ethicalabs/Research-Intent-Collab huggingface dataset:
Knowledge-based lean six sigma system for enhancing quality management performance in healthcare environment

PurposeThis paper aims to present the development of a knowledge-based system (KBS) to support the implementation of Lean Six Sigma (L6s) principles applied to enhance quality management (QM) performance within a health-care environment.  Design/methodology/approachThe process of KBS building has been started by acquiring knowledge from experts in field of L6σ and QM in health care. The acquired knowledge has been represented in a rule-based approach for capturing L6σ practices. These rules are produced in IF […].THEN way where IF is the premise and THEN is the action. The produced rules have been integrated with gauging absence pre-requisites (GAP) technique to facilitate benchmarking of best practice in a health-care environment. A comprehensive review of the structure of the system is given, detailing a typical output of the KBS.  FindingsImplementation of L6s principles to enhance QM performance in a health-care environment requires a pre-assessment of the organisation’s competences. The KBS provides an enhanced strategic and operational decision-making hierarchy for achieving a performance benchmark.  Research limitations/implicationsThe KBS needs validation in real health-care environment, which will be done in Oman’s hospitals.  Practical implicationsThe paper is intended to benefit QM practitioners in the health-care sector during decision-making to achieve performance improvement against a best practice benchmark.  Originality/valueThis research presents a novel application of a hybrid KBS with GAP methodology to support the implementation of L6s principles to enhance QM performance in a health-care environment.
Threats to Global Mental Health From Unregulated Digital Phenotyping and Neuromarketing: Recommendations for COVID-19 Era and Beyond

The new era of digitalized knowledge and information technology (IT) has improved efficiency in all medical fields, and digital health solutions are becoming the norm. There has also been an upsurge in utilizing digital solutions during the COVID-19 pandemic to address the unmet mental healthcare needs, especially for those unable to afford in-person office-based therapy sessions or those living in remote rural areas with limited access to mental healthcare providers. Despite these benefits, there are significant concerns regarding the widespread use of such technologies in the healthcare system. A few of those concerns are a potential breach in the patients' privacy, confidentiality, and the agency of patients being at risk of getting used for marketing or data harnessing purposes. Digital phenotyping aims to detect and categorize an individual's behavior, activities, interests, and psychological features to properly customize future communications or mental care for that individual. Neuromarketing seeks to investigate an individual's neuronal response(s) (cortical and subcortical autonomic) characteristics and uses this data to direct the person into purchasing merchandise of interest, or shaping individual's opinion in consumer, social or political decision making, etc. This commentary's primary concern is the intersection of these two concepts that would be an inevitable threat, more so, in the post-COVID era when disparities would be exaggerated globally. We also addressed the potential “dark web” applications in this intersection, worsening the crisis. We intend to raise attention toward this new threat, as the impacts might be more damming in low-income settings or/with vulnerable populations. Legal, health ethics, and government regulatory processes looking at broader impacts of digital marketing need to be in place.
LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks

Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications. However, a hitherto overlooked problem in SP-NNs is that the underlying silicon photonic devices suffer from intrinsic optical loss and crosstalk noise, the impact of which accumulates as the network scales up. Leveraging precise device-level models, this paper presents the first comprehensive and systematic optical loss and crosstalk modeling framework for SP-NNs. For an SP-NN case study with two hidden layers and 1380 tunable parameters, we show a catastrophic 84% drop in inferencing accuracy due to optical loss and crosstalk noise.
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)

While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing development framework. We evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The corpora were derived from texts from three different institutions (Mayo Clinic, University of Kentucky, University of Minnesota). The gold standard annotations were tested with a single institution's (Mayo) ruleset. This resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study and adoption. Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.
An exact solution of cosmic ray modulation problem in a stationary composite heliosphere model

ABSTRACT                A new theoretical approach to describe the physical processes of energy particle propagation is proposed. This approach is based on the analytically iterative method for solving closed cosmic ray (CR) modulation problems, which was proposed by Shakhov and Kolesnyk. First, we have applied the approach on a simple model of the heliosphere, wherein the diffusion coefficients κ for each region of CR modulation are constants. This approach produced a very good matching of the obtained solution and also provided a numerical solution and an analytical solution. Finally, a modern problem of CR modulation in a stationary composite model of the heliosphere was considered. This model includes an environment that contains adjacent spherically symmetric regions with different modes of propagation of the solar wind (SW) speed for each layer. The CR scattering is due to different factors for each layer of the environment, as characterized by relevant κ values that simultaneously have dependence on the momentum of the particle p and the particle speed $\upsilon$, i.e. $\kappa \propto p\upsilon$. The local interstellar spectra (LISs) are given by a power-law unmodulated spectrum with the slope of the initial spectrum α, i.e. LIS ∝ p−α. An exact solution of the problem of CR modulation for low-energy particles and high-energy particles was first derived and qualitatively compared against the Voyager 1 data.
The Transformation of Extralegal Principles into Legal Principles by the International Court of Justice in The Hague: Illustrated by a Peaceful Delimitation of «the Promised Land»

