Search Results (78)

Family structure has long been regarded as an important determinant of household saving, yet the empirical evidence for developing economies remains limited. Using the 2018–2022 panels of the China Family Panel Studies (CFPS), a nationwide survey that follows 16,519 households across three waves, the present study investigates how family size, the elderly share, and the child share jointly shape saving behavior. A household fixed effects framework is employed to control for time-invariant heterogeneity, followed by a sequential endogeneity strategy: external-shock instruments are tested and rejected, lagged two-stage least squares implement internal instruments, and a dynamic System-GMM model is estimated to capture saving persistence. Robustness checks include province-by-year fixed effects, inverse probability weighting for attrition, balanced-panel replication, alternative variable definitions, lag structures, and sample filters. Family size raises the saving rate by 4.6 percentage points in the preferred dynamic specification (p < 0.01). The elderly ratio remains insignificant throughout, whereas the child ratio exerts a negative but model-sensitive association. A three-path mediation analysis indicates that approximately 26 percent of the total family size effect operates through scale economy savings on quasi-fixed expenses, 19 percent is offset by resource dilution pressure, and less than 1 percent flows through a precautionary saving channel linked to income volatility. These findings extend the resource dilution literature by quantifying the relative strength of competing mechanisms in a middle-income context and showing that cost-sharing economies dominate child-related dilution for most households. Policy discussion highlights the importance of public childcare subsidies and targeted credit access for rural parents, whose saving capacity is the most constrained by additional children. The study also demonstrates that fixed effects estimates of family structure can be upward-biased unless dynamic saving behavior and internal instruments are considered. Full article

This paper investigates systemic tail dependence within the crypto-asset ecosystem by examining interconnectedness across eight major tokens spanning Layer 1 cryptocurrencies, DeFi tokens, stablecoins, and infrastructure/governance assets. We employ a novel partial correlation-based network framework and quantile-specific connectedness measures to examine how co-movement patterns evolve under normal and extreme market conditions from September 2021 to March 2025. Unlike conventional correlation or variance decomposition approaches, our methodology isolates direct, tail-specific transmission channels while filtering out standard shocks. The results indicate strong asymmetries in dependence structures. Systemic risk intensifies during adverse tail events, particularly around episodes such as the Terra/Luna crash, the USDC depeg, and Bitcoin’s 2024 halving cycle. Our analysis shows that ETH, LINK, and UNI are key assets in spreading losses when the market falls. In contrast, the stablecoin DAI tends to absorb some of the stress, helping reduce risk during downturns. These results indicate critical contagion pathways and suggest that regulation targeting protocol-level transparency, liquidity provisioning, and interoperability standards may reduce amplification mechanisms without eliminating interdependence. Our findings contribute to the emerging literature on crypto-systemic risk and offer actionable insights for regulators, DeFi protocol architects, and institutional investors. In particular, we advocate for the incorporation of tail-sensitive network diagnostics into real-time monitoring frameworks to better manage asymmetric spillover risks in decentralized financial systems. Full article

Inspired by the single-blade hyperboloid, a new type of multi-bar shock absorber was designed, which can recover vibration energy. Its principle is to convert the droop reciprocating vibration of the vehicle in the spatial domain into the reciprocating rotational motion in the plane through the trajectory and force characteristics of the single-blade hyperboloid moving along the space. To improve the efficiency of energy regeneration, a mechanical motion filtering mechanism was designed. Through theoretical derivation, the energy regeneration formula of a new type of multi-rod shock absorber was obtained. After simulation analysis and experimental verification, under the input excitation of 1.82 Hz, the maximum instantaneous output voltage can reach 29 V, the maximum excitation current is 0.58 A, and the maximum power is 16.84 W. The efficient recovery and utilization of energy have been achieved, and the ride comfort of the vehicle has been improved. Full article

