Search Results (691)

This study enhances macroeconometric modelling by utilising an I(2) cointegration framework to analyse the dynamic link between tourism prices and inflation in Slovenia and the Eurozone. Using monthly data from 2000 to 2017, we estimate cointegrated VAR models that capture long-run equilibria, short-run adjustments, and persistent deviations inherent in I(2) processes. The results reveal strong spillover effects from Slovenian tourism and input prices to Eurozone inflation and hospitality prices in the short run, while Eurozone-wide shocks dominate the long-run dynamics. By explicitly accounting for nonstationarity, structural breaks, and seasonal patterns, the I(2) model provides a more reliable framework than traditional I(1)-based approaches, which are often prone to misspecification when higher-order integration and persistent deviations are ignored. The findings contribute to macroeconometric theory by demonstrating the value of I(2) cointegration in modelling complex price systems and offer policy insights into inflation management and competitiveness in tourism-dependent economies. Full article

Predictive analytics for financial distress plays an important role in enterprise risk management and everyday business decisions. Most past studies mainly use accounting indicators that come from standard financial reports. This study adds analyst-forecast financial indicators and places them in a data-driven business intelligence setup to improve how companies predict financial distress. We work with seven real datasets to test several predictive models and run statistical checks to see how analyst forecasts work with historical financial data. The results show that analyst-forecast indicators can clearly improve prediction accuracy and make the results easier to understand. From an enterprise systems view, this study pushes traditional financial distress prediction toward a smarter analytics setup that supports real-time, explainable, and data-based risk assessment. The findings provide useful ideas for both the theory and practice of designing business intelligence systems and financial decision-support tools for companies. Full article

The frequent occurrence of fires has prompted China to accelerate the development of community fire prevention and emergency management systems. Language, serving both communicative and affective functions by facilitating the flow of information and fostering mutual understanding, runs through the entire process of community fire emergency management. In response to the early-stage nature of this field and the lack of a systematic framework, this study constructs a dynamic capability evaluation system for urban community fire-related emergency language services (FELS) by integrating multi-source and heterogeneous data. First, by adopting a hybrid approach combining dynamic capability theory and text mining, a three-level indicator system is established. Second, based on domain knowledge, quantitative methods and scoring rules are designed for the third-level qualitative indicators to provide standardized input for the model. Third, a weighting and integration framework is developed that simultaneously considers the internal mechanism characteristics and statistical properties of indicators. Specifically, a knowledge-driven weighting approach combining FAHP and fuzzy DEMATEL is employed to characterize indicator importance and interrelationships, while the CRITIC method is used to extract Data-Driven weights based on data dispersion and information content. These knowledge-driven and Data-Driven weights are then integrated through a multi-feature fusion weighting approach. Finally, a linear weighting model is applied to combine the normalized indicator values with the integrated weights, enabling a systematic evaluation of the dynamic capabilities of community FELS. To validate the proposed framework, application tests were conducted in four representative types of urban communities, including internationally developed, aging and vulnerable, newly developed, and economically diverse communities, using fire emergency scenarios as the entry point. The external validity and internal robustness of the proposed model were verified through these tests. The results indicate that the evaluation system provides accurate, objective, and adaptive assessments of dynamic capabilities in FELS across different community contexts, offering a governance-oriented quantitative tool to support grassroots fire prevention and to enhance community resilience. Full article

In the context of global warming, the continued increase in the frequency of compound events—where drought and high-temperature extremes coincide—has led to severe natural disasters and substantial socio-economic losses. To systematically reveal the evolution of summer dry-heat compound events in Liaoning Province, this study constructs a whole-chain analysis framework of “identification–feature extraction–multivariate probability assessment”. Based on the Standardised Precipitation Index (SPI) and the Standardised Temperature Index (STI), we develop the Standardised Dry-Heat Index (SDHI) to identify dry-heat compound events. Run theory is applied simultaneously to extract key attributes for three types of events—drought, high temperature, and dry-heat compound events—and the Mann–Kendall test is used to detect their temporal mutation characteristics. By combining Copula functions with spatial analysis techniques, we further establish a whole-chain analysis method from “identification–feature extraction–hazard quantification”. The results show that during 1961–2020, summer drought, high-temperature, and dry-heat compound events occurred 4, 14, and 10 times, respectively, in Liaoning Province, with all three types showing a significant increase in frequency after the late 1990s. Spatially, zones of high drought intensity are mainly located in western Liaoning; the duration and severity of high temperatures are most pronounced in inland basin areas; and regions with high compound hazard intensity of dry-heat events largely coincide with urbanised areas. Climate propensity analyses further reveal that the province is experiencing an increasingly dry-heat-prone climate, with high temperatures being the dominant factor driving the enhanced hazard associated with dry-heat compound events. This study overcomes the limitations of traditional single-event analyses and provides a more accurate scientific basis for hazard assessment and zonal prevention and control of dry-heat disasters in Liaoning Province. Full article

