Search Results (3,228)

Degraded industrial sites in arid and semi-arid regions often suffer from loose surface substrates, weak water-retention capacity, high wind-erosion risk, and poor early vegetation establishment. Combining photovoltaic (PV) deployment with ecological utilization may improve near-surface habitats by shading, reducing wind speed, and regulating soil heat and moisture. This study investigated an ash storage yard of a coal-fired power plant in Ordos, Inner Mongolia, China, by comparing soil temperature, soil moisture, and near-surface wind-speed responses under three representative fixed PV tilt angles of 36°, 43°, and 50°, together with the corresponding early plant-growth suitability. A multi-physics model coupling near-surface airflow, water-vapor transport, and porous-media hydrothermal migration was established. A Gaussian suitability function combined with AHP-CRITIC weighting was used to construct a model-based comprehensive growth index (CGI) from soil temperature and moisture, while short-term field monitoring was used to validate afternoon soil hydrothermal trends. Among the three scenarios, the 36° configuration produced the widest horizontal heat–moisture-affected zone and the highest CGI values for alfalfa and Elymus nutans, reaching 0.7741 and 0.6875, respectively. Relative to the outside reference area, the rear PV zone reduced the near-surface wind speed by 33–40% and increased the plant heights of alfalfa and Elymus nutans by 49.4% and 37.8%, respectively. A first-order PVsyst assessment showed that the 43° configuration achieved the highest specific energy yield of 1814 kWh kWp⁻¹ year⁻¹, whereas the annual grid-connected output at 36° was only 0.59% lower. These findings indicate that the 36° configuration may provide a favorable compromise between early vegetation establishment and photovoltaic electricity generation among the tested scenarios. By linking renewable-energy production with microenvironment regulation and early vegetation establishment, the proposed framework provides a decision basis for the multifunctional and sustainable reuse of degraded industrial land. Nevertheless, the results represent a site-specific, single-season assessment and should not be interpreted as a universal optimum. Full article

Four-dimensional chaotic systems have garnered significant attention due to their complex nonlinear dynamics, high-dimensional complexity, and wide range of applications in science and engineering. This paper proposes and investigates a novel four-dimensional chaotic system in both constant- and variable-order frameworks to reveal the influence of memory effects on its dynamical behavior. The variable-order formulation is established using the Liouville–Caputo fractional derivative, while an efficient numerical scheme based on Lagrange interpolation is developed to approximate the variable-order derivative accurately. A rigorous local stability analysis is first conducted to characterize the equilibrium points and establish their instability and non-hyperbolic nature under the different derivative formulations. The nonlinear dynamics of the proposed system are then comprehensively examined through phase portraits, time series, bifurcation diagrams, and Lyapunov exponent analysis. The results demonstrate that the variable-order model preserves the fundamental topological characteristics of the chaotic attractors while introducing adaptive transient responses and significantly richer dynamical behaviors than the corresponding constant fractional-order model. Furthermore, the proposed system generates previously unreported chaotic attractors and phase-space patterns, enriching the class of known four-dimensional chaotic systems and demonstrating the variable-order framework’s enhanced capability to produce diverse nonlinear phenomena through adaptive memory effects. Full article

Soft-ground finite-element analyses commonly reduce curved Oedometer compression to one constant slope, obscuring where stress-level curvature affects boundary-value predictions. AJOP-T embeds the differentiable Arc Joint via Optimum Parameters map in continuous teardrop bounding-surface plasticity while retaining the inherited yield geometry, non-associated flow, radial mapping and SMP-transformed stress. High-order denotes only the map’s derivative hierarchy: its first two derivatives define tangent hardening and hardening curvature, not gradient, fractional, nonlocal or rate order. This first-phase formulation is deliberately rate-independent and retains constant κ to isolate compression-map hardening; time-dependent and nonlinear cyclic swelling responses are outside its claims. The formulation recovers constant-slope hardening asymptotically, yields a closed-form admissibility boundary, is invariant under SMP, and recovers the parent isotropic normally consolidated settlement equation. Four natural-clay compression maps were fitted; triaxial evidence is fitted for comparison except for one held-out Eastern Osaka extension path. Three implementations agree to at least five significant figures. Paired undrained strip-footing analyses reduce centre settlement by 31.8% in the curved regime but only 0.27% near the high-stress asymptote. A predicted 1.6% low-stress strength-ratio drift is below the reviewed data scatter and is not claimed as experimentally validated. Full article

