Search Results (1,266)

In the context of an increasingly complex and dynamic digital ecosystem, accurately assessing the value of Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises is crucial for making effective decisions. Traditional valuation methods struggle to effectively address issues such as the high R&D expenditures and significant operational risks associated with these enterprises. This study proposes an interpretable intelligent decision-support framework for valuing SRDI enterprises listed on the Beijing Stock Exchange (BSE), constructing a multidimensional indicator system that encompasses solvency, profitability, and R&D capabilities. Feature importance screening using the XGBoost algorithm was conducted to identify key indicators as input variables for a backpropagation (BP) neural network. Concurrently, the Particle Swarm Optimization (PSO) algorithm was applied to the neural network to optimize initial weights and thresholds, thereby modeling nonlinear valuation relationships. Empirical analysis of 770 SRDI firms listed on the Beijing Stock Exchange from 2020 to 2024 indicates that the XGBoost-PSO-BPNN model achieved a coefficient of determination of 0.8083 on the test set, outperforming traditional linear models and benchmark models such as single-tree models. SHAP explainability analysis further reveals that current asset turnover, return on assets, and equity concentration are the primary value drivers. This study employs various clustering methods to further classify enterprises into three categories and proposes recommendations for differentiated regulatory policies, providing intelligent decision support for enterprises operating within complex digital ecosystems. Full article

To achieve scientific cost forecasting, this study investigates structural changes in mining cost driven by the widespread adoption of mechanized mining, increased mining depths, and significant operational variations. Based on the backpropagation (BP) neural network, this study systematically analyzes the cost-composition characteristics of modern mining operations and applies activity-based costing to achieve refined cost accounting for each mining operation unit. Ten key influencing factors, including working space, stope temperature, stope depth, haulage distance, worker seniority and work efficiency, scraper efficiency, equipment service life, fuel and lubricant consumption rates, are identified by analyzing cost variation patterns. Principal component analysis (PCA) is used to reduce the dimensionality of the ten factors to simplify this model and enhance prediction accuracy. The PCA-BP neural network mining cost forecasting model is built with the principal components extracted as input variables. Actual cost data from an underground metal mine in Shandong Province is used for our model training and validation, with adopting linear regression, eXtreme Gradient Boosting (XGBoost), and a traditional BP neural network as the comparison models for performance evaluation. Our prediction results indicate that the PCA-BP model achieves an average relative error of 3.80% and a root mean square error of 1.43, both significantly outperforming the comparison models. The results demonstrate superior predictive accuracy and stability of our model. Validated with data from a typical deep mechanized gold mine in eastern China, the PCA-BP cost forecasting model requires parameter retraining based on local production conditions for applications in other regions. This study confirms that the model aligns well with the cost characteristics of modern underground metal mines and produces effective predictions, offering reliable quantitative support for the development of cost control strategies and optimization of cost planning in mining enterprises. Full article

Rail-transit station areas concentrate commercial services, employment, and intensive land development, but their vitality is shaped by multiple built-environment conditions rather than rail accessibility alone. Focusing on 20 stations along the Osaka Metro Midosuji Line in Japan, this study uses Japanese chome units, which are small neighborhood-level address and statistical units, within an 800 m pedestrian catchment as analytical units and measures commercial-service agglomeration intensity, employment intensity, and commercial–employment composite vitality. The composite indicator measures the static co-concentration of commercial-service provision and employment carrying capacity, with pedestrian flow, consumption activity, and dwell time treated as separate dimensions of station-area vitality. Ten station-area environmental variables are examined using ordinary least squares (OLS), Lasso, Random Forest, Back-Propagation (BP) Neural Network, and extreme gradient boosting (XGBoost) models, with Shapley additive explanations (SHAP) applied to interpret variable contributions and nonlinear responses. Results show that nonlinear models generally outperform linear models. Development intensity, officially assessed land price, and network distance to the nearest metro station are the most influential variables, showing threshold, marginal, and non-monotonic effects. Split models indicate that commercial-service agglomeration is more sensitive to rail proximity and street-network conditions, whereas employment intensity is more associated with development intensity and land price. These findings support fine-grained station-area renewal and mixed-function planning. Full article

