Search Results (8,228)

Real-time seismic reservoir monitoring of geologic reservoirs requires both large-scale data management and efficient computational workflows. Addressing these challenges is facilitated by developing techniques capable of selectively capturing critical geologic features, thereby increasing computational efficiency and reducing data storage requirements. This paper proposes a double-compression framework integrating Haar wavelet transforms with machine learning (ML) for efficient multiparameter seismic inversion. First, Haar wavelet compression significantly reduces the dimensionality of the input elastic models, preserving essential geologic structures while limiting data volumes. Next, a convolutional neural network with the long short-term memory (CNN-LSTM) architecture, including dual encoders and multi-decoders, compresses seismic data into a latent space to generate a multi-scale P-wave velocity estimate. By leveraging transfer learning to speed up convergence and enhance prediction accuracy, we fine-tune the latent representation to estimate the P-to-S-wave velocity ratio and acoustic impedance at multiple resolution scales. Tests on the synthetic ${\mathrm{CO}}_2$-injection Kimberlina model show that wavelet-based compression—including detuning large-scale trends—minimizes artifacts in simulated wavefields and accelerates neural-network training. The results demonstrate that combining wavelet-based pre-compression for reservoir models with data-driven latent encodings for seismic data achieves high compression ratios, reduces computational costs, and maintains the fidelity of subsurface imaging. Compared with a redundant-decimation baseline, the proposed framework reduces network training time by approximately 70% and GPU memory usage by 33–73%, achieves a wavefield energy loss below 0.1% at a 16:1 model-dimension reduction, and produces multi-resolution predictions of $V_{\mathrm{P}}$, $V_{\mathrm{P}}/V_{\mathrm{S}}$, and acoustic impedance with normalized errors below 0.04 across all six wavelet decomposition levels. Thus, the double-compression framework enables robust and scalable seismic monitoring of elastic reservoir parameters. Full article

Load forecasting is no longer only a point-prediction problem for aggregate demand. In renewable-rich and electric-vehicle-intensive power systems, forecasts must support net-load balancing, charging-demand management, uncertainty-aware operation, and spatially coupled decision-making. This review presents a quantitative evidence map based on a curated DOI-linked corpus of 116 papers published between 1960 and 2026. Each paper is coded by dominant model family, application theme, forecast horizon, and frontier feature tags. Publication era and dominant model family are strongly associated (${\chi }^2\left(21\right)=93.69$, $p=3.70\times {10}^{-11}$, Cramérś $V=0.519$). Post-2020 studies are sharply enriched in transformer/graph-neural-network/foundation-model content (13/43 versus 0/73; Haldane-corrected odds ratio 65.07; Fisher $p=6.65\times {10}^{-7}$), electric-vehicle or charging themes (7/43 versus 0/73; odds ratio 30.21; $p=6.91\times {10}^{-4}$), and deep-learning content (14/43 versus 7/73; odds ratio 4.36; $p=2.76\times {10}^{-3}$). To address category coarseness, the frontier family is further decomposed into transformer-only, graph-neural-network-only, hybrid spatiotemporal, and foundation-model subfamilies. The central conclusion is that the most important forecasting topic for current electrical power systems is not generic short-term load forecasting, but the integrated forecasting stack required by electrified, renewable-rich, and spatially coupled grids. Full article

The Transformer architecture drives modern Artificial Intelligence (AI), yet the physical principles that may constrain self-attention training remain poorly characterized. We develop a thermodynamic framework for attention training, drawing on the established Boltzmann correspondence between softmax attention and equilibrium statistical mechanics, and we propose a First Law analogue that decomposes the training energy budget into a heat term (the entropic cost of ordering attention) and a work term (the gain in mutual information about the target). From this framework we derive a Landauer-type bound on learning, which states that the loss reduction during training is bounded below by the entropic cost of structuring attention against thermal noise. The bound is satisfied across all configurations tested: 625 grid points spanning three datasets on a compact Vision Transformer trained from scratch (MNIST, CIFAR-10, and OrganAMNIST), and ten temperatures on a pretrained ViT-Small fine-tuned on Food-101. Reusing the same physical principles at inference time, we show that the thermodynamic work performed by each input patch provides a quantitative, energy-based measure of feature importance that outperforms standard attention weights and Integrated Gradients on ImageNet across pretrained ViT-Small, ViT-Base, and ViT-Large (22M to 304M parameters). The result is an integrated diagnostic framework that links phase structure, training-time bounds, and inference-time attribution within a single empirically falsifiable thermodynamic apparatus. Full article

