Search Results (1,363)

Background: Conventional analyses of physiological recovery often rely on discrete metrics that assume independence across time points, thereby ignoring intrinsic temporal continuity and masking substantial interindividual heterogeneity. This proof-of-concept study assesses the efficacy of Functional Data Analysis (FDA) as a promising framework for characterizing individual response dynamics following a functional threshold power (FTP) test. Methods: Physiological time-series data (including blood lactate, heart rate, blood pressure, and glucose levels) collected from 21 trained cyclists (10 professionals, 11 amateurs) were represented as functional objects using FDataGrid on the original sampling grid (0, 3, 5, 10, 20 min), without basis expansion or smoothing. We conducted unsupervised functional clustering (K-means; Fuzzy K-means) and supervised classification (Maximum Depth with Modified Band Depth, K-Nearest Neighbors, Nearest Centroid, functional QDA with parametric Gaussian covariance). Model performance was estimated via Repeated Stratified 5-Fold Cross-Validation with 10 repetitions (50 folds), reporting accuracy, balanced accuracy (mean ± SD), 95% CIs, permutation p-values, and sensitivity/specificity from aggregated confusion matrices. Results: Lactate (CL) and diastolic blood pressure (DBP) provided useful and statistically significant discrimination across several classifiers (e.g., KNN, Nearest Centroid, functional QDA), whereas heart rate showed modest discriminative value and glucose intermediate performance. Unsupervised analyses revealed distinct lactate recovery profiles and graded membership for hemodynamic/metabolic variables, supporting the value of FDA for resolving heterogeneity beyond group-average trends. Conclusions: FDA offers a feasible and informative approach for classifying recovery phenotypes while preserving temporal structure. Findings are promising but should be interpreted with caution due to the small sample size, sparse time points, and the need for external validation in larger, independent cohorts before translation into routine decision-making. Full article

Micro-meteorological monitoring systems have been widely deployed in power grids, providing essential data to support the prevention and mitigation of ice- and wind-related disasters. However, understanding of the associated error mechanisms and quantitative evaluations under freezing rain and snow remains limited, particularly in complex field environments. This study presents a field-based quantitative assessment of two key variables, air temperature and wind speed, based on comparative observations collected over multiple winter icing cycles. We analyze the coupled effects of low temperature, ice accretion, and solar radiation on temperature measurements through multi-configuration sensor comparison, and characterize the dynamic response of cup anemometers under icing conditions using cross-correlation lag analysis. Results show that temperature error is dominated by sensor installation configuration and solar radiation. Under weak solar radiation, unshielded sensors tend to record lower temperatures than a standard Stevenson screen, but once radiation exceeds 200 W/m², they warm rapidly and exhibit maximum positive biases of ~8–10 °C. Ice accretion further induces a cold bias of ~1 °C and a response lag of 5–18 min, while suppressing the rapid warming driven by shortwave radiation. For wind measurements, cup anemometers show clear underestimation during ice accretion, with the error increasing nonlinearly with ice thickness to ~20% before freezing-induced failure occurs. These findings provide a basis for improved sensor deployment and interpretation of field monitoring data in cold, humid, and icing-prone environments, although the quantitative results are site-dependent. Full article

Lithium-ion batteries in hybrid solar-grid systems experience complex electro-thermal dynamics and stochastic mode switching that threshold-based battery management systems fail to capture. This paper proposes a hybrid deviation detection framework that treats anomaly detection as a trajectory-consistency problem over a power-feasible Markov jump nonlinear system. A disturbance-robust invariant operating region is first established under explicit current bounds. A reachable-set equivalence is then derived, linking residual consistency to disturbance-augmented trajectory membership. Building on this structure, Isolation Forest empirically estimates the support of admissible electro-thermal trajectories, capturing nonlinear and mode-dependent behaviors not fully described by the analytical disturbance model. A unified sequential detection rule integrates structural constraint violations, model-based residual deviations, and empirical support inconsistencies into a coherent real-time monitor. The framework is validated on a hybrid solar-grid platform with a 6 W photovoltaic panel, a 3.7 V 1820 mAh lithium-ion battery, and a Raspberry Pi, collecting 3976 samples over four days. Results demonstrate early detection of depletion events and mode-transition anomalies before hard threshold violations, with zero false alarms during steady operation and an overall deviation rate of 4.8%, aligning with the configured contamination level. Early warning was observed at 20% state of charge, providing a 10% margin before the hardware threshold of 10%, while 88% of detected anomalies occurred in sequences, validating the persistence rule. Real-time inference required 47 ms per cycle with a 156 MB memory footprint, confirming edge deployment feasibility. Full article

