Search Results (3,229)

The high accuracy achieved by deep learning techniques has made them indispensable in computer vision applications. However, their substantial memory demands and high computational complexity limit their deployment in resource-constrained environments. To address this challenge, this study introduces a Feature Enhancement Module (FEM) as part of a unified hybrid compression framework that combines mixed-precision quantization and structured pruning to improve model efficiency. Experimental results on the Tiny ImageNet dataset using ResNet50 and MobileNetV3 architectures demonstrate the strong adaptability and scalability of the proposed approach. Compared with state-of-the-art compression methods, the proposed FEM-based framework achieves up to 6% improvement in Top-1 accuracy, while reducing memory usage by 32.26% and improving inference speed by 66%. Furthermore, the ablation study demonstrates that incorporating the FEM module leads to up to 24% improvement over the baseline model, highlighting its effectiveness. The results further show that FEM effectively preserves inter-channel feature representation stability even under aggressive compression, making it well suited for real-time processing and practical Artificial Intelligence (AI) applications. By maintaining semantic richness while significantly reducing computational cost, the proposed method bridges the gap between high-performance deep models and lightweight, deployable solutions. Overall, the FEM-based hybrid compression framework establishes a scalable and architecture-independent foundation for sustainable deep learning in resource-limited environments. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

The fuel consumption of power-split hybrid electric vehicles (PSHEVs) can be effectively reduced via mode transition that includes the engine process. However, factors such as engine torque ripple, system parameter uncertainties, and variations in torsional vibration characteristics can easily induce drivetrain vibration. These factors not only degrade ride comfort but also lead to a fundamental control challenge. The inherent trade-off between rapid response and stability is difficult to reconcile. In addition, the lack of adaptive mechanisms further limits consistent performance under varying conditions. To tackle these problems, a scenario-adaptive composite robust control (SACRC) strategy is proposed. The strategy consists of a UIO (unknown input observer)-based torque observation module, an adaptive VSS-LMS approach, and an H∞ controller with self-tuning parameters. Firstly, a six-degree-of-freedom dynamic model of the PSHEV transmission system is established with excitation sources, considering the characteristics of dual elastic elements. Secondly, a UIO-based torque observer is designed using a simplified dual-elastic-element model. By using engine speed and output shaft speed, the observer can accurately identify the torque transmitted by the torsional damper and drive shaft. Then, an adaptive VSS-LMS and H∞ controller with self-tuning parameters is constructed to ensure a balanced performance between fast torsional vibration suppression and control stability. Finally, simulation and experimental results demonstrate that the proposed strategy provides favorable adaptability to complex scenarios, and unifies the performance goals of rapidity, stability, and robustness. Full article

This study proposes a novel data-driven machine learning (ML) framework for multi-criteria environmental, social, and governance (ESG) evaluation. The framework aims to address the transparency, consistency, and subjectivity limitations of existing ESG evaluation systems by employing a fully data-driven, modular, and ML-supported architecture. It comprises three main modules: (i) ESG data preprocessing with missing-data imputation by the MissForest algorithm; (ii) a three-plane ESG feature selection workflow that integrates clustering, feature importance, and classification algorithms to identify representative ESG indicators; and (iii) a hybrid weighting and ranking procedure that combines unsupervised principal component analysis (PCA), criteria importance through inter-criteria correlation (CRITIC), and technique for order preference by similarity to ideal solution (TOPSIS) methods. A recent 2024 real-world application involving 57 listed Chinese pharmaceutical and biotechnology companies and 70 ESG indicators demonstrates the framework’s practical utility in producing transparent and objective ESG rankings. The main contributions of this work are fourfold: (1) the development of an end-to-end, entirely data-driven ML framework for ESG evaluation; (2) the introduction of an innovative three-plane ESG feature selection workflow within the framework; (3) the first study for designing a hybrid PCA-CRITIC-TOPSIS approach in ESG weighting and ranking; (4) the validation of the framework through a real-world industry application using recent and authentic ESG data. Full article

Thermochromic materials (TCMs) have attracted increasing attention as passive adaptive materials for solar regulation in buildings because they can reversibly change their optical properties in response to temperature without external energy input. Owing to this temperature-triggered optical modulation, they have been widely investigated for smart windows, temperature indicators, anti-counterfeiting labels, and flexible devices. In recent years, representative systems such as VO₂-based materials, polymers, hydrogels, and organic–inorganic hybrids have shown steady progress, especially in transition-temperature tuning, spectral selectivity, and cycling stability. This review summarizes the main classes of TCMs as well as their color-changing mechanisms, preparation methods, and performance-regulation strategies, with an emphasis on building energy efficiency and passive solar regulation. Typical applications and current bottlenecks are also discussed, including response speed, durability, environmental compatibility, and large-scale manufacturing. Finally, several practical directions for future work are highlighted, particularly low-cost synthesis, multifunctional integration, and application-oriented material design. Full article

