Biomechanical analysis in hip joints according to sagittal pelvic tilt in non-ambulatory flaccid neuromuscular scoliosis: a finite element study.

Stochastic Schrödinger equation (SSE) methods provide a powerful approach for simulating open quantum system dynamics by unraveling the influence functional into an ensemble average of pure-state trajectories. However, long-time simulations using SSE methods are hindered by the inherent growth of ensemble variance. An alternative strategy is to extract essential memory kernels or transfer tensors from short-time dynamics and extrapolate to arbitrarily long times, as in the transfer tensor method (TTM). Conventional TTM implementations, however, require dynamical maps constructed from an informationally complete set of N2 initial states─where N is the system dimension─limiting their applicability to small-scale problems. Here, we show that the SSE naturally provides an overcomplete set of Kraus operators that captures the essential information needed to construct dynamical maps for TTM. This connection enables the extension of TTM to high-dimensional systems using results obtained from a single converged SSE simulation with a sufficient number of stochastic realizations. We validate our approach with two representative SSE methods applied to a symmetric spin-boson model and a 24-site Fenna-Matthews-Olson (FMO) complex. The results demonstrate that SSE-TTM offers an accurate and computationally efficient strategy for simulating long-time quantum dynamics in relatively large-scale systems.

This study investigates the role of symmetric probabilistic models in predicting financial distress in the automotive industry, with a focus on companies operating in the Slovak Republic. Financial distress prediction represents a binary classification problem characterized by an inherent symmetry between healthy and distressed firms. To capture this structure, two widely used symmetric models—logit and probit—are applied and systematically compared. The modeling framework incorporates LASSO regression for variable selection, enabling dimensionality reduction while preserving the most informative financial indicators. The empirical analysis is conducted on a dataset of 351 manufacturing enterprises. The results indicate that both models achieve comparable predictive performance, with the logit model reaching an accuracy of 78.9% and the probit model 77.8%. The area under the ROC curve further confirms the strong discriminatory power of both approaches. The findings highlight that the symmetric nature of the applied link functions contributes to model stability, interpretability, and balanced classification behavior. This study extends existing research by explicitly linking symmetry concepts with financial distress prediction in a sector-specific context. The proposed approach provides a transparent and practically applicable framework for early risk identification in industrial enterprises.

Emergent dynamical quantum phase transition in a Z3 symmetric chiral clock model

The cosmological tensions present in the Λ cold dark matter model that have emerged and strengthened over recent years motivate model independent approaches to analysing data. Cosmography is useful for interpreting data in cosmology without imposing assumptions about the field equations of gravity or the matter content in the Universe. Some cosmography methods, denoted covariant cosmography, go even further and stay agnostic to the underlying spacetime metric. Due to their high level of generality, covariant cosmography methods can incorporate the anisotropies and inhomogeneities in the observer's vicinity, and may in turn inform us about the associated curvature of the relevant structures in our cosmic neighbourhood. Thus, covariant cosmography is a powerful, model-independent tool for analysing cosmological data while also enabling the mapping of our local cosmic neighbourhood. In order to explore the covariant cosmography framework to its fullest, it must be tested in tractable models and simulations. In this paper we derive the cosmography for luminosity distance to fourth order in redshift and investigate it in the special case of axially symmetric Szekeres models. We compare the numerical results for the distance-redshift relations of synthetic observers placed within the Szekeres structures with the predictions from the cosmography, and comment on the found level of approximation of the cosmography in relation to other results in the literature.

For single-point constrained and nonproportionally damped substructures, a data-driven method is proposed to reconstruct an underdetermined second-order model based on frequency response data at the statically determinate interface. By structured rational fitting of the interface acceleration impedance and applying a coordinate transformation to its state-space realization, the method yields a symmetric model that facilitates subsequent dynamic analysis. The main theoretical contribution is the inverse construction of a complex modal matrix that admits both the fitted interface impedance and the structural condition for model symmetry, with its general expression derived, and thereby enables an algorithm to seek mass positive definiteness. Numerical case studies on a lumped-parameter system and a solar array finite element model are performed to verify the method. The results demonstrate that the reconstructed models achieve high accuracy, which can serve as a reduced-order surrogate model for modal synthesis of spacecraft assembly.

