Search Results (14,723)

Inflation expectations are central to monetary policy transmission, yet relatively little is known about whether individuals’ own investment experiences are associated with how they form such expectations across different forecast horizons. This study examines the association between self-reported past investment performance and horizon-specific expected cumulative consumer price changes at the one-, three-, and five-year horizons using a large-scale online survey of 157,523 active Japanese investors. Because the survey asks respondents how consumer prices will change over each horizon, the three- and five-year responses are interpreted as expected cumulative price changes rather than annualized inflation rates. Ordered probit models are estimated while controlling for demographic, socioeconomic, and behavioral characteristics. The results show a horizon-dependent conditional association: self-reported investment performance is not significantly associated with one-year expectations in the full specification, whereas it is positively and significantly associated with three- and five-year expectations. Formal stacked OLS interaction tests indicate that the association differs significantly across horizons, and additional threshold-specific probit models show that the pattern is most evident for moderate inflation-expectation thresholds. The economic magnitudes are statistically precise but modest. Heterogeneity analyses further suggest that the association is weaker among respondents with higher financial literacy and higher assets, and stronger among respondents with a more myopic view of the future. Because the analysis relies on cross-sectional observational data and subjective performance measures, the findings should be interpreted as conditional associations rather than causal effects. Full article

To address the major safety hazard that heat released from mining polyurethane (PU) reinforcement materials may induce spontaneous combustion of residual coal in goaf, this study selected No. 3 coal from Wangzhuang Coal Mine, Shanxi Lu’an, as the research object. A self-developed large-capacity, large-scale experimental system was used to conduct programmed heating experiments on 2.0 kg multi-particle-size coal-PU mixed samples. The effects of PU content on characteristic gas release, crossing point temperature (CPT), residue morphology, and TGA-DSC characteristic temperatures were systematically investigated, and the reaction-kinetic evolution was further analyzed using the distributed activation energy model (DAEM). The results show that coal and PU exhibit a significant synergistic enhancement effect during co-heating. As the PU content increased, the release concentrations of CO, C₂H₄, and C₂H₆ increased markedly, and their initial release temperatures decreased, whereas CH₄ generation was inhibited by hydrogen-radical competition; no C₂H₂ was produced below 400 °C. The CPT decreased linearly with an increasing PU content, with an average decrease of approximately 8.5 °C for every 10% increase in PU content. Residue morphology showed clear critical features: glassy agglomerates appeared when the PU content exceeded 16.67%, and dense bulk coking occurred when the PU/coal mass ratio was greater than 1:10. TGA-DSC analysis showed that when the PU/coal ratio was lower than 1:10, the ignition temperature of the mixed sample was higher than that of pure coal, indicating an inhibitory synergistic effect. When the ratio exceeded 1:10, the ignition temperature decreased significantly, and the synergy shifted to promotion; increasing the heating rate shifted the characteristic temperatures to higher values and increased the reaction intensity. DAEM analysis further confirmed that when the PU ratio exceeded 1:10, the apparent activation energy of the mixed samples was lower than that of pure coal. Coal powder also acted as a physical skeleton that effectively dispersed molten PU, eliminated the activation-energy peaks of pure PU in the conversion ranges of 30–50% and 70–90%, and substantially improved combustion stability. Mechanistically, low-temperature PU melting and coating optimized heat and mass transfer, medium-temperature pyrolysis released active radicals and combustible gases that altered coal pyrolysis pathways and the radical reaction environment, and high-temperature hydrogen-radical competition reshaped the gas-product distribution. Together, these processes form a complete chain of synergistic spontaneous combustion. This study identifies key safety threshold parameters for PU reinforcement materials, recommends a PU content of ≤9.10%, and identifies CO and C₂H₄ as priority early-warning gases, providing direct experimental evidence for characteristic-gas-based early warning and mine fire prevention. Full article