The existing arresting efficiency evaluation method overlooks corrosion defects in its formulation. If directly applied to evaluate and design arrestors for corroded sandwich pipes, it often leads to conservative evaluations of arresting efficiency and unreasonably designed arrestors. Based on this, this paper first verifies the reliability of numerical simulation results through physical experiments. On this basis, the influence of the structural parameters and material parameters of the arrestor on the arresting efficiency of the integral arrestor is analyzed. The results show that an increase in the length, thickness and material strength of the arrestor not only affects the arresting efficiency of the arrestor but also changes the arresting crossing mode, from parallel crossing to orthogonal crossing. A chart of arresting efficiency suitable for engineering design is proposed. Finally, a systematic comparison is conducted of different modeling methods. The results show that, considering both prediction accuracy and training efficiency, the Genetic Algorithm–Back Propagation (GA-BP) model significantly outperforms the empirical model, the Whale Optimization Algorithm–Back Propagation (WOA-BP) model, and the Particle Swarm Optimization–Back Propagation (PSO-BP) model. The average prediction error is only 6.56%, and 94.42% of the data error is less than 20%. The model provides a theoretical basis for the arrestor design and failure assessment of sandwich pipes with corrosion defects and has clear engineering guidance value. Full article

Accurate air temperature observation requires minimizing solar radiation-induced deviations, which are strongly influenced by radiation shield performance. However, conventional shields often produce significant errors under strong solar radiation or weak ventilation. In this study, an air temperature observation system integrating a radiation shield and a backpropagation (BP) neural network-based correction method is proposed. Computational fluid dynamics (CFD) simulations were conducted to quantify radiation-induced temperature deviations under representative meteorological conditions, and the simulated dataset was used to train and test the neural network model. Initial field comparison experiments were performed using a 076B forced-ventilation system as a reference, where measured differences were treated as experimental deviations and model outputs as predicted deviations. The results show that, before correction, the proposed system exhibited a maximum deviation of 1.05 °C and a mean deviation of 0.26 °C, while the root mean square error and mean absolute error between experimental and predicted deviations were 0.30 °C and 0.23 °C, respectively. The correction significantly reduced temperature deviations, demonstrating the effectiveness of the proposed system in improving measurement accuracy. Full article

With the advancement of China’s carbon peaking and carbon neutrality targets and the low-carbon upgrading of the construction industry, steel fiber recycled aggregate concrete (SFRAC) has attracted increasing attention as a sustainable construction material due to its advantages in resource recycling and enhanced mechanical performance. However, its compressive strength is influenced by multiple interacting factors, making accurate prediction challenging when using conventional empirical or regression-based methods. To enhance predictive performance, a compressive strength database was established based on published experimental data. The input layer included seven mixture parameters: water content, cement content, fine aggregate content, natural coarse aggregate content, recycled coarse aggregate content, steel fiber content, and superplasticizer dosage, with the 28-day compressive strength serving as the output variable. Using this database, four prediction models were developed, including a back-propagation (BP) neural network and three optimized variants—GA–BP, PSO–BP, and GA–PSO–BP, optimized by genetic algorithm (GA) and particle swarm optimization (PSO)—were developed. Their performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Among the four models, GA–PSO–BP produced the best predictive performance, with a best-run R² of 0.9308 on the validation set, exceeding the BP, GA–BP, and PSO–BP neural networks by 0.0642, 0.0326, and 0.0512, respectively. Over 10 independent runs, it attained an average R² of 0.8822 and consistently delivered the lowest RMSE and MAE with small standard deviations, confirming its superior predictive accuracy and stability. These findings suggest that integrating GA and PSO can effectively enhance the predictive accuracy and stability of the BP neural network, thereby providing a dependable reference for compressive strength prediction and mix proportion optimization of steel fiber recycled aggregate concrete. Full article

