Search Results (618)

Conventional multispectral photodetectors typically rely on multiple electrical terminals to discriminate different wavelengths, which inevitably increases structural complexity. Here, we break this paradigm by demonstrating a dual-color visible–infrared photodetector based on a simple two-terminal Au/MoS₂/Te heterostructure. The device operates through a bias-switching mechanism: reversing the voltage polarity selectively activates either the MoS₂/Au Schottky junction for visible-light detection (520 nm) or the Te/MoS₂ heterojunction for infrared detection (1550 nm). This bias-controlled wavelength selectivity is unambiguously verified by scanning photocurrent mapping. Beyond dual-color discrimination, an adaptive convolutional neural network is employed to decode the nonlinear current–voltage characteristics and enable precise spectral identification, achieving a reconstruction error of approximately 4.5%. Furthermore, high-fidelity dual-color imaging is demonstrated at room temperature. These results establish a hardware–algorithm co-design strategy based on a minimalist two-terminal architecture, providing a viable route toward compact and intelligent spectral-sensing systems. Full article

Entropic soft-min operators are widely used to obtain smooth approximations of minimum and argmin mechanisms in optimization, machine learning, and reinforcement learning. The quality of this approximation is controlled by an inverse temperature parameter that governs the trade-off between smoothness and fidelity, yet its selection is usually based on global heuristics or worst-case bounds that do not account for the geometry of the candidate cost vector. This study investigates the calibration of the inverse temperature parameter from a geometry-aware perspective, with explicit guarantees on the approximation error between the entropic soft-min and the exact minimum value. After establishing the structural properties of the relaxation error, including monotonicity with respect to the inverse temperature and its dependence on the geometry of the near-optimal set, we introduce a conformal calibration rule that selects the smallest inverse temperature, ensuring that a prescribed upper quantile of the approximation error remains below a target tolerance with distribution-free finite-sample validity. The resulting selector adapts to the geometry distribution represented in the calibration population and provides a principled alternative to mean-based and worst-case tuning rules. Numerical experiments, including geometry-controlled benchmarks and a contextual bandit setting illustrating the impact of geometry-aware calibration on decision-making under estimated action values, show that the proposed method accurately tracks oracle calibration temperatures, preserves the desired operator-level coverage, and makes explicit how geometric heterogeneity governs the effective sharpness required by the soft-min approximation. Additional shifted evaluations illustrate the role of exchangeability in the validity guarantee and the consequences of transferring temperatures across populations with different near-optimal geometries. Full article

Nonlinear model predictive control (NMPC) delivers high tracking accuracy for industrial processes but requires solving a nonlinear program at each sampling instant, limiting its applicability under tight real-time constraints. The Koopman operator provides a principled route to circumvent this limitation by embedding nonlinear dynamics into a higher-dimensional space where the evolution becomes linear, thereby reducing the online optimization to a convex quadratic program. This paper presents a Koopman-based MPC framework (K-MPC) that incorporates three algorithmic contributions. First, an adaptive radial basis function dictionary learning procedure selects lifting functions from process data, eliminating manual basis selection and improving approximation fidelity for systems with localized nonlinearities. Second, a recursive least-squares update rule adjusts the Koopman matrix online as new measurements arrive, enabling the controller to track slow parameter drifts without full model recomputation. Third, a tube-based constraint tightening strategy accounts for the residual linearization error, preserving recursive feasibility under bounded Koopman approximation mismatch. Simulations on a Van der Pol oscillator, a continuous stirred-tank reactor (CSTR), and a four-state Tennessee Eastman-inspired distillation column demonstrate that K-MPC achieves root-mean-square tracking errors within 11–16% of NMPC while reducing average per-step computation time by a factor of 14 to 18. The recursive update mechanism reduces prediction error by 80% compared to the fixed offline Koopman model when reactor feed concentration drifts by 15% from its nominal value. Ablation experiments confirm that adaptive dictionary learning and online updating each contribute measurably to closed-loop performance. Full article