Rapid urbanization has significantly altered urban landscape composition and configuration, making it a key driver exacerbating the urban heat island (UHI) effect. As a rapidly expanding inland city in Central China, Zhengzhou is highly sensitive to changes in landscape composition and spatial configuration. Therefore, clarifying the nonlinear relationship between landscape patterns and the urban thermal environment is of great significance for sustainable urban planning and thermal environment regulation. Taking the main urban area of Zhengzhou as the study area, this paper retrieves land surface temperature (LST) using the radiative transfer equation method based on Landsat 8 remote sensing images from August 2015 to August 2024, and constructs the surface urban heat island intensity (SUHII) index. By integrating multi-dimensional landscape pattern indices, the XGBoost machine learning model, and the SHAP interpretability method, this study systematically analyzes the nonlinear response mechanisms of landscape composition and configuration to SUHII, key regulatory thresholds, and their changes between 2015 and 2024. The results show that: (1) The SUHII in Zhengzhou was substantially higher in 2024 than in 2015. The area proportions of strong and extremely strong heat islands were higher in 2024 (26.16% and 2.34%) than in 2015 (2.22% and 0.12%), and the thermal environment differed between 2015 and 2024, shifting from a localized patch pattern to a more continuously expanding pattern. (2) Landscape area-related indices are the key factors. The areas of green space and water bodies, along with the landscape diversity index, show significant negative correlations, while built-up area and aggregation index show significant positive correlations. (3) SHAP feature importance indicates that water body area is the primary cooling factor, whereas built-up area is the primary warming factor, jointly dominating the spatial pattern of the thermal environment in Zhengzhou. (4) Landscape composition and configuration exhibit significant nonlinear responses to SUHII with region-specific thresholds, and these thresholds were higher/lower in 2024 than in 2015, suggesting a possible association with urban expansion. Specifically, stable cooling effects occurred when the water body area exceeded 3.5 km² in 2015, with the threshold rising to 4.2 km² in 2024. The warming threshold for built-up area decreased from 18.8 km² to 8.5 km², suggesting a higher sensitivity of the thermal environment to built-up area expansion in 2024 compared to 2015, characterized by a regulation pattern of “dominant scale effect and weakened configuration effect”. This study identifies thresholds specific to Zhengzhou’s main urban area at two time points (2015 and 2024), providing quantitative support and scientific basis for blue–green space optimization, precise heat island mitigation, and territorial spatial planning in Zhengzhou. These findings are based on a comparison of two time points (2015 and 2024) and do not directly capture continuous temporal dynamics. Full article

Chronic kidney disease, acute kidney injury and cardiorenal syndrome are major determinants of cardiovascular morbidity and mortality, yet conventional renal assessment based on serum creatinine, estimated glomerular filtration rate and urine output often fails to detect early structural injury or pathway-specific cardiorenal risk. This narrative review synthesized recent evidence on emerging renal and cardiorenal biomarkers with potential value for cardiovascular risk stratification beyond creatinine. Literature published between 2015 and April 2026 was reviewed, focusing on biomarkers of tubular injury, functional renal impairment, fibrosis/remodeling and mineral metabolism. NGAL and KIM-1 may detect tubular stress and proximal tubular injury before overt functional decline and have shown relevance in heart failure, acute coronary syndromes and post-cardiac surgery settings. Cystatin C and pro-enkephalin refine functional renal assessment and may improve prognostic classification when creatinine is confounded by frailty, muscle mass or acute hemodynamic changes. Soluble ST2 and galectin-3 reflect inflammation, fibrosis and cardiorenal remodeling, while FGF-23 links kidney dysfunction to cardiovascular risk through phosphate imbalance, vascular calcification and myocardial hypertrophy. Multi-biomarker panels may help identify dominant cardiorenal phenotypes and personalize monitoring intensity. However, routine implementation requires standardized assays, validated thresholds, cost-effectiveness data and prospective evidence that biomarker-guided management improves clinical outcomes. Full article

