Search Results (302)

The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems. Full article

Advanced driver assistance systems (ADAS) require a comprehensive understanding of multiple tasks related to the physical and mental states of drivers and traffic situations. Existing ADAS studies perform driver emotion recognition (DER), driver behavior recognition (DBR), traffic context recognition (TCR), and vehicle behavior recognition (VBR) using models designed based on single-task learning, thereby failing to reflect the interactions among tasks in real driving environments. This paper proposes perception and recognition with inter-modal selective multi-task learning (PRISM-MTL), an integrated multimodal and multi-task learning framework that jointly recognizes DER, DBR, TCR, and VBR. The proposed PRISM-MTL consists of a hierarchical stage-wise attention network (HSA-Net)-based multimodal encoder that extracts spatial features from heterogeneous multimodal inputs and task-specific modality fusion (TSMF), which selectively learns effective modality information for each task. This design addresses negative transfer, a key challenge in multi-task learning. In the multimodal encoder, HSA-Net extracts visual modality tokens that emphasize global structural patterns and key spatial regions from multi-view images, while Token-SE generates joint modality tokens that reflect the spatial configuration of joint data. TSMF generates task-specific fusion features that selectively emphasize the modality cues for each task. The generated task-specific fusion features are summarized through temporal mean pooling, and final predictions of driver states and traffic situations are produced by each task head. Experimental results show that the proposed PRISM-MTL achieves state-of-the-art performance on the public AIDE database, with an mAcc of 86.25% ± 0.35 for multi-task recognition of driver states and traffic situations. Full article

Urban traffic congestion remains a major barrier to sustainable mobility, necessitating a transition from static signaling to Intelligent Traffic Light Systems (ITLS). This study presents a systematic review in computer vision, artificial intelligence, and adaptive control algorithms for urban intersection optimization. Following a methodological framework informed by PRISMA guidelines, the study examines state-of-the-art architectures, including deep learning-based perception models and reinforcement learning agents. The findings indicate that, although two-stage detectors provide benchmark accuracy for vehicle perception, single-stage models and Vision Transformers offer the high-speed processing required for real-time traffic management. In addition, deep reinforcement learning enables autonomous, lane-specific optimization that outperforms traditional actuated and fixed-time systems. The review also identifies a persistent research gap in the deployment of these computational frameworks in the resource-constrained and heterogeneous infrastructures of developing countries. For the urban context of Riobamba, Ecuador, a phased implementation strategy is proposed that balances computational demands with the city’s morphological and social characteristics. By bridging the gap between high-fidelity simulation and practical field deployment, this review provides a scalable framework for improving throughput, reducing emissions, and enhancing safety. Overall, these advances offer a promising pathway toward more resilient transportation systems in rapidly evolving Andean urban centers. Full article

Arterial corridors present complex safety challenges because signalized intersections, access points, mixed traffic movements, and speed variation can increase the likelihood of severe crash outcomes. Although Advanced Driver Assistance Systems (ADAS) are becoming increasingly common in modern vehicles, limited evidence exists regarding their safety performance in real-world arterial environments. This study investigates crash injury outcomes involving ADAS-equipped vehicles along the US-98 corridor, in Panama City, Florida. Police-reported crash records from 2022–2024 were integrated with vehicle-level ADAS data derived from the National Highway Traffic Safety Administration (NHTSA) Vehicle Product Information Catalog (vPIC) VIN decoding. Multinomial Logistic Regression (MNL) and Random Forest (RF) models were used to examine factors associated with injury severity. MNL results indicate that crashes involving ADAS-equipped vehicles were associated with an approximately 59% lower relative risk of severe injury, while no statistically significant association was observed for moderate injury outcomes. Speeding, alcohol involvement, intersection-related crashes, dark–not-lighted conditions, and rural roadway context were associated with elevated severe injury risk. These findings suggest that ADAS technologies may contribute to severe injury mitigation, but their safety relevance depends on broader behavioral, environmental, and roadway conditions. Full article

