Interval valued fuzzy logic can more flexibly and comprehensively characterize and handle uncertaintyand fuzziness in information by extending the membership degree of fuzzy sets from a single numericalvalue to a closed interval form. This provides a new technological path to solve the complex processingproblems of public health monitoring signals. This article focuses on the application of interval valuedfuzzy logic in public health monitoring signal processing. Firstly, the sources and characteristics ofuncertainty in public health monitoring signals were analyzed, and the adaptability advantage ofinterval valued fuzzy logic was clarified. Subsequently, a processing model based on interval valuedfuzzy logic was constructed from three key steps: signal denoising, feature extraction, and anomalydetection. This study used monitoring data of influenza like cases in a certain region from 2023 to 2024,collecting a total of 1200 continuous monitoring signals. The data types included normal signals (60%),abnormal warning signals (30%), and noise interference signals (10%). In the anomaly detection stage,establish interval value fuzzy anomaly judgment rules, match the fluctuation range of monitoring signalswith the public health safety threshold in intervals, and achieve early warning of public healthemergencies. The experimental group adopts interval valued fuzzy logic algorithm, which enhancesuncertainty handling ability by expanding the membership degree of fuzzy sets into closed intervals; Thecontrol group used traditional fuzzy logic algorithm and BP neural network algorithm. Compared withtraditional fuzzy logic and BP neural network algorithms, the processing model based on intervalvalued fuzzy logic improves the accuracy of uncertainty information processing by 15% -20%. In termsof false positive rate and noise processing, it maintains high stability in complex scenarios with lowsignal-to-noise ratio and incomplete data. At the same time, it performs well in alarm response time,and its signal recognition accuracy and noise filtering efficiency are better than the control groupalgorithm.

To enhance operational safety and reduce maintenance costs, this study investigates the fault diagnosis of hydro-power units, where the BP neural network and XGBoost algorithm are employed. To filter environmental noise, a combination of the least squares method and dispersion analysis is utilized to filter out irrelevant and erratic operational data. Following this, the random forest algorithm is applied to rank the significance of characteristic parameters, ensuring that only the most relevant features are selected for fault diagnosis. The BP neural network, integrated with expert knowledge, is then used to extract fault characteristics, improving model accuracy. To further refine fault detection and reflect the hydro-power unit’s real-time operation, the XGBoost algorithm is employed for fault identification. A case study demonstrates the model’s ability to predict fault characteristics 16 h in advance, confirming the effectiveness and reliability of the proposed diagnostic approach.

Retraction Note: Application of BP neural network algorithm in visualization system of sports training management

To address the prediction and allocation challenges of emergency medical supplies during the middle and late stages of major outbreaks, we proposed an end-to-end ICSL deep learning algorithm architecture. This approach takes into account the characteristics of infectious diseases and the impact of government quarantine measures on their spread, enabling the prediction of the maximum demand for emergency medical supplies. Based on this, a multi-objective scheduling and allocation model was constructed, considering urgency, scheduling time, and cost. We designed a multi-objective particle swarm optimization algorithm to solve this model. Finally, we validated the algorithm using data from the Wuhan pandemic control measures. The results showed that the parameter update method improved the prediction accuracy of the LSTM model, increasing accuracy by 29.37% compared to the traditional LSTM algorithm and by 8.63% compared to the improved BP neural network algorithm. The proposed scheduling and allocation model optimizes delivery timeliness while also considering the urgency and cost-effectiveness of the resource allocation. The research findings can provide decision-making insights for the allocation of emergency medical supplies during public health emergencies.

Eel (Anguilla) is an aquatic animal with high nutritional value and multiple health benefits for the human body. To fully utilize its processing by-products fish bone, this study optimized the enzymatic preparation process of using BP neural network and GA genetic algorithm, with collagen extraction yield as the key evaluation metric, and characterized the properties of the obtained collagen. The results demonstrated that the optimal extraction conditions for eel bone collagen were as follows: enzyme dosage of 2%, hydrolysis time of 2.65 h, solid-to-liquid ratio of 1:22, and ultrasonic pretreatment for 21 min at 250 W power, achieving an extraction yield of 57.6%. The main amino acids identified were glycine, glutamic acid, proline, and arginine. SDS-PAGE electrophoresis revealed that eel bone collagen exhibited structural characteristics of type I collagen. Raman spectroscopy and X-ray diffraction indicated an intact triple-helix structure with partial ordered features. The DSC and TGA results demonstrated good thermal stability, with a denaturation temperature of 106.73 °C. SEM imaging displayed a loose, porous fibrous network structure, while rheological analysis suggested potential biomedical material properties. The findings of this study provide fundamental data for the high-value utilization and development of eel bone resources.

