Search Results (9,543)

With the widespread application of motor drive systems in fields such as industrial automation and new energy vehicles, the impact of their nonlinear dynamical behavior on control accuracy and stability has become increasingly significant. Chaos theory provides new insights for revealing and regulating complex nonlinear phenomena in motor systems. Based on chaos theory, this paper takes the synchronous reluctance motor as the research object and proposes, for the first time, a fractional-order mathematical model of the synchronous reluctance motor based on chaotic neurons. Then, the chaotic dynamical behaviors of the fractional-order mathematical model at orders of 0.99 and 0.97 were analyzed. Through bifurcation analysis, Lyapunov exponents, Poincare sections, and attraction domains reveal the mechanism of chaotic oscillation induced by external excitation current and multiple parameter modulation factors. Subsequently, a closed-loop control system based on the Zeroing Neural Network (ZNN) algorithm was designed, which effectively suppressed the chaotic behavior in the motor system’s mechanical rotor angular velocity, phase current, and rotor electrical angular velocity, thereby significantly enhancing the system’s stability. Finally, the effectiveness of the proposed method was validated through simulation experiments, providing theoretical support for the design of motor drive systems. Full article

To address the challenge that microelectromechanical system (MEMS) gyroscope electrostatic force-modulated closed-loop self-excited driving circuits experience significant dynamic variations in capacitive load and driving demand under different operating conditions, such as start-up, steady-state resonance maintenance, and environmental perturbations, making it difficult to simultaneously achieve strong driving capability, stable oscillation, and low power consumption, this paper proposes a high-energy-efficiency adaptive output operational amplifier architecture. Based on a dynamic load-sensing mechanism, the design introduces a three-threshold decision scheme combining a high threshold, a low threshold, and a mid-supply reference voltage. By coordinating a continuous-time voltage detection circuit with a bidirectional shift register, the proposed approach enables accurate identification of the output state and the load level. A time-division-multiplexed two-stage control strategy is adopted to rapidly compensate for the drive capability under abrupt load changes, while proactively disabling redundant output units under steady-state conditions, thereby achieving power delivery on demand. The output stage employs a Class-AB push–pull structure integrating an improved low-leakage single-pole double-throw (SPDT) switch, which hard shuts off the power transistors in the non-operating state to effectively eliminate the subthreshold leakage current. Circuit simulations in a 0.18 μm CMOS process demonstrate that the proposed operational amplifier can adaptively regulate its output current in real time according to variations in the gyroscope driving demand, ensuring sufficient an electrostatic driving force and oscillation stability during transient conditions while significantly reducing static power consumption during the resonance steady state. The proposed design provides an effective solution for high-performance and high-energy-efficiency interface circuit design in MEMS gyroscope electrostatic force-modulated closed-loop self-excited driving systems. Full article

Model predictive control (MPC) has emerged as one of the most effective control strategies for autonomous vehicle path tracking owing to its capability to explicitly handle dynamic constraints and optimize future control actions. However, its tracking performance strongly depends on the appropriate tuning of prediction, control, and weighting parameters, which remains a challenging and computationally demanding task under varying driving conditions. This paper proposes an Adaptive Curvature-Aware MPC (CAMPC) framework optimized through a hybrid Bayesian Optimization–Tree-structured Parzen Estimator (BO–TPE) approach to automatically identify optimal MPC parameters while accounting for upcoming road curvature. The proposed controller incorporates future curvature information to adapt the vehicle speed profile and steering behavior, thereby improving tracking accuracy and control smoothness in complex road geometries. The framework is evaluated in the CARLA autonomous driving simulator under three challenging driving scenarios, including a roundabout, an urban environment, and a highly curved road. Experimental results demonstrate that the proposed CAMPC consistently outperforms a conventional PID controller, achieving improvements of 47%, 58%, and 85% in trajectory-tracking performance across average and maximum lateral and angular errors while reducing steering, braking, and acceleration efforts by more than 80%. Furthermore, the controller satisfies real-time execution requirements with an average computation time of 31 ms and a 95th-percentile latency of 35 ms, confirming its suitability for practical autonomous driving applications. These results demonstrate that integrating curvature-aware prediction with adaptive BO–TPE parameter optimization significantly enhances the robustness, accuracy, computational efficiency, and real-time capability of MPC for autonomous vehicle path tracking in challenging driving environments. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

