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In multihead weighers, packaging processes seek to find the best combination of passage hoppers whose product content provides a total package weight as close as possible to its (nominal) label weight. The weighing hoppers arranged in these machines dispense the product quantity that each package contains through computer algorithms designed and executed for this purpose. For its part, in the packaging process for double-layered multihead weighers, all hoppers are arranged in two levels. The first layer comprises a group of weighing hoppers, and the second comprises a set of booster hoppers placed uprightly or diagonally to each weighing hopper based on design of the machine. In both processes, the initial machine configuration is the same; however, the hopper selection algorithm works differently. This paper proposes a new packaging process optimization algorithm for double-layer upright and diagonal machines, wherein the hopper subset combined has previously been defined, and the packaging weight is expressed as actual values. As part of its validation, product filling strategies were implemented for weighing hoppers to assess the algorithm in different scenarios. Results from the process performance metrics prove that the new algorithm improves processes by reducing variability. In addition, results reveal that some machine configurations were also able to improve their operation.

Assembly optimization of printed circuit boards (PCBs) has received considerable research attention because of efforts to improve productivity. Researchers have simplified complexities associated with PCB assembly; however, they have overlooked hardware constraints, such as pick-and-place restrictions and simultaneous pickup restrictions. In this study, a hybrid group search optimizer (HGSO) was proposed. Assembly optimization of PCBs for a multihead placement machine is segmented into three problems: the (1) auto nozzle changer (ANC) assembly problem, (2) nozzle setup problem, and (3) component pick-and-place sequence problem. The proposed HGSO proportionally applies a modified group search optimizer (MGSO), random-key integer programming, and assigned number of nozzles to an ANC to solve the component picking problem and minimize the number of nozzle changes, and the place order is treated as a traveling salesman problem. Nearest neighbor search is used to generate an initial place order, which is then improved using a 2-opt method, where chaos local search and a population manager improve efficiency and population diversity to minimize total assembly time. To evaluate the performance of the proposed HGSO, real-time PCB data from a plant were examined and compared with data obtained by an onsite engineer and from other related studies. The results revealed that the proposed HGSO has the lowest total assembly time, and it can be widely employed in general multihead placement machines.

Shield tunneling machines are paramount underground engineering equipment and play a key role in tunnel construction. During the shield construction process, the “mud cake” formed by the difficult-to-remove clay attached to the cutterhead severely affects the shield construction efficiency and is harmful to the healthy operation of a shield tunneling machine. In this study, we propose an enhanced transformer-based detection model for detecting the cutterhead clogging status of shield tunneling machines. First, the working state data of shield machines are selected from historical excavation data, and a long short-term memory-autoencoder neural network module is constructed to remove outliers. Next, variational mode decomposition and wavelet transform are employed to denoise the data. After the preprocessing, nonoverlapping rectangular windows are used to intercept the working state data to obtain the time slices used for analysis, and several time-domain features of these periods are extracted. Owing to the data imbalance in the original dataset, the k -means-synthetic minority oversampling technique algorithm is adopted to oversample the extracted time-domain features of the clogging data in the training set to balance the dataset and improve the model performance. Finally, an enhanced transformer-based neural network is constructed to extract essential implicit features and detect cutterhead clogging status. Data collected from actual tunnel construction projects are used to verify the proposed model. The results show that the proposed model achieves accurate detection of shield machine cutterhead clogging status, with 98.85% accuracy and a 0.9786 F 1 score. Moreover, the proposed model significantly outperforms the comparison models.

