ethicalabs/Research-Intent-Collab huggingface dataset:
Virginia Tech GIS & Remote Sensing 2014 Research Symposium - Converging Surface Water Distributions in US Cities and Agriculture Dr. Meredith Steele, Assistant Professor, Crop & Soil Environmental Sciences

Converging Surface Water Distributions in US Cities and Agriculture Dr. Meredith Steele, Assistant Professor, Crop & Soil Environmental Sciences The annual 2014 Virginia Tech GIS and Remote Sensing Research Symposium provides a venue to share information about recent advances in geographic information systems and remote sensing applications and research. The Symposium focuses on interaction among participants and the sharing of data, applications, and techniques. It includes both presentation and poster sessions as well as a keynote speaker.
Abstract P214: Shotgun Metagenomic Analysis of Fecal Microbiome Reveals Profound Functional Differences in Patients with Hypertension

Objectives:             Recent studies have implicated changes of the gut pathology and altered microbiome in the animals of various hypertension models. However, these relationships in human hypertension are poorly understood. Thus, our objectives in this study was to test the hypothesis that microbiome from hypertensive patients (HTN) would be taxonomically and functionally differ from those of normotensive subjects.                                   Design and Method:             Fecal samples were collected from HTN (n=22, mean SBP 155.8±3.4mmHg) and reference subjects without hypertension (REF) (n=18, mean SBP 121.1±1.5mmHg) (see ClinicalTrials.gov, NCT02188381 for detailed protocol). Gut microbiomes were analyzed using shotgun metagenomics and the USEARCH6.1 algorithm for OTU clustering. Taxonomy was assigned with Metaphlan. Qiime, Phyloseq and Galaxy web applications were used to further analyze the data.                                   Results:             Two beta diversity measures, principal coordinates analysis and partial least squares discriminant analysis, showed significantly different microbiome composition between the two groups (ANOSIM p=0.012). A heatmap based on significantly enriched functional genes from each cohort (P&lt;0.05) also showed clustering of samples by group. Metagenomic analysis showed that the butyrate kinase gene and the abundance of butyrate producing bacteria were negatively correlated with SBP (R             2             =0.10 and P&lt;0.05 for both). Additionally, functional analysis showed that HEME biosynthesis and hydroxyphenylacetate degradation were decreased in the HTN cohort (LDA score 2.15 and 1.95 respectively) suggesting inefficient nitric oxide synthesis and polyphenol digestion in HTN patients.                                   Conclusions:             1) Significant difference in taxa of HTN from REF was observed. 2) Microbial genes linked to butyrate, nitric oxide and polyphenol were significantly decreased in HTN. Gene products of all these were shown to have important implications in cardiovascular diseases. Thus, our observations suggest that targeting of gut and its microbiota can offer a novel strategy for hypertension control.
Academic Collaboration Recommendation for Computer Science Researchers Using Social Network Analysis

In order to improve the quality and quantity of the performance in computing in Nigeria Universities, there is need for a functioning research networking application through which they can exchange ideas. This is because academics tend to be more comfortable communicating with each other, asking questions and more importantly, such networking will provide mentorship in the Nigeria academic community. In order to make for this, several recommendations have been suggested but not scientific. Therefore to bridge this gap, this paper examines the properties of co-authorship network in Covenant University, Computer Science department. Properties of network such as centrality measures, network coefficient and so on are discovered. A collaboration recommendation application is then developed using the link prediction based on the Adamic-Adar Index measure. In conclusion, the result gotten from the network analysis is a valuable source of information for accessing the different centrality values of researchers in computer science. It also formed the foundation for developing an academic collaboration recommendation system for a small world research network. This will therefore improve the quantity and quality of performance of computer science academics in Nigeria.
Gender Equality for Some at the Cost of Others : deciphering the Intersectional Discrimination of Racialized Care Workers in France and Germany

