Transfer Entropy Granger Causality between News Indices and Stock Markets in U.S. and Latin America during the COVID-19 Pandemic

The relationship between three different groups of COVID-19 news series and stock market volatility for several Latin American countries and the U.S. are analyzed. To confirm the relationship between these series, a maximal overlap discrete wavelet transform (MODWT) was applied to determine the specific periods wherein each pair of series is significantly correlated. To determine if the news series cause Latin American stock markets’ volatility, a one-sided Granger causality test based on transfer entropy (GC-TE) was applied. The results confirm that the U.S. and Latin American stock markets react differently to COVID-19 news. Some of the most statistically significant results were obtained from the reporting case index (RCI), A-COVID index, and uncertainty index, in that order, which are statistically significant for the majority of Latin American stock markets. Altogether, the results suggest these COVID-19 news indices could be used to forecast stock market volatility in the U.S. and Latin America.     

The Causality and Uncertainty of the COVID-19 Pandemic to Bursa Malaysia Financial Services Index’s Constituents

Valued in hundreds of billions of Malaysian ringgit, the Bursa Malaysia Financial Services Index’s constituents comprise several of the strongest performing financial constituents in Bursa Malaysia’s Main Market. Although these constituents persistently reside mostly within the large market capitalization (cap), the existence of the individual constituent’s causal influence or intensity relative to each other’s performance during uncertain or even certain times is unknown. Thus, the key purpose of this paper is to identify and analyze the individual constituent’s causal intensity, from early 2018 (pre-COVID-19) to the end of the year 2021 (post-COVID-19) using Granger causality and Schreiber transfer entropy. Furthermore, network science is used to measure and visualize the fluctuating causal degree of the source and the effected constituents. The results show that both the Granger causality and Schreiber transfer entropy networks detected patterns of increasing causality from pre- to post-COVID-19 but with differing causal intensities. Unexpectedly, both networks showed that the small- and mid-caps had high causal intensity during and after COVID-19. Using Bursa Malaysia’s sub-sector for further analysis, the Insurance sub-sector rapidly increased in causality as the year progressed, making it one of the index’s largest sources of causality. Even after removing large amounts of weak causal intensities, Schreiber transfer entropy was still able to detect higher amounts of causal sources from the Insurance sub-sector, whilst Granger causal sources declined rapidly post-COVID-19. The method of using directed temporal networks for the visualization of temporal causal sources is demonstrated to be a powerful approach that can aid in investment decision making.     

Dynamics of Information Flow between the Chinese A-Share Market and the U.S. Stock Market: From the 2008 Crisis to the COVID-19 Pandemic Period

The relationship between the Chinese market and the US market is widely concerned by researchers and investors. This paper uses transfer entropy and local random permutation (LRP) surrogates to detect the information flow dynamics between two markets. We provide a detailed analysis of the relationship between the two markets using long-term daily and weekly data. Calculations show that there is an asymmetric information flow between the two markets, in which the US market significantly affects the Chinese market. Dynamic analysis based on weekly data shows that the information flow evolves, and includes three significant periods between 2004 and 2021. We also used daily data to analyze the dynamics of information flow in detail over the three periods and found that changes in the intensity of information flow were accompanied by major events affecting the market, such as the 2008 financial crisis and the COVID-19 pandemic period. In particular, we analyzed the impact of the S&P500 index on different industry indices in the Chinese market and found that the dynamics of information flow exhibit multiple patterns. This study reveals the complex information flow between two markets from the perspective of nonlinear dynamics, thereby helping to analyze the impact of major events and providing quantitative analysis tools for investment practice.     

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.     

Impact of Coronavirus Outbreaks on Science and Society: Insights from Temporal Bibliometry of SARS and COVID-19

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both scientific and social standpoints. Despite several documented similarities, the coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20,000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. Unlike the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic to study changes in thematic foci and societal pressures dictating research over the course of a crisis.     

