Search Results (162)

Periodic and non-periodic disturbances significantly affect the tracking accuracy of servo systems. A dual-motor drive composite control strategy based on iterative learning feedforward control and super-twisting sliding mode observer is proposed. Initially, a novel reaching law capable of dynamically adjusting gain coefficients based on system states is introduced, leading to the design of a sliding mode controller with proven asymptotic stability. To address non-periodic total disturbances, a super-twisting sliding mode observer is developed, and Lyapunov stability theory is employed to demonstrate system stability and error convergence to zero. A resonant controller is designed to suppress medium- to high-frequency periodic disturbances. For periodic total disturbances, a parameterized feedforward controller based on iterative learning is devised, and an input-shaping filter is introduced to refine the input trajectory. The feedforward control parameters are iteratively updated using a data-driven approach. Experiments are conducted on a differential dual-drive servo system. The nut motor adopts the sliding mode controller with an observer. The screw motor employs the iterative learning feedforward controller. Results show effective suppression of the disturbances. Speed ripple is reduced, and tracking accuracy is significantly improved. The study demonstrates the feasibility and advantage of combining robust control with iterative learning in high-precision servo systems. Full article

A comprehensive numerical method was developed to ensure energy-efficient operating modes of a linear motion module powered by an induction motor. The proposed approach is based on minimizing inertial torque, accounting for the inertial properties of the drive components and the load carriage, followed by structural-parametric optimization and dynamic modeling. For the optimization of the drive system, comprising an intermediate gear stage and a primary ball screw mechanism, a normalization-based method combined with numerical parameter sweep was employed. The optimization process yielded optimal values of the screw lead and the number of gear teeth, which were further validated in terms of Pareto optimality. The carriage design was optimized with respect to mass, strength constraints, and dynamic stiffness using the finite element method. For the developed linear motion module, dynamic behavior was simulated by means of a system of nonlinear differential equations, taking into account the electromagnetic characteristics of the induction motor and the nonlinearities of the gear mesh. As a result of the comprehensive approach, the kinematic, force, and energy characteristics of the linear motion module, which was optimized at the design stage, were determined. Full article

This study investigates the relationship between the Quasi-Biennial Oscillation (QBO) and precipitation anomalies over sub-Saharan Africa (SSA) during the December–February season (DJF season) using ERA5 reanalysis and CHIRPS observations. The ERA5 accurately reproduces CHIRPS precipitation patterns, but their QBO relationships differ: ERA5 shows QBO correlates with the second and third precipitation mode, while CHIRPS exhibits stronger correlation with the first mode. Based on CHIRPS, the net QBO effect identifies southern, central SSA, and southern Madagascar as regions sensitive to precipitation reduction, with significant phase dependence and regional heterogeneity. During the westerly QBO phase, precipitation increases over southern SSA and northern Madagascar but decreases over central SSA. During the easterly phase, drying dominates most of SSA. Southern SSA drying is primarily linked to the easterly phase, while central SSA and southern Madagascar drying is associated with the westerly phase. Diagnostic analysis reveals the westerly phase enhances ascent and moisture convergence over southern SSA, while the easterly phase induces subsidence and moisture divergence. This study reveals the differential impacts and mechanisms of various QBO phases on precipitation over SSA, clarifies the phase dependence of regional responses and their physical processes, and provides a new stratospheric predictor for understanding the driving mechanisms of precipitation variability over extratropical Africa and improving regional precipitation prediction. Full article

Marine heatwaves (MHWs) in the Eastern China Seas exert profound ecological and economic impacts, highlighting the need for reliable indicators to support early prediction. Based on observations from 1982 to 2022, this study identifies three characteristic patterns linking annual maximum sea surface temperature (T_max) with summer MHWs: in July, the northern region follows a pattern where earlier T_max favors more frequent MHWs; in August, the whole study area is dominated by a mode where T_max coincides with the seasonal threshold peak, driving widespread MHWs; and in September, the southern region exhibits a pattern where later T_max favors more frequent MHWs. A threshold-based method integrating both T_max and its timing demonstrates strong skill in assessing MHW occurrence and exhibits practical utility when validated with independent observations from 2023 to 2024. Long-term warming of T_max, together with regionally divergent trends in its seasonal timing, closely aligns with observed increases in MHW days. Significant correlations between T_max and preceding monthly mean SST suggest that T_max integrates accumulated thermal conditions and carries seasonal memory, offering a potential pathway from seasonal SST prediction to early MHW risk assessment. These findings clarify the structured and regionally differentiated T_max–MHW relationship, demonstrate the feasibility of a T_max-based assessment framework, and provide a scientific basis for improving seasonal monitoring and early warning of MHWs under sustained climate warming. Full article

