Search Results (888)

Daily nighttime light (NTL) observations made by remote sensing satellites can monitor human activity at high temporal resolution, but are often constrained by residual physical disturbances. Even in standard products, such as NASA’s Black Marble VNP46A2, factors related to sensor viewing geometry, lunar illumination, atmospheric conditions, and seasonality can introduce noise into daily radiance retrievals. This study develops a locally adaptive framework to diagnose and correct residual disturbances in daily NTL data. By estimating location-specific regression models, we quantify the residual sensitivity of VNP46A2 radiance to multiple disturbance factors and selectively remove statistically significant components. The results show that the proposed approach effectively removes statistically significant residual disturbances from daily NTL data in the VNP46A2 product. An application for COVID-19 containment periods in China demonstrates the effectiveness of the proposed approach, where corrected daily NTL data exhibit enhanced temporal stability and improved interpretability. Further analysis based on event study approaches demonstrates that corrected daily NTL data enable the identification of short-run policy effects that are difficult to detect with lower-frequency indicators. Overall, this study enhances the suitability of daily NTL data for high-frequency socioeconomic applications and extends existing preprocessing approaches for daily NTL observations. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

To address the path-planning challenge for unmanned aerial vehicles (UAVs) in complex environments, this study presents an improved pelican optimization algorithm enhanced with multiple strategies (MIPOA). The proposed method introduces four main improvements: (1) using chaotic mapping to spread the initial search points more evenly, thereby increasing population variety; (2) incorporating a random Lévy-flight strategy to improve the exploration of the search space; (3) integrating a differential evolution approach based on Cauchy mutation to strengthen individual diversity and overall optimization ability; and (4) adopting an adaptive disturbance factor to speed up convergence and fine-tune solutions. To evaluate MIPOA, comparative tests were carried out against classical and modern intelligent algorithms using the CEC2017 and CEC2022 benchmark sets, along with a custom UAV environmental model. Results show that MIPOA converges faster and achieves more accurate solutions than the original pelican optimization algorithm (POA). On CEC2017 in 30-, 50-, and 100-dimensional cases, MIPOA attained the best average ranks of 1.57, 2.37, and 2.90, respectively, and achieved the top results on 26, 21, and 19 test functions, outperforming both POA and other advanced algorithms. For CEC2022 (20 dimensions), MIPOA obtained the highest Friedman average rank of 1.42, demonstrating its effectiveness in complex UAV path-planning tasks. The method enables the generation of faster, shorter, safer, and collision-free flight paths for UAVs, underscoring the robustness and wide applicability of MIPOA in real-world UAV path-planning scenarios. Full article

Green hydrogen production and the use of fuel cells (FCs) in microgrid (MG) systems have become viable and feasible solutions due to their continuous cost reduction and advancements in technology. Furthermore, green hydrogen electrolyzers and FC can mitigate fluctuations in renewable energy generation and various demand-related disturbances. Proper incorporation of electrolyzers and FCs can enhance load frequency control (LFC) in MG systems. However, they are subjected to multiple false data injection attacks (FDIAs), which can deteriorate MG stability and availability. Moreover, most existing LFC control schemes—such as conventional PID-based methods, single-degree-of-freedom fractional-order controllers, and various optimization-based structures—lack robustness against coordinated and multi-point FDIAs, leading to significant degradation in frequency regulation performance. This paper presents a new, modified, multi-degree-of-freedom, cascaded fractional-order controller for green hydrogen-based MG systems with high fluctuating renewable and demand sources. The proposed LFC is a cascaded control structure that combines a 1+TID controller with a filtered fractional-order PID controller (FOPIDF), namely the cascaded 1+TID/FOPIDF LFC control. Furthermore, another tilt-integrator derivative electric vehicle (EV) battery frequency regulation controller is proposed to benefit from EVs installed in MG systems. The proposed cascaded 1+TID/FOPIDF LFC control and EV TID LFC methods are designed using the powerful capability of the exponential distribution optimizer (EDO), which determines the optimal set of design parameters, leading to guaranteed optimal performance. The effectiveness of the newly proposed cascaded 1+TID/FOPIDF LFC control and design approach employing multi-generational-based two-area MG systems is studied by taking into account a variety of projected scenarios of FDIAs and renewable/load fluctuation scenarios. In addition, performance comparisons with some featured controllers are provided in the paper. For example, in the case of fluctuation in RESs, the measured indices are as follows: ISE (1.079, 0.5306, 0.3515, 0.0104); IAE (15.011, 10.691, 9.527, 1.363); ITSE (100.613, 64.412, 53.649, 1.323); and ITAE (2120, 1765, 1683, 241.32) for TID, FOPID, FOTID, and proposed, respectively, which confirm superior frequency deviation mitigation using the proposed optimized cascaded 1+TID/FOPIDF and EV TID LFC control method. Full article