Structural and topological phase transitions on the German Stock Exchange

We find numerical and empirical evidence for dynamical, structural and topological phase transitions on the (German) Frankfurt Stock Exchange (FSE) in the temporal vicinity of the worldwide financial crash. Using the Minimal Spanning Tree (MST) technique, a particularly useful canonical tool of the graph theory, two transitions of the topology of a complex network representing the FSE were found. The first transition is from a hierarchical scale-free MST representing the stock market before the recent worldwide financial crash, to a superstar-like MST decorated by a scale-free hierarchy of trees representing the market’s state for the period containing the crash. Subsequently, a transition is observed from this transient, (meta)stable state of the crash to a hierarchical scale-free MST decorated by several star-like trees after the worldwide financial crash. The phase transitions observed are analogous to the ones we obtained earlier for the Warsaw Stock Exchange and more pronounced than those found by Onnela–Chakraborti–Kaski–Kertész for the S&P 500 index in the vicinity of Black Monday (October 19, 1987) and also in the vicinity of January 1, 1998. Our results provide an empirical foundation for the future theory of dynamical, structural and topological phase transitions on financial markets.     

Four Non-Gaussian-Type Tube Models of Rubber-Like Elasticity: Analysis of Tensile Data for Lightly and Highly Carbon-Black Filled Styrene-Butadiene Elastomers

Recently, using a new, non-Gaussian, tube-based entropy model for active-filler filled elastic networks with the polymer phase influenced by the filler loading, proposed by the author, a data analysis of literature tensile data for carbon-black filled styrene-butadiene rubber (SBR) networks of different loadings was carried out; the data-fittability and the particular influence of the filler loading were examined in detail. In this report, the data-fitting performances of three other non-filled network models, all tube-based and finite chain extensible, along with a common strain-amplification factor that is necessary to extend them to the analysis of the filled networks, as well as the model which was employed in the above study were compared. As tensile data for this analysis, those observed for the same unfilled SBR, the same lightly loaded SBR and one of the same highly loaded SBR networks, as in our previous study, were chosen.     

Buffer-gas effect on the rotovibrational line intensity distribution: Analysis of possible mechanisms

Line intensities A_m () of the HF fundamental band (T=293 K) are found to decrease linearly with the buffer-gas (Xe) density (-16 Amagat). The obtained slopes of the A _m (d)/ A _m (0) vs. plots are maximum at | m | =1 () and rapidly drop with |m|. Many possible mechanisms are considered; the most effective one appears to be the HF-Xe bimer formation, with the equilibrium constant strongly depending on the rotational quantum number. The rigid-rotator approximation used gives the density derivatives considerably smaller than the measured ones. The disbalance may be lessened for vibrating rotators by allowance for the interband intensity transfer induced by the vibrational modulation of short-range forces. Received 17 January 2000     

Effect of the potential function and strain rate on mechanical behavior of the single crystal Ni-based alloys: A molecular dynamics study

Molecular dynamics has been widely used to study the fundamental mechanism of Ni-based superalloys. However, the effect of the potential function and strain rate on mechanical behavior has rarely been mentioned in the previous molecular dynamics studies. In the present work, we show that the potential function of molecular dynamics can dramatically influence the simulation results of single crystal Ni-based superalloys. The microstructure and mechanical behavior of single crystal Ni-based superalloys under four commonly used potential functions are systematically compared. A most suitable potential function for the mechanical deformation is critically selected, and based on it, the role of strain rate on the mechanical deformation is investigated.     

Unexpected reduction of the spin-exchange cross-section for fast 3He+ ions incident on Rb atoms

The spin-exchange cross-section, , was measured for a 6.33 keV/amu He ion incident on a polarized Rb atom. The result is cm², which is unexpectedly an order of magnitude smaller than the theoretical value cm² evaluated by the semiclassical impact parameter method assuming formation of a single molecular state. Received 11 September 1999 and Received in final form 28 December 1999     

Non-partial-wave Coulomb-Born theory for the excitation of many-electron atomic ions II: Numerical description and application

