Search Results (1,396)

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems. Full article

Addressing challenges such as the non-Newtonian fluid characteristics of melt, significant system hysteresis, and rheological thermal drift in large-scale glass fiber-reinforced polypropylene (PP-GF) screw-extrusion additive manufacturing (SEAM), this paper proposes a composite pressure control strategy based on inverse model feedforward and variable-structure feedback (VSFC-Smith). This strategy establishes a dynamic pressure benchmark through an inverse rheological model, utilizes a Smith predictor to compensate for time delay, and introduces dead-zone variable-structure feedback to smoothly suppress thermal drift. Experimental results demonstrate that, compared to traditional PID (Proportional-Integral-Derivative) controller, the VSFC-Smith strategy reduces the step pressure overshoot from 23.37% to 17.37%, decreases steady-state screw speed fluctuation by approximately 50%, and limits the error within ±0.04 MPa during complex trajectory tracking. In practical molding validation, this strategy effectively suppressed surface ripples, reducing the surface roughness (Sa) by 14.5% to 124.41 μm; simultaneously, the Z-directional interlayer tensile strength reached 12.63 MPa (a 22.5% improvement compared to open-loop control). This study successfully overcomes the limitations of traditional high-gain feedback, achieving synergistic optimization of the macroscopic morphology and microscopic mechanical properties of composite parts. Full article

Wire rope isolators (WRIs) exhibit typical nonlinear and asymmetric hysteretic behavior, with their mechanical performance being significantly influenced by the coupled effects of multiple parameters. This study investigates the dynamic response of large-sized spiral WRIs under axial loading. Within the framework of an asymmetric hysteresis model, a novel algebraic closed-form formulation is adopted for parameter identification and numerical simulation. Furthermore, a characteristic parameter, A, is introduced to quantify the unique mechanical behavior induced by the structural configuration of WRIs. Five types of large-sized spiral WRIs are selected as test specimens. For each WRI, tests are conducted under 30 distinct working conditions, yielding a total of 150 cyclic loading tests across all scenarios. By systematically varying the displacement amplitude, loading frequency, and preloading pressure, the influences of these key parameters on the dynamic characteristics of WRIs are comprehensively analyzed. These characteristics encompass the axial hysteresis loop shape, energy dissipation capacity, equivalent viscous damping, and average secant stiffness. The results indicate that these three loading parameters exert substantial effects on the mechanical properties of large-sized WRIs. Additionally, the simulated hysteresis curves derived from the identified parameters exhibit excellent agreement with the experimental observations. Compared with conventional mechanical models, the proposed algebraic closed-form model demonstrates slightly higher fitting accuracy, thereby validating its effectiveness and applicability in characterizing the mechanical behavior of large-sized WRIs. This research provides a crucial reference for the engineering application of large-sized spiral WRIs and facilitates the broader adoption of the proposed modeling approach. Full article

Storm-driven turbidity is a major water-quality concern in urban watersheds, reflecting the mobilization and transport of fine sediment during runoff events. This study examines how seasonal storm characteristics influence turbidity and associated sediment transport responses in the Middle Fork of Beargrass Creek, Louisville, Kentucky, over a two-year period. Forty-one erosive storm events were identified and characterized using high-resolution rainfall data to capture storm magnitude and structure. Study objectives were to: (1) quantify event-scale turbidity responses to erosive storms, (2) compare upstream and downstream turbidity behavior to assess spatial variability, (3) evaluate seasonal variation in these relationships, and (4) assess implications for sediment-focused best management practice (BMP) design. Event-based regression models related downstream turbidity to lagged upstream turbidity and downstream erosivity. Turbidity ratios and turbidity–discharge hysteresis characterized spatial and temporal sediment transport dynamics. Results showed that winter and spring storms exhibited longer durations, stronger upstream–downstream turbidity coupling, and more stable lag relationships, indicating integrated sediment transport. Short-duration, high-intensity summer storms produced elevated turbidity ratios, pronounced clockwise hysteresis, and greater model sensitivity, consistent with localized sediment mobilization. Findings support seasonally adaptive BMP strategies, with volume-reduction approaches most effective during winter–spring and source control measures critical during summer-fall. Full article