This paper provides shadowgraphy and molecular tagging velocimetry (MTV) acquisition results and validates a computational fluid dynamics (CFDs) simulation for an underexpanded supersonic gas jet in a plenum pointed toward a wall with an aligned converging pipe outlet. Flow configurations of this type are encountered in pulse-jet systems for online industrial gas filter regeneration. Although previous CFD validation efforts for pulse-jet systems have relied on static pressure measurements, this work expands the validation data using high-resolution flow visualization and velocimetry techniques. Simulations were performed with an axisymmetric two-dimensional Reynolds-averaged Navier-Stokes model and are in close agreement with the shadowgraphy and MTV data, including the description of Mach disks, barrel shocks, and reflected shocks in the underexpanded jet. The CFD model was finally applied to study the role of the converging tube downstream of the jet. Full article

This study examines the relationship between banking sector development (credit and deposits) and economic growth in Ecuador, using quarterly data for the period 2000–2022. An ARDL approach with Bound Test cointegration is employed, incorporating structural breaks using the Bai–Perron test and controlling for macroeconomic shocks. In addition, time transformation methodologies are applied to harmonize the frequency of the series: the monthlyization of GDP is performed using the Chow-Lin method, and the imputation of missing unemployment data using the Kalman filter. The results reveal a significant long-run elasticity between bank deposits and GDP (0.45%), while credits do not present a statistically significant effect, possibly due to high delinquency and institutional weakness. Granger causality tests confirm a unidirectional relationship between banking variables to economic growth. These findings highlight the importance of strengthening financial supervision and improving institutional quality to enhance the effect of bank intermediation. The study provides robust and contextualized empirical evidence relevant to resource-dependent economies with concentrated financial systems, contributing to the debate on the relationship between finance and growth in developing countries. Full article

In the era of Artificial Intelligence, the magic of achieving results at the “speed of light” for tasks that until recently required a lot of work and effort shocks, arouses enthusiasm and generates fears at the same time. Therefore, starting from this reality of our days, we proposed within the current research to study the relationship between the factors of technostress (techno-overload, techno-invasion, techno-complexity, techno-insecurity, techno-uncertainty) perceived as a result of the implementation of AI at the societal level and the quality of life, filtering the relationship through the “lens” of the positive and negative affect mediators. The mediation analyses, conducted on a sample of 217 adult Romanian citizens (18–62 years old), suggested that although AI-related technostress does not directly influence quality of life, it has a significant indirect impact through affective traits—general tendencies to frequently experience positive or negative emotions. This indicates that technostress contributes to variations in quality of life by influencing emotional experiences, which mediate the relationship. These findings emphasize not only the absence of a direct effect, but also the importance of the indirect pathway in understanding how individuals are affected by AI-related stress. We believe that the results of the current study can be equally useful in raising awareness of the psychological mechanisms responsible for the quality of life and in understanding the importance of implementing official programs, both technically, regarding the development of skills to understand and work with AI, and psychological support programs, considering the management of emotions, with reference to this technology. Full article

With the acceleration of urbanization, citizens are facing more pandemic challenges. A deeper understanding of constructing more resilient cities can help citizens be better prepared for potential future pandemics or disasters. In this study, a static–dynamic analytical framework for urban health resilience evaluation and influencing factor exploration was proposed, which helped not only to analyze the basic static urban health resilience (SUHRI) under normal conditions but also to evaluate the dynamic urban health resilience (DURHI) under an external epidemic shock. The epidemic dynamic model could reasonably simulate the epidemic change trend and quantitatively evaluate resistance and recovery capacity, and the proposed influencing factor exploration model improved the model fitness by filtering out the influence of population flow autocorrelation existing in the residuals. SUHRI and DUHRI, and their corresponding key influencing factors, were compared and discussed. The results of the static–dynamic integration and difference score showed that 60.6% cities within the study area had a similar performance on SUHRI and DUHRI, but there was also a typical difference: some regional hubs exhibited high SUHRI but had only mid-level DUHRI, which was attributed to stronger external disturbances such as higher population mobility. The key influencing factors for static and dynamic urban health resilience also vary. Hospital capacity and income had the strongest influence on static urban health resilience but a relatively weaker or even non-significant correlation with dynamic urban health resilience sub-indices. The extracted population flow eigenvector collection had the strongest influence on dynamic urban health resilience, as it represents the population flow connection among cities, which could reflect the information of policy response, such as policy stringency and support intensity. We hope that our study will shed some light on constructing more resilient urban systems and being prepared for future public health emergencies. Full article

