Exploring the asymmetric effect of COVID‑19 pandemic news on the cryptocurrency market:evidence from nonlinear autoregressive distributed lag approach and frequency domain causality

This paper explores the asymmetric effect of COVID-19 pandemic news,as measured by the coronavirus indices(Panic,Hype,Fake News,Sentiment,Infodemic,and Media Coverage),on the cryptocurrency market.Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heter-oskedasticity model,the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns,indicating fear of missing out behavior does not prevail.Furthermore,when the nonlinear autoregressive distributed lag model is esti-mated,both positive and negative shocks in pandemic indices promote Bitcoin’s daily changes;thus,Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil.The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns,whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index.These findings may have significant policy implications for investors and governments because they highlight the impor-tance of news during turbulent times.The empirical results indicate that pandemic news could significantly influence Bitcoin’s price.

Herbal hepatotoxicity:Challenges and pitfalls of causality assessment methods

The diagnosis of herbal hepatotoxicity or herb induced liver injury(HILI) represents a particular clinical and regulatory challenge with major pitfalls for the causality evaluation.At the day HILI is suspected in a patient,physicians should start assessing the quality of the used herbal product,optimizing the clinical data for completeness,and applying the Council for International Organizations of Medical Sciences(CIOMS) scale for initial causality assessment.This scale is structured,quantitative,liver specific,and validated for hepatotoxicity cases.Its items provide individual scores,which together yield causality levels of highly probable,probable,possible,unlikely,and excluded.After completion by additional information including raw data,this scale with all items should be reported to regulatory agencies and manufacturers for further evaluation.The CIOMS scale is preferred as tool for assessing causality in hepatotoxicity cases,compared to numerous other causality assessment methods,which are inferior on various grounds.Among these disputed methods are the Maria and Victorino scale,an insufficiently qualified,shortened version of the CIOMS scale,as well as various liver unspecific methods such as thead hoc causality approach,the Naranjo scale,the World Health Organization(WHO) method,and the Karch and Lasagna method.An expert panel is required for the Drug Induced Liver Injury Network method,the WHO method,and other approaches based on expert opinion,which provide retrospective analyses with a long delay and thereby prevent a timely assessment of the illness in question by the physician.In conclusion,HILI causality assessment is challenging and is best achieved by the liver specific CIOMS scale,avoiding pitfalls commonly observed with other approaches.

Drug and herb induced liver injury: Council for International Organizations of Medical Sciences scale for causality assessment

Causality assessment of suspected drug induced liver injury(DILI) and herb induced liver injury(HILI) is hampered by the lack of a standardized approach to be used by attending physicians and at various subsequent evaluating levels. The aim of this review was to analyze the suitability of the liver specific Council for International Organizations of Medical Sciences(CIOMS) scale as a standard tool for causality assessment in DILI and HILI cases. PubMed database was searched for the following terms: drug induced liver injury; herb induced liver injury; DILI causality assessment; and HILI causality assessment. The strength of the CIOMS lies in its potential as a standardized scale for DILI and HILI causality assessment. Other advantages include its liver specificity and its validation for hepatotoxicity with excellent sensitivity, specificity and predictive validity, based on cases with a positive reexposure test. This scale allows prospective collection of all relevant data required for a valid causality assessment. It does not require expert knowledge in hepatotoxicity and its results may subsequently be refined. Weaknesses of the CIOMS scale include the limited exclusion of alternative causes and qualitatively graded risk factors. In conclusion, CIOMS appears to be suitable as a standard scale for attending physicians, regulatory agencies, expert panels and other scientists to provide a standardized, reproducible causality assessment in suspected DILI and HILI cases, applicable primarily at all assessing levels involved. 2014 Baishideng Publishing Group Co., Limited. All

Gut microbiota and diabetes: From correlation to causality and mechanism

In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for diabetes.On a global scale,diabetes causes a significant negative impact to the health status of human populations.This review covers type 1 diabetes and type 2 diabetes.We examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes diseases.It appears that the human oral and gut microbiota are deeply interdigitated with diabetes.It is that simple.Recent studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and diabetes.These studies focus on the role and the potential impact of intestinal microflora in diabetes.We paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in humans.The microflora seems to have an endless capacity to impact and transform diabetes.We conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.

