Search Results (1,197)

Background/Objectives: Hemodialysis catheter-related bloodstream infection (HD-CRBSIs) is a main cause of morbidity in hemodialysis. New preventive strategies have emerged, such as using lock solutions with antiseptic or antibiotic capacity. In this study, the antimicrobial effect was analyzed in vitro and with a catheter model of lock solutions of gentamicin (LSG), gentamicin/heparin (LSG/H), and gentamicin/citrate (LSG/C) in clinical and ATCC strains of Pseudomonas aeruginosa and Staphylococcus aureus. Methods: The formation, minimum inhibitory concentration, and minimum inhibitory concentration of the biofilm and minimum biofilm eradication concentration of the lock solutions were determined. Additionally, colony-forming unit assays were performed to evaluate the antimicrobial efficacy of the lock solutions in a hemodialysis catheter inoculation model. Results: The minimum inhibitory concentration (MIC) of planktonic cells of both P. aeruginosa and S. aureus for LSG/H and LSG/C was 4 µg/mL. In the minimum biofilm inhibitory concentration (MBIC) tests, the LSG/H was less effective than LSG/C, requiring higher concentrations for inhibition, contrary to the minimum biofilm eradication concentration (MBEC), where LSG/H was more effective. All lock solutions eradicated P. aeruginosa biofilms in the HD catheter model under standard conditions. Nevertheless, under modified conditions, the lock solutions were not as effective versus ATCC and clinical strains of S. aureus. Conclusions: Our analysis shows that the lock solutions studied managed to eradicate intraluminal mature P. aeruginosa in non-tunneled HD catheters under standard conditions. Biofilm inhibition and eradication were observed at low gentamicin concentrations, which could optimize the gentamicin concentration in lock solutions used in HD catheters. Full article

Contextual multi-armed bandits (CMABs) are vital for sequential decision-making in areas such as recommendation systems, clinical trials, and finance. We propose a simulation framework integrating Gaussian Process (GP)-based CMABs with vine copulas to model dependent contexts and GARCH processes to capture reward volatility. Rewards are generated via copula-transformed Beta distributions to reflect complex joint dependencies and skewness. We evaluate four policies—ensemble, Epsilon-greedy, Thompson, and Upper Confidence Bound (UCB)—over 10,000 replications, assessing cumulative regret, observed reward, and cumulative reward. While Thompson sampling and LLM-guided policies consistently minimize regret and maximize rewards under varied reward distributions, Epsilon-greedy shows instability, and UCB exhibits moderate performance. Enhancing the ensemble with copula features, GP models, and dynamic policy selection driven by a large language model (LLM) yields superior adaptability and performance. Our results highlight the effectiveness of combining structured probabilistic models with LLM-based guidance for robust, adaptive decision-making in skewed, high-variance environments. Full article

The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs by exploring advances in degradation modeling, adaptive Battery Management Systems (BMSs), electronic component simulations, and real-world usage profiling. The authors have systematically analyzed over 80 recent studies using a PRISMA-guided review protocol. A novel comparative framework highlights gaps in current literature, particularly regarding real-world driving impacts, ripple current effects, and second-life battery applications. This review article critically compares model-driven, data-driven, and hybrid model approaches, emphasizing trade-offs in interpretability, accuracy, and deployment feasibility. Finally, the review links battery life extension to broader sustainability metrics, including circular economy models and predictive maintenance algorithms. This review offers actionable insights for researchers, engineers, and policymakers aiming to design longer-lasting and more sustainable electric mobility systems. Full article

Biofilms, structured communities of microorganisms encased in an extracellular matrix, are a major cause of persistent infections, particularly when formed on medical devices. This study investigated the kinetics of biofilm formation by Escherichia coli and Pseudomonas aeruginosa, two clinically significant pathogens, on two medical-grade polymers: polyether ether ketone (PEEK) and polyamide 12 (PA12). Using a modified crystal violet staining method and spectrophotometric quantification, we evaluated biofilm development over time on polymer granules and catheter segments composed of these materials. Results revealed that PEEK surfaces supported significantly more biofilm formation than PA12, with peak accumulation observed at 24 h for both pathogens. Conversely, PA12 demonstrated reduced bacterial adhesion and lower biofilm biomass, suggesting surface characteristics less conducive to microbial colonization. Additionally, the study validated a reproducible protocol for assessing biofilm formation, providing a foundation for evaluating anti-biofilm strategies. While the assays were performed under static in vitro conditions, the findings highlight the importance of material selection and early prevention strategies in the design of infection-resistant medical devices. This work contributes to the understanding of how surface properties affect microbial adhesion and underscores the critical need for innovative surface modifications or coatings to mitigate biofilm-related healthcare risks. Full article

