Search Results (2,325)

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

Spores of filamentous fungi are common biological particles in indoor air that can negatively impact human health, particularly among immunocompromised individuals and patients with chronic respiratory conditions. Airborne viruses represent an equally pervasive threat, with some carrying the potential for pandemic spread, affecting both healthy individuals and the immunosuppressed alike. This study investigated the abundance and diversity of airborne fungal spores in both hospital and residential environments, using custom designed air samplers with or without the presence of negative air ions (NAIs) inside the sampler. The main purpose of investigation was the assessment of biological effects of NAIs on fungal spore viability, deposition, mycelial growth, and sporulation, as well as airborne viral load. The precise assessment of mentioned biological effects is otherwise difficult to carry out due to low concentrations of studied specimens; therefore, specially devised and designed, ion-bioaerosol interaction air samplers were used for prolonged collection of specimens of interest. The total fungal spore concentrations were quantified, and fungal isolates were identified using cultural and microscopic methods, complemented by MALDI-TOF mass spectrometry. Results indicated no significant difference in overall spore concentration between environments or treatments; however, presence of NAIs induced a delay in the sporulation process of Cladosporium herbarum, Aspergillus flavus, and Aspergillus niger within 72 h. These effects of NAIs are for the first time demonstrated in this work; most likely, they are mediated by oxidative stress mechanisms. A parallel experiment demonstrated a substantially reduced concentration of aerosolized equine herpesvirus 1 (EHV-1) DNA within 10–30 min of exposure to NAIs, with more than 98% genomic load reduction beyond natural decay. These new results on the NAIs interaction with a virus, as well as new findings regarding the fungal sporulation, resulted in part from a novel interaction setup designed for experiments with the bioaerosols. Our findings highlight the potential of NAIs as a possible approach for controlling fungal sporulation and reducing airborne viral particle quantities in indoor environments. Full article

Traumatic injury remains a major cause of death worldwide, with bleeding being one of its most critical and life-threatening consequences. Whole-body computed tomography (WBCT) has become a standard diagnostic method in trauma settings; however, timely interpretation remains challenging for acute care physicians. In this study, we propose a new automated method for detecting traumatic bleeding in CT images using a three-dimensional object detection model enhanced with an atrous spatial pyramid pooling (ASPP) module. Furthermore, we incorporate a false positive (FP) reduction approach based on multi-organ segmentation, as developed in our previous study. The proposed method was evaluated on a multi-institutional dataset of delayed-phase contrast-enhanced CT images using a six-fold cross-validation approach. It achieved a maximum sensitivity of 90.0% with 587.3 FPs per case and a sensitivity of 70.0% with 46.9 FPs per case, outperforming previous segmentation-based methods. In addition, the average processing time was reduced to 4.2 ± 1.1 min. These results suggest that the proposed method enables rapid and accurate bleeding detection, demonstrating its potential for clinical application in emergency trauma care. Full article

This study explores asymmetric volatility structures within multivariate hysteretic autoregressive (MHAR) models that incorporate conditional correlations, aiming to flexibly capture the dynamic behavior of global financial assets. The proposed framework integrates regime switching and time-varying delays governed by a hysteresis variable, enabling the model to account for both asymmetric volatility and evolving correlation patterns over time. We adopt a fully Bayesian inference approach using adaptive Markov chain Monte Carlo (MCMC) techniques, allowing for the joint estimation of model parameters, Value-at-Risk (VaR), and Marginal Expected Shortfall (MES). The accuracy of VaR forecasts is assessed through two standard backtesting procedures. Our empirical analysis involves both simulated data and real-world financial datasets to evaluate the model’s effectiveness in capturing downside risk dynamics. We demonstrate the application of the proposed method on three pairs of daily log returns involving the S&P500, Bank of America (BAC), Intercontinental Exchange (ICE), and Goldman Sachs (GS), present the results obtained, and compare them against the original model framework. Full article

This study investigates the short- and long-run drivers of natural gas consumption in the European Union using an ARDL bounds testing approach. The analysis incorporates GDP per capita, liquid fuel use, and solid fuel use as explanatory variables. Augmented Dickey–Fuller tests confirm mixed integration orders, allowing valid ARDL estimation. The results reveal a statistically significant long-run relationship (cointegration) between gas consumption and the energy–economic system. In the short run, the use of liquid fuel exerts a strong positive influence on gas demand, while the effects of GDP materialise only after a two-year lag. Solid fuels show a delayed substitutive impact, reflecting the ongoing transition from coal. An error correction model confirms rapid convergence to equilibrium, with 77% of deviations corrected within one period. Recursive residual and CUSUM tests indicate structural stability over time. These findings highlight the responsiveness of EU gas demand to both economic and policy signals, offering valuable insights for energy modelling and strategic planning under the European Green Deal. Full article