To address the challenges of low automatic leveling efficiency and insufficient control precision for small tracked operation chassis navigating uneven terrain in hilly and mountainous areas, this study proposes a leveling control system that integrates a dual-layer threshold strategy with a BP neural network algorithm. The system is developed based on a four-point lifting leveling mechanism. Building upon this foundation, the conventional single-threshold angle error compensation control strategy was optimized to meet the specific leveling demands of chassis operating in such complex environments. A co-simulation platform was established using Matlab/Simulink-AMEsim for subsequent simulation and comparative analysis. Simulation results demonstrate that the proposed method achieves a 15.6% improvement in leveling response speed and a 21.3% enhancement in leveling accuracy compared to the classical single-threshold PID control algorithm. Static test results reveal a smooth leveling process devoid of significant overshoot or hysteresis, with the leveling error consistently maintained within 0.5°. Field tests further indicate that at a travel speed of 3 km/h under a 50 kg load, the platform stabilization time is reduced by an average of 1.3 s, while the leveling angle error remains within 0.5°. The proposed system not only improves leveling response speed and precision but also effectively enhances the overall leveling efficiency of the tracked chassis system.

The application research of nuclear power mechanical equipment coupling-fault diagnosis technologies based on BP neural network and optimization algorithm

The cylinder expansion (CYLEX) test is commonly used to describe the adiabatic expansion of the detonation products and calibrate the equation of state (EOS) of detonation products. A CYLEX test method analogous to the T-20 (pre-set position and record the time of arrival) methodology was developed utilizing a set of probes with radial displacement difference and a high-speed pulse timer to record the radial expansion displacement of the cylinder wall. Two CYLEX tests for Dihydroxylammonium 5,5’-bitetrazole-1,1’-dioxide (TKX-50)-based explosives and two for 2, 4, 6-trinitrotoluene (TNT) were conducted using the CYLEX test method developed, and further Jones-Wilkins-Lee (JWL) EOS parameters of detonation products were determined using the BP neural network and genetic algorithm (BP-GA) program. Besides, the JWL EOS parameters obtained were numerically validated by performing the simulations of the CYLEX test, where the simulation results were consistent with the experimental data.

This study, leveraging near-infrared spectroscopy technology and integrating vegetation index analysis, aims to develop a hyperspectral imaging-based non-destructive inspection technique for swift monitoring of crop chlorophyll content by rapidly predicting leaf SPAD. To this end, a high-precision spectral prediction model was first established under laboratory conditions using ex situ lyophilized Leaf samples. This model provides a core algorithmic foundation for future non-destructive field applications. A systematic study was conducted to develop prediction models for leaf SPAD values of Korla fragrant pear at different growth stages (fruit-setting period, fruit swelling period and Maturity period). This involved comparing various spectral preprocessing algorithms (AirPLS, Savitzky–Golay, Multiplicative Scatter Correction, FD, etc.) and CARS Feature Selection methods for the screening of optimal spectral feature band. Subsequently, models were constructed using BP Neural Network and Support Vector Regression algorithms. The results showed that leaf samples at different growth stages exhibited significant differences in their spectral features within the 5000–7000 cm−1 (effective features for predicting chlorophyll (SPAD)) and 7000–8000 cm−1 (moisture absorption valley) bands. The Savitzky–Golay+FD (Savitzky–Golay smoothing combined with first-order derivative (FD)) preprocessing algorithm performed optimally in feature extraction. Growth period specificity models significantly outperformed whole growth period models, with the optimal models for the fruit-setting period and fruit swelling period being FD-CARS-BP (Coefficient of determination (R2) > 0.86), and the optimal model for the Maturity period being Savitzky–Golay-FD+Savitzky–Golay-CARS-BP (Coefficient_of_determination (R2) = 0.862). Furthermore, joint modeling of characteristic spectra and vegetation indices further improved prediction performance (Coefficient of determination (R2) > 0.85, Root Mean Square Error (RMSE) 2.5). This study presents a reliable method for non-destructive monitoring of chlorophyll content in Korla fragrant pears, offering significant value for nutrient management and stress early warning in precision agriculture.

ABSTRACT The discrete element method (DEM) represents a crucial numerical simulation approach for investigating the internal damage mechanisms of rocks. However, in order to construct an accurate simulation model, it is essential to set the correct microscopic parameters. Consequently, parameter calibration has emerged as a key area of focus within this field. The existing parameter calibration methods have yielded satisfactory results; however, there is still scope for further improvement and advancement. In this study, a novel intelligent parameter calibration method has been proposed, combining the benefits of the BP neural network and genetic algorithm (GA). The method constructs a parameter relationship model with micro‐parameters as inputs and macro‐parameters as outputs. Then GA is employed to invert the relationship model to calculate the parameter calibration. The results demonstrate that the method is capable of calculating a set of high‐precision micro‐parameter solutions in a mere 2 min, with the majority of its errors being within 5%.

