Search Results (138)

Standard scalar indicators of systemic dependence, such as the mean pairwise correlation, the absorption ratio, and the dispersion of the eigenvalue spectrum, summarise the magnitude of co-movement but are by construction blind to its spatial arrangement. We propose treating the time-varying cross-asset correlation matrix as a greyscale image and quantifying its spatial organisation with the multiscale gliding-box lacunarity. Using a controlled block-factor generative model in which the average correlation is held fixed while the sectoral block strength is varied, we show that lacunarity recovers the planted block structure almost perfectly (partial Spearman $\rho =0.92$ at fixed mean correlation), a recovery that persists under fat-tailed innovations, time-varying loadings, and overlapping communities, whereas the mean correlation and the absorption ratio remain flat. Applied to twenty years of daily data for sixty-two sector-spanning United States equities, lacunarity tracks a model-free index of block heterogeneity after controlling for correlation magnitude (partial Spearman $\rho =0.46$, ninety-five percent bootstrap interval $[0.33,0.58]$) and improves the out-of-sample prediction of block structure beyond the magnitude baselines. We are explicit about two boundaries. A simple permutation-invariant dispersion statistic, the standard deviation of the off-diagonal correlations, tracks block heterogeneity even more strongly than lacunarity, so lacunarity is not the most efficient estimator of that quantity; its distinct role, confirmed by a scrambling test, is that it responds to the spatial arrangement of dependence, which dispersion measures are invariant to, and it remains informative under a canonical clustering or spectral ordering. The measure is descriptive rather than predictive of future drawdowns. The results position correlation-image lacunarity as an interpretable, computationally light, and arrangement-sensitive complement to the existing magnitude and dispersion descriptors of systemic dependence. Full article

Aiming at the problems of the severe active–reactive power coupling, insufficient adaptive inertia–damping regulation, and degraded dynamic performance of virtual synchronous generators (VSGs) under the operating conditions of a weak grid, high resistance-to-reactance ratio, and large power angle, this paper proposes a cooperative control strategy that combines reactive power feedforward decoupling with adaptive damping–inertia regulation. First, a small-signal power model of the VSG is established, and a dynamic relative gain array is employed to quantitatively analyze the effects of the resistance-to-reactance ratio and power angle on power coupling characteristics, revealing that large power angles and high resistance-to-reactance ratios significantly aggravate active–reactive power coupling. Based on this analysis, a reactive-power-oriented feedforward decoupling strategy is designed to suppress the cross-coupling between reactive power and power angle while preserving the intrinsic inertia support characteristics of the active power loop. Eigenvalue migration analysis further demonstrates that the proposed reactive-power-oriented decoupling provides higher damping ratios and larger stability margins than conventional full active–reactive power decoupling. Furthermore, a deep deterministic policy gradient-based adaptive damping–inertia control method is developed by incorporating frequency deviation, power fluctuation, voltage deviation, and coupling degree into the state space, enabling the online coordinated optimization of virtual inertia and damping coefficients. The hardware-in-the-loop experimental results verify that the proposed strategy effectively suppresses active–reactive power coupling, reduces power overshoot and oscillation, enhances frequency support capability and dynamic response speed, and maintains superior stability under weak grid conditions. Sensitivity analysis under grid impedance estimation errors further confirms its strong robustness against parameter uncertainty, while tests under composite disturbance scenarios demonstrate excellent transient performance. The proposed strategy provides an effective solution for improving the grid-connected operation performance and adaptability of VSGs in low-inertia power systems. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of $0.168\pm 0.017$ PLI units and root mean squared error of $0.219\pm 0.045$ (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; $t\left(59\right)=22.61$, $p<0.001$). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s $d=0.85$, $p=0.001$; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article

The increasing penetration of renewable energy exposes doubly fed induction generator (DFIG)-based wind power systems to weak-grid conditions, making them susceptible to low-frequency and subsynchronous oscillations. Although grid-forming (GFM) control enhances weak-grid adaptability, the resulting high-order small-signal model complicates stability analysis and controller design. This paper establishes a 15th-order state-space model for a GFM-DFIG system. Eigenvalue analysis is performed to identify the dominant oscillation modes and to reveal their sensitivity to controller parameters and grid strength. To reduce computational burden, a model order reduction method combining singular perturbation theory and participation factor analysis is proposed, yielding an eighth-order model that preserves dominant oscillatory characteristics. An additional damping control strategy is then designed using the reduced model. Simulations validate the reduced model’s accuracy and demonstrate the damping control’s effectiveness in mitigating oscillations. This paper provides an effective framework for stability analysis, reduced-order modeling, and damping control design for GFM-DFIG systems. Full article