The turbine-driven coaxial boiler feed pump (TD-BFP) speed regulation system is a core auxiliary machine in thermal power generating units. Its complex physical characteristics, including strong square-law nonlinearity, multivariable coupling, and large inertia, pose significant challenges for conventional fixed-parameter PID controllers, which often suffer from severe regulation lag, integral windup, and high-frequency oscillation during wide-range operating condition transitions. To address these issues, an improved adaptive PID control strategy based on a Back Propagation (BP) neural network is proposed in this paper. Specifically, to overcome the negative control gradient loss caused by the square-law resistance in the physical model, a sign-preserving mapping logic ($u\mid u\mid$) is innovatively designed. Furthermore, a dynamic anti-integral windup mechanism with physical boundary constraints and a first-order inertial filtering algorithm is introduced. Comprehensive simulation experiments on the Matlab/Simulink platform under high-load step operating conditions (3683 r/min and 1104 t/h) reveal that the proposed algorithm achieves millisecond-level, zero-overshoot tracking. Quantitative evaluations demonstrate that, compared with the traditional PID controller, the proposed method reduces the Root Mean Square Error (RMSE) by 88.29% and the Integral of Absolute Error (IAE) by 93.75%, achieving a near-perfect goodness of fit ($R^2$) of 0.9998. Additionally, the Total Variation (TV) of the control command is substantially decreased. These results convincingly demonstrate that the proposed controller perfectly balances extremely high dynamic fitting accuracy with reduced mechanical wear, presenting exceptional engineering application value for the localization transformation of power plant control systems. Full article

Real-time pressure field measurement in aerospace vehicles is challenging because flexible sensor arrays must operate on curved surfaces under coupled thermal and pressure conditions. In this study, a temperature-compensated flexible capacitive pressure sensing system was developed for aerospace applications by integrating an 8 × 8 flexible sensor array, a multi-channel readout circuit based on time-division multiplexing and synchronous detection, and a Particle Swarm Optimization–Backpropagation (PSO-BP) neural network model. Calibration results showed high linearity, with a correlation coefficient of 0.9998 and a maximum relative error of 2.23%. Under coupled temperature–pressure conditions over 5–150 kPa and 10–110 °C, the average measurement error remained below 6%. Flight experiments further demonstrated valid in-flight data acquisition and trend-level pressure variations during key flight events, verifying the feasibility of the proposed approach for distributed aerospace pressure monitoring. Full article

To enhance the accuracy, stability, and interpretability of residential building cost prediction models, and thereby provide a reliable basis for project investment decision-making. This study takes Sichuan Province as the research area and develops an improved sparrow search algorithm (ISSA). The performance of the Genetic Algorithm (GA), Wolf Pack Algorithm (WPA), Sparrow Search Algorithm (SSA), and ISSA was first evaluated and compared using benchmark test functions. Subsequently, nine prediction models, including Back Propagation Neural Network (BP), GA-BP, WPA-BP, SSA-BP, ISSA-BP, Random Forest (RF), ISSA-RF, Extreme Gradient Boosting (XGBoost), and ISSA-XGBoost, were established for comparative analysis. Finally, SHapley Additive exPlanations (SHAP) were employed to rank the key factors affecting construction cost. The results show that: (1) The ISSA algorithm demonstrates excellent convergence accuracy, stability and speed on benchmark test functions. (2) The ISSA-BP model achieved an average coefficient of determination (R²) of 0.9773, an average root mean square error (RMSE) of 39.2339, an average mean absolute error (MAE) of 17.0973, an average mean absolute percentage error (MAPE) of 0.6293, and an average mean bias error (MBE) of 9.1583. Compared with the other models, ISSA-BP exhibited the best overall predictive performance. (3) SHAP analysis indicates that indicators such as total building area and structure type have the greatest impact on project cost, while roof form and roof waterproofing have the least influence. This study can serve as a reference for refining and intelligently managing construction project costs. Full article

