Search Results (760)

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

Objective: Cerebral ischemia/reperfusion (I/R) injury triggers oxidative stress, neuroinflammation, neuronal degeneration, and white matter damage not only in directly affected cerebral regions but also in remote brain areas such as the cerebellum. Skimmianine, a naturally occurring furoquinoline alkaloid, has been reported to possess antioxidant and anti-inflammatory properties. This study investigated the protective effects of skimmianine pretreatment against secondary cerebellar injury following experimental cerebral I/R. Materials and Methods: Thirty-two female Wistar rats were randomly assigned to sham, Skimmianine, I/R, and I/R + Skimmianine groups (n = 8/group). Cerebral I/R was induced by transient middle cerebral artery occlusion for 60 min followed by 23 h reperfusion. Skimmianine (40 mg/kg/day, intraperitoneally) was administered for 14 days before ischemia induction. Oxidative stress markers, neuroinflammatory mediators, histopathological alterations, behavioral outcomes, and ultrastructural changes were evaluated. In addition, network pharmacology and molecular docking analyses were performed to explore potential molecular mechanisms. Results: Cerebral I/R significantly decreased TAS levels compared with sham (0.89 ± 0.15 vs. 1.52 ± 0.18 mmol Trolox Eq/L) and increased TOS (15.60 ± 3.03 vs. 6.80 ± 1.41 µmol H₂O₂ Eq/L), OSI (17.48 ± 0.50 vs. 4.43 ± 0.47), TNF-α (68.4 ± 10.2 vs. 18.6 ± 4.4 pg/mL), Iba1 (41.3 ± 9.7 vs. 11.7 ± 1.6 pg/mL), and GFAP levels (334.5 ± 12.5 vs. 87.7 ± 9.5 ng/mL; all p < 0.001). I/R also impaired motor performance, as shown by increased beam crossing time (11.7 ± 2.2 vs. 4.8 ± 0.7 s) and grid foot fault rate (18.6 ± 4.0% vs. 3.4 ± 1.1%). Skimmianine pretreatment significantly improved these alterations, increasing TAS to 1.29 ± 0.20 mmol Trolox Eq/L and reducing TOS, OSI, TNF-α, Iba1, and GFAP levels to 9.20 ± 2.04, 7.07 ± 0.47, 34.9 ± 7.4, 24.2 ± 6.9, and 237.0 ± 7.9, respectively, compared with the untreated I/R group. Histopathological scores for Purkinje cell loss, edema, vascular congestion, and TNF-α expression were also significantly reduced by skimmianine. Quantitative TEM analysis showed that I/R reduced myelin thickness (0.29 ± 0.05 vs. 0.53 ± 0.07 µm), increased G-ratio values (0.75 ± 0.05 vs. 0.63 ± 0.04), and increased vacuolized fibers (24.70 ± 4.20% vs. 3.20 ± 1.10%), whereas skimmianine partially restored myelin thickness (0.42 ± 0.07 µm), reduced the G-ratio (0.68 ± 0.05), and decreased vacuolized fibers (11.20 ± 2.80%; p < 0.05 vs. I/R). Molecular docking demonstrated favorable binding between skimmianine and TNF-α, with a predicted binding energy of −6.953 kcal/mol. Conclusions: These findings indicate that skimmianine exerts neuroprotective effects against secondary cerebellar injury following cerebral I/R through coordinated modulation of oxidative stress, systemic neuroinflammatory responses, astroglial injury-associated pathways, and inflammation-related mechanisms. Full article

