Search Results (108)

Despite extensive advancements in Engineered Cementitious Composites (ECCs), mixture design remains predominantly empirical, due to the absence of a quantitative parameter directly linking fiber–matrix interfacial mechanics to strain-hardening performance. This study identifies fiber–matrix interfacial friction as a quantifiable parameter and establishes a micromechanics-guided interfacial regulation framework to enhance the toughness of ECC by regulating fiber failure modes. First, a critical fiber–matrix interfacial frictional stress, (τ₀)_crit, corresponding to the transition between fiber pull-out and fracture, was theoretically derived based on energy dissipation maximization during crack propagation. A back-calculation approach was further developed to determine interfacial frictional stress (τ₀) directly from tensile stress–crack opening responses under single-crack tension, eliminating reliance on single-fiber pull-out testing. Then, τ₀ was tuned toward (τ₀)_crit through interfacial regulation using fly ash. Experimental results demonstrate that the toughness of ECC is maximized when τ₀ approaches (τ₀)_crit, confirming the validity of the proposed toughness enhancement mechanism. The study establishes an explicit mechanistic linkage between interfacial micromechanics and macroscopic strain-hardening performance, providing a predictive and quantitative design pathway that transcends empirical mixture adjustment. Full article

The rapid growth of e-commerce has intensified operational and sustainability challenges in urban last-mile delivery, necessitating routing methods that perform reliably under realistic traffic and spatial conditions. This study evaluates three routing algorithms, Nearest Neighbor (NN), Clarke–WrightSavings (CWS), and Ant Colony Optimization (ACO), using 1764 real-world Amazon delivery stops grouped into ten operational clusters in the Nashville metropolitan area. Travel distances and times were obtained through the Google Maps Distance Matrix API in driving mode to reflect actual road network structure and typical traffic conditions. Substantial performance differences were observed across algorithms and cluster configurations. NN achieved a strong performance in compact clusters (18.43 miles and 58.48 min in Cluster 4) but performed poorly in dispersed clusters (82.44 miles and 196.48 min in Cluster 9), reflecting high sensitivity to spatial dispersion. In contrast, CWS consistently reduced travel distance and time across clusters, achieving the shortest observed route (18.50 miles and 47.82 min in Cluster 10). Relative to ACO, CWS reduced travel distance by up to 42% (Cluster 9) and reduced travel time by over 45% in high-dispersion clusters. ACO exhibited the highest variability, with distances reaching 98.77 miles and travel times exceeding 218 min. Multi-criteria evaluation using efficiency ratios, distributional analysis, performance quadrant visualization, and a Composite Performance Index (CPI) confirmed the dominance of CWS. CPI scores of 1.00 (CWS), 0.78 (NN), and 0.00 (ACO) reflected balanced spatial and temporal efficiency under identical traffic-informed inputs. The results demonstrate that deterministic savings-based routing provides superior stability, efficiency, and scalability in semi-static urban delivery systems. However, the present study did not benchmark the evaluated algorithms against state-of-the-art exact TSP solvers (e.g., Concorde, LKH) or more recent metaheuristics such as Genetic Algorithms or Variable Neighborhood Search. The objective was to provide a controlled empirical comparison under consistent traffic-informed cost matrices rather than to establish global optimality bounds. Consequently, while the findings strongly support the relative superiority of the Clarke–Wright Savings approach within the evaluated framework, future research incorporating advanced exact and hybrid optimization methods would further contextualize algorithmic performance. Full article