This study presents the development and experimental evaluation of HygroCatch, a portable hybrid fog water harvesting prototype that integrates active and passive collection mechanisms. The device operates by combining fog droplet ionization in a high-voltage direct-current (HV DC) electrostatic field, thermoelectric cooling based on the Peltier effect, and mechanical deposition of droplets on vertical rods of symmetrical triads of electrodes. This hybrid approach enables adaptive operation across a wide range of fog liquid water content (LWC) conditions. The work establishes operating parameters for stable electrostatic ionization and evaluates the contribution of thermoelectric cooling to additional water harvesting. The results indicate that an operating voltage of 13–14 kV provides a stable ionization over a broad LWC range. The average fog water harvesting rate reached 3.15 kg/m²/h, with a maximum observed value of 4.44 kg/m²/h. On average, 56% of the collected water was obtained through HV DC ionization, 25% through Peltier-based thermoelectric cooling, and 19% through mechanical deposition on electrode grids under high LWC conditions. The total electrical power consumption of the device did not exceed 38.3 Wh/kg. The results demonstrate that a hybrid fog water harvesting strategy enables stable and efficient water collection under environmental conditions in which individual passive or active methods become ineffective. Full article

The Xinqiao Cu-S polymetallic deposit is located in the Tongling ore concentration area of the Middle-Lower Yangtze River metallogenic belt. The orebodies consist of skarn orebodies and stratiform sulfide orebodies, but the genetic link between them remains controversial. In this study, magnetite was used as a proxy to systematically constrain the hydrothermal evolution from the intrusion to the contact zone and further to the stratiform orebodies. A representative drill hole (E603) was logged, and samples were systematically collected from the Jitou pluton outward to the contact zone. Composite samples from the 8–28 m interval were crushed and prepared as resin mounts for integrated TIMA automated mineralogy, BSE textural observation, and in situ LA-ICP-MS trace element analysis. Five types of magnetite (Mt1 to Mt5) were systematically identified. Mt1 occurs as inclusions within feldspar in the quartz monzodiorite. It exhibits typical magmatic magnetite characteristics and contains grid-like ilmenite exsolution, indicating crystallization during the late magmatic stage. Mt2 is distributed in the interstices of magmatic minerals, commonly showing hematitization and replacement of ilmenite exsolution lamellae by titanite. Its trace element geochemistry displays magmatic–hydrothermal transitional features. Mt3–Mt5 in the skarn and stratiform orebodies are paragenetic with retrograde alteration minerals (e.g., epidote, chlorite, and actinolite) and sulfides, and are characterized by low Ti, Al, and V contents and high Mg, Mn, and Sn contents, indicating a hydrothermal origin. From Mt3 to Mt5, (Ti + V) and (Al + Mn) decrease, while Zn and Mn increase, accompanied by a decrease in the (Si + Al)/(Mg + Mn) ratio. This reflects a trend of decreasing fluid temperature and progressively enhanced wall-rock buffering. The Mg-in-magnetite geothermometer yields relatively consistent results for Mt1–Mt3, but anomalously high temperatures for Mt4–Mt5. This suggests that the elevated Mg activity in the fluid, caused by reaction with carbonate wall rocks, can significantly influence the calculated temperatures. Therefore, this geothermometer should be used cautiously for magnetite in the outer skarn zone and interpreted in combination with other temperature constraints. The textures, paragenetic mineral assemblages, and trace element characteristics of magnetite collectively reveal a continuous mineralization process linking the skarn and stratiform orebodies at Xinqiao, providing robust mineralogical and geochemical evidence for the contribution of Yanshanian magmatic–hydrothermal activity to the stratiform mineralization. Full article

With the widespread adoption of electric vehicles (EVs), the management and scheduling of charging and discharging play a crucial role in the performance of both the electricity grid and electric vehicles. Particularly in the context of peak shaving, valley filling, and the promotion of the energy internet infrastructure, efficient management of the EV charging and discharging process is vital. This study investigates the control and management issues surrounding EV charging and discharging, proposing a management strategy based on deep reinforcement learning. By constructing an intelligent decision-making model, it integrates factors such as the operating conditions of the electrical grid, user behavioral preferences, EV battery characteristics, and renewable energy outputs. The study collects real-world EV usage data from a city, establishing an experimental environment to simulate the interaction between the electricity grid and electric vehicles. Using techniques such as Deep Q-Network (DQN) and policy gradients, it constructs a decision network to explore charging and discharging strategies across different time scales and load situations. Experimental results show that this strategy, compared to traditional charging schedule methods, can effectively reduce energy loss during charging, enhance battery life, and balance the grid load, while suppressing demand peaks, thus achieving intelligent optimization and reliability enhancement of the charging and discharging process. Particularly, an adaptive charging power adjustment technique within the strategy can dynamically adjust the charging power according to the real-time status of the EV and grid load without affecting the user’s daily use, thereby achieving the dual objectives of efficient energy saving and economy. The research also quantitatively analyzes battery degradation characteristics and the continuity of charging to ensure the long-term sustainability of the charging strategy. The research findings are significant for understanding and guiding the practical management of EV charging and discharging. Full article

