Search Results (6,473)

With the rapid advancement of construction machinery electrification, optimizing the energy efficiency of electric loaders requires representative duty cycles that accurately capture real-world operating characteristics. However, most existing studies rely on simplified test-track cycles, which fail to reflect the complexity of actual operations. To address this gap, this paper takes a commercial concrete mixing plant as a case study and proposes a cross-scenario generalization method for the duty cycle aggregation of electric loaders. The method integrates multi-source signal acquisition, task-segment partitioning, feature extraction, and dimensionality reduction via Principal Component Analysis (PCA), enabling the clustering of typical operating modes and reconstruction of representative duty cycles through segment concatenation. The aggregated duty cycles are validated using Jensen–Shannon divergence, showing similarity levels above 93% compared with field measurements from mixing plants in Yiwu and Kunshan. These results demonstrate the method’s strong temporal adaptability and cross-scenario transferability. The proposed approach provides a solid foundation for energy consumption assessment, powertrain matching, and control strategy optimization of electric loaders while also supporting the development of duty cycle databases and future industry standardization. Full article

Although traditionally contraindicated, the coadministration of tamoxifen and estradiol may hold clinical relevance in specific contexts, particularly in breast cancer survivors with premature menopause and a high risk of osteoporosis, thereby justifying the need to re-evaluate this therapeutic combination. This study presents an innovative physiologically based pharmacokinetic (PBPK) modeling approach to evaluate the coadministration of tamoxifen and estradiol in women with breast cancer and a high risk of osteoporosis. Using GastroPlus^® software, PBPK models were developed and validated for both drugs, based on physicochemical and kinetic data obtained from the literature and, where necessary, supplemented by estimates generated in ADMET Predictor^®. The simulations considered different hormonal profiles (pre and postmenopausal) and therapeutic regimens, evaluating potential interactions mediated by the CYP3A4 enzyme. Analysis of the pharmacokinetic parameters (F, C_max, T_max and AUC) revealed strong agreement between the simulated and experimental values, with prediction errors of less than twofold. The drug interaction studies, carried out in dynamic and stationary modes, indicated that estradiol does not significantly alter the pharmacokinetics of tamoxifen, even at increasing doses or in enlarged virtual populations. These results represent the first in silico evidence that, under certain conditions, the concomitant use of estradiol does not compromise the pharmacokinetic efficacy of tamoxifen. Although the study is computational, it provides a solid scientific basis for re-evaluating this therapeutic combination and proposes a pioneering model for personalized strategies in complex oncological contexts. All simulations assumed average enzyme abundance/activity without CYP polymorphism parameterization; findings are restricted to parent-tamoxifen pharmacokinetics and do not infer metabolite (e.g., endoxifen) exposure or phenotype effects. Full article

An optical vortex is a typical structured light field characterized by a helical wavefront and a central phase singularity. With its expanding applications in modern information technology, the demand for generating vortex beams with diverse topological charges continues to grow. Existing methods for modulating the topological charges of vortex beams involve complex operations and high costs. This study proposes a novel approach to modulate the topological charges of optical vortices through edge diffraction of a high-order Hermit–Gaussian (HG) mode laser. First, a high-order HG mode laser is built using off-axis pumping configuration. By selectively obscuring specific lobes of the high-order HG beam, various optical vortices are generated using a cylindrical lens mode converter. The topological charge can be continuously tuned by controlling the number of obscured lobes. This method substantially improves the efficiency of topological charge modulation, while also enabling the generation of fractional vortex states. These advancements show potential in mode-division-multiplexed optical communications and encryption. Full article

Single-phase ground faults (SPGFs) in isolated neutral medium-voltage networks are difficult to detect, especially under high transient resistance. This paper proposes a centralized ground fault protection unit (CGFPU) that combines zero-sequence current (ZSC) magnitude and phase-angle analysis to enhance selectivity. Simulation results show that as transient resistance increases from 1 Ohm to 10 kOhm, fault currents decrease significantly, yet the CGFPU reliably identifies the faulty feeder by exploiting the characteristic 180° phase shift of ZSC phasors. The method remains selective with angular deviations up to ±20° and distinguishes between feeder and busbar faults. Compared with conventional amplitude- or model-based techniques, the proposed approach achieves faster detection, lower computational complexity, and robustness against unbalanced and charging currents. Furthermore, the CGFPU operates adaptively in alarm or trip mode depending on fault severity, thus preserving continuity for high-resistance faults and ensuring rapid isolation of bolted faults. These contributions establish a practical, scalable, and future-ready solution for SPGF protection in medium-voltage isolated neutral networks. Full article