Intelligent bearing fault diagnosis is essential for maintaining the reliability of rotating machinery. However, deploying deep learning models in industrial environments is often constrained by a lack of labeled data, environmental noise, and strict hardware limits. To address these connected challenges, this paper proposes 1D-CNN-Trans, a flexible and resource-efficient hybrid framework. Designed for supervised diagnosis with restricted data, the configurable model combines a compact one-dimensional convolutional neural network (1D-CNN) for local feature extraction, a Transformer encoder for capturing long-range temporal dependencies, and an optional squeeze-and-excitation (SE) module for channel recalibration under favorable conditions. The method is evaluated on two standard mechanical benchmarks under limited sample conditions, controlled additive white Gaussian noise (AWGN), and dynamic non-stationary interference. Experimental results indicate that 1D-CNN-Trans shows improved robustness under interference compared to selected baselines, notably improving accuracy against a standard CNN backbone. Furthermore, findings indicate that while the Transformer ensures noise robustness, channel recalibration (via SE) introduces optimization instability under extreme sparsity and noise. Consequently, we reposition the architecture as a configurable framework where recalibration is conditionally activated. Finally, theoretical complexity analysis is provided to validate the model’s low computational burden, indicating its general feasibility for resource-constrained scenarios. Full article

We propose a novel self-coupled optical waveguide (SCOW+) architecture that enhances spectral control in integrated photonic circuits. Derived from the foundational SCOW platform, SCOW+ introduces a tunable ring resonator coupled with an all-pass filter to achieve sharp, periodic transmission dips with adjustable free spectral range and extinction ratio. This hybrid configuration supports multifunctional behavior, enabling the device to operate as a narrowband filter, modulator, or sensor depending on the tuning parameters. The SCOW+ structure leverages self-coupling and phase interference to induce coupled-resonator-induced transparency, offering fine control over spectral features. Using frequency-domain simulations, we validate the spectral response and tunability of SCOW+. Simulation results confirm that the device exhibits flexible tuning capabilities and dynamic reconfiguration of its transmission profile by adjusting ring length and coupling coefficient. SCOW+ enhances spectral shaping without significantly increasing device size. Its modularity and compatibility with standard fabrication processes underscore its potential for scalable integration in silicon photonics platforms. The results of this study highlight the versatility of SCOW-derived architectures and enable compact, tunable photonic components in next-generation integrated systems. Full article

To address the challenges faced by virtual power plants (VPPs) in uncertain market environments and complex distribution networks, including strong market coupling, difficulty in multi-resource coordination, and strict safety constraints, this paper proposes a Hierarchical Hybrid Intelligent Framework (H2IF). The proposed framework integrates a market-aware meta-game mechanism, a topology-embedded graph attention coordination method, and a risk-aware soft/hard constraint safety mechanism to achieve economically optimal dispatch of VPPs in complex dynamic scenarios. By explicitly modeling competitive market interactions, the proposed method enhances strategy robustness; by exploiting grid topology priors, it improves multi-agent coordination capability; and by combining differentiable projection with risk-constrained optimization, it jointly ensures operational safety and revenue stability. Simulation results on a modified IEEE 33-bus system demonstrate that H2IF outperforms mainstream deep reinforcement learning methods and rule-based dispatch strategies in overall performance. In the 24 × 300-step testing scenario, H2IF achieves an average single-episode operating cost of 38.23 k$, which is 28.9%, 40.4%, and 26.5% lower than those of MADDPG, SAC, and the rule-based method, respectively, while also yielding the lowest constraint violation level. Ablation studies further verify the effectiveness of each key module in improving profit, reducing operating costs, enhancing tracking performance, and strengthening safety. The results indicate that the proposed method enables coordinated optimization of economy, safety, and robustness for VPP dispatch under uncertain market and operating conditions. Full article