Islanded photovoltaic microgrids with limited inertial support can undergo steep frequency excursions after sudden generation loss or abrupt load changes. This paper develops and evaluates a synthetic inertia strategy supported by a supercapacitor energy storage unit for fast frequency containment in this type of system. The proposed approach commands rapid active-power injection or absorption from the measured rate of change of frequency, thereby emulating the immediate inertial contribution usually associated with rotating machines while preserving a simple and physically interpretable control structure. The supercapacitor is represented through a resistance–capacitance model that includes equivalent series resistance and is interfaced through a bidirectional buck–boost power converter subject to practical current, voltage, and power limits. Rather than claiming a fundamentally new storage-support concept, the contribution of this paper lies in providing a transparent and constraint-consistent benchmark that integrates measured operating profiles, explicit supercapacitor limits, hybrid frequency–RoCoF support, and stress-aware comparative assessment under a common set of plant assumptions. The methodology is assessed in time-domain simulations under representative benchmark disturbances, including an approximately ten percent photovoltaic generation loss, a ten percent load increase, and a combined event. Performance is evaluated through the peak rate of change of frequency, frequency nadir, integral error indices, time outside the admissible band, and supercapacitor stress indicators such as current peaks, voltage depletion, and energy throughput. An additional non-ideal assessment is also included to examine the behavior of the RoCoF-based support law under bounded frequency-measurement perturbations and delayed control action. A complementary variability-driven case based on a highly fluctuating measured irradiance window is also used to examine the behavior of the adaptive energy-management mechanism under repeated photovoltaic-power variations. A local small-signal analysis is also included to show that the selected gain region is dynamically plausible in the unsaturated regime. The results show that the proposed adaptive hybrid strategy improves the overall frequency response while maintaining admissible supercapacitor operation, thus providing a stronger methodological basis for rapid frequency support in islanded photovoltaic microgrids. Full article

Accurate wildfire impact assessment and understanding post-disturbance recovery are essential for land management in fire-prone regions. This study develops a Sentinel-2–based burned-area extraction framework and integrates NDVI time-series analysis with explainable machine learning to quantify vegetation resilience across five fire-affected regions in China. The burned-area map achieves an overall accuracy of 99.8%, substantially outperforming MODIS products (77.9% and 92.7%) by better detecting fragmented patches in complex terrain. NDVI trajectories reveal three resilience pathways: compensatory recovery, stable recovery without compensation, and persistent degradation. Recovery times ranged from approximately 2 months to over 6 months, with some high-altitude areas showing no effective recovery. An XGBoost–SHAP model explains spatial recovery variability (R² = 0.50–0.88) and identifies a consistent shift from early climate control to later topographic regulation. Landscape heterogeneity promotes recolonization only within intermediate thresholds, temperature exhibits optimal windows, and precipitation shows diminishing returns. Topography acts primarily by redistributing hydrothermal conditions rather than as an independent driver. The results demonstrate strong spatial variability in ecosystem stability and highlight nonlinear interactions among climate, terrain, and landscape structure as key determinants of resilience. The proposed framework improves burned-area monitoring and supports targeted ecological restoration and adaptive land-use planning in heterogeneous landscapes. Full article