Background/Objectives: Obstetrical monitoring following radical trachelectomy (RT) for cervical cancer is marked by the lack of a standardized protocol, which may lead to delays in the intervention for cervical shortening. In light of the typical cervical remodeling process that occurs at the onset of labor, we hypothesized that the onset of premature cervical shortening in patients who have undergone radiotherapy commences at the internal ostium. Methods: We introduced the concepts of internal distance (distance between internal cervical ostium and cerclage thread) and the latent shortening of internal distance, which is characterized as a painless reduction in the internal distance, serving as an early marker of preterm contractions, thus enabling timely tocolytic intervention. Results: Three patients spontaneously conceived after RT. They were obstetrically followed-up after RT, using a combined approach of transvaginal ultrasound cervical markers and cardiotocography. Active tocolysis was used if internal distance shortening was observed. All patients delivered term healthy babies. Conclusions: The consistent ultrasound evaluation of both internal and external distances permits the proactive diagnosis of premature contractions and enables swift therapeutic measures. 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

This paper addresses the stability of stochastic delayed recurrent neural networks (SDRNNs), identifying challenges in existing scalar methods, which suffer from strong assumptions and limited applicability. Three key innovations are introduced: (1) weakening noise perturbation conditions by extending diagonal matrix assumptions to non-negative definite matrices; (2) establishing criteria for both mean-square exponential stability and almost sure exponential stability in the absence of input; (3) directly handling complex structures like time-varying delays through matrix analysis. Compared with prior studies, this approach yields broader stability conclusions under weaker conditions, with numerical simulations validating the theoretical effectiveness. Full article

This study develops a dynamic IS-LM macroeconomic model that incorporates delayed taxation and a memory-dependent income effect, and calibrates it to quarterly data for Romania (2000–2023). Within this framework, fiscal policy lags are modelled using a “memory” income variable that weights past incomes, an approach grounded in distributed lag theory to capture how historical economic conditions influence current dynamics. The model is analysed both analytically and through numerical simulations. We derive stability conditions and employ bifurcation analysis to explore how the timing of taxation influences macroeconomic equilibrium. The findings reveal that an immediate taxation regime yields a stable adjustment toward a unique equilibrium, consistent with classical IS-LM expectations. In contrast, delayed taxation, where tax revenue depends on past income, can destabilise the system, giving rise to cycles and even chaotic fluctuations for parameter values that would be stable under immediate collection. In particular, delays act as a destabilising force, lowering the threshold of the output-adjustment speed at which oscillations emerge. These results highlight the critical importance of policy timing: prompt fiscal feedback tends to stabilise the economy, whereas lags in fiscal intervention can induce endogenous cycles. The analysis offers policy-relevant insights, suggesting that reducing fiscal response delays or counteracting them with other stabilisation tools is crucial for macroeconomic stability. Full article

Traumatic liver injury remains a significant contributor to trauma-related morbidity and mortality worldwide. In Saudi Arabia, motor vehicle accidents (MVAs) are the predominant mechanism of injury, particularly among young adults. This study aimed to evaluate the clinical characteristics, management strategies, and outcomes of patients with liver trauma over a ten-year period at a tertiary academic level-one trauma center. A retrospective cohort study was conducted from January 2015 to December 2024. All adult patients (aged 18–65 years) who sustained blunt or penetrating liver injuries and underwent a pan-CT trauma survey were included. Demographic data, Injury Severity Scores (ISSs), imaging timelines, management approach, and clinical outcomes were analyzed. Statistical analysis was performed using JASP software with a significance threshold set at p < 0.05. A total of 111 patients were included, with a mean age of 33 ± 12.4 years; 78.1% were male. MVAs were the leading cause of injury (75.7%). Most patients (80.2%) had low-grade liver injuries and received non-operative management (NOM), with a high NOM success rate of 94.5%. The median time to CT was 55 ± 64 min, and the mean time to operative or IR intervention was 159.9 ± 78.8 min. Complications occurred in 32.4% of patients, with ventilator-associated pneumonia (19.8%) being most common. The overall mortality was 6.3%. Multivariate analysis revealed that shorter time to CT significantly reduced mortality risk (OR = 0.5, p < 0.05), while a positive e-FAST result was strongly associated with increased mortality (OR = 3.3, p < 0.05). Higher ISSs correlated with longer monitored unit stays (ρ = 0.3, p = 0.0014). Traumatic liver injuries in this cohort were predominantly low-grade and effectively managed conservatively, with favorable outcomes. However, delays in imaging and operative intervention were observed, underscoring the requirement for streamlined trauma workflows. These findings highlight the requirement for continuous trauma system improvement, including protocol optimization and timely access to imaging and surgical intervention. Full article

