Search Results (50,274)

Globally, forest leisure and recreation (FLR) activities are widely recognized not only for their environmental and social benefits but also for their economic contributions. To better understand these economic contributions, it is vital to examine how the regional economic levels of customers vary when consuming FLR. This study aimed to empirically examine whether the regional economic level of residents (i.e., gross regional domestic product; GRDP) is classifiable using FLR expenditure data, and to interpret which variables contribute to its classification. We acquired anonymized credit card transaction data on residents of two regions with different GRDP levels. The data were preprocessed by identifying FLR-related industries and extracting key spending features for classification analysis. Five classification models (e.g., deep neural network (DNN), random forest, extreme gradient boosting, support vector machine, and logistic regression) were applied. Among the models, the DNN model presented the best performance (overall accuracy = 0.73; area under the curve (AUC) = 0.82). SHAP analysis showed that the “FLR industry” variable was most influential in differentiating GRDP levels across all the models. These findings demonstrate that FLR consumption patterns may vary and are interpretable by economic levels, providing an empirical framework for designing regional economic policies. Full article

Emergency communication networks play a crucial role in disaster relief operations. Current automated deployment strategies based on rule-driven or heuristic algorithms struggle to adapt to the dynamic and heterogeneous network environments in disaster scenarios, while manual command deployment is constrained by personnel expertise and response time requirements, leading to suboptimal trade-offs between deployment efficiency and reliability. To address these challenges, this study proposes a novel deep reinforcement learning framework with a fully convolutional value network architecture, which achieves breakthroughs in multi-dimensional spatial decision-making through end-to-end feature extraction. This design effectively mitigates the “curse of dimensionality” inherent in traditional reinforcement learning methods for topology planning. Experimental results demonstrate that the proposed method effectively accomplishes the planning tasks of emergency communication hub elements, significantly improving deployment efficiency while maintaining robustness in complex environments. Full article

A reliable precipitation dataset with high spatial resolution is essential for climate research in the Tarim Basin. This study evaluated the performances of four models, namely a random forest (RF), a long short-term memory network (LSTM), a support vector machine (SVM), and a feedforward neural network (FNN). FNN, which was found to be superior to the other models, was used to integrate eight precipitation datasets spanning from 1990 to 2022 across the Tarim Basin, resulting in a new monthly high-resolution (0.1°) precipitation dataset named MoHiPr-TB. This dataset was subsequently bias-corrected by the China Land Data Assimilation System version 2.0 (CLDAS2.0). Validation results indicate that the corrected MoHiPr-TB not only accurately reflects the spatial distribution of precipitation but also effectively simulates its intensity and interannual and seasonal variations. Moreover, MoHiPr-TB is capable of detecting the precipitation–elevation relationship in the Pamir Plateau, where precipitation initially increases and then decreases with elevation, as well as the synchronous variation of precipitation and elevation in the Tianshan region. Collectively, this study delivers a high-accuracy precipitation dataset for the Tarim Basin, which is anticipated to have extensive applications in meteorological, hydrological, and ecological research. Full article

Large turbine generators rely on shaft earthing brushes to safely divert harmful shaft currents to ground, protecting bearings from electrical damage. This paper presents a novel deep learning-based diagnostic framework to detect and classify faults in shaft earthing brushes of large turbine generators. A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. In the approach, raw time-domain signals are converted into informative time–frequency representations, which serve as input to a CNN model trained to distinguish normal and faulty conditions. The framework was evaluated using data from a fleet of large-scale generators under various brush fault scenarios (e.g., increased brush contact resistance, loss of brush contact, worn out brushes, and brush contamination). Experimental results demonstrate high fault detection accuracy (exceeding 98%) and the reliable identification of different fault types, outperforming conventional threshold-based monitoring techniques. The proposed deep learning framework offers a novel intelligent monitoring solution for predictive maintenance of turbine generators. The contributions include the following: (1) the development of a specialized deep learning model for shaft earthing brush fault diagnosis, (2) a systematic methodology for feature extraction from shaft current signals, and (3) the validation of the framework on real-world fault data. This work enables the early detection of brush degradation, thereby reducing unplanned downtime and maintenance costs in power generation facilities. Full article

