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Cognitive Learning Analytics (CLA) is an interdisciplinary domain that combines cognitive science and learning analytics to interpret and enhance human learning through theoretically grounded data analysis. It integrates learning analytics with models of cognition to support theoretically grounded interpretation of learner data. Learning analytics, since its inception in 2011, has developed as a research field and applied practice, focusing on “the measurement, collection, analysis, and reporting of data about learners and their contexts.” It focuses on understanding and optimizing learning processes and environments by leveraging large-scale, multimodal educational data. Cognitive science, in parallel, provides established theories of human learning, memory, attention, and metacognition. CLA links observable behaviors with theoretically defined cognitive mechanisms. Through the integration of cognitive theories and computational techniques, CLA models how learners process information, make decisions, and construct knowledge in digital learning environments. CLA employs diverse data sources—including clickstream logs, eye tracking, biometric signals, and linguistic traces—to infer learners’ cognitive and affective states. These inferences inform adaptive learning systems, personalized feedback mechanisms, and intelligent tutoring tools that respond dynamically to the learner’s mental workload, engagement, or metacognitive strategies. Full article

With the scale of new energy access expanding, the proportion of nonlinear loads in the power grid has increased, leading to frequent impact disturbance events. The types of power quality disturbances (PQDs) are becoming increasingly complex, placing greater demands on the accurate identification of disturbance signals. Therefore, this paper proposes a PQD recognition method based on adaptive KS transform and a Multimodal Feature Fusion Network (MFNet). Firstly, using an improved red-billed blue magpie optimization algorithm, the traditional KS transform window function parameters are adaptively optimized to achieve accurate time–frequency localization of PQD. Secondly, considering the differential characteristics of PQDs in different modes, combined with the proposed adaptive KS transform, a parallel MFNet with three branches in the time domain, frequency domain, and time–frequency domain is constructed; to further enhance feature extraction capability and reduce information loss, residual structures are introduced in the network. Multiple comparative experimental results show that the proposed method achieves an average classification accuracy of 99.52% at 20 dB of noise and demonstrates good noise resistance. Full article

The steady operation of underground gas storage (UGS) is significant for securing national energy. However, long-term cyclic injection-production operation causes the dynamic changes in formation stress, potentially leading to fault reactivation and slippage. This could affect the seal performance of the fault zone and cause disastrous consequences. In this paper, a mechanical analysis model for fault slip is constructed to study the dynamic seal performance in response to long-term injection-production cycles. An intelligent approach is proposed to predicate the fault slip value based on machine learning algorithms. It can realize long-term prediction of fault slip value under a new condition of injection-production operation. The study shows that (1) formation pressure tends to accumulate near the fault zone due to the low permeability, and the interface of the reservoir layer, cap layer, and fault zone is the seal weak position of UGS; (2) the response of fault slip is driven by the injection-production rate and the reservoir pressure. There is a significant coupling relationship between the fault slip value and the accumulated injection gas volume; (3) the intelligent prediction approach can capture the nonlinear dynamic characteristics of slip tendency accurately, and it exhibits good prediction performance and generalization ability under the new operating condition. This study effectively assesses the dynamic risk for fault slip of depleted hydrocarbon reservoir UGS during the long-term injection-production procedure. It provides an effective technical approach for fault slip tendency analysis and injection-production process optimization, which is important for the sustainable operation of UGS reducing the risk of seal failure and supporting gas storage security. Full article

Maintaining high power delivery under uncertain landing positions is a key challenge for wireless charging of unmanned aerial vehicles (UAVs). This paper presents a data-driven power optimization method based on the Soft Actor-Critic algorithm for multi-transmitter single-receiver wireless power transfer (MTSR-WPT) systems. To support effective learning without explicit online parameter identification, a physics-informed dual-current state representation is constructed from measurable current responses, combining a zero-phase current with the current response under the applied phase command. The agent is trained using a reward defined directly from normalized load power, and the transmitter voltage phases serve as the control actions. In simulations of a five-transmitter system, the learned policy achieves about 97% of the theoretical maximum power in the training region and about 96% in the expanded evaluation region. Additional robustness studies show strong performance under moderate measurement noise and substantial recovery under model mismatch after short fine-tuning. Experimental validation on a physical prototype confirms the effectiveness of the method, yielding an average power improvement of 188% from a zero-phase baseline and reaching 87% of the maximum power measured on the hardware platform. These results support the proposed method as a practical data-driven alternative to model-dependent MTSR-WPT power optimization for UAV wireless charging. Full article

