Search Results (8,034)

Resveratrol, a natural stilbene found in various plants, is mainly known for its strong antioxidant activities exhibiting a comprehensive range of treatments for some skin disorders such as skin cancer, photoaging, dermatitis, and melanogenesis. However, few studies have been conducted on the differences in biological activities between resveratrol and its derivatives. Therefore, we aimed to investigate the effects of resveratrol (Re) and its derivatives acetyl-resveratrol (Are), cis-trismethoxy resveratrol (Cre), dihydroresveratrol (Dre), and oxyresveratrol (Ore) on antioxidant and anti-tyrosinase effects using in vitro and in silico methods. In the in vitro results, Ore showed the highest antioxidant activity among the resveratrol derivatives and displayed stronger inhibitory activity against natural tyrosinase compared with that of kojic acid. Density functional theory (DFT) was used to calculate quantum chemical descriptors to understand the compounds’ electronic and physicochemical properties. Molecular docking and molecular dynamics simulations were also performed to explore the corresponding binding mode and structural behavior, revealing that Ore exhibited the strongest binding interactions among resveratrol derivatives, primarily through hydrogen bonds and hydrophobic interactions with key amino acid residues. Moreover, all the resveratrol compounds demonstrated drug-likeness properties with predicted safe skin toxicity profiles. In conclusion, Ore exhibited the strongest tyrosinase inhibition and antioxidant activity among resveratrol derivatives in both in vitro and in silico assessments. Further research on the development of medicines, cosmetics, and food supplements of such compounds should be conducted. Full article

As low-voltage distribution networks incorporate increasingly diverse loads, series arc faults exhibit weak characteristics that are easily masked by load currents, leading to high misjudgment rates in traditional detection methods. This paper proposes a series arc fault detection method based on an improved Light Gradient Boosting Machine (LightGBM) model. First, a test platform containing 12 household loads was built to collect arc data from both individual and composite loads. Composite loads refer to composite load conditions where multiple devices are running simultaneously and arcing occurs on some loads. To address the challenge of feature extraction, Variational Mode Decomposition (VMD) is employed to isolate the fundamental frequency component. To enhance high-frequency arc characteristics, singular value decomposition (SVD) is then applied. A multidimensional statistical feature set—comprising peak-to-peak value, kurtosis, and other indicators—is constructed. Finally, the LightGBM algorithm is used to identify arc faults based on these features. To overcome the LightGBM model’s limited ability to focus on hard-to-classify samples, a dynamic weighted hybrid loss function is developed. Experiments demonstrate that the proposed method achieves 98.9% accuracy across 223,615 sample groups. When deployed on STM32H723VGT6 hardware, the average fault alarm time is 83.8 ms, meeting requirements. Full article

Aircraft spin, a nonlinear phenomenon dominated by unsteady aerodynamics, is difficult to predict. This article proposes a novel approach using Hankel Dynamic Mode Decomposition with Control (HDMDc) to identify an aircraft plant model for spin motion directly from measurement data. A key challenge in real-world data-driven modeling is addressing noise in both input and output measurements, often termed errors in variables (EIV). The standard HDMDc does not account for the distinct noise characteristics of different sensors. To overcome this, modifications are proposed to the standard HDMDc algorithm using EIV approaches: total least squares and bias-eliminating least squares. The proposed algorithms are validated first with a simple nonlinear dynamical system exhibiting limit cycle oscillation. Further, the methodology is applied to the simulated steady spin of the T-2 aircraft and the oscillatory spin motion of the F-18 aircraft. It is demonstrated that models identified using HDMDc with the EIV approach predicted spin trajectories with high goodness-of-fit values, even for unseen control inputs and initial conditions that differed from the training data. Specifically, the predicted trajectories had a FIT% close to 90% in most cases, with the worst-case FIT% being 38%. In contrast, the standard HDMDc algorithm’s predicted trajectory was not even visually close to the actual system trajectory, highlighting the significant improvement of the modified approach. Full article