A non-partial-wave Coulomb-Born theory is recently formulated to treat the excitation of many-electron atomic ions for impact by an arbitrary charged particle [Y.B. Duan et al., Phys. Rev. A 56, 2431 (1997)]. The multiple expansion of the transition matrix element is decomposed into the target form factor and the projectile form factor. These are the matrix elements of the tensor operators between quantum states so that any complicated wave function for the target ion can be employed. In this formal theory, an infinitesimally small positive quantity is introduced artificially to guarantee the convergence of integrals. As a supplementary part of the theory, we discuss how to choose the value of . It is found that the should be taken as functions of the momentum transfer and multipolarity . Illustrations are carried out by calculating the cross-sections for some typical transitions n _a l _a -n _b l _b of hydrogen-like ions for impact by electron, positron, and proton, respectively. The resulting cross-sections are in good agreement with ones produced by using a method available for ion targets with Slater-type orbitals [N.C. Deb, N.C. Sil, Phys. Rev. A 28, 2806 (1993)]. Comparisons demonstrate that the Coulomb-Born theory with non-partial wave analysis provides a powerful method to treat the excitation of many-electron atomic ions impact by an arbitrary charged particle. Received 6 April 1999     

Effect of particle size distribution and dynamics on the performance of two-dimensional packing

Extensive computer simulation is used to revisit and to generalize two classical problems: (i) the random car-parking dynamics of A. Rényi and (ii) the irreversible random sequential adsorption (RSA) of parallel squares of same size on a planar substrate of area L². In this paper, differently from the classical RSA, the squares obey the size distribution n(a)=n(1)a^−τ, where a=1,2,3,… is the area of the squares. Using this scaling distribution and three classes of packing dynamics we study the final packing fraction of particles, ?(τ,L), and in particular its thermodynamic limit L→∞. We show that the efficiency to attain a high/low packing density of particles on the substrate is strongly dependent on the value of the exponent τ and on the characteristics of the dynamics.     

Methods for calculating the desorption rate of an isolated molecule from a surface: Water on MgO(0 0 1)

We present two types of Molecular Dynamics (MD) simulation for calculating the desorption rate of molecules from a surface. In the first, the molecules move freely between two surfaces, and the desorption rate is obtained either by counting the number of desorption events in a given time, or by looking at the average density of the molecules as a function of distance from the surface and then applying transition state theory (TST). In the second, the potential of mean force (PMF) for a molecule is determined as a function of distance from the surface and the desorption rate is obtained by means of TST. The methods are applied to water on the MgO(0 0 1) surface at low coverage. Classical potentials are used so that long simulations can be performed, to minimise statistical errors. The two sets of MD simulations agree well at high temperatures. The PMF method reproduces the 0 K adsorption energy of the molecule to within 5 meV, and finds that the well depth of the PMF is not linear with temperature. This implies the prefactor frequency f in the Polanyi-Wigner equation is a function of temperature, increasing at lower temperatures due to the reduction of the available configuration space associated with an adsorbed molecule compared with a free molecule.     

Improved radiative corrections for (e, e'p) experiments: Beyond the peaking approximation and implications of the soft-photon approximation

Analyzing (e, e'p) experimental data involves corrections for radiative effects which change the interaction kinematics and which have to be carefully considered in order to obtain the desired accuracy. Missing momentum and energy due to bremsstrahlung have so far often been incorporated into the simulations and the experimental analyses using the peaking approximation. It assumes that all bremsstrahlung is emitted in the direction of the radiating particle. In this article we introduce a full angular Monte Carlo simulation method which overcomes this approximation. As a test, the angular distribution of the bremsstrahlung photons is reconstructed from H(e, e'p) data. Its width is found to be underestimated by the peaking approximation and described much better by the approach developed in this work. The impact of the soft-photon approximation on the photon angular distribution is found to be minor as compared to the impact of the peaking approximation.     