This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms—dynamic_rows and dynamic_matrix—are proposed and tested. These algorithms select the update rule quasi-randomly based on the input vector, reducing the effectiveness of synchronization-based attacks. A series of simulations were conducted to evaluate the security implications under various configurations, including different values of K, N, and L parameters of neural networks. The results demonstrate that the proposed dynamic algorithms can significantly reduce the attacker’s synchronization success rate without requiring additional communication overhead. Both proposed solutions outperformed hebbian, an update rule-based synchronization method utilizing the percentage of attackers synchronization. It has also been shown that when the attacker chooses their update rule randomly, the dynamic approaches work better compared to random walk rule-based synchronization, and that in most cases it is more profitable to use dynamic update rules when an attacker is using random walk. This study contributes to improving QKD’s robustness by introducing adaptive neural-based error correction mechanisms. Full article

Background: Sarcopenia is a progressive muscle disease that compromises mobility and quality of life in older adults. Although dual-energy X-ray absorptiometry (DXA) is the standard for assessing Appendicular Lean Mass Index (ALMI), it is costly and often inaccessible. This study aims to develop machine learning models using anthropometric measurements to predict low ALMI for the diagnosis of sarcopenia. Methods: A cross-sectional study was conducted on 183 Mexican adults (67.2% women and 32.8% men, ≥60 years old). ALMI was measured using DXA, and anthropometric data were collected following the International Society for the Advancement of Kinanthropometry (ISAK) protocols. Predictive models were developed using Logistic Regression (LR), Decision Trees (DTs), Random Forests (RFs), Artificial Neural Networks (ANNs), and LASSO regression. The dataset was split into training (70%) and testing (30%) sets. Model performance was evaluated using classification performance metrics and the area under the ROC curve (AUC). Results: ALMI indicated strong correlations with BMI, corrected calf girth, and arm relaxed girth. Among models, DT achieved the best performance in females (AUC = 0.84), and ANN indicated the highest AUC in males (0.92). Regarding the prediction of low ALMI, specificity values were highest in DT for females (100%), while RF performed best in males (92%). The key predictive variables varied depending on sex, with BMI and calf girth being the most relevant for females and arm girth for males. Conclusions: Anthropometry combined with machine learning provides an accurate, low-cost approach for identifying low ALMI in older adults. This method could facilitate sarcopenia screening in clinical settings with limited access to advanced diagnostic tools. Full article

Protein and amino acid content are the crucial quality parameters in bottle gourd, and traditional measurement methods for detecting those parameters are complicated, time-consuming, and costly. In this study, we employed NIRS along with machine learning and neural network-based methods to model and predict protein and free amino acids (FAAs) of bottle gourd. Specifically, the content of protein and FAAs were measured through conventional methods. Then a near-infrared analyzer was utilized to obtain the spectral data, which were processed using multiple scattering correction (MSC) and standard normalized variate (SNV). The processed spectral data were further processed using feature importance selection to select the feature bands that had the highest correlation with protein and FAAs, respectively. The models for protein and FAAs estimation were developed using support vector regression (SVR), ridge regression (RR), random forest regression (RFR), and fully connected neural networks (FCNNs). Among them, ridge regression achieved the optimal performance, with determination coefficients (R²) of 0.96 and 0.77 on the protein and FAAs test sets, respectively, and root mean square error (RMSE) values of 0.23 and 0.5, respectively. Based on this, we developed a precise and rapid prediction model for the important quality indices of bottle gourd. Full article