Social norms are mixtures of extralegal and legal principles. The International Court of Justice‟ in The Hague‟s (ICJ) adjudication subtracts normative structures from different sources, moral, ethical, religious etc. dogmas amplifying its case law. The failing Israel-Palestine reconciliation that results from “power asymmetries” and “supreme emergency exception”, illustrates how absence of concomitant social norms fosters extremists destined to wage war. War is not the answer to the two-state talks‟ collapse but civilized methods of dispute settlement. Neither negotiations nor “Gods divine plan” for Jews has solved the Israel-Palestine boundary conflict. Prayers, pronouncements etc. may ease tensions, but has failed to bring peace. Since territorial demarcation is a secular event, religious leaders should request ICJ adjudication. Other peaceful mechanisms, i.a. ICJ-justification, is however imminent. The courts frames are well-known legal and extralegal principles i.a. religious morals incorporated into general principles of law. Thus, i.a. the World Council of Religious Leaders (WCRL) should trust that ICJ not only propels “world peace” but also the “work of the United Nations (UN) in our common quest for peace”. Such attainments assume Council‟s diplomatic pressure on Security Council (SC).
Evaluating diagnostic content of AI-generated radiology reports of chest X-rays

Radiology reports are of core importance for the communication between the radiologist and clinician. A computer-aided radiology report system can assist radiologists in this task and reduce variation between reports thus facilitating communication with the medical doctor or clinician. Producing a well structured, clear, and clinically well-focused radiology report is essential for high-quality patient diagnosis and care. Despite recent advances in deep learning for image caption generation, this task remains highly challenging in a medical setting. Research has mainly focused on the design of tailored machine learning methods for this task, while little attention has been devoted to the development of evaluation metrics to assess the quality of AI-generated documents. Conventional quality metrics for natural language processing methods like the popular BLEU score, provide little information about the quality of the diagnostic content of AI-generated radiology reports. In particular, because radiology reports often use standardized sentences, BLEU scores of generated reports can be high while they lack diagnostically important information. We investigate this problem and propose a new measure that quantifies the diagnostic content of AI-generated radiology reports. In addition, we exploit the standardization of reports by generating a sequence of sentences. That is, instead of using a dictionary of words, as current image captioning methods do, we use a dictionary of sentences. The assumption underlying this choice is that radiologists use a well-focused vocabulary of 'standard' sentences, which should suffice for composing most reports. As a by-product, a significant training speed-up is achieved compared to models trained on a dictionary of words. Overall, results of our investigation indicate that standard validation metrics for AI-generated documents are weakly correlated with the diagnostic content of the reports. Therefore, these measures should be not used as only validation metrics, and measures evaluating diagnostic content should be preferred in such a medical context.
EFaaS: Energy-efficient Function Orchestration in Serverless Edge Computing

Energy in computing is critical as sustainability concerns rise. The serverless edge computing’s energy consumption is significant and growing rapidly. We propose EFaaS, a serverless scheduler that dispatches and caches functions, integrated into an existing serverless platform, reducing the energy cost by jointly considering request dispatching and function caching. We evaluate it using simulations with real-world traces. EFaaS achieves superior performance with $36.5 \%$ reduction in energy cost.
CoLLM-NAS: Collaborative Large Language Models for Efficient Knowledge-Guided Neural Architecture Search

