Search Results (857)

To address the persistent challenges in diagnosing bearing faults, this study proposes an intelligent diagnostic framework based on the principle that mechanical faults manifest as symmetry-breaking phenomena in a system’s vibration signals. In a healthy motor, vibration signals exhibit a high degree of symmetry, whereas faults introduce identifiable and distinct asymmetries. This study constructs a high-fidelity digital twin model based on the five-dimensional model theory to simulate both the symmetrical (healthy) state and various asymmetrical faulty states of motor bearings—specifically, inner race, outer race, and rolling element faults—thereby effectively addressing the critical issue of data scarcity. Building upon this framework, fault features characterizing these asymmetries are accurately extracted using an optimized variational mode decomposition (VMD) algorithm and subsequently classified with a convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model. The results validate the model’s ability to accurately replicate bearing-fault data. The proposed diagnostic method achieves a stable and high average accuracy of 98.44 ± 0.41% over multiple runs on the simulation data. Furthermore, its effectiveness was validated on a public real-world bearing dataset, where it achieved an accuracy of over 95%, demonstrating its robustness and potential for industrial applications by effectively identifying fault-induced asymmetries. Full article

This study investigates the impact of the COVID-19 pandemic on the Tokyo mango market by combining transaction data from the Ota Fruit Market with Google Mobility indices. In Japan, mangoes are regarded as a luxury fruit, largely dependent on imports and associated with high domestic production costs, which positions them as premium commodities. To assess the influence of price dynamics and human mobility on mango trading volumes during the pandemic, this study employs an autoregressive distributed lag (ARDL) model. The long-run results indicate that mango demand was positively associated with increased residential activity: a 1% rise in time spent at home during the COVID era corresponded to an increase of 786 kg in trade volume. Similarly, a 1% increase in time spent in retail and recreation areas was associated with a 364 kg rise in trade volume. In contrast, time spent in grocery and pharmacy locations showed no statistically significant effect. In the short run, fluctuations in mobility patterns and price levels contributed to variations in demand, with sales volumes adjusting toward their long-run equilibrium. The mobility indices exhibited mixed short-term effects on trade volumes. Notably, the analysis revealed that mango trading volumes rebounded in 2022, coinciding with the easing of pandemic-related disruptions. Full article

Background and Objectives: The study aims to evaluate the effects of regular physical activity on ocular structures by comparing eye anterior segment structures and retinal nerve fiber layer (RNFL) parameters between elite individual athletes and sedentary persons. Materials and Methods: This cross-sectional observational study included 75 participants (33 female and 42 male) aged 18–32 years. Participants consisted of 33 elite individual athletes (66 eyes) aged 18–32 years and 42 sedentary individuals (84 eyes) aged 18–28 years. The elite athlete group consisted of participants who had trained ≥ 3 times per week for the past 5 years and had competition experience in tennis (n = 11), badminton (n = 8), and short/middle distance running (n = 14). Sedentary participants had not engaged in regular physical activity for the last 2 years. Anterior segment structures were measured with ultrasound biomicroscopy (UBM), RNFL parameters were measured with spectral-domain optical coherence tomography (SD-OCT), and intraocular pressure (IOP) was measured with a non-contact pneumotonometer. Results: When the anterior segment parameters were compared, a significance was found in the group effect (p = 0.021, ${ƞ}_p^2$ = 0.036) in the TIA500 value, but the effects of side and gender were insignificant (p > 0.05). While the gender effect was found to be significant for the AOD500 (p = 0.030, ${ƞ}_p^2$ = 0.032) and ARA500 (p = 0.019, ${ƞ}_p^2$ = 0.038) variables, the group and side effects were statistically insignificant (p > 0.05). There was a significant difference in IOP values between sedentary individuals (15.82 ± 2.69) and athletes (14.61 ± 1.80) (p = 0.004, ${ƞ}_p^2$ = 0.057). There was a significant difference between the right (15.71 ± 2.70) and left (14.87 ± 2.02) sides (p = 0.027, ${ƞ}_p^2$ = 0.033). The effect of gender was not significant (p > 0.05). When the results were evaluated, a significant effect of the TEMPORAL side in RNFL parameters (p = 0.003, ${ƞ}_p^2$ = 0.058) was observed. Correlations were seen between the anterior segment and RNFL parameters in both groups (p < 0.05). Conclusions: Athletes exhibited lower IOP compared with sedentary individuals, with similar RNFL parameters but distinct correlation patterns with anterior segment structures. These findings suggest that ocular parameters should be evaluated in an integrated manner and that physical activity may support ocular health by contributing to IOP reduction and potentially slowing the progression of eye diseases. Full article