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have achieved significant success in various computer vision applications, including the classification of high-resolution imagery. However, a notable limitation of these deep learning approaches is their tendency to inadequately preserve the precise edges and shapes of target objects. In contrast, Object-Based Image Analysis (OBIA) offers a methodology that emphasizes the preservation of object boundaries by segmenting images into meaningful objects. Combining CNNs and ViTs with OBIA leverages the feature extraction capabilities of these deep learning algorithms and the boundary-preserving advantages of OBIA, leading to enhanced classification accuracy and improved delineation of object boundaries in high-resolution images. Still, the main challenge for combining these methods lies in effectively aligning the irregularly shaped image objects produced by OBIA with the regular image patches required by CNNs and ViT architectures. In this study, we propose a novel approach that integrates superpixel segmentation with CNNs and ViTs for the automatic classification of benthic habitats using high-resolution orthomosaic images. Initially, the Simple Linear Iterative Clustering (SLIC) algorithm was applied to segment the high-resolution orthomosaic images into superpixels. Subsequently, the central points of the resulting superpixels were utilized to generate square image patches. These patches performed as inputs for ConvNeXt-Base and EfficientNet-B0 pre-trained CNNs to extract fine-grained features and Dinov2 ViTs to extract high-level features. Then, a Support Vector Machine (SVM) classifier was trained using these attributes to classify benthic habitats. Eventually, the classification label derived from the SVM defined the class of each superpixel segment. This method achieved an average overall accuracy of 0.96 in classifying benthic habitats. Overall, we demonstrate that combining CNNs, ViTs, and superpixel segmentation is an effective approach to benthic habitats classification, providing accurate high-resolution maps of heterogeneous reef environments. Full article

The flexible job shop scheduling problem (FJSP) is a typical combinatorial optimization problem in smart manufacturing. Although existing methods have considered machine competition relationships, they lack explicit structured modeling of machine competition relationships induced by candidate operations and are not systematically integrated across state representation, representation learning, and decision-making processes. To address this, this paper proposes a competition-aware dual-attention deep reinforcement learning method. We construct a dynamic heterogeneous graph representation, where machine competition is modeled as state-dependent edges instantiated via a 3D competition tensor, transforming machine competition relationships into structured information, thereby enhancing the model’s ability to characterize complex resource competition patterns. On this basis, we have designed the Competition-Aware Dual-Attention Network (CADAN), which injects competition information into both the attention computation and representation learning processes via a dual-path mechanism, enabling more expressive modeling of machine competition relationships, and which introduces a head-wise competition bias to capture heterogeneous competition patterns. Furthermore, we have developed an adaptive decision head to refine the scores of candidate actions. Our experimental results demonstrate that the proposed method outperforms classical dispatching rules and achieves competitive or superior performance compared with representative evolutionary and learning-based methods on synthetic datasets, public benchmark datasets, and a real-world industrial machining scenario involving mechanical transmission components. Full article

Fluid-dynamic drag accounts for a substantial fraction of energy consumption across air, ground, and maritime transport systems, making its reduction a critical lever for decarbonizing mobility. While active flow control (AFC) strategies have demonstrated significant drag reduction potential, their design remains constrained by heuristic physical assumptions about dominant flow structures. Recent developments in deep reinforcement learning (DRL) have emerged as a transformative paradigm, capable of autonomously discovering control strategies in high-dimensional turbulent environments. This perspective traces the evolution of drag reduction approaches from classical passive and active control approaches toward data-driven methods based on DRL. A particularly promising direction is the integration of explainable artificial intelligence (XAI) with DRL, which provides physically interpretable information about flow regions associated with drag generation and guides the learning process toward physically meaningful actuation schemes. As a result, XAI-guided DRL controllers have been shown in canonical configurations to achieve comparable or improved drag reduction with substantially lower actuation power than controllers trained directly for drag minimization. This transition from opaque optimization toward flow control informed by dynamical causal relationships represents a key step for the development of energy-efficient and sustainable flow-control solutions for transport systems. Full article