The integration of Large Language Models (LLMs) with Neural Architecture Search (NAS) has introduced new possibilities for automating the design of neural architectures. However, most existing methods face critical limitations, including architectural invalidity, computational inefficiency, and inferior performance compared to traditional NAS. In this work, we present Collaborative LLM-based NAS (CoLLM-NAS), a two-stage NAS framework with knowledge-guided search driven by two complementary LLMs. Specifically, we propose a stateful Navigator LLM to guide search direction, a stateless Generator LLM to synthesize high-quality candidates, and a Coordinator module to orchestrate inter-LLM communication and manage evaluation processes. CoLLM-NAS efficiently guides the search process by combining LLMs' inherent knowledge of structured neural architectures with progressive knowledge from iterative feedback and historical trajectory. Experimental results on ImageNet and NAS-Bench-201 show that CoLLM-NAS surpasses existing NAS methods and conventional search algorithms, achieving new state-of-the-art results while significantly reducing search costs by 4--10. Furthermore, CoLLM-NAS consistently enhances the performance and efficiency of various two-stage NAS methods (e.g., OFA, SPOS, and AutoFormer) across diverse search spaces (e.g., MobileNet, ShuffleNet, and AutoFormer), demonstrating its excellent generalization.
Additive Manufacturing Technologies: An Overview about 3D Printing Methods and Future Prospects - Article ID 9656938

El uso de métodos de fabricación convencionales está limitado principalmente por el tamaño de la producción y la complejidad geométrica del componente, y como resultado, en ocasiones nos vemos obligados a utilizar procesos y herramientas que aumentan el coste final del elemento que se produce. Las técnicas de fabricación aditiva proporcionan grandes ventajas competitivas debido a que se adaptan a la complejidad geométrica y al diseño personalizado de la pieza que se fabrica.
Modulation of cosmic rays in the heliosphere over 11 and 22 year cycles: a modelling perspective

The long term modulation of galactic cosmic rays in the heliosphere deals with the causes of the 1 l-year and 22year solar cycles, while short term modulation is associated with effects of the order of a few solar rotations. During the past decade, with four spacecraft (Pioneer 10 & 11, Voyager 1 & 2) at various relatively large distances from the Sun, and with Ulysses inside of 5 AU and IMP 8 at Earth, the emphasis has changed to understand modulation as being applicable to three distinctive areas of the heliosphere. They are conveniently called the inner, middle and outer heliosphere. The outer heliosphere is about studying the effects of the configuration of the heliosphere, its interfaces with the interstellar medium and the termination shock - all aspects of great importance to cosmic ray modulation. The middle heliosphere is the region where shorter term effects like corotating regions and the effects of coronal mass ejections lose their characteristic features and merge into bigger entities of different scales which have profound effects on medium to long term modulation. The inner heliosphere that Ulysses explores is characterized by many short term events, superimposed on the long-term baseline, which have apart from their strong temporal and radial dependence easily recognizable latitudinal and azimuthal features. Compatibility studies between the results of modelling and observations at Earth and beyond have been done with reasonable success over the years. This review focuses on theory and modelling, and the understanding of the basic mechanisms of galactic cosmic ray modulation in the heliosphere, with emphasis on long-term modulation cycles.
Success of a South-South collaboration on Human Resources Information Systems (HRIS) in health: a case of Kenya and Zambia HRIS collaboration

La pénurie de personnel de santé dans la plupart des pays africains est un obstacle majeur à la réalisation des objectifs de santé et de développement. Les pays sont encouragés à élaborer des stratégies fondées sur des données probantes pour renforcer leur personnel de santé afin de combler le fossé. Les collaborations Sud-Sud ont gagné en popularité en raison des similitudes dans les défis rencontrés dans la région. Cette stratégie a été utilisée dans les secteurs du commerce, de l'éducation et de la santé, entre autres. Ce document est une feuille de route d'utilisation d'une collaboration Sud-Sud pour développer un système d'information sur les ressources humaines (SIRH) pour informer la mise à l'échelle du personnel de santé. Au cours de la dernière décennie, le Kenya a mis en œuvre l'un des SIRH les plus complets d'Afrique. Le SIRH a été financé par le Plan d'urgence du président des États-Unis pour la lutte contre le sida (PEPFAR) par l'intermédiaire des Centers for Disease Control and Prevention (CDC) et mis en œuvre par l'Université Emory. L'équipe kenyane a collaboré avec l'équipe zambienne pour établir un SIRH similaire en Zambie. Cette étude de cas décrit les activités de collaboration entre la Zambie et le Kenya qui comprenaient l'évaluation des besoins, la création d'un bureau de projet, la sensibilisation des parties prenantes, l'assistance technique et le transfert de connaissances, la réutilisation des logiciels, la réutilisation des documents et des directives, la structure et la gestion du projet et l'évaluation formative du projet. En outre, il souligne la nécessité d'adopter des stratégies de communication efficaces, une planification collaborative, un travail d'équipe, une volonté d'apprendre et des compétences techniques minimales du pays bénéficiaire en tant qu'enseignements tirés de la collaboration. À la suite de la collaboration, alors que le Kenya a pris 5 ans, la Zambie a pu mettre en œuvre le projet en 2 ans, ce qui représente moins de la moitié du temps qu'il a fallu au Kenya. Ce cas présente une expérience unique dans l'utilisation de la collaboration Sud-Sud dans la mise en place d'un SIRH. Il illustre les étapes et les ressources nécessaires tout en identifiant les succès et les défis de cette collaboration.
Humanizing Online Learning: The Role of AI+XR-Based Virtual Tutors in Adaptive Education