Sentiment analysis, a key component of natural language processing, is of paramount importance in various fields such as strategic surveillance, online image management, and customer satisfaction assessment. However, despite recent advances, improving the accuracy and adaptability of models remains a crucial challenge. This paper presents a cutting-edge method that combines the Transformer with evolutionary optimization techniques to improve sentiment analysis results. We rely on the use of pre-trained models to obtain embeddings and language features, thus guaranteeing a rich and relevant textual representation. Our method, called OAST-MAGC (Optimal Architecture for a Sentiment Analysis Transformer with Multihead Attention and Genetic Crossover), is distinguished by incorporating a multihead attention mechanism optimized using a genetic crossover technique. The originality of our approach lies in the integration of dynamic pruning, which selects the most relevant attention heads before starting genetic optimization. This step reduces the search space, improves convergence speed, and produces a lighter and more efficient final model. Additionally, the weights of the final layers are combined through genetic crossover, which promotes more efficient generalization and reduces the risk of overfitting. This architecture, which merges the effectiveness of pre-trained models with a dynamic optimization approach, offers a significant improvement in the accuracy and efficiency of sentiment analysis tasks. Experiments have proven that our method outperforms current techniques in terms of performance and robustness, achieving an accuracy of 95.96% and an F1-score of 96%, opening the way to new possibilities in processing complex textual data.

Tea pest and disease detection is crucial in tea plantation management, however, challenges such as multi-target occlusion and complex background impact detection accuracy and efficiency. To address these issues, this paper proposes an improved lightweight model, WMC-RTDETR, based on the RT-DETR model. The model significantly enhances the ability to capture multi-scale features by introducing wavelet transform convolution, improving the feature extraction accuracy in complex backgrounds, and increasing detection efficiency while reducing the number of model parameters. Combined with multiscale multihead self-attention, global feature fusion across scales is realized, which effectively overcomes the shortcomings of traditional attention mechanisms in small target detection. Additionally, a context-guided spatial feature reconstruction feature pyramid network is designed to refine the target feature reconstruction through contextual information, thereby improving the robustness and accuracy of target detection in complex scenes. Experimental results show that the proposed model achieves 97.7% and 83.1% respectively in mAP50 and mAP50:95 indicators, which outperform the original model. In addition, the number of parameters and floating-point operations are reduced by 35.48% and 40.42% respectively, enabling highly efficient and accurate detection of pests and diseases in complex scenarios. Furthermore, this paper successfully deploys the lightweight model on the Raspberry Pi platform, which proves that it has good real-time performance in resource-constrained embedded environments, providing a practical solution for low-cost disease monitoring in agricultural scenarios.

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting.

Zenith Total Delay (ZTD) is integral to applications such as atmospheric water vapor inversion and precise positioning in the Global Navigation Satellite System (GNSS). The development of high-precision regional ZTD models has emerged as a significant area of research within the GNSS domain. This study addresses the challenges associated with achieving high-precision tropospheric delay predictions under specific conditions and the limitations of CNN-LSTM models, particularly regarding suboptimal hyperparameter optimization and convergence to local optima. We propose a novel regional ZTD prediction model, the CNN-LSTM-Multihead-Attention (CLMA) model, optimized using the Dung Beetle Optimization (DBO), referred to as ZTD-DBO-CLMA. This model synergistically integrates the spatial feature extraction capabilities of Convolutional Neural Networks (CNN) with the temporal sequence modeling strengths of Long Short-Term Memory (LSTM) networks, enhanced through advanced hyperparameter optimization techniques. The model facilitates synchronized learning of CNN and LSTM components via the DBO optimization algorithm and the incorporation of a multihead attention mechanism.In our study, we utilized five consecutive months of ZTD data from 40 International GNSS Service (IGS) stations within the European region, sampled at one-hour intervals, to investigate regional ZTD prediction models. We employed the ZTD-DBO-CLMA model and compared it to the ZTD-CLMA model, which lacks DBO optimization. The results indicate that the ZTD-DBO-CLMA model significantly enhances prediction accuracy, reducing the mean absolute error (MAE) and root mean square error (RMSE) by 0.31 mm and 1.38 mm, respectively, while increasing the coefficient of determination (R²) by 39.43%. Furthermore, the DBO algorithm consistently demonstrates its optimization efficacy across diverse weather conditions, thereby improving the precision of ZTD predictions.