Bee bread is a fermented product derived from bee pollen, whose fermentation improves preservation, nutrient bioavailability, and functional properties. However, the microbial succession driving this process, particularly the role of yeasts, remains poorly understood. This study investigated microbial dynamics during fourteen-day spontaneous fermentation of five fresh bee pollen samples from different geographical origins, mimicking natural bee bread formation. Cultivable yeasts and lactic acid bacteria were monitored by viable cell counts and molecular identification. Physicochemical parameters, nutritional components, bioactivities, and pollen structure were evaluated. A clear microbial succession was observed, with Starmerella sp. dominating the early stages and the osmotolerant yeast Zygosaccharomyces rouxii prevailing during the final phase. Together with Apilactobacillus kunkeei, these microorganisms may constitute a cultivable fermentative core involved in bee bread formation. Fermentation significantly increased protein availability, with increases of up to 18%, reduced pH, promoted bee pollen degradation up to 16%, and enhanced antimicrobial activity against Staphylococcus aureus and Listeria monocytogenes. Understanding of cultivable microbiota dynamics during spontaneous bee pollen fermentation could provide an effective natural strategy to stabilize bee pollen while improving its nutritional and functional properties, laying the foundation for developing controlled industrial fermentations to produce bee bread-like products with consistent quality and health-promoting characteristics. Full article

External corrosion of buried ferrous water mains remains the dominant driver of structural failure in aging water distribution networks, yet the mechanisms linking soil physical and chemical heterogeneity to corrosion kinetics and product phase evolution have not previously been synthesized into a unified critical framework. This review evaluates three partially competing accounts of electrochemical degradation—anodic dissolution coupled to oxygen reduction within porous rust layers, redox cycling of iron oxide phases driven by seasonal soil moisture fluctuations, and microbially influenced corrosion (MIC) mediated by direct extracellular electron transfer (EMIC) and chemical metabolite pathways (M-MIC)—and assesses the weight of evidence for each. We demonstrate that corrosion products retain electrochemical activity long after formation, functioning as dynamic redox mediators that continue the reactions responsible for their own growth: the reduction of lepidocrocite under anoxic conditions regenerates Fe²⁺ ions that sustain anodic dissolution and catalyze oxygen reduction, while repeated soil moisture cycles drive the irreversible transformation of γ-FeOOH to Fe₃O₄, which fundamentally alters the conductivity and cathodic capacity of the rust layer. The widely cited universal critical-moisture threshold of 65% water-holding capacity (WHC) is evaluated and found to be a single-point approximation contradicted by texture-resolved experimental data that show the critical degree of saturation ranges from S_r ≈ 0.5 in sand to S_r ≈ 0.8 in clay. Modern machine learning analyses of field corrosion databases confirm that chloride content, pH, pipe-to-soil potential, and water content are the four highest-ranked predictors of maximum pit depth, consistent with the mechanistic framework developed here. The classical cathodic depolarization model of SRB-driven corrosion is evaluated against EMIC evidence and found insufficient: measured pure-culture SRB corrosion current densities range from 14 to 135 µA cm⁻², not the milliampere-level values reported in some earlier reviews. An explicit research agenda is proposed to address the five most consequential unresolved mechanistic questions. Full article

Tidal-flats buffer coasts support biodiversity and are changing rapidly, yet reported global declines contrast with local expansions, leaving managers uncertain about the direction and drivers of change. Here we investigate this tension by systematically reviewing how monitoring by remote sensing has been used to observe, extract, validate, and interpret tidal-flat dynamics. We considered the literature from January 2000 to March 2025 and screened 951 records related to 339 research articles, classifying 63% (215/339) predominantly focused on tidal-flat extraction and area change, while 37% (124/339) emphasized the analysis of driving factors. Publications surged after 2019, and three tidal-flat extraction families dominate practice: waterline methods (most widely applied), image classification, and composite/optical–Synthetic Aperture Radar (SAR) fusion. Across studies, the prevailing trend is net decline in tidal-flat extent, attributed to reduced riverine sediment supply, sea level rise, and reclamation; however, robust local counter-examples exist (e.g., ~18% expansion in Germany’s Wadden Sea, 1998–2016), underscoring strong spatial heterogeneity. Although ~31% of global tidal-flats fall within protected areas, effectiveness is uneven under sustained human pressure. We synthesize common failure modes (tide/scale mismatch, validation gaps) and outline standards for multimodal data fusion and benchmarking. This review synthesizes current knowledge on tidal-flat remote sensing methods, identifies key methodological challenges, and outlines priorities for future research to support reliable monitoring. Full article