This paper presents detailed small-signal modeling and modal analysis of a 1.5 MW grid-connected doubly fed induction generator (DFIG) wind turbine. A full nonlinear model capturing stator, rotor, and grid-side converter dynamics, DC-link voltage behavior, and the wind-turbine electromechanical subsystem is developed in the synchronously rotating d-q frame and linearized around a realistic steady-state operating point. The resulting state-space representation is utilized to investigate the intrinsic dynamic characteristics of the DFIG through eigenvalue analysis, modal classification, and participation factor evaluation. The results show that the open-loop DFIG contains a weakly damped electrical mode, a slowly growing unstable mode, and a near-integrator mode linked to the DC-link voltage, all of which strongly influence system behavior under disturbances. Parameter-sensitivity studies reveal how rotor speed, stator voltage, and rotor resistance affect the dominant modes, highlighting significant deterioration under low-voltage and low-speed operating conditions. Time-domain small-signal responses to temporary voltage sags further expose the vulnerability of DC-link voltage and power outputs when no coordinated control is applied. Overall, the study establishes a rigorous dynamic baseline for DFIG systems and provides the foundational insight needed for a follow-up paper focused on advanced damping and robustness-enhancing controllers. Full article

This study examines the temporal evolution of low-carbon energy production in the European Union (EU27) using annual data for 1990–2022, focusing on the dynamic interaction between nuclear, renewable, and biofuel production at the EU aggregate level. After evaluating stochastic properties via Augmented Dickey–Fuller (ADF) tests and assessing long-run cointegration through the Johansen framework, short-run interactions are modeled using a Vector Autoregression (VAR) of order one. Dynamic responses and innovation variances are analyzed using impulse response functions (IRFs) and forecast error variance decomposition (FEVD). The Augmented Dickey–Fuller (ADF) results suggest both series are I(1). The Johansen test fails to reject the null of no cointegration, implying that there is no stable long-run equilibrium relationship between the two series over 1990–2022. VAR-based IRFs show small, short-lived cross-responses that dissipate within a few years. FEVD results indicate that variance shares are horizon-dependent and sensitive to the Cholesky ordering. Granger causality tests provide limited evidence of short-run directional predictability. A Zivot–Andrews test does not reject the unit-root-with-break null. These findings suggest that nuclear and renewables follow largely independent dynamics in the EU27 aggregate. A key limitation is that EU27 aggregation masks cross-country heterogeneity (e.g., Germany vs. France) and excludes policy variables, prices, and demand-side drivers. The estimated VAR(1) satisfies the stability condition: all eigenvalues of the companion matrix lie inside the unit circle (modulus < 1), confirming that the system is dynamically stable. Full article

Photovoltaic desert control (PVDC), an innovative model integrating clean energy development and desertification control, faces complex coordination challenges among local governments, local communities, and photovoltaic enterprises. This study constructs a tripartite evolutionary game model to identify the conditions that drive PVDC toward coordinated governance. The model defines a three-dimensional strategy space: government regulatory intensity (Strong vs. Lax), community willingness to cooperate (Active Cooperation vs. Passive Resistance), and enterprise ecological integration (Active Ecological Integration vs. Passive Land Occupation). Replicator dynamic equations are derived to characterize nonlinear interactions, and the stability conditions of eight pure-strategy equilibrium points are identified through Jacobian matrix eigenvalue analysis. Numerical simulations are conducted using a baseline parameter set that satisfies the Evolutionary Stable Strategy conditions for the ideal equilibrium E8, namely Strong Regulation, Active Cooperation, and Active Ecological Integration. The results show that the system can converge to E8 when higher-level rewards cover government regulation, subsidy, and community-support costs; when community cooperation benefits exceed livelihood opportunity costs and compensation incentives from resistance; and when enterprises’ effective ecological integration costs are lower than the combined benefits of subsidies, avoided fines, and long-term returns. Sensitivity analysis further indicates that government subsidies, fines, community support, cooperation income, and enterprise long-term benefits are key drivers of system evolution, while excessive regulation costs, high opportunity costs, and high ecological integration costs may hinder coordination. Qualitative evidence from four PVDC-related cases in Xinjiang provides practical illustrations broadly consistent with the model mechanisms. This study offers a dynamic analytical framework for designing incentive-compatible governance mechanisms in PVDC and similar multi-stakeholder ecological restoration projects. Full article