Photocatalytic CO₂ reduction to high-value-added C₂+ products is a practical route from an economic viewpoint for advancing the industrialization of CO₂ conversion. Despite significant progress in catalyst modification in recent years (such as defect engineering, heterostructure construction, and single-atom modification), the generation of C₂+ products still faces challenges due to the slow kinetics of multi-electron reactions and the high thermodynamic barrier for C-C coupling. Moreover, the severely imbalanced molar ratio of CO₂ to H₂O in the traditional liquid-phase reaction systems exacerbated the challenge to the unfavorable situation. This article summarizes various strategies to improve the yield of C₂+ products through the regulation of reaction environments, including: (1) increasing the partial pressure of CO₂ to enhance its solubility; (2) using alternative solvents like ionic liquids to reduce water content; (3) transitioning the reaction system from liquid phase to gas phase; (4) designing a three-phase (gas–liquid–solid) interface or floating photocatalysts to optimize reactant transfer and local concentration; (5) utilizing photothermal synergistic effects to enhance the reaction temperature and efficiency under concentrated light. It also discusses the role of different reactor designs in improving the reaction environment. Finally, it emphasizes that future research should pay more attention to the optimization of the reaction environment engineering in addition to catalyst design, providing new perspectives for achieving efficient and highly selective C₂+ products in CO₂ photoreduction. Full article

The stable and accurate output of the inertial measurement unit array (IMU) of a micro-electro-mechanical system (MEMS) is the key to ensuring the data fusion of the MEMS IMU array. However, due to the large number of MEMS IMUs contained in the MEMS IMU array, it is susceptible to interference and has difficulty avoiding failures. The output of the MEMS IMU contains noise, outliers, and other related errors, which can seriously lead to low fault detection and isolation accuracy in the MEMS IMU. In this study, a new method of fault detection and isolation based on multivariate variational mode decomposition (MVMD), a whale optimization algorithm (WOA), and a generalized likelihood test (GLT) is proposed, which is called WOA-MVMD-GLT. Firstly, a multi-index fitness function WOA is proposed to optimize the parameters of MVMD. Secondly, MVMD is used to extract the features of the MEMS IMU’s signals. Finally, a GLT is used to construct a fault detection function and a fault isolation function to detect and isolate the faults of gyroscopes and accelerometers. The experimental results show that the method proposed in this paper can significantly reduce the false alarm rate and false isolation rate. Full article

Accurate and stable acquisition of the spatial distribution of soybean planting areas is essential for supporting precision agricultural monitoring and ensuring food security. However, crop remote-sensing mapping for specific regions still faces critical data bottlenecks: high-precision, large-scale pixel-level annotation is costly, resulting in scarce available labeled samples that make it difficult to construct large-scale training datasets. Although parameter-intensive models such as FCN and SegNet can achieve sufficient end-to-end training on large-scale public remote sensing datasets like LoveDA, when directly applied to the data-limited dataset in this study area, the models are prone to overfitting, leading to a significant decline in generalization ability. To address these issues, this study proposes a lightweight U-shaped semantic segmentation model, SimSDRU-Net. The model utilizes a pre-trained VGG-16 backbone to extract shallow texture and deep semantic features. The pre-trained weights mitigate the impact of overfitting in data-limited settings. In the decoding stage, a parameter-free lightweight SimAM attention module enhances effective soybean features and suppresses soil background redundancy, while an embedded S-DRU unit fuses multi-scale features for deep complementary reconstruction to improve edge detail capture. A label dataset was constructed using Sentinel-2 images as the data source and Menard County (USA) as the study area. The USDA CDL was used as a foundation for the dataset, with Google high-resolution images serving as visual interpretation aids. In the context of the experiment, Deeplabv3+ and U-Net++ were compared with U-Net under identical conditions. The results demonstrated that SimSDRU-Net exhibited optimal performance, with MIoU of 89.03%, MPA of 93.81%, and OA of 95.96%. Specifically, SimSDRU-Net uses the SimAM attention module to generate spatial attention weights by analyzing feature statistical differences through an energy function, so as to adaptively enhance soybean texture features. Meanwhile, the S-DRU unit groups, dynamically weights, and cross-branch reconstructs multi-scale convolutional features to preserve fine boundary details and achieve accurate segmentation of soybean plots. The present study demonstrates that SimSDRU-Net integrates lightweight design and high precision in data-limited scenarios, thereby providing effective technical support for the rapid extraction of soybean planting areas in North America. Full article