L’égalité des genres pour les unes au détriment des autres : de la discrimination intersectionnelle des travailleuses du care en France et en Allemagne                 Cette thèse applique le concept analytique d’ « inégalité intersectionnelle des genres » à l’analyse des implications du développement du secteur privé du care sur les inégalités structurelles fondées sur le genre, la classe et la race en France et en Allemagne. Je pose la question suivante : les politiques publiques régissant le secteur du care renforcent-elles les inégalités intersectionnelles par leur manque de compréhension des phénomènes de discrimination intersectionnelle ? Dans quelle mesure les politiques publiques dans les domaines de l’immigration, de l’emploi, de la famille et de l’égalité professionnelle entre hommes et femmes prennent-elles en compte l’intersection des désavantages structurels ? J’aborde la notion d’ « égalité des genres » d’un point de vue intersectionnel dans le sens où la catégorie « femme » est envisagée comme un groupe hétérogène et non universel. Les inégalités au sein de cette catégorie sont donc au centre de l’attention. Le transfert du travail reproductif des femmes employées sur le marché du travail formel à d’autres femmes en situation marginale pose des questions de hiérarchie basée sur la classe et sur la race à l’intérieur de la catégorie ‘femmes’. Ma problématique pourrait donc être reformulée comme suit : Comment les politiques promouvant le développement des services de care privés influencent-elles l’égalité entre les hommes et les femmes, et l’égalité entre les femmes ? Une analyse approfondie des politiques discursives et des cadres politiques liés aux problèmes du care et de l’égalité des genres a permis de comprendre les diverses représentations et constructions de ces questions politiques. La recherche conclut que les divers cadres discursifs des problèmes politiques de l’égalité des genres, de l’immigration et de l’emploi dans le secteur du care ont un impact direct sur la formulation des politiques publiques se rapportant au travail du care, ce qui perpétue un processus de discrimination que je décrit en conceptualise comme inégalité intersectionnelle des genres.
Sustainability transformations, environmental rule of law and the Indian judiciary: Connecting the dots through climate change litigation

There is an urgency to address societal challenges due to earth's environmental crisis and its capacity to sustain human well-being. In this context, ‘transformations towards sustainability’ move to centre-stage and are increasingly institutionalised within global scientific and policy discourses. Sustainability transformations involve reorientation and restructuring of governance processes and actions. Though the governance of transformation involves multiple actors, this article examines the role of the judiciary in steering a transformation process towards a sustainable and equitable future. Judicial intervention, as a strategic tool, can effect change in human action thereby enabling transformative changes. Drawing on social science literature, the article offers a novel interdisciplinary analysis of illustrative Indian climate change legal decisions located within the sustainability transformations discourse underpinned by the environmental rule of law. The Indian judiciary, noted for expansive thinking, and acting as a ‘lever of transformation’, is slowly addressing climate cases. These cases categorised as – climate conscious, climate accountability and climate futurity – reflect progressive cumulative outcomes, albeit incremental, but they nevertheless enable conditions for transformative change.
Cosmic-Microwave-Background Power Spectrum Modeled by Tabletop Experiments of Magnetic Crystal Vibration and Casimir-Effect (HQC-VI)

The cosmic microwave background (CMB) power spectrum is commonly interpreted as the macroscopic imprint of primordial quantum fluctuations amplified during cosmic inflation. However, the physical origin of these fluctuations by which real photons emerge remain conceptually open. We propose two types of photons contributing to CMB power spectrum modeled by tabletop experiments. One is “Vibrating Cosmos Photon” corresponding to coherent vibration of cosmic ball just after cosmic inflation, which is modeled by a tabletop experiment of a vibrating crystalline solid. Another is “Casimir-Photon” generated during Hubble cosmic expansion, which is modeled by the dynamic Casimir Effect experiment. The CMB power spectrum may be sum of those two types. In the last section, a quantized space model of Casimir effect is discussed in detail. This framework invites renewed examination of cosmological data from the perspective of quantized space.
Chemistry and electrochemistry of composite LiFePO4 materials for secondary lithium batteries

Abstract   In this paper, we report some practical data concerning the electrochemical behaviour of composite lithium iron phosphates, at higher rates of charge–discharge, at lower operating temperatures and during long cycle life tests.  These results were collected on composite LiFePO 4  powders (whose submicrometer particles were coated by carbon or boron-based wraps), all obtained from optimized syntheses using mechanochemical activation of an iron (II) precursor as main starting material.
A Systematic Review of Natural Language Processing for Knowledge Management in Healthcare

Driven by the visions of Data Science, recent years have seen a paradigm shift in Natural Language Processing (NLP). NLP has set the milestone in text processing and proved to be the preferred choice for researchers in the healthcare domain. The objective of this paper is to identify the potential of NLP, especially, how NLP is used to support the knowledge management process in the healthcare domain, making data a critical and trusted component in improving the health outcomes. This paper provides a comprehensive survey of the state-of-the-art NLP research with a particular focus on how knowledge is created, captured, shared, and applied in the healthcare domain. Our findings suggest, first, the techniques of NLP those supporting knowledge management extraction and knowledge capture processes in healthcare. Second, we propose a conceptual model for the knowledge extraction process through NLP. Finally, we discuss a set of issues, challenges, and proposed future research areas.
Applying parallel processing to natural-language processing

