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Accurate prediction of vehicle maintenance demands is crucial for enhancing service longevity and minimizing costs. However, current methods are limited to predicting maintenance demands for individual vehicle components. They fail to offer a comprehensive prediction that encompasses diverse maintenance demands. Additionally, vehicle maintenance demand prediction must consider the interrelationships among various maintenance projects and maintenance project records. To address these issues, we propose a vehicle maintenance demand prediction method that employs a collaborative approach. This method utilizes both static and dynamic correlation-aware learning techniques. We design a static correlation-aware method for maintenance project representation learning by leveraging prior statistical data from various maintenance projects. To effectively capture the temporal correlations inherent in different maintenance project records, we propose an attention-based dynamic correlation-aware technique. Experiments conducted on real-world datasets demonstrate that the proposed model outperforms existing methods.

Here, analytical extended multi-state complete active space second-order perturbation method (XMS-CASPT2) gradients are used to rationalize the decreasing triplet quantum yield trend in 2-nitronaphthalene, 1-nitronaphthalene, and 2-methyl-1-nitronaphthalene, a series of nitro-substituted aromatic compounds. Comparison with the XMS-CASPT2//CASSCF (where CASSCF stands for complete active space self-consistent field method) results highlights the importance of dynamic correlation in geometry optimization and challenges the validity of an XMS-CASPT2//CASSCF approach: XMS-CASPT2 S1 minima leads to planar structures, while CASSCF optimizations trigger a pyramidalization of the nitro group. The XMS-CASPT2 results correlate the reported decreasing triplet quantum yield trend in these species to a decrease in S1 to T2 population transfer and an increase in S1–S0 decay, while no such correlation is observed when using XMS-CASPT2//CASSCF data. [ABSTRACT FROM AUTHOR]

This study examines the dynamic correlations and hedge ratios of precious metal stock returns of the Johannesburg stock exchange in pre- and post-COVID scenarios to determine if they can be used to hedge against adverse market movements. The study uses daily return series of four gold stocks and three platinum stocks listed on the Johannesburg Stock Exchange (JSE) spanning from 11 November 2016 to 31 December 2019, for the pre-COVID period and 02 January 2020 to 10 February 2023, for the post-COVID period. Using t-copula-DCC-GJR-GARCH-skew-t and t-copula-aDCC-GJR-GARCH-skew-t models for pre- and post-COVID periods, respectively and the R-vine copula model for tail dependence analysis during times of extreme market conditions; our findings show that gold stocks are better hedge assets than platinum stocks during downturn market. Further, dynamic hedge ratios show that it is more expensive to hedge against long positions in the JSE index in the pre-COVID period than in the post-COVID period. Hedging effectiveness demonstrates that the dynamic portfolio weights strategy is better than hedge ratios when hedging against the JSE index. Based on the findings in this study, the economy of South Africa could possibly benefit if the government and the private sector reinvigorate the precious metal mining sector.

This study aims to analyze the effect of oil price volatility, gold price volatility and exchange rate volatility on the dynamic relationship between Indonesian and United States capital market. The data used in this study are daily closing prices of oil, gold and exchange rates (USD/IDR) as well as Indonesian capital market (JKSE) and United States capital market (DJIA) composite indices during period of January 2005 to October 2020. This study uses DCC-GARCH method to calculate the dynamic correlation between two capital markets and GARCH with the GED parameter to analyze oil volatility, gold volatility and exchange rate volatility on the integration of Indonesian capital market and United States capital market. The results of this study show positive and strong results on the integration of Indonesian and United States capital markets, thus proving that the movements of Indonesian market and American market tend to be strong and mutually influence the two capital markets. Moreover, the oil, gold and exchange rates volatilities have a negative effect on the integration of the Indonesia capital market and the US capital market. This finding implies investors should take oil, gold and exchange rates volatilities in their investment consideration.

This paper aimed to explore the impact of the dynamic relationship between broiler price and pig price on the healthy and stable development of the livestock and poultry breeding industry. Selecting monthly data on broiler price and pig price from January 2020 to August 2023, this study empirically explores the dynamic correlation between broiler price and pig price using VAR models, impulse response functions, and Granger causality. The results showed that an increase in the price change rate of broiler can effectively increase the price change rate of pig, and an increase in the price change rate of pig can also effectively increase the price change rate of broiler. There is a bidirectional causal relationship between the price of broiler and the price of pig. Compared to the price of pig, the price change rate of broiler is mainly influenced by their own prices. The price change rate of pig is mainly influenced by both the price change rate of broiler and their own price change rate, and the degree of influence of the price change rate of broiler is higher than that of their own. The price change rate of broiler plays a dominant role in the fluctuations of broiler price and pig price. The research shows that there is a dynamic correlation between the price of broiler and the price of pig, which can effectively improve the information judgment ability of price changes, in order to help the livestock breeding industry to improve quality and efficiency. [ABSTRACT FROM AUTHOR]

