Search Results (3,312)

Integrated transportation hubs are characterized by fast-varying and strongly coupled electricity, hydrogen-refueling, and thermal demands driven by traffic activities. To characterize their short-term dynamic interactions, this paper develops a compact system-level model of a core electricity–hydrogen–heat coupling subsystem comprising a PEM electrolyzer, a hydrogen storage tank, a fuel cell, and a thermal side. Power- and temperature-dependent off-design models are established for the PEM electrolyzer and fuel cell, while a lumped-parameter thermodynamic model with real-gas correction is developed for the hydrogen storage tank. The electrolyzer and fuel-cell models achieve calibration MAPEs of 0.39% and approximately 0.81%, respectively, against published reference data. Two typical disturbance scenarios are then investigated. Under a 30 kW electrical-load step, the grid-power deviation is reduced from a peak of approximately 29.4 kW to about 9.1 kW, while the hydrogen-refueling-demand disturbance produces only a minor thermal-side temperature variation. The results reveal distinct propagation magnitudes and time-scale characteristics across the electrical, hydrogen, and thermal domains. The proposed framework provides a compact and physically interpretable tool for short-term cross-domain dynamic analysis of integrated transportation hubs. Full article

Distributed Denial of Service (DDoS) attacks are one of the most important cybersecurity problems today since they are one of the biggest threats against network service continuity and accessibility. Increasing network traffic volume combined with heterogeneous data structures and variations in attack types creates greater complexity in detection and mitigation. Traditional methods based on signatures and static rule sets are not adequate, especially for unknown and evolving attack types. In recent years, deep learning techniques have become a powerful alternative for DDoS detection systems with their ability to automatically extract features from high-volume network traffic data. This study tries to give a detailed comparison of deep learning-based DDoS detection approaches by examining their datasets, model architecture, evaluation metrics, and computational limitations. The review shows that many recent studies have only focused on accuracy; however, critical system parameters such as latency, computational cost, and energy consumption are insufficiently evaluated. Furthermore, generalization problems and dependencies on specific datasets create important vulnerabilities. In this context, this work tries to define future discussions for resource-aware, generalizable, and real-time DDoS detection systems. Full article

Accurate short-term traffic forecasting and efficient dynamic route guidance are pivotal for mitigating urban congestion. However, accurately capturing complex temporal dependencies and nonlinear variations in traffic flow remains challenging, while conventional path-planning algorithms may also suffer from local-optimum problems. To address these challenges, this study proposes a hybrid framework integrating an optimized prediction model with an enhanced ant colony optimization algorithm. First, a GA-BiGRU-ATT model is developed for traffic state prediction. By combining a bidirectional gated recurrent unit (BiGRU), a temporal attention mechanism, and genetic algorithm-based hyperparameter optimization, the model captures contextual temporal dependencies within the observed historical input window and emphasizes critical time-step features. Under the evaluated model configurations, the proposed approach achieved traffic-state classification accuracies of 90.28% and 87.50% on Segments 1 and 2, respectively. Second, based on the predicted traffic states, an Improved Ant Colony Algorithm (IACA) incorporating traffic-state feedback is proposed to mitigate slow convergence and local-optimum entrapment in conventional ACO. Within the ant-colony-based comparison, the IACA reduced the average computational time by 49.57% relative to the conventional ACA. Furthermore, under the evaluated SUMO evening-peak scenario, periodic dynamic guidance reduced the average travel time of the selected guided vehicles by 12.06% and increased their average speed by 27.07%. These results demonstrate the potential of prediction-guided dynamic rerouting under the evaluated simulation conditions. Full article

Remote fault-diagnosis services in dynamic edge–cloud environments depend on timely monitoring-data upload, remote inference, and result delivery, making their communication layer sensitive to variations in available bandwidth, link latency, and packet loss rate. This study addresses the network-state forecasting layer that supports such services rather than the fault-classification model itself. We propose Horizon-Aware Transformer–Ridge Fusion (HATR-Fusion), which combines a nonlinear Transformer expert with a low-variance ridge-regression expert for joint short- and long-horizon forecasting. Historical available bandwidth, link latency, packet loss rate, mobility, and offered load are used as inputs. Preprocessing statistics are estimated using the training set only, and validation-calibrated convex fusion weights are frozen before test inference. Experiments on controlled synthetic trajectories from eight links sampled at 1-min intervals, using 10 neural-network initialization seeds and ten baselines including DLinear and iTransformer, show that HATR-Fusion reduces mean absolute error (MAE) relative to the standalone Transformer by 6.94–8.31% over the 10-min horizon and by 3.02–4.40% over the 60-min horizon, with all six paired improvements remaining significant after Holm correction. Against iTransformer, HATR-Fusion is significantly more accurate for short-horizon bandwidth and latency, whereas iTransformer is significantly more accurate for long-horizon latency and packet loss; short-horizon packet loss and long-horizon bandwidth are not significantly different after Holm correction. The six-task mean normalized mean absolute error (NMAE) is 0.07106 for HATR-Fusion and 0.07047 for iTransformer, indicating comparable overall accuracy with task-dependent differences between the two methods. Ablation results show complementary short- and long-range contributions from ridge regression and Transformer, while input-quality sensitivity analysis identifies a limitation of the fixed fusion weights under corrupted or missing history. The conclusions are therefore restricted to scenarios with relatively stable input quality and distribution shifts comparable to those evaluated in this study. Full article