Sea surface temperature (SST) modes of climate variability in the South Atlantic Ocean remain a challenging topic. To improve the understanding of this subject, this study assesses the influence of two commonly discussed SST variability modes, the South Atlantic Dipole (SAD) and the Southwestern South Atlantic (SWSA), on South America (SA) during the present-day climate conditions and discusses, based on the previous literature, their development. Complementing previous analyses based on annual or seasonal scales, the analysis is performed at the monthly scale, given its relevance for subseasonal-to-seasonal (S2S) forecasts. Empirical Orthogonal Function (EOF) analysis was applied to standardized monthly SST anomalies relative to the period 1991–2020, using data from the Extended Reconstructed Sea Surface Temperature (ERSST). After characterizing the SAD and SWSA modes, composites of different variables, such as precipitation anomalies, were constructed for the different phases of each pattern. The results show that the SAD is the dominant mode of SST variability, mainly influencing tropical latitudes by modulating the Intertropical Convergence Zone (ITCZ). During its positive (negative) phase, the ITCZ shifts southward (northward). In contrast, the SWSA exhibits a more localized subtropical–extratropical structure, characterized by SST anomalies along the south–southeastern coast of Brazil, and is closely associated with variability in the South Atlantic Convergence Zone (SACZ). The relationship between the SWSA and SACZ appears strong during the austral extended summer, when warmer waters during the positive (negative) SWSA phase are associated with wetter (drier) conditions over southeastern SA and drier (wetter) conditions over the continental and oceanic branches of the SACZ. Full article

Detection and simultaneous monitoring of multiple trace gases is vital in scientific and industrial processes. Here, we use coherent control in quartz-enhanced photoacoustic spectroscopy (COCO-QEPAS) with an in situ learning method for rapid fingerprinting of trace gases to identify and monitor arbitrary gases at very low concentrations, without prior knowledge of gas composition. We validate this on various mixtures, including CH₄/C₂H₂/C₂H₄/C₂H₆/NO₂/NH₃. To this end, we demonstrate real-time analysis of mixtures containing up to four trace gases at ppm-level, monitoring changes in seconds using linear regression. The scalability of simultaneously distinguishable gases is straightforward. Furthermore, we expand fingerprinting to 10 ppm with a detection limit of 180 ppb CH₄, and apply empirical mode decomposition as an adaptive, data-driven filtering method to recover characteristic spectral features at the noise floor. For quantitative analysis in the ppb regime, we employ principal component regression as a calibration model that exploits correlations across the full spectrum. Consequently, our method offers significant potential for sensing applications where speed, accuracy, and simplicity are critical. Full article

The Canadian Prairies are a region of critical importance to continental hydroclimate and agriculture, exhibiting high sensitivity to variability in atmospheric moisture transport. This study investigates the seasonal and interannual variability of integrated moisture flux over the Canadian Prairie region (96° W–114° W, 49° N–53° N) using the National Centers for Environmental Prediction (NCEP) Reanalysis dataset from 1979 to 2023. We employ a combination of composite analysis and Empirical Orthogonal Function (EOF) analysis to identify the dominant modes of variability and their associated large-scale synoptic drivers. Our results confirm a strong seasonal reversal: winter moisture flux is predominantly zonal (westerly), contributing an average of 90% to total inbound flux, while summer flux is primarily meridional (southerly), contributing a dominant 72.6%. Composite analysis of extreme moisture years reveals that anomalously high-moisture winters are associated with an intensified Aleutian Low and a strengthened pressure gradient off the North American west coast, facilitating enhanced westerly flow. Conversely, a strengthened continental high-pressure system characterizes anomalously low-moisture winters. During summer, high-moisture years are driven by an enhanced southerly component of the flow, likely linked to a strengthened Great Plains Low-Level Jet (GPLLJ). The first EOF mode for winter explains 43% of the variance in eastward flux and is characterized by a pattern consistent with the El Niño Southern Oscillation (ENSO) teleconnection pattern. These findings underscore the control of Pacific-centric circulation patterns on Prairie hydroclimate in winter and have significant implications for predicting seasonal water availability. Full article

