Search Results (2,285)

The rapid growth of modular construction has increased the demand for accurate and computationally efficient methods for predicting the shear performance of cold-formed steel members. Modular construction-optimised beams, characterised by a mono-symmetric triangular hollow flange geometry, exhibit shear behaviour that is not well represented by existing analytical formulations. This study proposes an explainable machine learning framework to predict the ultimate shear capacity of cold-formed steel modular construction-optimised beams using a validated finite-element dataset comprising 105 parametric models. Six supervised machine learning algorithms are trained and evaluated using resampling-based validation and statistical performance metrics. Categorical boosting achieved the best predictive performance, with a coefficient of determination of 95.9% and a mean absolute percentage error of 6.49% under 50 repeated train and test splits. Model transparency is supported using Shapley Additive Explanations, which confirm thickness and yield strength as the most influential inputs within the investigated domain. In addition, prediction uncertainty was quantified using empirical 95% prediction intervals, and the modelling workflow was strengthened by explicitly defining reproducibility and no-leakage conditions. Overall, the proposed framework provides an efficient and interpretable finite element surrogate tool for rapid design-oriented estimation of modular construction-optimised beam shear capacity within the defined parameter ranges and loading configuration. Full article

Satellite-based precipitation estimates (SPE) provide essential spatial coverage and near real-time availability for hydrological applications but often exhibit systematic biases in regions characterized by complex terrain and strong climatic variability, limiting their reliability for flood-related studies. To address these limitations, this study proposes an Adaptive Regularization framework integrated within a Long Short-Term Memory (LSTM) model to enhance satellite–gauge rainfall fusion beyond conventional optimization strategies. The framework dynamically adjusts learning rate and weight decay during training based on validation performance and overfitting indicators, improving training stability, data efficiency, and model generalization across diverse precipitation regimes. The proposed approach was applied to refine Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-Final) daily rainfall estimates over the flood-prone east coast of Peninsular Malaysia. Model performance was assessed against ten optimization algorithms using correlation coefficient (CC), mean absolute error (MAE), normalized root mean squared error (NRMSE), percentage bias (PBias), and Kling–Gupta efficiency (KGE). Results show that the Adaptive Regularization framework consistently outperforms all benchmark optimizers, achieving an MAE of 6.87, CC of 0.68, NRMSE of 1.84, and KGE of 0.56. Overall, the proposed framework enhances spatial consistency and robustness across monsoon seasons, offering a scalable solution for improving SPE in flood-prone regions. Full article

Optical-fibre daylighting systems (OFDS) harvest solar energy as a renewable lighting resource by delivering sunlight deep into green buildings. This emerging technology for sustainable infrastructure reduces electric-lighting demand; however, reported performance is difficult to compare across heterogeneous designs, metrics, and validation practices. Therefore, a PRISMA 2020–reported systematic literature review (SLR) of OFDS studies from three databases (Google Scholar, Scopus, and Web of Science; 2000–2025) was conducted, synthesising primary research that quantifies system- or component-level performance, with a focus on (i) plastic optical fibre (POF) transmission characteristics; and (ii) POF-based illuminance model validation. After de-duplication and screening, 106 primary studies were included, and two meta-analyses were performed where data were harmonised from 29 studies in total. Across reported POF configurations, attenuation ranged from 150 to 800 dB/km with a pooled mean of 332.8 dB/km, corresponding to a mean 1 m transmission of 92.7% and median design length scales of ∼3.7 m for 80% transmission and ∼11.6 m to half-power. Across illuminance validation datasets, models showed high linear agreement with experimental measurements (coefficient of determination ($R^2$) = 0.99; slope = 0.99) but typically underpredicted illuminance (geometric mean ratio = 1.16; mean absolute error (MAE) = 27.3 lux; mean absolute percentage error (MAPE) = 17.6%). These findings underscore the need for a standardised evaluation framework, including consistent metric definitions, robust uncertainty reporting, and reusable validation datasets to enable variance-weighted synthesis, while also identifying short-run POF routing as a key lever for improving system efficiency. In addition to providing the OFDS research agenda, this study serves as a roadmap for the industrial development of daylighting systems for green buildings based on harvesting solar energy, with its novelty lying in the PRISMA-guided evidence synthesis and quantitative meta-analytic consolidation of POF transmission and illuminance-validation performance. Full article