Due to the recent improvement in the additive manufacturing field, aerospike engines have been reconsidered as a possible alternative to the traditional bell-shaped nozzles. The former offer higher thrust and specific impulse during the launcher ascension phase because they are theoretically able to adapt the gas expansion ratio, reaching the optimal condition for a wide range of ambient pressure values, while bell-shaped nozzles can achieve the optimal expansion condition only at the design altitude. This capability has been proved for full-length plug nozzles, which, however, have some drawbacks, like a low thrust-to-weight ratio and challenging design of the cooling system at the spike tip. Therefore, research is moving towards truncated spike geometries, which allow the previously mentioned issues to be overcome. The aim of this work is to verify the expansion adaptation ability of a specific truncated aerospike geometry at different ambient pressures and to develop a simplified theory to estimate the upper bound of the base thrust coefficient. The analysis has been addressed by running numerical fluid dynamics simulations performed with an OpenFOAM solver. Full article

Developed and developing economies use globalization and foreign direct investment (FDI) to pave the way and to maximize economic growth. This study aims to investigate the impact of globalization and FDI on the economic growth of South Africa over the period from 1998 to 2022. The study employed the autoregressive distributed lag (ARDL) approach on annual data from the World Bank and the KOF index of globalization. ARDL tests reveal a long-run positive and statistically significant relationship of 12.7% in the case of economic globalization. This indicates that there is a reasonable level of the emergence of a globalized economy to integrate new and diverse systems, within internal economic growth forces that are supporting the globalization and endogenous growth theories. Political globalization is negative and statistically significant, while social globalization is positive but is used to depress long-run economic growth because of its insignificant status. The novelty of this study is to focus on the impacts of economic, social, and political globalization and FDI on the economic growth of South Africa, through direct and interactive procedures. The findings can be used by South African policymakers and other countries to prioritize reaping the benefits of globalization. These outcomes can be used to sensitize and promote policies that can attract relevant FDI, while enhancing economic growth. Full article

Amid intensifying challenges of global climate change, China—as the world’s largest carbon emitter and a major manufacturing hub—occupies a pivotal position in the global industrial green transformation. Drawing on environmental federalism theory and China’s decentralized governance model, this study develops a framework of “green finance–local government competition–industrial green transformation.” Using panel data from 283 cities in China, we employ spatial econometrics and mediation effect models to test the dual mechanisms by which green finance promotes industrial green transformation. The findings indicate that (1) green finance promotes industrial green transformation; (2) green finance advances industrial green transformation by dismantling China’s traditional local government competition–based development model and removing the institutional suppression arising from “race-to-the-bottom competition”; (3) the effect of green finance exhibits long-run characteristics and a “benchmark–imitation” pattern; (4) baseline environmental conditions strengthen the influence of green finance on industrial green transformation; (5) incorporating ecological civilization development into officials’ performance evaluations can effectively reshape policy incentives and amplify the positive role of green finance. Thus, we propose differentiated green finance policies, the construction of a governance mechanism that integrates fiscal–financial–ecological compensation, and the optimization of ecological civilization assessment indicators to curb campaign-style governance. Full article

This study examines the impact of health expenditure on economic growth in the BRICS countries during the period 2000–2021. Economic growth is measured by GDP per capita, while per capita health expenditure serves as the principal explanatory variable. Consistent with the framework of endogenous growth theory—which conceptualizes health as a form of human capital that enhances productivity—we additionally incorporate natural capital, education, and population share as control variables. Methodologically, the analysis employs panel unit root tests under cross-sectional dependence and estimates a dynamic panel ARDL model to assess both short- and long-term effects. To further validate the robustness of the model, additional explanatory variables relevant to endogenous growth theory are also evaluated. The results indicate that, in the long run, all explanatory variables exert a statistically significant influence on the economic growth of the BRICS countries. In the short run, however, only per capita health expenditure demonstrates a positive and statistically significant effect on GDP per capita, whereas the other variables do not yield significant short-term effects. Full article

