Search Results (75)

Photovoltaic (PV) power generation is inherently intermittent due to unpredictable irradiance variations, posing significant challenges for grid integration. While conventional power smoothing strategies mitigate short-term fluctuations, they do not explicitly enforce the tracking of a scheduled power trajectory. This paper proposes a dispatchable PV framework that integrates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for precise day-ahead power forecasting with a real-time supercapacitor (SC) compensation strategy. The CNN-LSTM network captures complex spatiotemporal meteorological dependencies to generate a robust day-ahead reference trajectory. Concurrently, a supercapacitor energy storage system (SC-ESS) integrated at the DC-link level via a bidirectional buck–boost converter actively balances the instantaneous mismatch between this forecast trajectory and the actual PV generation. Unlike filter-based hybrid methods, the SC-ESS is employed as a direct forecast error actuator in a closed-loop control scheme. This strategy strictly enforces real-time forecast tracking while preserving maximum power point tracking (MPPT) and DC-link voltage stability. Simulations and laboratory experiments under rapidly varying irradiance confirm that the proposed method significantly reduces power deviations from the forecast reference and improves short-term power predictability without imposing excessive stress on the SC. This forecast-aware strategy effectively enhances the dispatchability of PV systems, providing a practical solution for grid-supportive operation. Full article

Glacial lake outburst floods (GLOFs) are high-impact hazards in mountain regions, yet many events remain poorly documented because field access is limited and lake evolution can occur on sub-weekly time scales. Here, we used high spatiotemporal resolution PlanetScope imagery (3 m) to quantify the seasonal evolution and abrupt drainage of a moraine-dammed glacial lake in August 2025 in northern Pakistan. Historical lake dynamics were reconstructed using PlanetScope (2016–2024) imagery and multi-decadal Landsat observations (1992–2018). Climatic conditions were evaluated using ERA5-Land temperature data, and seasonal snow dynamics were characterized using MODIS and PlanetScope-based snow cover analyses. Multi-decadal satellite imagery indicates that lake formation in this catchment was historically intermittent, with no evidence of abrupt drainage before 2025, highlighting the anomalous nature of the event. PlanetScope observations show steady lake expansion throughout summer 2025, reaching a maximum area of 0.052 km² prior to the GLOF on August 22. Pre- and post-event imagery reveals no discernible landslide or impact trigger. Instead, the observations are most consistent with a failure mechanism driven by meltwater-driven lake growth and overtopping or erosion of the moraine dam. The 2025 summer season (June to September) was characterized by exceptionally warm conditions and unprecedented early snow depletion relative to the 2000–2024 baseline, suggesting a strong climatic and cryospheric contribution to the outburst. These results demonstrate the value of integrating dense time series of satellite observations and climatic data for capturing glacial-lake life cycles and diagnosing likely controls on outburst initiation. The study highlights the critical role of high-frequency satellite remote sensing for improving GLOF monitoring and early-warning capabilities in data-scarce mountain environments. Full article

Soil salinization constrains the sustainability of irrigated oasis agriculture in arid regions. Using repeated post-harvest monitoring of 125 fixed cropland sites in Bachu County, southern Xinjiang, from 2018 to 2021, this study investigated the short-term spatiotemporal variability of topsoil total salt content (TSC) and pH. Descriptive statistics, one-way ANOVA with Tukey’s HSD test, Universal Kriging interpolation, class-transition analysis, hotspot recurrence, centroid migration, and principal component analysis were used to characterize temporal variation, spatial structure, and environmental gradient associations. TSC showed a mitigation–rebound sequence, decreasing to 4.88 ± 5.21 g kg⁻¹ in 2020 and increasing to 6.90 ± 5.93 g kg⁻¹ in 2021, whereas pH increased first and then declined. Salinity remained consistently concentrated in downstream cropland, while pH showed weaker and more year-dependent zonal differentiation. Class-transition analysis revealed marked salinity reorganization in 2021, mainly driven by conversion from lower-salinity classes to moderately and severely saline classes. Severe-salinity hotspots were temporally intermittent but spatially recurrent in the downstream zone, whereas high-pH hotspots were short-lived and mainly confined to the upstream zone. PCA further showed that TSC and pH were aligned with different environmental gradient combinations. Overall, the four-year sequence should be interpreted as short-term interannual variability rather than a robust long-term sequence. These results indicate that TSC and pH should not be treated as interchangeable indicators in oasis cropland assessment, and they provide a transferable basis for zone-specific salinity monitoring and management, with priority given to persistent downstream sink areas. Full article