Polyurethane O-ring seals are vital for the service life and sealing reliability of hydraulic systems, yet internal hysteresis heat generation under reciprocating motion causes localized temperature rise, altering contact pressure distribution and impairing sealing performance. This study aimed to clarify the coupled effects of reciprocating motion parameters on O-ring hysteresis heat generation and sealing performance. A unified hysteresis heat generation rate expression was derived by combining the time–temperature superposition principle with the Maier–Göritz model, and the heat source model was integrated into a thermo-mechanically coupled finite element analysis (FEA) framework, validated by matching simulated and experimental temperature rise histories. Under baseline conditions, hysteresis heating causes the O-ring’s peak contact pressure to decrease by approximately 0.4 MPa during the outward stroke. Parametric analysis revealed that elevated operating parameters increase contact pressure to maintain effective sealing, but simultaneously intensify hysteresis heating. Quantitatively, the maximum O-ring temperature was highly sensitive to operating conditions, reaching 63.6 °C at 8 MPa hydraulic pressure, 60.0 °C at a 90 Hz reciprocating frequency, and up to 81.5 °C for a friction coefficient of 0.2. Although the current framework is limited by the exclusion of interfacial frictional heating, it enables the reliable quantitative prediction of thermal loads. Ultimately, this study provides a robust method for assessing sealing safety margins and offers theoretical guidance for the structural optimization of hydraulic sealing systems. Full article

In the essay we introduce present-day systems concepts, such as resilience, tipping points, and hysteresis effects, via the concept of fast–slow dynamical systems (whether explicit in the models or implicit through bifurcation and stability behaviours). These lead naturally to ideas first propagated within catastrophe theory, fifty years ago. We discuss the historical catastrophe (the backlash) that befell such an abstract yet mathematically grounded (and thus inescapable) theory within economics and also its subsequent re-appraisal and re-adoption. Finally, we discuss some of the challenges inherent in anticipating tipping points from live systems data (observations), within systems-theoretic interpretations, and whether methods from topological data analysis might respond to them. While it is fashionable for national, governmental and policy institutions to speak of “resilience” in all manner of national systems contexts, we aver that it is foolishly inadequate to do so without an understanding and consideration of tipping points and hysteresis (sometimes termed “path dependence”), giving rise to “lock-in”. Full article

The development of eco-efficient construction materials requires optimisation strategies that reduce cement consumption, valorise industrial by-products, and enhance performance without increasing material demand. Clay–cement sealing suspensions used in geotechnical engineering offer significant sustainability potential due to their high mineral content and compatibility with supplementary cementitious materials such as siliceous fly ash. The early-age rheological properties are essential for the design of geotechnical sealing barriers, yet the influence of chemical additive sequencing on flow behaviour remains poorly understood. This study examines how the priority of sodium silicate addition—introduced either before cement and siliceous fly ash (the “Prior” series) or after them (the “After” series)—affects the flow curves, yield stress, thixotropy, and equilibrium shear stress of clay–cement–fly ash sealing suspensions. Ascending flow curves were fitted to the Casson, Herschel–Bulkley, and Ostwald–de Waele models, and a shear-rate-resolved thixotropic power density analysis was applied to decompose the hysteresis behaviour. The results demonstrate that the Prior series produces deflocculated colloidal clay networks with localised cementitious agglomerates, exhibiting lower shear stresses at low shear rates but markedly higher yield stress amplitudes and larger hysteresis loop areas. The After series yields more uniformly distributed nucleation–coagulation networks with smaller hysteresis loops and pronounced structural rebuilding at low shear rates during the ramp-down phase. These findings provide a physicochemical framework for tailoring the early-age rheology of clay–cement suspensions through controlled additive sequencing, with direct implications for pumpability, injectability, and post-placement structural recovery in geotechnical applications. Full article

Solar greenhouses are indispensable infrastructure for winter vegetable production in high-latitude regions, maintaining favorable growing conditions without auxiliary heating through superior thermal insulation and solar radiation capture. However, extreme meteorological events disrupt this equilibrium, and soil temperature prediction remains challenging due to thermal hysteresis—time-lagged responses to environmental drivers that threaten crop viability during pre-dawn periods. Existing studies focus on air temperature or short-term horizons (3–6 h), leaving the critical 12 h prediction window inadequately addressed. This study develops a hybrid machine learning framework for 12 h soil temperature prediction in a solar greenhouse. High-resolution data were collected using soil temperature sensors at 200 mm depth (tomato root zone) during the winter growing season. Five algorithms were evaluated: RF and XGBoost (Bagging/Boosting for trend/residual capture), LSTM and GRU (temporal memory), and a novel RF-XGBoost hybrid with two-stage residual correction. The hybrid model achieved R² = 0.9927, improving standalone RF (R² = 0.9883) by 0.45% with MSE, RMSE, MAE reductions of 18.1%, 9.6%, 13.9%. Maximum 12 h error was 2.52 °C versus 3.12–4.60 °C for standalone models. The 12 h horizon enables preemptive heating activation, mitigating frost risk while avoiding unnecessary energy expenditure. Full article