This study investigates the dynamic interdependencies among key sectors of Borsa Istanbul—industrial, services, technology, banking, and electricity—using a novel network-geometric framework. Daily closure prices from 2022 to 2024 are transformed into logarithmic returns and analyzed via a sliding window approach. In each window, mutual information is computed to construct weighted networks that are filtered using Triangulated Maximally Filtered Graphs (TMFG) to isolate the most significant links. Forman–Ricci curvature is then calculated at the node level, and entropy measures over k-neighborhoods ($k=1,2,3$) capture the complexity of both local and global network structures. Cross-correlation, Granger causality, and transfer entropy analyses reveal that sector responses to macroeconomic shocks—such as inflation surges, interest rate hikes, and currency depreciation—vary considerably. The services sector emerges as a critical intermediary, transmitting shocks between the banking and both the industrial and technology sectors, while the electricity sector displays robust, stable interconnections. These findings demonstrate that curvature-based metrics capture nuanced network characteristics beyond traditional measures. Future work could incorporate high-frequency data to capture finer interactions and empirically compare curvature metrics with conventional indicators. Full article

This research seeks to investigate the viability of using Tectona grandis wood powder as a reinforcement material in polymer matrix composites because of the increasing awareness of natural fibers that offer impressive characteristics and cost-effectiveness in addition to being biodegradable. The fibers were mixed with epoxy resin, and the mixture was passed through a filter to remove fiber bundles and then compression molded to form composites, which were cured in an oven. Different experiments were performed on the composite to measure its mechanical characteristics. The tests performed were a tensile test to measure the mechanical properties of the material like strength and elastic properties, a compression test for evaluating the behavior of the material under a compressive load, a hardness test for the rate of indentation resistivity, and an impact test for the material’s ability to withstand shock loads. The results showed that fiber reinforcement caused a significant enhancement in the mechanical aspect of the composite, where the compression strength obtained was 249.83 MPa, and the tensile strength obtained was 17.98 MPa. SEM microstructural analysis and a moisture absorption test were performed, while an additional analysis was carried out using Ansys work bench software. This research proves that Tectona grandis wood powder improves the mechanical properties of polymer composites and represents a viable substitute for synthetic reinforcements. Full article

Shock wave boundary/layer interactions (SWBLIs) are critical in high-speed aerodynamic flows, particularly within supersonic regimes, where unsteady dynamics can induce structural fatigue and degrade vehicle performance. Conventional measurement techniques, such as pressure-sensitive paint (PSP), face limitations in frequency response, calibration complexity, and intrusive instrumentation. Similarly, MEMS-based sensors, like Kulite^® sensors, present challenges in terms of intrusiveness, cost, and integration complexity. This study presents a flexible, lightweight polyvinylidene fluoride (PVDF) piezoelectric sensor array designed for high-resolution wall-pressure measurements in SWBLI research. The primary objective is to optimize low-frequency pressure fluctuation detection, addressing SWBLI’s need for accurate, real-time measurements of low-frequency unsteadiness. Fabricated using a double-sided screen-printing technique, this sensor array is low-cost, flexible, and provides stable, high-sensitivity data. Finite Element Method (FEM) simulations indicate that the sensor structure also has potential for high-frequency responses, behaving as a high-pass filter with minimal signal attenuation up to 300 kHz, although the current study’s experimental testing is focused on low-frequency calibration and validation. A custom low-frequency sound pressure setup was used to calibrate the PVDF sensor array, ensuring uniform pressure distribution across sensor elements. Wind tunnel tests at Mach 2 verified the PVDF sensor’s ability to capture pressure fluctuations and unsteady behaviors consistent with those recorded by Kulite sensors. The findings suggest that PVDF sensors are promising alternatives for capturing low-frequency disturbances and intricate flow structures in advanced aerodynamic research, with high-frequency performance to be further explored in future work. Full article