Although previous studies composited response of sea surface temperature (SST) to typhoon sea surface wind (SSW) forcing around typhoon center, how SST responded spatiotemporally along the typhoon track over the ocean remains unclear. Through Empirical Orthogonal Function (EOF) analysis, several isolated typhoons in the Western North Pacific (WNP) from 2021 to 2024 were investigated. Two SSW forcing modes and two SST response modes were identified. The first SSW mode spatially reflects the overall distribution of SSW along the track, centering at its maturation position. And the first SST mode exhibits a high spatial correlation ($\left|R\right|>0.85$) with this SSW mode. The second SSW mode displays a distinct track-long scale dipole pattern along the path of the typhoon, representing its intensity variation during the “development–maturation–decay” lifecycle. Similarly, the second SST response mode shows a significant but lower correlation with this second SSW mode. Both corresponding SST response modes typically lag behind their respective wind-forcing by approximately 2 to 4 days, indicating that these SST response modes are direct reactions to SSW forcing. These cases implies that two track-long scale SSW modes are generally present during the lifecycle of typhoons and that their corresponding SST responses are dominated accordingly. Full article

As the core component in intelligent manufacturing systems, the precise control of the welding clamp’s electrode pressure plays a decisive role in ensuring the quality of spot welding. This paper proposes a novel pressure control strategy for robotic welding clamp based on partitioned adaptive RBF neural networks: (1) Deformation of the clamp body can lead to deviations in workpiece positioning. To address this issue, a deflection compensation method for robot welding clamp based on the PSO-RBF neural network is proposed. By leveraging pre-calibrated empirical data, the intrinsic mapping relationships are identified, and the derived deflection compensation value is integrated into the real-time position command of the robot end-effector. (2) During electrode motion, the system is subjected to external disturbances such as friction and gravitational forces. So, a sliding mode control strategy incorporating adaptive RBF disturbance compensation is proposed to achieve robust speed regulation. Furthermore, the electrode’s reference velocity is dynamically adjusted based on the welding force error and improved admittance control algorithm, enabling indirect regulation of the welding force to reach the desired set value. The results demonstrate that the proposed composite control strategy reduces electrode pressure overshoot to less than 5% and enhances steady-state control accuracy to ±1.5%. Full article

This study investigates the effects of surface roughness and interfacial agents on the bond performance of geopolymer–concrete composites (GCCs). Firstly, cement concrete substrates with four surface roughness conditions, including cast surface, drawn surface, chiseled surface and split surface, were prepared and their surface roughness was quantitatively characterized by the Joint Roughness Coefficient (JRC) based on the 3D surface morphology reconstruction technique. The GCC specimens were prepared by casting geopolymer concrete on cement concrete substrates and using three interfacial agents in the bonding interface. Then, the splitting tensile tests were conducted on GCC specimens and the effect of surface roughness and interfacial agents on the bonding strength and failure behavior of GCC was discussed. Finally, the empirical model of the bonding strength of the GCC was proposed by considering surface roughness, interfacial agent, and geopolymer tensile strength simultaneously. The results show that with increasing JRC, the bonding strength of GCC shows a trend of slow increase followed by significant increase, and the failure modes transitioned from interfacial debonding to concrete matrix failure. Among the bonding agents, geopolymer slurry achieved the highest bonding strength, followed sequentially by untreated interfaces, SBR-modified cement paste, and expansive agent-modified cement paste. The results also show that the empirical model can accurately predict the interface splitting tensile strength of GCC under different surface roughness and interfacial agents, with a prediction accuracy of 0.92. Full article

Accurate probabilistic load forecasting is essential for secure power system operation and efficient energy management, particularly under increasing renewable integration and demand-side complexity. However, traditional forecasting methods often struggle with issues such as non-linearity, non-stationarity, feature redundancy, and quantile crossing, which hinder reliable uncertainty quantification. To overcome these challenges, this study proposes a hybrid probabilistic load forecasting framework that integrates empirical mode decomposition (EMD), LASSO-based feature selection, and a monotone composite quantile regression neural network (MCQRNN) enhanced with kernel density estimation (KDE). First, EMD decomposes the raw load series into intrinsic mode functions and a trend component to mitigate non-stationarity. Then, LASSO selects the most informative features from both the decomposed components and the original time series, effectively reducing dimensionality and multicollinearity. Subsequently, the proposed MCQRNN model generates multiple quantiles under monotonicity constraints, eliminating quantile crossing and improving multi-quantile coherence through a composite loss function. Finally, Gaussian kernel density estimation reconstructs a continuous probability density function from the predicted quantiles, enabling full distributional forecasting. The framework is evaluated on two public datasets—GEFCom2014 and ISO New England—using point, interval, and density evaluation metrics. Experimental results demonstrate that the proposed EMD–LASSO–MCQRNN–KDE model outperforms benchmark approaches in both point and probabilistic forecasting, providing a robust and interpretable solution for uncertainty-aware grid operation and energy planning. Full article