Water electrolysis has emerged as a strategically appealing route for the sustainable production of green hydrogen (H₂) via the hydrogen evolution reaction (HER), though the sluggish kinetics of the oxygen evolution reaction (OER) remains a bottleneck hindering large-scale practical applications. In this regard, an attractive solution is offered by the integration of the ethanol oxidation reaction (EOR) into hybrid water-splitting systems, favorably reducing anodic overpotentials. Nonetheless, an open challenge is related to the fabrication of eco-friendly and economically viable catalysts free from noble metals, combining efficiency and stability. Herein, we explore nickel-oxide-based nanostructures grown onto porous Ni foam scaffolds by a scalable hydrothermal (HT) approach as EOR electrocatalysts. Material properties arising from modulation of the sole HT growth time are investigated by complementary structural, microscopic, and spectroscopic techniques. Electrochemical tests demonstrate good durability and very attractive EOR performances, mainly influenced by the morphology and the NiOOH surface content of the target systems. Overall, the present work advances an attractive route to transition-metal-based electrocatalysts for efficient alcohol-oxidation-assisted water electrolysis. Full article

Driver emotion recognition is a crucial method for reducing traffic accidents. Most existing research focuses on experienced drivers as the primary research subjects, overlooking novice drivers, who are inexperienced in driving. However, novice drivers can easily lose control of their emotions due to the high mental load during driving, which can lead to serious traffic accidents. Therefore, to recognize the emotions of novice drivers for timely warnings, we propose an emotion recognition model based on multimodal information. The model consists of a facial feature extraction module, an eye movement feature extraction module and a classifier. The facial feature extraction module uses the ViT-B/16 to extract the facial features of novice drivers. The eye movement feature extraction module is a hybrid network containing Bi-LSTM and Transformer. It extracts eye movement features of novice drivers. Facial features and eye movement features are fused and fed to the classifier. The classifier can output the five major emotion categories of surprise, anger, calm, happy, and other for novice drivers. The experimental results demonstrate that our model accurately recognizes the emotions of novice drivers with an accuracy of 98.72%, surpassing that of other models. Full article

Aquaculture sustainability in rapidly urbanizing regions is increasingly threatened by heavy metal contamination originating from complex anthropogenic land-use patterns. This study used an integrated model to evaluate the molecular-to-human health continuum in hybrid catfish (Clarias gariepinus × Clarias macrocephalus) sourced from Pathum Thani, Thailand’s primary aquaculture hub. We integrated geospatial land-use data with heavy-metal quantification, oxidative-stress biomarkers, and transcriptional profiling to assess how canal-specific water quality modulates fish health and consumer risk. The results revealed significant spatial heterogeneity in metal concentrations, corresponding to the province’s 27% urban–industrial land-use footprint. While water quality generally met regulatory limits, a pronounced aqueous–biotic discrepancy, “bioaccumulation paradox” was identified at certain sites, where muscle and hepatic tissues exhibited lead (Pb), chromium (Cr), and nickel (Ni) levels that substantially exceeded international safety standards. Biochemical and molecular analyses provided functional evidence of physiological distress, specifically significantly elevated malondialdehyde (MDA) levels, and the transcriptional modulation of cat, cyp1a, gpx, met, tnf, and star genes indicated that chronic metal exposure overwhelmed antioxidant defenses and induced potential endocrine disruption. Moreover, human health risk assessments revealed that the hazard index (HI) and target cancer risk (TR) exceeded unacceptable thresholds at multiple hotspots, indicating that Cr is a primary carcinogenic driver. These findings highlight a “GAP Paradox,” where farm-level certifications are insufficient to mitigate risks posed by the surrounding canal network. This study presents vital evidence-based risk profiles that necessitate a transition to a spatially based regulatory framework, incorporating geospatial land-use monitoring into national food safety policies to protect both aquaculture viability and public health. Full article

Skin cancer represents a significant public health challenge, necessitating early detection and timely treatment for optimal management. Timely and accurate evaluation of skin lesions is crucial, as delays can lead to more severe outcomes. However, identifying skin lesions accurately can be challenging due to differences in color, shape, and the various types of imaging equipment used for diagnosis. While recent studies have demonstrated the potential of ensemble convolutional neural networks (CNNs) for early diagnosis of skin disorders, these models are often too large and inefficient for processing contextual information. Although lightweight networks like MobileNetV3 and EfficientNet have been developed to reduce parameters and enable deep neural networks on mobile devices, their performance is limited by inadequate feature representation depth. To mitigate these limitations, we propose a new hybrid attention dual-stream deep learning model for skin lesion detection. Our model uses one training process to preprocess the images and splits the task into two branches. Each branch extracts different features using multi-stage and multi-branch attention techniques, improving the model’s ability to detect skin lesions accurately. The first branch processes the original image using a convolutional layer integrated with three novel attention modules: Enhanced Separable Depthwise Convolution (SCAttn), stage attention, and branch attention. The second branch utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the input image, improving local contrast and revealing finer details. The integration of CLAHE with SCAttn modules leverages enhanced local contrast to capture more nuanced features while maintaining computational efficiency. A classification module receives the concatenated hierarchical characteristics that were taken from both branches. Utilizing the PAD2020 and ISIC 2019 datasets, we assessed the proposed model and obtained an accuracy rate of 98.59% for PAD2020, surpassing the state-of-the-art performance by 2%, and stable performance accuracy for the ISIC 2019 dataset. This illustrates how well the model can integrate several attention mechanisms and feature enhancement methods, providing a reliable and effective means of detecting skin cancer. Full article

