Search Results (296)

This paper presents a method for the dynamic characterisation of load cells and force transducers designed to be straightforward to implement and based on instrumentation commonly used in structural dynamics testing. Impact hammers simultaneously provide an impulsive force to the sensor under test and measure the actual input to estimate the frequency response function. The bandwidth of the sensor is evaluated using an analytical single- or multi-degree-of-freedom frequency-domain model that is fitted to the experimental frequency response function using a Non-Linear Least-Squares approach. This paper provides guidelines for selecting the model complexity and the fitting frequency range and demonstrates the procedure on an experimental strain-gauge load cell. For the investigated transducer, the identified first natural frequency was 481.4 Hz, resulting in bandwidths of 47.9 Hz, 105.1 Hz, and 154.2 Hz for allowable deviations from the static sensitivity of 1%, 5%, and 10%, respectively. Moreover, for the single-degree-of-freedom case, the effect of masses added under operating conditions is modelled, with a worst-case relative error below 1% in the prediction of the natural frequency for the validation measurements, thus enabling the actual bandwidth to be estimated. Full article

The hybrid operation of grid-following (GFL) and grid-forming (GFM) renewable energy units can lead to phase-reference inconsistencies and distorted transient impedance trajectories, which in turn cause maloperation or failure-to-operate of conventional distance protection. To address this issue, this paper proposes a novel distance protection method based on transient impedance fitting. First, a dynamic phase transformation is applied to map the currents of GFL units into a unified reference frame, enabling consistent representation of heterogeneous currents from the hybrid renewable energy station. Second, a transient equivalent impedance model is established based on the transient voltage-current relationship of the transmission line, revealing the influence mechanisms of the rates of change in current amplitude and phase angle on the transient additional impedance. Finally, the magnitude of the transient impedance within a short post-fault data window is selected as the fitting object. The least-squares method is employed to extract the linear fitting slope and intercept, which characterize the evolution trend and initial position of the transient impedance trajectory, respectively, thereby forming the criteria for distinguishing internal and external faults. Simulation results demonstrate that the proposed method correctly identifies fault sections under various fault locations, transition resistances, and renewable power output conditions, with the protection decision completed within 15 ms. The proposed method effectively overcomes the susceptibility of conventional distance protection to maloperation and failure-to-operate in scenarios with high renewable energy penetration. Full article

The increasing use of germanium in advanced technologies and its potential release into aquatic environments demand sensitive and accessible analytical methods for its determination at trace levels. This work reports the development of an adsorptive stripping voltammetric method for Ge(IV) determination in freshwater samples using an ex situ bismuth film electrode and quercetin as a complexing agent. The stoichiometry of the Ge(IV)–quercetin complex was established as 1:2 by UV-Vis spectrophotometric titration and non-linear least-squares fitting. The effect of the sulfonic substituent was evaluated by comparing quercetin with quercetin-5′-sulfonic acid under otherwise identical conditions. The method was optimized with respect to pH, buffer concentration, ligand concentration, accumulation potential and time, and electrode cleaning conditions. Under optimized conditions, the method exhibited a linear range of 0.23–24.4 µg L⁻¹ (3.17–335.9 nmol L⁻¹), a limit of detection of 0.08 µg L⁻¹ (1.10 nmol L⁻¹), and a repeatability of 4.41%. The higher sensitivity achieved with quercetin relative to quercetin-5′-sulfonic acid was attributed to the neutral charge of the Ge(IV)–quercetin complex, which favors adsorption at the electrode surface. The method was successfully applied to groundwater, river water, and tap water samples with relative errors below 5.0%, demonstrating its suitability for environmental water analysis. Full article