Characterizations of q-symmetric continuous distributions based on properties of order statistics of entropy-types

Destination image formation in the digital era operates through asymmetric, emotionally co-created mechanisms that traditional static and symmetric assessment models cannot capture. This study introduces a computational Kano-IPA framework that integrates sentiment-emotion analysis of Reddit-sourced user-generated content (UGC) with the Kano model and modified importance-performance analysis to assess asymmetrically valued destination attributes at scale. The framework pursues three objectives: first, to operationalize and validate a computational protocol for deriving Kano categories from unsolicited UGC at scale; second, to validate this protocol against independent expert coding; and third, to apply the framework comparatively to English-language backpacker discourse on China and India. Results demonstrate that China’s; image concentrates Attractive Kano attributes in the Leverage quadrant, reflecting a low-risk, high-reward satisfaction architecture, while Must-be attributes in the Monitor quadrant for India signal asymmetric dissatisfaction risk rooted in unmet functional expectations. Empirical validation confirms robust agreement between computational and human classifications (N = 1149; Cohen’s; κ = 0.839; accuracy = 90.1%). These findings carry direct policy implications for global destination marketers: uniform attribute prioritization strategies generate misaligned campaigns, whereas market-specific asymmetric profiling derived from platform-mediated UGC enables culturally calibrated positioning and resource allocation across heterogeneous international consumer segments.

ABSTRACT This paper examines whether crude oil prices and oil price volatility have asymmetric effects on Korea’s bilateral exports and imports with its ten major trading partners. Using monthly data from January 2000 to September 2024, we estimate both linear and nonlinear autoregressive distributed lag (ARDL) models to distinguish between symmetric and direction-dependent adjustments. The linear results show that income variables primarily drive long-term trade dynamics, that oil prices have a stronger impact on imports than on exports, and that oil price volatility has limited long-term effects under symmetry. Conversely, the nonlinear model uncovers partner-specific asymmetric responses, with short-term asymmetries being more common than long-term ones. Notably, oil price volatility, which is mostly insignificant in the symmetric model, displays direction-dependent effects in certain bilateral relationships. Overall, accounting for asymmetries enhances the understanding of energy shock transmission across trade segments and offers a more detailed view of energy – trade connections in an energy-importing nation.

Achieving sustainable development requires decoupling economic growth from environmental degradation. In this context, this study examines the effects of tourism arrivals on CO2 intensity and energy intensity, two key indicators of environmental sustainability aligned with SDGs 7 and 13. Panel autoregressive distributed lag (ARDL) and nonlinear ARDL models are employed using a balanced panel of 54 countries over the period 1996–2023. In addition, Wald tests for long-run asymmetry, dynamic multiplier analysis, and Dumitrescu–Hurlin causality tests are applied. The results confirm the existence of stable long-run relationships between tourism arrivals and both CO2 intensity and energy intensity. In the symmetric framework, tourism growth is associated with significant long-run reductions in CO2 and energy intensity, while short-run effects are negative and significant only for CO2 intensity. In the asymmetric framework, positive tourism shocks generate stronger and more persistent reductions in both intensity measures, whereas negative shocks lead to weaker environmental efficiency gains. Moreover, the Wald test shows the existence of long-run asymmetry between positive and negative tourism shocks. In addition, the dynamic multiplier analysis confirms that environmental intensity adjusts gradually over time following tourism shocks. Finally, Dumitrescu–Hurlin causality tests indicate bidirectional Granger causality relationships between tourism arrivals and environmental intensity indicators. The findings are robust to dynamic endogeneity, the COVID-19 shock, and country heterogeneity. Overall, the findings indicate that tourism arrivals contribute to lowering long-term environmental intensity, consistent with relative decoupling and the goals of sustainable tourism development.