Intrabasinal low uplifts in lacustrine rift basins are key targets for sedimentological and petroleum geological research, as they can act as local source areas and exert critical controls on intrabasinal “source-to-sink” systems. Due to the discontinuous sediment supply, these systems often demonstrate the subtle and intermittent nature, and their roles in the development of depositional systems are usually overlooked. To clarify the controlling effect of intrabasinal local provenances on sedimentary system evolution, this study reconstructed the dynamic tectonic evolution of the Weixinan Low Uplift in the Beibuwan Basin, and systematically analyzed its control on “source-to-sink” systems and sedimentary filling using integrated high-resolution 3D seismic, core, well logging and geochemical data. Our results demonstrate that the activity of Fault 3 dominated the paleogeomorphic evolution of the Weixinan Low Uplift and its surrounding areas, which further governed the spatiotemporal development of the “source-to-sink” system and the distribution of sedimentary systems, with distinct evolutionary stages as follows: During the Ls2 Member stage (48.6–40.4 Ma), Fault 3 was inactive, the Weixinan Low Uplift was manifested as a gently dipping subaqueous slope under the influence of regional lacustrine transgression, and only small-scale braided river deltas were developed on the slope belt with weak sediment supply from the Qixi Uplift. During the Ls1 Member stage (40.4–33.9 Ma), the Ls13 Sub-member stage (lower Ls1 Member stage) was characterized by initiation of Fault 3 with segmented activity, triggering the formation of the Eastern Sub-sag of the Haizhong Sag and subaqueous uplift of the Weixinan Low Uplift; clastic sediments from the central Qixi Uplift were transported northeastward, developed braided river deltas and large-scale basin-floor lacustrine fans. In the Ls12 Sub-member stage (middle Ls1 Member stage), Fault 3 continued to propagate and was gradually linked, leading to further uplift of the Weixinan Low Uplift and expansion of the Haizhong Sag; Clastic materials from the central Qixi Uplift were almost entirely trapped in the Eastern Sub-sag of the Haizhong Sag. During the Ls11 Sub-member stage (upper Ls1 Member stage), further intensification of Fault 3 activity caused the Weixinan Low Uplift to be subaerially exposed and evolve into an intrabasinal local provenance, which supplied clastic sediments to surrounding sags and developed braided river deltas on the gentle slope belts and small-scale lacustrine fans on the lower slope. This study demonstrates that the tectonic evolution of the Weixinan Low Uplift has induced prominent changes in the basin paleogeomorphology, which in turn triggered dynamic shifts in the provenance and sediment transport pathways, and thus gave rise to complex local “source-to-sink” systems and depositional styles. Full article

Decarbonizing the maritime sector requires not only adopting alternative fuels and propulsion technologies but also quantitatively assessing their impacts on coastal and urban air quality. This study develops a stochastic, time-resolved air-quality modelling framework to evaluate ship-related pollutant dispersion in port environments. The approach integrates Automatic Identification System (AIS) trajectories, vessel-specific emission factors, and meteorological inputs within a moving-source Gaussian dispersion model to simulate the spatio-temporal evolution of pollutant concentrations. A 24 h case study for the Ports of Los Angeles and Long Beach demonstrates highly intermittent emission behaviour, with peak aggregated emission rates reaching approximately 1.2 kg/s for CO₂ and 3.8 g/s for SO₂. Temporally integrated concentration fields reveal maximum cumulative dosages of 0.145 g·s/m³ for NO_x, 0.023 g·s/m³ for SO₂, 0.014 g·s/m³ for total PM, and 7.5 g·s/m³ for CO₂ in near-port traffic corridors. Sensitivity analysis indicates that effective emission height variations alter cumulative exposure by up to 17%, whereas temporal resolution changes produce deviations below 7%, confirming numerical stability. Monte Carlo uncertainty propagation demonstrates bounded but non-negligible variability in exposure estimates under realistic emission and wind uncertainties. Results show that cumulative exposure patterns differ substantially from short-term concentration peaks, highlighting the importance of time-integrated and receptor-based metrics for port air quality assessment. The proposed AIS-driven stochastic framework provides a reproducible and computationally efficient tool for evaluating operational mitigation strategies and supporting evidence-based maritime decarbonization pathways. Full article