Arid inland saline lakes are key components of basin ecosystems. As the largest saline lake in Xinjiang and a critical ecological barrier in northwest China, Ebinur Lake’s area dynamics are vital to regional sustainable development. This study integrates Landsat imagery (1992–2024) with meteorological and socio-economic data to investigate optimal water extraction methods, spatio-temporal lake area variations, and driving mechanisms. Multiple methods were employed, including water index comparison, Mann–Kendall test, Pearson correlation, and ridge regression. Results show that: (1) the Normalized Difference Water Index (NDWI) maintains high, stable classification accuracy across years and months, making it suitable for long-term monitoring; (2) from 1992 to 2024, lake area demonstrates a significant fluctuating downward trend without abrupt change points, indicating continuous degradation. During the growing season (April–October), it first decreases and then increases, with larger early-season areas, minima in August and September, coinciding with peak agricultural irrigation demand; (3) regarding driving mechanisms, socio-economic factors dominate (approximately 70%), while meteorological factors play a weakly regulatory role (about 30%). Population growth and increased water consumption are the primary drivers, with obvious seasonal differences. Meteorological changes, socio-economic development, and ecological measures jointly influence lake area. Although extreme events (e.g., anomalous precipitation) induce short-term fluctuations, they do not alter the long-term degradation trend dominated by human activities. This study provides methodological support for long-term monitoring of arid saline lakes and scientific evidence for ecological conservation and water resource management in the Ebinur Lake Basin. Full article

In the context of escalating implementation of electric vehicles (EVs), further research is required to investigate the impact of empirical driving conditions on energy demand and battery performance. In spite of the fact that microscopic traffic simulation and EV energy modelling are already used extensively, their use is still limited in studies focusing on batteries. Specifically, in most existing approaches, the effect of traffic-induced variability on battery stress is not explicitly accounted for. The study presented here examines a systematic framework that combines energy demand, traffic dynamics and battery behaviour. Using the SUMO simulator, vehicle trajectories are converted into electric vehicle (EV) energy profiles via a physics-based longitudinal model, thereby estimating battery power, energy consumption, regenerative effects and changes in state of charge (SOC). Next, a variety of indicators related to the battery management system (BMS) are introduced, including the Battery Stress Index (BSI), a traffic–energy severity (TES) indicator and event-based measures for transient conditions. The results show that traffic variability leads to significant fluctuations in battery load, which are not fully captured by conventional energy metrics. The proposed indicators provide additional information on cumulative and dynamic battery solicitation while remaining physically interpretable. Taken together, this framework links traffic conditions and battery solicitation in a coherent approach, thereby creating a scalable approach to traffic-aware energy analysis. Full article