Origin–destination (OD) matrix forecasting is essential for urban railway operations because it enables simultaneous understanding of the direction and magnitude of passenger flows. However, OD matrices in large-scale subway networks are difficult to predict owing to their high dimensionality and sparsity, and existing approaches often rely on station-level predictions or complex structural designs. This study addresses the offline full-matrix OD forecasting problem, where complete historical OD sequences are available at prediction time, and proposes Metro-GATF, a spatiotemporal forecasting framework that jointly models railway topology and dynamic OD interactions. The model employs a GATv2-based spatial encoder to learn static inter-station relationships and encodes time-varying interactions using sparse OD graphs. A non-autoregressive transformer decoder generates future multi-step node representations in parallel, whereas origin–destination factorization and sparsity-aware gating are used to reconstruct the full OD matrix. Experiments on minute-level AFC-based OD data from a 637-station metropolitan subway network demonstrated that Metro-GATF achieved the lowest sMAPE among the compared full-matrix models. These results indicate that the proposed framework effectively captures complex spatiotemporal OD patterns and offers a practical end-to-end framework for forecasting urban railway demand. Full article

This study examines individualized teaching, personalized learning, and adaptive learning within the framework of constructivist pedagogy in higher education. The aim is to systematically analyze and map conceptual and empirical literature published between 2019 and 2026 to identify dominant research trends, methodological approaches, and key findings related to student-centered instructional models. A systematic mapping review was conducted using a structured research matrix aligned with PRISMA guidelines to map and compare existing studies on the selected concepts. The analysis focused on how individualized, personalized, and adaptive approaches are operationalized in higher education practice and how they contribute to student-centered learning environments. The findings indicate that although these approaches are widely discussed in the literature, they are often conceptually fragmented and inconsistently defined across studies. Several research gaps were identified, particularly regarding the integration of technological and pedagogical dimensions and the lack of coherent conceptual frameworks that connect the three approaches. Based on a synthesis of the findings, the study proposes directions for future research and suggests developing a more integrated conceptual orientation for student-centered teaching in higher education. Building on these patterns, the Transformative-Dynamic Learning and Teaching Approach (TDLTA) is introduced as a potential framework for further theoretical refinement and empirical validation. Full article

In the epistemological constellation of contemporary visual education, web comics establish themselves as semiotic devices with a dual pedagogical value. On the one hand, they represent the advanced synthesis of multimodal codes (visual, textual, sound); on the other, they structure participatory learning environments typical of digital culture. This contribution starts from the theoretical premise that digital comic narratives, in their hypertextual and algorithmic essence, constitute true liminal spaces where ontological meanings are negotiated and transformative visual literacy skills are developed. The research, conducted according to the methodological paradigm of design-based research and rooted in the framework of multimodal social semiotics, demonstrates how sequential narrative structure and visual metaphors reduce cognitive load, through scaffolding. The interactive mechanisms typical of the medium (comments, sharing, remixes) promote an aesthetic of participation, transforming students from passive users to producers of knowledge, according to the principles of connected learning. The analysis focuses on: Panda Likes Bevilacqua, Totally Unnecessary Comics by Leone; Rossoni’s Rouge Worms, Lele Corvi’s strips, Natangelo’s cartoons. The limitations of the study lie in the limited sample and its preliminary nature, as it analyses the device itself without evaluating its implications in the classroom or the professional skills (TPACK) necessary for teachers. Large-scale teaching feasibility remains to be investigated in future applied experiments, which involve the direct involvement of classes and teachers. From a pedagogical perspective, web comics are effective teaching tools for students with ASD. Their community-based nature requires a recalibration of traditional pedagogical frameworks towards more ecological approaches. Full article

Detecting previously unseen malware remains a critical challenge for modern cybersecurity systems due to the rapid evolution of malicious software and the limitations of traditional supervised detection models. This paper proposes a Dual-Channel Mamba-Based Semantic–Behavioral Feature Learning framework for zero-day malware detection that jointly models static malware artifacts and dynamic execution traces within a unified representation space. The proposed architecture employs two parallel encoders that extract semantic features from executable structures and behavioral features from API call sequences. These features are integrated through a cross-channel fusion mechanism and processed using a Mamba-based selective state space architecture, which efficiently captures long-range dependencies in malware behavior while maintaining linear computational complexity. To address the zero-day detection problem, a prototype-guided inference strategy is introduced that enables similarity-based classification of previously unseen malware families within the learned embedding space. Extensive experiments conducted on multiple malware datasets demonstrate that the proposed framework consistently outperforms strong deep learning baselines. The model achieves an average classification accuracy of 96.01% ± 0.35 and an F1-score of 95.56% ± 0.36, while the zero-day detection rate reaches 88.93% ± 0.98, significantly improving detection performance compared with transformer and recurrent architectures. Runtime analysis further shows that the proposed framework achieves efficient inference with an average latency of approximately 8 ms per sample, making it suitable for real-time malware analysis systems. These results indicate that combining dual-channel feature learning with Mamba-based sequential modeling provides an effective and scalable solution for detecting both known and previously unseen malware threats. Full article