Zenith Tropospheric Delay (ZTD) and its associated atmospheric water vapor information constitute essential environmental variables for Earth observation (EO)-based atmospheric monitoring and environmental variable retrieval. High-quality ZTD products are therefore of great importance for the post-processing, refinement, and reconstruction of atmospheric environmental variables at regional scales. Among existing observation techniques, Global Navigation Satellite System (GNSS) measurements provide high-precision ZTD estimates and have become an important means for retrieving tropospheric delay and water vapor. However, the sparse and uneven spatial distribution of GNSS stations limits their direct applicability for continuous environmental monitoring. Reanalysis-based products, such as ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), offer EO big data with excellent spatiotemporal continuity but suffer from pronounced systematic biases compared to precision GNSS retrievals, restricting their direct use in high-accuracy regional applications. To address these limitations, this study proposes a Residual Correction Kriging method for ZTD (RK ZTD) that integrates GNSS ZTD and ERA5 ZTD grids through a multi-source data fusion framework. High-precision GNSS ZTD is treated as reference data, and the differences between GNSS ZTD and ERA5 ZTD at modeling stations are defined as residuals to characterize the systematic bias in ERA5 ZTD grids. A Kriging interpolation algorithm is then employed to model the spatial distribution of these residuals and generate residual correction grids. By superimposing the interpolated residual grids onto the ERA5 ZTD grids, a refined and high-precision regional ZTD product is reconstructed. Experiments were conducted using observations collected in 2023 from 36 GNSS stations in the Netherlands, including 10 modeling stations and 26 independent validation stations, together with concurrent ERA5-derived ZTD grids. The results demonstrate that the proposed RK ZTD model provides spatially robust and high-precision ZTD products across the study region. The RK ZTD achieves a Root Mean Square Error (RMSE) of 5.70 mm, representing improvements of 58.4% and 35.4% compared with the original ERA5 ZTD (13.69 mm) and the GNSS-Kriging ZTD (8.82 mm), respectively. Moreover, the absolute bias is reduced to 0.41 mm, in contrast to 5.15 mm for the ERA5 ZTD, indicating that systematic biases are effectively mitigated. Spatial and seasonal analyses further confirm that the proposed method maintains stable performance across all seasons and significantly alleviates interpolation inaccuracies caused by sparse GNSS stations, even under extreme weather conditions such as Storm Ciarán, proving its value for advanced Earth environmental science applications. Full article

Fault detection in microgrids is a critical element of system stability and uninterrupted power delivery. Herein, a comparative study using LSTM and bidirectional LSTM networks is performed based on three-phase current data for multi-class fault classification. Five major fault types, namely LG, LL, LLG, LLL, and LLLG, were simulated using a Real-Time Digital Simulator (RTDS) under grid-connected and islanded modes. Collected current signals were preprocessed, normalized, and segmented for sequence learning. Later, both models were trained using the best hyperparameter setting to enhance their capabilities and classify faults. To measure how well they identified faults, evaluation metrics, like accuracy, precision, recall, F1-score, and ROC-AUC, were calculated. The results revealed that the Bi-LSTM outperformed the LSTM and classical machine learning models consistently, with more than 99% accuracy for most fault types. More importantly, the proposed framework also checked classification performance for LLLG faults, with the Bi-LSTM model having a test accuracy of 98.8%. These results confirm that the Bi-LSTM model can robustly and precisely classify and detect faults in real time within specific phases of microgrids; therefore, it provides a scalable foundation for the development of intelligent protection in smart power systems. Full article