Currently, reinforced concrete (RC) columns rely heavily on closely spaced steel ties for confinement and ductility under axial compression. However, tie congestion and construction limitations often reduce confinement efficiency, creating a need for alternative hybrid reinforcement solutions. A series of ten one-third scale circular RC short column specimens of 240 mm diameter and 1.2 m height were fabricated to investigate the effects of different internal confinement configurations. The columns were reinforced longitudinally with six steel rebars of 10 mm diameter (a steel ratio of 1%), and transversely with 6 mm diameter steel ties at varying spacings. All column specimens were cast using 25 MPa concrete, and their cores were internally wrapped with welded wire mesh (WWM) and carbon fiber reinforced polymer (CFRP) strips to enhance their confinement performance. The experimental program focused on evaluating the axial load capacity, axial strain, and ductility. Compared to control RC columns having conventional steel rebar ties, the specimens incorporating hybrid internal confinement of WWM and CFRP exhibited up to 38% and 180% increases in peak loads and ductility, respectively, and failed by buckling of the longitudinal steel bars, followed by rupture of the CFRP strips/WWM layers. These findings suggest that the use of internally wrapped composite systems in RC columns is particularly suitable for applications where dimensional constraints are critical. Additionally, an analytical model was proposed to predict the peak loads of the confined columns. The peak load predictions for various confinement configurations aligned well with the corresponding peak loads measured in experiments. Full article

Traffic congestion poses a significant challenge in urban environments. The use of digital techniques has emerged as a pivotal trend, as it offers substantial safety to and mitigates stress and frustration for road users. The purpose of this survey was to explore the current approaches and digital techniques for managing traffic congestion. We address this through a systematic literature review (SLR) approach by adopting PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We began by exploring the key techniques of topological data analysis (TDA), machine learning (ML) and deep learning (DL) for modeling urban traffic prediction. We evaluated the robustness of the topological data analysis technique (Persistent Homology (PH)) against deep learning frameworks (Graph Convolutional Neural Networks (GCNNs)). We found that each framework has its own strengths and weaknesses, and neither of the frameworks independently provides a complete solution. PH may offer richer structural insights and robustness to noise but may struggle with direct predictive implementation, while deep learning models do better at extracting dynamic predictive patterns but are assumed to lack interpretability and generalizability. Therefore, the integration of multiple techniques, either PH with stacking ensemble methods or deep learning with stacking ensemble methods, can improve prediction and generalization of the model while at the same time reducing over-reliance on local graph assumptions. Future research should focus not only on performance metrics or methods but also on explainability, transferability, adaptability across heterogeneous road environments and computational cost. Full article

Spectrum prediction is essential for implementing dynamic spectrum management and mitigating spectrum congestion. However, spectrum data in real electromagnetic environments exhibit high non-stationarity, multi-scale features, and complex non-Euclidean spatio-temporal coupling characteristics, which limit the prediction accuracy of existing models. To address these issues, this paper proposes an attention-based spectrum prediction method for spatio-temporal feature synergy (SSA-A-BiGCRNN). First, Singular Spectrum Analysis (SSA) is introduced to decompose and reconstruct the non-stationary spectrum signals, filtering out high-frequency burst noise and extracting core evolutionary trends. Second, a spatial topology graph among multiple frequency bands is constructed based on the Spearman rank correlation coefficient. A Bidirectional Graph Convolutional Recurrent Neural Network is then designed to simultaneously capture the spatial dependencies between frequency bands and the bidirectional evolutionary patterns in the time dimension. Finally, an attention mechanism is incorporated during the feature fusion stage to evaluate and focus on critical spatio-temporal information, further enhancing global prediction accuracy. Experimental results based on a real electromagnetic monitoring dataset demonstrate that the proposed model achieves an accuracy of 96.82%, a coefficient of determination ($R^2$) of 0.9966, a Root Mean Square Error (RMSE) of 0.5597, and a Mean Absolute Error (MAE) of 0.4031, significantly outperforming existing models. Full article