Railway transport management has changed dramatically over the past 50 years. The advent of computer technology and the capacity for information transmission brought greater safety and the ability to remotely control interlocking devices. These enable the centralisation of railway transport management, leading to higher operational efficiency and reduced staffing costs. At the same time, this technological progress has enabled the development of additional automation functions, which we can abbreviate as ARS (Automated Route Setting). The international designation Automatic Route Setting (ARS) includes actions that enable the automation tool to execute instructions to the signal box without the intervention of operating personnel (the dispatcher). Their importance increases with line speed and the size of the remotely controlled area. Thanks to them, the dispatcher gains time because the ARS can automatically resolve some operational situations or allow the dispatcher to address them in advance, thereby distributing the workload over a wider time window. However, the interlocking system itself remains the primary safety mechanism and will prevent ARS if any element of the infrastructure is occupied. At the same time, it is not possible to automate safety-critical functions that require direct assistance from the operating personnel. In the article, the authors analysed functions in which ARS is currently widely used. In the next part, they focused on the possible expansion of the palette of these functions that could be included in the ARS regime using multi-criteria analysis. The WSA method was applied using data obtained from routine users of the system. This approach enabled the incorporation of practical operational experience into the evaluation process and provided an empirical basis for assessing and prioritising the analysed functions. The next step was a safety-critical analysis and determination of the conditions under which they could be included in the ARS regime. The safety-critical functions are left aside. It is assumed that these will still have to be performed by the operator, not by the ARS. Detailed implementations and quantification of their impacts on the dispatcher’s activities are then carried out for selected ARS functions. The analysis therefore yields a prioritised ranking of ARS functions, indicating the order in which their implementation would be most appropriate from an operational perspective. This ranking provides a systematic basis for the phased deployment of ARS functionalities, considering their expected operational benefits and practical applicability in railway traffic management. The last part of the article is a look into the future, because the development in the field of safe communication between the train and the infrastructure (V2I) and the transmission of valid information provides many new challenges not only in the field of ARS itself, but also in the optimisation of the entire process of managing and organising rail transport. If we can use the ARS functions today, it is only a matter of technical development to be able, for example, to guide trains to the exact time when a train route will be built for this train. This will also enable optimising the train’s energy consumption and tracking capacity use. The ideal state is when the infrastructure fully communicates with the train in GoA4 mode and optimises both the train’s ride and the use of the infrastructure. Full article

Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of $N=150$ observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved $R^2=0.842$ and $RMSE=0.0705$ on a normalized target scale, corresponding to a $7.05\%$ error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached $91.1\%$ accuracy (Cohen’s Kappa $=0.91$). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline ($\mathrm{\Delta }{R}^2=+0.052$) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions. Full article

Air pollution from particulate matter (PM2.5 and PM10) poses a serious public health risk in urban environments, particularly in areas with heavy vehicular traffic. Against this backdrop, the present study proposes an Internet of Things (IoT)-based system designed to support air quality monitoring and evidence-based decision-making regarding PM2.5 and PM10 concentrations, integrating low-cost sensors with a machine learning prediction module. The study follows an experimental-applied design with a quantitative–comparative approach. Its scientific contribution is organized around an integrated IoT-ML framework addressing a concrete gap in the literature: the lack of local empirical evidence regarding which family of machine learning algorithms delivers the greatest accuracy, stability, and computational efficiency for particulate matter forecasting in mid-altitude urban environments using low-cost sensors. On one hand, the framework proposes and deploys a four-node IoT network for continuous PM2.5 and PM10 monitoring in high-traffic urban microenvironments—representing one of the first sustained deployments with low-cost, high-temporal-resolution sensors (10-min intervals) in Arequipa, Peru. On the other hand, the study presents the most extensive benchmarking reported in the local literature: a systematic evaluation of 44 machine learning algorithms under homogeneous experimental conditions, covering classical statistical models, traditional machine learning techniques, deep learning architectures, and hybrid approaches, along with an analysis of ensemble learning strategies using Ridge stacking and K-Fold cross-validation. This unified comparative analysis—applying consistent metrics (MAE, RMSE, R², and MAPE), the same prediction horizon, and a shared dataset—provides replicable empirical evidence that had not previously been reported for the urban context of Arequipa. The results show that traditional statistical models perform poorly overall, while tree-based and boosting algorithms consistently achieve R² values above 0.90 for both pollutants. Ensemble models, particularly stacking with Ridge regression and cross-validation, yielded the strongest overall performance, demonstrating greater robustness and prediction stability. Explainability criteria were also incorporated, enabling an assessment of each base model’s individual contribution and identifying the variables most relevant to the prediction process. The methodological contribution provides future researchers with a rigorous reference framework for algorithm selection in environmental IoT systems. Taken together, the findings demonstrate that combining low-cost IoT networks with advanced machine learning and ensemble learning techniques constitutes an effective, scalable, and cost-efficient alternative for air quality monitoring, predictive analysis, and the support of informed mitigation strategies in urban environments. Full article