Ecological restoration of mine wastelands is central to biodiversity conservation and ecosystem recovery worldwide. However, the long-term ecological consequences of active restoration versus natural regeneration remain debated, particularly in mountainous karst landscapes. Using a space-for-time substitution, we established a five-stage chronosequence—recently abandoned, 10 years, 20 years, 30 years, and a late-successional forest (>35 years)—in a typical underground coal-mine wasteland in eastern Yunnan, southwest China. Each age class contained paired active restoration and natural regeneration sites; the late-successional forest served as a reference. We surveyed nested vegetation plots (20 × 20 m with shrub and herb subplots) in summer and autumn, recorded vertebrate species with camera traps, and quantified α-diversity (species richness, Shannon–Wiener diversity, Simpson’s diversity, Pielou’s evenness) and β-diversity (Bray–Curtis dissimilarity, non-metric multidimensional scaling). Overall plant α-diversity was highest in natural regeneration and lowest in active restoration, whereas tree-layer diversity was highest in active restoration and shrub and herb layers were richer under natural regeneration. Preliminary data from our camera traps suggested that animal species richness ranked late-successional forest > natural regeneration > active restoration, but evenness peaked in active restoration, suggesting early-stage homogenization. Plant β-diversity indicated stronger compositional divergence among active restoration sites and greater similarity between natural regeneration and the reference forest; both modes converged toward the reference forest over time but followed distinct patterns. These findings suggest that active restoration accelerates structural development yet increases between-site heterogeneity, whereas natural regeneration maintains higher overall diversity and compositional similarity to reference communities. Our results provide preliminary empirical guidance for selecting restoration strategies in similar karst coal-mine landscapes. Full article

This study presents a strut-and-tie modeling (STM) framework for reinforced concrete (RC) deep beams strengthened with intraply hybrid composites (IRCs), integrating comprehensive experimental data from beams with three different span lengths (1.0 m, 1.5 m, and 2.0 m). Although the use of fiber-reinforced polymers (FRPs) for shear strengthening of RC members is well established, limited attention has been given to the development of STM formulations specifically adapted for hybrid composite systems. In this research, three distinct IRC configurations—Aramid–Carbon (AC), Glass–Aramid (GA), and Carbon–Glass (CG)—were applied as U-shaped jackets to RC beams without internal transverse reinforcement and tested under four-point bending. All experimental data were derived from the authors’ previous studies, ensuring methodological consistency and providing a robust empirical basis for model calibration. The proposed modified STM incorporates both the axial stiffness and effective strain capacity of IRCs into the tension tie formulation, while also accounting for the enhanced diagonal strut performance arising from composite confinement effects. Parametric evaluations were conducted to investigate the influence of the span-to-depth ratio (a/d), composite configuration, and failure mode on the internal force distribution and STM topology. Comparisons between the STM-predicted shear capacities and experimental results revealed excellent correlation, particularly for deep beams (a/d = 1.0), where IRCs substantially contributed to the shear transfer mechanism through active tensile engagement and confinement. To the best of the authors’ knowledge, this is the first study to formulate and validate a comprehensive STM specifically designed for RC deep beams strengthened with IRCs. The proposed approach provides a unified analytical framework for predicting shear strength and optimizing the design of composite-strengthened RC structures. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