Lake Taihu is a vital source of surface water for the Yangtze River Delta region, so effective monitoring of its water quality is essential for protecting the water source. However, most existing studies on unmanned aerial vehicle (UAV)-based water quality remote sensing have focused on single large rivers or lakes, primarily employing validation methods involving randomly selected samples. This makes it difficult to assess the generalisability of the models to unfamiliar watercourses. This study focuses on 13 inflow rivers on Xishan Island, a central island in Lake Taihu, which are characterized by short flow paths, independent catchment areas, and varying land use influences. Using a UAV multispectral remote sensing platform, we have designed a water quality monitoring and assessment framework tailored to multi-river systems with small sample sizes. First, various water body indices were developed and analysed for correlation with measured water quality parameters. Then, machine learning algorithms such as Backpropagation (BP) neural networks, Random Forest, XGBoost, Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) were selected to construct retrieval models. For accuracy evaluation, a spatial independent validation strategy was employed whereby one sample was forcibly set aside from each river to constitute the validation set. Using this method, we generated spatial distribution maps of water quality parameters for the inflow rivers and evaluated the influencing factors of spatial variation in water quality by area, taking into account water body functional types and ecological characteristics. The experimental results indicate that under the conditions of spatial independent validation strategy, the SVM model achieved the highest retrieval accuracy for dissolved oxygen (R² = 0.892, RMSE = 0.414 mg/L and MRE = 0.057), whereas the XGBoost model achieved the highest retrieval accuracy for turbidity (R² = 0.853, RMSE = 0.632 NTU and MRE = 0.065). The spatial pattern of water quality exhibited a pronounced gradient: dissolved oxygen (DO) concentrations followed the order of aquaculture area rivers > agricultural area rivers > urban area rivers, while turbidity displayed the opposite trend, reflecting that surrounding land use types, phytoplankton density, and human activity intensity are the dominant factors driving the spatial differentiation of river water quality on Xishan Island in spring. The full-chain technical framework of “multi-river synchronous retrieval—spatially independent validation strategy—area mechanistic assessment” proposed in this study provides a replicable evaluation paradigm for rapid water quality monitoring of Lake Taihu islands and similar watersheds, and holds significant implications for the construction of the Lake Taihu Eco-Island and the protection of the water environment. Full article

In this article, a data-driven adaptive neural network additional damping controller (DDANN-ADC) is proposed to regulate rotor-side converters of a DFIG-based power system to suppress sub-synchronous control interaction (SSCI). Firstly, a back propagation (BP) intermediate variable observer is designed to construct a dynamic model of DFIG-based wind farms based on real-time input–output measurement data. Subsequently, a modified cost function is developed for a BP online controller to generate a target control law, thereby contributing additional damping to the DFIG-based power system. The proposed DDANN-ADC can effectively utilize limited data generated during the control process to achieve online system identification and precise control of the system. Then, the stability of DFIG-based power system under the proposed DDANN-ADC is demonstrated with the Lyapunov function. Finally, simulation results reveal that the proposed DDANN-ADC methodology outperforms the traditional method with better adaptability and robustness under different operational conditions. Full article

