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This study presents an innovative air quality prediction framework that integrates factor analysis with deep learning models for precise prediction of original variables. Using data from Beijing’s Tiantan station, factor analysis was applied to reduce dimensionality. We embed the factor score matrix into the Transformer model which leveraged self-attention to capture long-term dependencies, marking a significant advancement over traditional LSTM methods. Our hybrid framework outperforms these methods and surpasses models like Transformer, N-BEATS, and Informer combined with principal component and factor analysis. Residual analysis and evaluation confirmed superior accuracy and stability, with the maximum likelihood factor analysis Transformer model achieving an MSE of 0.1619 and of 0.8520 for factor 1, and an MSE of 0.0476 and of 0.9563 for factor 2. Additionally, we introduced a cutting-edge CNN-BILSTM-ATTENTION model with discrete wavelet transform, which optimizes predictive performance by extracting local features, capturing temporal dependencies, and enhancing key time steps. Its MSE was 0.0405, with values all above 0.94, demonstrating exceptional performance. This study emphasizes the groundbreaking integration of factor analysis with deep learning, transforming causal relationships into conditions for predictive models. Future plans include optimizing factor extraction, exploring external data sources, and developing more efficient deep learning architectures.

Manufacturing workshops operate in dynamic and complex environments, where multiple orders are processed simultaneously through interdependent stages. This complexity makes it challenging to accurately predict the remaining completion time of ongoing orders. To address this issue, this paper proposes a data-driven prediction approach that analyzes key features extracted from multi-source manufacturing data. The method involves collecting heterogeneous production data, constructing a comprehensive feature dataset, and applying feature analysis to identify critical influencing factors. Furthermore, a deep learning optimization model based on a Convolutional Neural Network (CNN)–Bidirectional Long Short-Term Memory (BiLSTM)–Attention architecture is designed to handle the temporal and structural complexity of workshop data. The model integrates spatial feature extraction, temporal sequence modeling, and adaptive attention-based refinement to improve prediction accuracy. This unified framework enables the model to learn hierarchical representations, focus on salient temporal features, and deliver accurate and robust predictions. The proposed deep learning predictive model is validated on real production data collected from a discrete manufacturing workshop equipped with typical machines. Comparative experiments with other predictive models demonstrate that the CNN–BiLSTM–Attention model outperforms existing approaches in both accuracy and stability for predicting order remaining completion time, offering strong potential for deployment in intelligent production systems.

Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory–Attention (CNN–BiLSTM–Attention) model. By integrating multi-source data, including meteorological parameters (e.g., temperature, humidity, wind speed) and aircraft trajectory data (e.g., altitude, longitude, latitude), the framework achieves high-precision prediction of aircraft noise. The Haversine formula and inverse distance weighting (IDW) interpolation are employed to effectively supplement missing data, while spatiotemporal alignment techniques ensure data consistency. The CNN–BiLSTM–Attention model leverages the spatial feature extraction capabilities of CNNs, the bidirectional temporal sequence processing capabilities of BiLSTMs, and the context-enhancing properties of the attention mechanism to capture the spatiotemporal characteristics of noise. The experimental results indicate that the model’s predicted mean value of 68.66 closely approximates the actual value of 68.16, with a minimal difference of 0.5 and a mean absolute error of 0.89%. Notably, the error remained below 2% in 91.4% of the prediction rounds. Furthermore, ablation studies revealed that the complete CNN–BiLSTM–AM model significantly outperformed single-structure models. The incorporation of the attention mechanism was found to markedly enhance both the accuracy and generalization capability of the model. These findings highlight the model’s robust performance and reliability in predicting aviation noise. This study provides a scientific basis for effective aviation noise management and offers an innovative solution for addressing noise prediction problems under data-scarce conditions.

To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs Softmax regression. The experimental results show that the data processed by Kalman filter is more accurate in predicting the results on the CNN-BiLSTM-Attention model. Compared with the CNN-BiLSTM model, the Root Mean Square Error (RMSE) of the Kal-CNN-BiLSTM-Attention model is reduced by 17.58 and Mean Absolute Error (MAE) by 12.38, and the accuracy of the improved model is almost free from non-working days. To further verify the model’s applicability, the experiments were re-run using two other sets of fluctuating data, and the experimental results again demonstrated the stability of the model. Therefore, the Kal-CNN-BiLSTM-Attention traffic flow prediction model proposed in this paper is more applicable to a broader range of data and has higher accuracy.