This study employed quarterly data spanning from 2005 to 2024 to investigate the factors affecting China’s tea exports to Malaysia using demand theory. The Autoregressive Distributed Lag (ARDL) approach and Granger causality test were applied to examine the long-run and short-run impacts of key variables, including the prices of China’s tea and coffee imported by Malaysia, Malaysia’s GDP, Malaysia’s tea production, and the international oil price. The ARDL bounds testing confirmed the existence of a long-run equilibrium among these variables. The empirical findings revealed that an increase in the price of China’s tea significantly reduced export volumes, whereas Malaysia’s GDP exerted a strong positive influence. The price of coffee exhibited a significantly negative effect, suggesting an unconventional substitution relationship with tea. Both Malaysia’s domestic tea production and the international oil price imposed downward pressures on China’s tea exports. Furthermore, the Granger causality analysis indicated that the price of China’s tea, the price of coffee, and Malaysia’s GDP all exerted short-run effects on China’s tea exports to Malaysia. These findings contribute to the export demand literature and offer implications for policies aiming to enhance bilateral tea trade between China and Malaysia. Full article

The prevailing view is that ESG performance contributes to corporate financial stability, particularly regarding long-term sustainability objectives. However, there is a notable lack of critical research exploring its short-term financial effects, especially within capital-intensive sectors experiencing green transformation. This study examines the theoretical gap by investigating whether increased ESG performance may unintentionally heighten the financial burden and default risk in the short run. To verify the stability of each variable in the series, we employed the short-panel unit root test on panel data from 234 Chinese energy industry companies covering the years 2015 to 2023. Including enterprise fixed effects as well as time fixed effects, we find that higher ESG ratings increase the possibility of default risk in the Chinese energy sector. This effect remains robust after controlling firm size, financial leverage, return on assets, return on equity, earnings per share, beta and firm age. In addition, we conduct robustness checks using alternative default risk measures, both endogeneity- and component-based, and the outcomes demonstrate that the impact is substantial and consistent. Consequently, we may draw the conclusion that raising the ESG rating has an adverse effect on reducing corporate default risk, which fills the knowledge gap regarding the influence of listed companies’ default risk on China’s energy sector. Moreover, it has been found that green innovation plays a strengthening role in the analysis of the interaction term between green innovation and ESG on default risk. This suggests that while green innovation is a strategic initiative aimed at long-term sustainability, it requires a significant amount of capital and resources in the short term, which may result in higher default risk in the beginning. Full article

This study investigates the short-run and long-run dynamics of the financial cycle in South Africa, focusing on its macroeconomic drivers and their asymmetric effects across different phases. It addresses the persistent challenge in emerging market economies of balancing financial development and stability amidst volatile conditions. Using monthly data from 2000 to 2024, the research employs a quantile autoregressive distributed lag (QARDL) model to capture the heterogeneity and persistence of macro-financial linkages across the financial cycle’s distribution. The use of the QARDL model in this study allows for capturing asymmetric and quantile-specific relationships that traditional linear models might overlook. Findings reveal that monetary policy, and the housing sector are key drivers of long-term financial development in South Africa, showing positive effects. Conversely, exchange rate movements, inflation, money supply, and macroprudential policy dampen financial development. Short-term financial booms are associated with GDP growth, credit, share, and housing prices. Money supply and inflation are more closely linked to burst phases. These results underscore the importance of policy coordination, particularly between monetary and macroprudential authorities, to balance promoting financial development and ensuring stability in emerging markets. This study contributes to the empirical literature and offers practical insights for policymakers. Full article