Modeling and forecasting nonstationary and nonlinear economic time series remain fundamentally challenging due to structural breaks, volatility clustering, and noise contamination that distort the intrinsic stochastic structure. To address these limitations, this study proposes a novel three-stage hybrid statistical framework that systematically integrates multi-level signal decomposition with structured parametric modeling to enhance predictive accuracy. The proposed hybrid architectures—EMD–EEMD–ARIMA, EMD–EEMD–GMDH, and EMD–EEMD–ETS—employ a hierarchical decomposition–reconstruction strategy before forecasting. In the first stage, Empirical Mode Decomposition (EMD) decomposes the observed series into intrinsic mode functions (IMFs) and a residual component. In the second stage, Ensemble Empirical Mode Decomposition (EEMD) is applied to further refine the extracted components, mitigating mode mixing and improving signal separability. In the final stage, each reconstructed component is modeled using ARIMA, Exponential Smoothing State Space (ETS), and Group Method of Data Handling (GMDH) frameworks, and the individual forecasts are aggregated to obtain the final prediction. Empirical evaluation based on a recursive one-step-ahead forecasting scheme demonstrates consistent numerical improvements across all standard accuracy measures. In particular, the proposed EMD–EEMD–ARIMA model achieves the lowest forecasting error, reducing the root-mean-square error (RMSE) by approximately 6–7% relative to the best-performing single-stage model and by about 3–4% relative to the two-stage EMD-based hybrids. Similar improvements are observed in mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), indicating enhanced stability and robustness of the three-stage architecture. The results provide strong numerical evidence that multi-level decomposition combined with structured statistical modeling yields superior predictive performance for complex nonlinear and nonstationary time series. The proposed framework offers a mathematically coherent, computationally tractable, and systematically structured hybrid modeling strategy that effectively integrates noise-assisted decomposition with parametric and data-driven forecasting techniques. Full article

Tight conglomerate reservoirs are characterized by strong heterogeneity, significant in-situ stress differences, and unbalanced fracturing stimulation, which make fracture pressure prediction challenging and severely restrict the effectiveness of reservoir stimulation and ultimate recovery. Although acid pretreatment is an effective means to reduce fracture pressure, its quantitative relationship with fracture pressure remains unclear. There is an urgent need to establish a systematic method that integrates reservoir heterogeneity characterization, data augmentation, and intelligent prediction. Aiming at the tight conglomerate reservoir in the MH Block, this study proposes an intelligent fracture pressure prediction and acid pretreatment optimization method that integrates Self-Organizing Maps (SOMs), Generative Adversarial Networks (GANs), and Transformer models. First, SOM is used to perform unsupervised clustering of logging parameters to identify different geological feature categories and achieve fine-scale characterization of reservoir heterogeneity. Second, to address the issue of limited samples within each cluster, GAN is employed for high-quality data augmentation to expand the training sample set. Finally, a fracture pressure prediction model is constructed based on the Transformer architecture, and the influence of acid treatment parameters on fracture pressure is quantitatively analyzed using the SHAP method and laboratory experiments. The results show that the proposed model achieves a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 2.38 MPa, and a mean absolute percentage error (MAPE) of 2.02% on the test set, with prediction accuracy significantly outperforming benchmark models such as BPNN, XGBoost, and LSTM. Ablation experiments verify that both the SOM clustering and GAN augmentation modules effectively enhance model performance. Analysis of acid treatment parameters indicates that hydrofluoric acid (HF) concentration is the dominant factor influencing fracture pressure reduction, and the mud acid system exhibits a stronger synergistic effect compared to the single hydrochloric acid system. Reasonable optimization of acid concentration and dosage can significantly reduce fracture pressure (3.14–5.28 MPa). This method provides a theoretical basis and engineering guidance for accurate fracture pressure prediction and optimal design of acid pretreatment in tight conglomerate reservoirs. Full article

This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R² reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R²) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R² > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making. Full article

Accurate assessment of the State of Health (SOH) is critical for battery management systems in aviation. As a step towards this goal, this study presents a proof-of-concept for a novel SOH estimation method based on an Improved Particle Swarm Optimization-Extreme Learning Machine (IPSO-ELM) model, validated under controlled laboratory cycling conditions. Although traditional Extreme Learning Machines (ELM) are widely used due to their fast computation and good generalization, their random parameter initialization often leads to unstable convergence and limited accuracy. To address these limitations, this paper proposes a novel SOH estimation method based on an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the key parameters of ELM. Three health indicators (HI)—constant-current charging time, equal-voltage-drop discharge time, and average discharge voltage—were extracted from charge–discharge curves as model inputs. The IPSO algorithm dynamically adjusts the inertia weight, introduces a constriction factor and a termination counter to enhance global search capability and avoid local optima. Experimental results on open-source datasets (B005, B007, B0018) and laboratory datasets (A001, A002) demonstrate that the proposed IPSO-ELM model achieves a Root-Mean-Square Error (RMSE) below 0.7% and a Mean Absolute Percentage Error (MAPE) below 0.5%. Compared with standard ELM and PSO-ELM models, it significantly outperforms them in accuracy (e.g., for B0018, RMSE is reduced to 0.21% and MAPE to 0.14%), convergence speed, and robustness, establishing a foundation for future development of aviation-ready SOH estimators. Full article