Today, distributed optical fiber sensors are widely used in structural health monitoring due to their high sensitivity and long-distance applicability. However, when embedded in pavement structures, distributed optical fiber sensors are always installed in a slotted buried fashion, which not only affects current pavement durability but also reduces pavement construction efficiency. In order to design clear requirements of in situ-embedded distributed optical fiber sensors for pavement construction, this study analyzes the micro-mechanical behavior of optical cables under the ultimate pavement compaction state based on a coupled DEM-FDM approach. According to the study results, it is found that when the pavement subbase was compacted, the maximum contact force of 13.2 mm aggregates in the Z-direction exceeds 150 N, which is the main resistance of the external load during pavement construction. The tight-buffered optical cable without reinforcement element and armored layer cannot withstand the vibration load. The inclusion of GFRP strengthening components and an armored layer decreased maximum stress by 38.2% (X), 30.6% (Y), and 30.9% (Z), as well as displacement by 64.6% (X), 45.5% (Y), and 66.7% (Z). Additionally, the thickness of the outer sheath enhanced the ability to withstand tension but not compression. The increase in the thickness of the armored layer can improve the ability to withstand tension and compression. Full article

Accurate extraction of crop planting structures is important for crop area and yield estimation, but complex and fragmented cropping patterns with overlapping phenology in the Hetao Irrigation District hinder reliable crop discrimination. This study proposes a hierarchical workflow that integrates vegetation masking with multi-source feature optimization for crop mapping. First, dual-temporal Sentinel-2 imagery (May and August) is used to generate a vegetation region-of-interest(ROI) mask via Otsu thresholding applied to the Normalized Difference Vegetation Index (NDVI), combined with pixel-wise maximum-value fusion to reduce phenology-driven omissions and background interference. Second, within the vegetation mask, Sentinel-2 spectral, vegetation-index, and texture features are combined with Sentinel-1 synthetic aperture radar (SAR) backscatter and SAR texture features to construct a multi-source feature set. Random Forest(RF) feature-importance ranking is used to select an effective feature subset, and four classifiers (RF, support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and convolutional neural network (CNN)) are compared under the same training/validation setting. The vegetation extraction achieves an overall accuracy of 91% (Kappa = 0.80). Using Sentinel-2 features only, the optimized subset with CNN attains the best performance (overall accuracy = 95%, Kappa = 0.93). Adding Sentinel-1 SAR texture features provides an additional improvement (overall accuracy = 96%, Kappa = 0.94), particularly for classes prone to confusion in fragmented plots. Area proportions derived from the final map are consistent with statistical yearbook data (percentage errors: maize 3.45%, sunflower 2.66%, wheat 0.11%, tomato 0.92%) under the study conditions. This workflow supports practical crop-structure monitoring in complex irrigation districts. Full article