The convergence of sensing and processing is a critical frontier in the development of energy-efficient spiking edge intelligence. This paper presents a novel hardware implementation of a sensory neuron evolving from the leaky integrate-and-fire (LIF) model by coupling a volatile memristor with an LC tank circuit. The proposed memristor–resistor–inductor–capacitor (MRLC) neuron embeds electromagnetic sensing directly into neuronal dynamics, enabling direct transduction of proximity information into spike trains. We demonstrate that the circuit functions as a metal-sensitive proximity sensor with spiking output in both simulation and physical experiments. Moreover, the MRLC neuron exhibits rich dynamical regimes, including regular spiking, bursting with 2–5 spikes per burst, and quasi-chaotic behavior, as well as sensing memory provided by hysteresis-like multistability, which is a notable advancement over simple rate-encoding LIF neurons. Full article

Accurate modeling of smart composite dampers is crucial for simulation and model-based control. This study focuses on the constitutive modeling of a novel damper that synergistically combines an Entangled Metallic Wire Mesh (EMWM) with a magnetorheological (MR) fluid. Unlike traditional MR dampers, the interaction between the field-responsive MR fluid and the rate-sensitive, deformable EMWM matrix introduces strong coupled current–frequency dependence. To capture this essential characteristic, a control-oriented, bivariate (current–frequency) hysteresis model is formulated, wherein all parameters are explicit, continuous functions of both the control current (I) and excitation frequency (f). A systematic two-step identification method is employed to derive these functions from dynamic tests. A key finding is that the identified damping exponent (α) consistently exceeds unity across the tested operational range. This quantitatively indicates a transition from viscous-dominated to inertial-flow-dominated dissipation within the EMWM matrix, a distinctive mechanism attributed to non-Darcian flow in its porous structure. The fully parameterized model demonstrates high fidelity (R² > 0.99) within the characterized low-frequency, small-amplitude regime and shows reliable predictive capability for interpolated conditions. The presented model serves as a ready-to-use constitutive tool for the simulation and design of low-frequency vibration isolation systems utilizing EMWM-MR composites, and the revealed inertial flow mechanism provides fundamental insight for the development of next-generation adaptive dampers. Full article

Accurate prediction of surface rupture induced by seismogenic fault displacement is essential for the seismic safety assessment of major engineering projects. Most existing numerical simulations adopt quasi-static approaches, in which the effect of fault displacement is simplified as static loading. As a result, these methods cannot represent the dynamic characteristics of the fault rupture process, such as stress-wave propagation, soil inertial effects, and the influence of dynamic loading paths on rupture extension in soil layers. To address this issue, a full-process simulation method is established for simulating rupture of overlying soil subjected to dynamic fault displacement: Firstly, a non-uniform dynamic fault displacement loading is formulated for the two sides of the fault based on viscoelastic artificial boundaries, allowing the differential motion of the bedrock on both sides of the fault to be represented. Secondly, an improved dynamic skeleton curve constitutive model of soil is developed by introducing a minimum modulus constraint, providing an improved description of soil nonlinear dynamic behavior from small-strain hysteresis to large-strain shear failure. The reliability of the proposed method is verified through element-level tests and horizontal-site response simulation. As a benchmark, its ability to reproduce key rupture characteristics under quasi-static conditions is also assessed by comparison with classical quasi-static rupture studies. The method is then applied to simulate rupture extension and deformation response of overlying soil under strike-slip fault displacement. The results show that, compared to quasi-static analysis, dynamic fault displacement produces similar cumulative slip for surface rupture initiation and full connection, but induces transient amplification of peak surface displacement and a wider deformation zone with gentler displacement gradients. These findings demonstrate the necessity of considering dynamic fault dislocation of bedrock–overlying soil interaction in seismic assessments of engineering projects crossing active faults. Full article

