Search Results (2,866)

Several iterative integro-differential formulations for two-point, second- and third-order, nonlinear, boundary-value problems of ordinary differential equations based on Green’s functions and the method of variation of parameters are presented. It is shown that the generalized or dual Lagrange multiplier method (GVIM) previously developed for the iterative solution of nonlinear, boundary-value problems of ordinary differential equations that makes use of modified functionals and two Lagrange multipliers, is nothing but an iterative Green’s function formulation that does not require Lagrange multipliers at all. It is also shown that the two Lagrange multipliers of GVIM are associated with the left and right Green’s functions. The convergence of iterative methods based on both the Green function and the method of variation of parameters is proven for nonlinear functions that depend on the dependent variable and is illustrated by means of two examples. Several new iterative integro-differential formulations based on Green’s functions that use a multiplicative function for convergence acceleration are also presented. Full article

Obesity is a major global health concern, being associated with insulin resistance and multiple metabolic disorders. Gremlin 1 (GREM1), a bone morphogenetic protein (BMP) antagonist, is increasingly recognized as a key regulator of adipose tissue dysfunction and impaired thermogenesis in obesity. Orlistat, a lipase inhibitor that reduces dietary fat absorption, is one of the most commonly used pharmacological agents for obesity management. White tea has demonstrated antioxidant and anti-obesity properties in experimental models. The aim of this study was to evaluate the effects of white tea on metabolic parameters (HOMA-IR, BMP4, Gremlin1) and GREM1 expression in rats made obese by a high-fat diet (HFD). A total of 40 male Sprague-Dawley rats were randomized into five groups: a standard diet group (STD); a high-fat diet group (HFD); an HFD + orlistat group (ORL); an HFD + 50 mg/kg white tea group (WT50); and an HFD + 150 mg/kg white tea group (WT150). Obesity was induced by feeding the rats a 45% high-fat diet for 3 weeks. Serum insulin, glucose and HOMA-IR levels were measured. Levels of GREM1 and BMP4 in serum and retroperitoneal adipose tissue were assessed. White tea supplementation significantly reduced weight gain and HOMA-IR compared to the HFD group. GREM1 mRNA expression in visceral adipose tissue decreased markedly in the WT50 and WT150 groups (p = 0.002 and p = 0.017, respectively). Serum GREM1 levels were significantly lower in the white tea-treated groups than in the HFD group (p = 0.011). Tissue BMP4 levels were only significantly reduced in the WT50 group (p = 0.005), indicating a non-linear dose–response pattern. There was a negative correlation between serum BMP4 levels and weight gain (rho = –0.440, p = 0.015). White tea was associated with improvements in metabolic parameters in an HFD-induced obesity model. These observations suggest a potential association between white tea bioactives and adipose tissue-related molecular pathways implicated in obesity. Given the short intervention duration and the exploratory design of this animal study, the findings should be interpreted with caution. Full article

This paper introduces a new framework for high-dimensional covariance matrix estimation, the Blockwise Exponential Covariance Model (BECM), which extends the traditional block-partitioned representation to the log-covariance domain. By exploiting the block-preserving properties of the matrix logarithm and exponential transformations, the proposed model guarantees strict positive definiteness while substantially reducing the number of parameters to be estimated through a blockwise log-covariance parameterization, without imposing any rank constraint. Within each block, intra- and inter-group dependencies are parameterized through interpretable coefficients and kernel-based similarity measures of factor loadings, enabling a data-driven representation of nonlinear groupwise associations. Using monthly stock return data from the U.S. stock market, we conduct extensive rolling-window tests to evaluate the empirical performance of the BECM in minimum-variance portfolio construction. The results reveal three main findings. First, the BECM consistently outperforms the Canonical Block Representation Model (CBRM) and the native 1/N benchmark in terms of out-of-sample Sharpe ratios and risk-adjusted returns. Second, adaptive determination of the number of clusters through cross-validation effectively balances structural flexibility and estimation stability. Third, the model maintains numerical robustness under fine-grained partitions, avoiding the loss of positive definiteness common in high-dimensional covariance estimators. Overall, the BECM offers a theoretically grounded and empirically effective approach to modeling complex covariance structures in high-dimensional financial applications. Full article