Parasitoids are key natural antagonists of forest insect pests and are gaining importance in integrated forest protection under increasing climate-related disturbances. This study aimed to quantify the influence of vegetation diversity and canopy structure on the abundance and diversity of the overall insect community responses to vegetation structure and to provide an ecological context. Second, detailed analyses focused on three focal parasitoid families (Braconidae, Ichneumonidae, Tachinidae), which are of particular relevance for integrated forest protection due to their central role in integrated forest protection and in pesticide-free regulation approaches for risk mitigation in forest ecosystems. Malaise traps were deployed at eight randomly selected broadleaf and coniferous sites, and insect samples from six sampling dates in summer 2024 were analyzed. The sampling period coincided with the full development of woody and vascular plants, representing the phase of highest expected activity of phytophagous insects and associated parasitoids. Vegetation surveys (Braun–Blanquet), canopy closure, and canopy cover were recorded for each site. Across all samples, five arthropod classes, 13 insect orders, and 31 hymenopteran families were identified, with pronounced site-specific differences in community composition and abundance. Our results suggest that broadleaf-dominated sites, characterized by higher plant species richness and greater structural heterogeneity, support a more diverse assemblage of phytophagous insects, thereby increasing host availability and niche diversity for parasitoids. Parasitoid communities generally showed higher diversity at broadleaf sites. Spearman correlations and multiple linear regressions revealed a strong negative relationship between canopy cover and total insect abundance ρ (Spearman’s rank correlation coefficient (Spearman ρ = −0.72, p = 0.042; p = 0.012, R² = 0.70), R² (coefficient of determination), whereas parasitoid diversity (Shannon index) and the relative proportion of Ichneumonidae were positively associated with canopy cover (ρ = 0.85, p = 0.008). In addition, canopy cover had a significant positive effect on overall insect diversity (Shannon index; p = 0.015, R² = 0.63). Time-series analyses revealed a significant seasonal decline in parasitoid abundance (p < 0.001) and parasitoid diversity (p = 0.018). Time-series analyses revealed seasonal dynamics characterized by fluctuations in parasitoid abundance and diversity and a general decrease over the course of the sampling period. The findings demonstrate that structurally diverse mixed forests, particularly those with a high proportion of broadleaf trees mixed forests with heterogeneous canopy layers can enhance the diversity of specialized natural enemies, while dense canopy cover reduces overall insect abundance. These insights provide an ecological basis for silvicultural strategies that strengthen natural regulation processes within integrated forest protection. Full article

The precision drive industry has seen rapid growth, leading to an increased demand for actuators that are both highly accurate and responsive. Among these, non-resonant piezoelectric motors are particularly noteworthy. These motors are extensively employed in applications such as high-precision manufacturing, precision drug delivery, and cellular puncture, owing to their adaptable drive control and resistance to external disturbances. Given the specific requirements of these applications, it is crucial to quickly determine the relationship between the motor input parameters and output characteristics—a challenging endeavor. In this research, we examine a typical non-resonant piezoelectric motor using multiple sets of experimental data. A machine learning algorithm is employed to swiftly establish the correlation between electromechanical input parameters and output trajectory characteristics. Data are analyzed using a random forest model to understand the underlying influence mechanisms. Based on this analysis, predictions and recommendations are made to achieve optimal operating conditions for the motor. This study demonstrates that machine learning serves as an effective tool for predicting piezoelectric motor performance, facilitating rapid assessment of motor output capabilities. Full article

