ethicalabs/Research-Intent-Collab huggingface dataset:
A feature binding model in computer vision for object detection

In this paper, the authors propose the “Feature Binding (FB)” strategy in computer vision, a method combined with the biological visual perception theory. Based on feature subspace, the proposed method refers to the biological model and binds features according to certain rules. All features bound in a group are taken as a whole. Besides, all groups with different weight coefficients according to different importance are used to determine the object and its location. The position of the object can be determined based on the calculation according to the corresponding criteria. Feature Binding can significantly enhance the accuracy of object detection and localization. Moreover, the method can accelerate object detection and resist external interference in the unbound feature subspace. Feature Binding has good accuracy not only for the whole object but also for the obscured object. It also has good robustness for different algorithms, which are based on features, including traditional methods and deep learning algorithms. The object positioning system can detect the partially occluded objects more accurately in practice.
Cosmic-ray modulation and the heliospheric magnetic field

Abstract   The heliospheric magnetic field plays a key role in any model for the modulation of cosmic rays. It enters into all diffusion coefficients, and its magnitude, spatial gradient and direction determine drifts patterns of cosmic rays in the heliosphere. While the first axisymmetric model of E.N. Parker proved quite successful to explain in situ measurements in the ecliptic plane, new insight into the origin and the nature of the field, especially at high heliographic latitudes, has led to the development of complex fully-three-dimensional, time-dependent models. In this review, we discuss a selection of models for the heliospheric magnetic field, and discuss how some of the more recent Fisk-type models affect the modulation of cosmic rays.
Qualität von additiv hergestellten PLA-Bauteilen*/Quality of additively manufactured PLA components – How the print direction influences the mechanical properties and the distortion of 3D-printed PLA components

Die additive Fertigung eröffnet neue Gestaltungs- und Optimierungsfreiheitsgrade für oberflächen- und strukturoptimierte Bauteile. Mit diesen Verfahren lassen sich selbst komplexe räumliche Strukturen kostengünstig herstellen. Der vorliegende Artikel untersucht den Einfluss der Druckrichtung und des Füllgrads auf die mechanischen Eigenschaften, den Verzug sowie die erreichbaren Formgenauigkeiten am Beispiel von mit 3D-Druck über den FDM-Prozess erzeugten PLA-Bauteilen.           &amp;nbsp;           Additive manufacturing opens up new possibilities for optimizing components with regard to surfaces and structures. These processes allow producing even the most complex three-dimensional structures at comparatively low cost. This paper analyzes how the print direction and the filling ratio affect the mechanical properties, the distortion, as well as the achievable geometrical accuracies, by using 3D-printed PLA components produced by FDM processes.
The Quantum Interference Computer: error correction

An error correcting mechanism is proposed in the context of the Quantum Interference Computer approach
Zoo of quantum-topological phases of matter

What are topological phases of matter? First, they are phases of matter at zero temperature. Second, they have a non-zero energy gap for the excitations above the ground state. Third, they are disordered liquids that seem have no feature. But those disordered liquids actually can have rich patterns of many-body entanglement representing new kinds of order. This paper will give a simple introduction and a brief survey of topological phases of matter. We will first discuss topological phases that have topological order (ie with long range entanglement). Then we will cover topological phases that have no topological order (ie with only short-range entanglement).
A Methodology for Player Modeling based on Machine Learning

AI is gradually receiving more attention as a fundamental feature to increase the immersion in digital games. Among the several AI approaches, player modeling is becoming an important one. The main idea is to understand and model the player characteristics and behaviors in order to develop a better AI. In this work, we discuss several aspects of this new field. We proposed a taxonomy to organize the area, discussing several facets of this topic, ranging from implementation decisions up to what a model attempts to describe. We then classify, in our taxonomy, some of the most important works in this field. We also presented a generic approach to deal with player modeling using ML, and we instantiated this approach to model players' preferences in the game Civilization IV. The instantiation of this approach has several steps. We first discuss a generic representation, regardless of what is being modeled, and evaluate it performing experiments with the strategy game Civilization IV. Continuing the instantiation of the proposed approach we evaluated the applicability of using game score information to distinguish different preferences. We presented a characterization of virtual agents in the game, comparing their behavior with their stated preferences. Once we have characterized these agents, we were able to observe that different preferences generate different behaviors, measured by several game indicators. We then tackled the preference modeling problem as a binary classification task, with a supervised learning approach. We compared four different methods, based on different paradigms (SVM, AdaBoost, NaiveBayes and JRip), evaluating them on a set of matches played by different virtual agents. We conclude our work using the learned models to infer human players' preferences. Using some of the evaluated classifiers we obtained accuracies over 60% for most of the inferred preferences.
QUESTIONNAIRE BASED ASSESSMENT ON KNOWLEDGE AND ATTITUDE TOWARDS ANTIMICROBIAL RESISTANCE &amp; ANTIMICROBIAL STEWARDSHIP PROGRAM AMONG SECOND YEAR MEDICAL UNDERGRADUATES OF TERTIARY CARE TEACHING HOSPITAL, KANPUR