With the rapid growth of unmanned aerial vehicles (UAVs) and IoT users, spectrum resources are becoming increasingly scarce, making cognitive radio (CR) technology a key approach to improving spectrum utilization. However, traditional antennas are difficult to meet the lightweight, compact, and low-drag requirements of small UAVs due to spatial constraints. This paper proposes a tri-mode frequency reconfigurable flexible antenna that can be conformally integrated onto UAV wing arms to enable CR dynamic frequency communication. The antenna uses a polyimide (PI) substrate and has compact dimensions of 31.4 × 58 × 0.05 mm³. A microstrip line-based frequency-selective network is designed, incorporating PIN and varactor diodes to realize three operation modes, dual-band (2.25~3.55 GHz, 5.6~6.75 GHz), single-band (3.35~5.3 GHz), and continuous tuning (4.3~6.1 GHz), covering WLAN, WiMAX, and 5G NR bands. Test results show that the antenna maintains stable performance under conformal conditions, with frequency shifts less than 4%, gain (3.65~4.77 dBi), and radiation efficiency between 67.2% and 82.9%. The tuning ratio reaches 38.8% in the continuous mode. This design offers a new solution for CR communication in compact UAV platforms and shows promising application potential. Full article

To align with the adoption of electric vehicles in the transportation sector, this paper proposes the use of electric heavy-duty trucks for the logistics and distribution of large prefabricated building components. This approach aims to address the problems of high total costs and significant energy waste in prefabricated component transportation. Focusing on the multi-to-multi distribution mode, a two-level optimization model is constructed. The upper-level model is responsible for the reasonable allocation of demand points. The lower-level model optimizes the selection of road network nodes and charging stations along the delivery routes. It also dynamically adjusts charging timing and volume according to the real-time power situation. To enhance solution performance, a two-level multi-objective evolutionary algorithm based on Pareto theory is designed. This algorithm simultaneously optimizes distribution costs while coordinating path planning and charging strategies. Comparative experiments across different cases show that, compared with traditional single-level and multi-stage models, the proposed algorithm improves both solution accuracy and quality. Additionally, when compared with the scheduling scheme based on the full-charge capacity strategy, the dynamic charging strategy proposed in this paper reduces the total distribution cost by approximately 15.83%. These findings demonstrate that the constructed model and algorithm can effectively optimize the logistics and distribution of prefabricated components. They also provide a feasible solution for the practical application of electric vehicles in engineering logistics. Full article

Despite extensive research, indoor localization techniques remain an open problem, with Bluetooth Low Energy (BLE) continuing to be a dominant technology even in the presence of ultrawideband and Bluetooth 5.1. This study proposes a novel approach for indoor mobile device localization using BLE. Unlike traditional methods relying on the Received Signal Strength Indicator (RSSI), this technique employs spatial signal coverage analysis from multi-power Bluetooth emulators, with data collected by an array of receivers. These coverage patterns form a probability grid, which is processed to accurately determine the mobile device’s location. The method accounts for the intrinsic properties of antennas and the operational ranges of multiple beacon emulators, thereby enhancing localization precision. By utilizing receiver range data rather than RSSI, localization outcomes demonstrate greater consistency. Static measurements show an average error of 1.83 m, a median error of 1.73 m, and a mode error of 2.35 m. In dynamic settings, a moving robot exhibited a measurement error of 3.6 m for 70% of samples and 4.6 m for 94% of samples. This solution is currently being implemented to track attendees at trade fairs, providing metrics to inform stand rental pricing and insights for optimizing stand distribution to encourage visitor exploration. Full article

In the research, a novel accelerometer concept leveraging the mode-localization phenomenon is put forward. The sensor measures external acceleration through monitoring changes in the relative amplitude ratio among coupled resonators. The sensing part of the presented accelerometer comprises a doubly clamped beam coupled with a cantilever beam. Its design ensures the initial bending mode of the clamped beam approximates the secondary bending mode of the cantilever. Drawing on Euler–Bernoulli beam theory, the governing formulas of the coupled resonators are deduced and analyzed via Galerkin discretization integrated with the multiple-scale method. During working in both linear as well as nonlinear operating regions, this sensor’s dynamic behavior can be tuned by adjusting the drive voltage. The obtained results demonstrate that the nonlinear dynamics increases the accelerometer sensitivity, which can be further enhanced by adjusting the coupling voltage without severe mode overlap. The presented model offers one viable method to enhance the overall performance in multi-mode MEMS accelerometers. Full article