Multi-object tracking (MOT) has attracted increasing attention and achieved remarkable progress. However, accurately tracking objects with homogeneous appearance, heterogeneous motion, and heavy occlusion remains a challenge because of two problems: (1) missing association due to recognizing an object as background and (2) false prediction caused by the predominant utilization of linear motion models and the insufficient discriminability of object appearance representations. To address these challenges, this paper proposes a lightweight, generic, and appearance-independent MOT method with an unscented Kalman filter (UKF) on a Lie group called LUKF-Track. The method utilizes detection boxes across the entire range of scores in data association and matches objects across frames by employing a motion model, where the propagation and prediction of object states are formulated using a UKF on the Lie group. LUKF-Track achieves state-of-the-art results on three public benchmarks, MOT17, MOT20, and DanceTrack, which are characterized by highly nonlinear object motion and severe occlusions. Full article

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning. Full article

Background: Renal and cardiac dysfunction are major determinants of adverse outcomes following transcatheter aortic valve replacement (TAVR). The ratio of blood urea nitrogen to left ventricular ejection fraction (BUN/EF) integrates renal and cardiac status into a single physiological index. This study aimed to evaluate the prognostic value of both baseline and temporal (48–72 h) BUN/EF ratios for predicting mortality after TAVR. Methods: A total of 429 patients (mean age 76 ± 8 years; 51% female) who underwent TAVR for severe aortic stenosis between 2017 and 2025 were retrospectively analyzed. The primary endpoint was long-term all-cause mortality; in-hospital mortality was secondary. Receiver operating characteristic (ROC) curves, Cox regression, and reclassification metrics (NRI, IDI) assessed prognostic performance. Restricted cubic spline (RCS) analysis explored non-linear associations. Results: During a median follow-up of 733 days, overall and in-hospital mortality rates were 37.8% and 7.9%, respectively. Both baseline and 48–72 h BUN/EF ratios were independently associated with mortality (HR = 3.46 and 3.79 per 1 SD increase; both p < 0.001). The temporal ratio showed superior discrimination for in-hospital mortality (AUC = 0.826 vs. 0.743, p = 0.007). Adding baseline BUN/EF to EuroSCORE II significantly improved model performance (AUC 0.712 vs. 0.668, p = 0.031; NRI = 0.33; IDI = 0.067). RCS analysis revealed a linear relationship for baseline and a steep, non-linear association for temporal ratios with mortality risk. Conclusions: The 48–72 h BUN/EF ratio is a robust dynamic biomarker that predicts early mortality after TAVR, while baseline BUN/EF identifies patients at long-term risk. Integrating this simple bedside index into risk algorithms may refine postoperative monitoring and improve outcome prediction in TAVR populations. Full article