This paper develops a unified asymptotic theory for inverse-probability-weighted conditional U-statistics of arbitrary fixed order in the presence of missing-at-random responses and infinite-dimensional functional covariates. The target is a conditional higher-order functional generated by a measurable response kernel and evaluated locally on a separable Banach space. Localization is formulated through delta sequences, providing a common framework for kernel, partition, regressogram, orthogonal series, and related smoothing procedures without recourse to finite-dimensional density arguments. For bounded kernels, we establish uniform almost-complete convergence over pseudo-compact functional domains and obtain a sharp decomposition into deterministic localization bias and stochastic fluctuation. The latter is governed by the localized-kernel variance, the envelope of the delta sequence, the metric complexity of the indexing domain, and the small-ball concentration of the functional covariate. Unbounded kernels are treated under explicit weighted moment, truncation, and summability conditions. The feasible theory quantifies the additional perturbation induced by estimating the propensity score and identifies conditions under which this first-stage uncertainty is asymptotically negligible. Pointwise distributional theory is derived through a denominator linearization combined with the Hoeffding decomposition of the centered localized kernel. The Gaussian limit is driven by the first projection, while the higher-order canonical components are shown to be negligible under explicit local-mass, moment, and noncancellation assumptions. This yields oracle-equivalent feasible inference, a consistent first-projection variance estimator, and asymptotically valid studentized confidence intervals. A finite-grid adaptive comparison principle is also developed for data-driven resolution selection. The scope of the theory is illustrated through conditional rank functionals, discrimination with incomplete labels, metric-learning criteria, and functional prediction. Synthetic and semi-synthetic studies based on functional classification, phoneme log-periodograms, and growth trajectories document the finite-sample interaction between covariate-dependent label observation, local information loss, propensity estimation, and inverse-weighting variance. Full article

Freeform machining of impellers involves extended cycle times, leading to high energy consumption and costs necessitating efficient process optimization. This study develops a CAD/CAM-integrated hybrid Taguchi-Artificial Neural Network (ANN) model to optimize machining parameters for a freeform impeller. Four controllable factors, namely cutting feed ($C_f$), feed Z ($F_z$), retract feed ($R_f$), and cutter diameter ($C_D$), were investigated at five levels using an L25 orthogonal array, with machining time as the response. Taguchi analysis identified cutting feed as the most dominant factor, while retract feed was insignificant, and a first-order regression model yielded an R² of 95.88%. A two-layer feedforward neural network with six hidden neurons achieved an R² of 0.9999 and a mean absolute error of 0.0976 min. To rigorously validate generalization, leave-one-out cross-validation was employed, identifying three hidden neurons as optimal with a cross-validated R² of 0.9823, RMSE of 0.5350 min, and MAE of 0.3429 min. The final model trained on all samples achieved an R² of 0.9996. Comparison with a quadratic regression model on the same test set confirmed the superior predictive capability of the ANN ($R^2=0.9992$ vs. $0.9983$). Optimal parameters ($C_f=\mathrm{12,000} \mathrm{m}\mathrm{m}/\mathrm{m}\mathrm{i}\mathrm{n}$, $F_z=600 \mathrm{m}\mathrm{m}/\mathrm{m}\mathrm{i}\mathrm{n}$, $R_f=4000 \mathrm{m}\mathrm{m}/\mathrm{m}\mathrm{i}\mathrm{n}$, $C_D=6 \mathrm{m}\mathrm{m}$) were validated through simulation, yielding a machining time of 11.05 min, representing a 52.6% reduction from 23.32 min. The hybrid Taguchi–ANN framework effectively optimizes freeform impeller machining, significantly enhancing productivity while maintaining process reliability. Full article

Rapid cooperative detection of stationary targets by multiple unmanned aerial vehicles (UAVs) is important in time-critical missions such as search and rescue. However, the online coordination of probabilistic inference, distributed communication, and detection–motion decisions under local information remain challenging when targets exhibit spatial correlations that existing methods typically neglect. To address this challenge, we develop a distributed event-driven Bayesian search framework for stationary, spatially correlated targets at unknown locations. The framework couples three components. A pairwise spatial model and a distance-dependent Neyman–Pearson detector yield a Bayesian belief update whose unclipped product form is order-invariant to event-processing sequence. A distributed selective flooding algorithm propagates only positive detection events, achieving finite-time event-set consensus over connected graphs while avoiding full-map exchange. A decoupled detection–motion planner exhausts high-belief cells within each UAV’s field of view before selecting a waypoint that balances surrogate detection probability against travel cost, with responsibility regions dynamically renegotiated among neighbors when local high-value cells are depleted. In numerical experiments, the proposed method achieved zero uncoordinated repeat detection in all simulations and significantly reduced first-discovery coverage relative to static-partition and no-communication baselines, while adapted external baselines required 90-fold and 6-fold larger communication payloads and had nonzero repeat-detection rates. The framework thus occupies a specific tradeoff point of zero revisit, sparse communication, and early discovery gain in scenes where targets span multiple UAV search regions. Full article

