Search Results (8)

Background/Objectives: Comorbid post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD) patients are at a significantly higher risk of adverse outcomes, including opioid use disorder, depression, suicidal behaviors, and death, yet limited treatment options exist for this population. This study aimed to build on previous research by incorporating drug target information into a novel deep learning model, T-DeepBiomarker, to predict adverse outcomes and identify potential therapeutic medications. Methods: We utilized electronic medical record (EMR) data from the University of Pittsburgh Medical Center (UPMC), analyzing 5565 PTSD + AUD patients. T-DeepBiomarker was developed by integrating multimodal data, including lab results, drug target information, comorbidities, neighborhood-level social determinants of health (SDoH), and individual-level SDoH (e.g., psychotherapy and veteran status). The model was trained to predict adverse events, including opioid use disorder, suicidal behaviors, depression, and death, within three months following any clinical encounter. Candidate medications targeting significant proteins were identified through literature reviews. Results: T-DeepBiomarker achieved high predictive performance with an AUROC of 0.94 for adverse outcomes in PTSD + AUD patients. Several medications, including OnabotulinumtoxinA, Dronabinol, Acamprosate, Celecoxib, Exenatide, Melatonin, and Semaglutide, were identified as potentially reducing the risk of adverse events by targeting significant proteins. Conclusions: T-DeepBiomarker demonstrates high accuracy in predicting adverse outcomes in PTSD + AUD patients and highlights candidate drugs with potential therapeutic effects. These findings advance pharmacotherapy for this high-risk population and identify medications that warrant further investigation. Full article

Heart failure (HF) is a leading cause of mortality, morbidity, and financial burden worldwide. The emergence of advanced artificial intelligence (AI) technologies, particularly Generative Pre-trained Transformer (GPT) systems, presents new opportunities to enhance HF management. In this review, we identified and examined existing studies on the use of ChatGPT in HF care by searching multiple medical databases (PubMed, Google Scholar, Medline, and Scopus). We assessed the role of ChatGPT in HF prevention, diagnosis, and management, focusing on its influence on clinical decision-making and patient education. However, ChatGPT faces limited training data, inherent biases, and ethical issues that hinder its widespread clinical adoption. We review these limitations and highlight the need for improved training approaches, greater model transparency, and robust regulatory compliance. Additionally, we explore the effectiveness of ChatGPT in managing HF, particularly in reducing hospital readmissions and improving patient outcomes with customized treatment plans while addressing social determinants of health (SDoH). In this review, we aim to provide healthcare professionals and policymakers with an in-depth understanding of ChatGPT’s potential and constraints within the realm of HF care. Full article

Purpose: This research aimed to examine the effect of green logistics (GL) and green human resource management (GHRM) on the performance of environmentally friendly manufacturing industries oriented toward sustainable development of organizations (SDO) through the role of sustainable production (SP) as a mediating variable. Methodology: A quantitative approach was employed through a standardized questionnaire to obtain data from 110 manufacturing industries in Indonesia that implemented environmentally friendly practices. Advanced statistical techniques, such as structural equation modeling (SEM) and data analysis using Smart PLS (partial least square) version 4 were utilized to analyze the collected data. Findings: The results showed that the model supported the statistical significance of all seven hypotheses and confirmed the direct and mediating effects of GL, GHRM, and SP on SDO. Practical implications: This research added critical insights into the theory and practice of GL and GHRM to realize SDO through the role of SP as a mediator in environmentally friendly manufacturing industries. Originality: This research contributes to the existing literature by adding to the effect of SP mediation on the relationship of GL and GHRM on SDO. There was no previous research that discussed the important role of SP mediation in influencing the relationship between GL and GHRM on SDO. Full article

Technologies and systems for indoor positioning, mapping, and navigation (IPMN) have rapidly developed over the latest decade due to advanced radio and light communications, the internet of things, intelligent and smart devices, big data, and so forth. Thus, a group of surveys for IPMN technologies, systems, standards, and solutions can be found in literature. However, currently there is no proposed solution that can satisfy all indoor application requirements; one of the biggest challenges is lack of standardization, even though several IPMN standards have been published by different standard developing organizations (SDOs). Therefore, this paper aims to re-survey indoor positioning and mapping technologies, in particular, the existing standards related to these technologies and to present guidance in the field. As part of our work, we provide an IPMN standards system architecture consisting of concepts, terms, models, indoor positioning technologies, software and tools, applications, services and policies, and indoor mapping and modelling; and, we present IPMN standards developed for our projects in practice, such as multi-source fusion positioning data interfaces; seamless cooperative positioning service interfaces; content model for indoor mapping and navigation, and specification for digital indoor map products. Full article

This paper comprehensively reviews the spiral dynamics optimization (SDO) algorithm and investigates its characteristics. SDO algorithm is one of the most straightforward physics-based optimization algorithms and is successfully applied in various broad fields. This paper describes the recent advances of the SDO algorithm, including its adaptive, improved, and hybrid approaches. The growth of the SDO algorithm and its application in various areas, theoretical analysis, and comparison with its preceding and other algorithms are also described in detail. A detailed description of different spiral paths, their characteristics, and the application of these spiral approaches in developing and improving other optimization algorithms are comprehensively presented. The review concludes the current works on the SDO algorithm, highlighting its shortcomings and suggesting possible future research perspectives. Full article