Rapid identification of radioactive cesium (Cs) aerosols is important for nuclear severe-accident monitoring. However, conventional analytical methods often require extended measurement times or complex instrumentation. Here, we present a sensitive and compact sensing approach that integrates laser-induced plasma RGB imaging with a convolutional neural network (CNN). A detection configuration was established in which laser irradiation generated plasma from Cs-containing aerosols within a flowing gas stream, and the resulting emission was directly captured using a CMOS camera. Rather than resolving individual emission lines, the CNN learned subtle Cs-dependent variations in the spatial and RGB intensity distributions of the plasma images. To simplify training under limited-data conditions, the model was designed to address a binary classification task, distinguishing Cs-negative conditions (normal) from Cs-positive conditions (abnormal). Predictions from 100 consecutive laser shots were aggregated to provide a sensing decision within 5 s. Using a criterion requiring Cs-positive classification in at least 99% of independent measurements, the operational limit of detection (LOD) was determined to be 0.03 μg/m³, approximately one order of magnitude lower than values reported for the closest comparable laser-based cesium aerosol measurements. These results demonstrate that plasma RGB imaging combined with a CNN algorithm can provide a compact, highly sensitive, and real-time platform for cesium aerosol monitoring. Full article

Human-occupied vehicles (HOVs) provide an ideal platform for high-resolution near-bottom magnetic anomaly detection. However, complex platform-generated magnetic interference severely limits the reliable extraction of weak magnetic signals. The conventional Tolles–Lawson (T–L) model relies on large-amplitude attitude maneuvers to estimate interference coefficients, but such maneuvers are infeasible for HOVs because of their large inertia, hydrodynamic coupling, and deep-sea safety constraints. To address this limitation, we propose a dynamic-excitation magnetic interference compensation method that exploits the inherent dynamic characteristics of the platform. By commanding the HOV to execute acceleration–deceleration cycles and horizontal S-shaped turns, the method indirectly excites pitch and roll variations through coupled vehicle dynamics and provides the attitude-dependent information required by the complete 18-term T–L model. The 18-term basis set is constructed from measured direction cosines and their time derivatives, with coefficient identifiability determined by the excitation data. Helicopter-based analog experiments showed that the proposed maneuver yielded improvement ratios of 6.6 and 12.1 on two test lines, comparable to the values of 6.3 and 12.5 obtained using conventional airborne calibration maneuvers. In an in situ trial with the Shenhai Yongshi (“Deep-Sea Warrior”) HOV, the method reduced the magnetic-field standard deviation from 0.8870 nT to 0.4157 nT, corresponding to an improvement ratio of 2.13. The compensated record exhibited a residual dynamic-field variation of 0.4157 nT under the tested maneuvering conditions. The helicopter experiment evaluated the shared excitation sequence and signal-processing workflow on a controllable airborne platform. These results demonstrate a practical engineering approach to suppressing dynamic magnetic-field variations below 1 nT in the tested HOV conditions. Full article

This study assessed the concentrations of Cd, Pb, Cu, Zn, Ni, Cr, Mn, and Fe in edible tissues of seven commercially important seafood species purchased from the central fish market in Jeddah, Saudi Arabia. Metal concentrations differed significantly among species (p < 0.05). Mean metal concentrations decreased in the order Fe > Zn > Cu > Cr > Mn > Ni > Pb > Cd. Overall accumulation ranking: Crustaceans > Mollusks > Cephalopods (species-dependent). The concentrations of Pb, Cu, Zn, Cr, Ni, Mn, and Fe ranged from 0.262–3.226, 0.949–18.979, 6.549–53.817, 1.007–5.686, 1.037–2.714, 0.791–4.606, and 2.183–77.490 mg/kg wet weight, respectively. Cd concentrations were below the limit of detection (LOD; 0.0234 mg/kg wet weight). Several species exceeded the applicable guideline limits for Cd and Pb. At the same time, the reference values for Cu, Zn, Ni, Mn, Fe, and Cr were interpreted descriptively, as no applicable maximum limit had been established. Individual target hazard quotient (THQ) values were below 1; however, hazard index (HI) values exceeded unity in selected species, indicating potential cumulative non-carcinogenic risks. Overall, the findings demonstrate species-specific variation in trace metal concentrations and highlight the importance of continued monitoring of commercially available seafood to support consumer safety and public health. Full article

