Search Results (343)

The increasing demand for high-power electric vehicle charging systems with Vehicle-to-Grid (V2G) capability highlights the need for modular, scalable power converters. This paper proposes a hierarchical control strategy for a high-power, multi-port electric vehicle charging station. The system, based on a Series-Parallel Modular Multilevel Converter (SP-MMC) with isolated modules, is managed by a coordinated control strategy that integrates proportional-integral-resonant regulators, nearest-level control with voltage sorting, and single-phase-shifted modulation. The proposed system enables simultaneous, independent regulation of multiple bidirectional, isolated direct current ports while maintaining grid-side power quality and internal variables of the SP-MMC. The proposed control is validated using real-time Model-In-the-Loop (MIL) simulations that include sequential port activation, bidirectional power flow, and charging operation. MIL results demonstrate stable operation with controlled DC-link voltage ripple, accurate per-port current tracking, and near-unity grid power factor under multi-port operation. Full article

We present a new method for mapping global gas flaring using a multiyear spatio-temporal database of VIIRS Nightfire (VNF) nighttime infrared detections from the Suomi NPP, NOAA-20, and NOAA-21 satellites. The method is designed to resolve closely spaced industrial combustion sources and to produce a stable, physically meaningful flare catalog suitable for long-term monitoring and emissions analysis. The method combines adaptive spatial aggregation of high-temperature detections with a hierarchical clustering that super-resolves individual flare stacks within oil and gas fields. Post-processing yields physically consistent flare footprints and attraction regions, allowing separation of closely spaced sources. Flare clusters are assigned to operational categories (e.g., upstream, midstream, LNG) using prior catalogs combined with AI-assisted expert interpretation. In this step, a multimodal large language model (LLM) provides contextual classification suggestions based on geospatial information, high-resolution daytime imagery, and detection time-series summaries, while final attribution is performed and validated by domain experts. Compared with annual flare catalogs commonly used for national flaring estimates, the new catalog demonstrates substantially improved performance. It is more selective in the presence of intense atmospheric glow from large flares, identifies approximately twice as many active flares, and localizes individual stacks with ~50 m precision, resolving emitters separated by ~400–700 m. For the well-defined class of downstream flares at LNG export facilities, the catalog achieves complete detectability. These improvements support more accurate flare inventories, facility-level attribution, and policy-relevant assessments of gas flaring activity. Full article

Background: Dictyostelium discoideum is widely used in developmental and evolutionary biology due to its ability to transition from a single cell to a multicellular organism in response to starvation. While transcriptome information across its life cycle is widely available, only early-stage data exist at the proteome level. This study characterizes and compares the proteomes of D. discoideum cells at the vegetative, aggregation, mound, culmination and fruiting body stages. Methods: Samples were collected from cells developing synchronously on nitrocellulose filters. Proteins were extracted and digested with trypsin, and peptides were analyzed by liquid chromatography–tandem mass spectrometry. Data were processed using Proteome Discoverer™ for protein identification and label-free quantification. Results: A total of 4502 proteins were identified, of which 1848 (41%) were present across all stages. Pairwise comparisons between adjacent stages revealed clear transitions, the largest ones occurring between the culmination and fruiting body and between the fruiting body and vegetative stage, involving 29% and 52% of proteins, respectively. Hierarchical clustering assigned proteins to one of nine clusters, each displaying a distinct pattern of abundances across the life cycle. Conclusions: This study presents the first complete developmental proteomic time series for D. discoideum, revealing changes that contribute to multicellularity, cellular differentiation and morphogenesis. Full article

