Search Results (11,451)

Chiller systems account for a substantial proportion of building energy consumption, where their operational efficiency and start–stop cycling frequency directly influence overall system energy use and equipment lifespan. In practical applications, load fluctuations and improper control settings often cause chillers to experience frequent cycling, leading to decreased efficiency and increased mechanical wear. While existing studies predominantly focus on real-time control or model predictive approaches, fewer investigations systematically identify stable operating regions and optimal control thresholds using historical operational data. This study proposes a data-driven method for identifying an operational threshold. Long-term historical data are analyzed to establish a start–stop event detection mechanism. A normalized power index is introduced, and multi-scenario classification—incorporating seasonal conditions and peak/off-peak periods—is employed to evaluate system behavior across different contexts. Furthermore, a quantile scanning approach combined with hysteresis simulation is utilized to identify optimal operational threshold intervals. Stability evaluation indices, based on cycling frequency, power variation rate, and load deviation magnitude, are constructed to quantify stability performance. To verify the robustness of these thresholds, K-fold cross-validation is performed. Results indicate that the identified thresholds effectively reduce cycling frequency and power fluctuations, thereby enhancing system stability. Specifically, the start–stop cycling frequency is reduced by approximately 75–90%, and the power variation rate decreases by up to 85% across various scenarios. This study provides an offline decision-support framework to assist operators in optimizing control parameters and strategies. These outcomes serve as a reference for chiller energy management and provide empirical evidence for the future design of control strategies. Full article

To improve the anti-slip performance and energy-efficient torque coordination of four-wheel-drive articulated tractors operating in hilly and mountainous terrains, this study proposes an integrated control framework that combines a 7-DOF tractor dynamics model, a GA-optimized fuzzy slip-ratio controller, and a three-level dynamic torque allocation strategy. First, a control-oriented full-vehicle dynamics model is established by integrating tractor body dynamics, wheel rotational dynamics, and the Dugoff tire model. Then, a fuzzy slip-ratio controller is designed using the slip-ratio tracking error and its rate of change as inputs, and its key parameters are optimized using a genetic algorithm. On this basis, a three-level dynamic torque allocation strategy is developed to coordinate the four in-wheel motors according to wheel-load distribution and slip-related constraints. MATLAB/Simulink (version 2023a) simulations and hardware-in-the-loop (HIL) tests are carried out to validate the proposed strategy. Under the straight-line driving condition, the RMSE of the proposed GA-fuzzy controller is reduced from 0.02716 to 0.00897. Under the steering condition, the average RMSE is reduced from 0.02079 to 0.01003. In addition, under the torque-allocation validation condition, the average four-wheel RMSE is reduced from 0.29632 under equal torque allocation to 0.02159 under the proposed three-level dynamic torque allocation strategy. The results indicate that the proposed method can effectively maintain the slip ratio near its target value, suppress excessive slip and redundant torque output, and improve the anti-slip and energy-efficient performance of articulated tractors. More importantly, the study provides an integrated control framework that unifies GA-optimized slip regulation and three-level torque coordination specifically for four-wheel-drive articulated tractors. Full article

Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS degradation, radio-frequency interference, and intentional jamming can disrupt positioning and communication, thereby reducing mission effectiveness and safety. Recent surveys show that operation in GNSS-denied environments remains a major challenge and often requires alternative perception, localization, and control strategies. In response, this article investigates a reinforcement learning (RL)-based decision-support system for the autonomous control of a quadrotor UAV in a three-dimensional simulated environment. Rather than following pre-programmed waypoints, the UAV learns a control policy through interaction with the environment and reward-driven adaptation. The proposed system is designed for mission execution under uncertainty, limited external guidance, and partial observability. Two policy-gradient approaches are implemented and compared: classical REINFORCE and Proximal Policy Optimization (PPO) with an Actor–Critic architecture. The study presents the simulation environment, state and action representation, reward formulation, staged training procedure, and comparative evaluation. The results indicate that, within the considered unseen test scenario, the PPO-based configuration achieved higher mission effectiveness than REINFORCE in the final unseen test scenario, supporting the practical relevance of structured deep reinforcement learning for UAV operation in GPS-denied and communication-constrained environments. Full article