Tubular flux-switching permanent-magnet linear machines (TFSPMLMs) are difficult to optimize using a single core material because conventional axial laminations suffer from severe in-plane eddy-current loss, whereas soft magnetic composites (SMCs) exhibit lower permeability and higher hysteresis loss. To address this trade-off, three hybrid SMC–laminated steel core configurations were investigated: H1, with radially laminated steel in the yoke; H2, with axially laminated steel in the tooth; and H3, with circumferential laminated steel segments. A reference SMC model (R1) and the three hybrid models were comparatively evaluated using three-dimensional finite element analysis (3D FEA). H1 and H2 showed degraded performance due to an interfacial micro-gap along the main flux path and additional in-plane eddy currents in the laminated steel regions. To mitigate these limitations, circumferential segmentation was applied to the laminated steel parts. With eight segments, H2 achieved a thrust force of 278.8 N, comparable to that of R1, while reducing iron loss by 22.5%; even a two-segment structure provided noticeable improvement. Among the investigated models, H3 showed the best overall performance by avoiding a micro-gap on the main flux path, achieving 285.5 N, and 3.9% higher thrust force and 18% lower iron loss than R1. These results indicate that H3 is the most effective hybrid-core configuration for maximizing both thrust force and loss reduction, whereas segmented H2 is an attractive practical option when manufacturability and low-loss operation are considered. Full article

Electric vehicle(EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid systems framework that captures these complexities by integrating microfounded household choice, capacity-constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang–bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. Beyond theoretical characterization, the framework is structurally calibrated to match the order-of-magnitude effects reported in leading empirical and simulation-based studies, including network diffusion models, agent-based simulations, bass-type specifications, and fuel-price shock analyses. The hybrid formulation reproduces short-run percentage-point subsidy effects, long-run forecast dispersion under alternative network assumptions, and policy-induced equilibrium shifts observed in the applied literature while providing a unified geometric interpretation of these heterogeneous results through explicit basin boundaries and regime switching. The framework provides a complex systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies. Full article

Magnetorheological (MR) semi-active suspensions offer clear advantages in improving ride comfort and handling stability, yet their engineering applications are often hindered by strong nonlinear hysteresis of the damper, the randomness of road excitations, and the reliance on manual tuning of controller parameters. To address these issues, this paper proposes an integrated framework of “experimental modeling–semi-active implementation–adaptive control.” First, characteristic tests of the MR damper are conducted, based on which a current-dependent Bouc–Wen forward model is established. Tianji’s Horse Racing Optimization (THRO) is then employed for parameter identification to reproduce the hysteresis behavior accurately. Second, a back propagation (BP) neural network-based inverse current model is developed to achieve rapid mapping from “desired damping force” to “driving current,” enabling semi-active actuation. Furthermore, a radial basis function (RBF) neural network is embedded into the active disturbance rejection control (ADRC) structure to estimate the system Jacobian online and to tune key extended state observer (ESO) gains in real time, forming the proposed RBF-ADRC strategy and thereby enhancing disturbance observation and compensation capability. Simulation results under pulse-road and Class-C random-road excitations show that, compared with the passive suspension, the proposed method reduces the root mean square error values of sprung-mass acceleration, suspension dynamic deflection, and tire dynamic load by 25.14%, 18.71%, and 11.61%, respectively, while also outperforming skyhook control and fixed-gain ADRC. Frequency-domain results further show stronger attenuation in the low-frequency band relevant to body vibration. Under pulse excitation, RBF-ADRC yields smaller peak and trough body accelerations and faster post-impact recovery. Under ±30% sprung-mass variations, it achieves the best worst-case and fluctuation-range robustness among the compared strategies and remains close to offline retuning. These results demonstrate that the proposed method improves both control performance and robustness while reducing the need for repeated manual calibration. Full article

Numerical simulations employing the dynamic mesh method were performed to investigate the unsteady aerodynamics of variable-sweep wings during morphing. Quasi-steady and unsteady aerodynamic characteristics were compared, and the effects of key operating conditions (freestream velocity, angle of attack, morphing period, wingspan, chord length) on unsteady aerodynamics were analyzed. To enable the rapid prediction of unsteady aerodynamics, a Kriging surrogate model was established and validated against high-fidelity CFD results. The results indicate that unsteady effects manifest as hysteresis loops in aerodynamic coefficients within the morphing cycle. The wing morphing period, angle of attack, freestream velocity, and wingspan have a pronounced impact on the unsteady aerodynamic characteristics, whereas the effect of chord length is negligible. Reduced morphing periods, increased angles of attack, and increased wingspans amplify the hysteresis loop size and enhance the unsteady effects. An increase in the freestream velocity intensifies unsteady effects in the subsonic flow, while it attenuates unsteady effects in the supersonic flow. Compared to direct CFD simulations, the Kriging model for unsteady aerodynamic characteristics prediction achieves a 97% improvement in overall computational efficiency, while its predicted hysteresis loops are in good agreement with CFD results in both trend and magnitude, with an average prediction error below 4% and a maximum error of less than 6%. The Kriging surrogate model developed in this study offers substantial practical value for engineering applications by meeting the demand for rapid aerodynamic computation in the concept design phase for morphing aircraft. Full article