Selecting the relevant covariates in high-dimensional panel data remains a central challenge in applied econometrics. Conventional fixed effects and random effects models are not designed for systematic variable selection under model uncertainty. In addition, many existing models such as LASSO in machine learning or Bayesian approaches like model averaging, Bayesian Additive Regression Trees, and Bayesian Variable Selection with Shrinking and Diffusing Priors have been primarily developed for time series analysis. This paper develops and applies Bayesian Panel Variable Selection (BPVS) models to simulation and empirical applications. These models are designed to assist researchers in identifying which input covariates matter most, while also determining whether their effects should be treated as fixed or random through Bayesian hierarchical modeling and posterior inference, which jointly accounts for variable importance ranking. Both the simulation studies and the empirical application to socioeconomic determinants of subjective well-being show that Bayesian panel models outperform classical models, especially in terms of convergence stability, predictive accuracy, and reliable variable selection. Classical panel models, in contrast, remain attractive for their computational efficiency and simplicity. The Hausman test is used as a robustness check. The study adds an econometric approach for dealing with model uncertainty in high-dimensional panel analysis and offers open-source R 4.5.1 code to support future applications. Full article

Driver fatigue detection based on electroencephalography (EEG) signals has gained increasing attention for enhancing road safety. However, existing deep learning models often treat EEG data as generic time-series inputs, neglecting the inherent hierarchical and spatial–temporal structure of brain activity, which limits their interpretability and generalization. To address this, we propose a novel Residual Polynomial Network (RPN) that explicitly models the positive and negative activation patterns in EEG signals through a polarity-aware architecture. The RPN integrates polarity decomposition, residual learning, and hierarchical feature fusion to capture discriminative neurophysiological dynamics while maintaining model transparency. Extensive experiments are conducted on a real-world driving fatigue dataset using a subject-wise 10-fold cross-validation protocol. Results show that the proposed RPN achieves an average classification accuracy of 97.65%, outperforming conventional machine learning and deep learning baselines including SVM, KNN, DT, and LSTM. Ablation studies confirm the effectiveness of each component, and Sankey diagram analysis provides interpretable insights into feature-to-class mappings. This work not only advances the state of the art in EEG-based fatigue detection but also offers a more transparent and physiologically plausible deep learning framework for brain signal analysis. Full article

China’s coastal wetlands play a crucial role in maintaining biodiversity and providing essential ecosystem services. However, the absence of high-resolution wetland type maps poses substantial challenges for effective conservation and management. This study proposes a two-step classification framework that integrates pixel-based Random Forest algorithms with object-based hierarchical decision trees, utilizing Landsat-8 time-series imagery to generate a detailed wetland map comprising 10 wetland types and 5 non-wetland categories. The results reveal distinct spatial patterns along China’s coastline: freshwater wetlands and riverine systems dominate the northern regions, whereas southern coastal zones feature extensive tidal flats, aquaculture ponds, and mangrove ecosystems. The proposed method achieved an overall accuracy of 89.76% and a Kappa coefficient of 0.891, demonstrating its effectiveness for large-scale wetland mapping. This study provides robust technical support for the sustainable conservation and ecological management of coastal wetlands. Full article

Understanding the spatial and temporal structure of crustal deformation is essential for identifying tectonic blocks, assessing seismic hazard, and detecting precursory deformation associated with major megathrust earthquakes. In this study, we analyze twenty years of continuous GNSS observations from the Japanese Islands to identify coherent deformation domains and anomalous regions using an integrated time-dependent clustering framework. The workflow combines six machine learning algorithms (Hierarchical Agglomerative Clustering, K-means, Gaussian Mixture Models, Spectral Clustering, HDBSCAN and consensus clustering) and constructs a set of deformation-related features including steady-state velocities, strain rates, co-seismic and post-seismic displacements, and spatial distance metrics. Optimal cluster numbers are determined by validity metrics, and the most robust segmentation is obtained using a consensus approach. The resulting spatiotemporal domains reveal clear segmentation associated with major geological structures such as the Fossa Magna graben, the Median Tectonic Line, and deformation belts related to Pacific Plate subduction. The method also highlights deformation patterns potentially associated with the preparation stages of megathrust earthquakes. Our results demonstrate that machine learning-based clustering of long-term GNSS time series provides a powerful data-driven tool for quantifying deformation heterogeneity and improving the understanding of active geodynamic processes in subduction zones. Full article