The optimal planning of electric vehicle (EV) charging stations (ECSs) with advanced control algorithms is very important to accelerate the development of EVs, which is a promising solution to reduce carbon emissions of conventional internal combustion engine vehicles (ICEVs). The large and fluctuant load currents of ECSs can bring negative impacts to both EV-related power converters and power distribution systems if the energy flow is not regulated properly. Recent review papers related to EVs found in open literature have mainly focused on the design of power converter-based chargers and power interfaces, analyses of power quality (PQ) issues, the development of wireless charging techniques, etc. There is currently no review paper that focuses on key technologies in various system configurations, optimal energy management and advanced control issues in practical applications. To compensate for this insufficiency and provide timely research directions, this paper reviews 143 previously published papers related to the aforementioned topics in recent literature including 17 EV-related review papers found in Institute of Electrical and Electronics Engineers (IEEE)/Institution of Engineering and Technology (IET) (IEEE/IET) Electronic Library (IEL) and ScienceDirect OnSite (SDOS) databases. In this paper, existing system configurations, related design methods, algorithms and key technologies for ECSs are systematically reviewed. Based on discussions given in the reviewed papers, the most popular ECS configuration is a hybrid system design that integrates renewable energy (RE)-based power generation (REBPG), various energy storage systems (ESSs), and utility grids. It is noteworthy that the addition of an ESS with properly designed control algorithms can simultaneously buffer the fast, fluctuant power demand during charging, smooth the intermittent power generation of REBPG, and increase the overall efficiency and operating flexibility of ECSs. In addition, verifying the significance of the flexibility and possible profits that portable ESSs provide in ECS networks is a potential research theme in ECS fields, in which the potential applications of portable ESSs in the grid-tied ECSs are numerous and could cover a full technical spectrum. Full article

We developed an automated pattern recognition code that is particularly well suited to extract one-dimensional curvilinear features from two-dimensional digital images. A former version of this Oriented Coronal Curved Loop Tracing (OCCULT) code was applied to spacecraft images of magnetic loops in the solar corona, recorded with the NASA spacecraft, Transition Region And Coronal Explorer (TRACE), in extreme ultra-violet wavelengths. Here, we apply an advanced version of this code (OCCULT-2), also, to similar images from the Solar Dynamics Observatory (SDO), to chromospheric H-α images obtained with the Swedish Solar Telescope (SST) and to microscopy images of microtubule filaments in live cells in biophysics. We provide a full analytical description of the code, optimize the control parameters and compare the automated tracing with visual/manual methods. The traced structures differ by up to 16 orders of magnitude in size, which demonstrates the universality of the tracing algorithm. Full article

Recent advances in space-based heliospheric observations, laboratory experimentation, and plasma simulation codes are creating an exciting new cross-disciplinary opportunity for understanding fast energy release and transport mechanisms in heliophysics and laboratory plasma dynamics, which had not been previously accessible. This article provides an overview of some new observational, experimental, and computational assets, and discusses current and near-term activities towards exploitation of synergies involving those assets. This overview does not claim to be comprehensive, but instead covers mainly activities closely associated with the authors’ interests and reearch. Heliospheric observations reviewed include the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) on the National Aeronautics and Space Administration (NASA) Solar Terrestrial Relations Observatory (STEREO) mission, the first instrument to provide remote sensing imagery observations with spatial continuity extending from the Sun to the Earth, and the Extreme-ultraviolet Imaging Spectrometer (EIS) on the Japanese Hinode spacecraft that is measuring spectroscopically physical parameters of the solar atmosphere towards obtaining plasma temperatures, densities, and mass motions. The Solar Dynamics Observatory (SDO) and the upcoming Solar Orbiter with the Heliospheric Imager (SoloHI) on-board will also be discussed. Laboratory plasma experiments surveyed include the line-tied magnetic reconnection experiments at University of Wisconsin (relevant to coronal heating magnetic flux tube observations and simulations), and a dynamo facility under construction there; the Space Plasma Simulation Chamber at the Naval Research Laboratory that currently produces plasmas scalable to ionospheric and magnetospheric conditions and in the future also will be suited to study the physics of the solar corona; the Versatile Toroidal Facility at the Massachusetts Institute of Technology that provides direct experimental observation of reconnection dynamics; and the Swarthmore Spheromak Experiment, which provides well-diagnosed data on three-dimensional (3D) null-point magnetic reconnection that is also applicable to solar active regions embedded in pre-existing coronal fields. New computer capabilities highlighted include: HYPERION, a fully compressible 3D magnetohydrodynamics (MHD) code with radiation transport and thermal conduction; ORBIT-RF, a 4D Monte-Carlo code for the study of wave interactions with fast ions embedded in background MHD plasmas; the 3D implicit multi-fluid MHD spectral element code, HiFi; and, the 3D Hall MHD code VooDoo. Research synergies for these new tools are primarily in the areas of magnetic reconnection, plasma charged particle acceleration, plasma wave propagation and turbulence in a diverging magnetic field, plasma atomic processes, and magnetic dynamo behavior. Full article