Tunnel anchorages are critical components in long-span suspension bridges, transferring immense cable forces into the surrounding rock mass. Although previous studies have advanced the understanding of their pullout behavior through field tests, laboratory models, numerical simulations, and theoretical analyses, significant challenges remain in predicting their performance in complex geological conditions. This study investigates the pullout failure mechanism and bearing behavior of tunnel anchorages situated in heterogeneous conglomerate rock, with application to the Wujiagang Yangtze River Bridge in China to employ a tunnel anchorage in such strata. An integrated research methodology is adopted, combining in situ and laboratory geotechnical testing, a highly instrumented 1:12 scaled field model test, and detailed three-dimensional numerical modeling. The experimental program characterizes the strength and deformation properties of the rock, while the field test captures the mechanical response under design, overload, and ultimate failure conditions. Numerical models, calibrated against experimental results, are employed to analyze the influence of key parameters such as burial depth, inclination, and overburden strength. Furthermore, the long-term stability and creep behavior of the anchorage are evaluated. The results reveal the deformation characteristics, failure mode, and ultimate pullout capacity specific to weakly cemented and stratified rock. The study provides novel insights into the rock–anchorage interaction mechanism under these challenging conditions and validates the feasibility of tunnel anchorages in complex geology. The findings offer practical guidance for the design and construction of future tunnel anchorages in similar settings, ensuring both safety and economic efficiency. Full article

In this study, a colorimetric–photothermal dual-mode immunosensor based on Fe(Ⅲ)–gallic acid composite Prussian blue nanozyme (FGAN@PB NPs) was developed for the highly sensitive detection of malachite green (MG) in aquatic products. This strategy addresses the stability limitations associated with conventional horseradish peroxidase (HRP). In the colorimetric mode, the immunosensor exhibited an IC₅₀ of 7.56 ng/mL with a linear detection range of 2.21–25.84 ng/mL. In the photothermal mode, the linear range was 0.262–25.6 ng/mL, with a detection limit (LOD) of 0.31 ng/mL. The results from the two detection modes were mutually corroborative. Moreover, the detection of the proposed immunosensor was strongly correlated with the LC-MS/MS, offering a promising approach for the rapid on-site screening of MG and improving its applicability in complex sample matrices. Full article

Interferometric Synthetic Aperture Radar (InSAR) technology is crucial for large-scale land subsidence analysis in cultivated areas within hilly and mountainous regions. Accurate prediction of this subsidence is of significant importance for agricultural resource management and planning. Addressing the limitations of existing subsidence prediction methods in terms of accuracy and model selection, this paper proposes a deep neural network prediction model based on Variational Mode Decomposition (VMD) and the Snake Optimizer (SO), termed VMD-SO-CNN-LSTM-MATT. VMD decomposes complex subsidence signals into stable intrinsic components, improving input data quality. The SO algorithm is introduced to globally optimize model parameters, preventing local optima and enhancing prediction accuracy. This model utilizes time–series subsidence data extracted via the SBAS-InSAR technique as input. Initially, the original sequence is decomposed into multiple intrinsic mode functions (IMFs) using VMD. Subsequently, a CNN-LSTM network incorporating a Multi-Head Attention mechanism (MATT) is employed to model and predict each component. Concurrently, the SO algorithm performs global optimization of the model hyperparameters. Experimental results demonstrate that the proposed model significantly outperforms comparative models (traditional Long Short-Term Memory (LSTM) neural network, VMD-CNN-LSTM-MATT, and Sparrow Search Algorithm (SSA)-optimized CNN-LSTM) across key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Specifically, the reductions achieved are minimum improvements of 29.85% for MAE, 8.42% for RMSE, and 33.69% for MAPE. This model effectively enhances the prediction accuracy of land subsidence in cultivated hilly and mountainous areas, validating its high reliability and practicality for subsidence monitoring and prediction tasks. Full article

Accurate prediction of the degradation trajectory and estimation of the remaining useful life (RUL) of lithium-ion batteries are crucial for ensuring the reliability and safety of modern energy storage systems. However, many existing approaches rely on deep or highly complex models to achieve high accuracy, often at the cost of computational efficiency and practical applicability. To tackle this challenge, we propose a novel hybrid deep-learning framework, IVCLNet, which predicts the battery’s state-of-health (SOH) evolution and estimates RUL by identifying the end-of-life threshold (SOH = 80%). The framework integrates Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Variational Mode Decomposition (VMD), and an attention-enhanced Long Short-Term Memory (LSTM) network. IVCLNet leverages a cascade decomposition strategy to capture multi-scale degradation patterns and employs multiple indirect health indicators (HIs) to enrich feature representation. A lightweight Convolutional Block Attention Module (CBAM) is embedded to strengthen the model’s perception of critical features, guiding the one-dimensional convolutional layers to focus on informative components. Combined with LSTM-based temporal modeling, the framework ensures both accuracy and interpretability. Extensive experiments conducted on two publicly available lithium-ion battery datasets demonstrated that IVCLNet significantly outperforms existing methods in terms of prediction accuracy, robustness, and computational efficiency. The findings indicate that the proposed framework is promising for practical applications in battery health management systems. Full article