The purpose of this research was to investigate whether music empirically associated with therapeutic effects contains intrinsic informational structures that differentiate it from other sound sequences. Drawing on ontology, phenomenology, nonlinear dynamics, and complex systems theory, we hypothesize that therapeutic relevance may be linked to persistent structural patterns embedded in musical signals rather than to stylistic or genre-related attributes. This paper introduces the Discriminating Music Sequences (DiMuSes) method, an unsupervised, structure-oriented analytical framework designed to detect such patterns. The method applies 24 scalar evaluators derived from statistics, fractal geometry, nonlinear physics, and complex systems, transforming sound sequences into multidimensional vectors that characterize their global temporal organization. Principal Component Analysis (PCA) reduces this feature space to three dominant components (PC1–PC3), enabling visualization and comparison in a reduced informational space. Unsupervised k-Means clustering is subsequently applied in the PCA space to identify groups of structurally similar sound sequences, with cluster quality evaluated using Silhouette and Davies–Bouldin indices. Beyond clustering, DiMuSe implements ranking procedures based on relative positions in the PCA space, including distance to cluster centroids, inter-item proximity, and stability across clustering configurations, allowing melodies to be ordered according to their structural proximity to the therapeutic cluster. The method was first validated using synthetically generated nonlinear signals with known properties, confirming its capacity to discriminate structured time series. It was then applied to a dataset of 39 music and sound sequences spanning therapeutic, classical, folk, religious, vocal, natural, and noise categories. The results show that therapeutic music consistently forms a compact and well-separated cluster and ranks highly in structural proximity measures, suggesting shared informational characteristics. Notably, pink noise and ocean sounds also cluster near therapeutic music, aligning with independent evidence of their regulatory and relaxation effects. DiMuSe-derived rankings were consistent with two independent studies that identified the same musical pieces as highly therapeutic.The present research remains at a theoretical stage. Our method has not yet been tested in clinical or experimental therapeutic settings and does not account for individual preference, cultural background, or personal music history, all of which strongly influence therapeutic outcomes. Consequently, DiMuSe does not claim to predict individual efficacy but rather to identify structural potential at the signal level. Future work will focus on clinical validation, integration of biometric feedback, and the development of personalized extensions that combine intrinsic informational structure with listener-specific response data. Full article

Background: Mechanical loading is a fundamental regulator of bone remodelling; however, the mechanotransduction mechanisms governing alveolar bone adaptation under tensile-dominant orthodontic loading remain insufficiently defined. In particular, the molecular pathways associated with tension-driven cortical modelling in the periodontal ligament (PDL)–bone complex have not been systematically interpreted in the context of advanced biomechanical simulations. Methods: A nonlinear finite element model of the alveolar bone–PDL–tooth complex was developed using patient-specific CBCT data. Three loading configurations were analysed: (i) conventional orthodontic loading, (ii) loading combined with corticotomy alone, and (iii) a translation-dominant configuration generated by the Bone Protection System (BPS). Pressure distribution, displacement vectors, and stress polarity within the PDL and cortical plate were quantified across different bone density conditions. The mechanical outputs were subsequently interpreted in relation to established mechanotransductive molecular pathways involved in osteogenesis and angiogenesis. Results: Conventional loading generated compression-dominant stress fields within the marginal PDL, frequently exceeding physiological thresholds and producing moment-driven root displacement. Corticotomy alone reduced local stiffness but did not substantially alter stress polarity. The BPS configuration redirected loads toward a tensile-favourable mechanical environment characterised by reduced peak compressive pressures and parallel (translation-dominant) displacement vectors. The predicted tensile stress distribution is compatible with activation profiles of key mechanosensitive pathways, including integrin–FAK signalling, Wnt/β-catenin–mediated osteogenic differentiation and HIF-1α/VEGF-driven angiogenic coupling, suggesting a microenvironment that may be more conducive to cortical apposition than to resorption. Conclusions: This study presents a computational–molecular framework linking finite element–derived tensile stress patterns with osteogenic and angiogenic signalling pathways relevant to alveolar bone remodelling. The findings suggestthat controlled redirection of orthodontic loading toward tensile domains may shift the mechanical environment of the PDL–bone complex toward conditions associated with osteogenic than resorptive responses providing a mechanistic basis for tension-induced cortical modelling. This mechanobiological paradigm advances the understanding of load-guided alveolar bone adaptation at both the tissue and molecular levels. Full article

This paper investigates the optimal control problem of orbital rendezvous for spacecraft in near-circular orbits with a low-thrust propulsion system. Two optimality criteria are considered: time-optimal and motor-time-optimal control. A linearized mathematical model of relative motion between the active and passive spacecraft is employed, which is formulated in dimensionless variables that characterize secular, periodic, and lateral motion components of the relative motion. By applying Pontryagin’s Maximum Principle, the equations governing the optimal relative motion of the spacecraft are derived. To address the discontinuities associated with the bang–bang switching function inherent in the motor-time-optimal problem, and the lack of a suitable initial guess, a homotopy method is adopted, in which the solution to the rendezvous time-optimal problem is used as an initial guess and is gradually deformed into the motor-time-optimal control. Considering the errors introduced by the linearization of the relative motion model, the obtained control law is validated via numerical simulations based on the original nonlinear dynamics of the system. Simulation results demonstrate that the proposed trajectory optimization methodology achieves high success rates and rapid convergence, providing valuable theoretical support and practical guidance for mission scenarios with similar trajectory design requirements. Full article