Under fault conditions, Photovoltaic-Storage-Charging Integrated Stations (PSCISs) are regarded as a key resource for enhancing distribution network resilience. However, traditional centralized optimization fails to account for conflicts of interest between the distribution network and PSCISs and neglects the actual response behavior of EV users. To address these issues, a coordinated emergency operation strategy for distribution networks and PSCISs based on the master–slave game is proposed. Firstly, a bilevel optimization framework based on the master–slave game is constructed, where the upper level performs system-level coordination and the lower level handles autonomous decision-making. For the upper level, the minimization of distribution network operation cost is set as the optimization objective by the dispatching center to determine power purchase prices and load shedding rates, which serve as guidance signals for lower-level PSCISs. In terms of the lower level, a dual-factor S-shaped response curve is introduced into the lower-level model to precisely characterize EV users’ nonlinear response behavior to price incentives. Furthermore, based on the signals received from the upper level, the maximization of each PSCIS’s profit is set as the optimization objective to determine the PV output, storage dispatch, and V2G incentive prices. Subsequently, Model Predictive Control (MPC) is employed to implement rolling optimization during the fault period, addressing the source-load uncertainties. Finally, an improved IEEE 33-node distribution network is used for case analysis and validation of the proposed operation strategy. The results indicate that the proposed strategy can effectively coordinate the interests of multiple parties, achieving synergistic improvements in both the economy and reliability of the distribution network. Full article

The development of high-performance electrochemical sensors requires precise integration of electrode active materials that provide both superior electrocatalytic activity and long-term structural stability. Herein, we report a systematically optimized, one-pot electrochemical deposition approach for the fabrication of nanographenide-based nanoarchitectures, incorporating either a conducting polymer (PEDOT-NG) or Prussian blue (PB-NG). Derived from optimization-driven structural refinement—including applied potential, electrodeposition time, and precursor concentration—the robust nanoarchitecture exhibits a hierarchical morphology that provides an expanded electroactive surface area, accelerating charge transfer and enhancing electrochemical catalytic activity. The optimized PEDOT-NG exhibits exceptional sensitivity for the simultaneous determination of ascorbic acid (AA), dopamine (DA), and uric acid (UA), achieving wide linear ranges with low detection limits of 4.1, 0.12, and 0.18 μM, respectively. The PB-NG achieves a limit of detection of 4.39 μM, driven by highly reversible and stable redox kinetics. This performance is underpinned by narrowed peak-to-peak separations (ΔE) and reduced redox potentials. These results underscore the pivotal role of precise parametric control in developing high-performance electrochemical sensors. Furthermore, this work establishes a comprehensive strategy for designing resilient electrode active materials, thereby paving the way for next-generation electrochemical platforms tailored for diverse and robust sensing environments. Full article