Piezoelectric materials are fundamental elements in modern science and technology due to their unique ability to convert mechanical and electrical energy bidirectionally. They are widely employed in sensors, actuators, and energy-harvesting systems. In this work, we investigate the behavior of commercial lead zirconate titanate (PZT) sensors under frequency-mode excitation using a combined approach of impedance spectroscopy and optical interferometry. The impedance spectra reveal distinct resonance–antiresonance features that strongly depend on geometry, while interferometric measurements capture dynamic strain fields through fringe displacement analysis. The strongest deformation occurs near the first kilohertz resonance, directly correlated with the impedance phase, enabling the extraction of an effective piezoelectric constant (~40 pC/N). Moving beyond the linear regime, laser-induced excitation demonstrates optically driven activation of piezoelectric modes, with a frequency-dependent response and nonlinear scaling with optical power, characteristic of coupled pyroelectric–piezoelectric effects. These findings introduce a frequency-mode approach that combines impedance spectroscopy and optical interferometry to simultaneously probe electrical and mechanical responses in a single setup, enabling non-contact, frequency-selective sensing without surface modification or complex optical alignment. Although focused on macroscale ceramic PZTs, the non-contact measurement and activation strategies presented here offer scalable tools for informing the design and analysis of piezoelectric behavior in micro- and nanoscale systems. Such frequency-resolved, optical-access approaches are particularly valuable in the development of next-generation nanosensors, MEMS/NEMS devices, and optoelectronic interfaces where direct electrical probing is challenging or invasive. Full article

The aim of this study is to outline a theoretical framework for exploring possible interactions between the concepts of information, intelligence, and the experience of beauty, including efforts to communicate the latter. These notions are inherently polysemantic, which renders their interrelations highly complex. The problem is further complicated by the impossibility of comparing what might be described as the ‘experience of information’ with the traditionally understood experience of beauty. Still, the question remains whether a theoretical inquiry—conceived as an attempt to test the usefulness of selected notions of information in the context of conveying aesthetic experience—can yield valuable insights. Surprisingly, highly abstract definitions, when combined with a particular view of intelligence, prove especially illuminating, particularly given the near-ubiquity of AI-based systems. Moreover, this perspective makes it possible to approach beauty—and, more broadly, aesthetics—as a highly specific process of information transmission. Intelligence, understood in this context as a uniquely human and concrete capacity for actively interpreting, transforming, and generating information, together with selected abstract notions of information, plays a fundamental role in this process. A noteworthy outcome is that, in such applications, abstract and formally defined concepts often prove more effective than more intuitive approaches, such as semantic conceptions of information. The study concludes by stressing the importance of protecting, cultivating, and promoting those modes of being that remain distinctive to human existence. Full article

Earth-rock dams are critical components of hydraulic engineering, undertaking core functions such as flood control and disaster mitigation. However, the potential occurrence of dam breach poses a severe threat to regional socioeconomic stability and ecological security. To address the limitations of traditional Bayesian network (BN) in capturing the complex nonlinear coupling and dynamic mutual interactions among risk factors, they are integrated with machine learning techniques, based on a collected dataset of earth-rock dam breach case samples, the PC structure learning algorithm was employed to preliminarily uncover risk associations. The dataset was compiled from public databases, including the U.S. Army Corps of Engineers (USACE) and Dam Safety Management Center of the Ministry of Water Resources of China, as well as engineering reports from provincial water conservancy departments in China and Europe. Expert knowledge was integrated to optimize the network topology, thereby correcting causal relationships inconsistent with engineering mechanisms. The results indicate that the established hybrid model achieved AUC, accuracy, and F1-Score values of 0.887, 0.895, and 0.899, respectively, significantly outperforming the data-driven model G1. Forward inference identified the key drivers elevating breach risk. Conversely, backward inference revealed that overtopping was the direct failure mode with the highest probability of occurrence and the greatest contribution. The integration of data-driven approaches and domain knowledge provides theoretical and technical support for the probabilistic quantification of earth-rock dam breach and risk prevention and control decision-making. Full article