Organic-rich shale contains chemically heterogeneous mineral–organic interfaces that produce strong and spatially variable proton surface relaxation, complicating the identification of clay-bound water and the conversion of NMR relaxation time into pore size. Conventional interpretations commonly treat surface relaxivity as a constant, while the respective contributions of water-retaining surface chemistry, molecular restriction, and paramagnetic centers remain insufficiently separated. In this study, Wufeng–Longmaxi shale samples from the Yongchuan Block were investigated using mineralogical and pore structural characterization, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, cation exchange capacity, zeta potential, electron paramagnetic resonance, controlled hydration, one- and two-dimensional low-field time domain NMR, and molecular dynamics simulations. Under the present fluid and acquisition conditions, strongly surface-associated water was operationally identified mainly at T₂ < 1.6 ms and T₁ < 85 ms, while the effective transverse surface relaxivity ranged from 3.6 to 9.1 μm/s. Water retention was more closely associated with cation exchange capacity and surface –OH/O–C environments, whereas relaxation efficiency was controlled more directly by EPR-detectable paramagnetic centers and restricted molecular motion of interfacial water. Simulations of Na-smectite, chlorite, illite, and kerogen-covered illite revealed systematic differences in water density layering, adsorption strength, hydrogen bond persistence, molecular residence, translational diffusion, rotational reorientation, and proton–proton dipolar correlation. A chemistry-informed model combining the EPR-derived paramagnetic center density with a surface area-weighted molecular restriction index explained 86% of the measured relaxivity variation, with an adjusted R² of 0.83 and leave-one-out cross-validation RMSE and MAE values of 0.71 and 0.57 μm/s, respectively. At the core scale, the variable relaxivity interpretation reduced the mean absolute percentage error of characteristic pore diameter from 21.9% to 4.5% and the RMSE of the clay-bound water fraction from 3.4 to 0.4 percentage points relative to the fixed relaxivity method. Transfer to NMR logging further reduced lithology-dependent biases in pore size conversion and clay-bound water partitioning. These results define shale surface relaxivity as an emergent interfacial property arising from coupled magnetic and molecular controls and provide a mechanistic basis for NMR analysis of chemically heterogeneous shale materials. Full article

This work presents an experimental investigation of droplet evaporation dynamics using a step-index multimode fiber-tip (MMF) interferometer. Distilled water, ethanol, isopropyl alcohol (IPA), and their binary mixtures with water were analyzed through complementary frequency- and time-domain approaches. Fast Fourier Transform (FFT) analysis was used to identify the dominant spectral components over selected evaporation intervals, while the instantaneous phase obtained from the analytic signal was used to track time-dependent variations in the optical response. For water, dominant components at 8.34 and 9.87 Hz corresponded to thickness-variation rates of −4.85 and −5.75 µm/s, respectively. Ethanol exhibited a dominant component at 15.8 Hz, corresponding to −9.01 µm/s, whereas IPA showed components at 13.2 and 34.8 Hz, associated with rates of −7.46 and −19.6 µm/s. Binary mixtures exhibited multiple spectral components and stronger temporal variability, indicating a nonstationary optical response during evaporation. The frequency- and time-domain results therefore provide complementary descriptions: the FFT identifies the dominant components over the selected interval, whereas instantaneous-phase analysis reveals their temporal evolution. Because the analysis was performed over short, selected evaporation intervals, the refractive index was assumed to remain approximately constant, and the measured phase variations were therefore attributed predominantly to changes in droplet thickness. The retrieved values are consequently interpreted as thickness-variation rates rather than direct mass-loss rates. The proposed approach provides a simple and compact method for monitoring droplet evaporation. Full article