The integration of Artificial Intelligence (AI) and Extended Reality (XR) in education has opened new possibilities for adaptive and personalized learning. Among these, Virtual Humans (VH), which are AI-driven XR avatars capable of real-time humanized interaction, have emerged as promising tools for content adaptation, emotional support, and learner engagement. This paper examines the roles of VH in fostering adaptive learning, emphasizing their capability to meet diverse learner needs through responsive, individualized content adaptations, based on a literature review. This study investigates the effectiveness of a VH-based system, VirtualTutor, developed to enhance online learning in a Computer Science course. A case study involving 265 undergraduate students evaluated the impact of the system on engagement, perceived usefulness, and comprehension. The results of this study are presented in this paper. Findings support the potential of VH to foster inclusive, scalable, and learner-centered educational environments, while also identifying key areas for system refinement and future research.
Research on Influence Diffusion in Social Network

With the wide spread of internet and big data research and applications,influence diffusion research in social network becomes one of the hot topics in data mining and social network analysis in recent years.The main results on social influence diffusion research from the field of computer science in the last decade,which covers the three main areas-- influence diffusion modeling,influence diffusion learning,and influence diffusion optimization,were summarized.Different techniques,such as stochastic modeling,data mining,algorithmic optimization,and game theory,were demonstrated in their application to influence diffusion research.Finally,some discussions on the current issues,challenges and future directions in influence diffusion research and applications were provided.
Dark Matter and Dark Energy: Summary and Future Directions

This paper reviews the progress reported at this Royal Society Discussion Meeting and advertizes some possible future directions in our drive to understand dark matter and dark energy. Additionally, a first attempt is made to place in context the exciting new results from the WMAP satellite, which were published shortly after this Meeting. In the first part of this review, pieces of observational evidence shown here that bear on the amounts of dark matter and dark energy are reviewed. Subsequently, particle candidates for dark matter are mentioned, and detection strategies are discussed. Finally, ideas are presented for calculating the amounts of dark matter and dark energy, and possibly relating them to laboratory data.
An Examination of the Limits of Multilateral Diplomacy: Negotiation, Mediation and Peace Agreement in the ongoing Russia-Ukraine War. A Conceptual Analysis.

Abstract  This study was intended to develop a new conceptual framework for the conflict governance and peacebuilding through the examination of the limits of multilateral diplomacy: negotiation, mediation and peace agreement in the ongoing war between Russia and Ukraine. Case study design was adopted with thematic analysis for data analysis. From the study outcome, the new conceptual framework suggested that when negotiation, mediation and peace agreement are in action, it would influence the Russo-Ukraine conflict drivers. The framework also showed that sustainable peace/conflict rest on the multilateral diplomacy in action and the conflict drivers. This is a reverse reaction where ineffective/effective action of multilateral diplomacy to bring Russo-Ukraine war to an end would deescalate or escalate conflict through the same limitations. Potential intervention suggested from the study findings include: Open communication, understanding power dynamics, historical, cultural, economic, and political relevance, avoidance of isolation of actors to conflict, establishment of hegemonic leader, impartiality on diplomatic engagement, willingness and commitment to implement peace agreement and joint cooperation  from all parties to dispute.  Keywords: Multilateral diplomacy, negotiation, mediation, peace agreement, conflict
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs

The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types. A recent work has generalized the Hawkes process to a neurally self-modulating multivariate point process, which enables the capturing of more complex and realistic impacts of past events on future events. However, this approach is limited by the number of possible event types, making it impossible to model the dynamics of evolving graph sequences, where each possible link between two nodes can be considered as an event type. The number of event types increases even further when links are directional and labeled. To address this issue, we propose the Graph Hawkes Neural Network that can capture the dynamics of evolving graph sequences and can predict the occurrence of a fact in a future time instance. Extensive experiments on large-scale temporal multi-relational databases, such as temporal knowledge graphs, demonstrate the effectiveness of our approach.