We develop a predictive–prescriptive decision-support framework for football squad composition under budget and positional constraints, in which mixed-integer optimization is the central methodological contribution. The framework integrates four components: (i) construction of a Sports Performance Index (SPI) via principal component analysis (PCA) of standardized per-90 performance features; (ii) leakage-free, player-wise, five-fold cross-fitted prediction of seasonal goals plus assists using ordinary least squares (OLS) as the primary input model, regularized linear models as additional baselines, and gradient boosting (XGBoost) as a nonlinear benchmark and uncertainty-estimation component, with each player-season receiving an out-of-fold prediction; (iii) a mixed-integer linear program (MILP) that selects a player-season portfolio under budget, position composition, squad-size, and duplicate-player constraints, with a tunable weight balancing predicted attacking output against the performance index; and (iv) a robust counterpart that incorporates prediction uncertainty into the optimization. The framework is implemented on English Premier League data covering the 2023–2024 and 2024–2025 seasons (N = 218 forward and midfielder player-seasons with at least 1800 min of playing time). Cross-fitted OLS attains a five-fold mean R² = 0.76 ± 0.03 with stable performance across folds. The exact MILP outperforms a benefit-to-cost greedy heuristic by up to 9.4% and the best of 1000 random feasible rosters by 41.0–53.0% in objective value, while guaranteeing feasibility of all composition and budget constraints. Sensitivity analyses across budget tightness, position bounds, squad size, and cost proxy demonstrate qualitative robustness. The robust counterpart with risk-aversion parameter κ ∈ {0, 0.5, 1.0} produces a Jaccard roster overlap of 0.75 relative to the nominal solution and reduces average selected-player prediction uncertainty from 2.65 to 2.49. The contribution is a mathematically rigorous, integer-programming-centered decision-support framework that integrates machine learning with optimization for sports business analytics and is directly applicable to club-level squad planning. Full article

We study prediction intervals for the semi-functional partial linear model (SFPLM) under stationary, geometrically $\beta$-mixing dependence. We analyze a split conformal procedure based on a three way data partition with buffer gaps, a functional principal component projection semi-metric on the functional covariate, profiled least squares estimation of the parametric component, kernel estimation of the nonparametric component and of the conditional standard deviation, and a studentized absolute residual score. Marginal validity of split conformal prediction with a trained score under $\beta$-mixing is available from generic results of Oliveira et al. and of Barber and Pananjady, without any buffer and without accuracy requirements on the fitted estimators. Our main result is complementary to those guarantees: a finite sample marginal lower coverage bound whose theoretical finite-sample coverage penalty decomposes additively into seven interpretable components expressed in the structural primitives of the SFPLM, quantifying the price of replacing the ideal SFPLM score by the estimated score inside the proof. The penalty plays no role in the computation of the interval, involves unknown structural constants, and is not an operational correction. The bound requires no parametric error model, but it is not assumption free; it holds under explicit structural conditions, including geometric $\beta$-mixing, conditionally centered sub-Gaussian errors, a fractal small ball regime for the projected functional covariate, and local regularity of the score distribution. Simulations under a protocol frozen before outcome computation, spanning mild and strong dependence, a misspecification stress test, and a dependent-score design with exactly quantified score autocorrelation, show near nominal coverage for all methods, with the gapped and contiguous variants statistically indistinguishable in coverage. Studentization showed no systematic coverage advantage, while interval-length differences were systematic. The value of the analysis lies in the explicit model-specific estimation layer of the coverage decomposition, not in a numerical gain over naive split conformal. Full article

Parsimonious emulators (PEs) trained on complex climate models (CCMs) are useful when global variables like global mean surface temperature and climate-system energy content are sought. CCM runs over millennia extracted from the LongRunMip repository are used to construct and test PEs for global mean temperature and net incoming radiation flux. For the temperature, the PE is a linear impulse response in the form of a superposition of k decaying exponentials, comprising k weight coefficients and k decay times to be estimated by least-square fitting to the temperature from CCM runs with abrupt step-function forcing. The model fit for $k\ge 3$ is good on all time scales, and the fitted model seems to perform even better for smoother forcing scenarios, suggesting that it reflects essential features of the CCM to which it is fitted. Data for radiation flux are combined with temperature data to produce low-order polynomial fits to Gregory plots and analytic expressions for the evolution of the effective feedback parameter, the radiation fluxes, the evolution of climate-system energy content, and an effective system heat capacity. The analysis reveals four stages of the ocean heat uptake, characterised by increasing effective heat capacity. From these Pes, one can compare the global performance of CCMs under different forcing scenarios, highlighting distinguishing features, such as evolution of albedo feedback and cloud radiative effect. Producing Gregory plots for all-sky and clear-sky outgoing long-wave and short-wave radiation, varying cloud albedo is identified as the main contributor to model spread of equilibrium climate sensitivity. Full article