A high-stability actively mode-locked fiber laser (AMLFL) is proposed and experimentally demonstrated. This AMLFL is based on a distributed feedback laser diode (DFB-LD) injection locking with Fabry-Perot (F-P) etalon frequency stabilization. In this system, a wavelength modulation method is employed to generate the feedback signal for frequency stabilization. The stabilization mechanism utilizes the linear response characteristic of the first-order derivative of the F-P etalon transmission peak. This achieves wavelength stabilization of the DFB-LD. Subsequently, the stabilized light source is injected into the ring cavity of the AMLFL. The proposed system does not require modification to the existing AMLFL cavity. It also features a simple structure and low implementation cost. Experimental results show that, with frequency stabilization, the wavelength drift of a selected spectral line is reduced to within the 10 pm resolution of the OSA. Meanwhile, the standard deviations of the 5 GHz spectral component power fluctuation and the average output optical pulse power are 0.01 dB and 0.01 dB, respectively. Full article

Recently, drive-by bridge structural health monitoring has gained increasing attention due to its potential to be a cost-effective way to monitor the highway infrastructure. The pre-installed sensory system on a passing vehicle is used to capture the spatiotemporal response of the bridge for structural health monitoring. The vehicle passing over the bridge is a time-varying process, and it is a big challenge to extract the time-varying characteristics of vehicle–bridge interaction systems for structural health monitoring. This paper aims to develop a drive-by time-varying feature extraction approach for bridge structural damage detection using the second-order synchrosqueezing transform. The research first examined the impact of various factors on the frequency changes in VBI systems, including the vehicle mass, stiffness, speed, road surface profiles, measurement noise, and different types of damage. When compared with traditional synchrosqueezing transform, the proposed method provides a clearer and more accurate time–frequency representation. A 6 m-long two-span bridge model is also built in the laboratory and the pre-installed wireless sensory system on a passing vehicle captures the vehicle and bridge interaction response. The time-varying features are extracted from dynamic responses of the vehicle passing over the bridge using the proposed method. Numerical and experimental results show that the proposed approach is effective and accurate enough to extract the time-varying features for bridge damage detection. Full article

Background: A Phase I trial to study the efficacy and safety of MR-guided FUS-MB (focused ultrasound–microbubble) treatment combined with radiotherapy (RT) using a fully electronically MRI-guided steerable focused ultrasound system was performed. The investigation here carried out an exploratory quantitative analysis of images for the first ten participants who were treated with MRgFUS-MB therapy, identifying texture-based MRI features that changed significantly after treatment and indicating probable treatment-related changes in tumour morphology. Methods and Findings: Ten patients with stage I-IV breast cancer (Age: 64 ± 10.8) underwent 2 FUS-MB therapies throughout their radiation treatments, as deemed appropriate by a multidisciplinary team. MRI scans were performed prior to treatment and at 1- and 3-month follow-up. The tumours were segmented, and 2D and 3D features, including shape, first-order and texture features, were extracted for original and wavelet-filtered T1-weighted fat-saturated MR images. The overall significance of radiomic features in association with morphological changes in the target tumour over 1-month and 3-month follow-up in comparison to the first treatment day was investigated. Results: The five most significant features (p < 0.05) were selected using a sample-related t-test, out of which most features were wavelet-filtered first-order features. First-order wavelet-filtered robust mean absolute deviation (RMAD) emerged as the most consistent significant feature for both 2D and 3D features with sampled grid spacings of 0.5 mm and 1 mm. The volume of the tumour demonstrated a decline for 80% of patients over 3 months, as measured by the 3D mesh volume of the tumour ROI. There was a significant decline in tumour size and image heterogeneity, indicating plausible changes in the tumour microenvironment possibly related to the treatment. Conclusions: Previous preclinical studies and clinical evaluations have demonstrated the efficacy of MR-guided FUS-MB therapy. MRI image-based features from the T1-weighted MR scans over various follow-up times provide potential evidence of changes in tumour tissue heterogeneity that might be treatment-related. MRgFUS MB therapy can possibly evolve as a mode of radiotherapy enhancement in the future, which warrants further controlled validation. The availability of adequate data in the future might possibly lead to the identification of image biomarkers related to treatment response, thus providing better clinical evaluations. Full article