Freight-dominant port collection and distribution road networks exhibit strong spatial congestion, early spillback, and heterogeneous vehicle dynamics that challenge conventional traffic signal control strategies. Although Max-Pressure (MP) signal control provides strong decentralized stability properties, its classical queue-based formulation lacks sensitivity to incipient spatial congestion and performs poorly when heavy-duty vehicles (HDVs) dominate traffic composition. This paper proposes a gain-modulated Max-Pressure (Gain-MP) control framework, in which conventional pressure computation is augmented by an occupancy-dependent feedback gain that dynamically adjusts phase priorities according to real-time spatial congestion states and current right-of-way conditions. Without altering the decentralized structure of MP, the proposed method introduces a nonlinear feedback mechanism that enhances system responsiveness to congestion formation while suppressing excessive phase switching. The approach is evaluated using microscopic simulation on a signalized grid network representing port access corridors under time-varying demand and high HDV penetration. Results demonstrate that the dynamic Gain-MP controller performs better than classical queue-based MP, PCU-weighted MP, and fixed-time control. Moreover, constant-demand experiments indicate that the dynamic Gain-MP controller maintains bounded vehicle accumulation over a wider empirical demand range than the benchmark MP-based methods under the tested settings. Full article

Traditional pedestrian routing algorithms typically minimize physical distance or travel time, often overlooking contextual factors that influence route choice in digitally connected environments. As public WiFi infrastructure becomes increasingly prevalent in smart-city districts and university campuses, digital connectivity may influence pedestrian mobility decisions. This study introduces P-WARP, a multi-factor routing and inference framework that reconstructs latent pedestrian preferences by integrating physical effort, environmental walkability, and WiFi connectivity within a unified semantic graph. The empirical analysis is conducted on the Chiang Mai University campus, a digitally connected environment serving as a smart campus testbed. The framework integrates heterogeneous spatial datasets, including OpenStreetMap topology, Shuttle Radar Topography Mission elevation data, environmental walkability grids, and WiFi roaming logs collected via a custom mobile sensing application from 21 volunteers across 71 verified walking trips. Two routing strategies are evaluated: a Global Static Model, representing infrastructure-based connectivity assumptions, and a Trip-Centric Dynamic Model, incorporating realized connectivity histories. Model parameters are calibrated using Bayesian Optimization with five-fold cross-validation. Results show that incorporating realized connectivity reduces trajectory reconstruction error by 6.84% relative to the baseline. The learned parameters reveal a notable detour tolerance, suggesting that stable digital connectivity can influence pedestrian route choice in digitally instrumented environments. Full article

Spring vegetation phenology is highly sensitive to climate change; however, climate drivers and their threshold responses at the urban scale remain insufficiently and systematically quantified. Focusing on 31 provincial capitals and municipalities in mainland China, this study integrated MODIS MCD12Q2-derived start-of-season (SOS) for spring green-up and TerraClimate climate data (2001–2023) at a 500 m grid resolution. SOS trends were characterized using the Mann–Kendall test and the Theil–Sen slope estimator. Building on these trend metrics, we developed an XGBoost–SHAP framework using the interannual rate of temperature change (tem_slope) and the interannual rate of precipitation change (pre_slope) as input features, to quantify the nonlinear contributions of climate-change rates to SOS trends and to identify key thresholds. Results indicate that the multi-year mean SOS across China’s provincial capitals and municipalities is primarily distributed between approximately DOY 74 and 138, exhibiting a clear spatial pattern of earlier green-up in the south, later green-up in the north, and delayed green-up on plateaus, with pronounced shifts in distribution centers and dispersion among climatic zones and cities. At the city level, the mean SOS trend shows an overall advancing rate of 0.81 d·year⁻¹ (i.e., the average of city-mean Sen slopes across the 31 cities). Pixel-level trend analyses show that advancing and delaying trends commonly coexist within most cities; among pixels with significant or marginally significant SOS trends identified by the Mann–Kendall test (MK p < 0.10) across all cities, advancing and delaying SOS pixels account for 75.02% and 24.98%, respectively. At the city scale, the proportions of advancing versus delaying pixels vary markedly among cities, forming directional structures characterized by advance-dominant, delay-dominant, or bidirectional coexistence patterns. SHAP dependence relationships further reveal that the effects of tem_slope and pre_slope on SOS trends are generally nonlinear and piecewise, with substantial heterogeneity across climate zones and cities. The identified tipping points and associated sensitive ranges collectively delineate spatially differentiated climate-sensitive intervals, which define the nonlinear response boundaries of spring SOS to sustained warming and precipitation changes. This study provides quantitative evidence for regional differences in urban spring phenological responses to climate change across major Chinese cities and offers a methodological reference for identifying actionable climate thresholds in urban greening design and climate-adaptive management. Full article

Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning. Full article

This article examines the Unité Pédagogique d’Architecture in Toulouse (UPAT) as a paradigmatic example of the palimpsestic architectures that characterize many contemporary university campuses. Conceived in the immediate aftermath of May 1968, the school emerged at a moment when pedagogical reform, political commitment, and architectural experimentation became closely intertwined. These conditions gave rise to a singular spatial organization based on a combinatory grid, intended to give architectural form to a democratic ideal of education grounded in openness, flexibility, and collective agency. The study adopts a historical–critical methodology based on the systematic analysis of primary and secondary sources, complemented by original graphic interpretations. This approach makes it possible to read the UPAT simultaneously as a didactic instrument and as an ideological manifesto, one whose ambitions were inherently marked by internal tensions and contradictions. A diachronic examination of subsequent extensions and transformations reveals how these founding intentions were progressively reinterpreted, constrained, or displaced in response to changing institutional, social, and cultural conditions. Taken as a whole, the evolving trajectory of this “manifesto school” illuminates the ways in which architectural ideals—particularly the pursuit of openness—are negotiated over time, offering a critical perspective on the reciprocal shaping of architecture, pedagogy, and institutional identity within the history of university buildings. Full article

Approximately one quarter of the European Union’s (EU’s) CO₂ emissions originate from the transport sector, of which road transport, such as cars and heavy-duty vehicles, contributes roughly 72%. Moreover, according to the European Automobile Manufacturers’ Association, 92% of cars in the EU are internal combustion engine vehicles powered by fossil fuels. Therefore, boosting the adoption of Electric Vehicles (EVs) is considered one of the most prominent solutions for reducing GHG emissions and achieving the EU’s climate targets. To increase EV adoption and fulfil the demand of EV users, adequate EV Charging Stations (EVCSs) are required. Nevertheless, since most EVCSs are supplied by electricity grids that remain predominantly fossil fuel-based, their operation entails substantial indirect GHG emissions. A prominent approach to reducing grid-related emissions is integrating renewable energy sources (RESs) with EVCSs, thereby lowering emissions and alleviating grid stress. Although promising, the energy, economic, and environmental (3E) benefits of this integration remain insufficiently explored. Therefore, this study develops and applies a 3E optimisation framework to assess the feasibility and performance of RES-powered EVCS at NOVA University Lisbon (UNL). Data was collected from the UNL parking area, such as time of arrival, and time of departure. Also, a rule-based algorithm was developed to curate data and estimate the EVCS load profile. Furthermore, HOMER optimisation software was employed to evaluate four scenarios, including (i) an EVCS based on PV, Wind Turbine (WT), and the grid, (ii) an EVCS based on PV and the grid, (iii) an EVCS based on WT and the grid, and (iv) an EVCS based only on energy withdrawal from the grid (base scenario). Under the adopted techno-economic assumptions, in the most optimised scenario, economic and environmental analyses illustrate significant improvements over the base scenario: CO₂ emissions are five times lower, and cost of energy is significantly lower, resulting in significantly lower EV charging costs for users. The results demonstrate that, through developed feasibility studies, researchers, decision-makers, and stakeholders can reach better conclusions about EVCS planning and management. Full article

As distributed terminals are increasingly integrated into modern power systems with high penetration of renewable energy and decentralized resources, access control mechanisms must support continuous and highly detailed trust assessment. Existing approaches based on machine learning primarily rely on network traffic features from a single source and analyze terminals in isolation, which limits their ability to capture complex device states and correlated attack behaviors. This paper presents a trust assessment framework for distributed power grid terminals that combines multidimensional behavioral modeling with dual domain graph neural networks. Behavioral features are collected from network traffic, runtime environment, and hardware or kernel events and are fused into compact representations through a variational autoencoder to mitigate redundancy and reduce computational overhead. Based on the fused features and observed communication relationships, two graphs are constructed in parallel: a feature domain graph reflecting behavioral similarity and a topological domain graph capturing communication structure between terminals. Graph convolution is performed in both domains to jointly model individual behavioral risk and correlation across terminals. A fusion mechanism based on attention is further introduced to adaptively integrate embeddings specific to each domain, together with a loss function that enforces both shared and complementary representations across domains. Experiments conducted on the CIC EV Charger Attack Dataset 2024 show that the proposed framework achieves a classification accuracy of 96.84%, while maintaining a recall rate above 95% for the low trust category. These results indicate that incorporating multidimensional behavior perception and dual domain relational modeling improves trust assessment performance for distributed power grid terminals under complex attack scenarios. Full article