Traffic congestion and air pollution caused by rapid urbanization have emerged as critical challenges in metropolitan areas worldwide. Urban air mobility (UAM), particularly electric propulsion-based systems, has gained attention as a promising solution. For the successful commercialization of UAM, a lightweight airframe design with ensured structural integrity is essential. This study proposes an optimized lightweight design process that integrates automated composite manufacturing with artificial intelligence (AI)-based material property prediction. Finite-element analysis (FEA) was performed on glass fiber-, basalt fiber-, and carbon fiber-reinforced polymers under identical deformation conditions to derive design material properties in terms of elastic modulus and weight reduction. A large-scale dataset of fiber-reinforced plastics was established through an automated manufacturing process, and a deep learning regression model was developed using Altair AI Studio to predict mechanical properties under untested material and process conditions. The predicted properties were applied to a UAM fuselage model, and FEA results demonstrated that composite structures achieved equivalent or superior stiffness with up to 50% weight reduction compared to aluminum. In addition, inverse reserve factor (IRF) analysis confirmed structural safety, with all configurations maintaining IRF values below 1. The proposed AI-driven framework provides a scalable and data-driven lightweight design methodology applicable to next-generation UAM and advanced air mobility structures. Full article

Multi-intersection traffic signal control must jointly address local coordination and delayed traffic propagation under strongly time-varying conditions. Existing offline sequence-imitation methods mainly recover actions from historical trajectories and make limited use of short-term future traffic evolution in shared-representation learning. To address this issue, we propose PR-STLight, a prediction-regularized spatio-temporal extension of TransformerLight for offline multi-intersection traffic signal control. PR-STLight introduces short-term future inbound-queue evolution as structural supervision for shared representation learning. The model combines neighborhood-constrained spatial self-attention, causal temporal self-attention, and a Topology-Recurrent Queue Predictor (TRQP) to capture topology-aware spatio-temporal dependencies and near-future congestion dynamics. Training adopts a two-stage strategy, namely queue-prediction pretraining followed by joint control-prediction optimization, to improve optimization stability on a fixed offline replay buffer. In experiments on the adopted CityFlow benchmarks, PR-STLight obtains average travel times of 274.39 s on Jinan $3\times 4$ and 288.09 s on Hangzhou $4\times 4$, corresponding to 1.14% and 2.82% lower travel times than the strongest non-PR baseline, and 21.27% and 22.54% lower travel time than the TransformerLight backbone, respectively. It also achieves the lowest average inbound queue on Hangzhou and remains competitive on Jinan. These results show that PR-STLight provides an effective offline spatio-temporal sequence framework for coordinated multi-intersection signal control. Full article

Traffic assignment is crucial for urban traffic regulation and management. Based on this background, this study proposes a heterogeneous graph neural network that integrates Transformer-based multi-head self-attention for traffic assignment in urban road networks. The model builds a heterogeneous graph with both physical road links and virtual origin–destination links. It features a dual-encoder structure: the V-Encoder and the R-Encoder. The V-Encoder employs Transformer multi-head self-attention to capture long-range spatial relationships between origin and destination nodes. In contrast, the R-Encoder aggregates local topological features to characterize the transmission of flow across road segments. A combined loss function that includes flow conservation constraints is designed to ensure predictions are both accurate and physically realistic. Experiments on the Sioux Falls and EMA networks demonstrate that the method outperforms baseline models under various congestion conditions, exhibiting high accuracy and efficiency. Ablation tests show that Transformer multi-head self-attention is vital for performance enhancement. The approach also remains robust under abnormal conditions, such as in the case of incomplete OD demands, making it a practical solution for efficient, low-carbon, and sustainable traffic management. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article