BackgroundYouth labour markets across Sub-Saharan Africa combine high aspirations for secure wage work with entrenched informality and underemployment.AimThis article clarifies how structural constraints shape stated employment preferences and entrepreneurial orientation among youth in Rwanda and Sierra Leone.DesignA constrained comparative secondary analysis drew on Afrobarometer Round 9 microdata for Sierra Leone and triangulated these findings with nationally reported labour-market and policy indicators for Rwanda. Public microdata for Rwanda were unavailable, so a bounded-evidence strategy transparently distinguishes verifiable survey statistics from document-based context.FindingsSierra Leonean youth express a cautious economic outlook—fewer than half expect improvement within a year—yet most endorse redistributive growth and demand stronger gender equity in job access. Preference for self-employment appears necessity-driven where institutional trust and public-goods provision remain moderate, rather than an indicator of opportunity entrepreneurship. Rwanda’s reported labour-absorption pressure and pervasive informality reinforce this reading, suggesting that education-fuelled aspiration gaps widen when formal job creation lags.ContributionThe study advances an “institutionally conditioned planned-behaviour” framework that integrates the Theory of Planned Behaviour with opportunity-structure theory and human-capital expectations. Methodologically, it demonstrates how rigorous comparative inference can be maintained through explicit verification protocols when evidence access is asymmetric, and it outlines a reproducible pathway for symmetric modelling once Rwandan microdata become publicly available.Practical implicationsPolicies that promote youth entrepreneurship without parallel investment in inclusive growth, reliable public goods, and transparent digital governance risk shifting systemic labour-market failure onto young people rather than resolving it.

Impaired awareness of hypoglycemia (IAH) significantly impacts efforts to maintain optimal glycemia. IAH occurs when recurrent episodes of hypoglycemia (HG) occur within a short period of time, but the mechanisms are unknown. Upregulation of glucose transport may contribute to the development of IAH. To determine if brain glucose transport is upregulated in response to recurrent HG in people with type 1 diabetes (T1D) and normal awareness of hypoglycemia. Forty-five subjects enrolled and 30 completed the entire 3-day protocol. Participants underwent magnetic resonance spectroscopy scans at 3 tesla during which they were clamped at 150, 225 or 300 mg/dL before and after exposure to three 2-hr HG (target = 50mg/dL) clamps over 2 days between December 2020 and June 2023. The primary metabolite of interest was glucose. Brain glucose transport kinetics were measured in the hypothalamus (HTL) and prefrontal cortex (PFC) during hyperglycemic clamps using reversible symmetric Michaelis-Menten model. Academic medical center. Participants were recruited from a clinical registry. Inclusion criteria were diagnosis of T1D, 18-65 years, hemoglobin A1C <8.5%, and diabetes duration of 2-25 years. Exclusion criteria included IAH on the Clarke and Gold questionnaires. Three 2-hr HG clamps over 2 days. The ratio of maximal transport rate to cerebral metabolic rate of glucose (Vmaxt/CMRglc). Brain glucose transport kinetics measured during hyperglycemic clamps were not different before vs. after exposure to recurrent HG clamps. Short-term exposure to recurrent HG does not upregulate glucose transport kinetics in the setting of T1D.

This study examines the risk level of the logistics sector, which is a key sub-component of the service sector in Türkiye, by comparing it with other service sub-sectors that have a high share within the sector, namely retail trade, electricity, gas, and steam, and telecommunications indices. Daily data from the period between March 2, 2020, and October 8, 2025, when volatility in financial markets increased significantly after COVID-19, was used for the analysis. First, returns for the indices were generated, and various non-linearity tests were used to determine whether these returns were non-linear. Next, the stationarity properties of the series were tested using non-linear unit root tests, and the appropriate ARMA(p,q) model was determined for each series based on information criteria. To examine the volatility structure of the return series, symmetric conditional heteroskedasticity models (GARCH, GARCH-M) and asymmetric effect models (T-GARCH, E-GARCH, and GJR-GARCH) were applied. The most suitable model was selected by considering error criteria and log-likelihood values. The findings reveal that the risk levels among the service sector indices are close to each other but show sectoral differentiation. According to the analysis, the telecommunications sector had the highest risk during the period under review, followed closely by the logistics sector. In contrast, the retail trade sector was identified as the sub-sector with the lowest volatility.