This paper proposes the Branch-Priority Exploration (BPE) framework for autonomous coverage in confined industrial corridor environments. BPE integrates three components: (1) a symmetry-aware LiDAR branch detector; (2) a hierarchical BFS/DFS mode-switching policy; and (3) a barrier-based branch memory. Frontier-based methods often struggle in industrial corridors where branches split off from the main corridor. The symmetric layout of such environments, featuring T-shaped junctions and L-shaped turns, creates recurring geometric patterns that conventional frontier scoring fails to exploit. When the robot reaches a junction, nearby frontier candidates often receive similar scores, causing repeated target switching as the local map changes. Meanwhile, frontier cells inside a branch tend to score lower than those along the main corridor; so, the robot often bypasses the branch and continues forward, which leads to additional backtracking later. Even when the robot eventually returns, residual frontier cells near the entrance may attract the planner repeatedly, causing redundant re-entry into already-covered branches. To address these issues, a branch-priority exploration framework is developed. A symmetry-aware branch detection module uses LiDAR range measurements from multiple directions to identify T-shaped junctions and lateral openings, applying identical geometric criteria to lateral openings on either side of the robot. This allows branch entry to be triggered by explicit geometric evidence, rather than frontier score comparisons that tend to be unreliable near intersections. When a branch is detected, the robot transitions from BFS mode to DFS mode for systematic branch coverage. Entry and post-return locks prevent mode reversal before the robot commits to the new heading. Once a branch is completed, a permanent virtual barrier is placed at its entrance; so, the planner no longer routes the robot back into that branch. The framework is formalized as a constrained coverage problem on occupancy grids, and monotonic coverage progress and finite branch completion under barrier memory are established theoretically. A fully reproducible ROS implementation on a wheeled platform with differential drive is validated. Experiments span several corridor environments of increasing topological complexity. Compared to a nearest-frontier baseline, the proposed method substantially reduces both exploration time and goal cancellations while achieving complete coverage across all trials. The cancellation count matches the number of T-branches per environment, with near-zero variance across repeated runs. Full article

Dual-motor electric drive axles (e-axles) can realize basic torque vectoring through motor-torque allocation. However, without an inter-wheel power-transfer path, they still face structural limitations under motor torque–speed envelopes and severe left–right adhesion asymmetry. To address this issue, this paper proposes a reconfigurable dual-motor e-axle based on fixed-carrier compound planetary gear trains and two cross-axle clutches. By switching between controlled-slip and lock-coupled states, the proposed topology creates a switchable inter-wheel power-transfer path. As a result, it enhances yaw-rate regulation capability under high-adhesion conditions and improves escape capability under severe adhesion asymmetry. A unified kinematic–static analytical framework is established to derive closed-form capability boundaries and compact structural indices for parameter matching. Vehicle-level co-simulation on a representative rear-wheel-drive platform is then carried out for validation. Under severe split-$\mu$ conditions, the peak high-adhesion wheel torque increases from 241.72 to 695.57 N·m, and the escape time decreases from 0.43 to 0.19 s. In a representative high-adhesion step-steer case, the mean yaw-rate tracking error is reduced from 6.75 to 0.20 deg/s, while the mean differential wheel torque reaches 1.83 times that of the baseline mode. The other high-adhesion cases show the same trend. These results verify the vehicle-dynamics significance and engineering feasibility of the proposed architecture. Full article