Accurate photovoltaic (PV) power forecasting serves as a critical foundation for economic dispatch and reliable grid operation. To address the inherent uncertainty in PV power generation, this study proposes a short-term PV power interval prediction method based on Bayesian-optimized CNN-BiLSTM-attention (BO-CNN-BiLSTM-attention) that accounts for conditional dependencies in prediction errors. The methodology comprises three main stages: first, PV output data undergoes preprocessing and feature selection. Second, a Bayesian-optimized CNN-BiLSTM-attention model achieves high-precision point forecasting for target time periods. Finally, the K-shape time series clustering algorithm matches point predictions with temporally similar historical data, while adaptive bandwidth kernel density estimation models the probability distribution of prediction errors from similar patterns, thereby enabling interval prediction. Experimental validation on a photovoltaic plant in Xinjiang, China demonstrates that the proposed method achieves superior prediction accuracy compared to various single and ensemble forecasting models, while outperforming multiple interval construction approaches in terms of prediction effectiveness.

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made.

To address the high computational cost in global sensitivity analysis of large shipborne equipment, a CNN-BiLSTM-Attention model optimized by a Genetic Algorithm-Particle Swarm Optimization with adaptive weight updating (AGPSO) is proposed. This model is combined with Sobol’ global sensitivity analysis for efficient multi-input multi-output (MIMO) sensitivity evaluation of complex structural systems. Firstly, an AGPSO-CNN-BiLSTM-Attention model is developed to automatically optimize three key hyperparameters: learning rate, batch size, and L2 regularization, thereby obtaining the optimal network. Comparative results demonstrate that it achieves training accuracies of 99.49% for relative displacement and 99.12% for absolute acceleration, significantly improving prediction performance and computational efficiency. Secondly, integrated with the Sobol’ method, a MIMO sensitivity framework is established to quantify parameter influence. Finally, applied to a gas turbine isolation system, the approach identifies critical isolator and limiter parameters affecting shock response. The method significantly improves efficiency and generalization, offering practical value for structural design and optimization of marine equipment.

Raw material inventory control is indispensable for ensuring the cost reduction and efficiency of enterprises. Silica powder is an essential raw material for new energy enterprises. The inventory control of silicon powder is of great concern to enterprises, but due to the complexity of the market environment and the inadequacy of information technology, inventory control of silica powder has been ineffective. One of the most significant reasons for this is that existing methods encounter difficulty in effectively extracting the local and long-term characteristics of the data, which leads to significant errors in forecasting and poor accuracy. This study focuses on improving the accuracy of corporate inventory forecasting. We propose an improved CNN-BiLSTM-attention prediction model that uses convolutional neural networks (CNNs) to extract the local features from a dataset. The attention mechanism (attention) uses the point multiplication method to weigh the acquired features and the bidirectional long short-term memory (BiLSTM) network to acquire the long-term features of the dataset. The final output of the model is the predicted value of silica powder and the evaluation metrics. The proposed model is compared with five other models: CNN, LSTM, CNN-LSTM, CNN-BiLSTM, and CNN-LSTM-attention. The experiments show that the improved CNN-BiLSTM-attention prediction model can predict inbound and outbound silica powder very well. The accuracy of the prediction of the inbound test set is higher than that of the other five models by 7.429%, 11.813%, 15.365%, 10.331%, and 5.821%, respectively. The accuracy of the outbound storage prediction is higher than that of the other five models by 14.535%, 15.135%, 1.603%, 7.584%, and 18.784%, respectively.

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model’s prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions.

Background Understanding the spatiotemporal characteristics of depression risk in middle-aged and elderly individuals is crucial for early identification and intervention. However, current research predominantly employs machine learning (ML) methods to predict depression risk, often overlooking the spatiotemporal heterogeneity of this risk. Methods This study utilized five waves of data from the China Health and Retirement Longitudinal Study (CHARLS) and constructed nine long short-term memory (LSTM) frameworks using CNN, BiLSTM, and Attention mechanisms to improve the accuracy and stability of depression risk prediction. Dynamic time windows were employed to handle time data sequences of inconsistent lengths, aligning with the structure of public databases. SHAP (SHapley Additive exPlanations) analysis was used to quantify and visualize the impact of each feature on the prediction results. Results Among the nine LSTM frameworks, the CNN-BiLSTM-Attention model demonstrated a potential improvement in predictive performance (AUC between 0.68 and 0.71). It also exhibited the highest stability during feature reduction (∆AUC = 0.0052). SHAP analysis for the LSTM and CNN-BiLSTM-Attention models identified health status and functionality as key factors influencing depression risk in middle-aged and elderly individuals, with pain, gender, sleep duration, and IADL (Instrumental Activities of Daily Living) being the most significant factors. Conclusions The LSTM + SHAP analysis framework showed significant application value in handling complex, high-dimensional spatiotemporal data. Future clinical interventions and public health policies should focus more on pain management and chronic disease management in middle-aged and elderly populations to reduce the risk of depression.