In recent years, Intelligent Transportation Systems (ITSs) have emerged as a solution to mitigate the problem of traffic congestion. Understanding human driving styles such as aggressive, normal, and cautious is crucial for safe driving. In particular, predicting lane-change manoeuvres may be further supported by combining vehicle state information with driving style information. However, existing vehicle trajectory datasets lack driving style information, making classification challenging. To address this limitation, this paper proposes a fuzzy logic-based driving style classification framework in a Vehicle-to-Everything (V2X) environment. The model uses vehicle state information, including speed, longitudinal acceleration, lateral acceleration, and distance headway to classify style as cautious, normal, or aggressive. The proposed system is interpretable, aligns with human reasoning, and remains computationally efficient for real-time applications. The performance of the proposed work has been evaluated through comprehensive experiments on highway data. Results show a separation of driving styles, achieving 77% accuracy on a balanced dataset, showing moderate agreement with deterministic labelling while maintaining interpretability. In V2X-enabled lane-change prediction scenarios, computational latency is essential, as Roadside Units (RSUs) must understand driving style and update prediction models. Since lane-change intentions should be predicted around 3 s before manoeuvre, delays in inference reduce reaction time. The proposed classifier achieves an inference latency of approximately 8 ms, ensuring that it does not become a bottleneck in real-time systems. Furthermore, the usefulness of driving style information is tested by integrating it into a lane-change prediction task. Experimental results demonstrate that incorporating driving style enhances prediction accuracy from 75% to 84%. Lastly, the proposed method provides a balanced result between interpretability, computational efficiency, and predictive performance, supporting RSUs to issue timely warnings and support safer decision-making in highway environments. Full article

The smart environment hides numerous security challenges that need to be addressed promptly. Smart cities have emerged as a novel concept, integrating emerging technologies and data-driven solutions to improve urban living conditions. Traffic surveillance cameras at intersections enable continuous traffic monitoring and rapid incident detection, optimizing signal timing to improve road safety and reduce traffic congestion and travel delay. These cities present new challenges for the police force, forcing them to blend into the environment. The paper proposes novel hierarchical Police Internet of Things (PIoT) concepts that should enable and secure timely, high-priority policing forecasting and decision-making processes in smart cities. Hierarchical edge, fog, and cloud computing were presented according to the police decision-making process. This concept is carefully developed to improve the timeliness of predictive policing, planning, management, and decision-making using artificial intelligence and fuzzy logic. The proposed vertical PIoT concept is supported by vertical data processing. In hierarchical computing, machine learning models for time series prediction and fuzzy-logic-based decision-making are applied to enable comprehensive analysis in a smart environment. Two case studies dealing with crime and traffic issues are presented in detail. Full article

The demand for the fabric warehouse presents obvious characteristics of hockey stick effect. This leads to problems such as peak congestion and labor shortages during its operation. In order to alleviate this phenomenon, we propose a combination strategy that uses a SARIMA–Markov hybrid model for demand forecasting, and then applies Actor-Critic reinforcement learning for dynamic pricing. This model integrates SARIMA with Markov chains for residual correction, capturing linear trends and seasonal patterns while correcting residuals, yielding more accurate predictions for highly volatile demand in textile logistics. Experimental results indicate that our approach achieves better performance than SARIMA, Temporal Fusion Transformer (TFT), and Ensemble, especially in identifying and reproducing sharp demand peaks. By combining forecasting results with price elasticity, the proposed dynamic pricing scheme cuts peak-hour demand by 12.54%, which in turn eases pressure on labor scheduling and boosts the efficiency of workforce allocation. This work offers a data-driven approach to flattening demand fluctuations via intelligent pricing, improves operational efficiency without requiring extra hardware investment, and provides a practical response to a long-standing bottleneck in the textile logistics sector. Full article