Financial time-series labeling remains fundamentally limited by three critical deficiencies: temporal rigidity (fixed horizons regardless of market conditions), scale blindness (single-resolution analysis), and correlation-causation conflation. These limitations cause systematic failure during regime shifts. We introduce Adaptive Event-Driven Labeling (AEDL), integrating three core innovations: (1) multi-scale temporal analysis capturing hierarchical market patterns across five time resolutions, (2) causal inference using Granger causality and transfer entropy to filter spurious correlations, and (3) model-agnostic meta-learning (MAML) for adaptive parameter optimization. The framework outputs calibrated probability distributions enabling uncertainty-aware trading strategies. Evaluation on 16 assets spanning 25 years (2000–2025) with rigorous out-of-sample validation demonstrates substantial improvements: AEDL achieves average Sharpe ratio of 0.48 (across all models and assets) while baseline methods average near-zero or negative (Fixed Horizon: −0.29, Triple Barrier: −0.03, Trend Scanning: 0.00). Systematic ablation experiments on a 12-asset subset reveal that selective innovation deployment outperforms both minimal baselines and maximal integration: removing causal inference improves performance to 0.65 Sharpe while maintaining full asset coverage (12/12), whereas adding attention mechanisms reduces applicability to 2/12 assets due to compound filtering effects. These findings demonstrate that judicious component selection outperforms kitchen-sink approaches, with peak individual asset performance exceeding 3.0 Sharpe. Wilcoxon tests confirm statistically significant improvements over Fixed Horizon baseline (p = 0.0024). Full article

Predicting the Remaining Useful Life (RUL) of engine oil is critical for proactive maintenance and fleet reliability. However, irregular and noisy single-point sampling presents challenges for conventional prognostic models. To address this, a hierarchical physics-informed transfer learning (TL) framework is proposed that reconstructs nonlinear degradation trajectories directly from non-time-series data. The method uniquely integrates Arrhenius-type oxidation kinetics and thermochemical laws within a multi-level TL architecture, coupling fleet-level generalization with engine-specific adaptation. Unlike conventional approaches, this framework embeds physical priors directly into the transfer process, ensuring thermodynamically consistent predictions across different equipment. An integrated uncertainty quantification module provides calibrated confidence intervals for RUL estimation. Validation was conducted on 1760 oil samples from dump trucks, dozers, shovels, and wheel loaders operating under real mining conditions. The framework achieved an average R² of 0.979 and RMSE of 10.185. This represents a 69% reduction in prediction error and a 75% narrowing of confidence intervals for RUL estimates compared to baseline models. TL outperformed the asset-specific model, reducing RMSE by up to 3 times across all equipment. Overall, this work introduces a new direction for physics-informed transfer learning, enabling accurate and uncertainty-aware RUL prediction from uncontrolled industrial data and bridging the gap between idealized degradation studies and real-world maintenance practices. Full article

Accurate identification of tool wear conditions is of great significance for extending tool life, ensuring processing quality, and improving production efficiency. Current research shows that signals collected by a single sensor have limited dimensions and cannot comprehensively capture the degradation process of tool wear, while multi-sensor fusion recognition methods cannot effectively handle the complementarity and redundancy between heterogeneous sensor data in feature extraction and fusion. To address these issues, this paper proposes Hi-MDTCN (Hierarchical Multi-scale Dilated Temporal Convolutional Network). In the network, we propose a hierarchical signal analysis framework that processes the signal in segments. When processing intra-segment signals, we design a Multi-channel one-dimensional convolutional network with attention mechanism to capture local wear features at different time scales and fuse them into a unified representation. When processing signal segments, we design a Bi-TCN module to further capture long-term dependencies in wear evolution, mining the overall trend of tool wear over time. Hi-MDTCN adopts a dilated convolution mechanism, which can achieve an extremely large receptive field without building an overly deep network structure, effectively solving problems faced by recurrent neural networks in long sequence modeling such as gradient vanishing, low training efficiency, and poor parallel computing capability, achieving efficient parallel capture of long-range dependencies in time series. Finally, the proposed method is applied to the PHM2010 milling data. Experimental results show that the model’s tool condition recognition accuracy is higher than traditional methods, demonstrating its effectiveness for practical applications. Full article