Airports, as integral components of the global aviation industry, experience dynamic changes in air passenger traffic load, which are also related to the trends of local urban development and airspace restrictions of nearby airports. Using time-series data from 2006 to 2019, this study comprehensively applied the autoregressive distributed lag and vector auto-regression mode approaches to identify causal relationships between the urban development factors, including GDP, population, tourism industry, industrial structure, etc., of Tianjin city (China); the Tianjin airport passenger flow; and the Beijing Capital airport's airspace restriction factor, namely, airport aircraft sorties. The results show that the growth of Tianjin city's GDP, primary industry and disposable income per capita was accompanied by a long-term decline in the passenger flow at the Tianjin airport. In addition, increased aircraft sorties of Tianjin airport, as well as the growth of primary, secondary and tertiary industries in Tianjin city, led to a short-term decline in passenger flow at the Tianjin airport. In general, there is variability in the long- and short-term impacts of urban economic structure on airport passenger flow, and this variability applies to other airports. The increased aircraft sorties at the Beijing Capital airport had a short-term positive impact on passenger flow at the Tianjin airport but resulted in a long-term decline of the latter's aircraft sorties. This phenomenon indicates that there is interaction between airports and that this influence varies depending on the competition and cooperation mechanisms between airports. The findings of this study are considered instrumental in guiding the competitive and cooperative strategies of nearby airports and predicting the coupled trends of the airport and urban development.

This research explores the spillover effects in the directional movement of returns and the persistence of shocks among three prominent energy spot markets: b transfer facility for natural gas, Brent crude oil and electricity markets from monthly price data spanning January 2010 to September 2022. Methodologically, we initially employ bivariate vector autoregressive models to detect potential lagged return effects from one spot market on another. Then, we examine the impact on the conditional mean returns and volatility across these spot markets using the standard dynamic conditional correlation (DCC) model, as well as the respective asymmetric (ADCC) and flexible (FDCC) extensions. In addition, we accommodate innovative insights that include recent datasets on the COVID-19 crisis and the Ukrainian war, which constitute a new addition to the existent literature. The empirical findings confirm the significant impact of these two unprecedented moments of contemporaneous history, given that both events are substantiated by an exponential increase in prices and by a rise in volatility. However, the effect on returns was not uniform across the time series. Specifically, there was a consistent increase in volatility for natural gas and electricity from the start of 2020 until the end of 2022, while Brent oil exhibited a substantial peak only in the first half of 2020. This study also reveals that previous lagged returns within each market, particularly for Brent oil and electricity, had statistically significant effects on current returns. There was also a robust unidirectional positive spillover effect from the Brent oil market to the returns of electricity and the natural gas markets. The study also reveals the presence of a weak positive autocorrelation between natural gas and electricity returns, and positive shocks to returns had a more pronounced impact on volatility compared to negative shocks across all the time series.

The load in the power grid is often affected by many factors, and the coupling relationship among them changes dynamically with time. This study proposed a short-term load forecasting technique based on time pattern attention and a long short-term memory network considering time-dependent intrinsic cross-correlation (TDICC) for smart grids with massive amounts of data. Based on source-load dynamic correlation analysis, The proposed TDICC to track and correct the multi-timescale dynamic correlation of two signal overruns or lags, which realizes the transformation of correlation description from two-dimensional space to three-dimensional space and expands its ability to describe multi-timescale dynamic correlation. Finally, the actual load data are used for example analysis, and the results show that the proposed method can tap the dynamic correlation between multiple influencing factors and load and has higher prediction accuracy compared with other models, which provides a more accurate database for power system dispatching.

This study analyzes the spillover effect and dynamic correlation of the China-US bean futures markets and discusses the relationship between the dynamic correlation of the bean futures price index and investor sentiment. First, the spillover effect of the China-US bean futures markets is analyzed through the BEKK-GARCH model. Then, the DCC-GARCH model is used for obtaining the dynamic correlation coefficients of the China-US bean futures markets. Next, the principal component analysis method is chosen to construct a comprehensive investor sentiment index. Lastly, the dynamic impacts between the change in investor sentiment and the correlation of the China-US bean futures price index are discussed through the ensemble empirical mode decomposition and impulse response analysis. The results show that the spillover effect of different degrees and directions exists between the China-US bean futures markets, and the dynamic correlation coefficients among different bean futures are also different. Besides, a certain degree of interactions exists between the high-frequency and low-frequency components of the comprehensive investor sentiment index and the dynamic correlations of bean futures price indexes.