Reliable short-term traffic flow prediction is crucial for intelligent transportation systems to enable real-time control, mitigate congestion, and improve urban mobility. However, traffic dynamics are inherently uncertain, temporally dependent, and subject to pronounced intraday variability, making accurate forecasting challenging. To address these issues, this study introduces a Functional AutoRegressive (FAR) model that represents daily traffic profiles as continuous stochastic functions rather than discrete observations, thereby preserving temporal continuity and capturing underlying symmetric structures. The model is developed using high-frequency traffic data collected at 15-min intervals from the Dublin Airport Link Road, Ireland, covering January 2022 to December 2024; data from 2022–2023 are used for model estimation, while 2024 data are reserved for one-day-ahead out-of-sample evaluation. A moving-window filtering technique is incorporated to enhance robustness by probabilistically identifying outliers and reducing noise. The proposed FAR approach is benchmarked against conventional models, including autoregressive (AR), autoregressive moving average (ARMA), nonparametric autoregressive (NPAR), and vector autoregressive (VAR) models. Empirical results demonstrate that the FAR model consistently achieves superior forecasting performance across all traffic conditions, yielding a full-day MAPE of 9.160% compared to 11.623% for the VAR model, along with lower MAE (76.772) and RMSE (131.767). It also performs best on both workdays and weekends, with MAPEs of 8.129% and 10.438%, respectively. Moreover, the model remains robust across peak and off-peak periods, effectively capturing both symmetric and asymmetric traffic variations while offering a more interpretable representation of intraday patterns. These findings suggest that functional time series modeling provides an effective and computationally efficient framework for traffic forecasting, with strong potential for application in next-generation intelligent transportation systems. Full article

Centralized reactive orchestration in Low Earth Orbit (LEO) networks struggles with heavy-tailed traffic surges that trigger signaling storms and topology instability. To address this challenge, we develop a LEO-specific predictive resource allocation framework that integrates spectral-aware distributional forecasting with risk-aware allocation. The forecasting module pairs cascaded dual-scale Exponential Moving Average (EMA) decomposition with a direct multi-step decoder to suppress autoregressive error accumulation. A Spectral Penalty operating in the frequency domain enhances sensitivity to orbital harmonics, while nonuniform quantization yields calibrated probabilistic bounds that preserve heavy-tailed characteristics. On the allocation side, the predictive standard deviation serves as an endogenous risk index amplified by service priority to form a capacity bound that is explicitly aware of risk. A companion demand model structurally reserves a fixed control plane bandwidth floor, insulating signaling from data plane congestion. Simulation results show that the forecasting module reduces the Continuous Ranked Probability Score (CRPS) by up to 5.9% relative to the strongest compared distributional baseline across prediction horizons of 30–105 min. Under a 300% traffic shock, the distributed allocation mechanism maintains 99.99% satisfaction for the highest priority service class and keeps control plane overflow below 0.05%. Lower-priority traffic is curtailed through compression governed by priority, and the per-node memory consumption is sufficiently low for deployment on current onboard satellite processors. Full article

As data-intensive applications continue to scale reconfigurable array processors (RAPs), electrical networks-on-chip (NoCs) are increasingly constrained by energy-delay bottlenecks due to RC-delay constraints. Hybrid optoelectronic NoCs (HONoCs) suffer from a fundamental medium-selection dilemma: optical circuit switching incurs microsecond-scale setup overheads for long flows, whereas static distance thresholds fail to capture the spatiotemporal heterogeneity of traffic, causing wavelength waste for bursty flows and congestion diffusion under non-stationary loads. This paper presents an adaptive switching framework that is aware of spatiotemporal locality. We introduce the Temporal-Spatial Locality Index (TSLI) to classify flows into Electrophilic (EF), Photophilic (PF), and Hybrid-sensitive (HF) categories, and propose Cross-layer Congestion Entropy (CCE) to unify electrical and optical resource states. Based on these metrics, an Adaptive Medium Selection State Machine (AMSSM) dynamically switches among Electro-Dominant (EDM), Electro-Optical Synergistic (EOSM), and Optical-Dominant (ODM) modes, while a Weighted Multi-dimensional Medium Matching (WMMM) algorithm performs fine-grained channel selection. A Predictive Optical Path Provisioning (POPP) mechanism further amortizes setup latencies via trend-aware pre-establishment. Evaluation on an 8 × 8 mesh HONoCs demonstrates 22% higher saturation throughput, 38% lower energy-delay product (EDP), and 57% reduction in average latency under non-stationary traffic, compared to static thresholds. The proposed mechanisms provide a theoretical foundation and engineering paradigm for efficient on-chip interconnects. Full article