Saudi Arabia is actively boosting women’s labor force participation, exceeding 2022 targets to enhance human capital. The purpose of this study is to examine the nexus between women’s empowerment and economic growth using secondary data from 1997 to 2022. Empowerment was proxied by Saudi women employed in government, with Gross Domestic Product (GDP) and female B.Sc. holders used as explanatory variables. The result of the Johansen test depicts a long-run equilibrium relationship between these variables. The Vector Error Correction Model (VECM) revealed a significant negative short-term impact of GDP on women’s empowerment at the 5% level, while female graduates had a positive short-term effect. The model results indicated rapid adjustment, correcting about 71% of disequilibrium per period towards long-run equilibrium. Importantly, a significant positive long-run relationship exists between women’s empowerment and economic growth. Diagnostic tests confirmed the VECM’s reliability, reflected by normally distributed residuals, with no significant autocorrelation, and overall model stability. The study findings contribute valuable insights for policymakers and stakeholders striving to achieve gender equity and sustainable economic development. Moreover, qualitative methods could be employed in future research to enhance the comprehensive understanding of the cultural and social barriers to maximize the long-run virtuous cycle of empowerment and economic growth. Full article

Peruvian Creole goats (PCGs) represent a unique genetic resource shaped by adaptation to diverse environments and traditional breeding practices. In this study, we performed a genomic analysis of six regional populations (Ancash, Ica, Lambayeque, Lima, Piura, and Tumbes) using high-density SNP genotype data. Principal component analysis revealed a moderate genetic structure, with the Ica population showing clear separation and northern populations exhibiting overlap. Runs of homozygosity were predominantly short, and specific regions on chromosome 6 were shared across populations. Inbreeding coefficients were generally low, with Ancash showing the highest values. Linkage disequilibrium decayed rapidly over genetic distance, especially in Piura, indicating higher genetic diversity. Estimates of effective population size revealed decreasing trends across populations, with Piura maintaining the largest recent population size. These findings offer valuable insights into the population structure of Peruvian Creole goats, providing guidance for conservation and sustainable breeding efforts. Full article

Russia’s full-scale aggression against Ukraine has led to profound disruptions in local and global agri-food markets. Since Ukraine is one of the world’s largest maize exporters, the war also contributed to considerable changes in trade reallocation, as well as an increase in the significance of the European Union in Ukrainian exports. This study analyses the effects of the Russian–Ukrainian war on horizontal maize price transmission between Ukraine and the EU countries. The panel autoregressive distributed lag model (ARDL) was applied to investigate the impact of the war on the price pass-through between those countries. The econometric analysis was performed on a weekly feed maize export price series for Ukraine and 14 selected EU countries. The time frame of research, January 2019 to December 2024, was split into pre-war and war periods. The study indicates that with the outbreak of the war, the long-term relationship between Ukraine and the EU’s maize prices has weakened. At the same time, there was an increase in the short-run maize price transmission between Ukraine and the Eastern EU countries. This proves that in the face of the conflict, market participants in these countries are increasingly guided by the market situation in Ukraine when making economic decisions. Full article