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

Personalized transdermal drug delivery systems (TDDS) represent a transformative approach in precision medicine by enabling patient-specific, non-invasive, and controlled therapeutic administration. Conventional transdermal patches are limited by fixed dosing, passive diffusion, and interindividual variability in skin permeability and metabolism, often leading to suboptimal therapeutic outcomes. Recent advances in materials science, nanotechnology, microneedle engineering, and digital health have enabled the development of next-generation personalized TDDS capable of programmable, adaptive, and feedback-controlled drug release. Smart wearable patches integrating biosensors, microfluidics, microneedles, and wireless connectivity allow real-time monitoring of physiological and biochemical parameters, enabling closed-loop drug delivery tailored to individual metabolic profiles. Nanocarriers such as lipid nanoparticles, polymeric nanoparticles, and stimuli-responsive hydrogels further enhance drug stability, penetration, and controlled release, while 3D-printing technologies facilitate patient-specific customization of patch geometry, drug loading, and release kinetics. Artificial intelligence (AI) and machine learning tools are increasingly being employed to predict drug permeation behavior, optimize enhancer combinations, and personalize dosing regimens based on pharmacogenomic and pharmacokinetic data. Despite these advances, regulatory complexity, manufacturing standardization, long-term biocompatibility, and cybersecurity considerations remain critical challenges for clinical translation. This review highlights recent innovations in personalized TDDS, discusses their clinical potential, and examines regulatory and technological barriers. Collectively, these emerging smart transdermal platforms offer a promising pathway toward adaptive, patient-centered therapeutics that can significantly improve treatment efficacy, safety, and compliance. Future research should focus on integrating multimodal biosensing, advanced biomaterials, scalable manufacturing strategies, and robust regulatory frameworks to enable clinically validated, fully autonomous transdermal systems that can dynamically adapt to real-time patient needs in diverse therapeutic settings. Full article

Autonomous path planning in obstacle-dense environments remains challenging for swarm intelligence methods due to infeasible initialization, insufficient exploration–exploitation balance, and poor trajectory smoothness for real-robot execution. To address these issues, this paper proposes a Kinematic-Aware Improved Hippo Optimization algorithm (KA-IHO) for mobile robot path planning. The proposed method integrates four components: an elite safety pool initialization strategy to improve feasible solution generation in dense maps, a hierarchical elite-scout update mechanism to better balance global exploration and local exploitation, anti-stagnation mechanisms including a Population Stagnation Restart strategy and a 10-Direction Radial Micro-Search to guarantee high feasibility rates across all map complexities, and a late-stage Laplacian Line-of-Sight Ironing Operator to reduce path redundancy and improve trajectory smoothness. Comparative experiments are conducted on five reproducible grid maps with different complexity levels ($40\times 40$ and $80\times 80$), where KA-IHO is evaluated against six representative algorithms, including HO, SBOA, PSO, GWO, ARO, and INFO, over 20 independent runs. The results show that KA-IHO consistently achieves collision-free planning and obtains lower mean fitness values with smaller standard deviations than the compared methods, indicating improved robustness and solution quality. In addition, hardware closed-loop experiments on a differential-drive mobile robot demonstrate that the planned paths can be executed reliably in real environments, with trajectory tracking errors controlled within $\pm 4$ cm. Full article

Ensuring DC bus voltage stability is a key enabler for the sustainable development of photovoltaic-storage DC microgrids (PV–storage DC MGs), which are regarded as critical infrastructure for high-penetration renewable energy utilization. However, the inherent randomness of PV power generation seriously threatens this stability. This paper proposes a novel model predictive control (MPC) scheme for the energy storage system (ESS) to mitigate voltage fluctuations and enhance system stability. To improve the model precision, a forgetting-factor-augmented recursive least squares (RLS) algorithm is employed for online identification and correction of the estimated equivalent impedance between the ESS and the DC bus. Rigorous Lyapunov stability analysis is performed to obtain the sufficient stability conditions and quantitative tuning rules for the weighting coefficients, which transforms the qualitative parameter selection into a theoretical constrained optimization. The state of charge (SOC) of the ESS is set as a security constraint to avoid excessive charge/discharge and extend battery service life. A distinguished advantage of the proposed strategy is that it generates ESS power commands solely based on local measurements, eliminating the dependence on external communication and improving system reliability. Simulation results on MATLAB R2021b/Simulink and hardware-in-the-loop experiments based on RT-Lab and DSP demonstrate that the proposed MPC method significantly reduces the DC bus voltage deviation, accelerates the dynamic recovery process, and maintains stable ESS operation under both normal PV fluctuations and sudden PV outage conditions. Full article