Background/Objectives: Adults with intellectual disability (ID) experience high rates of psychiatric comorbidity but often face diagnostic challenges and treatment barriers, leading to inappropriate psychotropic medication use. This study examined the extent to which specialized psychiatric assessment and improved diagnostic accuracy had an impact on medication management and clinical outcomes in adults with ID and co-occurring psychiatric disorders. Methods: This observational retrospective study analyzed medical records from 25 adults with ID who underwent specialized psychiatric assessment at a community-based service in Italy between January 2023 and January 2024. Psychopathological diagnoses were established according to Diagnostic Manual—Intellectual Disability, Second Edition (DM-ID2) criteria, based on clinical observation and a comprehensive assessment using validated instruments. Clinical outcomes were assessed using a psychometric tool encompassing multiple psychopathological and behavioral dimensions. Data on psychotropic prescriptions and side effects were also collected. Non-parametric analyses were performed, with significance set at α = 0.05. Results: The proportion of patients with a psychiatric diagnosis increased from 32% to 96% after specialized assessment (p < 0.001), with notable rises in depressive (0% to 32%), bipolar (8% to 36%), anxiety (4% to 24%), and impulse control (0% to 16%) disorders. First-generation antipsychotic prescriptions decreased (from 36% to 8%, p = 0.023), while antidepressant use increased (from 12% to 52%, p = 0.004). The mean number of side effects per patient declined from 1.6 to 0.5 (p < 0.001), particularly the elevated prolactin level and psychomotor retardation. Significant improvements were observed in symptom intensity and frequency across multiple domains, including aggression, mood disturbances, and compulsions (p < 0.001). Conclusions: In this single-center retrospective study, specialized psychiatric assessment was associated with improved diagnostic accuracy, medication management, and clinical outcomes in adults with ID. The increase in psychiatric diagnoses likely reflects improved identification, addressing key challenges in precision diagnosis for people with neurodevelopmental disorders. Although the overall number of prescribed medications remained stable, optimization of treatment regimens reduced first-generation antipsychotic use and related adverse effects. These findings indicates that access to specialized assessment and precision diagnosis could improve psychopharmacological interventions and outcomes for this vulnerable population, but larger, multi-center and longer-term studies are needed to confirm these results. Full article

Precisely assessing electric vehicle hosting capacity (EVHC) is critical for ensuring the secure integration of EVs and optimizing the use of distribution network resources. Although optimization-based methods such as Particle Swarm Optimization (PSO) can identify a high theoretical HC under steady-state voltage constraints, these static formulations fail to capture short-term dynamics such as photovoltaic (PV) intermittency and uncoordinated EV arrivals. As a result, the hosting capacity that can actually be used in practice is often reduced to a much lower capacity to keep the system operating safely. This study compares optimization-based and simulation-based HC assessments and introduces a Weighted Average Power Estimator (WAPE)-based dynamic control framework to preserve the higher HC identified by optimization under real-world conditions. Case studies on a modified IEEE 13-bus system show PV drops of 90% during a 4-s cloud event. Studies also demonstrate that a sudden clustering of multiple EVs would significantly lower effective HC. With WAPE control, the system maintains stable operation at full HC, holding the bus voltage within an acceptable range (400–430 V) during the two events, representing a 2–3% voltage improvement. In addition, WAPE allows the EV to continue charging at a lower rate during disturbances, reducing the total charging time by almost 10% compared with completely stopping the charging process. Overall, the proposed WAPE substantially improves the usable and sustainable HC of distribution networks, ensuring reliable EV integration under dynamic and uncertain operating conditions. Full article

