Search Results (36,160)

Handover control in human–robot collaboration remains a significant challenge. This paper proposes a three-step vision-based human–robot handover system (VHS). Vision inputs are used to perceive the environment and enable adaptive control of the robotic arm. Moreover, a three-step behavior cloning learning strategy is designed. Furthermore, a modified Temporal Difference (TD) loss function based on transfer models is proposed to train the algorithm to improve policy exploration and convergence. The proposed method results in substantial enhancements in comparative experimental validation in a simulation environment with a realistic dynamic hand model. Full article

The performance and durability of lithium metal solid-state batteries are governed by the dynamic evolution of the lithium/solid-electrolyte (Li/SSE) interface, where electrochemical reactions, mass transport, and mechanical constraints are intrinsically coupled. This review presents an integrated electro-chemo-mechanical framework that links interfacial stripping dynamics to distinct degradation regimes controlled by current density, stack pressure, and thermal activation. We show that stable cycling emerges only within a narrow flux-balance window in which lithium creep and vacancy diffusion compensate stripping-induced volume loss without triggering electrolyte fracture or filament penetration. By synthesizing recent experimental, modeling, and materials engineering advances, the review maps the transitions between void-dominated instability, pressure-assisted stabilization, and stress-limited failure. Particular emphasis is placed on adaptive pressure strategies, compliant interlayer design, and microstructural interface engineering as pathways to expand the operational stability window. The analysis highlights that interfacial stability is not solely a materials property but a systems-level outcome arising from coupled electro-mechanical boundary conditions and temperature-dependent transport processes. This perspective provides design principles for developing next-generation solid-state batteries capable of stable high-rate cycling and long-term reliability. Full article

Neural ordinary differential equations (NODEs) have emerged as an effective methodology in artificial neural networks (ANNs) and deep learning for capturing unknown or unmodeled dynamics in compartmental and dynamical mathematical models arising from real-life applications, particularly under limited-data conditions, through learned data-driven corrections. Nevertheless, accurate one-step prediction errors do not necessarily guarantee reliable long-horizon rollouts. In this work, we study residual Neural ODE models of the form $\widehat{f}=f+h_{\theta }$ and derive a priori rollout-error estimates showing that long-time prediction behavior is generated by the incremental stability structure of the learned dynamics. Contracting regimes produce uniformly controlled rollout errors, whereas weakly contractive or expansive regimes can amplify persistent approximation errors over long time horizons. The analysis is illustrated on a flow-reactor chemical reaction network (CRN), where the washout parameter controls rollout reliability on the data-supported region. Numerical experiments further demonstrate that models with comparable empirical one-step prediction losses may exhibit substantially different multi-step behaviors. Rollout-error analysis and projected-gradient-descent (PGD) sensitivity directions additionally reveal that locally expansive regions align with worst-case perturbation amplification. Full article

The classical localizationist framework in biology and neuroscience has provided a powerful approach for linking structure to function. However, increasing evidence indicates that biological functions emerge from distributed interactions across complex systems. While network and systems-based approaches have advanced this transition, they often remain focused on connectivity patterns or statistical dependencies. In this review, I argue that a further conceptual step is required: a coordination-based framework in which biological function emerges from the context-dependent selective stabilization of interactions among distributed components that become causally relevant for specific outcomes. I develop this perspective comparing brain network organization and plant signaling, two systems that exhibit adaptive behavior without relying on centralized control. Across both domains, function depends on the dynamic coordination of heterogeneous processes operating across multiple spatial and temporal scales. This framework acknowledges structural specialization but argues that specialized components become effective through coordinated interaction regimes. I further discuss how this perspective extends current systems biology approaches by prioritizing temporally structured interaction patterns as the primary explanatory target. Finally, I outline empirically testable predictions suggesting that biological function is better captured by time-resolved coordination dynamics, hub-mediated integration, and metastable interaction regimes than by localized activity or static connectivity. Full article