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

Estimating heterogeneous treatment effects (HTEs) across multiple correlated outcomes poses significant challenges due to complex dependency structures and diverse data types. In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory networks) architecture to capture nonlinear dependencies and temporal dynamics in multivariate treatment effect estimation. The empirical copula transformation, a rank-based nonparametric approach, preprocesses input covariates to better represent the underlying joint distributions before modeling. We compare this method with a baseline CNN-LSTM model lacking copula preprocessing and a nonparametric tree-based approach, the Causal Forest, grounded in generalized random forests for HTE estimation. Our framework accommodates continuous, count, and censored survival outcomes simultaneously through a multitask learning setup with customized loss functions, including Cox partial likelihood for survival data. We evaluate model performance under varying treatment perturbation rates via extensive simulation studies, demonstrating that the Empirical Copula CNN-LSTM achieves superior accuracy and robustness in average treatment effect (ATE) and conditional average treatment effect (CATE) estimation. These results highlight the potential of copula-based deep learning models for causal inference in complex multivariate settings, offering valuable insights for personalized treatment strategies. Full article

Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications. Full article

Epilepsy remains a major therapeutic challenge, with approximately one-third of patients experiencing drug-resistant epilepsy (DRE) despite the availability of multiple antiseizure medications (ASMs). This review aims to evaluate emerging ASMs—cenobamate, fenfluramine, ganaxolone, ezogabine (retigabine), and perampanel—with a focus on their mechanisms of action, pharmacological profiles, and potential role in precision medicine. A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science to identify preclinical and clinical studies evaluating the pharmacodynamics, pharmacokinetics, efficacy, and safety of the selected ASMs. Relevant trials, reviews, and mechanistic studies were reviewed to synthesize the current understanding of their application in DRE and specific epilepsy syndromes. Each ASM demonstrated unique mechanisms targeting hyperexcitability, including the modulation of γ-aminobutyric acid receptor A (GABA-A) receptors, sodium and potassium channels, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPA receptors), and serotonin systems. These mechanisms correspond with specific pathophysiological features in syndromes such as Dravet and Lennox–Gastaut. Evidence from clinical trials supports their use as adjunctive therapies with generally favorable tolerability, though adverse events and variable efficacy profiles were noted. The mechanistic diversity of these emerging ASMs supports their value in personalized epilepsy management, particularly in treatment-resistant cases. While the promise of precision medicine is evident, further studies are required to address challenges related to long-term safety, cost, and equitable access. Full article

Biomimetic lattice structures, inspired by natural architectures such as bone, coral, mollusk shells, and Euplectella aspergillum, have gained increasing attention for their exceptional strength-to-weight ratios, energy absorption, and deformation control. These properties make them ideal for advanced engineering applications in aerospace, biomedical devices, and structural impact protection. This study presents a comprehensive bibliometric analysis of global research on biomimetic lattice structures published between 2020 and 2025, aiming to identify thematic trends, collaboration patterns, and underexplored areas. A curated dataset of 3685 publications was extracted from databases like PubMed, Dimensions, Scopus, IEEE, Google Scholar, and Science Direct and merged together. After the removal of duplication and cleaning, about 2226 full research articles selected for the bibliometric analysis excluding review works, conference papers, book chapters, and notes using Cite space, VOS viewer version 1.6.20, and Bibliometrix R packages (4.5. 64-bit) for mapping co-authorship networks, institutional affiliations, keyword co-occurrence, and citation relationships. A significant increase in the number of publications was found over the past year, reflecting growing interest in this area. The results identify China as the most prolific contributor, with substantial institutional support and active collaboration networks, especially with European research groups. Key research focuses include additive manufacturing, finite element modeling, machine learning-based design optimization, and the performance evaluation of bioinspired geometries. Notably, the integration of artificial intelligence into structural modeling is accelerating a shift toward data-driven design frameworks. However, gaps remain in geometric modeling standardization, fatigue behavior analysis, and the real-world validation of lattice structures under complex loading conditions. This study provides a strategic overview of current research directions and offers guidance for future interdisciplinary exploration. The insights are intended to support researchers and practitioners in advancing next-generation biomimetic materials with superior mechanical performance and application-specific adaptability. Full article

Autism Spectrum Disorder (ASD) encompasses various neurological disorders with symptoms varying by age, development, genetics, and other factors. Core symptoms include decreased pain sensitivity, difficulty sustaining eye contact, incorrect auditory responses, and social engagement issues. Diagnosing ASD poses challenges as signs can appear at early stages of life, leading to delayed diagnoses. Traditional diagnosis relies mainly on clinical observation, which is a subjective and time-consuming approach. However, AI-driven techniques, primarily those within machine learning and deep learning, are becoming increasingly prevalent for the efficient and objective detection and classification of ASD. In this work, we review and discuss the most relevant related literature between January 2016 and May 2024 by focusing on ASD detection or classification using diverse technologies, including magnetic resonance imaging, facial images, questionnaires, electroencephalogram, and eye tracking data. Our analysis encompasses works from major research repositories, including WoS, PubMed, Scopus, and IEEE. We discuss rehabilitation techniques, the structure of public and private datasets, and the challenges of automated ASD detection, classification, and therapy by highlighting emerging trends, gaps, and future research directions. Among the most interesting findings of this review are the relevance of questionnaires and genetics in the early detection of ASD, as well as the prevalence of datasets that are biased toward specific genders, ethnicities, or geographic locations, restricting their applicability. This document serves as a comprehensive resource for researchers, clinicians, and stakeholders, promoting a deeper understanding and advancement of AI applications in the evaluation and management of ASD. Full article