Promoting the effective use of abandoned farmland has become a key policy priority for strengthening food security in China. However, disentangling the decision-making processes among diverse participating actors is a foundational prerequisite for addressing the governance challenge of abandoned farmland utilization. Building on this, the present study employs a four-actor evolutionary game model and sensitivity analysis of key parameters to systematically examine the interactions among four key actors—local governments, village collectives, family farms, and farmers—and to identify the corresponding evolutionarily stable strategies (ESSs) across different stages of abandoned farmland utilization. The results show that: (1) Multi-actor strategic interactions in abandoned farmland utilization exhibit a multi-stage evolutionary trajectory, in which all actors gradually shift their strategic choices under changing cost–benefit structures, regulatory intensity, and coordination conditions, leading to different evolutionary stable equilibria across governance stages. (2) The configuration in which local governments adopt loose regulation, the village collective plays an active coordinating role, family farms pursue long-term operations, and farmers choose recultivation is a key condition for achieving a Pareto-optimal equilibrium. (3) Although farmers’ production willingness and behavioral choices form the basis for the utilization of abandoned farmland, spontaneous individual action alone is insufficient to address the structural contradictions currently facing abandoned farmland utilization in China. To effectively promote the evolution of abandoned farmland governance toward a stable collaborative equilibrium and ultimately realize sustainable utilization, it is necessary to further optimize governmental administrative control models and incentive mechanisms, strengthen the organizational and coordinating functions of village collectives, and improve long-term operational support systems for family farms. This study systematically elucidates the underlying logic of China’s abandoned farmland utilization from the perspective of multi-actor behavioral decision-making, providing policy-referential insights for optimizing policy design, reducing coordination costs, and improving the efficiency of abandoned farmland utilization. Full article

To address load–energy dynamic coupling in heterogeneous unmanned aerial vehicle (UAV) emergency rescue, this paper proposes an energy-coupled heterogeneous UAV task allocation (EC-HUTA) model that explicitly characterizes nonlinear interdependencies among payload, velocity, and power consumption, minimizing aggregate mission costs subject to physical and temporal constraints. To tackle the resulting high-dimensional, nonconvex problem, we introduce a multi-strategy improved stellar oscillation optimizer (MISOO), establishing a closed-loop synergistic system through three coupled stages: (i) evolutionary game-theoretic strategy competition via replicator dynamics for adaptive exploration–exploitation balance; (ii) intuitionistic fuzzy entropy (IFE)-driven dimension-wise parameter control, where IFE calibrates global exploration intensity while dimension-specific crossover probabilities accommodate heterogeneous convergence; and (iii) memory-driven differential escape mechanisms modulated by historical memory parameters to evade local optima. Cross-stage coupling through IFE ensures state information flows across the “strategy selection-refined search-dynamic escape” pipeline. Coupled with a dual-layer encoding scheme, this framework ensures efficient feasible search. Ablation studies validate each mechanism’s contribution. Evaluations on CEC2017 benchmarks demonstrate MISOO’s superior convergence against six metaheuristics. Large-scale earthquake rescue simulations confirm that EC-HUTA/MISOO strictly adheres to nonlinear energy constraints while enhancing task completion and temporal compliance. These results validate the framework’s efficacy for time-critical emergency resource allocation. Full article

This study evaluates the biological effectiveness of camel compound feeds produced using an optimized drying–grinding–extrusion technology and enriched with Artemisia lerchiana (wormwood). Building on a previously published process optimization study, the present work focuses on the effects of the developed feeds on milk productivity and quality in lactating camels. Eighteen lactating dromedary camels were randomly assigned to three dietary treatments (n = 6): a control diet without wormwood and experimental diets containing 10% and 15% wormwood (dry matter basis). The feeding trial lasted 45 days, including a 15-day adaptation period and a 30-day measurement period. Milk yield was recorded daily, and milk composition was analyzed weekly. Statistical analysis was performed using one-way and repeated-measures ANOVA (p < 0.05). Wormwood supplementation resulted in higher milk yield and significantly increased milk fat and protein content, with the strongest effects observed at the 15% inclusion level. No adverse effects on lactose content, physicochemical properties, or milk hygienic quality were detected. The results confirm that combining extrusion-based processing with phytogenic supplementation is an effective strategy for improving camel milk productivity. Full article