The sustainability of apiculture within Mediterranean forest ecosystems is contingent upon the extent and health of melliferous tree habitats. This study outlines a five-year initiative (2020–2024) aimed at mapping and monitoring four principal honey-producing tree species—pine (Pinus halepensis and Pinus nigra), Greek fir (Abies cephalonica), oak (Quercus ithaburensis subsp. macrolepis), and chestnut (Castanea sativa)—across Evia, Greece. This is achieved through the utilization of high-resolution Sentinel-2 satellite imagery in conjunction with a hierarchical deep learning framework. Distinct from prior vegetation mapping endeavors, this research introduces an innovative application of a hierarchical framework for species-level semantic segmentation of apicultural flora, employing a U-Net convolutional neural network to capture fine-scale spatial and temporal dynamics. The proposed framework first stratifies forests into broadleaf and coniferous types using Copernicus DLT data, and subsequently applies two specialized U-Net models trained on Sentinel-2 NDVI time series and DEM-derived topographic variables to (i) discriminate pine from fir within coniferous forests and (ii) distinguish oak from chestnut within broadleaf stands. This hierarchical decomposition reduces spectral confusion among structurally similar species and enables fine-scale semantic segmentation of apicultural flora. Our hierarchical framework achieves 92.1% overall accuracy, significantly outperforming traditional multiclass approaches (89.5%) and classical ML methods (76.9%). The results demonstrate the framework’s efficacy in accurately delineating species distributions, quantifying the ecological and economic impacts of the catastrophic 2021 forest fires, and projecting long-term habitat recovery trajectories. The integration of a novel hierarchical approach with Deep Learning-driven monitoring of climate- and disturbance-driven changes in honey-producing habitats marks a significant step towards more effective assessment and management of four major beekeeping tree species. These findings highlight the significance of such methodologies in guiding conservation, restoration, and adaptive management strategies, ultimately supporting resilient apiculture and safeguarding ecosystem services in fire-prone Mediterranean landscapes. Full article

Urban land subsidence poses a significant threat in rapidly urbanizing regions, threatening infrastructure integrity and sustainable development. This study focuses on Mandalay, Myanmar, and presents a novel clustering framework—Dynamic Time Warping and Trend-based Longest Common Subsequence with Agglomerative Hierarchical Clustering (DTLCS-AHC)—to classify spatiotemporal deformation patterns from Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) time series derived from Sentinel-1A imagery covering January 2022 to March 2025. The method identifies four characteristic deformation regimes: stable uplift, stable subsidence, primary subsidence, and secondary subsidence. Time–frequency analysis employing Empirical Mode Decomposition (EMD) and Discrete Fourier Transform (DFT) reveals seasonal oscillations in stable areas. Notably, a transition from subsidence to uplift was detected in specific areas approximately seven months prior to the Mw 7.7 earthquake, but causal relationships require further validation. This study further establishes correlations between subsidence and both urban expansion and rainfall patterns. A physically informed conceptual model is developed through multi-source data integration, and cross-city validation in Yangon confirms the robustness and generalizability of the approach. This research provides a scalable technical framework for deformation monitoring and risk assessment in tropical, data-scarce urban environments. Full article