The prevention of bubble coalescence is essential in various industrial processes, such as mineral flotation, where the stability of air–liquid interfaces significantly affects performance. The combined influence of multiple physicochemical parameters on bubble coalescence remains insufficiently understood, particularly under conditions relevant to flotation. This study explores the key factors that influence the inhibition of bubble coalescence in aqueous solutions containing methyl isobutyl carbinol (MIBC), providing a systematic comparative analysis to assess the effect of each variable on coalescence inhibition. An experimental method was employed in which two air bubbles were formed from identical capillaries and brought into contact either head-to-head or side-by-side, then held until coalescence occurred. This setup allows for reliable measurements of coalescence time with minimal variability regarding the conditions under which the bubbles interact. The study examined the effects of several factors: temperature, pH, salt concentration and type, bubble approach speed, contact area, and contact configuration. The results reveal that coalescence is delayed at lower temperatures, alkaline pH conditions, high salt concentrations, and larger interfacial contact areas between bubbles. Within the range studied, the influence of approach speed was found to be insignificant. These findings provide valuable insights into the fundamental mechanisms governing bubble coalescence and offer practical guidance for optimizing industrial processes that rely on the controlled stabilization of air–liquid interfaces. By understanding and manipulating the factors that inhibit coalescence, it is possible to design more efficient and sustainable mineral flotation systems, thereby reducing environmental impact and conserving water resources. Full article

Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. Full article

This research is part of a broader investigation into innovative simulation-based approaches for improving traffic efficiency and road safety in roundabout corridors. These corridors, composed of successive roundabouts along arterials, present systemic challenges due to the dynamic interactions between adjacent intersections. While previous studies have addressed localized inefficiencies or proposed isolated interventions, this paper introduces possible replicable methodology based on a microsimulation and surrogate safety analysis to evaluate roundabout corridors as integrated systems. In this context, efficiency refers to the ability of a road corridor to maintain stable traffic conditions under a given demand scenario, with low delay times corresponding to acceptable levels of service. Safety is interpreted as the minimization of vehicle conflicts and critical interactions, evaluated through surrogate measures derived from simulated vehicle trajectories. The proposed approach—implemented through Aimsun Next and the SSAM tool—is tested on two real-world corridors: Via Aurelia Nord in Pisa (Italy) and Route de Marseille in Avignon (France), assessing multiple intersection configurations that combine roundabouts and signal-controlled junctions. Results show how certain layouts can produce unexpected performance outcomes, underlining the importance of system-wide evaluations. The proposed framework aims to support engineers and planners in identifying optimal corridor configurations under realistic operating conditions. Full article

Route planning under uncertain traffic conditions requires accounting for not only expected travel times but also the risk of late arrivals. This study proposes a mean-upper partial moment (MUPM) framework for pathfinding that explicitly considers travel time unreliability. The framework incorporates a benchmark travel time to measure the upper partial moment (UPM), capturing both the probability and severity of delays. By adjusting a risk parameter ($\theta$), the model reflects different traveler risk preferences and unifies several existing reliability measures, including on-time arrival probability, late arrival penalty, and semi-variance. A bi-objective model is formulated to simultaneously minimize mean travel time and UPM. Theoretical analysis shows that the MUPM framework is consistent with the expected utility theory (EUT) and stochastic dominance theory (SDT), providing a behavioral foundation for the model. To efficiently solve the model, an SDT-based label-correcting algorithm is adapted, with a pre-screening step to reduce unnecessary pairwise path comparisons. Numerical experiments using GPS probe vehicle data from Louisville, Kentucky, USA, demonstrate that varying $\theta$ values lead to different non-dominated paths. Lower $\theta$ values emphasize frequent small delays but may overlook excessive delays, while higher $\theta$ values effectively capture the tail risk, aligning with the behavior of risk-averse travelers. The MUPM framework provides a flexible, behaviorally grounded, and computationally scalable approach to pathfinding under uncertainty. It holds strong potential for applications in traveler information systems, transportation planning, and network resilience analysis. Full article