There is a strong and growing need for low environmental impact, fluorine-free finishes that deliver durable water repellency and stain resistance to leather while preserving its original appearance. This work successfully addresses this need by introducing a simple, robust, and scalable two-step coating strategy that endows leather surfaces with excellent hydrophobic and self-cleaning properties. The process relies on a straightforward spray application of functionalized silica nanoparticles followed by a hydrophobic silane, namely hexadecyltrimethoxysilane (HDTMS), enabling precise control over surface properties through the number of applied layers. Comprehensive characterization by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS) confirmed the effective formation and uniformity of the coating. Performance testing demonstrated excellent functional outcomes: the optimized coating achieved a water contact angle (WCA) of 128° and maintained values above 125° even after abrasion, highlighting its durability. Treated leather exhibited resistance to common liquid stains such as tea and coffee, maintaining a clean surface. These functional gains were achieved without compromising the leather’s natural look or soft feel, even after multiple coating cycles. This work delivers a fluorine-free solution offering an effective route to high-value water- and stain-resistant leather finishes that respect both environmental and aesthetic requirements. Full article

The spine enhances the flexibility of quadrupeds during locomotion. Inspired by this biological mechanism, this study incorporates an actuated spinal joint into a quadruped robot, enabling more natural motion and posture adjustment. To improve the motion stability of spinal robots in complex environments, a deep reinforcement learning framework that integrates a central pattern generator (CPG) with the twin delayed deterministic policy gradient (TD3) algorithm is proposed to optimize the gait motion of the spinal quadruped robot. First, the structure and parameters of the quadruped robot with a spinal joint are analyzed and a CPG coupling model incorporating spinal motion parameters is designed. Subsequently, a TD3–CPG algorithm framework based on a joint incremental strategy is proposed to optimize the robot’s gait, exploring optimal control strategies for terrain adaptation through spinal motion integration. Finally, experiments are conducted on various obstacle terrains to validate the proposed algorithm. Simulation and experiment results demonstrate the effectiveness of the algorithm in optimizing the gait of the spinal quadruped robot, showing significant improvements in walking stability, speed, and terrain adaptability across different terrains. Full article

Background: Hormone receptor-positive/HER2-negative (HR+/HER2−) early breast cancer (EBC) presents a persistent risk of relapse, even beyond 5 years, driving the need for adjuvant intensification strategies. This review analyzes the clinical evidence for CDK4/6 inhibitors (CDK4/6i) in the adjuvant setting. This evidence is then integrated with molecular findings to support the concept of the “carry-over” effect, which is understood as a lasting benefit that persists after the end of active treatment, reflected by a sustained separation of invasive disease-free survival (iDFS) curves during follow-up. Relevant Sections: The main adjuvant trials in EBC are reviewed, with consideration of the “carry-over” effect. Emerging biomarkers and the impact of financial toxicity are also described. Results: PALLAS did not demonstrate a clear on-treatment or post-treatment benefit, whereas PENELOPE-B suggested, at most, a transient early advantage that was not maintained with longer follow-up; therefore, neither trial provides convincing evidence of a durable “carry-over” effect. In contrast, monarchE (abemaciclib) and NATALEE (ribociclib) showed significant improvements in iDFS and, in the case of abemaciclib, a signal of benefit in overall survival, supporting the existence of a clinically relevant post-treatment effect. Conclusions: From a biological perspective, the review proposes that the “carry-over” effect should not be considered a uniform class effect, but rather the result of a sequence of events modulated by pharmacological selectivity (CDK4 vs. CDK6 and additional targets), the induction of cellular senescence, and immunomodulatory effects that could favor the control of micrometastases. In addition, elements that influence interpretation and the need to optimize adherence and toxicity management to “materialize” the benefit in a potentially curable context are discussed. Full article