Search Results (411)

In Optical Camera Communication (OCC), precise localization of LED arrays under complex tilt conditions is a core challenge for reliable decoding. This paper proposes an OCC reception scheme for RGB-LED arrays that integrates YOLO-OBB rotated object detection with two-stage geometric correction. The system first employs a YOLOv8n-OBB model to extract a quadrilateral region of interest that tightly encloses the LED array boundary. This effectively suppresses background interference caused by superimposed perspective tilt and in-plane rotation. A coarse-to-fine two-stage correction framework is then applied. The first stage rapidly eliminates the dominant perspective distortion based on the detected bounding-box corners. The second stage performs a refined correction using the actual LED center positions. Two homography matrices are cascaded into a combined transformation, achieving two-stage correction accuracy through a single coordinate mapping. In the corrected image, K-Means clustering constructs a 16 × 16 LED topological grid. A locking strategy is adopted so that subsequent frames skip repeated LED detection and clustering. The steady-state per-frame processing time is reduced to approximately 78.9 ms. Experiments covered 16 cross-combinations of vertical tilt from 0° to 45° (0°, 15°, 30°, 45°) and in-plane rotation from 0° to 40° (0°, 15°, 30°, 40°). The uncorrected scheme and the horizontal-box scheme experienced severe bit errors or complete failure under complicated distortion. The proposed scheme maintained error-free transmission under all 16 tested conditions. The ratios of opposite sides of the corrected LED grid remained stable between 0.997 and 1.004. The system simultaneously achieves high reliability and low-latency real-time processing under complex geometric distortions. Full article

This paper investigates the magnetization reversal processes in spring-exchange magnetic composites featuring irregular, dendritic structures. A disorder-based cluster Monte Carlo method combined with a Diffusion-Limited Aggregation (DLA) algorithm was used to model a fractal-like soft magnetic phase (Fe) embedded in a high-coercivity hard matrix (Fe-Nb-B-Dy). A multiparameter analysis was performed by varying the soft phase volume fraction (10–30%), intergrain exchange coupling via contact bridges (25–100%), system scale factors (1–20), surface-to-volume anisotropy ratios (K_S/K_V = 1–20), and the degree of random anisotropy contribution (RAC = 0–100%). The simulations reveal that highly branched fractal structures enhance the interfacial contact area, which accelerates the nucleation of domain reversal driven by the soft phase, paradoxically lowering the overall coercivity compared to compact morphologies. Furthermore, a lack of easy magnetization axis coherent alignment triggers a cascading reversal mechanism through local “weak links”, severely degrading the coercive field from approximately 4.2 T to below 0.4 T in extreme cases (at 30% Fe, 25% coupling and high K_S/K_V ratio). These findings suggest potentially the most important factors and their impact that should be taken into account in the design and optimization of next-generation powder-sintered permanent magnets. Full article

Existing ground-slotted coplanar waveguide (CPW) spoof surface plasmon polariton (SSPP) low-pass filters (LPFs) remain constrained by the difficulty of achieving a wide stopband while maintaining a compact size, as well as by undesired radiation leakage arising from their open-aperture slot configuration. To address these issues, a grounded coplanar waveguide spoof surface plasmon polariton (GCPW-SSPP) low-pass filter based on a multi-arm star-shaped slot (MASS) loading topology is proposed. An equivalent-circuit interpretation and full-wave dispersion analysis show that the multi-arm slots introduce enhanced distributed reactive loading, thereby lowering the asymptotic frequency and enabling compact SSPP implementations. The near-field characteristics further demonstrate tighter electromagnetic confinement, as reflected by an approximately 48% reduction in the electric-field confinement width along the z-direction. To alleviate the trade-off between miniaturization and wide-stopband performance in cascaded SSPP LPFs, the single-cell S-parameters of the proposed topology are investigated. A single MASS unit exhibits a sharp cutoff and a deep transmission notch, allowing a wide stopband to be obtained with fewer cascaded cells. Radiation characteristics are subsequently quantified by a loss-decomposition method, and the MASS topology is found to suppress the radiation leakage of open-aperture ground-slotted structures, yielding a maximum radiation-loss reduction of approximately 75%. To validate the design methodology, a MASS-loaded GCPW-SSPP LPF is designed, fabricated, and measured. The measured results are in good agreement with the simulated ones, confirming the effectiveness of the proposed scheme. By simultaneously achieving a wide stopband, compact size, and suppressed radiation leakage, the proposed filter offers a promising low-interference filtering solution for highly integrated microwave and RF front-end systems. Full article