Simultaneous and stochastic optimization of open-pit mining complexes at the medium-term level aims to maximize expected profits, manage technical risk for integrated value chains, and enhance the operational feasibility of the long-term plan while still meeting its targets to achieve long-term value. To address two key operational feasibility challenges over a twelve-month horizon, the proposed framework integrates two features that provide a more detailed operational evaluation during decision-making than existing methods. The first involves explicit tracking of shovel movements to align optimized extraction sequences with the operational capabilities of loading equipment. The second uses high-order simulation to produce probability distributions for processing plant responses based on geometallurgical properties of the material being scheduled and selected operating modes. To efficiently optimize schedules with this detailed evaluation, a solution method is proposed using an online machine learning model to rank moves in a metaheuristic search procedure. The framework is demonstrated using a gold mining complex and results show realistic extraction sequences with an increase in metal production and cashflow when compared to a regression-based processing model. Full article

In short-duration power-support applications of energy storage stations, state of power (SOP) estimation should reflect the constant-power boundary over the target horizon, while constant-current extrapolation may misrepresent the current rise caused by voltage decline. This study proposes a 30 s constant-power SOP evaluation framework for portable inspection, decoupling parameter inversion from boundary propagation. The method uses a single-particle model with electrolyte dynamics (SPMe) with degradation factors for ohmic resistance, kinetics, and diffusion. The ohmic degradation factor is determined through time-zero voltage-drop hard calibration, while the kinetic and diffusion degradation factors are identified from 30 s constant-current pulse responses using physics-informed neural network (PINN)-based inversion, and the constant-power boundary is solved by Runge–Kutta integration and bisection search. In model-consistent closed-loop verification, which assesses numerical and inversion consistency under matched-model assumptions rather than independent physical accuracy, the method achieved a mean absolute error (MAE) of 0.100%, a 95th-percentile error of 0.503%, and a maximum error of 2.019%, below the constant-current approximation and first-order equivalent circuit model baselines within the matched-model synthetic setting. Its Jetson Nano-equivalent runtime was approximately 0.630 s. An external proxy comparison using 154 discharge pulses from a public HPPC dataset for an LCO-graphite cell showed an MAE of 0.41 W relative to the pulse-power proxy. This result measures agreement with the selected pulse-power proxy rather than accuracy against a strictly defined 30 s constant-power ground truth. The 10 mV-noise case increased the SOP MAE to 3.868%, indicating substantial sensitivity to voltage-measurement disturbance and the need for validated signal conditioning. These results indicate a physically interpretable and computationally feasible candidate framework for rapid battery power screening, while direct constant-power experiments, broader chemistry coverage, and measured-noise validation remain necessary before field deployment. Full article

Microelectromechanical systems (MEMS) have a widespread use in several applications such as signal filtering, time referencing, and sensing. This paper explores the nonlinear dynamic behavior of a MEMS resonator using a reduced-order modeling approach. The study focuses on how multi-modal formulation and detuning affect the system’s response under primary resonance. Using the method of multiple scales, amplitude–phase response equations are derived, and time-domain simulations are generated with the Runge–Kutta method. Two mode combinations are examined: the first mode combined with the second mode and the first mode with the third mode for multi-modal influence evaluation. Results indicate that the first mode provides the dominant behavior to MEMS response, while the second and third modes exhibit minimal participation despite the nonlinearities retained in the presented multi-modal model. Additionally, a detuning study reveals that the geometric and forcing nonlinear effects are stronger near resonance and diminish as the system moves away from it. The analysis suggests that the significant features of the response can be captured in the case of primary resonance using only the first mode, which offers an effective modeling approach. From a design perspective, finding that the first mode alone is sufficient means that the essential dynamic behavior of the MEMS resonator can be predicted and controlled by focusing on its first mode of vibration. In practical terms, this greatly allows engineers to optimize geometry, driving voltage, or control parameters to target the first mode natural frequency without accounting for higher modes, which reduces computational cost and design complexity. Full article

