Search Results (1,208)

Phase-sensitive surface plasmon resonance (SPR) detection is widely employed in molecular dynamics studies and SPR imaging owing to its real-time capability, high sensitivity, and compatibility with imaging systems. A key research objective is to achieve higher measurement resolution of refractive index under optimal dynamic range conditions. We present an enhanced SPR phase imaging system combining a quad-polarization filter array for phase differential detection with a novel polarization pair, block matching, and 4D filtering (PPBM4D) algorithm to extend the dynamic range and enhance resolution. By extending the BM3D framework, PPBM4D leverages inter-polarization correlations to generate virtual measurements for each channel in the quad-polarization filter, enabling more effective noise suppression through collaborative filtering. The algorithm demonstrates 57% instrumental noise reduction and achieves 1.51 × 10⁻⁶ RIU resolution (1.333–1.393 RIU range). The system’s algorithm performance is validated through stepwise NaCl solution switching experiments (0.0025–0.08%) and protein interaction assays (0.15625–20 μg/mL). This advancement establishes a robust framework for high-resolution SPR applications across a broad dynamic range, particularly benefiting live-cell imaging and high-throughput screening. Full article

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility. Full article

Inconsistency in the structural strengths of a membrane wing under positive and negative loads has undesirable impacts on the aeroelastic deflections of the wing, which results in more significant flight control system modeling errors and worsens the performance of the aircraft. In this paper, an integrated dynamic model is derived for a membrane-wing aircraft based on the structural dynamics equation of the membrane wing and the flight dynamics equation of the traditional fixed wing. Based on state feedback control theory, an autopilot system is designed to unify the flight and control properties of different flight and wing deformation statuses. The system uses models of different operating regions to estimate the dynamic response of the vehicle and compares the estimation results with the sensor signals. Based on the compared results, the autopilot can identify the overall flight and select the correct operating region for the control system. By switching to the operating region with the minimum modeling error, the autopilot system maintains good flight performance while flying in turbulence. According to the simulation results, compared with traditional rigid aircraft autopilots, the proposed autopilot can reduce the absolute maximum attack angles by nearly 27% and the absolute maximum wingtip twist angles by nearly 25% under gust conditions. This enhanced robustness and stability performance demonstrates the autopilot’s significant potential for practical deployment in micro-aerial vehicles, particularly in applications demanding reliable operation under turbulent conditions, such as military surveillance, environmental monitoring, precision agriculture, or infrastructure inspection. Full article

High-speed switching chips are key components of network core devices in the high-performance computing paradigm, and their scheduling algorithm performance directly influences the throughput, latency, and fairness within the system. However, traditional scheduling algorithms often encounter issues such as high implementation complexity and high communication overhead when dealing with bursty traffic. To addressing the issue of bottlenecks in high-speed switching chip scheduling, we propose a low-complexity and high-performance scheduling algorithm called m-RGA, where m represents a priority mechanism. First, by monitoring the historical service time and load level of the VOQs at the port, the priority of the VOQs is dynamically adjusted to enhance the efficient matching and fair allocation of port resources. Additionally, we prove that an algorithm achieving a 2× speedup under a constant traffic model can simultaneously guarantee throughput and latency, making this algorithm theoretically as excellent as any maximum matching algorithm. Through simulation, we demonstrate that m-RGA outperforms Highest Rank First (HRF) arbitration in terms of latency under non-uniform and bursty traffic patterns. Full article

This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets. Full article

The non-uniform thermal distribution in the active neutral-point clamped (ANPC) topology causes significant thermal gradients during high-power operation, restricting its use in large-capacity power conversion systems like variable-speed pumped storage. This study introduces a novel hybrid fundamental frequency modulation strategy. Through a dynamic allocation mechanism based on a reference signal, this strategy alternates inner and outer power switches at the fundamental frequency, ensuring balanced switching frequency across devices. Consequently, it effectively mitigates the inherent loss imbalance in conventional ANPC topologies. Quantitative analysis using a power device loss model shows that, compared to conventional carrier phase-shift modulation, the proposed method reduces total system losses by 39.98% and improves the loss-balancing index by 18.27% over inner-switch fundamental frequency modulation. A multidimensional validation framework, including an MW-level hardware platform, numerical simulations, and test data, was established. The results confirm the proposed strategy’s effectiveness in improving power device thermal balance. Full article