Background/Objectives: Although deep learning methods have achieved promising performance in recent years, comparatively less attention has been given to explicitly modeling periodic and multi-scale temporal dynamics for ECG-specific representation learning within TimesNet-based frameworks. In this work, we propose an ECG-specific TimesNet-based framework for multi-label classification of multi-lead ECG recordings that incorporates periodicity-aware temporal modeling. Methods: The proposed framework utilizes Fast Fourier Transform (FFT)-guided temporal decomposition to identify dominant frequency components and reshapes ECG sequences into period-aligned representations to better capture intra-period morphological patterns and inter-period rhythm dependencies. Multi-scale convolutional TimesBlocks are further employed to learn rhythm-aware and morphology-aware temporal representations. Results: The proposed framework was evaluated on the PTB-XL dataset using two experimental settings: Three-Class classification (NORM, AFIB, PVC) and Five-Class classification (NORM, AFIB, MI, PVC, STTC). Experiments were conducted using a one-vs-rest multi-label learning strategy with independent class probability estimation. The framework achieved mean one-vs-rest test AUC values of 0.956 and 0.913 for the Three-Class and Five-Class settings, respectively. Experimental results indicated that the reduced classification complexity in the Three-Class setting was associated with improved feature separability, more stable decision boundaries, and enhanced discriminative representation learning. Latent-space visualization using UMAP and PCA demonstrated clearer clustering in the Three-Class configuration, while gradient-based interpretability analysis highlighted physiologically relevant ECG waveform regions contributing to model predictions. In addition, computational profiling demonstrated practical feasibility with approximately 1.957 million trainable parameters, 13.14 GFLOPs computational complexity, 5.230 ms average inference latency per ECG recording, and a throughput of approximately 191 ECG recordings per second on GPU hardware. Conclusions: These findings suggest that periodicity-aware temporal modeling can improve ECGF representation learning while demonstrating practical potential for computationally efficient and interpretable automated ECG analysis applications. Full article

Vessel traffic service systems in remote or newly established maritime regions face significant operational limitations due to the scarcity of historical AIS data, which undermines the reliability of trajectory-based situational awareness and collision risk assessment. Existing deep learning models, predominantly validated on data-rich major shipping corridors, suffer severe performance degradation under cross-domain deployment, rendering them impractical for vessel traffic management in underserved waters. To bridge this operational gap, this study proposes a Boundary-Aware Distillation and LoRA-Based Transfer (BD-LT) framework that enables reliable trajectory prediction with as few as 132 target-domain trajectories. The framework integrates HDBSCAN-based geographic-semantic domain partitioning, a Time-Aware Transformer with Time2Vec encoding for irregular AIS sampling, hybrid knowledge distillation with error-boundary gating for selective cross-domain transfer, and LoRA-based parameter-efficient adaptation to mitigate overfitting. Validated on NOAA full-scale AIS measurements, the framework reduces the 60 min Final Displacement Error by 35.2% relative to the no-framework baseline, consistently outperforming state-of-the-art models across all prediction horizons, with statistical reliability confirmed via bootstrap resampling. These results demonstrate the practical feasibility of deploying data-driven trajectory prediction in maritime regions where conventional approaches have historically been inapplicable, with direct implications for collision avoidance decision support and port approach traffic management. Full article