Complex dynamical systems are characterized by inherent nonlinearity, high dimensionality, spatiotemporal uncertainty, and implicit symmetry, posing fundamental challenges for their mathematical modeling and multi-step evolution prediction. For example, wind power exhibits strong randomness, intermittency, and latent temporal symmetry. To address this, this paper proposes a symmetry-guided deep generative model, the bi-directional recurrent generative adversarial network (BDR-GAN), for the multi-step rolling prediction of such systems. The BDR-GAN formalizes multi-step evolution as a conditional probability distribution learning problem. It systematically integrates three forms of symmetry to enhance modeling validity: bi-directional temporal symmetry captured by a BiLSTM-based generator, structural symmetry within the adversarial learning framework between the generator and a 1D-CNN discriminator, and rolling symmetry enabled by a recursive prediction strategy that supports cyclic state updates. Theoretical analysis demonstrates that this symmetry-embedded adversarial mechanism enables BDR-GAN to effectively approximate the underlying dynamic operators and the conditional distribution of future states, improving the learned model’s generalization. Experimental validation on wind power datasets confirms the framework’s superiority. Compared to benchmark models, BDR-GAN achieves superior prediction accuracy (e.g., RMSE 0.236, MAPE 5.12%), provides reliable uncertainty quantification (PICP 95.5%), and exhibits enhanced robustness against noise and variability. This work provides a generalizable, symmetry-guided modeling framework for the multi-step evolution of complex dynamical systems, offering theoretical and technical support for high-precision prediction in critical applications such as wind power integration and smart grid operation. Full article

Wastewater-Based Epidemiology (WBE) has emerged as a critical tool for monitoring SARS-CoV-2 circulation at the community level. This study assessed spatiotemporal viral dynamics in Las Heras, Mendoza, Argentina, by comparing wastewater samples from six sewer maintenance holes and three wastewater treatment plants (WWTPs) between January and June 2021, and by conducting long-term surveillance at Campo Espejo WWTP during epidemic (2020–2021) and endemic (2024–2025) phases of COVID-19. Viral particles from sewer manholes and WWTPs samples were concentrated by polyethylene glycol precipitation or aluminum polychloride adsorption–precipitation methods, and then SARS-CoV-2 RNA was quantified by reverse transcription quantitative polymerase chain reaction targeting N1 and N2 nucleocapsid viral markers. Results showed consistent detection of viral RNA across all sites, with peaks in wastewater preceding diagnosed COVID-19 cases increases, confirming WBE as an early-warning system. Localized sewer sampling identified urban hotspots, while WWTPs monitoring captured broader epidemiological trends. Recently, COVID-19 surveillance showed lower and intermittent viral loads, decoupled from diagnosed cases, compared to epidemic phase, indicating a transition to endemic circulation. Overall, combining upstream and downstream WBE enhanced spatial and temporal resolution, demonstrating its utility for public health monitoring during both epidemic and endemic phases. Full article

With the ongoing transformation of energy systems and the expanding scale of multi-park integrated energy systems, this paper proposes a novel multi-spatiotemporal scale scheduling framework that integrates robust optimization with distributed coordination to address the challenges of complex spatiotemporal coupling and significant uncertainties in the coordinated operation of transmission grids and multi-park integrated energy systems under high renewable energy penetration. The proposed framework establishes a hierarchical optimization model encompassing day-ahead, intra-day rolling, and real-time scheduling stages, incorporating multi-energy coupling constraints and accounting for load uncertainty. Robust optimization is employed to effectively manage source-load fluctuations arising from renewable intermittency. For solution implementation, the analytical target cascading (ATC) method is adopted to enable distributed collaborative optimization between the transmission system and individual park-level systems. Simulation results demonstrate that the proposed approach significantly enhances both the economic efficiency and operational reliability of the integrated energy system. Full article

Indoor air quality (IAQ) in healthcare facilities is increasingly recognized as a key determinant of occupant health, comfort, and operational performance. Owing to heterogeneous space functions, varying occupancy patterns, and dynamic operational conditions, IAQ parameters may exhibit marked spatial and temporal variability within the same facility. University-affiliated healthcare buildings, where clinical services coexist with academic and administrative activities, represent particularly complex indoor environments that remain relatively underexplored in the current IAQ literature. This study examines the spatiotemporal variability of indoor carbon dioxide (CO₂) and fine particulate matter (PM_2.5) concentrations across four representative functional zones within a university-affiliated healthcare facility, including a patient waiting room, an academic office, an administrative office, and a restorative dental clinic. Continuous, long-term monitoring was conducted over a multi-month period to capture both spatial differences and diurnal dynamics under real operational conditions. Daily mean CO₂ concentrations varied across functional zones, ranging from approximately 540 to 620 ppm, with higher levels generally observed in spaces with sustained occupancy and limited ventilation. Daily mean PM_2.5 concentrations ranged from approximately 13 to 18 µg/m³, with greater variability detected in zones associated with intermittent activities and procedural sources. Unlike many IAQ studies focusing on single departments or short-term campaigns, this multi-zone, long-term assessment within a shared building infrastructure enables direct comparison of functional spaces and identification of time-specific exposure patterns. Overall, the findings highlight that IAQ conditions within healthcare facilities are shaped by both space function and temporal factors, even under shared ventilation infrastructure. The results emphasize the value of zone-specific and time-resolved IAQ assessment approaches and provide evidence-based insights to support targeted ventilation strategies, activity-aware operational controls, and improved indoor environmental management in healthcare settings. Full article