Massively parallel computers offer not only improved speed but also a new perspective on computer vision, production systems, neural networks, and other AI applications. However, not much work has been done to apply parallel processing to natural-language processing, even though most sequential natural-language systems slow down as knowledge bases grow to realistic sizes and as linguistic features are added to handle special cases. To demonstrate the potential of parallel systems for natural-language processing, we selected an inherently parallel knowledge representation and reasoning method (marker-passing in a semantic network) and then developed a natural-language processor based on it. We implemented the memory-based parsing system/spl minus/called Parallel/spl minus/on a marker-passing parallel computer especially designed for natural-language processing. >
Pharmacology of Stem Cell Therapy and Regenerative Medicines

Regenerative medicine is a rapidly evolving multidisciplinary, translational research enterprise whose explicit purpose is to advance technologies for the repair and replacement of damaged cells, tissues, and organs. Scientific progress in the field has been steady and expectations for its robust clinical application continue to rise. The major thesis of this review is that the pharmacological sciences will contribute critically to the accelerated translational progress and clinical utility of regenerative medicine technologies. In 2007, we coined the phrase “regenerative pharmacology” to describe the enormous possibilities that could occur at the interface between pharmacology, regenerative medicine, and tissue engineering. The operational definition of regenerative pharmacology is “the application of pharmacological sciences to accelerate, optimize, and characterize (either in vitro or in vivo) the development, maturation, and function of bioengineered and regenerating tissues.” As such, regenerative pharmacology seeks to cure disease through restoration of tissue/organ function. This strategy is distinct from standard pharmacotherapy, which is often limited to the amelioration of symptoms. Our goal here is to get pharmacologists more involved in this field of research by exposing them to the tools, opportunities, challenges, and interdisciplinary expertise that will be required to ensure awareness and galvanize involvement. To this end, we illustrate ways in which the pharmacological sciences can drive future innovations in regenerative medicine and tissue engineering and thus help to revolutionize the discovery of curative therapeutics.
Attitudinize Psychotherapy Interventions for Mental Health: A Handbook of Islamic Clinical Psychology

This handbook has been developed to provide a comprehensive framework for understanding and applying Attitudinize Psychotherapy Interventions within the field of Islamic Clinical Psychology. Its purpose is to bridge the gap between traditional Islamic concepts of the human psyche and contemporary clinical practices, offering a holistic approach to mental health care. By emphasizing the transformative power of attitudes, intentions, and spiritual grounding, the handbook seeks to empower clinicians, researchers, and students to integrate faith-based principles with evidence-based psychological interventions. It is designed not only as a theoretical resource but also as a practical guide, with case studies, worksheets, and structured exercises that can be directly applied in therapeutic settings.
The impact of box size on the properties of dark matter haloes in cosmological simulations

Submitted to MNRAS; 10 pages, 12 figures
Redefining the User in Human-Generative AI Collaboration: Insights from Music Composition.

The rise of Generative Artificial Intelligence (GenAI) is transforming the role of end users. We investigate human-AI collaboration in music composition as an illustrative example. Existing studies analyze the impact of current AI music models on composers, focusing on challenges and strategies for interaction. However, these studies often neglect the multifaceted nature of music composition, influenced by personal aspirations, social and cultural context, and distinct genre characteristics. We propose an ethnographic approach to explore composers’ practices and needs, which can inform the design of human-AI collaborative tools that empower and support them.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years for this task, there has been no standard comprehensive setting for performance evaluation. Consequently, it has been difficult to understand which methods work well and when under a broad range of settings. To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights. (1) We benchmark the outlier detection performance of 14 methods ranging from classical matrix factorization to the latest graph neural networks. (2) Using nine real datasets, our benchmark assesses how the different detection methods respond to two major types of synthetic outliers and separately to organic (real non-synthetic) outliers. (3) Using an existing random graph generation technique, we produce a family of synthetically generated datasets of different graph sizes that enable us to compare the running time and memory usage of the different outlier detection algorithms. Based on our experimental results, we discuss the pros and cons of existing graph outlier detection algorithms, and we highlight opportunities for future research. Importantly, our code is freely available and meant to be easily extendable: https://github.com/pygod-team/pygod/tree/main/benchmark
A Machine Learning Approach to Reduce Latency in Edge Computing for IoT Devices