In recent years, unmanned aerial vehicles (UAVs) and Big Data analytics have both emerged as increasingly important approaches in advanced traffic monitoring. UAVs provide high-resolution spatial data and operational flexibility, supporting automated vehicle detection and the construction of origin–destination (O/D) matrices through video processing. Conversely, Big Data offers a passive and non-invasive approach based on heterogeneous sources such as mobile devices, satellite navigation systems, and digital applications, ensuring continuous temporal coverage for mobility pattern analysis. This study evaluates the combined use of UAVs and Big Data for traffic flow monitoring as an alternative to traditional manual methods. Focusing on two case studies in Trapani (Italy), the research assesses the advantages and limitations of each technology and their complementary use. Results show that Big Data effectively captures large-scale temporal dynamics but lacks accuracy for detailed O/D estimation, while UAVs provide precise spatial and behavioural information despite operational constraints. A key objective of this study is to investigate the potential complementarity between UAV observations and Big Data traffic monitoring technologies, highlighting the main strengths and limitations of each method under complex study sites and challenging operational conditions for traffic data acquisition using UAVs. Full article

Critical infrastructures (CI) are essential to modern society, providing vital services such as energy, water, and transportation. However, these systems are increasingly targeted by sophisticated cyberattacks, exploiting vulnerabilities in both IT (Information Technology) and OT (Operational Technology) environments, posing significant risks to safety, economic stability, and national security. Despite advancements, current anomaly detection models for CI often cannot effectively integrate diverse data sources or provide detailed attack classifications. To address these challenges, we propose a novel Graph Convolutional Network (GCN) model integrated with Long Short-Term Memory (LSTM) layers for effective anomaly detection and attack classification in CI. The model leverages Cyber Threat Intelligence (CTI) and MITRE ATT&CK techniques, integrating network traffic and physical device data to enhance detection of sophisticated threats. Unlike approaches using binary classification, our model performs multiclass classification to recognize specific attack types, bridging the gap in understanding complex attack patterns within CI. By incorporating Indicators of Compromise (IoCs) from MISP (Malware Information Sharing Platform) with the SWAT (Secure Water Treatment) dataset, we developed a graph-based data structure where nodes represent entities like SCADA tags and IP addresses. The model processes this dynamic graph using convolutional layers for spatial feature extraction and LSTM layers for temporal dependencies. Results indicate a significant improvement over existing solutions, achieving a test accuracy of 99.04% and a macro F1-score of 0.9151. The integration of multiple data sources enhances the model’s capacity to handle evolving cyber threats, making it well-suited for protecting CI. Full article

Real-time navigation systems are increasingly used to provide optimal driving routes together with accurate travel time predictions that reflect dynamic urban traffic conditions. Recent advances have focused on integrating structured traffic data from traditional APIs with unstructured, context-rich information extracted via semantic crawling of news websites and social media platforms. This survey reviews state-of-the-art approaches that combine these heterogeneous data sources to improve route planning and travel time estimation, with special attention to the challenges posed by incident detection, event extraction, and multimodal data fusion. We discuss core methodologies including natural language processing techniques for event recognition, machine learning models for traffic prediction, and graph-based routing algorithms, highlighting their advantages and limitations. Finally, we outline open research directions for building context-aware navigation systems able to adapt to real urban mobility conditions. Full article