The practical performance of building-integrated solar systems in cold climates is strongly governed by temperature-grade matching between solar energy output and space-heating demand. However, year-round field evidence comparing solar thermal collectors, photovoltaic systems, and photovoltaic/thermal systems under the same building, climatic, and heating-network boundary conditions remains limited. This study conducted a year-round field evaluation of solar collector (SC), photovoltaic (PV), and photovoltaic/thermal (PVT) systems installed in an office building in Tianjin, China. Continuous operating data collected from November 2022 to October 2023 were used to assess seasonal thermal output, electricity generation, effective heat supply, solar utilization efficiency, carbon reduction, and payback period. During the heating season, SC exhibited the strongest direct-heating capability among the investigated systems, delivering 817.50 MJ/m² of useful heat. In contrast, under the investigated system configuration without heat-pump assistance, the outlet temperature of the PVT subsystem remained below the 45 °C direct-heating threshold, and its thermal output could not be directly utilized for winter space heating. This result is specific to the investigated operating conditions and does not exclude the potential application of PVT systems coupled with heat pumps or low-temperature heating terminals. During the non-heating season, the investigated PVT subsystem simultaneously produced electricity and usable low-temperature heat, with heat and electricity accounting for 61.3% and 38.7% of its useful output, respectively, indicating its potential for combined energy harvesting. Under the investigated climatic, system, cost, and energy-demand conditions, the entropy-weighted TOPSIS assessment ranked SC highest when non-heating-season heat demand was present, whereas PV was more suitable when such heat demand was absent. Furthermore, a demand–output matching method was developed to support SC/PV area allocation for different building types. Under the investigated climatic and energy-demand assumptions, the recommended PV area ratios were 54.5%, 67.4%, and 79.7% for residential, office, and commercial buildings, respectively. These results provide field evidence for effective heat evaluation, temperature-grade matching, and component selection in solar-assisted heating systems for cold-climate buildings. Full article

This study examines the relationship between digitalization, e-commerce capability, and firm value in Gulf Cooperation Council (GCC) countries. Using an unbalanced panel of 200 non-financial firms over the period of 2015–2024, the analysis employs a panel threshold regression model to investigate nonlinearities in the digitalization–performance relationship. The results indicate that digitalization (DIG) is positively associated with firm value, although the relationship is regime-dependent. A significant threshold effect is identified at DIG = 0.37, with the coefficient of digitalization declining from 0.405 in the low-digitalization regime to 0.162 in the high-digitalization regime, suggesting diminishing marginal returns to digital transformation. The findings also show that e-commerce capability becomes more strongly associated with firm value in the high-digitalization regime, where the interaction coefficient reaches 0.291 (p < 0.01), while remaining statistically insignificant below the threshold. These findings highlight the complementary relationship between digital infrastructure and e-commerce capability and suggest that the economic value of digital transformation depends on firms’ level of digital maturity. This study contributes to the literature by providing a nonlinear perspective on the digitalization–performance relationship and by offering new firm-level evidence from GCC economies. The results provide useful implications for firms and policymakers seeking to design effective digital transformation strategies. Full article

Aluminum alloy hot rolling features diverse varieties, large variations in incoming strip thickness, and strong process nonlinearity. Traditional rolling force prediction models rely on simplified physical assumptions and poor adaptability, making it hard to satisfy high-precision production requirements. This paper presents a mechanism–data dual-driven PSO-BP neural network method for rolling force prediction which is applicable to the rolling temperature range of 320 °C to 520 °C. The SIMS mechanism model is employed as a physical constraint, and a hybrid PSO-GD algorithm optimizes the initial weights and thresholds of the BP network, avoiding the local optimum issue of conventional BP. The rolling mechanism model is embedded into the loss function to deeply integrate physical laws and data-driven learning. Validation using 508 sets of field data from 5083 aluminum alloy hot rolling shows that the model achieves a M_APE of 5.0794% and R² of 0.9254, significantly outperforming the traditional mechanism model (8.91%) and standard BP (8.77%). The proposed model preserves physical interpretability while utilizing data-driven adaptability, offering an effective approach for high-precision rolling force prediction and improving the dimensional accuracy of hot-rolled aluminum alloy sheets. Full article