Small-disturbance damping characteristics have become a critical concern in renewable-dominated power systems under low short circuit ratio (SCR) conditions. In heterogeneous systems composed of grid-following photovoltaic (GFL-PV) and grid-forming energy storage system (GFM-ESS) units, strong dynamic coupling may weaken the damping of critical oscillation modes, thereby complicating stability analysis and coordinated parameter tuning. This paper proposes a damping enhancement strategy for a low-SCR GFL-PV/GFM-ESS system. The main innovation is an integrated damping-oriented framework that links detailed small-disturbance modeling, dominant-mode identification, participation-factor analysis, parameter-sensitivity evaluation, and coordinated optimization. First, a dynamic model including GFL-PV, GFM-ESS, and their coupling is established, and the corresponding linearized model is verified. Then, eigenvalue, modal, participation-factor, and sensitivity analyses are performed to identify weakly damped modes, key state variables, and sensitive parameters. Furthermore, a Joint Opposite Selection-enhanced particle swarm optimization (JOS-PSO) strategy is proposed to tune multiple coupled parameters. Simulation results under different operating conditions show that the proposed method improves damping characteristics, small-disturbance stability, and dynamic performance. Full article

This paper investigates the dynamic behavior of non-Darcy seepage systems in post-failure rock. A one-dimensional non-Darcy seepage evolution equation is established, and a 6-dimensional nonlinear ordinary differential system is derived via the spectral truncation method. Eigenvalue analysis is adopted to determine the instability and bifurcation conditions, with the bifurcation diagram plotted. The fourth-order Runge–Kutta method is used to obtain phase trajectory patterns under different initial values. The results confirm the existence of transcritical bifurcations and fold bifurcations. The dynamic response of the system is discontinuous with control parameters, and phase trajectory symmetry breaking occurs with the increase in nonlinear terms. The reduced-order model shows diverse phase trajectories including equilibrium, periodic, chaotic attractors and unstable states. The system is sensitive to initial values, which significantly affect phase trajectory behaviors. The system may lose stability and trigger water inrush hazards under critical conditions. The bifurcation diagram and critical parameters obtained can provide a theoretical basis for the early warning, risk assessment and prevention of coal mine water inrush hazards. Full article

EigenPlaces is a state-of-the-art visual place recognition (VPR) method that constructs training classes via SVD-based focal points, where a fixed focal distance D controls how far the focal point is placed from each cell center. However, this globally fixed D cannot adapt to the diverse scene geometries encountered across different urban environments. In this work, we systematically analyze the sensitivity of D across multiple benchmark datasets and reveal that the optimal D value is highly dataset-dependent, with performance gaps of up to 4.4 percentage points between the best and worst D choices. We then propose a depth-aware adaptive D strategy that leverages monocular depth estimation to compute per-cell focal distances, combined with quantile mapping to ensure sufficient variance in the assigned D values. By establishing a principled connection between visual sensor data and geometric training supervision, our method enhances the environmental perception reliability of intelligent sensing platforms. Experiments on three benchmarks (Pitts30k, AmsterTime, SF-XL) validate the dataset-dependent nature of D and confirm that our depth-aware approach achieves the best same-distribution performance among all tested configurations. We further conduct a multi-strategy ablation comparing depth raw, depth quantile, and SVD eigenvalue ratio approaches, providing practical guidance for adaptive focal distance selection in VPR training pipelines. Full article

Mobile EEG enables investigating brain activity during real-world behaviour, but remains susceptible to motion artefacts, limiting signal interpretability and the use of advanced analytical techniques. Methods developed for removing motion-related artefacts induced by periodic activity like cycling, walking or juggling showed degraded performance with increasing movement variability and speed. To fill this gap, we developed a method based on generalized eigenvalue decomposition (GED) to identify and suppress highly variable, non-periodic—especially transient—artefacts due to very rapid, free full body movements of different types, as they occur during sports practice. By leveraging the contrast between covariance matrices of artefactual and resting-state EEG segments, this approach isolates motion-related components for removal during multichannel EEG signal reconstruction. The method was validated on two ecological datasets featuring stereotyped head and body movements and dynamic table tennis. Comparison with state-of-the-art technique showed superior performance of our method in terms of signal-to-error ratio (SER), artefact-to-residue ratio (ARR), brain spectral power preservation and computation time. Sensitivity analysis was applied to demonstrate the method’s robustness to parameter changes. These findings highlight the potential of the proposed method as a robust, generalizable approach for motion artefact suppression in mobile EEG, particularly when applied in extreme recording conditions like during active sports activity. Full article