Wireless Sensor Networks (WSNs) are widely deployed for long-term monitoring in environments characterized by nonstationary sensing dynamics, intermittent connectivity and continuously evolving network topologies, while reliable, fine-grained labeled data capturing faults and adversarial behaviors remain scarce. This survey systematically reviews and synthesizes recent research that integrates autoencoder-based representation learning with self-supervised learning (SSL) objectives to enhance anomaly detection under these practical constraints. We structure the existing literature through a unified taxonomy encompassing autoencoder variants, self-supervised pretext tasks, spatio-temporal encoding mechanisms and the increasing use of graph-structured autoencoders for topology-aware modeling. Across distinct methodological categories, SSL-augmented frameworks consistently demonstrate improved robustness and stability compared to purely reconstruction-driven baselines, particularly in heterogeneous, dynamic and temporally drifting WSN environments. Nevertheless, this review also highlights several unresolved challenges that hinder real-world adoption, including uncertain scalability to large-scale networks, limited model interpretability, nontrivial energy and memory overheads on resource-constrained sensor nodes and a lack of standardized evaluation protocols and reporting practices. By consolidating publicly available datasets, experimental configurations and comparative performance trends, we derive concrete design requirements for robust and resource-aware anomaly detection in operational WSNs and outline promising future research directions, emphasizing lightweight model architectures, explainable learning mechanisms and federated AE–SSL paradigms to enable adaptive, privacy-preserving monitoring in next-generation IoT sensing systems. Full article

The integration of high-penetration renewable energy sources (RESs) and electric vehicles (EVs) increases the risk of voltage fluctuations in distribution networks. Traditional static partitioning strategies struggle to handle the intermittency of wind turbine (WT) and photovoltaic (PV) generation, as well as the spatiotemporal randomness of EV loads. Furthermore, existing scheduling methods typically optimize EV active power or reactive compensation independently, missing opportunities for synergistic regulation. The main novelty of this paper lies in proposing a spatiotemporally coupled voltage-stability optimization framework. This framework, based on an hourly updated electrical distance matrix that accounts for RES uncertainty and EV spatiotemporal transfer characteristics, enables hourly dynamic network partitioning. Simultaneously, coordinated active–reactive optimization control of EVs is achieved by regulating the power factor angle of three-phase six-pulse bidirectional chargers. The framework is embedded within a hierarchical model predictive control (MPC) architecture, where the upper layer performs hourly dynamic partition updates and the lower layer executes a five-minute rolling dispatch for EVs. Simulations conducted on a modified IEEE 33-bus system demonstrate that, compared to uncoordinated charging, the proposed method reduces total daily network losses by 4991.3 kW, corresponding to a decrease of 3.9%. Furthermore, it markedly shrinks the low-voltage area and generally raises node voltages throughout the day. The method effectively enhances voltage uniformity, reduces network losses, and improves renewable energy accommodation capability. Full article

Current passive microwave satellites like SMAP still face limitations in observational frequency and responsiveness in regions with frequent cloud cover, dense vegetation, or complex terrain, making it difficult to achieve continuous global monitoring with high spatio-temporal resolution. To enhance global high-frequency monitoring capabilities, this study utilizes global reflectivity data provided by the Tianmu-1 (TM-1) constellation since 2023, combined with multiple auxiliary variables, including NDVI, VWC, precipitation, and elevation, to develop a 9 km resolution soil moisture retrieval model. Several spatial clustering and temporal partitioning strategies are incorporated for systematic evaluation. Additionally, since the publicly available TM-1 L1 reflectivity data does not provide separable polarization channels, this study uses DDM/specular point reflectivity as the primary observable quantity for modeling and mitigates non-soil factor interference by introducing multi-source priors such as NDVI, VWC, precipitation, terrain, and roughness. Unlike SMAP’s “single orbit daily fixed local time” observation mode, TM-1, leveraging multi-constellation and multi-orbit reflection geometry, offers more balanced temporal sampling and availability in cloudy, rainy, and mid-to-high latitude regions. This enables temporal gap filling and rapid event response (such as moisture transitions within hours after precipitation events) during periods of SMAP’s quality masking or intermittent data loss. Results indicate that the model combining LC-cluster with seasonal partitioning delivers the best performance at the cluster level, achieving a correlation coefficient (R) of 0.8155 and an unbiased RMSE (ubRMSE) of 0.0689 cm³/cm³, with a particularly strong performance in barren and shrub ecosystems. Comparisons with SMAP and ISMN datasets show that TM-1 is consistent with mainstream products in trend tracking and systematic error control, providing valuable support for global and high-latitude studies of dynamic hydrothermal processes due to its more balanced mid- and high-latitude orbital coverage. Full article

