Search Results (4,604)

This study investigated the effects of rubber substitution ratios (0%, 5%, 10%, 15%) on the frost resistance of rubber concrete with recycled brick–concrete aggregate (BRC). The freeze–thaw (F–T) damage model was established and improved, and the damage mechanism was revealed. The results showed that with the increase in rubber substitution ratio, the frost resistance indices of BRC did not improve or decline synchronously. An increase in rubber content could enhance one index, such as the relative compressive strength, but was often achieved at the expense of reductions in other indices, such as the relative dynamic elastic modulus (RDEM) and relative quality. Consequently, a single indicator was insufficient for evaluating the overall frost resistance. To address this limitation, an entropy weight-based evaluation system was developed. This system integrated the multiple indices into a unified damage score. When combined with defined damage grades, it enabled a holistic assessment of the damage state. For the nonlinear accelerated damage stage during freeze–thaw cycles, the Weibull distribution-based freeze–thaw damage model demonstrated higher prediction accuracy (R² > 0.85) compared to the conventional freeze–thaw fatigue model. The freeze–thaw damage in BRC originated from the competition between “pore deterioration and crack propagation at weak interfaces” and “the elastic buffering effect of rubber.” This study provided a reference for the frost-resistance design and freeze–thaw life prediction of BRC in cold regions. Full article

With the continuous development of power systems, WAMS have become increasingly important for real-time system monitoring. As the core devices of WAMS, PMUs can provide synchronized, high-precision, and high-resolution measurements of power system states. However, in practical applications, PMUs deployed in different regions often operate at different sampling rates, resulting in multi-rate measurement data and posing challenges for data fusion. To address this issue, this paper proposes a multi-rate PMU data fusion method based on low-rank TT. Specifically, the proposed method first performs tensor-based modeling of multi-rate measurement data, embedding multidimensional correlations into a high-order tensor representation. Then, a data completion model is constructed through low-rank TT decomposition to effectively capture cross-timescale dependencies. Finally, an efficient numerical solution is developed to expand low-resolution measurements into high-resolution data, thereby achieving unified data fusion. Case studies on both simulated and real-world PMU measurement data demonstrate that the proposed approach outperforms traditional interpolation and matrix completion methods, achieving superior reconstruction accuracy and robustness. Full article

This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increasing acceptance among states as a compliant measurement device. A comprehensive dataset was assembled spanning more than 1000 miles of roadways, capturing diverse marking materials, colors, installation methods, pavement types, and vehicle speeds. The final dataset used for model development focused on dry condition measurements and roadway segments most relevant to state transportation agencies. A detailed synchronization process was implemented to ensure the accurate pairing of retroreflectivity and LiDAR intensity values. Using these data, several machine learning techniques were evaluated, and an ensemble of gradient boosting-based models emerged as the top performer, predicting pavement retroreflectivity with an R² of 0.94 on previously unseen data. The repeatability of the predicted retroreflectivity was tested and showed similar consistency as the MRU. The model’s accuracy was confirmed against independent field segments demonstrating the potential for LiDAR to serve as a practical, low-cost alternative for MRU measurements in routine roadway inspection and maintenance. The approach presented in this study enhances roadway safety by enabling more frequent, network-level assessments of pavement marking performance at lower cost, allowing agencies to detect and correct visibility problems sooner and helping to prevent nighttime and adverse weather crashes. Full article

Understanding consciousness requires bridging theoretical models and clinically measurable brain dynamics. This review integrates three complementary frameworks that converge on a dynamical view of conscious processing: continuous formulations of Integrated Information Theory (IIT), attractor-landscape modeling of brain-state transitions, and perturbational complexity metrics from transcranial magnetic stimulation combined with electroencephalography (TMS-EEG). Continuous-time IIT formalizes how integrated information evolves across temporal hierarchies, while dynamical-systems approaches show that consciousness emerges near criticality, where metastable attractors enable flexible transitions between partially synchronized states. Perturbational-complexity indices capture these properties empirically, quantifying the brain’s capacity for integration and differentiation even without behavioral responsiveness. Across anesthesia, disorders of consciousness, epilepsy, and neurodegeneration, TMS-EEG biomarkers reveal reduced complexity and altered synchronization consistent with structural and functional disconnection. Integrating multimodal data—diffusion MRI, fMRI, EEG, and causal perturbations—is consistent with individualized modeling of consciousness-related dynamics. Standardized protocols, mechanistically interpretable machine learning, and longitudinal validation are essential for clinical translation. By uniting information-theoretic, dynamical, and empirical perspectives, this framework offers a reproducible foundation for consciousness biomarkers that mechanistically link brain dynamics to subjective experience, paving the way for precision applications in neurology, psychiatry, and anesthesia. Full article