This study investigates the impact of Internet of Things (IoT) demand on Digital Economic Growth (DEG) in Saudi Arabia between 2015 and 2023, employing both linear regression and a panel Autoregressive Distributed Lag (ARDL) model. The results show a long-term, significant, and positive association between IoT adoption and DEG, supported by the Technology Organization Environment (TOE) framework, highlighting the relevance of technology readiness and organizational capacity. Moreover, Internet penetration is a significant driver of digital transformation, aligned with the Diffusion of Innovations (DOI) theory, which emphasizes the role of connectivity in facilitating the adoption of digital devices. IoT will have little or no impact in the short term, but in the long run, the benefits are clear. Furthermore, despite the long- and short-term benefits of 5G deployment indicated by the results, a divergence between 5G deployment and electricity consumption is signaled by the significance of the error-correction term, which may be attributed to infrastructure and deployment prerequisites. Additionally, as an extension of the Resource-Based View (RBV) paradigm, the ultimate drivers of DEG through innovation and strategic resources highlight the importance of Research and Development (R&D) investment and Foreign Direct Investment (FDI) in inducing its growth. In contrast, inflation has an adverse impact on DEG, confirming macroeconomic instability as an obstacle to digital advancement, which relates to the environmental pillar of TOE. Policymakers can maximize Saudi Arabia’s digital economic growth on a sustainable, stronger path by investing in IoT infrastructure, increasing internet access and adoption, enhancing R&D and institutional support, and addressing challenges related to macroeconomic stability and 5G deployment. This study adds to the extant research by empirically evaluating the short- and long-term effects of IoT adoption on Saudi Arabia’s digital economic development, thereby providing insights into the roles of innovation, infrastructure, and institutional support in driving digital transformation. Full article

This paper explores the economic, environmental, and security issues created by the launch of satellite megaconstellations, which are networks of LEO (Low Earth Orbit) satellites planned to provide worldwide communications, data services, and research capabilities. Although such programs bring the potential to offer global coverage and substantial technology enhancements, they also pose significant challenges to fund and sustain. In order to address these issues, the approach assumes a Life Cycle Costing (LCC) scope that includes development, launch, operational, end-of-life, and environmental impacts. Based on this, we introduce an original model, which includes a Cooperative Game Theory component—more precisely the Shapley value—to devise fair and efficient cost-sharing mechanisms between multiple players. The model includes the effects of cooperation, free-rider phenomena, and the consideration of capacity limitations, providing a formalized approach to distribute costs fairly and ensure coalition stability. A three-operators case study demonstrates the real benefits achieved by collaboration: significant cost savings of up to 27% compared with independent approaches. However, the analysis also demonstrates the destabilizing effects of free riders, which undermine cooperation in the short run and may lead to a net increase in costs for contributing parties. The results indicate that resilient allocation mechanisms and policy protection are necessary to secure the sustainability of megaconstellations over the long time period, possibly also applicable to other critical infrastructures beyond space systems. Full article

Most existing depth-averaged granular flow theories assume that dry, cohesionless granular materials are incompressible, with the void ratio among grains remaining spatially and temporally invariant. However, recent large-scale experiments showed that the pore space among grains varies both spatially and temporally. This study, therefore, incorporates the effects of granular dilatancy to perform analytical and numerical investigations of granular flows down inclined planes. A high-resolution shock-capturing scheme is employed to numerically solve the compressible depth-averaged equations for temporal and spatial evolution of the flow thickness and depth-averaged velocity, as well as depth-averaged volume fraction. Additionally, a traveling wave solution is constructed. The comparison between analytical and numerical solutions confirms the accuracy of the numerical solution and also reveals that the gradient of the solids volume fraction, induced by granular dilatancy, results in a gentler slope of the granular front, in agreement with experimental observations. Furthermore, this numerical framework is applied to investigate granular flows transitioning from an inclined plane onto a horizontal run-out pad. The numerical solution shows that the incorporation of granular dilatancy causes the shock wave to propagate upstream more rapidly. As a result, the position and morphology of the mass deposit exhibit closer alignment with experimental data. Full article