The non-fixed location and intermittent emission of pollution sources are posing significant challenges for industrial ventilation and dust removal. Owing to its energy-saving and emission-reduction advantages, recirculating ventilation has emerged as a critical solution in industrial ventilation. However, the introduction of recirculated air leads to the accumulation of both pollutants and temperature in controlled environments. However, the accumulation situation and control methods within the recirculation air system are still not fully understood, especially for discrete pollution sources. Theoretical models were developed in this paper to quantitatively calculate these issues. Then, the spatiotemporal distributions of air temperature and pollutant concentrations were investigated within the controlled environment by numerical simulation. The result shows that upper limits exist for both pollutant and heat accumulation. Controlling the recirculation air ratio between 0% and 60% resulted in a corresponding increase in pollutant concentration ranging up to 28.58% and a temperature rise of up to 6.16%. At a 27.8% recirculation ratio, fresh air consumption decreased by 27.8%, mean pollutant concentration increased by 5.50%, and mean air temperature rose by 3.78%. In addition, the dynamic characteristics of pollutant concentration and temperature variation at different circulating air ratios were analyzed. The recirculating air system effectively conserves energy and reduces emissions. The quantitative calculation models can facilitate the adoption and promotion of such systems, thereby contributing to industrial green development. Full article

Geomagnetic activity reflects the complex coupling between the solar wind, magneto-sphere and ionosphere. While the global Kp index serves as a standard proxy for geo-magnetic disturbances, it obscures regional variations linked to local current systems and ionospheric conductivity. This study investigates regional features of geomagnetic activity using the local K index from the Almaty (AAA) observatory and compares its temporal dynamics with Kp for 2007–2025. A combination of statistical, spectral, wavelet, and nonlinear methods was applied, including power spectral density, continuous and cross-wavelet transforms, multifractal detrended fluctuation analysis, and permutation entropy. These approaches capture both linear and nonlinear features of variability and reveal scale-dependent structures in geomagnetic fluctuations. The results show a high correlation (r ≈ 0.84) between K (AAA) and Kp, but with a consistent positive offset of the local index, indicating sensitivity to regional ionospheric processes. Wavelet coherence highlights strong coupling in the 13–27-day band associated with solar rotation. Multifractal spectra reveal broader, more heterogeneous scaling in Kp and narrower, more intermittent dynamics in K during disturbed periods. Local indices, like K (AAA), thus provide essential insight into mid-latitude electrodynamics, complementing global measures in characterizing the nonlinear spatio-temporal complexity of geomagnetic activity. Full article

Accurate forecasting of photovoltaic (PV) power is crucial for real-time grid balancing and storage optimization. However, the intermittent, noisy, and nonstationary nature of PV generation, together with cross-site interactions, makes multi-site intra-hour forecasting challenging. In this paper, we propose a unified approach for multi-site PV power forecasting named WGL (Wavelet–Graph Learning). Unlike prior studies that treat denoising and spatio-temporal modeling separately or predict each station independently, WGL forecasts all PV stations jointly while explicitly capturing their inherent spatio-temporal correlations. Within WGL, Learnable Wavelet Shrinkage (LWS) performs end-to-end noise suppression; a Temporal Multi-Scale Fine-grained Fusion (T-MSFF) module extracts complementary temporal patterns; and an attention fusion gate adaptively balances TCN and LSTM branches. For spatial coupling, graph self-attention (GSA) learns a sparse undirected graph among stations, and a Factorized Spatio-Temporal Attention (FSTA) efficiently models long-range interactions. Experiments on real-world multi-site PV datasets show that WGL consistently outperforms representative deep and graph-based baselines across intra-hour horizons, highlighting its effectiveness and deployment potential. Furthermore, a comprehensive analysis of influencing factors for scheme implementation—encompassing safety, reliability, economic rationality, management scientificity, and humanistic care—is conducted, providing a holistic assessment of the framework’s feasibility and potential impact in real-world power systems. Full article