Problem: Controlling nonlinearly parameterized systems with unknown disturbances remains challenging because classical adaptive approaches rely on separation-of-variables and reparameterization techniques, leading to increased parameter dimensions, conservative stability bounds, and implementation complexity. Objective: This paper develops a data-driven predictive fuzzy adaptive control (DD-PFAC) framework that eliminates the need for separation techniques while achieving superior tracking performance and formally certified stability. Novelty: The key innovation is a two-layer architecture. Layer 1 provides direct fuzzy approximation of composite nonlinear functions (system dynamics plus disturbance bound) without parameter reparameterization, reducing parameter complexity from $O\left(qn\right)$ to $O\left(nN\right)$. Layer 2 employs Hankel matrix-based predictive optimization to adaptively tune both control gains $c_i\left(k\right)$ and adaptation rates ${\gamma }_i\left(k\right)$ online using 80–150 recent input–output samples. Methodology: A Lyapunov function augmented with a prediction-error term is used to prove uniform ultimate boundedness of all closed-loop signals. A projection-based recursive least-squares algorithm updates the gain parameters online while guaranteeing $c_i\left(k\right)\ge c_{min}>0$ at all times. Results: Comparative simulations demonstrate 31.4% reduction in integral square error, 27.8% reduction in mean absolute error, and 37.4% reduction in steady-state error versus traditional adaptive fuzzy control. A four-group ablation study confirms that adaptive gain scheduling contributes 27.7% and predictive compensation contributes 6.5% to the total MAE improvement. Robustness tests validate consistent 28–32% performance advantage across sinusoidal, pulse, step, and large-disturbance scenarios. Full article

Chaotic behavior in power systems that are integrated with permanent-magnet synchronous generators (PMSGs) poses a significant threat to stability and security. Existing control methods often suffer from slow convergence, reliance on precise system models, or the inability to guarantee convergence within a predefined time. To address these issues, this paper develops a predefined-time synchronization control scheme for chaotic PMSG systems under unknown nonlinearities and external disturbances. First, an adaptive neural network with variable exponent coefficients is constructed to approximate unknown system dynamics online. Second, a predefined-time stability criterion is established, ensuring global convergence of synchronization errors within a user-specified time, independently of initial conditions. Third, the proposed controller achieves superior disturbance rejection without requiring prior knowledge of disturbance bounds. Numerical simulations demonstrate that the proposed method outperforms conventional finite-time control in convergence speed, control smoothness, and robustness to parameter variations—offering a practical and theoretically guaranteed solution for enhancing the stability of PMSG-based power systems. Full article

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment. Full article

Cooperative multi-UAV pursuit–evasion under occlusions and sensor noise is challenged by intermittent observability of the evader, varying observation-window lengths, and non-stationary evader tactics, all of which destabilize prediction and undermine safety-constrained cooperation. To address these challenges, we propose a safe decision-making framework that uses behavior mode and subgoal inference as intermediate representations for interpretable, uncertainty-aware cooperation. Specifically, an observation-driven generative intent–subgoal model infers the evader’s behavior mode and subgoal from short observation windows. Building on this model, a length-agnostic trajectory predictor is trained via multi-window knowledge distillation and consistency regularization to produce future trajectory predictions with calibrated uncertainty for arbitrary observation-window lengths, thereby reducing cross-window inference inconsistency and lowering online computational cost. Based on these predictions, we derive belief and risk features and develop a belief–risk-gated hierarchical multi-agent policy based on soft actor-critic with a safety projection layer, enabling adaptive strategy switching and a controllable trade-off between efficiency and safety. Experiments in obstacle-rich pursuit–evasion environments with randomized layouts and diverse obstacle configurations demonstrate more stable cooperative capture, safer maneuvering, and lower decision variance than representative baselines, indicating strong robustness and real-time feasibility. Specifically, across different observation-window settings, the proposed method improves the normalized expected return by approximately 5–7% over the strongest baseline and reduces pursuer losses by roughly 22–25%. Moreover, its end-to-end decision latency consistently remains within the 50 ms control cycle. Full article

NetworkGuard is a modular edge-based virtual network sensing framework designed for residential smart home security. The system interprets network telemetry—such as DNS queries, firewall events, VPN latency, and connection establishment delay—as structured sensing signals for gateway-level monitoring. Implemented on a Raspberry Pi 4 and managed via an Android interface, NetworkGuard integrates DNS filtering (Pi-hole), firewall enforcement (UFW), encrypted VPN tunneling (WireGuard), and an AI-assisted advisory layer for contextual log interpretation. During a six-week residential deployment, DNS blocking efficiency improved from 81.2% to 97.0% following blocklist refinement, while VPN connection establishment time decreased from approximately 3012 ms to 2410 ms after configuration tuning. ICMP-based measurements indicated a stable tunnel latency under moderate traffic conditions. Controlled validation scenarios—including DNS manipulation attempts, port scanning, and VPN interruption testing—confirmed consistent firewall enforcement and tunnel containment. The results demonstrate that layered security principles can be adapted into a lightweight, reproducible edge architecture suitable for small-scale residential IoT environments without a reliance on enterprise infrastructure. Full article

