Search Results (1,875)

To address the energy management challenge for methanol range-extended series hybrid systems in port tugboats, characterized by highly transient and intermittent operations, this study proposes a real-time energy management strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A high-fidelity forward simulation model was constructed and validated to train the TD3 agent. In simulations of typical port operation cycles, TD3 reduced methanol consumption by approximately 18.5%, 10.2%, and 7.3% compared to rule-based (RB), equivalent consumption minimization strategy (ECMS), and deep deterministic policy gradient (DDPG) approaches, respectively. Emissions such as NO_x and carbon dioxide (CO₂) were also significantly reduced, while maintaining superior battery state of charge (SOC). Its overall performance approximates global optimal (DP) performance with a gap of less than 2.5%, while retaining real-time online decision-making capability. Hardware-in-the-loop (HIL) testing further demonstrates that TD3 exhibits less than 1.8% performance degradation under actual communication and execution conditions, validating its engineering feasibility and deployment potential. This study provides methodological and experimental foundations for developing high-performance, low-emission, real-time energy management algorithms for port tugboats. Full article

This paper presents an end-to-end framework for automated inspection and condition assessment of airport runway pavement using UAV-acquired imagery. The proposed approach integrates Unmanned Aerial Vehicle (UAV)-based data collection, deep learning-based pixel-level semantic segmentation of surface defects, and Geographic Information System (GIS)-based spatial aggregation to generate a georeferenced digital representation of airfield pavement condition. Multiple safety-critical defect types are detected and localized at pixel resolution, while spatially referenced processing enables a Pavement Condition Index (PCI)-inspired condition assessment based on defect density within predefined sampling units. The framework is validated through a real-world case study at Zadar Airport, where the entire runway was surveyed using high-resolution UAV imagery. The results demonstrate the system’s capability to identify and map multiple defect categories across the full runway extent and to produce a coherent, runway-scale condition map supporting maintenance prioritization and decision-making. Overall, the proposed solution provides a scalable, data-driven alternative to traditional manual runway inspection workflows and establishes a practical foundation for digital condition monitoring of airport pavement infrastructure. Full article

Solar panels are an important device converting light energy into electricity not only from the sun but also from artificial light sources such as light emitting diodes (LEDs) or lasers. Recent advances in solar cell technologies enable them to be flexible, allowing them to be attached to things with different sizes and shapes. Therefore, it is challenging for AI-equipped systems to automatically recognize and distinguish flexible solar panels from other surrounding objects in realistic, complicated environments. Traditional recognition methods usually suffer from low recognition accuracy and high computational cost. Hence, this paper proposes a deep learning method for solar panel recognition using a complete work flow that includes data acquisition and dataset construction, YOLOv8-based model training, real-time solar panel recognition, and extended functionality. The proposed method demonstrates the accurate identification of realistic flat and flexible solar panels, including bent and partially shaded panels, with a mean average precision (mAP)@0.5 of 99.4% and an mAP@0.5:0.95 of 90.4%. The Pareto front for the multi-objective loss function minimization problem is also investigated to determine the optimal set of weighting parameters for the loss components. Furthermore, another functionality is added to detect the sizes of different solar panels if multiple ones co-exist. These features provide a promising foundation for further usage of the proposed deep learning approach to recognize flexible solar panels in realistic contexts. Full article

Accurate diagnosis of nitrogen status is essential for precision fertilization in winter wheat. Single-modal or single-temporal remote sensing often fails to capture the multidimensional crop responses to nitrogen stress. In this study, we propose a hybrid framework based on CNN-LSTM-XGBoost for interpretable classification of wheat nitrogen stress gradients using multimodal unmanned aerial vehicle (UAV) multispectral and thermal infrared (TIR) imagery. Field experiments were conducted at the Xinxiang base in Henan Province during the 2023–2024, following a randomized block design involving 10 cultivars, four nitrogen levels, and four water treatments. Multisource UAV images acquired at jointing, heading, and filling stages were used to construct a multimodal feature set consisting of manual features (spectral bands, vegetation indices (VIs), TIR, and their interaction terms) and seven temporal statistical features. A deep learning model (CNN-LSTM) was utilized to further extract deep spatiotemporal features, and its performance was systematically compared with traditional machine learning models. The results show that multimodal feature fusion significantly enhanced classification performance. The CNN-LSTM model achieved an accuracy of 89.38% with fused multimodal features, outperforming all traditional machine learning models. Incorporating multi-temporal features improved the F1_macro of the XGBoost model to 0.9131, a 9.42 percentage-point increase over using the single heading stage alone. The hybrid model (CNN-LSTM-XGBoost) achieved the highest overall performance (Accuracy = 0.9208; F1_macro = 0.9212; AUC_macro = 0.9879; Kappa = 0.8944). SHAP analysis identified TIR × NDRE as the most influential indicator, reflecting the coupled physiological response of reduced chlorophyll content and increased canopy temperature under nitrogen deficiency. The proposed multimodal, multi-temporal, and interpretable framework provides a robust technical foundation for UAV-assisted precision nitrogen management. Full article