To address the complexity of tensile mechanical behavior in fissured rock masses, this study conducted Brazilian splitting tests and numerical simulations on yellow sandstone containing randomly distributed fissures. Based on secondary development of the ABAQUS platform, a numerical model considering the spatial distribution of mineral components was established. A random fissure network was generated using the Weibull distribution, and crack propagation was characterized by employing cohesive elements. The influence mechanisms of the fissure inclination angle (θ = 0°~90°) and fissure ratio (R = 3~15%) on Brazilian tensile strength, failure mode, and crack propagation were systematically analyzed. The research demonstrates the following: (1) Brazilian tensile strength exhibits an overall decreasing trend with an increasing fissure ratio, while the effect of the fissure inclination angle is non-monotonic: at a low fissure ratio (R = 3%), Brazilian tensile strength shows a “decrease–increase–decrease” characteristic; at a medium to high fissure ratio (R ≥ 9%), Brazilian tensile strength continuously increases with an increasing fissure inclination angle. (2) The fissure ratio dominates the deviation of the failure path (deviation intensifies when θ ≤ 67.5° and is minimal at θ = 90°). At the mesoscale, the proportion of tensile cracks increases with an increasing R, while the contribution of shear cracks significantly enhances with an increasing θ (sharply increasing after θ > 45°). (3) Crack propagation is controlled by the spatial interaction of initial cracks. Under the combined action of a high inclination angle (θ = 90°) and high fissure ratio (R = 15%), a tensile–shear composite failure pattern forms, characterized by dual-source crack initiation and central coalescence. This study provides a mesoscale mechanical basis for the stability assessment of engineering structures in fissured rock masses. Full article

The development of compact, energy-efficient integrated lasers operating at 1.3 µm re-mains a critical focus in silicon photonics, essential for advancing data communications and optical interconnect technologies. This paper presents a numerical study of distributed Bragg reflector (DBR) hybrid III-V-on-silicon lasers, analyzing design trade-offs and optimization strategies based on supermode theory. The III-V section of the design incorporates InAs/(Al)GaAs quantum dots (QDs), which offer improved temperature insensitivity at the cost of more complex III-V/Si optical coupling, due to the high refractive index of (Al)GaAs. Consequently, many current laser designs rely on silicon waveguides with a thickness exceeding 220 nm, which helps coupling but limits their compatibility with standard CMOS technologies. To address this challenge, we perform detailed simulations focusing on 220-nm-thick silicon waveguides. We first examine how the mode profiles jointly depend on the silicon waveguide dimensions and the geometry and composition of the III-V stack. Based on this analysis, we propose a novel epitaxial design that enables effective III-V/Si coupling, with the optical mode strongly confined within the III-V waveguide in the gain section and efficiently transferred to the silicon waveguide in the passive sections. Moreover, the final design is shown to be robust to fabrication-induced deviations from nominal parameters. Full article

Hypersonic glide targets (HGTs) pose significant challenges for radar tracking due to complex maneuver strategies and time-varying statistics of measurement noise. Conventional single-model tracking methods are generally insufficient to fully capture maneuver modes, while existing multiple-model methods face trade-offs between model set completeness and computational efficiency. In addition, existing tracking methods struggle to cope with the non-Gaussian noise during hypersonic flight. To overcome these limitations, a Hierarchical Adaptive Moment Matching (HAMM) multiple-model method is proposed in this paper. Firstly, a comprehensive model set is constructed to cover characteristic maneuver modes. Subsequently, a hierarchical multiple-model framework is developed where: (1) a coarse model set is dynamically adapted by multi-frame posterior probability evolution and Rényi divergence criteria; (2) a fine model set is generated based on the moment matching method. Furthermore, the minimum error entropy cubature Kalman filter (MEECKF) is proposed to suppress the non-Gaussian measurement noise with high stability. Monte Carlo simulations demonstrate that the proposed method achieves improved positioning accuracy and faster convergence. Full article

The optimal computing ability of spiking neural networks (SNNs) mainly depends on the connection weights of their synapses and the thresholds that control the spiking. In order to realize the optimization calculation of different objective functions, it is necessary to modify the connection weights adaptively and make the thresholds dynamically self-learning. However, it is very difficult to construct an adaptive learning algorithm for spiking neural networks due to the discontinuity of neuron spike sending process, which is also a fatal problem in this field. In this paper, an efficient adaptive learning algorithm for spiking neural networks is proposed, which adjusts the weights of synaptic connections by a learning factor adaptively and adjusts the probability of spike sending by the self-organizing learning method of the dynamic threshold, so as to achieve the goal of automatic global search optimization. The algorithm is applied to the learning task of global optimization, and the experimental results show that this algorithm has good stability and learning ability, and is effective in dealing with complex multi-objective optimization problems of spatiotemporal spike mode. Moreover, the proposed framework explicitly leverages problem and model symmetries. In Traveling Salesman Problems, distance symmetry (d(i, j) = d(j, i)) and tour permutation symmetry are preserved by our spike-train-based similarity and energy updates, which do not depend on node labels. Together with the homogeneous neuron dynamics and balanced excitatory–inhibitory populations, these symmetry-aware properties reduce the effective search space and enhance the convergence stability. Full article