This study investigates the population dynamics and seasonal reproductive patterns of Gambusia holbrooki, an invasive fish threatening biodiversity within arid springs of the Edgbaston Spring complex in Queensland, Australia. Using daily aging techniques, we uncover critical life history traits that inform targeted species management. Our findings reveal marked sex-specific mortality rates, with males exhibiting higher mortality than females, a pattern consistent with findings from Tasmania. Reproductive activity peaks were observed between September and November, but persisted throughout the year, excluding January and April of 2020, likely due to elevated water temperatures during these months. Growth modeling identified the power function as the best fit for describing G. holbrooki growth trajectories. These insights highlight the importance of seasonally informed control strategies to mitigate the ecological impact of this pest species. The study provides essential data to support conservation efforts and guide effective management of invasive fish in fragile arid spring ecosystems. Full article

Gantry-type dual-drive feed systems are widely used in high-precision CNC machine tools, and their synchronization performance directly affects machining accuracy and operational stability. To reduce synchronization errors caused by load-position variation, nonuniform stiffness distribution, and inertia mismatch, this study proposes a structural parameter optimization method for a gantry-type dual-drive feed system. The novelty of this work lies in integrating position-dependent dynamic modeling, critical-position identification, sensitive structural-parameter selection, and response-surface-based optimization into a unified framework for synchronization-error reduction. First, a position-dependent dynamic model is established using modal reduction, spline interpolation, and substructure synthesis. The dynamic model is then coupled with a servo control model to construct an electromechanical coupling model, which is validated experimentally on a gantry-type dual-drive feed system. Next, the synchronization-error distribution over the entire workspace is evaluated, and the critical position with the poorest synchronization performance is identified. Based on sensitivity analysis, the key structural parameters affecting synchronization error are selected as design variables. A response surface surrogate model is then constructed, and particle swarm optimization is used to obtain the optimal structural-parameter combination. The results show that the synchronization error at the critical position is reduced by 20.5%, while the average synchronization error at the validation positions is reduced by 17.3%. These results demonstrate that the proposed method can effectively improve the synchronization accuracy of gantry-type dual-drive feed systems and provide practical guidance for the structural design of high-precision dual-drive machine tools. Full article

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

To address the issues of sluggish dynamic response, significant steady-state fluctuations, and poor disturbance rejection associated with traditional proportional–integral (PI) and conventional speed control methods, a novel sensorless sliding-mode speed control strategy for permanent magnet synchronous motors (PMSMs) based on the super-twisting algorithm (STA) is proposed. First, an advanced sliding-mode speed controller is designed by integrating an integral nonsingular fast terminal sliding-mode surface with the STA, thereby enhancing the dynamic response and transient stability of the PMSM under speed variations. Subsequently, to mitigate inherent sliding-mode chattering, a novel load torque observer is developed. This observer continuously feeds forward real-time load estimates to the speed controller, which substantially improves the system’s robustness against external disturbances. Furthermore, to eliminate the reliance on mechanical sensors and ensure reliable operation across diverse scenarios, an improved sliding-mode observer (SMO) incorporating the STA is utilized to achieve more precise rotor position and speed estimation. Finally, an experimental platform is established to conduct comprehensive variable-speed and variable-load tests on the PMSM. Experimental results demonstrate that the proposed method improves the dynamic response and disturbance immunity of the PMSM by 58.33% and 71.75%, respectively, while reducing steady-state fluctuations by 33.33%. These results demonstrate the effectiveness of the proposed sensorless sliding-mode control strategy and show improved speed regulation performance for PMSM drives. Full article