Causality Diagram-based Scheduling Approach for Blast Furnace Gas System

Rational use of blast furnace gas(BFG) in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means(CFCM) clustering algorithm. In this algorithm,not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered.The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

Functional Causality between Oil Prices and GDP Based on Big Data

This paper examines the causal relationship between oil prices and the Gross Domestic Product(GDP)in the Kingdom of Saudi Arabia.The study is carried out by a data set collected quarterly,by Saudi Arabian Monetary Authority,over a period from 1974 to 2016.We seek how a change in real crude oil price affects the GDP of KSA.Based on a new technique,we treat this data in its continuous path.Precisely,we analyze the causality between these two variables,i.e.,oil prices and GDP,by using their yearly curves observed in the four quarters of each year.We discuss the causality in the sense of Granger,which requires the stationarity of the data.Thus,in the first Step,we test the stationarity by using the Monte Carlo test of a functional time series stationarity.Our main goal is treated in the second step,where we use the functional causality idea to model the co-variability between these variables.We show that the two series are not integrated;there is one causality between these two variables.All the statistical analyzes were performed using R software.

Exploring the Big Data Using a Rigorous and Quantitative Causality Analysis

Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods.

Using granger-geweke causality model to evaluate the effective connectivity of primary motor cortex, supplementary motor area and cerebellum

Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task can produce relatively reliable brain response. As an extension of our previous study, we developed an algorithm based on the classical Granger- Geweke causality model to further investigate the effective connectivity of three brain regions (left primary motor cortex (M1), supplementary motor area (SMA) and right cerebellum) that showed the most robust brain activations. Our computational results not only confirm the strong linear feedback among SMA, M1 and right cerebellum, but also demonstrate that M1 is the hub of these three regions indicated by the anatomy research. Moreover, the model predicts the high intermediate node density existing in the area between SMA and M1, which will stimulate the imaging experimentalists to carry out new experiments to validate this postulation.

Causality Is Logically Definable—Toward an Equilibrium-Based Computing Paradigm of Quantum Agent and Quantum Intelligence (QAQI) (Survey and Research)

A survey on agents, causality and intelligence is presented and an equilibrium-based computing paradigm of quantum agents and quantum intelligence (QAQI) is proposed. In the survey, Aristotle’s causality principle and its historical extensions by David Hume, Bertrand Russell, Lotfi Zadeh, Donald Rubin, Judea Pearl, Niels Bohr, Albert Einstein, David Bohm, and the causal set initiative are reviewed;bipolar dynamic logic (BDL) is introduced as a causal logic for bipolar inductive and deductive reasoning;bipolar quantum linear algebra (BQLA) is introdused as a causal algebra for quantum agent interaction and formation. Despite the widely held view that causality is undefinable with regularity, it is shown that equilibrium-based bipolar causality is logically definable using BDL and BQLA for causal inference in physical, social, biological, mental, and philosophical terms. This finding leads to the paradigm of QAQI where agents are modeled as quantum enssembles;intelligence is revealed as quantum intelligence. It is shown that the enssemble formation, mutation and interaction of agents can be described as direct or indirect results of quantum causality. Some fundamental laws of causation are presented for quantum agent entanglement and quantum intelligence. Applicability is illustrated;major challenges are identified in equilibriumbased causal inference and quantum data mining.