This is a comprehensive overview on our research work to link interdisciplinary modeling and simulation techniques to improve the predictability and reliability simulations (PARs) of compressible turbulence with shock waves for general audiences who are not familiar with our nonlinear approach. This focused nonlinear approach is to integrate our “nonlinear dynamical approach” with our “newly developed high order entropy-conserving, momentum-conserving and kinetic energy-preserving methods” in the quantification of numerical uncertainty in highly nonlinear flow simulations. The central issue is that the solution space of discrete genuinely nonlinear systems is much larger than that of the corresponding genuinely nonlinear continuous systems, thus obtaining numerical solutions that might not be solutions of the continuous systems. Traditional uncertainty quantification (UQ) approaches in numerical simulations commonly employ linearized analysis that might not provide the true behavior of genuinely nonlinear physical fluid flows. Due to the rapid development of high-performance computing, the last two decades have been an era when computation is ahead of analysis and when very large-scale practical computations are increasingly used in poorly understood multiscale data-limited complex nonlinear physical problems and non-traditional fields. This is compounded by the fact that the numerical schemes used in production computational fluid dynamics (CFD) computer codes often do not take into consideration the genuinely nonlinear behavior of numerical methods for more realistic modeling and simulations. Often, the numerical methods used might have been developed for weakly nonlinear flow or different flow types other than the flow being investigated. In addition, some of these methods are not discretely physics-preserving (structure-preserving); this includes but is not limited to entropy-conserving, momentum-conserving and kinetic energy-preserving methods. Employing theories of nonlinear dynamics to guide the construction of more appropriate, stable and accurate numerical methods could help, e.g., (a) delineate solutions of the discretized counterparts but not solutions of the governing equations; (b) prevent numerical chaos or numerical “turbulence” leading to FALSE predication of transition to turbulence; (c) provide more reliable numerical simulations of nonlinear fluid dynamical systems, especially by direct numerical simulations (DNS), large eddy simulations (LES) and implicit large eddy simulations (ILES) simulations; and (d) prevent incorrect computed shock speeds for problems containing stiff nonlinear source terms, if present. For computation intensive turbulent flows, the desirable methods should also be efficient and exhibit scalable parallelism for current high-performance computing. Selected numerical examples to illustrate the genuinely nonlinear behavior of numerical methods and our integrated approach to improve PARs are included. Full article

Background: Renal replacement therapy (RRT), widely used in the treatment of renal injury during sepsis, aims to eliminate the toxins and proinflammatory cytokines involved in the pathomechanism underlying septic shock. Dialysis filters are characterized by a high adsorption potential for cytokines in RRT in the case of septic renal injury. For the treatment of sepsis with antibiotics, it is of key importance to achieve the desired values of PK/PD indices. Continuous renal replacement therapy (CRRT) may affect antimicrobial clearance, increasing their elimination in some cases. Methods: The aim of this study was to determine the degree of adsorption for linezolid on three different types of filters used in CRRT. In our in vitro study, a continuous veno-venous hemofiltration (CVVH) was conducted using three types of filters: polysulfone (PS), polyethyleneimine-treated polyacrylonitrile (PAN PEI), and non-PEI-treated polyacrylonitrile (PAN). Each type of filter was used in three CVVH cycles, involving the use of 600 mg of linezolid dissolved in 700 mL of bovine blood or in 700 mL of 0.9% NaCl. In each case, the total volume of the obtained solution was 1000 mL. Blood samples were collected at particular time points to measure their drug concentration. The differences in mean drug/NaCl adsorption and drug/blood adsorption were determined using a one-way ANOVA with multiple comparisons via Tukey’s post hoc test; a p-value of <0.05 was considered significant. Results: A significant adsorption of linezolid was found for PAN PEI filters, both in samples obtained from bovine blood and 0.9% NaCl solutions, at the endpoint. In PAN PEI samples, the concentration of linezolid in 0.9% NaCl solutions decreased from 594.74 μg/mL to 310.66 μg/mL after 120 min (the difference was established at 52%). In blood samples, the initial concentration was 495.18 μg/mL, which then decreased to 359.84 μg/mL (73% of the beginning value). No significant adsorption was demonstrated on PAN or PS filters. Conclusion: There is a need for in vivo research to confirm the effect of filter type on linezolid concentration in patients undergoing CRRT. Full article