Objective -To assess the knowledge and attitude of second year medical undergraduates towards antimicrobial resistance (AMR) and antimicrobial stewardship program (ASP) Method –A cross-sectional questionnaire based study conducted among second year medical undergraduates of GSVM, Medical College, Kanpur in the form of pre-test and after sensitizing the students about AMR and ASP, again same questionnaire were given as post-test. Five Point likert scale has been used for assessment. Chi-square test was used and p-value &lt;0.05 was consider statistically signicant. Result- Total of 239 students, 234 students participated in pre-test and 232 participated in post-test. Percentage of male students in pre &amp; post-test were more (63.2% and 62.5%) compared to female students (36.8% and 37.5%).We have found highly signicant (p&lt;0.000001) improvement in knowledge and attitude towards AR and ASP at the end of post-test. Except for one question where almost equal percentage of students in both pre (68.4%) and post-test (69%) said they have never heard of ASP before the lecture taken. Conclusion- Signicant improvement in knowledge and positive attitude towards AMR and ASP has been found. Different methods of educational interventions (case problem study, practical session, and training) can be embedded for successful outcome.
Primary Care Availability, Safety Net Clinics, and Health Insurance Coverage

The objective of the study was to examine the relationship between physician/safety net availability and health insurance coverage and preventable hospitalizations (PHs) in nonelderly adults in an urban area. Preventable conditions (PHs) were identified for nonelderly adults in Harris County using the Texas Health Care Information Collection hospital database. Multivariable logistic regression models examined the association of health insurance and patient proximity to physicians and safety net clinics with the risk of a PH. Safety net availability reduced PH risk by 23% (P &lt; .05) but only among the uninsured. Lack of health insurance increased PH risk by 30% (P &lt; .05).
An ecological economic approach to forest and biodiversity conservation: The case of vanuatu

Abstract   The paper presents an ecological economic approach to forest and biodiversity conservation. Constructivist philosophy of social science is critically reviewed and adopted as the methodological basis of the approach. The paradigmatic basis of ecological economics is discussed and a model of human behavior suited to ecological economics is presented. This model accounts for altruistic, normative/affective, and irrational behavior, as well as the role of institutions. A discussion of participatory land use planning, which also accounts for intergenerational equity issues, completes the presentation of the ecological economic approach. Its application to the conservation of forests and biodiversity in Vanuatu demonstrates the operational applicability of the proposed approach.
Electronic nursing documentation for patient safety, quality of nursing care, and documentation: a systematic review

Objective: To evaluate the impact of electronic nursing documentation on patient safety, quality of nursing care and documentation. Method: The systematic review was conducted in December 2022, and comprised a comprehensive search on Scopus, ScienceDirect, ProQuest, PubMed, Cumulative Index to Nursing and Allied Health Literature, Sage Journals and Google Scholar databases for English-language human studies published between 2018 and 2022. The key words used in the search included "Nursing", "care", "documentation", "record", "electronic", "process" and "health services". The risk of bias was assessed using Strengthening the Reporting of Observational Studies in Epidemiology tool. Results: Of the 469 items initially identified, 15(3.2%) were analysed in detail, indicating a positive influence of electronic nursing documentation on patient safety, care quality, and documentation. However, shortcomings were observed in the development of electronic nursing documentation for optimal effectiveness. Conclusion: Electronic nursing documentation significantly enhanced patient safety, care quality and documentation. To facilitate its integration into clinical settings, a standardised and logically structured electronic nursing documentation system is essential. Key Words: Documentation, Electronic health documentation, Nursing, Patient safety, Quality of healthcare.
Explaining deep neural networks through knowledge extraction and graph analysis