To satisfy the stringent functional-safety requirements of steer-by-wire steering systems for advanced autonomous driving, this paper proposes a novel dual-motor collaborative fault-tolerant control strategy. The proposed approach aims to overcome the insufficient fault tolerance of conventional single-motor architectures, as well as the limited dynamic response and disturbance-rejection capability observed in existing multi-motor schemes. The key contribution is an integrated control framework consisting of two components: (i) dual-motor torque synchronization achieved via a fuzzy-PID–based mean-deviation coupling method, and (ii) a super-spiral sliding-mode control law optimized by an adaptive differential-evolution algorithm to enhance the dynamic performance and robustness of the current loop. Experimental results demonstrate that, relative to a non-synchronized baseline, the proposed strategy reduces the inter-motor current mismatch by 8.1–78.6% across multiple operating conditions. Moreover, following fault occurrence, the proposed Self-Adaptive Differential-Evolution-algorithm-based Super-Twisting Sliding-Mode Control method shortens the stabilization time by 50–70%, 9–20%, and 16.7% compared with conventional PID, Super-Twisting Sliding-Mode Control methods, and classical $H_{\infty }$ robust control, respectively. Overall, the developed solution meets functional-safety requirements and provides a highly reliable steering-actuation mechanism for advanced autonomous driving applications. Full article

Steatosis, characterized by excessive fat accumulation in the liver, is a significant precursor to chronic liver disease and hepatocarcinoma. This condition is influenced by multiple contributing factors such as obesity, alcohol consumption, and exposure to chemicals or drugs. Systems biology approaches including transcriptomics and metabolomics can aid in grouping chemicals according to their mode of action. In this study, we analyze transcriptomic and metabolomic data from primary human and transformed hepatocytes, respectively, to differentiate between steatotic and non-steatotic chemicals. Rather than assessing each steatotic compound individually, we pooled several steatotic chemicals in order to minimize compound-specific noise and better identify features associated with the underlying process of steatosis. Differential gene expression analysis revealed established mechanisms involved in steatosis, consistent with the recently updated adverse outcome pathway. Likewise, metabolomic data enabled clear discrimination between steatotic and non-steatotic chemicals. These findings highlight the potential of omics technologies to support chemical grouping based on insights into the molecular mechanisms that drive steatosis development. Full article

In order to protect the high-sensitivity optical lens of the “magnetic field and velocity field imager” in extreme deep space environments, this paper proposes a new type of dual redundant planetary differential lens cover drive mechanism. In view of the critical vulnerability that traditional single-motor direct drive is prone to sudden mechanical jamming and catastrophic single-point failure (SPF) in severe tasks such as Jupiter exploration, this study constructs a “dual input single output (DISO)” rigid decoupling architecture from the perspective of physical topology. Through theoretical analysis and kinematic modeling, the adaptive decoupling mechanism of the two-degree-of-freedom (2-DOF) system under unilateral mechanical stalling is revealed. Dynamic analysis shows that in the nominal dual-motor synergy mode, the system shows a significant “kinematic load-sharing effect”, thus greatly reducing the sliding friction and gear wear rate. In addition, under the severe dynamic fault injection scenario (maximum gravity deviation and sudden jam superposition of a single motor), the cold standby motor is activated and the dynamic takeover is quickly performed. The high-fidelity transient simulation based on ADAMS verifies that although the fault will produce transient global torque spikes and pulsed internal gear contact forces at the moment, all extreme dynamic loads remain well within the structural safety margin. The output successfully achieved a smooth transition, which is characterized by a non-zero-crossing velocity recovery. This research provides an innovative theoretical basis and a practical engineering paradigm for the design of high-reliability fault-tolerant mechanisms in deep space exploration. Full article

While existing research has established that air pollution-induced “avoidance behavior” significantly drives the growth of online food delivery volumes, the Average Order Value (AOV) remains unexplored. This study utilizes micro-transactional data provided by the store owner and employs machine learning algorithms to detect the impact of air quality (measured by the AQI) on online food delivery AOV and analyze the underlying consumer behavior. The findings indicate that: (1) Air quality deterioration significantly drives up the AOV. The global average response coefficient is 0.0053, showing a 2.4-fold acceleration effect once the AQI crosses the median (66). (2) Crucially, this growth stems from a directional divergence in consumer decision-making. Air pollution leads to the simultaneous occurrence of a reduction in average item quantity (impact coefficient: −0.0014) and a surge in Average Item Price (AIP) (impact coefficient: 0.0066). (3) Causal analysis further identifies a “substitution mechanism.” Specifically, every one-unit decrease in average item quantity induces a CNY 1.098 jump in average item price. These findings suggest a plausible behavioral logic where environmental stress may induce psychological fatigue but does not necessarily trigger “defensive frugality.” Instead, the observed pattern is consistent with a “decision avoidance” mode where consumers streamline item quantities; simultaneously, to hedge against potential experience risks resulting from simplified choices, they appear to utilize saved cognitive resources to target high-value “signature” items. Theoretically, this study fills the gap in environmental stress research regarding the price dimension of online consumption and reveals a behavioral evolution from “pure avoidance” to “value-oriented selection.” Practically, it provides empirical support for online food delivery merchants to optimize product selection, differentiate pricing, and implement precision marketing in dynamic environments. Full article