Basal cell carcinoma (BCC) is the most common malignancy worldwide, yet the role of the skin microbiome in BCC remains poorly defined. In this cross-sectional observational case series, we compared the cutaneous microbiome of BCC lesions with matched perilesional and control skin using whole-genome shotgun sequencing in an intra-patient, multi-site sampling design. BCC samples demonstrated reduced microbial richness and significant shifts in community composition compared with matched control skin. Specifically, BCC lesions exhibited significantly lower Chao1 diversity (β = −484.6, 95% CI: −772.1 to −197.2, p = 0.003). Differences in overall microbial composition were confirmed by PERMANOVA analysis based on Bray–Curtis and Jaccard distance metrics (R² = 12.6% and 9.7%, respectively; both p = 0.01). At the species level, Cutibacterium acnes was significantly reduced in BCC samples compared with controls (β = −0.31, 95% CI: −0.45 to −0.16, p = 0.0004), corresponding to an approximately 27% lower geometric mean relative abundance. Functional profiling suggested shifts in microbial metabolic potential, with pathways related to redox balance and lipid-associated processes differentially represented in BCC samples relative to controls. Together, these findings demonstrate that BCC lesions are associated with localized alterations in microbial diversity, community composition, and inferred functional potential. These results support the presence of a tumor-associated microbiome signature in BCC; however, further studies in larger and more diverse cohorts are needed to determine whether these changes contribute to tumor development or reflect adaptation to the tumor microenvironment. Full article

The actuator reliability of Steer-by-Wire (SBW) systems is critical to the functional safety of autonomous vehicles. However, existing control methods struggle to simultaneously enhance both response speed and fault-tolerant performance when facing multiple uncertainties such as parameter perturbations, external disturbances, and actuator faults. To address these issues, this paper proposes a synergistic fault-tolerant control (FTC) strategy combining guaranteed cost control (GCC) and integral sliding mode control (ISMC). First, a dynamic model of the SBW system incorporating the multiple uncertainties is established. Second, a GCC law is derived based on linear matrix inequalities (LMIs) to impose strict constraints on the system’s tracking accuracy and robustness. Building upon this, an ISMC is integrated to significantly accelerate the system’s dynamic response without sacrificing steady-state accuracy, thereby forming a synergistic fault-tolerant architecture characterized by both high precision and rapid response. The results indicate that, under typical fault modes and steering conditions, the response speed of GCC+ISMC is significantly improved compared with GCC alone, and the GCC+ISMC reduces tracking errors by approximately 35% compared to adaptive integral sliding mode control (AISMC). These findings demonstrate that the proposed approach effectively mitigates multiple system uncertainties, offering comprehensive advantages in tracking accuracy, response speed, and robustness. Full article

Small rotorcraft unmanned aerial vehicles (UAVs) are highly susceptible to wind disturbances when performing tasks such as fixed-point hovering, low-altitude inspection, and aggressive maneuvers. Under complex, variable meteorological conditions, attitude stability and position-holding accuracy are particularly critical. Although quadrotor UAVs exhibit structural and dynamic symmetry, real wind disturbances are often asymmetric, disrupting the original balance and leading to intensified attitude oscillations, position drift, and degraded data quality. To effectively address the challenges of wind-induced oscillation and positional deviation, this paper proposes a fuzzy logic-based linear active disturbance rejection control (Fuzzy-LADRC) strategy. This approach employs a hybrid algorithm combining particle swarm optimization and gray wolf optimization to optimize controller parameters and incorporates fuzzy logic to enhance the adaptive capability of the linear active disturbance rejection controller (LADRC). Simulation experiments conducted in MATLAB/Simulink under complex wind-field conditions demonstrate that the proposed method significantly outperforms traditional PID controllers: in the regulation of roll and pitch angles, control performance improves by approximately 5%, while in yaw angle control, the improvement reaches up to 30%. Furthermore, this method can significantly suppress position deviation and fluctuation in the X and Y directions, and reduce the overshoot in the Z-axis during the UAV’s takeoff phase by 75%. Full article