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article

Global environmental changes significantly alter ecosystem services (ESs), particularly in fragile regions like the Tibetan Plateau. While methodological advances have improved spatial assessment capabilities, understanding of how multiple drivers interact to shape ecosystem service heterogeneity remains limited to regional scales, especially across complex alpine landscapes. This study aims to clarify whether multi-factor interactions produce nonlinear enhancements in ES explanatory power and how these driver–response relationships vary across heterogeneous terrains. We quantified spatiotemporal patterns of four key ecosystem services—water yield (WY), soil conservation (SC), carbon sequestration (CS), and habitat quality (HQ)—across the southeastern Tibetan Plateau from 2000 to 2020 using multi-source remote sensing data and spatial econometric modeling. Our analysis reveals that SC increased by 0.43 t·hm⁻²·yr⁻¹, CS rose by 1.67 g·m⁻²·yr⁻¹, and HQ improved by 0.09 over this period, while WY decreased by 3.70 mm·yr⁻¹. ES variations are predominantly shaped by potent synergies, where interactive explanatory power consistently surpasses individual drivers. Hydrothermal coupling (precipitation ∩ potential evapotranspiration) reached 0.52 for WY and SC, while climate–vegetation synergy (precipitation ∩ normalized difference vegetation index) achieved 0.76 for CS. Such climate–restoration synergies now fundamentally shape the region’s ESs. Geographically weighted regression (GWR) further revealed distinct spatial dependencies, with southeastern regions experiencing strong negative effects of land use type and elevation on WY, while northwestern areas showed a positive elevation associated with WY but negative effects on SC and HQ. These findings highlight the critical importance of accounting for spatial non-stationarity in driver–ecosystem service relationships when designing conservation strategies for vulnerable alpine ecosystems. Full article

Permanent magnet synchronous motors (PMSMs) are widely preferred in modern applications due to their high efficiency, high torque-to-inertia ratio, high power factor, and rapid dynamic response. Achieving optimal PMSM performance requires precise control, which depends on accurate estimation of motor speed and rotor position. This information is traditionally obtained through sensors such as encoders; however, these devices increase system cost and introduce size and integration constraints, limiting their use in many PMSM-based applications. To overcome these limitations, sensorless control strategies have gained significant attention. Since PMSMs inherently exhibit nonlinear dynamic behavior, accurate modeling of these nonlinearities is essential for reliable sensorless operation. In this study, a Runge–Kutta Extended Kalman Filter (RKEKF) approach is developed and implemented to enhance estimation accuracy for both rotor position and speed. The developed method utilizes the applied stator voltages and measured phase currents to estimate the motor states. Experimental validation was conducted on the dSPACE DS1104 platform under various operating conditions, including forward and reverse rotation, acceleration, low- and high-speed operation, and loaded operation. Furthermore, the performance of the developed RKEKF under load was compared with the conventional Extended Kalman Filter (EKF), demonstrating its improved estimation capability. The real-time feasibility of the developed RKEKF was experimentally verified through execution-time measurements on the dSPACE DS1104 platform, where the conventional EKF and the RKEKF required 47 µs and 55 µs, respectively, confirming that the proposed approach remains suitable for real-time PMSM control while accommodating the additional computational effort associated with Runge–Kutta integration. Full article