The global energy transition and the implementation of carbon capture, utilization, and storage (CCUS) strategies require energy-efficient and scalable CO₂ separation technologies. Mixed-matrix membranes (MMMs), combining polymer matrices with functional inorganic or hybrid nanofillers, have emerged as advanced separation platforms capable of surpassing the conventional permeability–selectivity trade-off observed in neat polymer membranes. This review critically evaluates recent developments in modern hybrid membranes for CO₂ separation from synthetic and industrial gas mixtures, including CO₂/N₂ (flue gas), CO₂/CH₄ (natural gas and biogas upgrading), and syngas systems. Particular emphasis is placed on MMMs incorporating covalent organic frameworks (COFs), metal–organic frameworks (MOFs), graphene oxide (GO), MXenes, transition metal dichalcogenides (TMDs), carbon nanotubes (CNTs), g-C₃N₄, layered double hydroxides (LDH), zeolites, metal oxides, and magnetic nanoparticles. Reported performance ranges include CO₂ permeability (P_CO2) typically between 100 and 800 Barrer, CO₂/N₂ selectivity up to 319, and CO₂/CH₄ selectivity up to 249, depending on filler chemistry, loading, and interfacial compatibility. The mechanisms governing gas transport—molecular sieving, selective adsorption, facilitated transport, and diffusion-pathway engineering—are systematically discussed. Key challenges addressed include filler dispersion, polymer–filler interfacial defects, physical aging, moisture sensitivity, oxidation (particularly in MXenes), and scalability toward industrial membrane modules. Future perspectives focus on sub-nanometer pore engineering, surface functionalization to enhance CO₂ affinity, controlled alignment of 2D nanosheets to promote directional transport, multifunctional core–shell and hollow structures, and the integration of computational modeling and machine learning for accelerated material design. Modern hybrid MMMs are identified as strategically important materials enabling high-efficiency CO₂ separation processes aligned with decarbonization and energy transition objectives. Full article

The development of rice flowers and panicles critically affects grain yield and quality. LEAFY HULL STERILE1/OsMADS1, a grass-specific SEPALLATA-like MADS-box transcription factor, is essential for rice floral development and floral meristem activity maintenance. However, the mechanism through which OsMADS1 interacts with other genes to regulate floral organ identity and meristem determinacy remains unclear. In this study, we first generated OsMADS1 knockout mutants using CRISPR/Cas9. The mutant florets exhibited obvious morphological defects, which were categorized into five phenotypic classes. Yeast two-hybrid screening identified two OsMADS1-interacting proteins: OsMADS22, an STMADS11-like protein, and OsYABBY5, a YABBY transcription factor. Their physical interactions were validated both in vitro and in vivo, and were important for floral organ specification and meristem maintenance. Transcriptomic analysis revealed that OsMADS1 regulates numerous genes involved in hormone signaling and panicle/flower development. Furthermore, OsMADS1 acts together with OsMADS22 and OsYABBY5 to modulate the expression of the downstream target OsMADS55, thereby controlling rice spikelet development. Together, our results reveal that OsMADS1 executes diverse regulatory functions in floral organ specification and meristem identity by interacting with multiple developmental regulators, providing new insights into the molecular mechanisms of plant flower development. Full article

This study focuses on how mesoscale eddies at the Kuroshio boundary in the East China Sea modulate underwater acoustic propagation. Using high-resolution reanalysis data from the Hybrid Coordinate Ocean Model (HYCOM) and validated acoustic ray-tracing simulations, the OW + SLA method is employed for eddy identification and classification. Statistical analysis of 120 eddy events from 2015 to 2020 clarifies their seasonal variation characteristics. Warm eddies shift the convergence zone 15–30 km away from the sound source and broaden it by 20–40%, while cold eddies shift it 10–25 km toward the source and narrow it by 15–35%. A linear relationship exists between eddy amplitude and acoustic transmission loss (TL = 72.4 + 0.42 h, R² = 0.61), where TL is the transmission loss in decibels (dB) and h is the eddy amplitude in meters (m), and there are depth-dependent transmission loss modulation effects. These results provide practical guidance not only for sonar system design and acoustic communication optimization but also for error correction in underwater acoustic navigation systems operating in eddy-prone environments. Full article