Air pollution poses a major environmental and public health challenge in Metropolitan Lima, Peru, where complex topography, coastal meteorological conditions, and intense urbanization generate highly dynamic patterns of pollutant concentrations. Accurate multi-horizon forecasting is therefore essential for supporting environmental monitoring and early-warning systems. This study proposes a hybrid deep learning framework, called VMD-Attention-BiLSTM, for forecasting hourly PM_2.5, PM₁₀, and NO₂ concentrations using a decade of hourly air quality observations (2015–2024) collected by the National Meteorology and Hydrology Service of Peru (SENAMHI). The proposed methodology integrates a strictly causal preprocessing pipeline—including forward-only imputation, spatial-corroborated percentile-95 outlier detection, and pollutant-calibrated Variational Mode Decomposition (VMD)—with a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced by a Bahdanau-style attention mechanism. All transformations are fitted exclusively on the training partitions of a five-fold TimeSeriesSplit cross-validation to prevent information leakage. A systematic benchmark of 1008 imputation experiments was conducted to justify the choice of causal linear interpolation over Kalman Filter alternatives. Model performance was evaluated at 24-, 48-, and 72-h forecasting horizons. The optimized framework achieved competitive predictive performance across the evaluated horizons, yielding best RMSE values of 9.04, 19.93, and 9.41 µg/m³ for PM_2.5, PM₁₀, and NO₂ at 24 h, degrading to 9.79, 24.56, and 11.02 µg/m³ at 72 h. All metrics are reported on the original concentration scale. VMD sensitivity analysis revealed that the optimal mode count is pollutant-dependent ($K=12$ for particulate matter; $K=4$ for NO₂). Furthermore, the ablation study showed that the complete VMD-Attention-BiLSTM configuration provided competitive and frequently improved performance relative to the baseline and partial configurations, with the magnitude of the improvement varying according to pollutant and forecasting horizon. The obtained results indicate that integrating signal decomposition with attention-based bidirectional learning can provide complementary benefits for forecasting under highly non-stationary urban conditions, particularly at shorter forecasting horizons. The proposed framework provides a reproducible and scalable solution for intelligent air-quality forecasting and serves as a valuable decision-support tool for environmental monitoring and public health protection in complex metropolitan environments. Full article

In modern industrial settings, flexible production pipelines are increasingly essential to accommodate customized products and small-batch orders under dynamic operational conditions. Traditional execution and reasoning pipelines deployed at edge nodes typically rely on fixed topologies, where computation, memory access, and control flows are statically predefined. These rigid designs are ill-equipped to handle process variations or dynamic task scheduling, leading to execution discontinuities, state inconsistencies, and suboptimal runtime responsiveness in embodied systems. This paper proposes a sensor-driven, context-aware adaptive reasoning framework for embodied industrial production systems. The framework integrates heterogeneous industrial sensor perception with causal dependency graphs to dynamically reconfigure reasoning paths according to changing tasks, resource states, and real-time sensor observations. Multi-dimensional context embedding vectors generated from multi-modal sensor data are maintained in a lightweight context memory layer, ensuring seamless cross-node state continuity and efficient data migration. Furthermore, a predictive task scheduling model constructs and dismantles temporary execution chains on demand while continuously updating the global topology model. Experimental results demonstrate significant improvements in reasoning continuity, context consistency, and real-time task responsiveness compared with traditional fixed-topology reasoning pipelines. By tightly coupling sensor perception, context-aware reasoning, and adaptive execution, the proposed framework demonstrates the potential to support adaptive and distributed execution for next-generation embodied intelligence systems operating in dynamic industrial environments. Full article

Background/Objectives: Successful endodontic treatment depends on preserving sufficient radicular dentine to prevent perforations. Mandibular first molars present with complex root morphology and “danger zones,” especially of the mesial root. This study aimed to address the limited South African dentine thickness data. Methods: This cross-sectional study evaluated 77 first mandibular molars obtained from 45 Black South African mandibular micro-CT scans. A novel software program developed for this study was used for the first time to generate automated orthogonal virtual sections and measurements at 0.1 mm intervals along the entire root length adapting to its curved axis. Statistical analyses were performed to assess variations according to root surface, root level, sex, side, and age. Results: Despite statistically significant effects of co-factors on dentine thickness, variations between left and right molars, sexes, or in relation to age were not significant. In both roots dentine thickness tapered toward the apex. Thinner dentinal walls were noted in the mesial root (on distal aspect 0.98 mm at furcation tapering to 0.28 mm at apex) than the distal. Up to the apical third, buccal and lingual surfaces remained significantly thicker (2.61 and 2.48 mm respectively at furcation tapering to 0.66 and 0.52 mm at the apex) than mesial and distal. Conclusions: Comprehensive three-dimensional data on mandibular first molar dentine thickness in a Black South African sample is provided. Dentine thickness was thinner than reported using micro-CT scans and a novel software program (Unity 2022.3 LTS, Unity Technologies, San Francisco, CA, USA), emphasizing the need for increased clinical caution. Findings should be cautiously applied and experimentally validated. Full article