To address the critical challenge of extracting short-period surface deformation signals induced by extreme floods from InSAR time series, this study focuses on the catastrophic flood that struck the Haihe River Basin in July 2023 (hereinafter referred to as the “23·7” flood, with a total duration of approximately 65 days) and proposes a novel method for transient deformation signal extraction. Using Sentinel-1A satellite data and the PS-InSAR technique, we constructed a multivariate composite fitting function comprising a linear trend term, annual and semi-annual seasonal terms, a step term, and a logarithmic decay term. Through nonlinear least-squares fitting, this approach achieves effective separation of long-term tectonic deformation, seasonal fluctuations, high-frequency noise, and transient flood-related signals. The results show that the W-shaped floodplain east of Xiong’an New Area does not exhibit the expected subsidence induced by water loading but instead features pronounced surface uplift of up to 30 mm. Multi-physics forward modeling reveals the underlying mechanism: the elastic subsidence caused by surface water loading, calculated via the LoadDef spherical loading theory, amounts to only ~2 mm. In contrast, forward modeling based on the GMS three-dimensional groundwater seepage model and the principle of effective stress indicates that the pore water rebound effect can produce surface uplift of up to ~36 mm. The superposition of these two effects is highly consistent with InSAR observations in terms of magnitude, direction, and spatial distribution, confirming that the flood-induced surface deformation is dominated by the pore water rebound effect driven by rapid groundwater recharge, rather than subsidence from water loading. The proposed framework extends the application potential of geodetic techniques for monitoring short-period extreme hydrological events. Full article

Pseudotolithus elongatus is one of the most commercially important species supporting artisanal fisheries in Guinea-Bissau; however, its population biology and reproductive characteristics remain poorly understood, particularly in estuarine ecosystems. This study provides the first integrated biological characterisation of P. elongatus in the Cacine River Estuary, Guinea-Bissau, based on 1776 specimens collected through three independent sampling programmes. Length at first sexual maturity (L₅₀) was estimated using binomial generalised linear models (GLMs); length–weight relationships were assessed by ordinary least squares regression and analysis of covariance (ANCOVA); sexual size dimorphism was evaluated using a battery of parametric and non-parametric tests (ANOVA, Welch’s t-test, Kruskal–Wallis, Kolmogorov–Smirnov); and sex ratio was tested using chi-square goodness-of-fit tests with Yates’ continuity correction. Females attained sexual maturity at a significantly smaller size than males (L₅₀ = 224.9 mm vs. 306.8 mm), and both sexes exhibited positive allometric growth. Females attained larger body sizes and predominated in the largest length classes, confirming pronounced sexual size dimorphism. Reproductive activity occurred throughout the year, with a distinct spawning peak during the dry season (February–April), and the overall sex ratio remained close to parity despite significant variation among length classes and months. These findings provide a biological baseline to support evidence-informed fishery management of P. elongatus in Guinea-Bissau and highlight the need for long-term, standardised monitoring in the Cacine River Estuary. Full article

Nonlinear mixed-effects models (NLMMs) provide a flexible framework for modeling repeated-measures trajectories. However, how best to forecast future observations, especially at ages well beyond those represented in the data, remains relatively underexamined. In this study, we develop a Chapman–Richards NLMM with a spatial-power autocorrelation structure for irregularly spaced repeated measures and compare three forecasting strategies: (i) population-averaged forecasts based on the fixed-effects component only; (ii) subject-specific forecasts in which empirical best linear unbiased predictors (EBLUPs) of the random effects are obtained via a first-order Taylor series expansion with an iterative Newton–Raphson update, including the case of new progenies not used in model fitting; and (iii) forecasts that combine the population-averaged prediction with prior repeated measures through the fitted autocorrelation matrix. Forecast accuracy was assessed with progeny-level validation under fully held-out and partially observed scenarios, using root mean square prediction error (RMSPE) and mean absolute error (MAE), and was examined as a function of: (i) the number of available prior measures and (ii) the accuracy of the fixed-effects component of the NLMM. The methods were illustrated with repeated-measures data from hybrid spruce (Picea engelmannii Parry ex Engelmann × Picea glauca (Moench) Voss) progeny trials at three planting sites in British Columbia, Canada, with measurement ages from 2 to 42 years. Subject-specific forecasts had the lowest prediction errors when sufficient prior measures were available and were also the least affected by misspecification of the fixed-effects component. With only two prior measurements, autocorrelation-based forecasts had the lowest or tied-lowest observed errors, although differences among the three approaches were small. Using all measurements taken before age 42, subject-specific forecasts of height at age 42 achieved an RMSPE of 0.50 m. With only two prior measurements, the corresponding RMSPEs were approximately 1.31–1.33 m across the forecasting approaches. Although demonstrated with a single hybrid spruce dataset from three planting sites, the comparison is, in principle, applicable to other repeated-measures settings in which long-horizon predictions are required from short observation histories; broader applicability remains to be confirmed. Full article