Abstract Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. However, favoring a more simplified, spherically symmetric model for galaxy clusters can lead to biases in the estimates of cluster properties. In this work, we present a deep learning approach for estimating the triaxiality and orientations of massive galaxy clusters (those with masses ≳10 14 M ⊙ h −1 ) from 2D observables. We utilize the flagship hydrodynamical volume of the suite of cosmological-hydrodynamical MillenniumTNG (MTNG) simulations as our ground truth. Our model combines the feature extracting power of a convolutional neural network and the message passing power of a graph neural network in a multimodal, fusion network. Our model is able to extract 3D geometry information from 2D idealized cluster multiwavelength images (soft X-ray, medium X-ray, hard X-ray, and tSZ effect) and mathematical graph representations of 2D cluster member observables (line-of-sight radial velocities, 2D projected positions and V -band luminosities). Our network improves cluster geometry estimation in MTNG by 30% compared to assuming spherical symmetry. We report an R 2 = 0.85 regression score for estimating the major axis length of triaxial clusters and correctly classifying 71% of prolate clusters with elongated orientations along our line of sight.

In this paper, we construct two spherically symmetric thin-shell gravastar models within a BTZ geometry with minimum length. Therefore, in the inner region of the gravastar, we consider an anti-de Sitter metric with minimum length. Thus, for the first model, we introduce the minimum length effect using the probability density of the ground state of the hydrogen atom in two dimensions. For the second gravastar model, we adopt a Lorentzian-type distribution. Also in the outer region, we consider the BTZ black hole metric. So, by examining the inner spacetime, the thin shell, and the outer spacetime, we find that there are different physical characteristics regarding their energy densities and pressures that make the gravastar stable. This effect persists even when the cosmological constant is zero. In addition, we determined the entropy of the gravastar thin shell. Besides, we explore the thermodynamic properties of the BTZ black hole with minimum length in Schwarzschild-type form and also check its stability.

Viscoelastic materials and non-Newtonian fluids often exhibit a pronounced sensitivity to temperature, which significantly influences their mechanical response. Recently, Ruggeri proposed a nonlinear viscoelastic model within the framework of Rational Extended Thermodynamics and showed that, by a suitable modification of the production term, the same structure can also describe non-Newtonian fluids with a finite relaxation time in an isothermal setting. In this paper, we extend these models to non-isothermal processes in one spatial dimension in the absence of heat flux. By coupling the balance laws of momentum and energy with a balance law for an additional nonequilibrium stress variable and enforcing the entropy principle together with convexity, we derive a thermodynamically admissible system of nonlinear evolution equations. The resulting model is symmetric hyperbolic, which guarantees local well-posedness of the Cauchy problem and admits weak solutions, including shocks. For Newtonian production terms, we further verify the Shizuta–Kawashima condition, yielding global-in-time smooth solutions for sufficiently small initial data. A unified framework is thus obtained, capable of describing either nonlinear thermo-viscoelastic behavior or temperature-dependent non-Newtonian rheology in the parabolic (zero-relaxation) limit. The principal-subsystem viewpoint clarifies the nesting of reduced theories: in particular, the previously proposed isothermal model is recovered as a principal subsystem, and classical hyperelastic dynamics emerges as a common principal subsystem of the isothermal and Euler-type limits.

An important limitation of concentration-response (C-R) modeling of toxicity test data is the imposition of a shape for this relationship that can deviate from the underlying true relationship, thereby biasing estimates of effect concentrations (ECp's). In particular, the imposed mathematical model is often symmetric, whereas considerable asymmetry might be present in the underlying relationship that is either not evident or not addressed in standard toxicity tests with limited numbers of treatments. To evaluate asymmetry and its implications for ECp estimation, six simultaneous tests of NaCl chronic toxicity to Ceriodaphnia dubia were conducted, providing extensive information on inter-replicate variability and on the shape of the C-R relationship. An asymmetric C-R relationship derived from this large data set was used to simulate data sets with a more typical, smaller configuration, which were subject to C-R analysis using both symmetric and asymmetric models. Both models resulted in substantial uncertainties for estimating ECp's at low p values for this test configuration. There is a need for more work and care regarding the use of C-R analysis in developing effects assessments for low levels of effect.