Understanding the interactions between vegetation dynamics and surface water availability is essential for assessing the resilience of tropical floodplain ecosystems under increasing hydroclimatic variability. This study analyzes the spatio-temporal dynamics of vegetation cover, surface water, and land use in Zambrano, a floodplain-dominated sector of the lower Magdalena River basin (Colombian Caribbean), over the period 1990–2025. Multi-temporal Landsat imagery was used to derive the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), enabling the evaluation of seasonal and interannual ecohydrological variability under contrasting dry and rainy conditions. In addition, land-use classification was performed using a CORINE Land Cover methodology adapted for Colombia (CLC-C) to characterize the spatial organization of the landscape and its influence on vegetation–water interactions. Results show that vegetation dynamics are strongly controlled by hydroclimatic seasonality. Dense vegetation consistently expands during rainy periods, while dry seasons promote the expansion of open and sparse vegetation, reflecting seasonal vegetation stress rather than long-term degradation. NDWI patterns indicate that surface water and soil moisture are highly seasonal and spatially constrained, with open water largely confined to the Magdalena River channel and localized floodplain depressions. Extreme hydroclimatic events associated with the El Niño–Southern Oscillation (ENSO) produce abrupt but temporary changes in vegetation structure and surface moisture distribution. A strong inverse correlation between NDVI and NDWI reflects the contrasting spectral responses of vegetation and water surfaces resulting from the shared near-infrared (NIR) band in both indices. This spectral relationship is consistent with the observed seasonal variations in vegetation greenness and surface moisture across the floodplain. Land-use analysis reveals the progressive consolidation of the landscape, where the agropastoral matrix expanded from ~18,000 ha in 1990 to over 22,000 ha by 2025, driving a systematic reduction in natural and semi-natural forest structures. Forest conservation areas serve as critical ecological buffers, exhibiting lower seasonal variability in vegetation greenness. Overall, the results indicate that the Zambrano floodplain functions as a structurally stable yet highly responsive ecohydrological system, where vegetation dynamics and surface water availability are predominantly governed by interannual hydroclimatic pulses rather than long-term directional degradation. These findings demonstrate that while the structural matrix of the floodplain exhibits strong baseline resilience, its ecological functioning remains critically coupled with, and vulnerable to, the extreme phase shifts in ENSO cycles. Full article

The research investigates the psychopathological mechanisms through which perceived leader favoritism (PLF) can affect counterproductive work behavior (CWB) through the mediating effects of employee jealousy toward their coworkers and the specialized form of envy known as malicious envy. Integrating Social Comparison Theory with Affective Events Theory, the study proposes that perceived favoritism triggers unfavorable social comparisons, which in turn evoke negative emotional states that manifest in dysfunctional workplace behaviors. The research used a quantitative study design to gather information from 436 employees working in hotels. The researchers used structural equation modeling (SEM) to analyze the research data while they also conducted reliability and validity assessments. The study shows that perceived leader favoritism leads to higher organizational counterproductive work behavior (CWB-O) and higher interpersonal counterproductive work behavior (CWB-I) among employees. The study found that PLF leads to higher employee jealousy levels which result in higher malicious envy levels that drive CWB behavior. The research found that jealousy and malicious envy function as central psychological pathways which connect favoritism to workplace deviance. This study offers a new contribution to the existing body of knowledge about leadership by extending its scope to investigate how subtle favoritism practices lead to harmful results while explaining the emotional mechanisms which cause counterproductive behavior. The research develops an integrated framework which helps people comprehend how perceived leader favoritism affects emotional responses and behavioral patterns within modern workplaces. Full article

The sustainable management of land resources and the formulation of land policies are closely linked to the stability and health of terrestrial ecological systems, which in turn underpin sustainable regional economic, social, and environmental development. However, land use change has a time effect on the environment and requires long-term observation to discover its impact on landscape patterns. The Yellow River Basin functions as a critical ecological barrier in northern China, where land use changes are particularly intense in the transitional zone between its middle and lower reaches. Using Landsat imagery as the data source, this study adopts the Random Forest (RF) algorithm to classify eight sets of sequential data covering a 35-year period from 1990 to 2025 in the study area. Landscape pattern metrics and transfer matrices are employed to conduct qualitative and quantitative analyses of the spatiotemporal dynamics of land use changes. Additionally, land expansion analysis strategies and the RF algorithm are applied to identify the relative importance of different driving factors. The results show that: (1) The classification accuracy based on the Google Earth Engine (GEE) cloud platform remains consistently high, exceeding 90% across all phases. (2) Patch density decreases significantly, while the largest patch index continues to decline; the Shannon diversity index shows a fluctuating upward trend, and the aggregation index exhibits a slight increase. (3) Mutual conversions among farmland, forest, and grassland are the dominant processes driving land use changes in the region. (4) The Digital Elevation Model (DEM), construction land area distribution, and distance to primary roads are the key factors influencing land use patterns, with human activities acting as the primary driver of land use type transformations in the area. Full article