Broiler respiratory rate (RR) in cage systems is a core physiological indicator of health and stress. However, real-time, non-invasive continuous RR monitoring is difficult in a high-density breeding environment, thereby limiting precise poultry health management. This study developed a feasible non-contact broiler RR measurement method to address this gap. The proposed method integrates infrared thermal imaging and phase-based video magnification (PBVM). Using cage-reared white-feathered broilers as subjects, we selected the thoracodorsal and tail regions as regions of interest (ROI), applied PBVM to amplify subtle respiratory-related body surface movements, and extracted RR features via the Fast Fourier Transform (FFT). Two validation experiments were conducted under controlled laboratory conditions. One was an RR dynamic monitoring experiment covering the entire life cycle (4 to 36 days), which analyzed video data of 198 individual quiet broilers. The other was a multi-gradient heat stress experiment with temperature increases of +2 °C, +4 °C, and +5 °C, and analyzed video data of 162 individual quiet broilers. The method achieved favorable measurement accuracy: in the whole-life-stage experiment, the mean absolute error (MAE) was 0.036 Hz, the mean absolute percentage error (MAPE) was 4.461%, and the coefficient of determination (R²) reached 0.961; in the heat stress experiment, the MAE was 0.042 Hz, the MAPE was 3.270%, and the R² reached 0.928. Linear regression analysis confirmed that healthy broiler RR decreased linearly with increasing age, and verified that RR showed a stepwise response to thermal challenge with a positive correlation between RR increase and temperature increment, accompanied by growth stage specificity. This study provides a feasible non-invasive approach for broiler RR monitoring, offering preliminary reference data for early heat stress detection and sustainable poultry production. Full article

Pavement rutting is caused by grooves formed by vehicle traffic, affecting driving comfort, safety, and service life. Rutting detection methods have evolved from manual and automated approaches to intelligent detection for smart cities and maintenance. However, lightweight intelligent detection still faces challenges such as insufficient accuracy and technical complexity, and a mature system has yet to be established. This study aims to develop a portable intelligent terminal for pavement rut detection, which can address the challenges associated with traditional pavement rut detection while providing accuracy and reliability. In this study, rutting detection experiments were performed on a full-scale accelerated loading track to collect data on vibration acceleration, angular velocity, and attitude angles. Comparative experiments were carried out between traditional and lightweight detection methods. Subsequently, GRU-CNN, LSTM–Transformer, GRU, and LSTM models were developed to analyze and compare their performance in predicting rutting depth. The results show that the terminal operates stably, offering convenient usability and reliable data acquisition. Furthermore, vehicle angular velocity and roll angle emerge as critical indicators reflecting rutting impacts on driving states and prove suitable for pavement rut depth detection. The proposed GRU-CNN model achieves superior accuracy and overall performance relative to widely used models. Under synchronous detection conditions, the lightweight method yields a mean absolute error (MAE) of 1.22 mm, achieving performance improvements of 17.32%, 8.74%, and 10.08% over the LSTM–Transformer, GRU, and LSTM models, respectively. Additionally, the method yields a mean absolute percentage error of approximately 10.6%, representing error reductions of 15.87%, 19.08%, and 23.74% compared to the aforementioned baseline models, which meets application requirements. Innovation lies in the development of a lightweight intelligent terminal and GRU-CNN hybrid model that integrates vehicle dynamic parameters for large-scale pavement rutting detection. This study presents a lightweight, real-time pavement rutting detection method based on vehicle operation data for the construction and maintenance of smart cities and intelligent transportation infrastructure, combining the features of high cost effectiveness, high accuracy, and ease of large-scale application. Full article