Global Numerical Model for the Assessment of the Effect of Geometry and Operation Conditions on Insert and Orifice Region Plasmas of a Thermionic Hollow Cathode Electron Source

Thermionic hollow cathodes have been widely used in wide variety of areas such as spacecraft electric propulsion systems, material processing and lasers for more than half a century as efficient electron sources. Especially in electric propulsion systems, hollow cathodes are being used as electron sources for propellant ionization and ion beam neutralization. Moreover, it is also a promising candidate for utilization as a stand‐alone propulsion system in microsatellites or nanosatellites due to its small physical size, low power consumption and ease of operation. On the other hand, the small geometry of the typical orificed hollow cathodes makes the plasma diagnostics difficult which is why numerical studies become important for understanding the driving physical processes behind their operation, and the effects of the geometry and the operation parameters on cathode performance. In this paper, a global numerical model for the insert and orifice plasma of a hollow cathode is presented where volume averaged plasma parameters are considered for both regions. The results of this study show that the developed model can be used for designing and sizing orificed hollow cathodes as comparisons with the results of experimental and other numerical studies are in good agreement with the ones obtained from the developed model. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

石油及石油产品温度对能量色散X射线荧光光谱法测定硫含量结果影响的研究

考察样品温度对能量色散X射线荧光光谱法测定硫含量的影响;通过改变样品温度,观察测定结果的变化.结果表明:石油及石油产品的样品温度对X荧光光谱仪测试结果具有显著影响;多数含硫和高硫石油及其产品的样品温度对X荧光光谱仪测试结果的影响呈现正相关关系,低硫石油或低硫石油产品的样品温度对X荧光光谱仪测试结果的影响具有波动性.能量色散X射线荧光光谱法测定仪安装测试温度控制装置和改进样品温度自动校正曲线,对控制样品温度对X荧光光谱仪测试结果的准确度是十分必要的.     

光敏剂对肿瘤细胞损伤效应与孵育时间关系的拉曼光谱分析

利用拉曼光谱研究光敏剂与HepG2肝癌细胞不同孵育时间的光动力损伤效应。结果表明孵育时间与光动力损伤效应成正相关性,但孵育时间超过一定值,延长孵育时间并不能提高光动力损伤效应。     

X射线荧光光谱高压制样方法和技术研究

使用自行设计研制的高压制样模具和高压制样技术(专利号: 201310125772.5),对岩石、土壤,水系沉积物等地质样品进行高压制样研究,这是国内首次的高压制样尝试,并取得了显著的成果。不加粘结剂,用1600 kN高压能使各种类型地质粉末样品压制成型。而且,经高压制备的试样片表面致密、平整、光滑、光亮,不龟裂,不分层,不掉粉末,消除了对X射线荧光光谱仪分析室的污染,为X射线荧光光谱分析粉末制样开辟了一条新途径。通过对元素分析线、背景强度、校准曲线斜率、方法的精密度和制样的重现性的对比研究表明：高压(1 600 kN)制备的试样片较常规压力(400 kN)元素的峰背比值、灵敏度显著地提高,检出限明显降低、分析结果更接近标准值、精密度和样品制备的重现性均有较大提高。还利用电子显微镜和X射线衍射对这些高压试样片(1600 kN)和常规压力的试样片(400 kN)作了表面形态和结构的对照研究,研究表明：高压试样片(1 600 K)较常规压力的试样片(400 kN)二氧化硅谱峰的半高宽均变宽。表明峰形发生了变异,变化的原因可能是在高压下SiO₂晶格被破坏,粒度减小,因而使高压制备的试样片表面更加坚实、致密、平滑、不掉粉末,减少了颗粒度和矿物效应,提高了分析结果的精密度和准确度。     

碳纳米管应变传感测量与偏振拉曼控制方法研究

面向微尺度平面变形的精细测量对碳纳米管应变传感测量方法进行了理论与实验研究。利用碳纳米管的拉曼应变敏感性及其偏振选择性,针对各种典型的拉曼光谱系统偏振构型,推导建立了适于各种偏振拉曼构型的碳纳米管平面应变传感解析关系式。从测量学角度出发,对不同偏振构型下的应变传感进行了对比分析,得出入射与散射光偏振方向皆能够连续控制且始终保持平行的双偏协同构型最适合于平面应变传感,并进一步提出易于实现的双偏协同构型光路配置方式与控制方法。通过实验应用证明,所提出的方法能够有效的实现基于偏振拉曼的碳纳米管平面应变传感测量。     

斯特林制冷机通用化和系列化的技术经济效益分析

以实际数据分析了斯特林制冷机实行通用化、系列化所取得的和可以取得的技术、经济效益,阐明了在斯特林制冷机研制、生产中实施通用化、系列化的重要性。