-assisted approach enhances the temporal granularity of urban air quality predictions of traffic-related pollutants. Using the full capabilities of an IoT-edge-cloud-HPC architecture, this work demonstrates a scalable, adaptive, and privacy-conscious solution for urban pollution monitoring and establishes a foundation for next-generation IoT-driven environmental intelligence.
Rejoinder to "Equi-energy sampler with applications in statistical inference and statistical mechanics"

Rejoinder to ``Equi-energy sampler with applications in statistical inference and statistical mechanics'' by Kou, Zhou and Wong [math.ST/0507080]
The application of international cultural rights in protecting Indigenous peoples’ land property in Indonesia

Since the Indonesian government adopted the Agrarian Law 1960, which emphasises that any lands or territories without land title or land certificate are claimed belong to the state property, Indigenous peoples argue that the right over their land is based on a common recognition, instead of an official certification. This article aims to analyse the applicability of international cultural rights’ norms in protecting Indigenous rights to land in Indonesia. Several international instruments, such as the UNESCO Conventions, the International Covenant on Civil and Political Rights and the International Covenant on Economic, Social and Cultural Rights, have been urged that any government shall respect and protect cultural rights for everyone, including Indigenous peoples. Therefore, protecting Indigenous intangible and tangible cultural heritage would not be possible without protecting their ancestral lands, territories and resources. In other words, securing the right to traditional lands is a prerequisite for Indigenous communities’ cultural survival in Indonesia.
Epidemiology of Infectious Pathogens in Horses with Acute Respiratory Disease, Abortion, and Neurological Signs: Insights Gained from the Veterinary Surveillance System for Horses in The Netherlands (SEIN)

Monitoring infectious diseases is essential for safeguarding equine health and ensuring the sustainability of the horse industry. In 2019, the Royal Veterinary Association of the Netherlands (KNMvD) and Royal GD (GD Animal Health) launched SEIN (Surveillance of Equine Infectious diseases in the Netherlands), a voluntary surveillance system for laboratory-confirmed outbreaks of equid alphaherpesvirus 1 (EHV-1), equid alphaherpesvirus 4 (EHV-4), equine influenza virus (EIV), and Streptococcus equi subsp. equi. This retrospective study analyzed 364 confirmed outbreaks reported through SEIN between June 2019 and April 2023. S. equi was the most commonly reported pathogen overall (64%). Among outbreaks involving respiratory disease, S. equi accounted for 74% of cases, followed by EHV-4 (16%), EIV (6%), and EHV-1 (4%). The geographical distribution of outbreaks covered 80 of the 90 postal code regions (89%), and approximately half of all participating practices generated at least 1 alert. Vaccination data revealed low coverage against EHV-1/4, EIV, and S. equi among both affected horses and premises. Clinical signs overlapped between pathogens, but some were more pathogen-specific, e.g., coughing in EIV, and abscessation in S. equi. The SEIN system provided spatiotemporal information on confirmed outbreaks. These results underscore the importance of quick diagnostics and structured surveillance systems in guiding prevention strategies.
Virtual-organism toy-model as a tool to develop bioinformatics approaches of Systems Biology for medical-target discovery

Systems Biology has emerged in the last years as a new holistic approach based on the global understanding of cells instead of only being focused on their individual parts (genes or proteins), to better understand the complexity of human cells. Since the Systems Biology still does not provide the most accurate answers to our questions due to the complexity of cells and the limited quality of available information to perform a good gene/protein map analysis, we have created simpler models to ensure easier analysis of the map that represents the human cell. Therefore, a virtual organism has been designed according to the main physiological rules for humans in order to replicate the human organism and its vital functions. This toy model was constructed by defining the topology of its genes/proteins and the biological functions associated to it. There are several examples of these toy models that emulate natural processes to perform analysis of the virtual life in order to design the best strategy to understand real life. The strategy applied in this study combines topological and functional analysis integrating the knowledge about the relative position of a node among the others in the map with the conclusions generated by mathematical models that reproduce functional data of the virtual organism. Our results demonstrate that the combination of both strategies allows better understanding of our virtual organism even with the lower input of information needed and therefore it can be a potential tool to better understand the real life.