This study investigates the influence of loading pattern, analysis level, and nodal connection stiffness on the stability and dynamic behaviour of a low-rise steel dome. Three joint configurations and two loading scenarios, symmetrical and asymmetrical, were analysed. Structural stability was evaluated using linear buckling analysis, second-order analysis, full geometrically nonlinear analysis. Dynamic properties were determined by modal analyses performed with and without the geometric stiffness matrix. The results show that the structural response is governed primarily by the asymmetric distribution of axial forces. For the model with pinned joints, a 68.4% decrease in the critical load multiplier is observed. Modal analysis revealed that asymmetric loading not only reduced the first natural frequency but also fundamentally changed the modal structure. Global vibration modes disappeared, modal mass became distributed over a large number of modes, and the Modal Assurance Criterion remained below 0.11, confirming a qualitative change in the vibration mechanism. In contrast, symmetrical loading caused only minor frequency reductions while largely preserving the mode shapes. The results demonstrate that linear analyses may significantly overestimate the stability and dynamic performance of low-rise steel domes under asymmetric loading. Accurate assessment therefore requires second-order effects, geometric nonlinearity, and geometric stiffness to be considered. Full article

Dam removal is a direct measure to restore river connectivity, but the assessment of its ecological effects has long been constrained by the lack of continuous monitoring data across multiple trophic levels. Based on eight years (2017–2024) of continuous field monitoring data from the Heishui River, a first-order tributary of the Jinsha River, this study systematically analyzed the spatiotemporal dynamics of phytoplankton, zooplankton, and fish communities before and after the removal of the Laomuhe Dam. Ecopath models were constructed annually for six ecological units to quantitatively assess the restructuring effect of dam removal on the structure and function of the aquatic food web. The results showed that: (1) Plankton communities were highly sensitive to the disturbance of dam removal. In the early stage after dam removal (2019), the diversity of phytoplankton and zooplankton across the whole river suffered a cliff-like decline; the Shannon index of zooplankton in the former reservoir area plummeted from 3.06 to 0.81. During the recovery period, diatoms and rotifers acted as pioneer groups for phytoplankton and zooplankton, respectively, and were the first to recover. By 2024, the proportion of copepods rose to the highest level during the study period, and the dominant group of the community shifted from rotifers to copepods. (2) Fish communities responded rapidly and positively to the restoration of connectivity. In the upstream reach adjacent to the dam site, the number of species increased from 3 before dam removal to 12 afterward; in the downstream reach, it increased from 2 to 15, with diversity index increases of 172% and 246%, respectively. (3) Ecopath model analysis revealed a recovery pattern of “structure first, function delayed” in the food web—species number and community composition could be re-established shortly after connectivity restoration, but the recovery of functional indicators such as the system omnivory index (SOI) and energy transfer efficiency (total primary production/total respiration, TPP/TR) lagged significantly behind. By the end of the study period, the upstream dewatered reach had not yet reached the level of the natural reference reach, and the downstream recovery rate and degree were better than those upstream. This study provides comprehensive time-series evidence for the long-term response of aquatic ecosystems to low-head dam removal and offers valuable references for guiding adaptive management of mountain rivers after dam removal. Full article

Fractional-order models are useful for describing systems with memory, anomalous relaxation, and non-classical dynamic behavior. However, parameter estimation in these models may be affected by strong covariance between the kinetic coefficient and the fractional order, reducing the independent interpretability of the estimated parameters. This work proposes a reference-time scaling strategy for an unforced fractional-order decay model and a forced fractional-order step response model, aiming to improve the local conditioning of the estimation problem by making the sensitivity vectors of the dimensionless coefficient and the fractional order locally orthogonal. The covariance structure is analyzed through the local sensitivity matrix, and a sensitivity-based expression for the reference time is derived to set the off-diagonal term of the approximate Gauss–Newton covariance matrix to zero, thereby reducing first-order linear dependencies. The methodology is evaluated using previously reported experimental data from Amiodarone plasma concentration-time profiles for fractional pharmacokinetic modeling and from the temperature response of a didactic thermal system to a step change in the manipulated variable for fractional-order system identification. The results show that the fitted trajectories, residual sums of squares, dimensional kinetic coefficients, and fractional orders remain invariant under reference-time scaling. Nevertheless, the choice of reference time strongly affects the covariance and correlation between the dimensionless parameter $\mu$ or $\kappa$ and $\beta$, while the recovered dimensional coefficients m and k remain invariant. Conventional choices, such as the maximum, arithmetic mean, geometric mean, and harmonic mean of the experimental times, did not systematically reduce parameter correlation. In contrast, the proposed reference time, selected from the local sensitivity structure, reduced the first-order local correlation in both applications. The results indicate that reference-time scaling is a simple reparameterization tool that improves local statistical interpretability under the Gauss–Newton approximation of fractional-order parameter estimates without modifying the physical model or the quality of the fit. Full article