To reveal the electromagnetic response characteristics and hydro-thermal evolution mechanism of frozen soil under microwave irradiation, we used remolded frozen soil prepared from undisturbed parent soil collected in Hegang, China, as the research object. We conducted dielectric parameter tests across the 715–1150 MHz and 2250–2650 MHz frequency bands and 1.5 kW microwave heating tests on specimens with three gravimetric water contents (15%, 20%, and 25%) paired with a coupled numerical simulation of electromagnetic field-heat transfer-moisture migration. The results show that water content is the dominant factor controlling the dielectric response of frozen soil. The dielectric loss and water content sensitivity of frozen soil in the low-frequency band (dominated by unfrozen water) are significantly higher than those in the high-frequency band (dominated by ice phase and soil matrix). Microwave-induced temperature rise exhibits a three-stage characteristic, as follows: slow temperature rise, isothermal plateau at the freezing point, and rapid temperature rise. Specimens with a lower initial water content show a higher temperature rise efficiency in the late heating stage, with a maximum rate of 1.112 °C·s⁻¹ for the 15% water content specimen. Mass loss is negatively correlated with initial water content, with a maximum value of 1.8 g after 120 s of irradiation. In addition, the non-uniformity of the electromagnetic field results in a temperature field pattern characterized by a high-temperature core at the specimen center and lower temperatures at the edges. This study provides fundamental theoretical support and technical guidance for the application of microwave thawing technology in geotechnical engineering, particularly for frozen soil foundation treatment in cold regions. Full article

The concept of virtual water is currently one of the most important issues in water resource management, especially in a context marked by structural water scarcity. Beyond the analysis of virtual water flows, which has been widely studied in the literature, this study aims to better understand the relationship between virtual water exports and economic growth. This paper analyzes the dynamic relationship between Morocco’s economic growth and agricultural virtual water exports to the European Union over the period of 1986–2023. An ARDL model was used based on annual data to test cointegration and estimate short- and long-term effects, controlling for gross fixed capital formation and agricultural value added. The bounds test confirms the existence of a stable long-term relationship between the variables. The results suggest that export specialization may be associated with foreign earnings and agricultural activity while also coinciding with greater pressure on resources and potential adaptation costs, especially for blue water resources. However, estimates indicate that in the long term, investment is positively and significantly associated with growth, while virtual water exports are associated with a negative effect on GDP, suggesting that export gains may be offset by increasing water constraints and sectoral trade-offs, and that agricultural value added mainly influences short-term dynamics. The results highlight the importance of integrating water footprint and virtual water trade concepts, as well as climate constraints, into agricultural and trade strategy planning while strengthening policies on water efficiency, innovation, and governance. Full article

Combine harvesters in lodged, wet, weedy, uneven, or otherwise heterogeneous fields operate under rapidly changing feed rate, load, and material flow conditions. These disturbances often appear as drum overload, cleaning loss, grain breakage, impurity increase, and unstable travel, whereas conventional fixed-parameter operation still depends heavily on operator experience. This review examines intelligent perception and control technologies for combine harvesters from a mechanism-to-control perspective. The discussion covers dynamic load evolution, cleaning loss and grain damage mechanisms, multivariable coupling, pre-harvest perception, feed rate and internal state sensing, result layer loss and quality monitoring, forward speed control, threshing drum load regulation, adaptive cleaning control, and whole machine integration. The literature shows a clear shift from isolated sensing or single-parameter adjustment toward multimodal perception, state estimation, predictive control, digital twins, and edge deployment. At the same time, field robustness, cross-condition generalization, actuator bandwidth, sensing delay, and the coupling between result layer monitoring and closed-loop control remain the main barriers to deployment. The review, therefore, argues for a whole machine architecture that links environmental preview, internal state estimation, loss quality feedback, actuator-aware control, and cloud–edge–device collaboration for stable, low-loss, and autonomous harvesting in complex agricultural environments. Full article