Non-alcoholic fatty liver disease (NAFLD) is closely linked to metabolic syndrome and circadian rhythm disruption, yet the mechanisms by which lifestyle interventions restore circadian organization remain incompletely understood. In this study, we employed a stringent 3 h time-restricted feeding (TRF) regimen in a mouse model of high-fat diet (HFD)-induced metabolic dysfunction. TRF markedly improved metabolic outcomes, including lipid accumulation, glucose tolerance, and behavioral and physiological rhythms. Importantly, through transcriptomic profiling using RNA sequencing, we found that TRF induced circadian rhythmicity in previously arrhythmic hepatic genes. This approach revealed that TRF promotes transcriptional synchronization within key metabolic pathways. Genes involved in autophagy, fatty acid metabolism, and protein catabolism exhibited coherent peak expression at defined time windows, suggesting that TRF temporally restructures gene networks to enhance metabolic efficiency. This intra-pathway synchronization likely minimizes energy waste and enables cells to execute specialized functions in a temporally optimized manner. Together, our findings identify temporal reorganization of metabolic pathways as a mechanistic basis for the benefits of TRF, providing new insight into circadian-based strategies for managing metabolic disease. Full article

Achieving high reliability remains the critical challenge for pulsed power supplies (PPS), whose core components are susceptible to severe degradation and catastrophic failure due to long-term operation under electrical, thermal and magnetic stresses, particularly those associated with high voltage and high current. This reliability challenge fundamentally limits the widespread deployment of PPSs in defense and industrial applications. This article provides a comprehensive and systematic review of the reliability challenges and recent technological progress concerning PPSs, focusing on three hierarchical levels: component, system integration, and extreme operating environments. The review investigates the underlying failure mechanisms, degradation characteristics, and structural optimization of key components, such as energy storage capacitors and power switches. Furthermore, it elaborates on advanced system-level techniques, including novel thermal management topologies, jitter control methods for multi-module synchronization, and electromagnetic interference (EMI) source suppression and coupling path optimization. The primary conclusion is that achieving long-term, high-frequency operation depends on multi-physics field modeling and robust, integrated design approaches at all three levels. In summary, this review outlines important research directions for future advancements and offers technical guidance to help speed up the development of next-generation PPS systems characterized by high power density, frequent repetition, and outstanding reliability. Full article

With the continuous and rapid growth of global photovoltaic (PV) installed capacity, the fluctuation, intermittence, and randomness of its output aggravate the inertia loss of traditional power systems, which poses severe challenges to grid voltage stability, frequency regulation, and safe operation of equipment. Stability control of PV power stations has become a necessary aspect of technical support for the construction of new power systems (NPSs). In this paper, a technical analysis framework of stability control of photovoltaic power stations is systematically constructed. First, the core stability problems of photovoltaic systems are sorted out. Then, a technical review of the three control levels, namely the equipment, system, and grid, is carried out. At the same time, the application potential of emerging technologies such as data-driven and AI control, digital twin predictive control, and advanced grid-forming (GFM) inverters is described. Based on existing reviews, this paper proposes an equipment–system–grid hierarchical analysis framework and explicitly integrates emerging technologies with classical methods. This framework provides references for the selection, engineering deployment, and future research directions of stability control technologies for photovoltaic power plants, while also offering technical support for the safe and efficient operation of high-penetration renewable energy power grids. Full article