Conventional settlement monitoring techniques are inadequate for seawall construction environments due to severe physical impacts, the absence of terrestrial communication networks, and highly dynamic disturbances. This research proposes a multi-source fusion settlement monitoring system designed specifically for the construction phase to overcome these constraints. An integrated inclinometer–magnetoresistive sensing unit is the central component of this system. The unit achieves physical isolation from the severe impact loads of rock backfilling, guarantees protection in high-salinity and high-humidity environments, and accommodates the large deformations typical of soft foundations by utilizing a structural design that includes a rigid channel steel sheath, anti-corrosion sealing, and flexible joints. In terms of computation, a cascaded attitude fusion framework is developed that combines a Multiplicative Extended Kalman Filter (MEKF) with Quaternion Estimator (QUEST) initialization. High-precision displacement inversion via quaternion rotation is made possible by the introduction of an adaptive mechanism based on the Mahalanobis distance that precisely detects and suppresses transient acceleration disturbances induced by construction machinery and waves. Additionally, data transmission issues in remote offshore areas are resolved by combining solar power and BeiDou short-message communication technologies. This adaptive technique minimizes attitude estimate errors in dynamic situations by approximately 84.56%, as demonstrated by experimental and field validation. The system was deployed as a 165 m array comprising 49 sensing units and monitored continuously for 458 days, achieving a normalized RMSE of 9.44–11.02% compared to reference settlement tubes and capturing a maximum settlement of 1.7 m in the core high-fill section. These results confirm the system’s high monitoring accuracy and resilience in harsh construction conditions. Full article

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor speed and (ii) a linear quadratic integral (LQI) controller for regulating the peak DC-link voltage (PDV) of the QZS. Due to the nonlinear characteristics, parameter uncertainties, and external disturbances inherent in PMSM systems, achieving accurate speed tracking and stable DC-link voltage (DCV) regulation using a PDV control strategy under varying power flow conditions remains a significant challenge. In this study, the PMSM model is represented as a nonlinear system with strict feedback. Augmented feedforward control signals are incorporated to restructure the conventional cascade control architecture into a novel optimal control framework. Based on this formulation, a saturated adaptive optimal control law is proposed, relying on a near-optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equation. This solution is approximated using an online approximator combined with an integral reinforcement learning technique. Meanwhile, an LQI controller is employed to regulate the PDV and suppress voltage fluctuations in the QZS. Simulation results demonstrate that the proposed approach significantly improves speed tracking accuracy, DCV stability, and disturbance rejection capability while improving the overall performance and reliability of PMSM drive systems. The simulation results demonstrate that the proposed control strategies have strong potential for effective application in all-electric aircraft systems, meeting the requirements of high performance and energy efficiency. Full article

Rainstorm disasters rank among the most impactful natural hazards globally. The application of satellite precipitation products (SPPs) plays a crucial role in enhancing regional disaster prevention and mitigation strategies by improving the accuracy and timeliness of meteorological disaster warning. The Yangtze River Basin, historically prone to severe rainstorm disasters, was selected as the study area. This study evaluates the accuracy of China’s Fengyun series (FY-2 and FY-4; hereafter FY), IMERG-early and PERSIANN-CCS, constructs a basin-scale indicator system for rainstorm disaster risk assessment, and delineates rainstorm risk zones using different FY products. The results indicate that IMERG-early and FY-2G have the highest monitoring capability with a correlation coefficient (CC) of spatial distribution >0.80 and a time series CC >0.70, respectively. FY-4B and FY-4A exhibit similar performance, whereas FY-2H and PERSIANN-CCS show lower performance than the FY-4 series. Thus, a basin-scale rainstorm disaster risk assessment indicator system was developed using the domestic FY-2G product. The risk zoning results show that high- and medium-high-risk areas account for approximately 12.79% and 26.32% of the total basin area, respectively, and are predominantly concentrated in the middle–lower Yangtze region and the Sichuan plain. These high- and medium-high-risk zones exhibit a spatial overlap rate of 82.35% with the rainstorms and their cascading hazards documented in China’s Annual Top 10 Natural Disasters since 2019. Future research should prioritize dynamic exposure assessment and enhance monitoring and early warning applications for specific rainstorm events. Full article