Water vapor (H₂O) acts as both an essential thermodynamic driver and a primary source of chemical radicals in the middle atmosphere, playing an irreplaceable role in maintaining Earth’s radiative balance and indicating long-term climate variability. In this study, 24 years (2002–2025) of H₂O measurements from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on board the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite are systematically analyzed to characterize the H₂O spatiotemporal distribution throughout the middle atmosphere (specifically within the 20–80 km altitude range), with a focus on elucidating its evolutionary patterns across time, altitude, and latitude. Building upon this analysis, an empirical model for the bimonthly mean water vapor volume mixing ratio (VMR) is constructed based on actual measurements. Employing a nonlinear least-squares fitting algorithm, time-series fitting is performed on the data within distinct altitude and latitude grids. Consequently, a mathematical analytical expression for the time series was derived for each latitudinal band at every altitude grid point, alongside the determination of corresponding fitting parameter sets. By integrating these parameterized formulas and derived parameters, a comprehensive empirical H₂O VMR model spanning multiple altitude layers and a broad latitudinal range was ultimately established. Validation results demonstrate that the empirical model exhibits high consistency with the original observational data. The coefficients of determination ($R^2$) generally exceed 0.7 and strictly remain $\ge$ 0.6 in all cases. Furthermore, the model demonstrates strong linear correlation with actual observations (Pearson correlation coefficients typically exceeding 0.8) and maintains low bias, as evidenced by small root mean square errors (mostly < 0.35 ppmv) and mean absolute errors (mostly < 0.25 ppmv) across diverse spatial grids. These evaluation metrics collectively indicate excellent goodness-of-fit and robust reconstruction capabilities. This model provides a reliable empirical reference for investigating the spatiotemporal evolution of middle atmospheric H₂O VMR and serves as a potential data foundation for future optimizations of relevant radiative transfer models. Full article

Accurate dynamic parameters are required for model-based control of lower-limb exoskeletons, but limited excitation, transmission friction, and assembly-dependent uncertainty can degrade conventional estimates. This study examines a two-stage method that combines recursive least squares (RLS) with an adaptive grey wolf optimizer (AGWO). Offline RLS tracks the base-parameter trajectory and expands its post-convergence extrema to construct a finite search space; a non-smooth friction severity index then modulates the GWO convergence schedule. The method was evaluated on a pedestal-mounted, single-degree-of-freedom hip mechanism using a 5 s calibration trajectory and a separate 7 s validation trajectory. Deterministic least squares (LS) and bound-constrained least squares (BCLS) were compared with standard PSO, RLS–PSO, RLS–GA, RLS–GWO, and RLS–AGWO. Each stochastic method used a population of 30, with 80 iterations (2400 fitness evaluations) and 30 independent seeds. On the independent trajectory, BCLS obtained an RMSE of 0.1152 Nm. Median validation RMSEs were 0.1152, 0.1152, 0.1562, and 0.1516 Nm for RLS–PSO, RLS–GA, RLS–GWO, and RLS–AGWO, respectively. Thus, the adaptive schedule improved median GWO error by 3.0%, but deterministic BCLS was both more accurate and faster for the present linear-in-parameters model. AGWO is therefore not mathematically necessary for the current convex objective; its potential advantage should be tested with genuinely nonlinear friction parameterizations. The conclusions remain limited to a single-axis pedestal experiment and do not establish performance during human-worn gait. Full article

Urban parks contribute to emotional well-being, yet the relationship among park environments, users’ perceptions, recreational activities, and emotions across seasons remains insufficiently understood. Using Jiefang Park in Wuhan, China, as a case study, this study analyzed 2233 valid questionnaires from four-season field surveys across six surveyed spaces, together with measured spatial environmental data. Partial least squares structural equation modeling (PLS-SEM) was used to examine the associations and interaction effects of landscape perception, thermal perception, recreational activities, and positive and negative emotions. Hierarchical linear modeling (HLM) was used to account for respondents nested within 24 space-season units and examine cross-level statistical indirect associations. Emotional responses varied across seasons and among the six surveyed locations. The surveyed lawn space showed the highest positive-emotion and lowest negative-emotion scores, whereas the surveyed fitness activity space had the highest negative-emotion score. The surveyed fitness activity and waterfront spaces displayed smaller seasonal fluctuations. Thermal and visual perceptions were consistently associated with emotional responses in the season-specific models. Measurement invariance was not established, so path coefficients were not formally compared. Spatial environmental characteristics showed significant indirect associations with emotions mainly through perceptual variables, whereas indirect associations through recreational activities were not significant. Vegetation color richness showed favorable indirect associations, while green view, pavement visibility, and fitness facility quantity showed adverse indirect associations at the surveyed locations. These findings suggest that seasonally responsive park design should emphasize perceptual quality, thermal adaptability, and context-sensitive spatial configuration. Full article