This study presents a comprehensive performance analysis of four different control strategies, Proportional–Integral (PI), classical Sliding Mode Control (SMC), Super-Twisting SMC (ST-SMC), and Exponential Reaching Law SMC (ERL-SMC), applied to the speed regulation of a Hall-effect sensored Brushless DC (BLDC) motor. A mathematically detailed BLDC motor model, three-phase inverter structure with safe commutation logic, and a high-frequency PWM switching scheme were implemented in the MATLAB/Simulink-2024a environment to provide a realistic simulation framework. The control strategies were evaluated under multiple test scenarios, including variations in supply voltage, mechanical load disturbances, reference speed transitions, and steady-state operation. The comparative results reveal that the classical SMC and PI controllers suffer from significant oscillations, overshoot, and limited disturbance rejection capability, especially during voltage and load transients. The ST-SMC algorithm improves robustness and reduces the chattering effect inherent to first-order SMC but still exhibits noticeable oscillations near the sliding surface. In contrast, the proposed ERL-SMC controller demonstrates superior performance across all scenarios, achieving the lowest steady-state ripple, the shortest settling time, and the most stable transition response while significantly mitigating chattering. These results indicate that ERL-SMC is the most effective and reliable control strategy among the evaluated methods for BLDC speed regulation, which requires high dynamic response and disturbance robustness. The findings of this study contribute to the advancement of SMC-based BLDC motor control, providing a solid foundation for future research that integrates observer-based schemes, adaptive tuning, or real-time hardware implementation. Full article

Mixed traffic flow composed of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) represents a core characteristic of intelligent transportation systems. However, its operational efficiency is significantly constrained by lane management strategies and CAV cooperative driving behaviors. To investigate this, a cellular automata-based simulation model is developed that integrates multiple car-following rules, a lane-changing strategy, and a platoon coordination mechanism. Through a systematic comparison of 13 lane management strategies in one-way two-lane and three-lane configurations, this study analyzes the influence mechanisms of lane allocation and cooperative driving on traffic flow, considering fundamental diagram characteristics, operating speed, CAV degradation behavior, and maximum platoon size. The results indicate that the performance of different strategies exhibits phased evolution with increasing CAV penetration rates. At low penetration rates, providing relatively independent space for HDVs effectively suppresses random disturbances and improves throughput. At medium to high penetration rates, dedicated CAV lanes—especially those with spatial continuity—enable cooperative platoons to fully leverage their advantages, leading to significant improvements in traffic capacity and operational stability. These findings demonstrate an optimal alignment between cooperative driving mechanisms and lane configurations, offering theoretical support for highway lane management in mixed traffic environments. Full article

This paper develops a novel fault reconstruction (FR) method and an FR-based fault-tolerant control (FTC) scheme for systems suffering from both sensor and actuator faults based on the combination of a Luenberger-like reduced-order observer and an interval observer. Firstly, by introducing an output transformation, an auxiliary output that is able to decouple the sensor fault is obtained. Secondly, for addressing the external disturbance and actuator fault, a multiple unknown input (MUI) is formed, and a reduced-order observer that is able to decouple the MUI is constructed. Consequently, asymptotic convergence estimations of the state and the sensor fault can be accomplished. Thirdly, in order to obtain the asymptotic convergence actual FR (AFR), an interval observer is designed. After this, an algebraic connection of the MUI and the state error estimation is given, and, based on the algebraic relationship, an algebraic MUI reconstruction (MUIR) method is proposed. Finally, an FTC scheme is developed by using the state estimation and MUIR. Under the FTC, the closed-loop system is asymptotically stable even if it suffers from sensor and actuator faults simultaneously. Theoretical analysis demonstrates that the observer-based FTC mechanism satisfies the separation principle. At last, two simulation examples are given to verify the effectiveness of the proposed methods. Full article