Two-wheeled self-balancing robots exhibit nonlinear and inherently unstable dynamics due to their inverted-pendulum structure, making control design challenging under terrain variations and external disturbances. This paper proposes a hybrid master–slave fuzzy cascade controller with an additional wheel-synchronization loop to improve tracking performance and robustness. The architecture combines a master velocity PI loop with fuzzy-tuned integral action and a slave balance PD loop with fuzzy proportional control, while a differential synchronization mechanism compensates for motor mismatches without affecting the global balance dynamics. Local stability is analyzed through linearization and equivalent gain approximation within a sector-bounded framework. Experimental validation was conducted on an ESP32-based TWSBR under flat, uphill, and downhill conditions at reference velocities of $0.15$, $0.20$, and $0.30{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, including payload tests with additional masses of $0.279$ and $0.375\mathrm{kg}$. For each scenario, 30 independent trials were performed to compute the reported metrics. Compared with a conventional PID controller, the proposed strategy reduced the flat-terrain velocity RMSE from $0.0108$ to $0.0057{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, while also improving angular stabilization and robustness under slope and payload disturbances. Full article

Purpose: Obesity is a global public health issue, and natural products that promote white fat browning and enhance thermogenesis to consume energy represent promising strategy for addressing this problem. Forsythoside A (FTA) is key bioactive constituent isolated from the fruit of Forsythia suspensa. It has been reported that FTA can alleviate metabolic disorders such as hepatic lipid accumulation induced by high-fat diet (HFD). However, research on the role of FTA in alleviating obesity by promoting white fat browning remains scarce. Materials and Methods: We intervened in diet-induced obesity (DIO) mice and differentiated 3T3-L1 cells with FTA and detected thermogenic indices and the expression of thermogenesis-related genes under the guidance of network pharmacology. Mechanistically, molecular docking combined with molecular biology techniques was employed to verify the affinity of pathway-related proteins, and the AMPK inhibitor (BML-275) was used to intervene in 3T3-L1 cells to assist in demonstrating the main pathway through which FTA stimulates white fat browning. Results: FTA significantly attenuated lipid accumulation in both in vivo and in vitro models. Gene Ontology (GO) enrichment analysis revealed that FTA may promote white adipocyte browning and mitochondrial thermogenesis. Consistent with improved energy metabolism, FTA treatment increased oxygen consumption and carbon dioxide production in mice, while maintaining the respiratory exchange ratio (RER) at approximately 0.7. In vitro, FTA enhanced cellular oxygen consumption rate (OCR) and mitochondrial density. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis combined with molecular docking identified the AMPK signaling cascade as a key potential pathway mediating FTA action. Molecular biology assays further confirmed that FTA promotes AMPK phosphorylation and activates the canonical thermogenic downstream PGC-1α/UCP1 pathway. Consistently, inhibition of AMPK with BML-275 abolished the beneficial effects of FTA in 3T3-L1 adipocytes. Conclusions: This study reveals that FTA enhances white fat browning via the AMPK pathway while increasing thermogenesis in adipose tissue. Full article

To address DC-link voltage fluctuation, active-power imbalance between the machine side and the grid side, and double-frequency distortion in the grid current of a flywheel energy storage system (FESS) under symmetrical and asymmetrical voltage sag faults, this paper proposes a coordinated control strategy for the machine-side and grid-side converters to enhance low-voltage ride-through (LVRT) capability. Taking the DC-side energy imbalance as the coordination criterion, the machine-side converter adopts an online active-current-command reconstruction method based on cascaded limiting of DC-link voltage deviation. Under reactive-power-priority support and constrained active-power output on the grid side, the FESS can actively adjust its active-current command according to the DC-side energy state, thereby suppressing DC-link overvoltage/undervoltage and restoring the power balance between the machine side and the grid side. On the grid side, an improved linear active disturbance rejection control (LADRC) is introduced into the current inner loop. By optimizing the structure of the extended state observer, the observation and compensation capability for double-frequency disturbances is enhanced, thus improving grid-current quality under asymmetrical faults. In this way, power rebalancing between the machine side and the grid side, DC-link voltage stabilization, and grid-current disturbance suppression are incorporated into a unified coordinated control framework. Hardware-in-the-loop experimental results show that the proposed strategy can maintain DC-link voltage stability during both symmetrical and asymmetrical voltage sags, while keeping the maximum grid-current total harmonic distortion (THD) below 0.13%. Under asymmetrical voltage sag, the improved LADRC reduces the maximum interphase peak-current deviation from approximately 52 A under conventional PI control to 4.57 A, corresponding to a reduction of about 91.2%. These results indicate that the proposed strategy can effectively enhance DC-link voltage stabilization and improve grid-current quality during faults. Full article