Abstract The crucial role of green finance in environmental protection and climate change mitigation is well established in the literature. However, its effects are mostly analysed through a linear perspective, while potential heterogeneity arising from asymmetric dynamics and income thresholds is often overlooked. Symmetric ARDL models fail to capture these asymmetries and may misguide policy recommendations, particularly in contexts where long-run dynamics are crucial. By applying nonlinear ARDL (NARDL) model to a panel of 60 developing countries from 2000 to 2019, this study confirms significant nonlinear and asymmetric impacts of green finance, income and financial development on carbon emissions. Empirical results confirm that green finance exerts heterogeneous effects across different income groups, it has a statistically significant long-term mitigating effect in low- and upper-middle-income countries, but its impact is positive (i.e., emission-increasing) in lower-middle-income economies. However, the effects of green finance are significant only in the long run, underscoring the need for sustained policy commitment. Financial development and economic growth also exhibit income-dependent asymmetries in their environmental effects. These findings underscore that the effectiveness of green finance is highly contingent upon the level of economic development, requiring tailored climate finance strategies to each country’s specific developmental context. Policy implications include enhancing the targeting and design of green finance, providing subsidies for clean technologies, and implementing complementary financial and institutional reforms.

This study aims to evaluate the performance of elliptical and D-vine copula models for predicting discharge at one spring (S5) using observed discharge data from the other four springs (S1-S4) and concurrent rainfall within a single springshed of Central Himalaya, India, in the context that the geological typology of the study springs is similar. The hypothesis that simpler, symmetric dependence models may outperform highly flexible vine structures when hydrological connectivity is strong, and data length is limited, is tested. The results revealed that elliptical copulas substantially outperformed D-vine structures, with the best elliptical model achieving RMSE ≈ 0.11 lpm and R2 ≈ 0.76 under full conditioning, where both Gaussian and t copulas performed comparably. Among D-vine configurations, simplified hydrologically coherent structures (S1 → S2 → S3 → S5) markedly improved accuracy (R2 ≈ 0.58–0.59, RMSE ≈ 0.14 lpm) over complex multi-node D-vines, confirming that pairwise correlation strength and physical flow topology are critical determinants. A similar flow variability index and strong inter-spring correlations indicate that groundwater-spring linkages are reliably represented by symmetric dependence structures. This study demonstrates that copula-based dependence modeling offers powerful, data-efficient approaches for discharge prediction in monitoring-limited Himalayan springsheds, where limited hydrological monitoring constrains sustainable water resource management.

Under the standard model of hierarchical structure formation, the overall geometry of galaxy clusters is better described by a triaxial ellipse than by a sphere. As a result, the application of spherically symmetric models can result in significant biases, with masses derived from weak-lensing observations being particularly sensitive. These biases can be mitigated by fitting a triaxial model, but this requires deep multi-probe data along with a set of physically motivated models to describe them. We present a multi-probe triaxial analysis method based on the data available for galaxy clusters in the Cluster Heritage project with _ - Mass Assembly and Thermodynamics at Endpoint of structure formation (CHEX-MATE), which includes X-ray data from Sunyaev-Zel'dovich effect maps from and ACT, and weak-lensing data from Subaru. This work builds upon our previous development of a gas-only X-ray and Sunyaev-Zel'dovich triaxial fitting formalism in Paper I. After verifying our approach using mock observations of model clusters with known properties, we applied it to the CHEX-MATE galaxy cluster ļuster (Abell 1689). We found that the cluster is elongated along the line of sight relative to the plane of sky by a factor of mathcal R LP = 1.20 ± 0.04. As a result, the weak-lensing mass obtained from our triaxial fit, ^ $=(13.88_ -1.43 +1.73 ) $ ^ 14 is significantly lower than the value of $(17.77_ -1.75 +2.00 ) $ ^ 14 obtained from a spherically symmetric fit that otherwise employed the same method. Our triaxial fit finds a concentration of ţwoc$=8.66_ -1.70 +2.08 $, consistent with the spherically symmetric value of 9.99_ -1.78 ^ +2.26 , which suggests that the unexpectedly high concentration in Abell 1689 is not due to triaxiality and orientation. We also measured the nonthermal pressure fraction at radii between 0.18--1.37 Mpc and found a minimum of approximately 20% at intermediate radii, increasing to near 30% at the smallest and largest radii, and with a typical measurement precision of ± 5%.