Indoor environmental quality directly affects public health and quality of life, among which odor pollution is one of the primary drivers of indoor environmental complaints. Traditional research and management approaches, which rely predominantly on mass concentrations of individual chemical compounds, are fundamentally inadequate for addressing the inherent sensory complexity, dynamic evolution, and subjective perception of indoor odors. Through a systematic literature review, this paper for the first time establishes an integrated research framework for indoor odor pollution across the whole-life-cycle management of the built environment, structured around “source–evolution–evaluation–control”. This framework systematically analyzes emission characteristics of building-related pollution sources, revealing the profound impact of indoor dynamic chemical and biological transformation processes on odor properties. Sensory analysis, instrumental measurements, and intelligent sensing approaches are critically compared in terms of their underlying principles and application boundaries. From an engineering perspective, the effectiveness and limitations of source prevention, ventilation dilution, and terminal purification strategies are comprehensively evaluated. The analysis demonstrates that effective indoor odor management must transcend passive and fragmented mitigation practices, and that its future development depends on the deep integration of environmental chemistry, sensory science, materials science, and artificial intelligence. Finally, this review proposes that by constructing regulation systems based on real-time sensing, digital twins, and intelligent decision-making, indoor odor management can fundamentally shift from reactive complaint-driven responses to proactive health-oriented protection. This paradigm transformation provides a systematic theoretical foundation and a technological roadmap for achieving healthy, comfortable, and sustainable building environments. Full article

Acknowledging recent breakthroughs in the context of deep bio-inspired neural networks, several architectural deep network options have been deployed to create intelligent systems. The foundations of convolutional neural networks are influenced by hierarchical processing in the visual cortex. The graph neural networks mimic the communication of biological neurons. Considering these two computation methods, a novel deep ensemble network is used to propose a bio-inspired deep graph network for creating an intelligent supply chain model. An automated smart supply chain helps to create a more agile, resilient and sustainable system. Improving the sustainability of the network plays a key role in the efficiency of the supply chain’s performance. The proposed bio-inspired Chebyshev ensemble graph network (Ch-EGN) is hybrid learning for creating an intelligent supply chain. The functionality of the proposed deep network is assessed on two different databases including SupplyGraph and DataCo for risk administration, enhancing supply chain sustainability, identifying hidden risks and increasing the supply chain’s transparency. An average accuracy of 98.95% is obtained using the proposed network for automatic delivery status prediction. The performance metrics regarding multi-class categorization scenarios of the intelligent supply chain confirm the efficiency of the proposed bio-inspired approach for sustainability and risk management. Full article

Achieving successful primordial germ cell (PGC)-based genome editing requires a deep understanding of their molecular identity. For the first time, a comparative transcriptomic analysis of chicken PGCs and adult liver cells to define their specific gene expression signature was performed. PGCs were isolated from Rhode Island Red chicken embryos, cultured, and subjected to RNA sequencing alongside liver tissue. Differential expression analysis with Benjamini–Hochberg correction identified 1909 differentially expressed genes (DEGs). Functional annotation revealed that PGCs possess a unique transcriptional landscape, characterized not only by enhanced proliferation and metabolic activity but also by a profound molecular convergence with neural crest cells. This is evidenced by the upregulation of gene modules governing long-range migration, neuronal signaling, and specialized “neuro-lipid” metabolism (e.g., sphingolipid and plasmalogen pathways). Additionally, we identified unannotated transcripts linked to immune pathways and ciliary signaling. Our study expands the functional annotation of avian PGCs and reveals an unexpected evolutionary recruitment of conserved morphogenetic programs, providing a refined molecular foundation for advanced germline editing technologies. Full article

Diaphragm walls are widely used for deep foundation pit support and permanent underground structures. The joints between adjacent panels are critical weak points, significantly influencing the overall deformation and stress distribution of the structure. To address the insufficient shear and tensile capacity of existing diaphragm wall joints, this study proposes a novel rigid joint incorporating retractable shear studs. The joint features a straightforward and constructible design, primarily comprising retractable shear studs, H-section steel, and shear stud pop-out limit plates. By withdrawing the limit plates inserted into the H-section steel, the retractable shear studs mounted on the web automatically extend along their axis, penetrating into the adjacent reinforcement cage to form an intrusive lap joint. This mechanism effectively enhances the integrity and load-bearing capacity at the joint. To validate its mechanical performance, large-scale specimens featuring this new joint were fabricated and subjected to shear and tensile tests. The experimental results demonstrate that, compared to traditional H-section steel joints, the peak shear and tensile strengths of the proposed joint are increased by approximately 10 times and 16 times, respectively. These findings fully verify the excellent mechanical performance of the novel diaphragm wall joint structure. Full article