Hurricanes cause severe impacts on lives, livelihoods, and essential systems. Hurricane Melissa impacted Jamaica as a Category 5 cyclone, resulting in estimated losses of approximately 41% of national GDP (US$8.8 billion) and eliciting widespread damage to housing, healthcare, agriculture, and urban infrastructure. Agriculture sustained heavy losses, with 41,000 hectares of damaged farmland and the loss of more than 1 million livestock animals. These impacts resulted in exposed animal closures with biological hazards. Using systems thinking, the PESTHEEL framework, and a One Health lens, we argue for viewing Hurricane Melissa as series of cascading inter-related One Health threats of waterborne and vector-borne diseases, zoonoses, antimicrobial resistance, degraded indoor and outdoor air quality, chemical pollution, and shifting migration and border dynamics. These each unfold at different timings. A structured synthesis for Jamaica and other Caribbean Small Island Developing States is provided by integrating systems thinking, One Health, and the PESTHEEL framework. Immediate and lagged risk pathways are identified, and practical risk reduction actions are proposed to support anticipatory, multisectoral recovery: enhanced syndromic, laboratory, wastewater, vector, and rodent surveillance; resilient WASH and shelter systems; non-insecticidal and integrated vector management; biosecure aid and border protocols; environmental toxicology monitoring; and climate–health intelligence. Full article

Major depressive disorder (MDD) is a mental illness with high mortality, suicide, and relapse rates that could become the leading cause of health problems worldwide by 2030. The microbiota–gut–brain axis involves bidirectional communication between the human gut microbiota and the central nervous system (CNS). The gut microbiome is a complex ecosystem of approximately 100 trillion microorganisms, including viruses, bacteria, and fungi. The gut microbiota has recently been recognized for its impact on various diseases and health concerns. Several factors influence the composition and structure of gut microbes, ultimately affecting human physiology, with the nervous system being particularly vulnerable. The gut–brain–microbiota axis influences several important brain functions through numerous pathways, including vagus nerve signaling, gut microbial synthesis of metabolites, and immune-related chemicals. These factors can influence neurotransmitter activity, neuroinflammation, behavior, and mental health. Despite increased interest, the possibility of modifying the gut microbiota as a therapeutic approach remains unclear. Although numerous studies suggest that microbiota play an important role in many illnesses, the precise mechanisms are yet to be elucidated, and there are currently no evidence-based, microbiota-focused treatments for these illnesses. Recent research indicates that gut dysbiosis (GD) causes increased intestinal permeability (leaky gut), initiates systemic inflammation, and contaminates the blood. Opportunistic microbial metabolites cross the blood–brain barrier, triggering a neuroinflammatory cascade and apoptotic pathways while affecting neurogenesis and neurotransmitters, ultimately resulting in the development of MDD and anxiety. This review examined the factors influencing normal gut microbiota and GD-mediated MDD, as well as possible therapeutic options. The study outlines its objectives and methodological approaches, including the screening and filtering of research on GD-induced depression. Furthermore, it explored the daily use of dietary supplements, revealing new paths for clinical and preclinical research. Full article

Integrated energy systems (IES) are widely recognized as a key pathway toward carbon neutrality, enabling the coupling and coordinated optimization of electricity, heat, gas, and cooling. This review provides a structured, technology-oriented overview of IES based on a unified five-subsystem framework (production, conversion, transmission, storage, and consumption). It systematically covers: (1) renewable energy utilization—solar, wind, and geothermal—supported by a global spatial distribution map and representative top-performing commercial products; (2) energy cascade utilization, where combined heat and power/combined cooling, heating and power (CHP/CCHP) raises overall efficiency from approximately 35–40% to 70–90%; (3) multi-form energy storage—electrical, electrochemical, chemical, thermal, and mechanical—distinguishing short-term balancing (e.g., lithium-ion (Li-ion), flywheels, supercapacitors, with 85–95% round-trip efficiency) from long-duration and seasonal applications (e.g., pumped hydro, hydrogen/power-to-gas (P2G), redox flow batteries); and (4) forecasting, collaborative optimization, and the bidirectional integration of IES with smart grids and grid modernization. A strategic strengths, weaknesses, opportunities, and threats–Political, Economic, Sociological, Technological, Legal, and Environmental (SWOT–PESTLE) analysis is further presented to position IES within the global energy transition. The review highlights that IES and grid innovation are mutually enabling, and that realizing the full carbon-neutrality potential of IES requires coordinated progress in standardization, digitalization, long-duration storage, and cross-sector policy alignment. Full article