In this paper, a new solution is proposed for the problem of mooring safety of large ships in complex sea conditions. Firstly, a dual-mode mooring system is designed to adaptively switch between active control and passive energy storage, adjusting the mooring strategy based on real-time sea conditions. Second, a collaborative analysis platform based on AQWA-Python-MATLAB/Simulink was researched and developed. Thirdly, based on the above simulation platform, the performance of the mooring system and the effects of different configurations on the stability of ship motion and dynamic tension of the cable are emphasized. Finally, by comparing the different mooring positions under various sea conditions with the traditional mooring system, the results show that the constant tension mooring system significantly improves the stability and safety of the ship under both conventional and extreme sea conditions, effectively reducing the fluctuation of cable tension. Through the optimization analysis, it is determined that the configuration of bow and stern cables is the optimal solution, which ensures safety while also improving economic benefits. Full article

With the global transition towards renewable energy, pumped storage has become a pivotal technology for large-scale energy storage, playing an essential role in peak load regulation, frequency control, and ensuring the stability of modern power systems. As the core equipment of pumped storage power stations, pump-turbines operate under complex and frequently changing conditions. These units are required to switch repeatedly between pumping, generating, and transitional modes, giving rise to significant fluid–structure interactions (FSIs). Such interactions have a profound impact on the operational performance and stability of the units. This review provides a comprehensive summary of current research on FSIs in pump-turbines, encompassing both experimental investigations and numerical simulations. Key topics discussed include internal flow dynamics, vibration and acoustic characteristics, and structural responses such as runner deformation and stress distribution. Various numerical coupling strategies for FSI modeling are also examined in detail. Despite progress in this field, several challenges remain, including the complexity of multidisciplinary coupling, the difficulty in developing and solving accurate models, and limitations in predictive capabilities. This review highlights the critical requirements for advancing FSI research in pump-turbines and identifies gaps in the current literature that warrant further investigation. Full article

The organic Rankine cycle (ORC) is a key technology for the recovery of low-grade waste heat, but its efficient and stable operation is challenged by complex kinetic coupling. This paper proposes a model partitioning strategy based on gap measurement to construct a high-fidelity ORC system model and combines the setting of observer decoupling and multi-model switching strategies to reduce the coupling impact and enhance adaptability. For control optimization, the reinforcement learning method of deep deterministic Policy Gradient (DDPG) is adopted to break through the limitations of the traditional discrete action space and achieve precise optimization in the continuous space. The proposed DDPG-MGPC (Hybrid Model Predictive Control) framework significantly enhances robustness and adaptability through the synergy of reinforcement learning and model prediction. Simulation shows that, compared with the existing hybrid reinforcement learning and MPC methods, DDPG-MGPC has better tracking performance and anti-interference ability under dynamic working conditions, providing a more efficient solution for the practical application of ORC. Full article

Threshold-switching memristors (TSMs) are emerging as key enablers for hardware spiking neural networks, offering intrinsic spiking dynamics, sub-pJ energy consumption, and nanoscale footprints ideal for brain-inspired computing at the edge. This review provides a comprehensive examination of how TSMs emulate diverse spiking behaviors—including oscillatory, leaky integrate-and-fire (LIF), Hodgkin–Huxley (H-H), and stochastic dynamics—and how these features enable compact, energy-efficient neuromorphic systems. We analyze the physical switching mechanisms of redox and Mott-type TSMs, discuss their voltage-dependent dynamics, and assess their suitability for spike generation. We review memristor-based neuron circuits regarding architectures, materials, and key performance metrics. At the system level, we summarize bio-inspired neuromorphic platforms integrating TSM neurons with visual, tactile, thermal, and olfactory sensors, achieving real-time edge computation with high accuracy and low power. Finally, we critically examine key challenges—such as stochastic switching origins, device variability, and endurance limits—and propose future directions toward reconfigurable, robust, and scalable memristive neuromorphic architectures. Full article

The adaptive distributed type-2 fuzzy dynamic event-triggered (DET) formation control problem of switched nonlinear multi-agent systems (SNMASs) with actuator faults is addressed in this study. Each agent has a switching subsystem and the switching method of each subsystem is heterogeneous. Interval type-2 fuzzy logic systems (T2FLSs) are adopted to handle uncertain nonlinearities. To conserve communication resources (UCRs), a novel distributed DET controller with an event triggering mechanism is proposed. Additionally, Zeno behavior is excluded. Then, the formation objective can be achieved with a designed common Lyapunov function (CLF). Finally, simulation results confirm the validity of the proposed scheme. Full article