In high-voltage switchgear, partial discharge (PD) detection using six-degree-of-freedom (6-DOF) manipulators presents challenges. However, these involve inverse kinematics (IK) solution redundancy and the lack of synergistic optimization between end-effector positioning accuracy and energy consumption. To address these issues, a dual-layer adaptive optimization model integrating multiple algorithms is proposed. In the first layer, a spatio-temporal correlation particle memory-based particle swarm optimization BP neural network (STPSO-BP) is employed. It replaces traditional IK, while long short-term memory (LSTM) predicts particle movement trends, and trajectory similarity penalties constrain search trajectories. Thereby, positioning accuracy and adaptability are enhanced. In the second layer, a chaotic mapping-based simulated annealing (CM-SA) algorithm is utilized. Chaotic joint angle constraints, dynamic weight adjustment, and dynamic temperature regulation are incorporated. This approach achieves collaborative optimization of energy consumption and positioning error, utilizing cubic spline interpolation to smooth the joint trajectory. Specifically, the positioning error decreases by 68.9% compared with the traditional BP neural network algorithm. Energy consumption is reduced by 60.18% in contrast to the pre-optimization state. Overall, the model achieves significant optimization. An innovative solution for synergistic accuracy–energy control in 6-DOF manipulators for PD detection is offered.

With the popularization of higher education, the demand for counselor positions in private universities has been growing steadily. However, current job-person matching products on the market struggle to meet the actual needs of this position. This study focuses on counselor positions in private universities and constructs a job-person matching model based on the BP neural network algorithm, aiming to improve the accuracy and efficiency of counselor recruitment. By collecting and analyzing counselor competency feature data, the BP neural network model was trained and tested, verifying its feasibility and effectiveness. This study provides a scientific basis for counselor recruitment in private universities, helping to optimize the structure of the counselor team and enhance the work efficiency of counselors.

Abstract Amidst the swift progression of urbanization and the continuous amelioration of living standards in China, the exigencies placed upon urban water supply systems have escalated markedly. The extant urban water supply dispatching methodology, predicated on the BP neural network, is confronted with sundry obstacles, such as a languid rate of convergence and a propensity to ensnare in local minima. To efficaciously surmount these impediments, this treatise proffers an innovative resolution by amalgamating the simulated annealing algorithm with the BP neural network architecture. By invoking empirical case studies, the paper substantiates the augmented efficacy and pre-eminence of the BP neural network augmented with the simulated annealing algorithm in urban water supply dispatching. The findings indubitably illustrate that the BP-GSA model surpasses both the conventional BP model and the genetic algorithm (GA) model in terms of MSE, MAE, and R2. This enhancement markedly amplifies the prognostic precision and stability of the model. Additionally, based on a comprehensive survey, it is evident that the BP-GSA model also shows remarkable advantages in terms of water supply reliability, water supply cost, and overall customer satisfaction.

The cable intermediate joint plays a crucial role in power cable systems, as its temperature directly impacts insulation performance and longevity. Predicting temperature accurately poses challenges for operation and maintenance. This study introduces a model for electromagnetic‐thermal coupling of a 110 kV single‐core high voltage cable, enabling numerical simulation to determine temperature distributions within the cable body and middle joint. By employing a hybrid orthogonal design approach, training and test samples are generated from simulated temperature field data. Conductor current, ambient temperature, convective heat transfer coefficient, and insulation thermal conductivity coefficient of the intermediate joint are chosen as variables to compile the dataset. An inverse model‐based prediction method is developed using a firefly‐optimized BP neural network algorithm. Results demonstrate that the optimized model exhibits a correlation coefficient of 0.99, surpassing the prediction accuracy of traditional optimized BP neural networks. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

This article proposed the concept of "fit", focusing on the matching between learners and the learning environment, and digitally expressing the matching relationship from three levels: education, group, and technology. This study explores a reinforcement learning-based method for generating learning paths, aiming to realize the adaptive generation of learning paths. Experimental results showed this method feasible and effective, with the proposed adaptive BP neural network algorithm having the shortest path selection time (78 seconds) and higher optimization rate (38.75%) among the compared algorithms.