The increasing penetration of inverter-based renewable energy resources, especially photovoltaic (PV) systems, has decreased the available system inertia and introduced challenges in maintaining stable grid-forming operation. This paper presents a grid-forming photovoltaic multilevel inverter (MLI) with a modified delta-connected cascaded H-bridge (CHB) multilevel configuration. The proposed system decreases the number of semiconductor switches and provides inherent voltage balancing, while also achieving high power quality, rendering it suitable for grid-forming applications. Each H-bridge cell is connected to an isolated Cúk converter to enable maximum power point tracking (MPPT) of distributed PV modules, allowing for flexible and modular DC-side integration. The proposed MLI operates as a virtual synchronous generator. A control scheme is proposed to attain grid-forming capability, hence providing stable voltage and frequency support. Moreover, a DC-link voltage regulation strategy is also developed to maintain the DC-link voltage at the reference voltage. A detailed mathematical model is developed to characterize the associated dynamics of the proposed MLI and the control system with a grid interface. The model is built in the SIMULINK environment, and the simulation results are presented under variations in solar radiation and grid voltage disturbances to exhibit the functionality of the proposed system and the effectiveness of the control scheme in providing a well-damped frequency response and stable generated voltage and currents. The results demonstrate stable frequency regulation with a settling time of approximately 0.3 s, and the output current exhibits low harmonic distortion, with a Total Harmonic Distortion (THD) of about 0.53%. Simulation results show stable operation and confirm that the proposed approach is a competitive solution for PV-based grid-forming applications. Full article

A new class of modified minimum state error variance (MSEV) state-space based optimal linear quadratic Gaussian (LQG) regulators for closed-loop structures with estimated feedback has been proposed in this article. The negative feedback path is designed as the cascade of the digital LQG regulator and discrete Kalman state observer. The proposed design enables tracking of a time-varying reference input using the predictive control approach. Moreover, the proposed tracking method utilizes a multivariable continuous-time Cauchy state-space model of nonlinear, nonstationary dynamic systems. The resulting control strategy is approximately optimal, as the optimality of the LQG design holds locally for each linearized model around the respective operating point and does not extend to the global nonlinear system. In this sense, starting from the prespecified nominal state trajectory to be tracked, a numerical optimization procedure minimizing the squared tracking error at each step by using the Nelder–Mead direct search simplex algorithm under the required constraints on the input signal has been developed. The LQG regulator and Kalman state observer are designed by utilizing the linear discrete-time state variable models that properly approximate the nonlinear system dynamics across the nominal state trajectory. The performance of the proposed design is validated by simulating a six-degree-of-freedom nonlinear aircraft model across typical flight regimes. Full article

In this work, we applied Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) to predict key performance characteristics of quantum cascade lasers (QCLs), including material gain, current density, and emission frequency. By developing a machine learning-based surrogate modeling framework that replaces computationally expensive simulations of QCLs, we enable orders-of-magnitude-faster evaluation and optimization of a high-dimensional configuration space. The training dataset was generated using a numerical simulator based on the density-matrix transport model. By combining physics simulations with machine learning, we achieved reliable predictions of device characteristics, with standardized RMSE values ranging from 0.21 to 0.55 for RF, 0.16 to 0.51 for XGBoost, and 0.04 to 0.22 for the ANN model, demonstrating the superior predictive performance of the ANN across all investigated performance characteristics. The ANN was subsequently used to analyze the full configuration space defined by possible layer thicknesses and electric fields. Approximately 44 million configurations were evaluated in about five minutes, achieving a speedup of approximately 90,000 times over the numerical simulator for a single configuration. This approach allowed the identification of designs with improved material gain and facilitated the efficient optimization of key parameters while maintaining high prediction reliability. Full article