Deep learning has achieved impressive progress in automated electrocardiogram (ECG) analysis, yet its performance still relies heavily on large-scale labeled datasets. As ECG annotation requires cardiologists, this process is costly and time-consuming, limiting its scalability in clinical practice. Contrastive learning offers a promising alternative by enabling the extraction of generalizable representations from unlabeled ECG data. In this study, we propose Anatomy-Aware Contrastive Learning for ECG (ACL-ECG), a self-supervised method that incorporates cardiac anatomical relationships into contrastive learning. ACL-ECG employs a physiology-aware augmentation strategy to generate rhythm-preserving augmented views, including random scale cropping, cardiac-cycle masking, and temporal shifting. Furthermore, ECG leads are grouped into four anatomically meaningful regions—anterior, inferior, septal, and lateral—and region-level contrastive objectives are introduced to promote intra-region consistency while enhancing inter-region discriminability. Extensive evaluations of downstream tasks demonstrate that ACL-ECG consistently outperforms state-of-the-art contrastive baselines under linear probing, achieving improvements of up to 1.29% in the area under the receiver operating characteristic curve (AUROC) and 3.57% in the area under the precision–recall curve (AUPRC). Moreover, when fine-tuned using only 10% of labeled data, ACL-ECG attains a performance comparable to fully supervised training, effectively reducing annotation requirements by approximately 5∼8×. Ablation studies further confirm the contributions of both the physiology-aware augmentation strategy and the anatomy-aware contrastive objective. Overall, ACL-ECG enhances representation quality without increasing annotation burden, and provides a promising and anatomy-informed foundation for self-supervised ECG analysis in label-scarce settings. Full article

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article

To address the tradeoff between environmental robustness and fine-grained accuracy in single-sensor human behavior recognition, this paper proposes a non-contact system fusing 77 GHz SIFT (mmWave) radar and a 40 kHz ultrasonic array. The system leverages radar’s long-range penetration and low-visibility adaptability, paired with ultrasound’s centimeter-level short-range precision and electromagnetic clutter immunity. A synchronized data acquisition platform ensures multi-modal signal consistency, while wavelet transform (for radar) and STFT (for ultrasound) extract complementary time–frequency features. The proposed Attention-CNN-BiLSTM architecture integrates local spatial feature extraction, bidirectional temporal dependency modeling, and salient cue enhancement. Experimental results on 1600 synchronized sequences (four behaviors: standing, sitting, walking, falling) show a 98.6% mean class accuracy with subject-wise generalization, outperforming single-sensor baselines and traditional deep learning models. As a privacy-preserving, lighting-agnostic solution, it offers promising applications in smart homes, healthcare monitoring, and intelligent surveillance, providing a robust technical foundation for contactless behavior recognition. Full article

Increased global energy consumption contributes to higher operational costs in the energy sector and results in environmental deterioration. This study evaluates the effectiveness of integrating Internet of Things (IoT) sensors and machine learning techniques to predict adaptive thermostat setpoints to support behavior-aware Heating, Ventilation, and Air Conditioning (HVAC) operation in residential buildings. The dataset was collected over two years from 2080 IoT devices installed in 370 zones in two buildings in Halifax, Canada. Specific categories of real-time information, including indoor and outdoor temperature, humidity, thermostat setpoints, and window/door status, shaped the dataset of the study. Data preprocessing included retrieving data from the MySQL database and converting the data into an analytical format suitable for visualization and processing. In the machine learning phase, deep learning (DL) was employed to predict adaptive threshold settings (“from” and “to”) for the thermostats, and a gradient boosted trees (GBT) approach was used to predict heating and cooling thresholds. Standard metrics (RMSE, MAE, and R²) were used to evaluate effective prediction for adaptive thermostat setpoints. A comparative analysis between GBT ”from” and “to” models and the deep learning (DL) model was performed to assess the accuracy of prediction. Deep learning achieved the highest performance, reducing the MAE value by about 9% in comparison to the strongest GBT model (1.12 vs. 1.23) and reaching an R² value of up to 0.60, indicating improved predictive accuracy under real-world building conditions. The results indicate that IoT-driven setpoint prediction provides a practical foundation for behavior-aware thermostat modeling and future adaptive HVAC control strategies in smart buildings. This study focuses on setpoint prediction under real operational conditions and does not evaluate automated HVAC control or assess actual energy savings. Full article