Explainable Artificial Intelligence (XAI) has recently become an active research field due to the need for transparency and accountability when deploying AI models for high-stake decision making. Despite the success of Deep Neural Networks (DNNs), understanding their decision processes is still a known challenge. The research direction presented in this thesis stems from the idea that combining knowledge with deep representations can be the key to more transparent decision making. Specifically, we have focused on Computer Vision tasks and Convolutional Neural Networks (CNNs) and we have proposed a graph representation, called co-activation graph, that serves as an intermediate representation between knowledge encoded within a CNN and the semantics contained in external knowledge bases. Given a trained CNN, we first show how a co-activation graph can be created and exploited to generate global insights for the model’s inner-workings. Then, we propose a taxonomy extraction method that captures how symbolic class concepts and their hypernyms in a given domain are hierarchically organised in the model’s subsymbolic representation. We then illustrate how background knowledge can be connected to the graph in order to generate textual local factual and counterfactual explanations. Our results indicate that graph analysis approaches applied to co-activation graphs can reveal important insights into how CNNs work and enable both global and local semantic explanations. Despite focusing on CNN architectures, we believe that our approach can be adapted to other architectures which would make it possible to apply the same methodology in other domains such as Natural Language Processing. At the end of the thesis we will discuss interesting research directions that are being investigated in the area of using knowledge graphs and graph analysis for explainability of deep learning models, and we outline opportunities for the development of this research area
Content validation of a novel robotic surgical simulator

Study Type – Therapy (case series) Level of Evidence 4                                                           OBJECTIVE                     • To assess the content validity of an early prototype robotic simulator. Minimally invasive surgery poses challenges for training future surgeons. The Robotic Surgical Simulator (RoSS) is a novel virtual reality simulator for the da Vinci Surgical System.                                                           PATIENTS AND METHODS                     • Participants attending the 2010 International Robotic Urology Symposium were invited to experience RoSS. Afterwards, participants completed a survey regarding the appropriateness of the simulator as a teaching tool.                                                           RESULTS                                            • Forty‐two subjects including surgeons experienced with robotics (                       n                       = 31) and novices (                       n                       = 11) participated in this study.                                          • Eighty per cent of the entire cohort had an average of 4 years of experience with robot‐assisted surgery.                     • Eleven (26%) novices lacked independent robot‐assisted experience. The expert group comprised 17 (41%) surgeons averaging 881 (160–2200) robot‐assisted cases. Experts rated the ‘clutch control’ virtual simulation task as a good (71%) or excellent (29%) teaching tool.                     • Seventy‐eight per cent rated the ‘ball place’ task as good or excellent but 22% rated it as poor.                     • Twenty‐seven per cent rated the ‘needle removal’ task as an excellent teaching tool, 60% rated it good and 13% rated it poor.                     • Ninety‐one per cent rated the ‘fourth arm tissue removal’ task as good or excellent.                     • Ninety‐four per cent responded that RoSS would be useful for training purposes.                     • Eighty‐eight per cent felt that RoSS would be an appropriate training and testing format before operating room experience for residents.                     • Seventy‐nine per cent indicated that RoSS could be used for privileging or certifying in robotic surgery.                                                           CONCLUSION                     • Results based on expert evaluation of RoSS as a teaching modality illustrate that RoSS has appropriate content validity.
Toward Better Access to Health Insurance Coverage for U.S. Retirees in Mexico Mejor accesibilidad a los seguros de salud para jubilados estadunidenses residentes en México

Many retirees from the United States of America have limited health insurance coverage while living in Mexico. Medicare and Medicaid benefits are not portable to other countries and Medigap (private insurance that supplements Medicare) is very limited. This causes economic and medical hardships and serves as a barrier to retirement to Mexico. Increasing numbers of U.S. retirees will be interested in moving to Mexico in the future because of the climate, the culture, and the lower cost of living. The numbers are increasing as a result of several factors such as aging "baby boomers" and the rapidly growing Mexican-origin population in the U.S.A. who are citizens or permanent residents but would like to return to their communities of origin after working in the U.S.A. There are several policy initiatives that could provide opportunities for improving health insurance coverage for these retirees that could be cost-effective. The full version of this paper is available too at: http://www.insp.mx/salud/index.htmlMuchos jubilados estadunidenses cuentan con una cobertura de seguro médico restringida mientras viven en México. Los beneficios de Medicare y Medicaid no son transferibles a otros países, y el Medigap (el seguro privado que complementa a Medicare) es muy limitado. Esto genera privaciones económicas y médicas y es una barrera para el retiro en México. Un número creciente de jubilados estadunidenses estaría interesado en mudarse a México en el futuro, debido al clima, la cultura y el costo más bajo de la vida. Este número creciente es resultado de varios factores: el envejecimiento de los baby boomers y la creciente población de origen mexicano con ciudadanía estadunidense o con residencia permanente, a la que le gustaría regresar a su comunidad de origen después de haber trabajado en Estados Unidos de América. En este trabajo se presentan diversas iniciativas que podrían ofrecer oportunidades para mejorar la cobertura de seguro médico para los retirados y que, además, podrían ser costo-efectivas. El texto completo de este artículo también está disponible en: http://www.insp.mx/salud/index.html
Benchmark Force Fields for the Molecular Dynamic Simulation of G-Quadruplexes