Hosting capacity (HC) has become a key concept in planning and operating modern distribution networks to sustainably integrate distributed energy resources (DERs), including photovoltaic systems, wind generation, battery energy storage, and electric vehicles. However, the literature shows variation in HC definitions, assessment assumptions, limiting criteria, and reporting practices, complicating cross-study comparison and utility implementation. This paper examines HC from four interconnected perspectives: limiting factors and performance indices, assessment methods, enhancement strategies, and standardization efforts. The review examines the influence of voltage constraints, thermal loading, power quality, protection coordination, network topology, system inertia, regulatory requirements, and load diversity on HC. It compares major assessment approaches, including deterministic, stochastic, time-series, optimization-based, iterative, hybrid, data-driven, and artificial intelligence-based methods, highlighting their strengths, limitations, and suitable applications. The paper also reviews HC enhancement techniques for sustainable network capacity utilization, including network reinforcement, smart inverter control, demand-side flexibility, energy storage, and coordinated multi-layer control. Particular attention is given to emerging HC standardization and the gaps between formal standards and the research frontier, especially in probabilistic, dynamic, and real-time assessment. Overall, HC depends on binding network constraints, operating conditions, assumptions, and controls, while methodological and reporting gaps limit comparability and sustainable DER integration. Full article

Heavy liquid metals such as liquid lead (Pb) are attractive heat transfer media for advanced energy applications, despite their corrosive nature. In the search for heat-resistant austenitic materials that are corrosion resistant to liquid Pb at high temperature, three new Fe24Ni15Cr3Al-based alumina-forming austenitic (AFA) steels with slightly varying compositions have been developed. The present study investigates the corrosion behavior of these AFA materials using static exposure tests to molten Pb containing 1 × 10⁻⁷ wt.% dissolved oxygen. The corrosion tests are performed at 500 °C, 600 °C, and 700 °C for 1000 h, 2000 h, and 5000 h each. In addition to the variation in material, temperature, and exposure time, two different surface finishes are also used for the tests. Examination of the specimens after exposure shows the formation of oxide scales for temperatures up to 600 °C on all three AFA materials, with minor material-specific variations. Furthermore, the scale characteristics depend on the surface finish. Coarse ground surfaces exhibit thin Al-rich protective oxide scales, while fine ground surfaces show the formation of thick multilayer oxide scales susceptible to Ni dissolution and Pb penetration. Pb exposure at 700 °C leads to a severe corrosion attack of all three Fe24Ni15Cr3Al-based AFA steels. Full article

Karst groundwater systems are characterized by highly heterogeneous flow networks, resulting in complex responses of groundwater levels to precipitation variability. The Jinan Spring Basin, one of the most representative karst spring systems in northern China, has experienced substantial changes in spring discharge due to variations in precipitation, groundwater exploitation, and hydrogeological conditions. However, the temporal scales at which precipitation signals control groundwater-level fluctuations and the mechanisms governing their transmission within the karst aquifer remain poorly understood. In this study, daily precipitation data from 30 meteorological stations and groundwater-level records at Baotu Spring during 2016–2018 were analyzed using global wavelet spectrum (GWS) and wavelet transform coherence (WTC) approaches. The dominant precipitation cycles, scale-dependent precipitation–groundwater relationships, and phase-derived groundwater response lags were quantified to reveal the hydrological response characteristics of the Jinan karst system. Three prominent precipitation periods were identified at 17.37, 29.22, and 330.57 days. Groundwater responses presented clear temporal-scale dependence, with short-period signals showing rapid but localized responses, while intermediate and long-period signals demonstrated stronger and more persistent coherence. The percentage of significant coherence area (PASC) increased from 23.50% at the 0–17.37 day scale to 64.28% at the 29.22–330.57 day scale, indicating that accumulated precipitation rather than individual rainfall events exerts the dominant control on groundwater-level variations. The spatial distribution of response lags revealed that rapid responses (17.37 days) mainly occurred in the southern recharge areas, reflecting preferential recharge through well-developed karst conduits. Intermediate responses (29.22 days) showed a progressive increase in lag time from south to north, indicating the influence of regional groundwater flow and aquifer storage. Long-period responses (330.57 days) were locally enhanced near major faults, suggesting structural controls on deeper groundwater circulation. This study reveals that precipitation signals in the Jinan Spring Basin are transmitted through multiple groundwater circulation pathways with distinct temporal characteristics. The identified multi-scale response patterns provide new insights into the internal structure and hydrological functioning of karst aquifers and offer scientific support for sustainable management of spring water resources. Full article