The top oil temperature of a transformer is a vital sign reflecting its operational condition. The accurate prediction of this parameter is essential for evaluating insulation performance and extending equipment lifespan. At present, the prediction of oil temperature is mainly based on single-feature prediction. However, it overlooks the influence of other features. This has a negative effect on the prediction accuracy. Furthermore, the training dataset is often made up of data from a single transformer. This leads to the poor generalization of the prediction. To tackle these challenges, this paper leverages large-scale data analysis and processing techniques, and presents a transformer top oil temperature prediction model that combines multiple models. The Convolutional Neural Network was applied in this method to extract spatial features from multiple input variables. Subsequently, a Long Short-Term Memory network was employed to capture dynamic patterns in the time series. Meanwhile, a Transformer encoder enhanced feature interaction and global perception. The spatial characteristics extracted by the CNN and the temporal characteristics extracted by LSTM were further integrated to create a more comprehensive representation. The established model was optimized using the Whale Optimization Algorithm to improve prediction accuracy. The results of the experiment indicate that the maximum RMSE and MAPE of this method on the summer and winter datasets were 0.5884 and 0.79%, respectively, demonstrating superior prediction accuracy. Compared with other models, the proposed model improved prediction performance by 13.74%, 36.66%, and 43.36%, respectively, indicating high generalization capability and accuracy. This provides theoretical support for condition monitoring and fault warning of power equipment. Full article

High-speed communication can be achieved using laser inter-satellite links. However, laser terminals are highly sensitive to environmental conditions, which can lead to link disconnections. Therefore, an acquisition method capable of determining pointing errors is essential. In this study, a fast space–time fusion acquisition method was developed. This method establishes the relationship between satellite position, capture time, and azimuth and elevation angles. The performance of the proposed acquisition time optimization method was verified in a practical engineering application. Experimental results show that the pointing error was reduced by five times, the acquisition rate increased by 40%, the acquisition speed improved by 300 times, and multiple interference factors were effectively addressed. Full article

Accurate prediction of Air Quality Index (AQI) concentrations remains a critical challenge in environmental monitoring and public health management due to the complex nonlinear relationships among multiple atmospheric factors. To address this challenge, we propose a novel prediction model that integrates an adaptive-weight particle swarm optimization (AWPSO) algorithm with a back propagation neural network (BPNN). First, the random forest (RF) algorithm is used to scree the influencing factors of AQI concentration. Second, the inertia weights and learning factors of the standard PSO are improved to ensure the global search ability exhibited by the algorithm in the early stage and the ability to rapidly obtain the optimal solution in the later stage; we also introduce an adaptive variation algorithm in the particle search process to prevent the particles from being caught in local optima. Finally, the BPNN is optimized using the AWPSO algorithm, and the final values of the optimized particle iterations serve as the connection weights and thresholds of the BPNN. The experimental results show that the RFAWPSO-BP model reduces the root mean square error and mean absolute error by 9.17 μg/m³, 5.7 μg/m³, 2.66 μg/m³; and 9.12 μg/m³, 5.7 μg/m³, 2.68 μg/m³ compared with the BP, PSO-BP, and AWPSO-BP models, respectively; furthermore, the goodness of fit of the proposed model was 14.8%, 6.1%, and 2.3% higher than that of the aforementioned models, respectively, demonstrating good prediction accuracy. Full article

This study investigates how lighting conditions influence emotional and neural responses in a standardized, simulated museum environment. A multimodal evaluation framework combining subjective and objective measures was used. Thirty-two international students assessed their viewing experiences using 14 semantic differential descriptors, while real-time EEG signals were recorded via the EMOTIV EPOC X device. Spectral energy analyses of the α, β, and θ frequency bands were conducted, and a θα energy ratio combined with γ coefficients was used to model attention and comfort levels. The results indicated that high illuminance (300 lx) and high correlated color temperature (4000 K) significantly enhanced both attention and comfort. Art majors showed higher attention levels than engineering majors during short-term viewing. Among four regression models, the backpropagation (BP) neural network achieved the highest predictive accuracy (R² = 88.65%). These findings provide empirical support for designing culturally inclusive museum lighting and offer neuroscience-informed strategies for promoting the global dissemination of traditional Chinese culture, further supported by retrospective interview insights. Full article