This paper presents a comprehensive review of bridge health identification (BHI) within the emerging paradigm of intelligent infrastructure, with a particular focus on modal analysis and artificial intelligence (AI)-driven methodologies. Aging bridge networks, increasing traffic demands, and environmental stressors have significantly accelerated structural deterioration, necessitating advanced monitoring and diagnostic frameworks. Modal parameters, including natural frequencies, mode shapes, and damping ratios, are widely recognized as reliable indicators of structural condition and form the foundation of vibration-based BHI. This study systematically reviews operational modal analysis (OMA) techniques, including frequency-domain, time-domain, and hybrid approaches, highlighting their capabilities and limitations under real-world conditions. Furthermore, the integration of AI and machine learning (ML) methods, ranging from supervised and unsupervised learning to deep learning (DL) and reinforcement learning (RL), is critically examined in the context of data-driven damage detection, feature extraction, and predictive maintenance. Special attention is given to Automated Operational Modal Analysis (AOMA), where recent advances in FDD- and SSI-based frameworks have enabled scalable and user-independent modal identification. Despite significant progress, key challenges remain, including environmental variability, data scarcity, lack of interpretability, and deployment constraints. Finally, the paper identifies major research gaps and outlines future directions toward physics-informed AI, multi-modal data fusion, uncertainty-aware decision-making, and digital twin integration. The study provides a unified perspective bridging structural dynamics and intelligent data-driven approaches, contributing to the development of next-generation smart bridge monitoring systems. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Environmental noise is increasingly recognized as a major environmental and public health challenge, with road traffic identified as the dominant source of acoustic pollution across Europe. In this context, noise mitigation is directly linked to sustainable development goals related to human health and urban sustainability. Noise barriers are among the most widely implemented mitigation strategies; however, their spatial distribution and adequacy remain poorly documented, limiting their effectiveness for sustainable territorial planning. This study develops the first georeferenced database of highway noise barriers in Andalusia (Spain) and applies a reproducible, transdisciplinary geospatial workflow integrating field surveys, remote-sensing tools, and Geographic Information Systems (GIS). A total of 110 barriers were mapped, classified by material, geometry, and surrounding land use, and analyzed in relation to sensitive receptors, including dwellings, schools, and hospitals. Results show that only 1.6% of the Andalusian highway network is currently protected by noise barriers, with strong territorial disparities: over 50% of all structures are concentrated along coastal metropolitan corridors, while extensive inland areas remain unprotected. Misalignments were also detected between barrier placement and officially reported high-exposure segments, indicating limited correspondence between infrastructural deployment and planning-designated priority areas. Beyond generating a comprehensive regional dataset, the proposed methodology provides a scalable basis for national and European initiatives seeking to harmonize the mapping and assessment of noise-mitigation infrastructures. By offering an open-access, transferable framework, this work contributes to a more equitable distribution of environmental protection measures and supports policy professionals, environmental managers, and planners in advancing healthier and more sustainable urban and transport systems. Full article

The transjugular intrahepatic portosystemic shunt (TIPS) is a cornerstone intervention for complications of portal hypertension, including variceal bleeding and refractory ascites. As the population with cirrhosis ages, clinicians increasingly face the question of whether and how to perform TIPS safely in older adults. We reviewed observational cohorts, registry analyses, and systematic reviews/meta-analyses. Existing evidence does not support chronological age as an absolute contraindication; however, multiple studies suggest that advanced age is associated with higher rates of post-TIPS hepatic encephalopathy (HE), early mortality, and readmissions. These findings underscore the need to shift from a binary “eligible vs. ineligible” paradigm to a structured, actionable framework that addresses modifiable risks and anticipates age-related vulnerabilities. Recent clinical practice guidance emphasizes comprehensive pre-TIPS assessment and vigilant post-procedure care, with specific attention to HE risk factors (e.g., prior HE, hyponatremia, renal dysfunction, sarcopenia) and cardiopulmonary reserve. In this narrative review, we propose an elderly-focused clinical pathway built around a four-domain assessment (Liver–Brain–Body–Heart/Kidney) and a traffic-light risk tiering system to guide patient selection, procedural strategy, follow-up scheduling, and triggered management of HE, cardiac decompensation, and renal dysfunction. This pathway aims to preserve the benefits of portal decompression while reducing preventable complications and improving outcomes that are meaningful to older patients, including functional status and quality of life. This narrative review emphasizes that outcomes after TIPS in older adults are determined not by chronological age alone but by multidomain physiological reserve. The proposed pathway informs patient selection, procedural planning, and early post-discharge monitoring in older adults. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article