Addressing the thermal management challenges of prismatic aluminum shell lithium battery modules in electric vehicles under high-rate charge–discharge conditions, this study proposes a multi-objective optimization design method for integrated biomimetic liquid cooling plates. By integrating various highly efficient heat transfer structures from nature, including fractal-tree-like networks, leaf vein branching systems, and spider web radial distribution, a novel biomimetic liquid cooling plate topology was constructed. A multi-physics coupled numerical model considering electrochemical heat generation, thermal conduction, convective heat transfer, and thermal stress deformation was established. The NSGA-II algorithm was employed to globally optimize 12 design variables including channel geometric parameters, operating conditions, and structural dimensions, achieving collaborative optimization objectives of maximum temperature minimization, temperature uniformity maximization, pressure drop minimization, and structural lightweighting. The weight coefficients for the four optimization objectives were determined through the Analytic Hierarchy Process (AHP) with verified consistency (CR = 0.02 < 0.10), ensuring rational priority allocation aligned with automotive safety standards. The optimization results demonstrated that compared to the initial design, the optimal solution reduced the maximum temperature under 3C discharge conditions by 9.9% to 34.7 °C, decreased the temperature difference by 31.3% to 3.3 °C, lowered the pressure drop by 24.6% to 2150 Pa, reduced structural mass by 4.0%, and decreased maximum stress by 16.7%. Quantitative comparison with single biomimetic structures under identical boundary conditions showed that the integrated design achieved a 3.3% lower maximum temperature and 25.7% better flow uniformity than the best-performing single structure, demonstrating the synergistic advantages of multi-biomimetic integration. These synergistic performance improvements can be attributed to the hierarchical multi-scale architecture where fractal networks provide macro-scale flow distribution, leaf vein branches ensure meso-scale coverage, and spider web radials achieve micro-scale thermal matching. Long-term cycling tests conducted at 1C/1C rate with 25 ± 1 °C ambient temperature showed that the optimized design maintained a capacity retention rate of 92.3% after 1000 charge–discharge cycles, demonstrating excellent durability. The complex biomimetic channel structure can be fabricated using selective laser melting technology with minimum feature sizes below 0.3 mm, indicating promising manufacturing feasibility. The research findings provide theoretical guidance and technical support for the engineering design of high-performance battery thermal management systems. Full article

Industrial digitalization reshapes production processes and can potentially improve carbon productivity by optimizing factor allocation and energy efficiency. Using panel data for 30 Chinese provinces from 2012 to 2022, this study constructs a comprehensive industrial digitalization index with four dimensions and 13 indicators using the entropy method and examines its impact on carbon productivity (GDP per unit of CO₂ emissions). We employ the Dagum Gini coefficient and kernel density estimation to describe regional disparities and their evolution, a dynamic panel threshold model to test the nonlinear role of industrial transformation and upgrading, and a spatial Durbin model to identify spatial spillover effects. The results indicate that industrial digitalization has risen nationwide but remains uneven; industrial digitalization significantly enhances carbon productivity, with stronger effects in the eastern and western regions and in plain areas; the effect exhibits a double-threshold pattern with respect to industrial transformation and upgrading, implying a U-shaped relationship; and industrial digitalization generates positive spatial spillovers. These findings suggest that policy should coordinate digital infrastructure investment with industrial upgrading and regional collaboration to accelerate low-carbon, high-efficiency growth. Full article

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications. Full article

With the increasing emphasis and protection on forest resources worldwide, the development of non-wood plant fiber raw materials has become a key path to promote the green and sustainable development of China’s pulp and paper industry. In this study, Super-Arundo donax, a new non-wood fiber raw material, was systematically investigated for its applicability in the bleaching process. Firstly, by adjusting key bleaching technical variables such as alkali dosage, time, oxygen pressure and temperature, the oxygen delignification process of the Super-Arundo donax kraft pulp was optimized. The data revealed that under the experimental conditions of 3.0% alkali dosage, 60 min bleaching time, 100 °C bleaching temperature, 0.6 MPa oxygen pressure and 0.6% MgSO₄ dosage, the bleached pulp yield reached 91.58%, the brightness was 42.04% ISO, and its tensile index was 60.92 N·m/g, bursting index was 4.16 kPa·m²/g, and tear index was 5.45 mN·m²/g, respectively. To further enhance the bleaching effect, the study introduced the H₂O₂ enhanced oxygen delignification process. The alkali dosage, bleaching temperature and H₂O₂ dosage were selected as the process parameters, with the pulp yield and brightness as the response indicators. A central composite design was adopted to construct a response surface model, and the interaction effects among various factors were analyzed. The optimized optimal process conditions are as follows: pulp concentration 10%, alkali dosage 2.84%, bleaching temperature 105 °C, H₂O₂ dosage 4.85%, bleaching time 60 min, MgSO₄ dosage 0.6%. Under these conditions, the pulp yield was 89.76% and the brightness reached 53.85% ISO. Therefore, Super-Arundo donax possesses excellent pulp-making and papermaking properties, and is expected to serve as a high-quality non-wood fiber raw material to alleviate the pressure on traditional papermaking raw materials and contribute to the green, sustainable and low-carbon transformation of the pulp and paper industry. Full article