In response to the problem of neglecting the periodic and global characteristics of sequence data when predicting PM2.5 concentrations via machine learning models, a PM2.5 concentrations prediction model based on feature space reconstruction and multihead self-attention gated recurrent unit (FSR-MSAGRU) is proposed in this study. First, the raw sequence data are subjected to frequency spectrum analysis to determine the period value of the PM2.5 sequence data. Subsequently, the seasonal trend decomposition procedure based on loess (STL) is employed to capture the periodicity and trend information in the PM2.5 sequence data. Then, the feature space of the PM2.5 sequence data is reconstructed using the raw PM2.5 sequence data, decomposed seasonal components, trend components, and residual components. Finally, the reconstructed feature data are input into multihead self-attention gated recurrent unit (MSAGRU) with the ability to capture global feature information to predict PM2.5 concentrations. Favorable prediction results were attained by the proposed FSR-MSAGRU model across 6 distinct experimental datasets, with a PCC exceeding 0.98 and a decrease in the prediction accuracy metric SMAPE of at least 68% compared to that of the GRU model. Comparative experimental results with 13 reference models demonstrate that the proposed model exhibits better prediction performances and stronger generalization abilities.

Background Many biological studies have shown that lncRNAs regulate the expression of epigenetically related genes. The study of lncRNAs has helped to deepen our understanding of the pathogenesis of complex diseases at the molecular level. Due to the large number of lncRNAs and the complex and time-consuming nature of biological experiments, applying computer techniques to predict potential lncRNA-disease associations is very effective. To explore information between complex network structures, existing methods rely mainly on lncRNA and disease information. Metapaths have been applied to network models as an effective method for exploring information in heterogeneous graphs. However, existing methods are dominated by lncRNAs or disease nodes and tend to ignore the paths provided by intermediate nodes. Methods We propose a deep learning model based on hierarchical graphical attention networks to predict unknown lncRNA-disease associations using multiple types of metapaths to extract features. We have named this model the MMHGAN. First, the model constructs a lncRNA-disease–miRNA heterogeneous graph based on known associations and two homogeneous graphs of lncRNAs and diseases. Second, for homogeneous graphs, the features of neighboring nodes are aggregated using a multihead attention mechanism. Third, for the heterogeneous graph, metapaths of different intermediate nodes are selected to construct subgraphs, and the importance of different types of metapaths is calculated and aggregated to obtain the final embedded features. Finally, the features are reconstructed using a fully connected layer to obtain the prediction results. Results We used a fivefold cross-validation method and obtained an average AUC value of 96.07% and an average AUPR value of 93.23%. Additionally, ablation experiments demonstrated the role of homogeneous graphs and different intermediate node path weights. In addition, we studied lung cancer, esophageal carcinoma, and breast cancer. Among the 15 lncRNAs associated with these diseases, 15, 12, and 14 lncRNAs were validated by the lncRNA Disease Database and the Lnc2Cancer Database, respectively. Conclusion We compared the MMHGAN model with six existing models with better performance, and the case study demonstrated that the model was effective in predicting the correlation between potential lncRNAs and diseases.

The increasing integration of renewable energy sources into power grids necessitates efficient energy storage systems to balance supply and demand. Vanadium redox flow batteries (VRFBs) are becoming increasingly popular because of their long lifespan and flexible energy storage capabilities. Central to the effectiveness of VRFBs is the accurate estimation of future state of charge (SOC) levels. However, conventional SOC forecast frameworks suffer from poor generalization capabilities, which restrict their applicability in real‐life energy systems. This research introduces a sequential forecast framework that combines multihead self‐attention (MHA) with chained transfer learning (CTL) to estimate SOC sequences across multiple temporal horizons. The model performance is evaluated by forecasting SOC levels of the VRFB system operated under various charging and discharging current profiles. The results demonstrate that the change in the VRFB system’s operational dynamics significantly reduces the forecast accuracy of conventional frameworks, with the maximum MAE reaching 66%. Compared to the best‐performing baseline trained on a linear current profile, the CTL‐MHA‐gated recurrent unit (GRU) decreased the maximum MAE from 28.7% to below 1.5%. The generalization capability of the proposed framework addresses a critical barrier to the integration of SOC forecast frameworks with smart energy storage systems.