The exhaust gas recirculation (EGR) valve plays an important role in improving engine fuel economy and reducing emissions. In order to improve the positioning accuracy and robustness of the EGR valve under uncertain dynamics and external disturbances, this paper proposes a positioning servo system design for an electromotive (EM) EGR valve based on the Kalman filter. Taking a novel valve driven by a central winding limited angle torque motor (LATM) as the object, we have fully considered the influence of the motor rotor position and load current, as well as the magnetic field saturation and cogging effect, improved the existing LTAM model, and derived accurate torque expression. The parameter uncertainty of the above internal model and the external stochastic disturbance were unified as “total disturbance”, and a Kalman filter-based observer was designed for disturbance estimations and real-time feed-forward compensation. Furthermore, using non-contact magnetic angle measurements to obtain accurate valve position information, a position control model with real-time response and high accuracy was established. Numerous simulated and experimental data show that in the presence of ± 25% plant model parameter fluctuations and random shock-type disturbances, the servo system scheme proposed in this paper achieves a maximum position deviation of 0.3 mm, a repeatability of positioning accuracy after disturbances of 0.01 mm, and a disturbance recovery time of not more than 250 ms. In addition, the above performance is insensitive to the duration of the disturbance, which demonstrates the strong robustness, high accuracy, and excellent dynamic response capability of the proposed design. Full article

This comprehensive overview presents our continued efforts in high-order finite difference method (FDM) development for adaptive numerical dissipation control in the long-time integration of direct numerical simulation (DNS), large eddy simulation (LES), and implicit LES (ILES) computations of compressible turbulence for gas dynamics and MHD. The focus is on turbulence with shock wave numerical simulations using the adaptive blending of high-order structure-preserving non-dissipative methods (classical central, Padé (compact), and dispersion relation-preserving (DRP)) with high-order shock-capturing methods in such a way that high-order shock-capturing methods are active only in the vicinity of shock/shear waves, and high-gradient and spurious high-frequency oscillation regions guided via flow sensors. Any efficient and high-resolution high-order shock-capturing methods are good candidates for the blending of methods procedure. Typically, the adaptive blending of more than one method falls under two camps: hybrid methods and nonlinear filter methods. They are applicable to unstructured finite volume, finite element, discontinuous Galerkin, and spectral element methods. This work represents the culmination of over 20 years of high-order FDM developments and hands-on experience by the authors and collaborators in adaptive numerical dissipation control using the “high order nonlinear filter approach”. Extensions of these FDM versions to curvilinear nonuniform, freestream-preserving moving grids and time-varying deforming grids were also developed. By examining the construction of these two approaches using the high-order multistage type of temporal discretization, the nonlinear filter approach is made more efficient and less CPU-intensive while obtaining similar accuracy. A representative variety of test cases that compare the various blending of high-order methods with standalone standard methods is illustrated. Due to the fact that our nonlinear filter methods are not well known in compressible turbulence with shock waves, the intent of this comprehensive overview is for general audiences who are not familiar with our nonlinear filter methods. For readers interested in the implementation of our methods into their computer code, it is hoped that the long overview will be helpful. Full article

Over the years, oil prices and financial stock markets have always had a complex relationship. This paper analyzes the interactions and co-movements between the oil market (WTI crude oil) and two major stock markets in Europe and the US (the Euro Stoxx 50 and the SP500) for the period from 1990 to 2023. For that, I use both the time-varying and the Markov copula models. The latter one represents an extension of the former one, where the constant term of the dynamic dependence parameter is driven by a hidden two-state first-order Markov chain. It is also called the dynamic regime-switching (RS) copula model. To estimate the model, I use the inference function for margins (IFM) method together with Kim’s filter for the Markov switching process. The marginals of the returns are modeled by the GARCH and GAS models. Empirical results show that the RS copula model seems adequate to measure and evaluate the time-varying and non-linear dependence structure. Two persistent regimes of high and low dependency have been detected. There was a jump in the co-movements of both pairs during high regimes associated with instability and crises. In addition, the extreme dependence between crude oil and US/European stock markets is time-varying but also asymmetric, as indicated by the SJC copula. The correlation in the lower tail is higher than that in the upper. Hence, oil and stock returns are more closely joined and tend to co-move more closely together in bullish periods than in bearish periods. Finally, the dependence between WTI crude oil and the SP500 stock index seems to be more affected by exogenous shocks and instability than the oil and European stock markets. Full article