Land transition in China has led to the emergence of highly heterogeneous neighborhoods. This process challenges the social sustainability of public green spaces. This research investigates the driving mechanisms of place attachment within green space across diverse community typologies in Beijing. This study constructed a structural equation model (SEM) based on 626 valid questionnaires, using the Stimulus–Organism–Response (S-O-R) framework. The overall SEM results indicate that place identity significantly contributes to civic behavior (β = 0.439, p < 0.001). However, a persistent ‘value-action’ gap remains, with 65.81% of residents demonstrating high identity yet low participation. Furthermore, the multi-group analysis (MGA) reveals that place attachment logic diverges significantly across groups. Regarding user identity, public events promote visitors’ place identity, but this effect remains insignificant among residents (β = −0.064, p > 0.05). Regarding generational differences, the macro-spatial environment is significantly associated with place dependence for young people (β = 0.330, p < 0.001) but is insignificant for the elderly. Community heterogeneity reveals distinct failure modes. In commodity housing communities, a disconnect exists where daily usage fails to foster dependence (β = 0.026, p > 0.05). Conversely, urban–rural resettlement communities display an identity deficit where public events fail to translate into place identity (β = 0.131, p > 0.05). The study proposes differentiated renewal pathways tailored to three community types. For commercial housing communities, it advocates precise interventions that prioritize social engagement. Meanwhile, for urban–rural resettlement communities, the focus shifts to accessibility and culturally rooted activities to help reconnect displaced populations. Full article

Spacecraft pursuit–evasion in contested environments is complicated by strategic incompleteness: the evader can switch maneuvering modes and deploy multi-domain countermeasures that degrade the pursuer’s perception, leading to non-stationary information and distributionally ambiguous interference statistics. A dynamic time-window Nash equilibrium framework is developed for linearized Local Vertical Local Horizontal (LVLH) relative motion under interference-induced uncertainty. Perceptual degradation is modeled via an evidence–theoretic belief representation, and the Jensen–Shannon (JS) divergence is introduced to quantify discrepancies between nominal and interference-corrupted beliefs. The divergence metric drives an adaptive time-window partitioning policy and an uncertainty-aware running cost that balances nominal performance objectives with robustness regularization during high-degradation intervals. In each time window, sufficient conditions are provided for the existence of a local Nash equilibrium, and equilibrium strategies are characterized by the Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation. A global consistency result is established: assuming state continuity, additive cost decomposition, and dynamic-programming compatibility at window boundaries, concatenating the window-wise equilibria yields a Nash equilibrium over the entire horizon. Unlike conventional receding-horizon differential games with a fixed replanning grid, the proposed policy partitions the horizon online in response to perceptual-degradation events and stitches adjacent windows through a continuation value. This boundary stitching enables the global consistency guarantee under additive costs and state continuity. To hedge against ambiguity in interference intensity, a variational distributionally robust optimization (DRO) problem with moment-constrained ambiguity sets is formulated, and the dual worst-case distribution is derived. The resulting Karush–Kuhn–Tucker (KKT) system is reformulated as a finite-dimensional variational inequality, for which an accelerated Alternating Direction Method of Multipliers (ADMM) operator-splitting solver is proposed for efficient real-time computation. Numerical simulations validate the framework and demonstrate improved robustness and computational scalability under time-varying interference compared with fixed-window baselines. Full article