G-quadruplexes have drawn widespread attention for serving as a potential anti-cancer target and their application in material science. Molecular dynamics (MD) simulation is the key theoretical tool in the study of GQ’s structure-function relationship. In this article, we systematically benchmarked the five force fields of parmbsc0, parmbsc1, OL15, AMOEBA, and Drude2017 on the MD simulation of G-quadruplex from four aspects: structural stability, central ion channel stability, description of Hoogsteen hydrogen bond network, and description of the main chain dihedral angle. The results show that the overall performance of the Drude force field is the best. Although there may be a certain over-polarization effect, it is still the best choice for the MD simulation of G-quadruplexes.
Research On Factors Impacting On Corporate Social Responsibilities And Business Ethics Of Taxi Businesses In Ho Chi Minh City

The objectives of the study aimed to identify the impact factors and their level of influence on responsibilities and ethics of taxi businesses in Ho Chi Minh City.  An approved error correction model consists of five impact factors including: Legal regulations, Balance of interests between taxi businesses and customers, Competition in the market, Knowledge of customer, Individual manager of taxi business. The results of the study can bring about positive changes in the taxi business.
The Application of Artificial Intelligence in Root Cause Analysis of Financial Conflicts in Saudi Arabia: A Framework and Case Study Investigation to a More Effective Digital Judiciary

This research paper investigates the mounting pressure on Saudi Arabia's conventional systems of justice to respond to the increasingly complex financial litigation resulting from the economic reform of Vision 2030. It argues that in order for the Kingdom to sustain its high growth and maintain investor confidence, a more effective and transparent system of justice is not only convenient but necessary.  In order to overcome this challenge, this paper proposes a new conceptual model for applying Artificial Intelligence (AI) to Root Cause Analysis (RCA) of financial conflicts. The model based on AI is intended to transform judicial proceedings from reactive adjudication to proactive, system-based risk management.  Utilizing a positive research methodology, the study develops a total AI-RCA model and adequately tests its usefulness through three illustrative Saudi case studies: a giga-project building construction dispute, a corporate shareholder dispute, and an digital financial services compliance infringement. Analysis reveals the system's capability for dramatically shortening legal discovery time, presenting objective, data-based analysis free of human cognitive bias, and identifying the root causes of disputes with unparalleled precision.  The article concludes that AI integration in judicial RCA represents a paradigm change. It is a matter of strategy that will produce the robust, high-tech legal infrastructure that will drive the thriving, diversified economy Saudi Vision 2030 foresees.
How Does AI Enable Digital Phenotyping for Mental Health?

The proliferation of digital technology has ushered in a new era of healthcare, one that is more personalized, predictive, and participatory. At the forefron...
Characterizing Pairs Collaboration in a Mobile-equipped Shared-Wall Display Supported Collaborative Setup

Recent advancements in mobile devices encourage researchers to utilize them in collaborative environments as a medium to interact with large shared wall-displays. In this paper, we focus on a semi-controlled user study that we conducted to measure the collaborative coupling ratio between partners working in pairs in a collaborative setup equipped with a shared tiled-wall display and multiple mobile devices. We invited 36 participants in 18 pairs to take part in our experiment in order to analyze how they communicate and collaborate with each other during the experiment. We observed their collaborative coupling by measuring how often they verbally and visually communicated. Further, we found frequently used collaborative physical position patterns by observing the pairs' physical arrangements and standing positions. Moreover, we combined these factors to gain a clearer understanding of coupling in our setup, taking into account the mobility factor offered by the mobile devices. Results of the study show interesting findings about the coupling factors between the partners mainly due to the flexibility offered by including mobile devices in our collaborative setup.
A dataset and benchmark of carbonate thin-section images for deep learning

Deep learning has become a key tool for carbonate thin-section image analysis. However, the lack of large public datasets limits reproducibility and fair model comparison. To address this, we present DeepCarbonate, a cleaned and standardized benchmark dataset. Samples were collected from the Ediacaran Dengying, Cambrian Longwangmiao, and Triassic Leikoupo and Jialingjiang Formations in the Sichuan Basin, China, and the Cretaceous Mishrif Formation in the UAE. The dataset was curated by petroleum geology experts; invalid images (blurred, low brightness, or corrupted) were removed through expert voting and 2σ filtering, and all images were reorganized in the ImageNet format. DeepCarbonate contains 22 lithological categories, hierarchically organized by optical mode (PPL, XPL, R) and split into train, validation, and test subsets, ensuring standardized benchmarking and reproducible experiments. Using PyTorch with CUDA acceleration, we evaluated ResNet, VGG, DenseNet, MobileNet, and EfficientNet models under baseline, ablation, long tailed distribution, and balanced Top 9 subset experiments. Results highlight the dataset's value as a robust benchmark for carbonate petrography research and applications.