Nowadays, high latency in Edge Computing (EC) for Internet of Things (IoT) devices due to network congestion and online traffic reduces the acquired precision, performance, and processing power of the network. Data overload in IoT significantly impacts the real-time capabilities of user experience, decision-making efficiency, operational costs, and security in EC. By combining EC innovation and three Machine Learning (ML) models, namely Decision Trees (DT), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs), this research aims to tackle the inactivity of IoT devices and information cleaning from errors. Its purpose is to preserve information astuteness and highlight the efficacy of each model's execution by using the essential components of previous approaches. The proposed model evaluates the precision, performance, and quality enhancement by measuring the Mean Square Error (MSE), coefficient of determination (R2), and accuracy.
Applications of artificial intelligence in fault diagnosis and prognosis for industrial systems

Secure and predictable operation of industrial machinery relies heavily on the timely and accurate identification of faults, especially in complex systems where undetected issues can lead to critical failures, safety hazards, or substantial economic losses. This study provides a comprehensive review of the development, application, and comparative performance of various fault diagnosis (FD) techniques, offering a clear understanding of how industrial faults can be effectively detected, classified, and analyzed. The review begins with an overview of traditional FD approaches, outlining their principles and highlighting their limitations in addressing the growing complexity and dynamic nature of modern industrial environments. In response to these challenges, the study emphasizes the growing relevance of intelligent methods, particularly those based on machine learning (ML) and deep learning (DL), for achieving more effective fault diagnosis. The early fault diagnosis (EFD) method using ML is examined in detail, illustrating how learning algorithms can facilitate prompt identification of machine abnormalities while providing flexibility and adaptability in diverse operating conditions. Additionally, the study explores DL-based EFD techniques, which have shown exceptional capability in extracting features from high-dimensional data and in recognizing subtle fault patterns with high accuracy, even under noisy or nonlinear conditions. A comparative analysis is then presented to evaluate the advantages, disadvantages, and effectiveness of different intelligent fault diagnosis (IFD) models, enabling a deeper understanding of their respective roles in industrial applications. The review concludes by discussing the implications of adopting ML- and DL-based FD techniques for improving predictive maintenance strategies, minimizing unexpected downtime, and enhancing overall operational efficiency in industrial systems.
Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed.
Women's sexual and reproductive health rights under International Law : the case of obstetric violence

Este trabajo examina el concepto de violencia obstétrica en el contexto del Derecho Internacional Público. Para ello, se ha realizado un análisis doctrinal y normativo de los principales instrumentos jurídicos de derechos humanos en el ámbito de la salud y los derechos de la mujer. Adicionalmente, se ha complementado con una revisión jurisprudencial del Comité de la Convención sobre la Eliminación de todas las formas de Discriminación contra la Mujer (Comité CEDAW) en tres destacados casos sobre violencia obstétrica. Aunque el tema se aborda principalmente desde la perspectiva del Derecho Internacional Público y, más concretamente, desde la óptica del Derecho Internacional de los Derechos Humanos, se ha incluido una breve referencia de la situación y el marco jurídico español en relación con este tipo de violencia para su contraste con los estándares normativos del derecho internacional de los derechos humanos. Palabras clave: violencia obstétrica, derechos humanos, embarazo, derecho internacional, derechos sexuales y reproductivos de las mujeres, no discriminación, igualdad de género
A modeling system for monitoring the air - water pollution

Regular monitoring of key water quality parameters is important for assessing its the hydrological status in conjunction with air-pollution interaction. In this study, a new cost - effective technique based on the geo-ecological information-modeling system (GIMS) is implemented employing the combined use of simulation experiments and in field observations to investigate the problem of optimizing water quality monitoring. The GIMS, is accompanied by 39 elements selected in 8 management systems and 31 functional elements, which are described in detail. It is shown that the combined use of model and field observations allows reliable recording of water quality and optimization of the monitoring regime. Finally, simulation experiments in a lagoon are presented, demonstrating the fidelity of the proposed modelling system to optimize water quality control through regular in field measurements and simulations.
Hybrid multi-step estimators for stochastic differential equations based on sampled data

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