The transition to more-electric aircraft (MEA) is driving the replacement of centralized hydraulic and pneumatic systems with electrically powered alternatives. Within this paradigm, flight-control actuation remains one of the most demanding subsystems, combining strict requirements in force density, dynamic response, reliability, fault tolerance, thermal performance, and certification. Electromechanical actuators (EMAs) are a key enabling technology due to their potential for power-on-demand operation, reduction or elimination of hydraulic infrastructure, improved maintainability, and compatibility with distributed electrical architectures. However, their broader use in safety-critical flight-control applications remains constrained by mechanical jamming, thermal management, power-electronics robustness, fault tolerance, health monitoring, and system-level integration. This paper presents a structured, design-oriented review of electromechanical flight-control actuation systems within the MEA framework. It analyzes the evolution from hydraulic to fully electromechanical actuation, examines EMA architectures and subsystems, reviews energy-efficiency trade-offs at actuator and aircraft levels, and discusses fault modes, redundancy strategies, fault-tolerant design, and health-monitoring approaches. Finally, it identifies open challenges related to certification, jamming mitigation, high-voltage electrical architectures, wide-bandgap power electronics, thermal management, and digital twin-based health monitoring. The review provides a unified system-level perspective that supports the development of energy-efficient, fault-tolerant, and certifiable flight-control actuation systems. Full article

The Yellow River Basin (YRB) is a key ecological barrier and socio-economic region in China, but the spatiotemporal dynamics of its ecosystem carbon sink and the non-linear effects of environmental drivers remain insufficiently understood. This study estimated Net Ecosystem Productivity (NEP) in the YRB from 2001 to 2024 using MODIS Net Primary Productivity (NPP) data and an empirical soil heterotrophic respiration model, analyzed NEP trends with the Theil–Sen estimator and Mann–Kendall test, and quantified non-linear responses to climatic, temporal, and spatial factors using a Generalized Additive Model (GAM). The YRB acted as a persistent and strengthening net carbon sink, with annual total NEP increasing significantly from 39.85 Tg C yr⁻¹ in 2001 to 176.44 Tg C yr⁻¹ in 2024, at a rate of 5.65 Tg C yr⁻¹. NEP showed a clear southeast-to-northwest decreasing gradient, and 85.6% of the basin exhibited increasing trends, particularly on the Loess Plateau. The GAM captured non-linear associations of NEP with temperature, precipitation, solar radiation, relative humidity, year, and spatial location, and achieved a moderate pooled spatial block cross-validated R² of 0.723. NEP displayed a unimodal association with temperature—with a fitted peak near 0 °C reflecting the spatial transition from cold high-altitude to warmer water-limited regions—and a saturation-type response to precipitation, highlighting the joint control of hydrothermal conditions and pervasive water limitation. The fitted spatial smooth further revealed residual spatially structured variation that may be partly associated with irrigation and land management. These findings improve the understanding of carbon-sink dynamics in the YRB and provide scientific support for climate-adaptive ecosystem management and the regional implementation of China’s “dual carbon” goals. Full article

The dynamics of active particles are increasingly being leveraged to design and control micro-robotic swarms. Local interactions play a crucial role in the phase transitions of active particles; how the combined effects of alignment, short-range repulsion, and boundary interactions regulate their packing structure and collective order with different confinement scales remains less systematically explored. In this study, we investigate the packing of active particles within a confined region, focusing on the role of local interaction rules in shaping both the packing structure and the polar order parameter. The effects of key controlling variables related to local interaction rules, including interaction radius, repulsion radius, confined boundary radius, and noise strength, are numerically studied. Specifically, by comparing systems with and without velocity–alignment interactions, we reveal the role of alignment in dictating both structural and dynamical properties of the ensemble. To quantify the packing structure, we employ Voronoi tessellation to evaluate both local and global packing densities. The results show that strong confinement induces a jammed state in which alignment effects are suppressed, resulting in high global packing density and low polar order, regardless of the noise amplitude. Upon increasing the boundary radius beyond a critical threshold, the system unjams, enabling alignment interactions to significantly enhance both the polar order parameter and packing density. Interestingly, the relationship between global packing density and micro-structural parameters, such as coordination number and Voronoi tessellation metrics, is similar in the systems with and without alignment. Our results demonstrate that collective packing and phase behaviour of active matter are governed by the nontrivial interplay between alignment, confinement, and noise, with alignment interactions driving the transition from disordered to ordered states as geometric constraints are relaxed, offering critical insights for the design of targeted micro-robotic swarms and active microfluidic sorting systems. Full article