Geopolymer gel concrete (GPC) is a kind of environmentally friendly concrete, which has become a potential alternative material to replace ordinary concrete. Traditional mix design of GPC is carried out under experimental conditions, which is time-consuming and labor-intensive. Geopolymer concrete (GPC) is intended for use in hydraulic structures, which are often exposed to water environments. Water flow exerts significant abrasion and erosion on these structures. If the abrasion resistance (AR) of the material is poor, the service life and service quality of hydraulic structures will be substantially reduced under the action of water flow. Therefore, AR is a key performance indicator for GPC in hydraulic engineering applications. This abrasion resistance can be enhanced by using fibers (for example, steel fibers, polyvinyl alcohol (PVA) fibers, and basalt fibers) and nanomaterials. Furthermore, there is a complex nonlinear relationship between the proportions of fibers and nanoparticles added and the properties of GPC. In this study, the circular ring test method and the underwater steel ball test method were conducted to investigate the AR of nano-SiO₂ (NS) and hybrid fiber (NHF) reinforced geopolymer gel concrete (NHF-GPC). A backpropagation (BP) neural network (BPNN) model optimized by the Grey Wolf Optimizer (GWO) (GWO-BPNN) is established to predict the abrasion resistance strength (ARS) and the abrasion rate of NHF-GPC based on the circular ring test method. In addition, the ARS, abrasion rate, and average abrasion depth (AAD) based on the underwater steel ball test method were also predicted. The results indicate that the GWO-BPNN model demonstrates superior performance over the standard BPNN, exhibiting higher prediction accuracy, better fitting performance, and faster convergence speed. Specifically, for the circular ring test method abrasion rate prediction, GWO-BPNN reduced the root mean square error (RMSE) by 30.3% and lowered the mean absolute percentage error (MAPE) to 8.4%. The GWO-BPNN model established in this study can provide efficient and reliable theoretical support for the optimization of the NHF-GPC mix design. Full article

The integration of digital technologies is increasingly recognized as a critical enabler of lean practices in prefabricated construction projects. However, a systematic understanding of the underlying factors that drive this lean–digital transformation remains limited. To address the gap, this study identified 18 factors through an in-depth review of 30 papers and a follow-up questionnaire survey. The factors are divided into five dimensions, i.e., organizational, social, technological, economic and environmental, according to an extended framework of the Socio-Technical Systems (STS) and Technology–Organization–Environment (TOE). These 18 factors were then analyzed via a back propagation (BP) neural network model. The empirical data were collected from 148 practitioners across 11 regions in China where PC industrialization, digital technology adoption, and lean-related practices are relatively mature. These regions were selected because digitalization-driven lean practices are more observable in such contexts, allowing the BP model to capture the comprehensive contribution of key factors more effectively. The findings reveal that the effective implementation of the smart lean practices via digitalization is primarily driven by a systematic process, where greater attention should be directed toward simulation-based process optimization, robust information management, integrated design and construction, lean management systems, and the workers’ digital skills. Although the empirical evidence is derived from relatively mature PC and digital construction markets in China, the identified factors provide reference insights for broader PC projects including less mature regions to make effective measures to improve lean implementation. This study contributes to the existing knowledge body of lean in PC by extending the theories of STS and TOE to advance the understanding of digital drivers. Additionally, the results serve as a reference for stakeholders by informing strategic priorities such as resource allocation for workforce development, advancing the realization of smart lean prefabricated construction. Full article

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision. Full article

The temperature management models for solar greenhouses exhibit strong regional dependency. Their application in non-target environments often faces significant limitations, frequently resulting in severe temperature control deviations. To address this challenge, seven solar greenhouses located in Lingyuan (Liaoning Province) and Yinan (Shandong Province) were utilized as experimental platforms. Using real-time environmental data collected by the NEUT-80S IoT monitoring system, backpropagation (BP) neural network models were trained and validated. Multiple stepwise regression analysis identified total solar radiation and sunshine duration as the primary determinants of cucumber yield. Based on these findings, a dynamic weight matrix was constructed using a solar radiation clustering algorithm. By integrating similarity distance and similarity coefficient, a microclimate similarity determination logic was established, leading to the proposal of an automatic model selection strategy with an 11-day update cycle. Quantitative validation demonstrated that when the threshold conditions—a similarity coefficient (R) ≥ 0.6 and a similarity distance (D) ≤ 0.85—are met, triggering the optimally matched model significantly improves the simulation goodness-of-fit (R²) from 0.6716 in the unmatched state to 0.9851. This strategy effectively achieves the cross-regional adaptation of high-yield temperature management models, providing robust technical support for the advancement of precision protected agriculture. Full article