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Developing reasonable energy management strategy(EMS) to coordinate the working status of various power sources is the key to ascendancy the energy-saving advantages of power-split hybrid electric bus. However, few optimal control strategies have been conducted to utilize operating condition information to achieve approximate online global optimization of energy-saving algorithms, and maximize energy-saving potential. Aiming at the problem, a real-time adaptive EMS based on improved DP algorithm and control rule extraction is proposed for a power-split HEB in this paper. An improved DP algorithm based on secant method for solve the lack of theoretical basis for determining weight coefficients is constructed. The optimal control rules are extracted through outlier detection and graphical method. And an online adaptive algorithm within Relevance Vector Machine and particle swarm optimization algorithm is performed to achieve online adaptive optimal effect. Finally, the simulation and hardware-in-the-loop test are conducted. Simulation results validate that control algorithm proposed in this paper is close to DP results, while achieving the 14.6% oil saving compared with logic threshold control. Additionally, the control algorithm proposed in this paper also demonstrates good real-time performance. The main contribution of this paper is to explore a novel way to fully exert the fuel economy potential and adaptability of power-split HEB in various cycles.

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an RV2 of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the RV2 to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the RV2 to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes.

Background Metabolic syndrome (MetS) is a major risk factor for cardiovascular diseases. The objective of the study was to evaluate the updated prevalence of MetS and provide a comprehensive illustration of the possible temporal changes in MetS prevalence in China from 2011 to 2015. Methods The data for this study are from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). CHARLS is a nationally representative survey targeting populations aged 45 and above from 28 provinces in mainland China. A total of 11,847 and 13,013 participants were eligible for data analysis at the two time points. Results The estimated prevalence of MetS in 2015 was 20.41% (95% CI: 19.02–21.8%) by the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) criteria, 34.77% (95% CI: 33.12–36.42%) by the International Diabetes Federation (IDF) criteria, 39.68% (95% CI: 37.88–41.47%) by the revised ATP III criteria, and 25.55% (95% CI: 24.19–26.91%) by the Chinese Diabetes Society (CDS) criteria. The prevalence was higher among women and elderly adults and in urban and northern populations. Furthermore, the trends in the prevalence decreased significantly between 2011 and 2015 by the ATP III, revised ATP III and CDS criteria. However, trends increased significantly from 2011 to 2015 by the IDF criteria. Conclusions A higher prevalence of MetS is found in those who reported being middle aged and elderly, women, residing in northern China or living in urban areas. Additionally, temporal changes in the prevalence of MetS varied according to different criteria. Increased attention to the causes associated with populations who have higher levels of MetS is warranted.

Artificial intelligence and positive psychology play crucial roles in education, yet there is limited research on how these psychological factors influence learners’ use of AI, particularly in language education. Grounded in self-determination theory, this study investigates the factors influencing Chinese English learners’ intention to use AI for language learning. Utilizing structural equation modeling, this research examines the mediating roles of grit, flow, and resilience in the relationship between basic psychological needs and the intention to use AI. Data were analyzed using AMOS 26 and SPSS 26. The findings reveal that flow, grit, and resilience mediate the relationship between basic psychological needs and the intention to adopt AI tools for language learning. This study provides valuable insights into how educational environments can be designed to fulfill psychological needs, thereby fostering greater engagement and acceptance of AI in language education.

This paper proposes a patent citation classification model based on deep learning, and collects the patent datasets in text analysis and communication area from Google patent database to evaluate the classification effect of the model. At the same time, considering the technical relevance between the examiners’ citations and the pending patent, this paper proposes a hypothesis to take the output value of the model as the technology similarity of two patents. The rationality of the hypothesis is verified from the perspective of machine statistics and manual spot check. The experimental results show that the model effect based on deep learning proposed in this paper is significantly better than the traditional text representation and classification method, while having higher robustness than the method combining Doc2vec and traditional classification technology. In addition, we compare between the proposed method based on deep learning and the traditional similarity method by a triple verification. It shows that the proposed method is more accurate in calculating technology similarity of patents. And the results of manual sampling show that it is reasonable to use the output value of the proposed model to represent the technology similarity of patents.

In this study, the microstructure evolution of 7055-T76 aluminum alloy in the coupled thermal-mechanical severe plastic deformation process of friction stir welding was investigated. Significant differences in the grain size, texture, and precipitates among different zones in FSWed joint were determined by the complex coupled thermal-mechanical effect. The base material (BM) was mainly composed of deformation grain and Brass and S texture with a sizeable η phase. The growth, partial recrystallization, and fully recrystallization of grains occurred in heat affected zone (HAZ), thermo-mechanical affected zone (TMAZ) and nugget zone (NZ), respectively. The growth, re-dissolution, and re-precipitation of nano-precipitates occurred in HAZ and NZ, respectively. It was found that superior synthetic microstructure characteristics were obtained in NZ. The NZ of FSWed joint was composed of refined recrystallized equiaxed grains about 1.4 μm and textures of Goss {110} < 001 > , R {124} < 211 > , and P {011} < 112 > with the weakest intensity. The precipitates were fully re-dissolved, and little η ′ precipitates re-precipitated in NZ under deformation and high-temperature interaction. In addition, many high-angle grain boundaries existed in the BM and NZ.