To address the coordinated operation of renewable generation, load, and storage in zero-carbon parks, this paper proposes a source–load–storage (SLS) coordinated multi-resource optimization method. First, a parallel forecasting model combining partial least squares regression (PLSR) and ModernTCN, denoted PLSR-Modern TCN, is developed. PLSR extracts an eight-dimensional latent representation from each lagged-load window, while ModernTCN independently captures nonlinear temporal dependencies from the original one-channel sequence. The two representations are aligned by sample index, concatenated, and mapped to the one-day-ahead forecasting horizon. The model achieves an R² of 0.987, outperforming traditional TCN, convolutional neural network (CNN), random forest, and linear regression models. Based on the forecasts, a multi-objective SLS optimization model is established by considering time-of-use electricity prices, supply–demand balance, renewable curtailment, operation cost, carbon emissions, energy storage operation, PCC voltage, and equivalent harmonic power. The entropy weight method determines the objective weights, and an improved genetic algorithm (IGA) solves the optimization model. Simulation results show that IGA achieves the lowest mean comprehensive fitness and the smallest repeated-run variation among the compared algorithms. In the illustrative scheduling result, IGA provides a modest energy-related operating-cost reduction of approximately 0.11–0.13% and a carbon-emission reduction of approximately 2.2–4.5%. Full article

Anisotropic polymers fabricated via additive manufacturing exhibit complex thermal transport profiles that are challenging to characterize using steady-state techniques. We present a transient thermal method utilizing a short rectangular current pulse excitation to determine the directional thermal diffusivity and conductivity of anisotropic materials. The measurement is conducted on finite specimens, where the low diffusivity of the polymer media results in a highly attenuated and dispersed rear-side temperature profile over an extended transient window. Conduction losses to the adjacent coolers are accounted for by solving the one-dimensional heat conduction equation on an asymmetric multi-layer sandwich structure using the implicit Crank–Nicolson method. Because thermal diffusivity and conductivity are not independent quantities ($\lambda =a\rho c$), the inverse problem is deliberately formulated to estimate the diffusivity alone: the volumetric heat capacity is predetermined and held fixed, and the conductivity follows directly as $\lambda =a\rho c$. This removes the ill-conditioning that would otherwise arise from treating $\lambda$ and a as free, independent parameters in the fit. A two-parameter non-linear least-squares fit is applied to the rear-side temperature rise following Savitzky–Golay noise filtering to estimate the directional diffusivity and effective heat flux. The method is validated using an isotropic reference standard to rule out false system anisotropy, and is subsequently applied to additively manufactured polymer specimens to resolve print-induced directionality through sequential, axis-aligned (unidirectional) measurements along the axial and transverse printing directions. The validity of the one-dimensional reduction is confirmed quantitatively by two- and three-dimensional anisotropic simulations of the exact geometry, which bound the lateral-spreading bias below $0.01\%$ even for the highest-anisotropy specimen, and the robustness of the method to sensor thermal response, signal filtering, and effective-flux estimation is quantified. A rigorous evaluation of the expanded metrological uncertainty demonstrates the high accuracy and reliability of this low-energy excitation technique for highly dispersing media, making it a viable and highly accessible alternative for evaluating material anisotropy. Full article

Natural gas transportation and distribution networks are becoming increasingly heterogeneous due to the injection of biomethane, regasified LNG, and hydrogen-enriched natural gas, requiring distributed and continuous gas-quality monitoring. This work presents an industrial Raman-based instrument for in-line measurement of natural gas and hydrogen-enriched natural gas composition and related thermodynamic properties. The system employs a 450 nm broadband laser diode, a high-throughput custom spectrometer, and a pressure-rated gas cell integrated in an ATEX-certified enclosure. Gas composition is retrieved through calibration spectra and non-linear least-squares fitting, while higher heating value is calculated according to ISO 6976. The instrument was validated over pressures from 1.5 to 17 bara and temperatures from −20 °C to 55 °C using certified representative gas mixtures. The system achieved compliance with OIML R 140 Class A requirements, with HHV errors below ±0.5% and repeatability within 0.1%, while operating without carrier gases or sample manipulation. Long-term field operations in pressure-reduction stations confirmed stable performance over twelve months. The results demonstrate that Raman spectroscopy can provide a robust, low-maintenance solution for continuous natural-gas-quality monitoring and controlled hydrogen-blending applications. Full article