The prestressed concrete-filled double skin steel tube (CFDST) lattice tower has emerged as a promising structural solution for large-capacity wind turbine systems due to its superior load-bearing capacity and economic efficiency. The steel–concrete composite adapter (SCCA) is a key component that connects the upper tubular steel tower to the lower lattice segment, transferring axial loads. However, the compressive behaviour of the SCCA remains underexplored due to its complex multi-shell configuration and steel–concrete interaction. This study investigates the axial compression behaviour of SCCAs through refined finite element simulations, identifying diagonal extrusion as the typical failure mode. The analysis clarifies the distinct roles of the outer and inner shells in confinement, highlighting the dominant influence of outer shell thickness and concrete strength. A sensitivity-based parametric study highlights the significant roles of outer shell thickness and concrete strength. To address the high cost of FE simulations, a 400-sample database was built using Latin Hypercube Sampling and engineering-grade material inputs. Using this dataset, five neural networks were trained to predict SCCA capacity. The Dropout model exhibited the best accuracy and generalization, confirming the feasibility of physics-informed, data-driven prediction for SCCAs and outperforming traditional empirical approaches. A graphical prediction tool was also developed, enabling rapid capacity estimation and design optimization for wind turbine structures. This tool supports real-time prediction and multi-objective optimization, offering practical value for the early-stage design of composite adapters in lattice turbine towers. Full article

This study suggests a hybrid approach that combines improved feature selection and intelligent diagnosis to increase the operational safety and intelligent diagnosis capabilities of hydropower units. In order to handle the vibration data, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used initially. A novel comprehensive index is constructed by combining the Pearson correlation coefficient, mutual information (MI), and Kullback–Leibler divergence (KLD) to select intrinsic mode functions (IMFs). Next, feature extraction is performed on the selected IMFs using Refined Composite Multiscale Fluctuation Dispersion Entropy (RCMFDE). Then, time and frequency domain features are screened by calculating dispersion and combined with IMF features to build a hybrid feature vector. The vector is then fed into a CNN-GRU-Attention model for intelligent diagnosis. The improved slime mold algorithm (ISMA) is employed for the first time to optimize the hyperparameters of the CNN-GRU-Attention model. The experimental results show that the classification accuracy reaches 96.79% for raw signals and 93.33% for noisy signals, significantly outperforming traditional methods. This study incorporates entropy-based feature extraction, combines hyperparameter optimization with the classification model, and addresses the limitations of single feature selection methods for non-stationary and nonlinear signals. The proposed approach provides an excellent solution for intelligent optimized diagnosis of hydropower units. Full article

This study investigates the complex dynamics of new-type urbanization in resource-based county-level cities, using Wuan City in Hebei Province, China, as a representative case. As China pursues a high-quality development agenda, cities historically dependent on resource extraction face profound challenges in achieving sustainable and inclusive urban growth. This research employs a multi-method approach—including Theil index analysis, industrial shift-share analysis, a Cobb–Douglas production function model, and a composite urbanization index—to quantitatively diagnose the constraints on Wuan’s development and assess its transformation efforts. Our empirical results reveal a multifaceted situation: while the urban–rural income gap has narrowed, rural income streams remain fragile. The shift-share analysis indicates that although Wuan’s traditional industries have regained competitiveness, the city’s economic structure is still burdened by a persistent negative structural component, hindering diversification. Furthermore, the economy exhibits characteristics of a labor-intensive growth model with inefficient capital deployment. These underlying issues are reflected in a comprehensive urbanization index that, after a period of rapid growth, has recently stagnated, signaling the exhaustion of the city’s traditional development mode. In response, Wuan attempts an “industrial transformation-driven new-type urbanization” path. This study details the three core strategies being implemented: (1) incremental population urbanization through development at the urban fringe and in industrial zones; (2) in situ urbanization of the existing rural population; and (3) the cultivation of specialized “characteristic small towns” to create new, diversified economic nodes. The findings from Wuan offer critical, actionable lessons for other resource-dependent regions. The case demonstrates that successful urban transformation requires not only industrial upgrading but also integrated, spatially aware planning and robust institutional support. We conclude that while Wuan’s model provides a valuable reference, its strategies must be adapted to local contexts, emphasizing the universal importance of institutional innovation, human capital investment, and a people-centered approach to achieving resilient and high-quality urbanization. Full article