The unique structure of bearingless motors requires extra displacement sensors to monitor rotor movement, unlike conventional synchronous motors. However, this requirement inevitably escalates the cost and size of the motor. To address these issues, this paper proposes a novel approach: a bearingless synchronous reluctance motor (BSRM) without displacement sensors, utilizing the whale optimization algorithm–Elman neural network (WOA-ENN). The paper firstly introduces the suspension mechanism and mathematical model of the BSRM, upon which a function containing rotor position information is constructed. Subsequently, a sensorless method based on Elman neural network (ENN) is proposed, optimized using the whale optimization algorithm (WOA). Finally, the feasibility and reliability of the proposed approach are validated through simulations and experiments.

Accurate water demand prediction is essential for optimizing the daily operations of water treatment plants and pumping stations. To achieve accurate prediction of water demand for university campuses, this study utilizes real hourly water consumption data collected over 380 observation days from a water treatment plant located on a university campus in Zhenjiang, Jiangsu Province. Based on periodicity analysis of the original data through Fast Fourier Transform (FFT) and autocorrelation coefficients, the data were preprocessed and aggregated into two-hour intervals. The processed water consumption data, along with temporal information (month, day of the week, date, and hour) and weather conditions (daily average wind speed, maximum and minimum temperature), were used as model inputs. The first 352 days of data were utilized to train the model, followed by 14 days serving as the validation set and the final two weeks as the test set. A hybrid forecasting model for campus water demand was developed by integrating a Back Propagation (BP) neural network with a Long Short-Term Memory (LSTM) neural network. The model’s performance was compared with standalone BP, LSTM, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Simulation results demonstrate that, compared to other models, the proposed BP–LSTM hybrid model achieves a reduction in Mean Absolute Percentage Error (MAPE) ranging from 4.4% to 15.8%, and a decrease in Root Mean Squared Error (RMSE) between 2.5% and 16.8%. These findings indicate that the BP–LSTM model offers higher prediction accuracy and greater reliability compared to traditional single-model approaches.

As a core component of large marine engines, the compressor delivers robust and efficient power for propulsion. This study focuses on assessing and quantifying the uncertainty in the aerodynamic performance of a transonic rotor under various operating conditions, with the aim of investigating the impact of blade manufacturing variability on performance. Monte Carlo simulation (MCS) and sensitivity analysis were initially employed to identify parameters that significantly influence airfoil performance. Subsequently, a non-intrusive polynomial chaos (NIPC) uncertainty quantification model was developed to compare the effects of tip clearance deviation and surface geometry deviation on rotor performance. The study then analyzes how the geometric deviation at the different spanwise sections affects aerodynamic performance. The results reveal that geometric deviations have a more profound influence on aerodynamic performance than blade tip clearance. The impact of geometric deviations on average pressure ratio and efficiency of the transonic compressor rotor intensifies as the air mass flow rate approaches the near-stall point, while it decreases near the choking point. Interestingly, fluctuations in pressure ratio exhibit the opposite trend. Regarding spatial distribution, deviations in the upper half of the blade span (near the tip) exert a more dramatic influence on mass flow rate and pressure ratio fluctuation. A conceivable reason is that the inlet airflow velocity increases along the radial direction of the blade, and manufacturing variations in the same magnitude produce more notable relative geometric deviations in the upper half of the blade span. Centered on the machining tolerance guidelines for transonic compressor rotors, this work recommends stricter profile tolerance requirements for the upper half of the blade span.

Abstract To enhance sensor fault detection in bearingless permanent magnet synchronous motors, this paper proposes a multi-signal fusion fault diagnosis method that integrates the Gramian angular field (GAF) transformation, a parallel convolutional neural network (pCNN), and a bidirectional long short-term memory (BiLSTM) network. First, sensor data are transformed into two-dimensional (2D) images using the GAF method. These images are then processed by the pCNN to extract fault feature vectors. Finally, the BiLSTM network captures transient temporal features embedded in the fault characteristics, enabling accurate fault classification. Experimental results demonstrate that the proposed method not only ensures precise fault identification but also significantly outperforms other machine-learning-based fault diagnosis approaches in terms of classification performance, thereby confirming its feasibility and reliability. Graphical Abstract Graphical Abstract Flowchart of the GAF-pCNN-BiLSTM.