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article

This study developed an interpretable framework, RFE-SHAP, designed for yarn quality prediction. It integrates Recursive Feature Elimination (RFE) with SHapley Additive exPlanations (SHAP) theory to refine feature selection and mitigate data redundancy in small-sample environments. With Support Vector Regression (SVR) serving as the foundational evaluator, the RFE process iteratively identifies critical variables. Distinct from conventional methods, our approach employs SHAP values to quantify both the primary effects of individual features and the complex synergistic interactions among variables. This yields a transparent and intuitive strategy for identifying optimal feature subsets for two key quality indicators: yarn strength and hairiness H-value. To assess performance, a comparative analysis was performed between the traditional SVR-RFE method and the proposed RFE-SHAP method, using both as inputs for a Back-Propagation Artificial Neural Network (BP-ANN). The experimental results based on authentic production data demonstrate that the RFE-SHAP-BP model significantly enhances prediction reliability. Notably, compared to the baseline SVR-RFE-BP model, the proposed approach reduced the Mean Absolute Percentage Error (MAPE) by 0.73 and 1.01 percentage points for yarn strength and hairiness H-value, respectively. The final MAPE values reached 2.10% and 2.78%, confirming the model’s superior precision. These findings indicate that the RFE-SHAP method is highly feasible and effectively elevates prediction performance in data-limited industrial scenarios. Full article

Electronic nose systems are advanced technological tools that enable the objective evaluation of odors through sensor arrays mimicking the human olfactory mechanism and sophisticated data processing algorithms. These systems facilitate rapid, reproducible, and standardized measurement of chemical components in applications such as food safety, environmental monitoring, medical diagnostics, and industrial quality control. In this study, measurements obtained from electronic nose sensors were compared with olfactometry panelist assessments using n-butanol as a reference substance in accordance with the TS EN 13725 standard. Furthermore, machine learning algorithms, including Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), were applied to model the sensor data and evaluate their predictive accuracy. The results demonstrated the reliability and applicability of the electronic nose system, achieving training mean absolute percentage error (MAPE) values of 6.53% for PLS, 10.89% for SVR, and 0.15% for GPR. This study presents an innovative approach that systematically assesses the performance of electronic nose technology using a standardized reference odor and highlights the effectiveness of the modeling approach. Full article

Vertical prestressing is critical for shear resistance in long-span PC continuous rigid-frame bridges, yet existing design methods rely on the inaccurate superposition of single-tendon stress fields, neglecting mechanical interaction between adjacent tendons. This study derives the first closed-form elastic analytical solution for the vertical normal stress field under two interacting prestressing tendons, explicitly capturing the coupling term. Validated against high-fidelity Finite Element Analysis (FEA), the solution achieves a Mean Absolute Percentage Error (MAPE) below 6.8%, outperforming conventional superposition methods by 6.8–17.7 percentage points. The analysis reveals a transition from diffusion-dominated to superposition-dominated stress regimes and establishes a predictive linear relationship between tendon spacing and the depth of the prestressing blind zone. The section at one-fourth of the web height below the top edge is identified as the critical control section, leading to a proposed maximum spacing limit of 0.34 times the web height to ensure a stress uniformity coefficient greater than 0.95. This criterion represents a 13.3% increase over empirical rules and a 27.5% increase over the JTG 3362-2018 limit, enabling estimated savings of 52,000 CNY per typical four-span bridge while maintaining structural safety. This represents a 13.3% increase over empirical rules and a 27.5% increase over the limit in JTG 3362-2018, enabling estimated savings of 52,000 CNY per typical four-span bridge while maintaining structural safety. Full article

As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses on low-permeability reservoirs in the Changqing Oilfield, evaluating three surfactant systems—YHS-Z1 (a 7:3 mass ratio blend of hydroxypropyl sulfobetaine and cocamide), YHS-Z2 (a polyether carboxylate, a nonionic-anionic composite) and a middle-phase microemulsion system (Heavy alkylbenzene sulfonate and hydroxysulfobetaine were combined with a mass ratio of 7:3)—through a series of experiments including interfacial tension measurement, contact angle analysis, static and dynamic oil displacement tests, as well as emulsion transport/retention index assessments, to comprehensively characterize their oil displacement properties. Based on the experimental data, this study constructed four classical regression models: Ridge Regression, Random Forest (RF), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR), and conducted a comparative analysis of their predictive performance. The results demonstrate that the Random Forest (RF) model achieved the optimal prediction performance, with a Mean Absolute Error (MAE) of 1.8245, a Mean Absolute Percentage Error (MAPE) of 4.78%, and a coefficient of determination (R²) of 0.9428 on the training set. Further analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the retention index is the primary global factor (accounting for 49.79% of the variance), while significant intergroup differences exist in the primary factors across different surfactant systems. Concurrently, single-factor and multi-factor sensitivity analyses were conducted to elucidate synergistic effects and threshold behaviors among parameters. The optimal parameter combination, identified via a random search method, achieved a predicted recovery factor of 45.61%, representing a 6.57% improvement over the highest experimental value. This study demonstrates that machine learning methods can effectively identify the dominant factors in oil displacement and enable synergistic parameter optimization, thereby providing a theoretical foundation for the efficient development of surfactant flooding in low-permeability reservoirs. Full article