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The aim of this study is to investigate the relationship between economic policy uncertainty (EPU) and stock prices during the period from March 2003 to March 2021.

The study uses asymmetric and symmetric frequency domain causality tests and focuses on BRIC countries, namely, Brazil, Russia, India and China.

The findings of the symmetric causality test confirm unidirectional permanent causality from EPU to stock prices for Brazil and India and bidirectional causality for China. However, according to the asymmetric causality test, the findings for China show that there is no causality between the variables. The results for Brazil and India indicate that there is unidirectional permanent causality from positive components of EPU to positive components of stock prices. Moreover, for Brazil, there is unidirectional temporary causality from the negative components of EPU to the negative components of stock prices. For India, there is temporary causality in the opposite direction.

The reactions of financial markets to positive and negative shocks differ. In this context, to the best of the authors’ knowledge, this study is the first attempt to examine the causal relationships between stock prices and uncertainty using an asymmetric frequency domain approach. Thus, the study enables the analysis of the effects of positive and negative shocks in the stock market separately.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

The purpose of the paper is the simulation of nonuniform transmission lines.

The method involves a Magnus expansion and a numerical Laplace transform. The method involves a judicious arrangement of the governing equations so as to enable efficient simulation.

The results confirm an effective and efficient numerical solver for inclusion of nonuniform transmission lines in circuit simulation.

The work combines a Magnus expansion and numerical Laplace transform algorithm in a novel manner and applies the resultant algorithm for the effective and efficient simulation of nonuniform transmission lines.

Research on workplace stress measurements varied without much accuracy and effectiveness. The purpose of this paper is to introduce a new quantitative assessment tool emWave Pro Plus (Institute of HeartMath) and compare heart rate variability (HRV) results with the Personal and Organizational Quality Assessment (POQA) and the Perceived Stress Scale (PSS).

This research opted for a correlational study which involves 85 full-time employees who were working at least 40 h per week in a large corporation participated in this study. The POQA and PSS were used to correlate with HRV.

Astonishing findings emerged in this study. Significant positive correlations were found between emotional stress and HRV, and between intention to quit and HRV. In other words, the researchers have to make sense the following surprising findings: the higher the emotional stress an employee faces, the healthier they are. Healthier employees may have higher intentions of quitting their jobs. The surprising results may be attributed to personality, culture, emotional regulation and age among others.

This research fulfills an identified need to validate quantifiable stress measurements especially in a corporate environment. The research also shows promising results, and future studies should continue to tap into HRV as an objective measure of mental health and workplace stress.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

This study investigates information flow of market constituents and global indices at multi-frequencies.

The study’s findings were obtained using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN)-based cluster analysis executed for Rényi effective transfer entropy (RETE).

The authors find that significant negative information flows among sustainability equities (SEs) and conventional equities (CEs) at most multi-frequencies, which exacerbates diversification benefits. The information flows are mostly bi-directional, highlighting the importance of stock markets' constituents and their global indices in portfolio construction.

The authors advocate that both SE and CE markets are mostly heterogeneous, revealing some levels of markets inefficiencies.

The empirical literature on CEs is replete with several dynamics, revealing their returns behaviour for diversification purposes, leaving very little to know about the returns behaviour of SE. Wherein, an avalanche of several initiatives on Corporate Social Responsibility (CSR) enjoin firms to operate socially responsible, but investors need to have a clear reason to remain sustainable into the foreseeable future period. Accordingly, the humble desire of investors is the formation of a well-diversified portfolio and would highly demand stocks to the extent that they form a reliable portfolio, especially, amid SEs and/or CEs.

本研究擬審查多頻率的及為市場成份的信息流和全球指數。

研究人員使用基於改良完全集合經驗模態分解自適應噪聲(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)的聚類分析法,取得Rényi有效轉移熵,藉此得到研究結果。

我們發現、於大部份多頻率,在持續性股票和傳統股票間有顯著的負信息流動,這會增加多樣化的益處。這些信息流大部份是雙向的,這強調了股票市場成份及其全球指數在構建投資組合上的重要性。

我們認為持續性股票市場和傳統股票市場大多為異質市場,這顯示了市場的低效率,而且這低效率的程度頗大。

關於傳統股票的實證性文獻裡是充滿了變革動力的,這顯示了它們以多樣化為目的的回報行為。這使我們對關於持續性股票的回報行為、認識變得實在太少了。於此,大量的企業社會責任的新措施不斷提醒各公司、要本著企業社會責任的理念去營運;但投資者需清晰明白他們為何需在可見的將來保持可持續性。因此,他們卑微的願望是一個較好的多樣化投資組合得以形成,故此他們高度要求股票要有組成可靠投資組合的性質和能力,特別是在持續性股票和/或傳統股票當中。

The main purpose of the research is to determine if the relationship between trading volume and price changes is connected to market effectiveness and to use the volume-price relationship to compare the efficiency levels of foreign markets. The degree of the relationship is determined in this study, and the efficiency levels of different countries' capital markets are compared.

In this study, 1,024 observations are used as a data set, which includes daily closing prices and trading volume in the stock market indices of 25 countries between the dates of 01.12.2016 and 31.12.2020. In the first step of the analysis, descriptive statistics of price and volume series are examined. The stationarity of the series is then controlled using the ADF unit root test. Simple linear regression models with the dependent variable of trading volume are generated for all stock market indices after each series has reached stationarity, and the ARCH heteroscedasticity test is used to determine whether these models contain the ARCH effect. Because all models have the ARCH effect, autoregressive models are chosen, and EGARCH models are conducted for all indices to see whether there is an asymmetry in the price-volume relationship.

The study concludes that the stock market in the United States is the most effective, since it has the strongest relationship between trading volume and price changes. However, because of the financial distress caused by the COVID-19 pandemic, the relationship between price and trading volume is lower in Eurozone countries. The price-volume relationship could not be observed in some shallow markets. Furthermore, whereas the majority of countries have a negative relationship between price changes and transaction volume, China, the United Arab Emirates and Qatar have a positive relationship. When prices rise in these countries, investors buy with the sense of hope provided by the optimistic atmosphere, and when prices fall, they sell with the fear of losing money.

The study's most significant limitation is that it is difficult to ascertain a definitive conclusion about the subject under investigation. In reality, if the same research is done using data from different countries and time periods, the results are quite likely to vary.

As a result of the study, investors can decide which market to enter by comparing and analyzing the price-volume relationship of several markets. According to the study's findings, investors are advised to examine the price-volume relationship in a market before beginning to trade in that market. In this way, investors can understand the market's efficiency and whether it is overpriced.

The relationship between price movements and trade volume gives crucial information about a capital market's internal structure. Some concerns can be answered by assessing this relationship, such as whether the market has a speculative pricing problem, how information flows to the market, and whether investment decisions are rational and homogenous. Empirical studies on modeling this relationship, on the other hand, have not reached a definite outcome. The main reason for this is that the price-to-volume relationship fluctuates depending on the market structure. The purpose of this study is to fill a gap in the literature by presenting the reasons why this critical issue in the literature cannot be answered, as well as empirical findings.

The significance and originality of this research are that it examines the price-volume relationship to evaluate the efficiency levels of various markets. This relationship is being investigated in a number of multinational studies. These researches, on the other hand, were conducted to see if there is a relationship between trading volume and market volatility, and if so, how that interaction is formed. The size of the price and volume relationship is emphasized in this study, unlike previous studies in the literature.

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).