Background: The valorization of sustainable feed ingredients such fermented de-oiled rice bran meal (FDRBM) is crucial; however, the molecular mechanisms driving its benefits remain unclear. This study addresses this gap by investigating FDRBM as a dietary substitute for maize in rabbits to determine its effects on meat quality and underlying gut–liver axis communication. Methods: In an eight-week trial, New Zealand White rabbits were assigned to a control diet or the basal diet with a 20% substitution of either unfermented de-oiled rice bran (UFDRBM) or FDRBM. Post-trial, the researchers analyzed carcass traits, meat quality, and nutritional composition. A multi-omics approach integrates gene expression data from the ileum and muscle with liver metabolomics to model coordinated biological responses. Results: Although growth performance was similar, the FDRBM diet significantly improved meat quality by enhancing water-holding capacity and increasing essential amino acids (p < 0.05). Mechanistically, these improvements were associated with the upregulation of genes associated with oxidative muscle fiber (Tnnc1) and lipid metabolism. Analysis of the gut–liver axis revealed that FDRBM enhanced ileum antioxidant capacity, which coincided with profound reprogramming of liver metabolism (p < 0.01 *), identifying C17-sphinganine as a differential metabolite. Conclusion: This study provides novel insights into the mode of action of FDRBM, suggesting that it enhances rabbit meat quality in part by modulating metabolic gene expression and is associated with coordinated molecular changes across the gut–liver axis. Full article

Frequent accidents involving heavy trucks and the inadequacy of existing dynamic monitoring technologies pose significant challenges to accurate early warning risk and safety management. To address these issues, this study proposes a multi-dimensional risk measurement and real-time early warning method for heavy truck driving behavior based on trajectory data. By extracting multi-dimensional trajectory features such as lateral position, speed, and acceleration, quantitative indicators for driving stability and car-following risk were constructed. Integrated with the CRITIC objective weighting method and the K-means++ clustering algorithm, a comprehensive risk measurement model was established to systematically characterize the dynamic evolution of driving behavior, overcoming the limitations of single-dimensional risk analysis. Experimental results based on the CQSkyEyeX trajectory dataset demonstrate that the proposed method categorizes driving behavior into six risk levels. Low-risk behavior accounted for 66.70%, while medium- to high-risk behaviors mainly included serpentine driving (26.69%) and close following (4.18%). High-risk behavior constituted only 0.03%. A multi-strategy real-time warning mechanism was further developed, achieving a warning accuracy of 98.36% with the final-value method, significantly outperforming the mode method (83.62%). The outcomes of this study demonstrate the effectiveness and practical utility of the proposed model for risk identification and early warning. On a practical level, the developed risk classification framework and management strategy establish a quantitative basis for differentiated supervision, enabling a closed-loop management process of “identification–intervention–optimization”. Future work will focus on three key directions: integrating multi-source data, extending the model to other typical operational scenarios, and incorporating advanced machine learning techniques to further enhance its generalization capability and warning accuracy. Overall, this research provides a feasible technical pathway for the precise quantification, dynamic monitoring, and tiered intervention of driving behavior in heavy-duty trucks, thereby contributing to enhanced safety in road freight transportation. Full article

To address collaborative decision-making challenges in the pharmaceutical logistics supply chain amid public health emergencies, this study integrates disappointment aversion, delay effects, and pharmaceutical value attenuation, constructing a three-echelon system. It adopts a “differential game-system dynamics (SD)” two-layer dynamic research method for in-depth synergy. The differential game model focuses on multi-agent dynamic strategic interactions, deriving time-series equilibrium solutions for the optimal effort levels, transportation efficiency, and profits under four decision modes (decentralized, government subsidy, cost-sharing, centralized) to clarify the dynamic impact laws of the core parameters. Compensating for its limitations in complex environmental coupling and practical variable integration, the SD model incorporates the patient consumption rate, inventory fluctuations, weather disturbances and other real factors to build a dynamic feedback system. It not only verifies the practical adaptability of the differential game equilibrium solutions but also reveals the evolutionary laws of supply chain performance and the amplified inter-mode performance differences under multi-factor superposition. This study finds that centralized decision-making performs the best in terms of transportation efficiency peaking, profit stability, and attenuation control. Pharmaceutical stability and enterprise effort levels positively drive benefits, while disappointment aversion and excessive delays exert inhibitory effects. Government subsidies need to be paired with collaborative mechanisms to avoid policy dependence. Management implications suggest that enterprises should prioritize the collaborative centralized-decision-making mode, establish risk-sharing and benefit-sharing mechanisms, precisely regulate key variables, and implement gradient subsidies with exit mechanisms to enhance the supply chain’s dynamic adaptability and achieve the triple optimization of “efficiency–profit–stability”. Full article