This paper investigates the consensus tracking problem for a class of high-order nonlinear multi-agent systems subject to actuator faults, sensor faults, unknown disturbances, and model uncertainties. To effectively address this problem, a hierarchical fault-tolerant control framework with fuzzy adaptive mechanisms is proposed. First, a distributed output predictor based on a finite-time differentiator is constructed for each follower to estimate the leader’s output trajectory and to prevent fault propagation across the network. Second, a novel state and actuator-fault observer is designed to reconstruct unmeasured states and detect actuator faults in real time. Third, a sensor-fault compensation strategy is integrated into a backstepping procedure, resulting in a fuzzy adaptive consensus-tracking controller. This controller guarantees the uniform boundedness of all closed-loop signals and ensures that the tracking error converges to a small neighborhood of the origin. Finally, numerical simulations validate the effectiveness and robustness of the proposed method in the presence of multiple simultaneous faults and disturbances. Full article

Atrial fibrillation (AF) is the most common sustained arrhythmia, and its initiation and progression involve multiple mechanisms, including electrical remodeling, structural remodeling, inflammatory responses, and oxidative stress. In recent years, the ubiquitin–proteasome system (UPS), a central pathway for maintaining intracellular protein homeostasis, has attracted increasing attention in the pathogenesis of AF. By regulating the degradation and expression of ion channel proteins, Ca²⁺-handling molecules, and pro-fibrotic signaling factors, the UPS plays a pivotal role in key pathological processes such as electrical and structural remodeling. Several E3 ubiquitin ligases (e.g., NEDD4-1/2, MuRF1, WWP1/2, TRAF6), deubiquitinating enzymes (e.g., JOSD2), and immunoproteasome subunits (e.g., β5i) have been shown to exert critical regulatory effects on atrial electrophysiological disturbances, interstitial remodeling, and inflammation. This review provides a comprehensive summary of the regulatory mechanisms of the UPS in AF-associated pathological processes, outlines potential therapeutic targets, and highlights current intervention strategies, including proteasome inhibitors, selective E3 ligase modulators, and natural compounds. Moreover, we discuss the latest advances and future perspectives regarding the application of UPS-based interventions in AF, aiming to provide theoretical foundations and research insights for the mechanistic exploration and innovative therapeutic development of AF. Full article

Mining activities exert profound and long-lasting impacts on terrestrial eco-environmental systems, manifesting across multiple spatial and temporal scales throughout the mining lifecycle—from exploration and extraction to post-mining reclamation. Remote sensing technology serves as an advanced monitoring and analysis tool, playing a critical role in the continuous monitoring of mining-related eco-environmental disturbances. This work provides a systematic review of remote sensing applications for mining-related eco-environmental monitoring and assessment. We first outline the importance of mineral resource development and summarize the associated eco-environmental issues. The second section presents an overview of remote sensing platforms and data types currently employed for monitoring in mining areas. The third section systematically summarizes recent research advances in key mining-related eco-environmental dimensions, including spatiotemporal land-use and land-cover analysis, terrain and deformation monitoring, natural environmental factor disturbances assessment, comprehensive ecological-environment quality evaluation, and post-mining reclamation assessment. Finally, we analyze the opportunities, challenges and future perspectives associated with remote sensing applications in mining areas. This review aims to provide reference for advancing remote sensing-based eco-environmental monitoring in mining areas, thereby supporting more effective, long-term monitoring and informed decision-making within the mining sector. Full article

Rodents, as a core component of desert ecosystems and an important indicator of environmental changes, are ideal subjects for studying the impacts of fluctuations in climatic conditions on wildlife. Based on field data from the southern Alxa Desert (2014–2020), this study constructed an ecosystem structure network integrating local/metacommunities, climate, soil, and plant communities. Combined with structural equation modeling, we explored the response mechanisms of rodent communities to climatic conditions across multiple scales. The results showed the following: the α-diversity of local and metacommunities exhibited convergent seasonal patterns, with greater impacts from human disturbances than interannual effects, as well as coexisting species turnover and nesting in metacommunities. Precipitation directly affected metacommunity abundance and diversity and indirectly influenced both community types via vegetation, while temperature directly regulated community characteristics; metacommunities were formed via the coupling of local communities through species migration and habitat filtering, reflecting complex links between local and regional processes. This research provides scientific support for predicting desert ecosystem dynamics and guiding conservation management. Full article