This study presents the first comprehensive physicochemical and bioactive characterization of the fruit of Chondrodendron tomentosum Ruiz & Pav. (Menispermaceae). Biometric and physicochemical parameters were characterized across three fruit ripening stages (green, turning, ripe). Additionally, proximate composition was determined in ripe fruits, and methanol concentration (25–75%), ultrasonic amplitude (30–70%), and time (1–15 min) were optimized using response surface methodology with a Box–Behnken design. During ripening, weight increased by +47.7% (3.89 to 5.74 g; p < 0.0001), TSS by +26.1% (7.00 to 8.83 °Brix), pH decreased by 32.0% (6.28 to 4.27), and acidity increased by 276% (0.25 to 0.94%). The quadratic models demonstrated high predictive accuracy (R² > 96.5%; p < 0.004). Optimal conditions (57% methanol, 70% amplitude, and 15 min) maximized total anthocyanin content (120.71 ± 1.89 mg cyanidin-3-glucoside/L), total phenols (672.46 ± 5.84 mg GAE/100 g), and DPPH radical scavenging capacity (5857.55 ± 60.20 µmol Trolox/100 g) in ripe fruits. Unripe fruits do not contain anthocyanins, reaching 46.01 mg C3G/L in turning fruits and 120.71 mg/L in ripe fruits (162% higher than turning fruits). Principal component analysis (90.6% variance) revealed synchronized co-accumulation of anthocyanins and phenols, enhanced by vacuolar acidification. These results suggest ripe C. tomentosum fruits as a potential source for natural colorants, nutraceuticals, and functional foods, pending prior development of green, human-safe extraction processes. Full article

A fast modelling framework is presented for loss estimation in current-source microinverters. The power stage is modelled with ideal switches and simplified magnetics to keep simulations lightweight, while dedicated estimators reconstruct core, conduction, and switching losses from simulated waveforms using Steinmetz-based and analytical models. The method is demonstrated on an interleaved active-clamp flyback with H-bridge unfolder but remains topology-agnostic and applicable to other current source (CS) DC/DC variants. Control includes maximum power point tracking (MPPT) with voltage-reference tracking, a PID loop, simplified grid synchronization, and peak-current regulation. Dynamic tests under irradiance and grid-voltage variations confirm stable operation and correct MPPT behaviour. A steady-state loss breakdown at 0.75 p.u. irradiance predicts ~97% overall efficiency, consistent with reported microinverter performance. The framework enables rapid design exploration and efficiency prediction without full device-level modelling, balancing accuracy and computational speed. Full article

To solve slot temperature accumulation in high thrust density permanent magnet linear synchronous motors (PMLSMs), this paper proposes an additive manufacturing (AM)-based variable cross-section winding design for coating-cooled tapered PMLSMs. Integrating the magnetic circuit features of tapered PMLSMs and AM windings’ technical merits, the motor’s operating mechanism and electromagnetic distribution are analyzed. With the coating cooling structure as the thermal management foundation, simulation reveals the motor’s temperature distribution under water cooling, defining core slot thermal management requirements. A novel cross-section winding design is then presented: first, a lumped-parameter thermal network model quantifies the coupling between the winding cross-sectional area and slot heat source distribution; second, a greedy algorithm optimizes the winding cross-section globally to reduce the slot hot-spot temperature and suppress temperature rise. Validated by a fabricated tapered PMLSM stator prototype and static temperature-rise experiments, the results confirm that winding cross-section reconstruction optimizes heat distribution effectively, offering a new approach for temperature rise suppression in high thrust density PMLSMs. Full article