Accurate reservoir-fluid identification and water saturation prediction are essential for remaining-oil evaluation and water-flooding adjustment in heterogeneous oilfields. However, in the Youshashan Oilfield, southwestern Qaidam Basin, China, thin interbeds, strong reservoir heterogeneity, complex oil–water transitions, and inter-well logging-response differences make conventional single-task interpretation difficult. To address these problems, this study proposes a joint prediction method based on the Youshashan Fluid Prediction Network (YSF-Net) for six-class reservoir-fluid identification and continuous water saturation (S_w) prediction. A total of 200 wells were used and strictly divided by well into 140 training wells, 30 validation wells, and 30 independent test wells to avoid data leakage. Conventional logs were first processed through depth matching, outlier correction, robust standardization, and missing-value masking. Then, sliding-window logging sequences, regional stratigraphic embeddings, and reservoir-prior parameters, including shale volume, porosity, and permeability, were jointly input into the YSF-Net. The model uses a shared feature encoder with classification and regression branches to simultaneously identify oil layers, oil–water layers, water layers, and weakly, moderately, and strongly water-flooded layers, while predicting continuous S_w. A modified Simandoux-based physical consistency constraint was further introduced during training to improve the geological rationality of S_w prediction. Experimental results show that YSF-Net outperforms the CNN, BiLSTM, CNN-BiLSTM, and Transformer. It achieves an Accuracy of 0.926, Macro-F1 of 0.913, Macro-AUC of 0.968, S_w RMSE of 0.061, S_w MAE of 0.047, and S_w R² of 0.947. In direct cross-well testing without fine-tuning, YSF-Net obtains a Cross-well Accuracy of 0.918, Cross-well Macro-F1 of 0.904, and Cross-well S_w RMSE of 0.064. Ablation, transition-boundary, and typical well-interval analyses further demonstrate that regional constraints, reservoir-prior inputs, multi-task learning, and physical consistency improve class-boundary discrimination and S_w prediction reliability. The proposed method provides an accurate, consistent, and practical workflow for intelligent reservoir-fluid interpretation in heterogeneous reservoirs. Full article

This paper introduces a hardware/software co-designed 3D object detection pipeline based on the PointPillars architecture for low-power embedded MPSoC deployment. The proposed system accelerates the computationally intensive stages in programmable logic (PL), including ROI filtering, coordinate transformation, pillarization, centroid extraction, and INT8 neural inference, using Vitis high-level synthesis (HLS) and an integrated Deep Learning Processing Unit (DPU). Control-oriented and irregular operations, such as data acquisition, Direct Memory Access (DMA) control, lightweight Non-Maximum Suppression (NMS), visualization, and logging, remain on the processing system (PS). The design targets the AMD Kria KV260 platform and achieves an accelerated core pipeline latency of 11.4 ms per frame at 300 MHz, corresponding to 87.4 Hz throughput, with 6.842 W board-level power consumption. Including PS-side NMS, the practical end-to-end latency is approximately 12.2 ms for typical KITTI scenes. Compared with existing Field-Programmable Gate Array (FPGA)-based implementations implementations, the proposed design reduces latency by up to $33\times$. It achieves a $202\times$ improvement in on-chip BRAM efficiency across HLS optimization versions through FIFO streaming, dataflow execution, and array partitioning. Experimental validation on physical hardware confirms that the proposed PL-accelerated hardware/software co-design provides a practical and cost-effective solution for real-time 3D LiDAR perception on embedded FPGA platforms. Full article

This study examines whether decomposition-based deep learning forecasts of daily changes in natural rubber prices can appear directionally accurate while failing to preserve the dispersion of the target series—a failure mode that conventional accuracy metrics cannot detect. Using daily RSS3 FOB price changes in the period 2018–2026, a VMD-Augmented BiLSTM forecasting design is employed as the empirical vehicle for testing this question. Forecasts are evaluated jointly through Pearson correlation, directional accuracy, class-conditional recall, and the Standard Deviation Ratio (StdR), with StdR serving as a diagnostic for variance collapse on differenced series. The deployed model appends all Variational Mode Decomposition (VMD) components directly to the economic feature matrix and feeds the augmented sequence into a bidirectional LSTM encoder with temporal attention; VMD is fitted using an expanding-window procedure to prevent information leakage. The design is compared to a conventional per-IMF decomposition–forecast pipeline, a Vanilla LSTM, ARIMA(2,0,2), and a dual-pathway BiLSTM–Transformer control. On a 175-observation deduplicated test set, the deployed model attains Pearson correlation of $r=0.821\pm 0.016$, directional accuracy of $82.5\%\pm 1.8\%$, and StdR $=1.091\pm 0.060$ across five random seeds. The Vanilla LSTM baseline attains directional accuracy of $82.29\%\pm 0.00$—statistically indistinguishable from that of the deployed model—yet exhibits variance collapse (StdR $=0.210\pm 0.007$), confirming that DA alone cannot distinguish predictive skill grounded in conditional dynamics from forecasts that merely reproduce the unconditional sign distribution. The principal contribution is methodological: A variance-sensitive evaluation protocol that distinguishes forecast skill grounded in conditional dynamics from directional but underdispersed predictions, demonstrated across three empirically distinct mechanisms by which variance collapse arises in this setting. Full article