Background: Cenobamate (CNB) is an anti-seizure medication (ASM) approved for the treatment of drug-resistant focal epilepsy in adults. Notwithstanding significant proof of efficacy, real-world pharmacokinetics (PK) data are lacking, particularly regarding sex-based variations and the effect of concomitant ASMs. This exploratory study aimed to investigate the PK profile of CNB in responder adults with drug-resistant focal epilepsy and assess potential relationship with concomitant ASMs and clinical variables. Methods: We enrolled 17 patients receiving add-on CNB. The concentration-to-dose ratio (C/D), incremental slope (ΔC/ΔD), and dose-to-concentration AUC were calculated. Enrolled individuals were stratified into three exposure clusters (low, medium, and high). Univariate ANOVA was used to explore associations between PK parameters, clinical variables and concomitant ASMs. Results: Sex appeared to be associated with AUC cluster classification (p = 0.026), showing females predominating in the high-exposure group. A nonlinear dose-concentration relationship emerged from the ΔC/ΔD analysis, showing steeper slopes at low doses (12.5–50 mg), great variability at higher doses (100–200 mg), and a negative slope in some individuals. Higher CNB concentrations were observed in patients co-treated with lacosamide, while concomitant topiramate was associated with lower exposure. Carbamazepine and valproate showed non-significant trends consistent with their known enzyme-inducing and inhibiting properties. Conclusions: PK of CNB appears highly variable and seems to be influenced by sex and concomitant ASMs. These findings highlight the importance of therapeutic drug monitoring and individualized titration strategies to optimize efficacy and safety in clinical practice. These results should be regarded as exploratory and hypothesis-generating due to the small and monocentric sample size and need to be confirmed in larger, multicenter cohorts. Full article

This study investigates how gender-inclusive leadership and trade integration shape environmental sustainability in China, addressing a key gap in the literature where most prior work has focused on aggregate governance, finance, or growth without considering how gender representation in leadership and trade openness jointly relate to environmental outcomes. China provides a particularly relevant setting because it is both a leading global emitter and one of the world’s most trade-integrated and rapidly growing economies, so changes in leadership structures, financial deepening, and external openness can have sizable environmental consequences. Given the nonlinear and non-normal nature of the variables, the analysis relies on nonlinear econometric tools, specifically quantile-on-quantile ARDL and Quantile Granger Causality, applied to quarterly data from 1998Q1 to 2024Q4. The results show that the impact of gender-inclusive leadership on environmental sustainability is state-dependent, with improvements at lower environmental pressure but a predominantly negative long-run association at mid to upper quantiles, while financial development tends to support sustainability, and economic growth and trade openness are generally linked to lower sustainability across much of the quantile range. By narrowing the research gap on gender-inclusive leadership and explicitly motivating China as a critical case, this study offers context-specific evidence that can guide policies aimed at fostering inclusive leadership and greener finance while carefully managing the environmental consequences of rapid growth and deeper trade integration. Full article

Stochastically forced nonlinear wave systems are commonly associated with complex dynamical behavior, although little is known about the general interaction of nonlinear dispersion, irrational forcing frequencies, and multiplicative noise. To fill this gap, we consider a generalized stochastic SIdV equation and examine the effects of deterministic and stochastic influences on the long-term behavior of the equation. The PDE was modeled using a stochastic traveling-wave transformation that simplifies it into a planar system, which was studied using Darboux-seeded constructions, Poincaré maps, bifurcation patterns, Lyapunov exponents, recurrence plots, and sensitivity diagnostics. We discovered that natural, implicit, and unique seeds produce highly diverse transformed wave fields exhibiting both irrational and golden-ratio forcing, controlling the transition from quasi-periodicity to chaos. Stochastic perturbation is demonstrated to suppress as well as to amplify chaotic states, based on noise levels, altering attractor geometry, predictability, and multistability. Meanwhile, OGY control is demonstrated to be able to stabilize chosen unstable periodic orbits of the double-well regime. A stochastic bifurcation analysis was performed with respect to noise strength $\sigma$, revealing that the attractor structure of the system remains robust under stochastic excitation, with noise inducing only bounded fluctuations rather than qualitative dynamical transitions within the investigated parameter regime. These findings demonstrate that the emergence, deformation, and controllability of complex oscillatory patterns of stochastic nonlinear wave models are jointly controlled by nonlinear structure, external forcing, and noise. Full article