The foundation of an automatic sign language training (ASLT) system lies in word-level sign language recognition (WSLR), which refers to the translation of captured sign language signals into sign words. However, two key issues need to be addressed in this field: (1) the number of sign words in all public sign language datasets is too small, and the words do not match real-world scenarios, and (2) only single-view sign videos are typically provided, which makes solving the problem of hand occlusion difficult. In this work, we design an efficient algorithm for WSLR which is trained on our recently released NationalCSL-DP dataset. The algorithm first performs frame-level alignment of dual-view sign videos. A two-stage deep neural network is then employed to extract the spatiotemporal features of the signers, including hand motions and body gestures. Furthermore, a front-view guided early fusion (FvGEF) strategy is proposed for effective fusion of features from different views. Extensive experiments were carried out to evaluate the algorithm. The results show that the proposed algorithm significantly outperformed existing dual-view sign language recognition algorithms. Compared with several state-of-the-art methods, the proposed algorithm achieves Top-1 accuracy on the NationalCSL6707 dataset that is 10.29 and 11.38 higher than MViT and CNN + Transformer, respectively. Full article

To address the challenges of characterizing subway passenger flow fluctuations and overcoming the slow convergence and significant errors of existing intelligent optimization algorithms in tuning deep learning parameters for flow prediction, this study proposes a novel subway passenger flow fluctuation interval prediction model based on a Symmetry-Enhanced FIG-ICPO-XGBoost model. The core innovation is an Improved Cheetah Optimization Algorithm (ICPO), which incorporates enhancements including Circle mapping for population initialization, a hybrid strategy of dimension-by-dimension pinhole imaging opposition-based learning and Cauchy mutation to escape local optima, and adaptive variable spiral search with inertia weight to balance exploration and exploitation. The construction of this methodology embodies the concept of symmetry in algorithm design. For instance, Circle mapping achieves uniformity and ergodicity in the initial distribution of the population within the solution space, reflecting the symmetric principle of spatial coverage. Dimension-by-dimension pinhole imaging opposition-based learning generates opposite solutions through the principle of mirror symmetry, effectively expanding the search space. The adaptive variable spiral search strategy dynamically adjusts the spiral shape, simulating the symmetric relationship of dynamic balance between exploration and exploitation. Utilizing fuzzy-granulated passenger flow data (LOW, R, UP) from Harbin, the ICPO was employed to optimize XGBoost hyperparameters. Experimental results demonstrate the superior performance of the ICPO on 12 benchmark functions. The ICPO-XGBoost model achieves mean MAE, RMSE, and MAPE values of 10,291, 10,612, and 5.8%, respectively, for the predictions of the LOW, R, and UP datasets. Compared to existing models such as CPO-XGBoost, PSO-BiLSTM, GA-BP, and CNN-LSTM, these values represent improvements ranging from 4541 to 13,161 for MAE, 5258 to 14,613 for RMSE, and 2.6% to 7.2% for MAPE. The proposed model provides a reliable theoretical and data-driven foundation for optimizing subway train schedules and station passenger flow management. Full article

This study evaluates seepage behavior and hydraulic gradient conditions at the Lam Phra Phloeng Earthfill Dam in Nakhon Ratchasima, Thailand, by integrating long-term instrumentation records, updated geotechnical data, and deterministic numerical modeling. Piezometer and observation-well data collected between 2007 and 2023 were screened for reliability, revealing that several sensors exhibited abnormal or non-responsive behavior, limiting direct interpretation of phreatic surface variations in critical zones. Reliable datasets were incorporated into SEEP/W seepage simulations using representative dam cross-sections and soil parameters derived from recent drilling and laboratory testing. The results indicate that under normal reservoir operation, the phreatic surface remains within the core–drainage system and hydraulic gradients are well below estimated critical thresholds for the clayey foundation. Elevated reservoir levels lead to increased pore-water pressures and higher hydraulic gradients, particularly near the downstream zones and the deep central section of the dam. Rapid drawdown produces the most unfavorable hydraulic condition, generating steep transient pore-pressure gradients that approach critical values and reduce hydraulic safety margins. Although no immediate evidence of piping or uncontrolled seepage was identified, malfunctioning instrumentation creates monitoring blind spots that increase uncertainty in real-time seepage assessment. This study demonstrates that hydraulic gradient-based interpretation of deterministic seepage modeling provides a practical screening tool for dam safety evaluation under data-limited conditions. The findings emphasize the importance of enhanced monitoring redundancy and conservative operational control to support risk-informed management of aging earthfill dams under increasing hydrological variability. Full article