Investor sentiment has increasingly been recognized as a behavioural factor influencing asset prices beyond traditional rational asset pricing models, yet evidence from South Africa’s property remains limited. This study investigates the short-run and long-run relationship between investor sentiment and FTSE/JSE-listed property indices, to determine the influence of sentiment on property index pricing within the South African context. Using monthly data for selected JSE/FTSE property indices, a composite investor sentiment index was constructed through a principal component analysis (PCA) of multiple market-based sentiment proxies. Consequently, a Vector Error Correction Model (VECM) was estimated to examine both the long-run and short-run relationships, integrated with the VEC Granger causality tests to determine the direction of influence between variables. The findings report a novel relationship between investor sentiment and the FTSE/JSE property indices, as they provide new insights at the disaggregated level, which is overlooked in the literature. In the short run, the findings suggest that market psychology drives short-term property price adjustments. Moreover, in the long run, the relationship remains significant, indicating that this effect persists, underscoring the enduring influence of sentiment on market valuation. Additionally, the Granger causality results indicate uni-directional relationships, where investor sentiment drives listed property pricing and macroeconomic variables, reinforcing its predictive role. The study concludes that investor sentiment is a key determinant of South Africa’s listed property market, consistent with the rationale of behavioural finance theory, and underscores that investment decisions within this market are substantially influenced by investor psychology, contributing to property market volatility. Full article

As a foundational analytical tool, the discernibility matrix plays a pivotal role in the systematic reduction of knowledge in rough set-based systems. Recent advancements in rough set theory have witnessed the proliferation of discernibility matrix-based knowledge reduction algorithms, with notable applications in classical, neighborhood, covering, and fuzzy rough set models. However, the quadratic growth of the discernibility matrix’s complexity (relative to domain size) imposes fundamental scalability limits, rendering it inefficient for real-world applications with massive datasets. To address this issue, we introduced a discernibility hashing strategy to limit the growth scale of the discernibility attributes and proposed a feature selection algorithm via discernibility hash based on rough set theory. First, on the premise of keeping the information of the original discernibility matrix unchanged, the method maps the discernibility attribute set of all objects to the storage unit through a hash function and records the number of collisions to construct a discernibility hash. By using this mapping, the two-dimensional matrix space can be reduced to a one-dimensional hash space, which greatly removes invalid and redundant elements. Secondly, based on the discernibility hash, an efficient knowledge reduction algorithm is proposed. The algorithm avoids invalid and redundant element attribute sets to participate in the knowledge reduction process and improves the efficiency of the algorithm. Finally, the experimental results show that the method is superior to the discernibility matrix method in terms of storage space and running time. Full article

The objective of this study was to determine the most stable conformation of L-tryptophan (L-Tryp) on gold and silver nanoparticles. Additionally, this work investigated how these parameters were influenced by analyte concentration, nanoparticle size, and pH. The purpose of this study was to establish whether L-Tryp molecules interact with the nanoparticles through the carboxylate end, the amino group end, or both. This research has diverse applications in biophysics and medical diagnostics, potentially opening up new avenues in these fields. Moreover, it may enrich the disciplines of chemistry and nanotechnology by offering innovative approaches for future research. These findings represent a significant advancement in understanding the interactions between L-Tryp and nanoparticles, making a meaningful contribution to biophysics and medical diagnostics. Surface-Enhanced Raman Scattering (SERS) spectra of L-Tryp in the 100–4000 cm⁻¹ spectral range were obtained using a 785 nm laser for excitation. Gold (Au) and silver (Ag) nanoparticles (NPs) were synthesized using the citrate reduction method. The experimental procedure involved the use of electrolytes (such as NaCl) for colloid activation, which resulted in very high SERS signals. Modification of nanoparticle surface charge was achieved by adjusting the pH of Au and Ag colloidal suspensions between 2 and 11. The SERS spectra indicate that small-sized nanoparticles require high concentrations of L-Tryp to achieve high sensitivity, whereas larger nanoparticles perform effectively at lower concentrations. The pronounced enhancement of stretching vibrations in the COO⁻ group in the SERS spectra strongly suggests that the carboxylate group attaches to silver nanoparticles (AgNPs). Conversely, for gold nanoparticles (AuNPs), a new band at approximately 2136 cm⁻¹ was observed, indicating that the amino group of L-Tryp interacts with Au in its neutral form. These analyses were complemented by theoretical modeling, employing Density Functional Theory (DFT) calculations run using the Gaussian program to study molecular models in which L-Tryp interacted with AgNP and AuNP substrates in neutral, cationic, and anionic forms. Full article

This work explores the issue of identifying sub-synchronous oscillations (SSOs). Regular detection techniques face issues with response timings to variations in viewpoint and adaptability to variations in conditions of the system but our proposed method overcomes them. We have actually come up with a new framework called Tempered Fractional Deterministic Learning (TF-DL) that successfully combines tempered fractional calculus with deterministic learning theory. This method makes a memory-based learner that works best for oscillatory dynamics. This lets SSO identification happen faster through a recursive structure that can run in real time. Theoretical analysis validates exponential convergence in the context of persistent excitation. Simulations show that detection time is 62.7% shorter than gradient descent, with better convergence and better parameters. Full article