The decoding of motor imagery (MI) electroencephalogram (EEG) signals is crucial for motor control and rehabilitation. However, as feature extraction is the core component of the decoding process, traditional methods, often limited to single-feature domains or shallow time-frequency fusion, struggle to comprehensively capture the spatio-temporal-frequency characteristics of the signals, thereby limiting decoding accuracy. To address these limitations, this paper proposes a dual-branch neural network architecture with multi-domain feature fusion, the dual-branch spatio-temporal-frequency fusion convolutional network with Transformer (DB-STFFCNet). The DB-STFFCNet model consists of three modules: the spatiotemporal feature extraction module (STFE), the frequency feature extraction module (FFE), and the feature fusion and classification module. The STFE module employs a lightweight multi-dimensional attention network combined with a temporal Transformer encoder, capable of simultaneously modeling local fine-grained features and global spatiotemporal dependencies, effectively integrating spatiotemporal information and enhancing feature representation. The FFE module constructs a hierarchical feature refinement structure by leveraging the fast Fourier transform (FFT) and multi-scale frequency convolutions, while a frequency-domain Transformer encoder captures the global dependencies among frequency domain features, thus improving the model’s ability to represent key frequency information. Finally, the fusion module effectively consolidates the spatiotemporal and frequency features to achieve accurate classification. To evaluate the feasibility of the proposed method, experiments were conducted on the BCI Competition IV-2a and IV-2b public datasets, achieving accuracies of 83.13% and 89.54%, respectively, outperforming existing methods. This study provides a novel solution for joint time-frequency representation learning in EEG analysis. Full article

The Internet of Things (IoT) holds transformative potential in fields such as power grid optimization, defense networks, and healthcare. However, the constrained processing capacities and resource limitations of IoT networks make them especially susceptible to cyber threats. This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. Each model was assessed under balanced and imbalanced dataset configurations and evaluated using three loss functions (cross-entropy, focal loss, and dual focal loss). By analyzing model efficacy across these datasets, we highlight the importance of generalizability and adaptability to varied data characteristics that are essential for real-world applications. The results demonstrate that the CNN trained using the cross-entropy loss function consistently outperforms the other models, particularly on balanced datasets. On the other hand, LSTM and biLSTM show strong potential in temporal modeling, but their performance is highly dependent on the characteristics of the dataset. By analyzing the performance of multiple DL models under diverse datasets, this research provides actionable insights for developing secure, interpretable IoT systems that can meet the challenges of designing a secure IoT system. Full article

Surface, terrain, or even atmosphere analysis using images or their fragments is important due to the possibilities of further processing. In particular, attention is necessary for satellite and/or drone images. Analyzing image elements by classifying the given classes is important for obtaining information about space for autonomous systems, identifying landscape elements, or monitoring and maintaining the infrastructure and environment. Hence, in this paper, we propose a neural classifier architecture that analyzes different features by the parallel processing of information in the network and combines them with a feature fusion mechanism. The neural architecture model takes into account different types of features by extracting them by focusing on spatial, local patterns and multi-scale representation. In addition, the classifier is guided by an attention mechanism for focusing more on different channels, spatial information, and even feature pyramid mechanisms. Atrous convolutional operators were also used in such an architecture as better context feature extractors. The proposed classifier architecture is the main element of the modeled framework for satellite data analysis, which is based on the possibility of training depending on the client’s desire. The proposed methodology was evaluated on three publicly available classification datasets for remote sensing: satellite images, Visual Terrain Recognition, and USTC SmokeRS, where the proposed model achieved accuracy scores of 97.8%, 100.0%, and 92.4%, respectively. The obtained results indicate the effectiveness of the proposed attention mechanisms across different remote sensing challenges. Full article

This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. Full article