This study explored the preliminary educational effects of a tsunami evacuation program using a streetscape reconstructed from a real district with a 3D laser scanner. The study area was Ono-machi, Kanazawa City, Japan, where 652 scan positions were captured using a Leica BLK360; the resulting point clouds were registered, cleaned, converted into a mesh model, and imported into Unity to build a desktop-based 3D evacuation experience. Twenty-five residents participated, operating the system individually or in small groups, discussing evacuation decisions, and completing pre- and post-experience questionnaires. Exploratory pre–post comparisons using the Wilcoxon signed-rank test were conducted for the 22 complete paired responses. Because five corresponding pairs were tested, the possibility of Type I error inflation due to multiple comparisons was considered. The results were interpreted using both uncorrected p-values and a Bonferroni-adjusted significance threshold of 0.01. The largest improvement was observed in the understanding of hazardous locations, with a mean increase of 1.59 points and a large effect size. The improvement in consideration of detours and alternative routes also remained below the adjusted threshold. Other corresponding item pairs showed positive descriptive changes and uncorrected p-values below 0.05, but they did not meet the Bonferroni-adjusted threshold. Therefore, these findings should be interpreted as preliminary evidence that a locally grounded, point-cloud-derived 3D urban model may support residents’ place-based understanding of local hazards and evacuation-related reflection. By supporting local risk communication, preparedness, and evacuation-related reflection, this approach may contribute to sustainable community-based disaster-prevention education and the development of more resilient coastal communities. Full article

To achieve dual-carbon goals and advance the sustainable development of the automotive industry, China’s Dual-Credit Policy serves as the core long-term mechanism for the low-carbon transition of the automotive industry. Given the coexistence of fuel vehicles (FVs) and new energy vehicles (NEVs) in China, existing research often overemphasizes production output while neglecting energy consumption control, and focuses predominantly on NEVs at the expense of FV optimization. To address these gaps, this paper treats FV fuel consumption and NEV energy efficiency as core endogenous decision variables. We construct profit-maximizing optimal production decision models for both types of manufacturers under the Dual-Credit Policy. Through mathematical derivation, numerical simulations, and empirical tests using actual industrial parameters, this study verifies the existence and uniqueness of optimal solutions. It clarifies the influence mechanisms of policy and market factors on corporate energy decisions and identifies the rules of strategy dominance. The findings reveal that the optimal fuel consumption decisions of FV manufacturers exhibit distinct piecewise patterns and critical threshold effects. Specifically, credit prices, NEV quotas, and fuel consumption standards determine the dominance of compliant (low-consumption) versus non-compliant (high-consumption) strategies. Furthermore, the policy exerts a significant market-oriented positive incentive on the energy efficiency upgrading of NEV manufacturers, with credit prices, market demand, and R&D costs acting as core constraints. Notably, the transition-guiding effect of the policy has clear effective boundaries, and its efficacy highly depends on the alignment between parameter design and market conditions. This research provides theoretical support for manufacturers to formulate energy-optimized production decisions and offers actionable references for the continuous optimization of the Dual-Credit Policy system and the sustainable low-carbon transformation of China’s automotive sector. Full article

The classical monetary trilemma implies a binding trade-off among exchange rate stability, capital mobility, and monetary autonomy. Yet, emerging market economies increasingly operate hybrid policy configurations that depart systematically from the trilemma’s corner solutions. This paper proposes a continuous, time-varying measure of such departures—the Hybridity of Regime Index (HRI)—extracted via a dynamic factor model from sub-indices capturing exchange rate hybridity, capital account segmentation, and effective monetary autonomy for a balanced panel of thirty emerging markets over the period 2005–2024. The analysis yields four principal findings. First, a secular increase in average regime hybridity is observed, with a marked acceleration following the financial fragmentation shocks of 2022. Second, moderate hybridity is associated with attenuated output and inflation volatility, and local projections show that high-HRI economies experience milder output contractions in the immediate aftermath of global financial shocks. Third, panel threshold regressions identify an endogenous HRI level beyond which the stabilizing effect reverses: further hybridity amplifies macroeconomic volatility and erodes reserve adequacy. Fourth, the post-2022 geopolitical fragmentation of the international monetary system has amplified the pre-existing trend toward hybridity, with sanction-affected economies exhibiting discontinuous jumps in HRI that push them into the high-vulnerability regime. This paper characterizes this non-linear pattern as a resilience–vulnerability nexus and discusses its implications for early warning indicators and for the assessment of policy responses to the fragmentation of the international monetary system. Full article