This study investigates the contributions of five key sectors—insurance, materials, utilities, real estate, and transport—to the financial markets of six Gulf Cooperation Council (GCC) countries from 2004 to 2023. Grounded in the Sectoral Linkage Theory and Endogenous Growth Theory, the study employs a Panel Autoregressive Distributed Lag (Panel ARDL) model to examine both short-term and long-term sectoral impacts on financial market resilience. The findings reveal that the insurance and transport sectors offer short-term market stimulation, but lack persistent effects. Conversely, the materials, utilities, and real estate sectors exhibit strong, long-run contributions to financial stability and economic diversification. These results highlight the asymmetric impact of sectoral dynamics on market performance in resource-rich contexts. This research contributes to the literature by providing empirical evidence on sectoral interdependence in oil-dependent economies and highlights the importance of structural diversification for sustainable financial resilience. The study provides actionable insights for policymakers and investors seeking to enhance market resilience and reduce reliance on hydrocarbon revenues through targeted sectoral development. Full article

County economies are the cornerstone of China’s economic and social development but face challenges such as a singular industrial structure and the outflow of production factors. As an important policy tool for rural revitalization, the impact mechanism of National-Level Modern Agricultural Industrial Parks (NMAIPs) on county economies remains inadequately explored. This study aims to quantify the dynamic economic effects of the NMAIP policy through rigorous empirical analysis and elucidate the core pathways driving county economic growth. Based on panel data from 44 counties in six central Chinese provinces from 2014 to 2024, this study employs a Multi-Period Difference-in-Differences (DID) model and finds a significant one-year lag effect of the NMAIP policy: in the year following park establishment, county GDP increased by an average of 8.5%, and this positive effect persisted until the fourth year but showed a trend of marginal diminution. Pathway analysis reveals that agricultural scale expansion (measured by gross output value of agriculture, forestry, animal husbandry, and fishery) and production efficiency improvement (measured by the ratio of output value to agricultural expenditure) are the core driving mechanisms, accounting for 48% and 35% of the total effect, respectively. In contrast, the mediating roles of industrial integration (comprehensive index) and industrial structure upgrading (share of agricultural services) were not statistically significant in the short run. The policy lag primarily arises from the conversion cycle of infrastructure investment to economic output, while pathway differences are closely related to the maturity of the county’s agricultural industrial chain and resource allocation efficiency. This study provides robust empirical evidence for optimizing the timing and pathways of the NMAIP policy design: policy effect evaluations require a 1–2 year “window period”; resources should be prioritized for projects that can rapidly enhance scale and efficiency (e.g., scaled planting, technology-driven efficiency gains), laying a solid agricultural foundation before gradually fostering industrial integration. This aligns with the spirit of “avoiding industrial hollowing-out” proposed in the 2024 Central “Thousand Villages Project” and provides the Chinese experience for the policy evaluation and path selection of global agricultural parks. Full article

Gross primary productivity (GPP) is a key carbon flux in the global carbon cycle, and understanding the inhibitory effects of drought on GPP and its underlying mechanisms is crucial for understanding carbon–climate feedback. However, current research has not sufficiently addressed the threshold dynamics and regional differentiation of GPP responses to the synergistic effects of meteorological drought (MD) and soil moisture drought (SD), particularly in the drought-sensitive Mongolian Plateau. This study focuses on the Mongolian Plateau from 1982 to 2021, using the standardized precipitation index (SPI) and standardized soil moisture index (SSI) to characterize MD and SD, respectively. The study combines the three-threshold run theory, cross-wavelet analysis, Spearman correlation analysis, and copula models to systematically investigate the variation characteristics, propagation patterns, and the probability and thresholds for triggering GPP loss under different time scales (monthly, seasonal, semi-annual, and annual). The results show that (1) both types of droughts exhibited significant intensification trends, with SD intensifying at a faster rate (annual scale SSI12 trend: −0.34/10a). The intensification trend strengthened with increasing time scales. MD exhibited high frequency, short duration, and low intensity, while SD showed the opposite characteristics. The most significant aridification occurred in the central region. (2) The average propagation time from MD to SD was 11.22 months. The average response time of GPP to MD was 10.46 months, while the response time to SD was significantly shorter (approximately 2 months on average); the correlation between SSI and GPP was significantly higher than that between SPI and GPP. (3) The conditional probability of triggering mild GPP loss (e.g., <40^th percentile) was relatively high for both drought types, and the probability of loss increased as the time scales extended. Compared to MD, SD was more likely to induce severe GPP loss. Additionally, the drought intensity threshold for triggering mild loss was lower (i.e., mild drought could trigger it), while higher drought intensity was required to trigger severe and extreme losses. Therefore, this study provides practical guidance for regional drought early-warning systems and ecosystem adaptive management, while laying an important theoretical foundation for a deeper understanding of drought response mechanisms. Full article