This study aimed to assess the effect of exercise on serum electrophoretic protein pattern, C-reactive protein (CRP) and platelet aggregation in horses subjected to a jumping exercise. The possible relationship between acute-phase reactions and platelet reactivity in the context of exercise was investigated. Blood samples were collected from 10 jumper horses at rest (TREST), within 5 min from the end of exercise (TPE5), and 30 min (TPE30) and 60 min after exercise (TPE60). The serum values of total proteins; CRP; albumin; α1-, α-2, β1-, β2- and γ-globulins; and the maximum degree of aggregation and the initial velocity of aggregation (slope) were evaluated. According to one-way analysis of variance, CRP and α1-, α-2, β1- and β2-globulins increased after exercise compared with rest condition (p < 0.001), whereas albumin and platelet aggregation showed lower values after exercise than at rest (p < 0.001). CRP and α1-globulin values were negatively correlated with both platelet aggregation indices at TPE5, whereas no significant correlation among these parameters was found at TREST, TPE30 and TPE60. This study provides evidence that an acute-phase response occurred in horses after the jumping exercise and suggests a linkage between the inflammatory status and the platelet responsiveness in horses during exercise.

Air pollution is closely associated with human health and the economy. Therefore, it is important to understand variations in the spatiotemporal and sectoral emission distributions of major air pollutants and their drivers. The policies (APAPPC) promulgated by China in 2013 have also achieved remarkable results. Rate of change, trend analysis, and a geographically and temporally weighted regression model were used to study the effects of socioeconomic factors on NOx, SO2, and dust emissions in China during 2011–2017. During the study period, annual average emissions of NOx, SO2, and dust decreased by 11.45, 13.42, and 4.82 Mt (−47.64, −60.53, and −39.05%), respectively. Pollutant emissions were concentrated in North China, with Shandong and Hebei provinces exhibiting the highest NOx and SO2 and dust emissions, respectively. Pollutant emissions from the power and industrial sectors were mainly distributed in East (27.08 and 28.00%, respectively) and North China (23.57 and 20.04%, respectively), whereas emissions from the residential sector were mainly concentrated in North (22.48%) and Southwest China (20.07%). Pollutant emissions were positively correlated with electricity generation, urban population density, urban green spaces, private car ownership, the secondary industry as a share of regional GDP, and steel production and negatively correlated with disposable income and gross construction output. Per capita disposable income was the dominant driving factor.

Objective: Modelling dynamic nature of covariance of assets return almost always is a challenging area of finance. Econometrics models just pay attention to variance behavior longitudinally, however, core of numerous finance models need the analysis of the total covariance structure of returns. Method: Among the first models that analyze covariance behavior are multivariate GARCH models which were criticized for the need to estimate a large number of parameters. This paper is aimed to investigate the effect of stock return dynamic correlation structure on systematic risk, idiosyncratic risk and average stock return. To this end, a sample of 148 listed companies in Tehran Stock Exchange is examined during 2003 to 2014. GARCH framework is used for testing this claim. Results: According to the results, securities that were highly correlated with market wide risk factors in the past are likely to have low systematic risk, idiosyncratic risk and average return at present. It can be expected there is significant relationship between idiosyncrstic risk and correlation for lower turnover stock (information transparency proxy) although there is no relationship for smaller firms.

The stochastic characteristics of wind power and photovoltaic (PV) make the resource allocation of power system difficult. Therefore, it is necessary to consider the correlation between the power generation of wind and PV power stations to avoid resource waste and guarantee system power supply, while the traditional correlation analysis method cannot accurately describe the multiscale and time-varying characteristics of the correlation. In this article, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the time series of the wind and PV power generation was decomposed into multiscale components. Moreover, the time-dependent intrinsic correlation (TDIC) is introduced to excavate the local correlation of the power generation time series under the framework of a multi-time scale, the dynamic change of a correlation is captured by analyzing the TDIC plots. The analysis shows that the strength and nature of the association between wind and PV vary with time scales and time spells, reflecting rich, dynamic characteristics. The correlation variation of different scale components in local time is of great significance to power system operation, planning, and resource optimal allocation.

This study examines the static and dynamic correlations in the ASEAN equity markets. The importance of this research appears from the fact that practitioners can get the benefit if their investments yield the same or higher returns given lower or the same risk in their portfolio. Firstly, this advantage comes from including the assets that decrease volatility of the portfolio. Hence, the correlation between the ASEAN markets should be examined. Secondly, co-movements in market realizations may increase global financial instability. Its existence is important for international investors, financial institutions, and policy makers. The study locates the relationship between ASEAN and its major trading partners, including Japanese, US, and UK markets, in order to find more rational results. This study utilizes alternative multivariate GARCH forms to provide useful information on the dynamic evolution and implications of return volatilities. The results show that the volatilities of all the equity markets under study are persistent over time. The estimates from VEC model indicate that the movements of the US and UK equity market returns have some degree of influence on several of the ASEAN equity markets. The results imply that, first, most of the developing ASEAN equity markets work by its own information with small relation to the developed world. Second, it is still convincing to state that investing in ASEAN equity markets should provide investors a better mean-variance portfolio. And, third, buy-and-hold strategy seems to be more beneficial than readjusting the ASEAN equities portfolio.