In the context of China’s Carbon Peaking and Carbon Neutrality Goals, understanding how non-agricultural employment affects agricultural carbon emission intensity is critical for achieving green and sustainable agricultural development. This study asks whether the reallocation of labor away from agriculture increases or reduces agricultural carbon emission intensity, through which transformation-related pathways this relationship may operate, and whether the relationship changes across development stages and spatially connected regions. Using panel data from 295 prefecture-level cities in China from 2011 to 2023, this study constructs a factor-substitution framework and applies fixed-effects models, potential pathway analysis, threshold models, and Spatial Durbin Models. The results show that non-agricultural employment has a significant inverted U-shaped association with agricultural carbon emission intensity: at relatively low levels it is associated with higher emission intensity, whereas beyond the estimated turning point it is associated with lower emission intensity. Non-agricultural employment is also systematically associated with agricultural structural adjustment, mechanization transformation, and AI-related patenting activity, which are interpreted as potential pathways rather than definitive causal mediation channels. Threshold results indicate that economic development and urbanization condition the marginal effect of non-agricultural employment, while spatial estimates show that the nonlinear relationship extends to neighboring regions through significant spillover effects. These findings suggest that agricultural carbon-reduction policies should distinguish between cities at different stages of labor reallocation, promote low-carbon forms of capital and technology substitution, and strengthen cross-regional coordination in agricultural producer services and green technology diffusion. Full article

The large-scale integration of renewable energy has significantly improved the low-carbon performance of power systems, but has also increased operational uncertainty. Under extreme weather conditions, wind and solar power may experience consecutive days of simultaneous output shortfalls—referred to as “renewable energy drought”—leading to persistently high net load and severe challenges to supply guarantee. To address this issue, this paper proposes a multi-source coordinated supply-guarantee dispatch strategy for consecutive-day renewable energy drought scenarios. First, net load is defined as the total system load minus the available wind and solar output. Based on magnitude and duration thresholds, renewable energy drought events are extracted from historical data to generate representative scarcity scenarios. Second, a multi-source coordinated optimization dispatch model is constructed, incorporating wind power, solar power, thermal units, battery energy storage, and pumped-storage hydro. The objective is to minimize the total system operating cost, which includes thermal fuel cost, start-up/shut-down costs, storage cycling cost, wind/solar curtailment penalty cost, and load shedding penalty cost. The load shedding penalty coefficient is set to a magnitude much higher than conventional costs to highlight the priority of supply guarantee. The model accounts for operational constraints such as minimum up/down times, deep regulation capability, ramping limits of thermal units, and charge/discharge power limits of storage. Taking a provincial power system in China for the year 2030 as a case study, a dispatch case covering four consecutive days (96 time periods) is designed. Based on a baseline scenario, eight groups of sensitivity analyses are conducted to comprehensively investigate the impacts of key factors on the supply-guarantee strategy, including: the minimum up/down time of thermal units, deep regulation capability, load shedding penalty cost, load level, rated energy capacity and charge/discharge efficiency of battery energy storage, rated energy capacity and pumping/generating efficiency of pumped-storage hydro, thermal fuel cost coefficient, and renewable energy capacity. Simulation results show that the proposed strategy can effectively coordinate multiple resources under consecutive-day drought conditions; reducing the minimum up/down time of thermal units improves supply flexibility but increases start-up/shut-down costs; enhancing deep regulation capability optimizes storage utilization and reduces total system cost; the load shedding penalty cost directly determines the trade-off between supply guarantee and economic efficiency; and as load level decreases by 5%, 10%, and 15%, the total system operating cost reduces by approximately 6.3%, 12.5%, and 18.8%, respectively. This study provides a quantitative method and technical support for supply-guarantee dispatch decisions and resource allocation in high-renewable power systems under persistent drought conditions. Full article