The kiln head temperature of a rotary kiln is a core process parameter in cement clinker production, and its accurate prediction coupled with uncertainty quantification is crucial for process optimization, energy consumption control, and safe operation. To tackle the prediction challenges arising from strong multi-variable coupling and nonlinear time series characteristics, this paper proposes a prediction approach integrating feature selection, heterogeneous model ensemble, and probabilistic interval estimation. Firstly, the Relief algorithm is adopted to select key features and construct a time series feature set with high discriminability. Then, a hierarchical architecture encompassing deep feature extraction, heterogeneous model fusion, and probabilistic interval quantification is devised. CNN is utilized to extract spatial correlation features among multiple variables, while BiLSTM is employed to bidirectionally capture the long-term and short-term temporal dependencies of the temperature sequence, thereby forming a deep temporal–spatial feature representation. Subsequently, RF is introduced to establish a heterogeneous model ensemble mechanism, and dynamic weight allocation is implemented based on the Mean Absolute Error of the validation set to enhance the modeling capability for nonlinear coupling relationships. Finally, Gaussian probabilistic regression is leveraged to generate multi-confidence prediction intervals for quantifying prediction uncertainty. Experiments on the real rotary kiln dataset demonstrate that the R² of the proposed model is improved by up to 15.5% compared with single CNN, BiLSTM and RF models, and the Mean Absolute Error is reduced by up to 27.7%, which indicates that the model exhibits strong robustness to the dynamic operating conditions of the rotary kiln and provides both accuracy guarantee and risk quantification basis for process decision-making. This method offers a new paradigm integrating feature selection, adaptive heterogeneous model collaboration, and uncertainty quantification for industrial multi-variable nonlinear time series prediction, and its hierarchical modeling concept is valuable for the intelligent perception of complex process industrial parameters. Full article

To address the challenges of low production efficiency, high energy consumption, and frequent equipment failures in low-producing wells, this study proposes an intelligent production parameter optimization method based on deep learning and multi-indicator fusion. First, a Long short-term memory (LSTM)-based prediction model was developed using the displacement–load characteristics of pumping-unit dynamometer cards. The results show that the model achieves an average prediction metric Q = 0.08, outperforming Back Propagation and Recurrent Neutral Network (BP and RNN) models. Second, a Convolutional Neutral Network (CNN) was employed to extract the fluid supply capability features, achieving a recognition accuracy exceeding 98%, thereby validating the model’s effectiveness. Combined with three types of time series data (liquid supply degree, dynamic liquid level, and liquid production rate), a multi-index fusion parameter optimization method is proposed. A comprehensive decision-making model is constructed based on the Analytic Hierarchy Process (AHP), which takes long-term, short-term, and overall feature changes as the basis, and forms a hierarchical framework by decomposing objectives and quantifying feature weights. The consistency ratio (CR) of the model is less than 0.1, meeting the requirement of logical consistency and enabling the output of standardized regulation suggestions. Consequently, a closed-loop system of “data preprocessing—condition prediction—state identification—parameter optimization” was constructed, enabling early dynamometer card prediction, accurate fluid supply fluctuation identification, and automatic generation of optimization schemes. This system effectively enhances production efficiency and equipment stability in low-producing wells, providing a technical foundation for intelligent oilfield development. Full article

To address the challenge of accurately predicting carbon price fluctuations, which are influenced by multiple factors, a multisource, spatiotemporal, federated learning framework with cross-modal feature fusion is proposed. Firstly, a three-level hierarchical federated learning network, consisting of perception clients, regional (edge) nodes, and a central server, is designed. The server incrementally aggregates the parameters generated by the local large model of the perception client through incremental data training, improving the efficiency of parameter aggregation in federated learning and avoiding the problem of network traffic data exposure. Secondly, a cross-modal, spatiotemporal, enhanced attention model is proposed. In order to extract the joint features of carbon price time series data and spatial correlation, spatiotemporal feature encoding is adopted. In order to share the semantic space of aligning market factors and carbon emission data in the embedding layer, cross-modal alignment is adopted. Finally, the experimental results demonstrate that the proposed framework can effectively predict carbon prices. Full article