As MEMS design encounters growing challenges, particularly stiction between movable and stationary electrodes, dielectric charging, pull-in instability, and multi-valued response characteristics, the integration of dimple-equipped structures has emerged as a pivotal solution to mitigate these fundamental issues. Consequently, this study investigates the dynamic behavior of an electrostatically actuated double-clamped microbeam incorporating dimples and contact pads. While the dimples enhance the beam’s travel range, they may also induce an impact mode upon contact with the landing pads, leading to complex nonlinear dynamic phenomena. A reduced-order model was developed to numerically solve the governing equation of motion. The microbeam’s response was analyzed both with and without dimples using multiple analytical techniques, including bifurcation diagrams and discrete excitation procedures near the impacting regime. The findings demonstrate that the inclusion of dimples effectively suppresses stiction, pull-in instability, and multi-valued responses. The results indicate that upon contacting the landing pads, the beam exhibits pronounced nonlinear dynamic behaviors, manifesting as higher-period oscillations such as period-3, period-4 and period-5 and then fully developed chaotic attractors. Indeed, this specifically demonstrates the potential of using the dynamic transition from a steady-state to a chaotic response to build novel MEMS sensors. Full article

Accurate prediction of transformer top-oil temperature is crucial for insulation ageing assessment and fault warning. This paper proposes a novel prediction method based on Variational Mode Decomposition (VMD), kernel principal component analysis (Kernel PCA), a Time-aware Shapley Additive Explanations–Multilayer Perceptron (TSHAP-MLP) feature selection method, enhanced Bayesian optimization, and a Self-organized Time Series Forecasting System (SOFTS). First, the top-oil temperature signal is decomposed using VMD to extract components of different frequency bands. Then, Kernel PCA is employed to perform non-linear dimensionality reduction on the resulting intrinsic mode functions (IMFs). Subsequently, a TSHAP-MLP approach—incorporating temporal weighting and a sliding window mechanism—is used to evaluate the dynamic contributions of historical monitoring data and IMF features over time. Features with SHAP values greater than 1 are selected to reduce input dimensionality. Finally, an enhanced hierarchical Bayesian optimization algorithm is used to fine-tune the SOFTS model parameters, thereby improving prediction accuracy. Experimental results demonstrate that the proposed model outperforms transformer, TimesNet, LSTM, and BP in terms of error metrics, confirming its effectiveness for accurate transformer top-oil temperature prediction. Full article

To effectively address challenges posed by high-penetration renewable energy to power system operation and reserves, this paper proposes a novel research framework. The framework considers transmission–distribution coordinated dispatch and optimizes reserve capacity. First, the framework addresses the volatility and uncertainty of wind and solar power output. It constructs a three-dimensional objective function incorporating generation cost, spinning reserve cost, and linear wind/solar curtailment penalties as core components. The study uses the IEEE 30-bus system as the transmission network and the IEEE 33-bus system as the distribution network to build a transmission–distribution coordinated optimization model. Power dynamic mutual support across voltage levels is achieved through tie transformers. Second, the framework designs three typical scenarios for comparative analysis. These include separate dispatch of transmission and distribution networks, coordinated dispatch of transmission and distribution networks, and a fixed reserve ratio mode. The approach breaks through the limitations of traditional fixed reserve allocation. It optimizes the coordinated mechanism between reserve capacity spatiotemporal allocation and renewable energy accommodation. Case study results demonstrate that the proposed coordinated optimization scheme reduces total system operating costs and wind/solar curtailment rates. This is achieved by exploiting the potential of regulation resources on both the transmission and distribution sides. The results verify the significant advantages of transmission–distribution coordination in improving reserve resource allocation efficiency and promoting renewable energy accommodation. The approach helps enhance power grid operational economics and reliability. Full article

Fiber Bragg grating (FBG) sensing systems face significant challenges in resolving overlapping spectral signatures when multiple sensors operate within limited wavelength ranges, severely limiting sensor density and network scalability. This study introduces a novel Transformer-based neural network architecture that effectively resolves spectral overlap in both uniform and mixed-linewidth FBG sensor arrays, operating under bidirectional drift. The system uniquely combines dual-linewidth configurations with reflection and transmission mode fusion to enhance demodulation accuracy and sensing capacity. By integrating cloud computing, the model enables scalable deployment and near-real-time inference even in large-scale monitoring environments. The proposed approach supports self-healing functionality through dynamic switching between spectral modes during fiber breaks and enhances resilience against spectral congestion. Comprehensive evaluation across twelve drift scenarios demonstrates exceptional demodulation performance under severe spectral overlap conditions that challenge conventional peak-finding algorithms. This breakthrough establishes a new paradigm for high-density, distributed FBG sensing networks applicable to land monitoring, soil stability assessment, groundwater detection, maritime surveillance, and smart agriculture. Full article