Conventional object detection models suffer from significant performance degradation in dense urban traffic scenarios. To address these critical limitations and enable accurate real-time road congestion decision making, this study proposes an optimized YOLOv8-based detection paradigm that decouples multi-scale feature enhancement from dynamic focused bounding box regression. Specifically, a multi-scale feature enhancement (MFE) module is designed to extract high-resolution shallow features directly from the P2 layer of the YOLOv8 backbone. Then, a convolutional block attention module (CBAM) is embedded into the feature fusion neck to adaptively filter complex urban background noise and recalibrate channel–spatial feature responses for vehicle target saliency. Furthermore, the standard CIoU loss is replaced with the Wise-IoU (WIoU) dynamic focusing loss function, which suppresses gradient interference from low-quality, occluded samples and stabilizes bounding box regression for dense vehicle targets. The high-precision vehicle detection outputs are fed into a quantitative congestion index (CI) model, which fuses vehicle density and average speed to realize real-time congestion-level classification. Extensive experiments on the public UAVDT benchmark dataset demonstrate that the proposed model achieves an mAP@0.5 of 83.1% (3.8 percentage points higher than the YOLOv8 baseline), an mAP_S (small target) of 23.2% (a 4.3 percentage point improvement), and a real-time congestion decision accuracy of 83.8%. Ablation studies verify the independent and synergistic effectiveness of the MFE, CBAM, and WIoU modules, with the MFE module making the greatest contribution to small-target detection performance (+1.7% mAP@0.5). The proposed model maintains a real-time inference speed of 86 FPS (frames per second) on an NVIDIA RTX 3090 GPU, far exceeding the 30 FPS threshold for real-time traffic monitoring. Full article

This paper generalizes a fractional Cournot–Bertrand duopoly model with delayed self-feedback by replacing discrete memory effects with equal-mean, distributed Gamma memories. The resulting framework preserves the original economic structure while distinguishing the effect of the shape of the memory distribution from that of its average length. We derive the equilibria, the linearized characteristic equations, and real–imaginary crossing conditions for point, weak-Gamma and strong-Gamma memories. For the reference output-feedback configuration, the point-delay benchmark has the reported Hopf threshold ${\tau }_0=3.6355$ at frequency ${\psi }_0=0.5947$, whereas the two Gamma kernels remain separated from an imaginary-axis crossing over the tested equal-mean interval. At the reference crossing frequency, their feedback gains are approximately $0.42$ and $0.46$, respectively, compared with unit gain for the point delay. Numerical root tracking, stability diagnostics, parameter scans, and solver-convergence checks support the conclusion that distributed aggregation can materially enlarge the practically stable operating region. Full article

The aim of this meta-analysis was to synthesize current evidence on inter-limb asymmetries in youth athletes and to determine their magnitude, developmental determinants, and functional relevance. The review followed PRISMA 2020 guidelines, and the protocol was registered in PROSPERO. Six databases were searched from inception to October 2025. Studies assessing asymmetry as a between-limb difference in athletes aged 6–18 years were included. A total of 25 studies (N = 4125) were included qualitatively, with 24 included in the quantitative analyses. Meta-analyses were conducted for comparable outcomes (single-leg countermovement jump [SLCMJ], change of direction speed [COD], association with sprint performance, and maturation effects) using random-effects models and heterogeneity assessment (I², τ²). Mean asymmetry was 10.8% for SLCMJ (95% CI: 6.7–14.9; I² = 78%) and 7.4% for COD (95% CI: 0.5–14.2; I² = 64%). The association between asymmetry and sprint performance was small and not statistically significant (r = −0.27; 95% CI: −0.55 to 0.07). Maturation analysis showed a moderate effect (d = 0.35; 95% CI: 0.18–0.52), with peak asymmetry around peak height velocity (PHV). Heterogeneity was mainly explained by sport-specific demands and methodological differences. Asymmetries of approximately 10% are commonly observed in youth athletes in single-leg jump and change of direction tests, but their clinical relevance likely depends on sport-specific demands, maturation status, and testing modality, and should not be interpreted as a universal normative threshold. The lack of prospective injury data prevents the establishment of universal clinical thresholds. In conclusion, inter-limb asymmetries are common and developmentally dynamic in youth athletes, with functional relevance depending on biological and sport-specific context. Future research should prioritize methodological standardization and prospective designs. Full article