This paper addresses the problem of optimal stabilization for stochastic dynamical systems characterized by Markov switches and concentration points of jumps, which is a scenario not adequately covered by classical stability conditions. Unlike traditional approaches requiring a strictly positive minimal interval between jumps, we allow jump moments to accumulate at a finite point. Utilizing Lyapunov function methods, we derive sufficient conditions for exponential stability in the mean square and asymptotic stability in probability. We provide explicit constructions of Lyapunov functions adapted to scenarios with jump concentration points and develop conditions under which these functions ensure system stability. For linear stochastic differential equations, the stabilization problem is further simplified to solving a system of Riccati-type matrix equations. This work provides essential theoretical foundations and practical methodologies for stabilizing complex stochastic systems that feature concentration points, expanding the applicability of optimal control theory. Full article

Redox signaling is central to plant adaptation, influencing metabolic regulation, stress responses, and developmental processes through thiol-based oxidative post-translational modifications (oxiPTMs) of redox-sensitive proteins. These modifications, particularly those involving cysteine (Cys) residues, act as molecular switches that alter protein function, structure, and interactions. Advances in mass spectrometry-based redox proteomics have greatly enhanced the identification and quantification of oxiPTMs, enabling a more refined understanding of redox dynamics in plant cells. In parallel, the emergence of computational modeling, artificial intelligence (AI), and machine learning (ML) has revolutionized the ability to predict redox-sensitive residues and characterize redox-dependent signaling networks. This review provides a comprehensive synthesis of methodological advancements in redox proteomics, including enrichment strategies, quantification techniques, and real-time redox sensing technologies. It also explores the integration of computational tools for predicting S-nitrosation, sulfenylation, S-glutathionylation, persulfidation, and disulfide bond formation, highlighting key models such as CysQuant, BiGRUD-SA, DLF-Sul, and Plant PTM Viewer. Furthermore, the functional significance of redox modifications is examined in plant development, seed germination, fruit ripening, and pathogen responses. By bridging experimental proteomics with AI-driven prediction platforms, this review underscores the future potential of integrated redox systems biology and emphasizes the importance of validating computational predictions, through experimental proteomics, for enhancing crop resilience, metabolic efficiency, and precision agriculture under climate variability. Full article

This paper introduces an enhanced bi-directional full-bridge resonant converter designed for Vehicle-to-Grid (V2G) systems. A key innovation lies in the incorporation of an auxiliary LC resonant circuit connected via a tertiary transformer winding. This circuit dynamically modifies the magnetizing inductance based on operating frequency, enabling soft-switching across all primary switches, specifically, Zero-Voltage Switching (ZVS) at turn-on and near Zero-Current Switching (ZCS) at turn-off across the entire load spectrum. Additionally, the converter supports both Constant Current (CC) and Constant Voltage (CV) charging modes at distinct, fixed operating frequencies, thus avoiding wide frequency variations. A 3.3 kW prototype developed for onboard electric vehicle charging applications demonstrates the effectiveness of the proposed topology. Experimental results confirm high efficiency in both charging and discharging operations, achieving up to 98.13% efficiency in charge mode and 98% in discharge mode. Full article

Model predictive control (MPC) has become a popular method in motor control due to its high adaptability to multivariate control. However, one issue for this control system is constructing a reasonable cost function (CF) and obtaining appropriate weighting factors (WFs) within it. This paper addresses the issue of effectively reducing torque ripple and current harmonic content in permanent magnet synchronous motors (PMSM). Within the three-vector model predictive current control (TV-MPCC) strategy for PMSM, a new CF including current error and switching frequency terms is constructed. Combined with the technique for order preference by similarity to ideal solution (TOPSIS), the optimal control vector is obtained. Compared with traditional methods, this method reduces the complexity of adjusting WFs in the CF. Simulation results show that the motor’s torque ripple and current harmonic content are effectively reduced. Both the steady state and dynamic performance of the PMSM are also improved by means of the proposed multi-objective MPC for current error and switching frequency. Full article