The study investigates the contribution of technology (TECH), quantified by Internet penetration, in influencing tourism performance (TP) among the top ten touristic nations in Europe: France, Spain, Italy, Turkey, the United Kingdom, Germany, Greece, Austria, Portugal, and the Netherlands. Using panel data from 2000–2022, the study includes additional structural controls like environment quality, gross domestic production (GDP) per capita, exchange rate (ER), and safety index (SI). The Method of Moments Quantile Regression (MMQR) is employed to capture heterogeneous effects at different levels of TP, and Driscoll–Kraay standard error (DKSE) correction is employed to make the analysis robust against autocorrelation as well as cross-sectional dependence. Spectral–Granger causality tests are also conducted to check short- and long-run dynamics in the relationships. Empirical results are that TECH and SI are important in TP at all quantiles, but with stronger effects for lower-performing countries. Environmental quality (EQ) and GDP per capita (GDPPC) exert increasing impacts at upper quantiles, suggesting their importance in sustaining high-level tourism economies. ER effects are limited and primarily short-term. The findings highlight the need for integrated digital, environmental, and economic policies to achieve sustainable tourism development. The paper contributes to tourism research by providing a comprehensive, frequency-sensitive, and distributional analysis of macroeconomic determinants of tourism in highly developed European tourist destinations. Full article

Electric propulsion requires engines and transmission systems that run at higher speeds compared to combustion engines. For improving sustainability and environmental protection, biodegradable oils are suggested for the lubrication of high-speed gears that require particularly quick wetting of the steel surfaces. Newly developed promising candidates include short-chained polyalphaolefins. In the present work, a study on the applicability of such oil is presented and discussed with respect to different aging levels based on biodegradable properties. It focuses on the wettability of metallic surfaces investigated through time-resolved contact angle measurements. Carbon steels with different carbon contents and microstructures are selected as the most commonly used materials for gears. Effects of steel composition, surface roughness and oil oxidation are studied. The results show that in most cases, the application of biodegradable polyalphaolefins is not critical; however, a combination of steels with inhomogeneous microstructure, high surface roughness and aged oil can be critical because of limited wetting. Full article

This study examines the role of human capital, technological investment, and institutional quality in promoting economic complexity in South Africa, with implications for sustainable development and the strategic role of Small and Medium Enterprises. Motivated by the growing importance of productive sophistication for long-term development in emerging economies (notably SDG 8 and SDG 9), the study examines both long-run and short-run dynamics using the Autoregressive Distributed Lag approach, with robustness checks via Fully Modified Least Squares, Dynamic Least Squares, and Canonical Cointegration Regression. Structural Vector Autoregression is employed to assess the persistence of shocks, while the Toda–Yamamoto causality test evaluates causality. The results reveal that institutional quality significantly enhances economic complexity in the long run, while technological investment exhibits a negative long-run impact, potentially indicating absorptive capacity constraints within industries. Though human capital and income per capita do not influence complexity in the long run, they have short-term effects, with income per capita having the most immediate influence. Variance decomposition shows that shocks to technological investment are essential for economic complexity, and are the most persistent, followed by human capital and institutional quality. These findings show the need for institutional reforms that lower entry barriers for SMEs in industries, targeted innovation policies that support upgrading, and human capital strategies aligned with driven industrial transformation. The study offers insights for policymakers striving to influence structural drivers to advance sustainable industrial development and achieve the SDGs. Full article