Improving photovoltaic (PV) panel performance under extreme climatic conditions is critical for advancing sustainable energy systems. In hyper-arid regions, elevated operating temperatures significantly reduce panel efficiency. This study investigates and compares three cooling techniques—air cooling, water cooling, and porous media cooling—using thermal and electrical modeling based on CFD simulations in ANSYS. The numerical model replicates a PV system operating under peak solar irradiance (900 W/m²) and realistic ambient conditions in Adrar, Algeria. Simulation results show that air cooling leads to a modest temperature reduction of 6 °C and a marginal efficiency gain of 0.25%. Water cooling, employing a top-down laminar flow, reduces cell temperature by over 35 °C and improves net electrical output by 30.9%, despite pump energy consumption. Porous media cooling, leveraging passive evaporation through gravel, decreases panel temperature by around 30 °C and achieves a net output gain of 26.3%. Mesh sensitivity and validation against experimental data support the accuracy of the model. These findings highlight the significant potential of water and porous material cooling strategies to enhance PV performance in hyper-arid environments. The study also demonstrates that porous media can deliver high thermal effectiveness with minimal energy input, making it a suitable low-cost option for off-grid applications. Future work will integrate long-term climate data, real diffuser geometries, and experimental validation to further refine these models. Full article

Background and Clinical Significance: Tuberous sclerosis complex (TSC) is an autosomal dominant disorder caused by pathogenic variants in TSC1 or TSC2. Cardiac rhabdomyoma is a common prenatal finding and can be associated with severe complications, including pericardial effusion. We administered prenatal sirolimus to mitigate pericardial effusion, which led to postnatal complications. Case Presentation: A 28-year-old pregnant woman with no significant family history underwent routine fetal ultrasound at 28.1 weeks of gestation, which identified a large right ventricular mass consistent with rhabdomyoma. Further fetal brain MRI revealed cortical-subcortical tubers and subependymal nodules, leading to a clinical diagnosis of TSC. At 30.4 weeks, oral sirolimus (3 mg/day) was started due to the significant pericardial effusion. The effusion remained after treatment, requiring pericardiocentesis at 33.6 weeks. The sirolimus dosage was raised to 6 mg/day at 35.6 weeks, reaching a plasma level of 3.76 ng/mL, but there was no discernible improvement because of the continued fluid accumulation. The mother did not experience any adverse side effects from the procedure. Genetic testing confirmed a pathogenic variant in TSC2 (c.1372C>T). After birth, the neonate received a single dose of sirolimus but subsequently developed necrotizing enterocolitis (NEC), highlighting the potential adverse effects and the need for cautious consideration of treatment options. Conclusions: This case illustrates the complexities of managing prenatal tuberous sclerosis complex (TSC). While sirolimus has been explored for fetal cardiac rhabdomyoma and associated complications, its effectiveness in resolving pericardial effusion remains uncertain. Additionally, the development of NEC postnatally raises concerns about the safety of mTOR inhibitors in this context. Further studies are necessary to assess the risks and benefits of this approach in fetal therapy. Full article

Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus on their classification, mechanical behavior, and optimization methodologies. Stress distribution, deformation capacity, energy absorption, and computational modeling challenges are critically analyzed, highlighting the impact of manufacturing defects on structural integrity. The review explores the latest progress in hybrid additive manufacturing, hierarchical lattice structures, modeling and simulation, and smart adaptive materials, emphasizing their potential for self-healing and real-time monitoring applications. Furthermore, key research gaps are identified, including the need for improved predictive computational models using artificial intelligence, scalable manufacturing techniques, and multi-functional lattice systems integrating thermal, acoustic, and impact resistance properties. Future directions emphasize cost-effective material development, sustainability considerations, and enhanced experimental validation across multiple length scales. This work provides a comprehensive foundation for future research aimed at optimizing biomimetic lattice structures for enhanced mechanical performance, scalability, and industrial applicability. Full article

Mixed reality (MR) laboratories combine physical elements with virtual components, providing convenient experiential environments for testing engineering concepts. This article reports the design, validation, and implementation of an MR laboratory for engineering students to practice the implementation of control algorithms in microcontrollers. First, the design of the MR lab is described in detail. In this, a seesaw electromechanical system is emulated, being synchronized with electrical signals that represent sensors’ measurements and actuators’ commands. Thus, a control algorithm implemented by the students in a microcontroller can affect the simulated system in real time. The real seesaw system was used to validate the simulated plant in the MR lab, finding that the same control algorithm effectively controls both the simulated and physical seesaw systems. A practice, designed based on Kolb’s experiential learning cycle, where the students must implement P, PI, and PID controllers in the MR lab, was implemented. A survey was conducted to assess the students’ motivation, and a post-test was administered to evaluate their learning outcomes. Full article

Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks. Full article