The large-scale grid integration of distributed renewable energy enhances the flexible regulation capacity of the power system. However, the inherent randomness and volatility of its output, coupled with weak coupling access characteristics, pose severe challenges to the safe and stable operation of the power system. To address these issues, this paper proposes a power system planning method suitable for urban power grids. To accurately characterize the uncertainty of renewable energy output, the method incorporates the concept of multi-scenario stochastic optimization and introduces a dynamic scenario generation method for wind and solar power based on nonparametric kernel density estimation and standard multivariate normal distribution sequence sampling. This method generates a set of typical daily dynamic output scenarios for wind and solar power that closely match actual output characteristics. Considering the spatiotemporal response characteristics of flexible resources, the Soft Open Point (SOP) DC link enables flexible cross-node power transmission and spatiotemporal coupling regulation of flexible resources. Therefore, this paper constructs a mathematical model for the grid integration of flexible resources based on the SOP DC link. By integrating operational constraints such as power flow constraints in the power grid and source-load uncertainty constraints, a power system planning model is established. However, traditional convex optimization methods require approximate simplifications of the model, which can easily lead to a loss of accuracy. Although the Particle Swarm Optimization (PSO) algorithm is suitable for nonlinear optimization, it is prone to getting trapped in local optima. Therefore, this paper introduces an improved PSO algorithm based on refraction opposite learning, which enhances the algorithm’s global optimization capability by expanding the particle search space and increasing population diversity. Finally, simulation verification is conducted based on an improved IEEE-39 bus test system, and the results show that the proposed scenario generation method achieves a sum of squared errors of only 4.82% and a silhouette coefficient of 0.94, significantly improving accuracy compared to traditional methods such as Monte Carlo sampling. Full article

Reliable and efficient low-voltage distribution networks are critical for scaling mini-grid deployment and advancing universal electricity access, yet prevailing design practices remain manual, heuristic, and difficult to scale. This study presents a fully automated workflow that integrates geospatial feature extraction, distribution network layout, conductor sizing, mixed-integer linear programming-based phase balancing, nonlinear AC power flow validation, and system costing to generate rapid, standard-compliant techno-economic designs for greenfield mini-grid sites. The methodology is demonstrated across 62 rural sites to confirm practicality for large-scale rural electrification planning. Designs were evaluated for single-phase, three-phase, and hybrid low-voltage configurations. When design constraints were relaxed, single-phase networks achieved the lowest median voltage drop (~0.8%) and technical losses (~0.6%); however, under realistic voltage-drop and ampacity limits, compliance relied on conductor oversizing, resulting in low utilization (median loading <20%) and substantially higher costs. Fewer than half of the sites met construction feasibility limits for parallel conductors, and single-phase designs were typically 3–4× more expensive than multi-phase alternatives. Multi-phase layouts delivered comparable technical performance at significantly lower cost. Phase-balancing optimization reduced voltage drop by 15–20% and current unbalance by ~50%, enabling loss reduction and increased load accommodation. Overall, the results demonstrate that automated low-voltage network design can replace manual drafting with scalable, data-driven workflows that reduce soft costs while improving technical performance, constructability, and investment readiness. Full article