Tilt-rotor Vertical Takeoff and Landing Unmanned Aerial Vehicles (TR-VTOL UAVs) combine fixed-wing and rotary-wing configurations, offering optimized flight planning but presenting challenges due to their complex dynamics and uncertainties. This study investigates a multi-agent reinforcement learning (RL) control system utilizing Soft Actor-Critic (SAC) modules, which are designed to independently control each input with a tailored reward mechanism. By implementing a novel reward structure based on a dynamic reference response region, the multi-agent design improves learning efficiency by minimizing data redundancy. Compared to other control methods such as Actor-Critic Neural Networks (AC NN), Proximal Policy Optimization (PPO), Nonsingular Terminal Sliding Mode Control (NTSMC), and PID controllers, the proposed system shows at least a 30% improvement in transient performance metrics—including RMSE, rise time, settling time, and maximum overshoot—under both no wind and constant 20 m/s wind conditions, representing an extreme scenario to evaluate controller robustness. This approach has also reduced training time by 80% compared to single-agent systems, lowering energy consumption and environmental impact. Full article

Karst springs play a critical strategic role in regional economic and ecological sustainability, yet their spatiotemporal heterogeneity and hydrological complexity pose substantial challenges for flow prediction. This study proposes FMD-mGTO-BiGRU-KAN, a four-stage hybrid deep learning architecture for daily spring flow prediction that integrates multi-feature signal decomposition, meta-heuristic optimization, and interpretable neural network design: constructing an Feature Mode Decomposition (FMD) decomposition layer to mitigate modal aliasing in meteorological signals; employing the improved Gorilla Troops Optimizer (mGTO) optimization algorithm to enable autonomous hyperparameter evolution, overcoming the limitations of traditional grid search; designing a Bidirectional Gated Recurrent Unit (BiGRU) network to capture long-term historical dependencies in spring flow sequences through bidirectional recurrent mechanisms; introducing Kolmogorov–Arnold Networks (KAN) to replace the fully connected layer, and improving the model interpretability through differentiable symbolic operations; Additionally, residual modules and dropout blocks are incorporated to enhance generalization capability, reduce overfitting risks. By integrating multiple deep learning algorithms, this hybrid model leverages their respective strengths to adeptly accommodate intricate meteorological conditions, thereby enhancing its capacity to discern the underlying patterns within complex and dynamic input features. Comparative results against benchmark models (LSTM, GRU, and Transformer) show that the proposed framework achieves 82.47% and 50.15% reductions in MSE and RMSE, respectively, with the NSE increasing by 8.01% to 0.9862. The prediction errors are more tightly distributed, and the proposed model surpasses the benchmark model in overall performance, validating its superiority. The model’s exceptional prediction ability offers a novel high-precision solution for spring flow prediction in complex hydrological systems. Full article

This study develops a dynamical systems framework for analyzing the co-evolution of port–industry–city (PIC) systems, with particular attention to the data-limited contexts often encountered in developing coastal regions. The model integrates time-delay differential equations and stochastic disturbances to capture nonlinear behaviors such as investment cycles, policy lags, and external shocks. By introducing dimensionless indicators and dynamic parameter adjustment, the framework reduces dependence on extensive datasets and enhances cross-regional applicability. The Kribi Deep Seaport in Cameroon serves as an illustrative case, demonstrating how the approach can reveal emergent trajectories under alternative development regimes. Simulation results identify three distinct pathways: capital-driven expansion with risks of premature overinvestment, industrial clustering modes requiring coordinated urban services, and policy-led strategies constrained by ecological thresholds and institutional inertia. Compared with conventional static or equilibrium-based models, this approach provides a mathematically rigorous tool for examining delay-driven, nonlinear interactions in complex socio-ecological systems. The framework highlights the value of dynamical systems analysis for scenario exploration, policy design, and sustainable governance in resource-constrained environments. Full article

The dynamic behavior of reinforced concrete (RC) beams under impact loads is highly complex. In this work, the failure modes, impact force, displacement, and internal force of RC beams under impact loads were studied in detail with different research parameters. Firstly, a numerical model of RC was established, and its reliability was verified through a series of tests. Then, seventeen groups of different parameters were designed and analyzed. These parameters included the shear–span ratio of the RC beams, the impact velocity of a drop hammer, concrete strength, and boundary conditions. The results indicate that the shear–span ratio and boundary condition of RC beams have no noticeable influence on the maximum impact forces. The maximum displacement, residual displacement, and vibration period of RC beams with fixed-oundary conditions are obviously less than those of supported RC beams. The negative moment of RC beams subjected to impact loads needs to be considered in design due to the many cracks near the supports caused by the negative moments. The shear force of RC beams with a fixed condition is greater at the support section, which requires detailed consideration. Full article