To improve the safety of road transportation of Spent Nuclear Fuel (SNF), this paper proposes a novel approach for risk identification and chaotic synchronous control in SNF road transportation systems. Firstly, a dynamic risk evolution model for the road transportation of SNF is developed by analyzing the nonlinear interactions among vehicles, environmental conditions, and human factors using complex network analysis and nonlinear dynamics. Secondly, an enhanced K-shell decomposition method is applied to identify key risk nodes and assess the relative importance of different risk factors, providing a basis for targeted risk control. Finally, a chaotic synchronization control strategy based on Lyapunov stability is proposed to suppress risk divergence and restore system stability. Three targeted control schemes are evaluated by varying the control gain coefficients across the ‘Vehicle–Environment–Human’ dimensions. Simulation results indicate that the strategy prioritizing environmental and human risk control yields the fastest convergence, significantly outperforming vehicle-centric approaches. The results show that prioritizing both environmental and human-factor control is most effective for suppressing chaotic divergence. This provides a solid quantitative basis for the strategic shift from passive defense to active environmental warning, thereby significantly optimizing the dynamic risk management of the SNF transportation system. Full article

Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments. Full article

Dynamic network conditions in precision agriculture motivate a scalable, privacypreserving federated learning architecture that tightly integrates ground-based edge intelligence with a space-assisted hierarchical aggregation layer. In Phase 1, heterogeneous tractors act as intelligent farm nodes that train local models, form capability- and task-aware clusters, and employ Network Quality Index (NQI)-driven scheduling, similarity-based checkpointing, and compressed transmissions to cope with highly variable 3G/4G/5G connectivity. In Phase 2, cluster drivers synchronize with Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites that perform regional and global aggregation using staleness- and fairness-aware weighting, while end-to-end Salsa20 + MAC encryption preserves the confidentiality and integrity of all model updates. Across two representative tasks—nutrient prediction and crop health assessment—our full hierarchical system matches or exceeds centralized performance (e.g., AUC 0.92 vs. 0.91 for crop health) while reducing uplink traffic by ∼90% relative to vanilla FedAvg and cutting the communication energy proxy by more than 4×. The proposed fairness-aware GEO aggregation substantially narrows regional performance gaps (standard deviation of AUC across regions reduced from 0.058 to 0.017) and delivers the largest gains in low-connectivity areas (AUC 0.74 → 0.88). These results demonstrate that coupling on-farm intelligence with multi-orbit federated aggregation enables near-centralized model quality, strong privacy guarantees, and communication efficiency suitable for large-scale, connectivity-challenged agricultural deployments. Full article

To address the significant vibration and noise problems caused by the zero-order radial electromagnetic force (REF) in integer-slot permanent magnet synchronous motors (PMSMs), while simultaneously improving the motor’s overall electromagnetic performance, this paper proposes a hierarchical iterative optimization strategy integrating Taguchi methods and genetic algorithms. The optimization objectives include minimizing the zero-order REF amplitude, cogging torque, and torque ripple, while maximizing the average torque, with efficiency and back electromotive force total harmonic distortion (back-EMF THD) treated as constraints. First, an 8-pole 48-slot double-layer embedded PMSM model is constructed. An innovative parameter selection strategy, combining theoretical analysis with finite-element analysis, is employed to investigate the spatial order and frequency characteristics of the electromagnetic force. Subsequently, a sensitivity analysis is performed to stratify parameters: highly sensitive parameters undergo first-round optimization via the Taguchi method, followed by second-round optimization using a multi-objective genetic algorithm. The results demonstrate significant reductions in both the zero-order REF amplitude and cogging torque. Specifically, the motor’s peak vibration acceleration is reduced by 32.96%, and the peak sound pressure level (SPL) drops by 9.036 dB. Vibration acceleration and sound pressure across all frequency bands are significantly reduced to varying extents, validating the effectiveness of the proposed optimization approach. Full article

In this study, a layered multi-objective optimization design method is proposed for a segmented skewed pole permanent magnet synchronous linear motor (PMSLM), considering thrust ripple suppression. Based on a 2-D analytical model, the effects of end force, cogging force, and winding asymmetry force on thrust ripple in PMSLM are analyzed, and the correctness is verified using finite element analysis and experiments. On this basis, a layered multi-objective optimization method is proposed. The whole optimization is divided into three layers. Metamodels of optimal prognosis are established to optimize the structural parameters in a layered manner, achieving a compromise between reducing thrust ripple and increasing average thrust. The effectiveness of the layered multi-objective optimization method is verified through simulation and prototype experiments. The layered structure aims to improve efficiency while ensuring computational accuracy. Full article