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots. Full article

Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility remains challenging for nonlinear vehicle dynamics. Methods: This paper presents a feedforward neural network (FNN)-based MPC framework for autonomous vehicle trajectory tracking. The FNN approximates the coupled vehicle dynamics and visual preview error model using an algebraic sum of log-sigmoid functions. Three adaptive FNN parameter sets, namely, the scaling factor, convergence parameter, and time-shifting parameter, are jointly optimized using a hybrid algorithm that combines the global search capability of fractional particle swarm optimization (FPSO) with the local refinement of sequential quadratic programming (SQP). Results: Comprehensive scenario-based simulations are performed to evaluate trajectory tracking dynamics under dry conditions with an adhesion coefficient of 0.8 and a vehicle mass of 1723 kg moving at a speed of 80 km/h. The results are quantitatively compared with a traditional PID controller and a structurally comparable MPC framework from the literature under identical simulation conditions; related DRL- and RL-based methods are discussed qualitatively for contextual orientation only. The stability, reliability, and computational complexity of the proposed framework are examined based on the mean square error, fitness value, and computational budget in GFLOPs for 100 independent runs. Conclusions: The proposed FNN-based MPC framework demonstrates improved tracking accuracy and optimizer reliability in simulation. While the present results indicate promising computational behavior, real-time deployment will require further validation on embedded automotive hardware and under closed-loop real-time constraints. Full article

Fire protection remains one of the key challenges in the field of architectural heritage conservation, particularly for heritage buildings dominated by timber structures, which face greater difficulties in fire prevention and risk assessment. To systematically evaluate the fire safety performance of high-rise timber heritage buildings, this study takes the Shengjin Pagoda, a typical brick–timber pavilion-style ancient tower in Jiangxi Province, China, as the research object. A three-dimensional performance-based fire assessment framework was developed using Fire Dynamics Simulator (FDS) and PyroSim. Based on field survey data and historical documentation, the geometric characteristics, material properties, and vertical circulation system of the pagoda were reconstructed. Three representative fire scenarios, including bottom-floor ignition, simultaneous multi-level ignition, and wind-driven top-floor ignition, were established to investigate smoke propagation, thermal insulation degradation, and the thermal response of critical timber components under different fire conditions. The results show that brick walls provide effective thermal insulation during the early stages of fire, with efficiency exceeding 90%, but this decreases to approximately 55% in upper regions due to chimney-effect-driven smoke accumulation. Under wind-driven top-floor ignition, exposed dougong components can reach temperatures of 782 °C, resulting in a progressive “top-down and outside-in” failure mechanism. The study reveals the dominant smoke-driven heat transfer pathways and the failure sequence of critical load-bearing elements. Based on these findings, a performance-based fire protection strategy incorporating vertical virtual smoke control zoning and fire-resistance enhancement of key structural components is proposed to support the sustainable conservation of historic high-rise timber structures. Full article

Objectives: To address the critical limitations of current formulations that fail to simultaneously resolve indomethacin’s poor water solubility, susceptibility to gastric acid hydrolysis, and difficulty in balancing rapid onset with long-term sustained release, this study prepared solid dispersions via anti-solvent freeze-drying to improve drug dissolution, constructed oral buccoadhesive bilayer controlled-release tablets using direct powder compression, and elucidated the intrinsic relationships among polymer gel properties, swelling-erosion behavior, tablet integrity maintenance, and drug release mechanisms. Methods: Solid dispersions (SDs) were prepared by anti-solvent freeze-drying. Bilayer tablets (25 mg IND/tablet, 12.5 mg/layer) were fabricated via direct powder compression after optimizing disintegrants and polymer matrices. In vitro dissolution, surface pH, adhesion time, and adhesion strength were evaluated. Results: SDs enhanced dissolution by at least 30-fold in water and 2.4-fold at pH 6.8 within 2 h versus pure drug. Optimized bilayer tablets achieved 45% drug release at 20 min and 80% sustained release over 8 h, with surface pH of 6.8 ± 0.1, adhesion time of 8.3 ± 0.1 h, and adhesion strength of 57 ± 0.13 g. Conclusions: The physicochemical properties of polymeric excipients are critical for balancing drug release and mucoadhesion in buccal tablets. To achieve ideal controlled-release effects, in addition to focusing on the swelling and erosion characteristics of matrix-based tablets, the ability to maintain tablet integrity during dynamic dissolution must be further investigated, which is an essential factor for ensuring precisely modulated drug release. Meanwhile, when employing solid dispersions as solubilizing intermediates to prepare controlled-release formulations, the gelling properties of polymers in each formulation component should be fully considered to avoid incomplete disintegration and insufficient release at the initial dissolution stage. Full article