This paper proposes a machine-learning-assisted framework for modal sensitivity analysis of systems with viscoelastic damping elements, including both classical and fractional rheological models. Surrogate models are trained to approximate natural frequencies over a prescribed parameter space using two sampling strategies (Grid and Latin Hypercube) and two regression approaches: multi-layer perceptron (MLP) and Gaussian process regression (GPR). Sensitivities are obtained from the surrogates by finite differences and complemented by model-interpretability measures, namely permutation feature importance (PFI) and Shapley Additive Explanations (SHAP). The surrogate-based results are compared with analytically obtained sensitivities. Local first- and second-order sensitivities of natural frequencies are derived analytically using the direct differentiation method (DDM) for a nonlinear eigenvalue problem formulated in the Laplace domain and further transformed into dimensionless sensitivity measures. The methodology is demonstrated for a single-degree-of-freedom oscillator with classical and fractional Kelvin damper models and a two-story frame equipped with a fractional Kelvin damper. The results show very good agreement between analytical and surrogate-based sensitivities. Feature-importance rankings obtained by PFI and SHAP are consistent with the dimensionless sensitivities and capture changes in parameter influence under varying damping levels. Dispersion studies indicate only minor ranking variations. Full article

Background/Objectives: In the context of nursing research and interventions, caregiver preparedness emerges as a pivotal concept. Informal caregivers play a central role in providing older adults with the vital nursing and social support they require. The present study evaluated the psychometric performance of the Caregiver Preparedness Scale (CPS) and tested the hypothesis that CPS scores differentiate between theoretically relevant known groups, including caregiving exposure and relationship-based indicators. Methods: A cross-sectional, face-to-face survey was conducted in June 2025 among the general population of Czechia. A total of 1024 interviews were included in the analysis. The sample was randomly split for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The internal consistency of the scale was assessed using Cronbach’s α and McDonald’s ω, while inter-item associations were evaluated with Kendall’s tau-b. The known-groups validity was assessed through nonparametric group comparisons across caregiving exposure, relationship indicators within the caregiver–senior dyad, caregivers’ self-rated health, and their life satisfaction. Results: The CPS demonstrated high internal consistency (Cronbach’s α = 0.944; McDonald’s ω = 0.944), robust item–total correlations (0.730–0.863), and acceptable floor and ceiling effects. The EFA supported a dominant one-factor solution (eigenvalue = 5.749), which explained 71.9% of the variance and had strong loadings (0.750–0.894). The CFA demonstrated a good fit (RMSEA = 0.069, SRMR = 0.0155, CFI = 0.990, and TLI = 0.980) after allowing for a limited number of conceptually justified residual covariances. Known-groups analysis supported the sensitivity of the scale when the CPS scores were higher among primary (M = 25.30) and secondary (M = 22.73) caregivers in comparison to non-caregivers (M = 18.38). Moreover, statistically significant differences were observed among those who provided care during the past five years (M = 24.30) compared to those without such experience (M = 18.12). CPS scores also exhibited variation in relationship-focused indicators in the anticipated directions, and were lower among respondents reporting poorer health and lower life satisfaction. Conclusions: The study provided consistent evidence that CPS is a reliable, unidimensional measure with robust known-groups validity. The CPS can be regarded as a suitable research instrument for nursing research and for evaluating interventions aimed at supporting informal caregivers. Full article

Current diagnostic assessments of lung disease rely primarily on medical imaging and global pulmonary function tests, which either provide static structural information or collapse complex regional behavior into global indices. As a result, important information about regional mechanical heterogeneity and early pathological changes may remain inaccessible. In this work, we introduce a conceptual diagnostic framework for the lung based on mechanical system identification and evaluate its feasibility using simulation-based analysis. Rather than directly imaging internal lung structure, the lung–thorax system is treated as an identifiable viscoelastic dynamical system whose internal mechanical properties can be inferred from its response to controlled external excitation. A multi-degree-of-freedom mechanical representation of the lung was developed to capture the dominant low-frequency behavior of the chest wall and major lung regions. Sensitivity and Fisher-information analysis confirmed the structural identifiability of regional stiffness parameters (FIM eigenvalues λ₁ = 1.75 × 10⁻⁹ and λ₂ = 8.91 × 10⁻¹⁰). Inverse fitting experiments accurately recovered simulated stiffness perturbations (e.g., k₀₁ = 240 → 239.5; k₀₂ = 154 → 159.5) from noisy frequency response data, while classification experiments achieved the complete separation of simulated pathological configurations in an idealized synthetic scenario, supporting theoretical discriminability rather than clinical performance claims. These findings demonstrate the theoretical feasibility of a diagnostic paradigm in which regional lung mechanical alterations can in principle be identified through mechanical system identification rather than direct imaging, thereby suggesting a complementary approach for a non-invasive assessment of regional lung mechanics from externally measured responses. By quantifying regional stiffness and mechanical heterogeneity, this framework may also support the personalization and monitoring of pulmonary rehabilitation strategies in chronic respiratory disease. Full article