Soil fumigation effectively mitigates replanting obstacles induced by intensive cultivation, yet its non-targeted biocidal effects can suppress beneficial microbial activity, potentially compromising agricultural sustainability. Microbial inoculation, as a strategy to supplement beneficial microorganisms, is often employed to restore soil microbial communities. However, in practice, commonly used exogenous microbial consortia exhibit poor adaptability in non-native environments, frequently resulting in limited efficacy. To address this limitation, we propose an ecological intervention based on the reintroduction of indigenous cultivable microorganisms: cultivable microbial communities were isolated from healthy adjacent soils and inoculated into fumigated soils affected by replanting obstacles. The experimental soil consisted of black soil under continuous cropping, collected from Northeast China. The three treatments were continuous cropping soil (control), fumigated continuous cropping soil and fumigated continuous cropping soil after inoculation of indigenous cultivable microorganisms. Using high-throughput sequencing and agronomic–chemical analyses, combined with cross-domain networks and procrustes analysis, we systematically assessed the ecological effects of this approach on microbial restoration and the alleviation of replanting obstacles. The results showed that indigenous cultivable microorganism inoculation significantly increased the richness of bacterial and fungal communities in fumigated soils within 21 days, extending microbial richness and diversity. Furthermore, inoculation accelerated the reconstruction of dominant microbial community structures, with the relative abundance of dominant species reaching up to 80%. Positive synergistic interactions between bacteria and fungi increased by approximately 10%, enhancing network stability. Key bacterial taxa, such as Paenibacillus and Mycobacterium, were significantly correlated with available potassium and phosphorus content, while Micromonospora, Massilia, and Flavisolibacter influenced plant fresh weight, total nitrogen, and potassium accumulation. Key fungal taxa, such as Cryptococcus and Phialemonium, were significantly associated with soil organic matter stability, maize photosynthetic efficiency, plant dry weight, and total phosphorus content. This study confirms the ecological adaptability and functionality of indigenous cultivable microorganisms in soil ecosystem restoration, offering a low-risk, highly effective localized intervention strategy for sustainable agriculture. Full article

Access control systems rely increasingly on multimodal biometric and behavioral signals to enhance security and robustness against sophisticated attacks. However, when heterogeneous modalities provide conflicting evidence, such as valid biometric credentials accompanied by abnormal behavioral or acoustic patterns, traditional fusion strategies based on static thresholds or majority voting often fail, leading to false alarms or insecure authorization decisions. This paper addresses this critical limitation by proposing a contextual decision-making fusion framework designed to resolve conflicting multimodal evidence at the decision-making level. The proposed approach models access control as a decision-making problem in a context of uncertainty, where independent agents generate modality-specific evidence from authentication channels based on face, voice, and fingerprints. A centralized fusion mechanism integrates heterogeneous results using adaptive reliability weighting and contextual reasoning to resolve conflicts before operational decisions are made. Rather than treating each modality independently, the framework explicitly considers inconsistencies, uncertainties, and situational context when aggregating evidence. The framework is evaluated using public benchmarks, including VGGFace2, VoxCeleb2, and FVC2004, combined with controlled multimodal scenarios that induce conflicting evidence. Experimental results obtained under controlled contradiction scenarios show that the proposed fusion strategy reduces false alarms and improves decision consistency by approximately 18%. These results are interpreted within the scope of controlled multimodal simulations. Full article