Accurate estimation of state‐of‐charge (SOC) and state‐of‐health (SOH) is crucial for optimal battery system performance and longevity. To enhance SOC estimation precision, this study proposes a backpropagation neural network‐adaptive unscented Kalman filter (BP‐AUKF) algorithm for co‐estimating SOH and SOC. It is based on a second‐order Thevenin equivalent circuit model and the forgetting factor recursive least squares (FFRLS) algorithm. Initially, the FFRLS algorithm determines the model parameters. Subsequently, the BP neural network algorithm estimates SOH as the number of iterations varies. Utilizing the corrected effective battery capacity, the AUKF provides an initial SOC estimate, which the BP neural network algorithm then refines, eliminating estimation errors. The proposed algorithm's superiority is demonstrated through simulations under the US06 highway driving schedule, Beijing Dynamic Stress Testing, Federal Urban Driving Schedule, and constant current conditions. Compared to the AUKF algorithm, it exhibits enhanced SOC estimation accuracy, achieving a mean absolute error below 2% and a root mean square error below 2.1%. Thus, this method ensures high accuracy, strong adaptability, and safe lithium‐ion battery operation. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Sintering is an important process in the iron and steel industry. As the main raw material of blast furnace, the proportion of sinter is more than 70%. It is also an important work for steel plants to ensure and improve sintering yield. Sintering yield is inherently prone to significant variations, primarily driven by the quality of raw materials, the condition of equipment, and the intricacies of production operations. Additionally, the considerable time lag between changes in the ratios of raw materials, operational parameters and the ultimate sintering yield leaves a gap in the operational decision-making process. To address this, the present study employs time series correlation analysis to extract both dynamic and static characteristics intrinsic to sintering yield. In view of the advantages of CNN–LSTM (convolutional neural network–long short-term memory) in processing time-series data, we leverage a CNN–LSTM composite algorithm and a BP neural network algorithm to extract feature information in dynamic and static characteristics. The resulting sintering yield prediction model was rigorously trained and tested using actual sintering yield data from a large-scale iron and steel production enterprise. This model achieved a determination coefficient ( R 2 ) of 0.96, showcasing an impressive accuracy of 86.11% within an absolute error range of ±10 t·h −1 in forecasting the yield of finished products across a 72-hour continuous production cycle. The resluts indicate that the proposed approach significantly enhances sintering yield forecasting accuracy, providing a valuable tool for optimising production planning and decision making in the steel industry.

Optimization Design of Anti-slide Pile Reinforcement for Bedding Slopes Using BP Neural Network and Genetic Algorithm

Under the impetus of the "double - carbon" goal, wind power, as one of the main forms of new - energy power generation, has been growing in significance. However, the unpredictability of wind power output has presented difficulties for the secure and stable operation of the power system as well as real - time scheduling plans. Regarding the issues that the prediction of wind power output based on the traditional BP neural network has a slow convergence rate and is prone to getting trapped in local optima, this paper puts forward a hybrid wind - power prediction model (PSO - BP), where the BP neural network is enhanced by the particle - swarm optimization (PSO) algorithm. This approach optimizes the initial weights and thresholds of the BP neural network via the global search of the PSO algorithm. As a result, it enhances the model's convergence capacity and, to some degree, circumvents the problem of local optimality.Public wind - power datasets are utilized in the experiments. The PSO - BP and traditional BP models are trained and tested multiple times under the same circumstances. The outcomes indicate that, in comparison with the traditional BP model, the PSO - BP model, while ensuring the convergence speed, mitigates the local optimization issue of the neural network. It significantly improves the reliability and precision of the model's prediction results. Moreover, it offers robust technical backing for the short - term prediction of wind power, which is conducive to improving the power - system consumption plan and ensuring its safe operation.

Under the "dual carbon" goal， promoting energy conservation and emission reduction is the key to high-quality economic development. Through innovative analysis， we aim to analyze and predict the influencing factors of carbon emissions in Jiangsu Province from multiple dimensions and provide targeted strategies to reduce carbon emissions. Based on the STRIPAT extended model and LMDI model， we construct an index system of influencing factors of carbon emissions in Jiangsu Province and analyze the impact of different indicators on carbon emissions from multiple dimensions. Using ridge regression and factor analysis methods， we obtain the correlation and contribution rate between carbon emissions and various indicators and predict the carbon emissions in Jiangsu Province using the BP neural network algorithm. The results showed that the ranking of the influencing factors of carbon emissions in Jiangsu Province was： energy consumption， GDP， population， proportion of added value of the tertiary industry， energy consumption structure， proportion of added value of the secondary industry， and proportion of added value of the primary industry. Among them， the proportion of added value of the primary industry and the proportion of added value of the secondary industry had a restraining effect on the growth of carbon emissions， while the remaining factors had a promoting effect. At the same time， according to the prediction results， Jiangsu Province should adjust its industrial and energy structure between 2025 and 2035， increasing the proportion of non-fossil energy to 30%， reducing unit CO2 emissions by 28.6%， and achieving carbon peak. Around 2050， increasing the proportion of non-fossil energy to 50% and reducing unit energy consumption by 46.1% will lead to a rapid decline in CO2 emissions. Eventually， around 2060， the proportion of non-fossil energy will exceed 80%， unit energy consumption will decrease by 54.6%， and CO2 emissions will decrease by 77.9%， achieving carbon neutrality.