Research on the Occurrences of Causality Conjunctions in Korean Based on Corpus

Corpus is a kind of database that may contain texts in a single language(monolingual corpus)or text data in multiple languages(multilingual corpus).Most of the words can be involved in such a database,so it is provided with significant realistic meaning for language learners.Also,the use of causality conjunction in Korean plays a more important guiding role in listening,speaking,reading and writing of Korean.However,the status with regard to the application of causality conjunction in Korean is not optimistic and the problems such as the improper use of language,lack of knowledge about conjunction by learners,short of professional Korean teaching consciousness by teachers and others are being,thereby seriously hindering the learning interests of Korean learners and educational progress.Thus,in accordance with research status at home and abroad,this paper carries out indepth analysis on the basic theory of corpus and Korean conjunction as well as the application of causality conjunction in Korean based on the corpus.And it can be found that the research on the application of causality conjunction in Korean based on corpus is with important value for both the flexible use of language and the improvement of comprehensive application ability of language.

A Causality Analysis of Coal Consumption and Economic Growth for China and India

China and India are the two countries with the strongest economic growth in the world. Meanwhile they consume much of the global coal to fuel their economic development. With coal burning as a major factor contributing to global greenhouse gas emissions, China and India are confronted with a dilemma of economic growth and environment protection. Will coal consumption reduction cause economic shocks? Is there a causal relationship between coal consumption and economic growth in China and India? In this paper Granger causality tests were used to examine the relationship between coal consumption and GDP in China and India, using data for the period from 1965 to 2006. It was found that a unidirectional causality from GDP to coal consumption existed in China while a unidirectional causality from coal consumption to GDP did in India. Therefore, developing cleaner and more efficient technologies is essential to reduce their CO2 emissions to reach sustainable development.

On the Translation of Folding Beijing From Pym's Multiple Causality of Translation

Hao Jingfang's science-fiction novelette Folding Beijing was named the Best Novelette at the Hugos on August 21st, 2016. It is originally written in Chinese and gain recognition among foreign readers after being translated into English. The fact that the translation put Chinese science-fiction on the international map again after the Hugo-award winning science-fiction The Three Body Problem written by Liu Cixin is an important event in Chinese science-fiction translation history. The contribution of Ken Liu's English version should not be neglected. This paper looks at the successful translation and its communication from the perspective of Multiple Causality of Translation proposed by Anthony Pym to probe into the kind of causation operative in the translation of Folding Beijing, including the material or initial cause, the final cause, the formal cause, and the efficient cause. Ken Liu's English version has made such a great success that a study of its formation and development may provide insights for home translators longing to help Chinese science-fiction reach more readers.

Abnormal behavior detection by causality analysis and sparse reconstruction

A new approach for abnormal behavior detection was proposed using causality analysis and sparse reconstruction. To effectively represent multiple-object behavior, low level visual features and causality features were adopted. The low level visual features, which included trajectory shape descriptor, speeded up robust features and histograms of optical flow, were used to describe properties of individual behavior, and causality features obtained by causality analysis were introduced to depict the interaction information among a set of objects. In order to cope with feature noisy and uncertainty, a method for multiple-object anomaly detection was presented via a sparse reconstruction. The abnormality of the testing sample was decided by the sparse reconstruction cost from an atomically learned dictionary. Experiment results show the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases for abnormal behavior detection.

Drug-induced severe liver injury due to Sterculia versicolor: a case report with causality assessment from possible to probable

Sterculia gum,the dry exudate of Sterculia versicolor and other members of the same genus,is used as a thickener and emulsifier in foods.It is generally considered safe as a food or drug,and its adverse reactions,such as Sterculia-induced liver injury,have never been reported.A 46-year-old woman was admitted to hospital with fatigue,nausea,abdominal distension,jaundice and a>16-fold increase in transaminase and bilirubin level.The patient had used Sterculia gum prior to the onset of her symptoms.Her symptoms and clinical indicators improved after treatment.The possibility of acute viral hepatitis,autoimmune hepatitis,and metabolic liver disease was excluded.After discharge from hospital,the patient had a severe liver injury again when re-exposed to Sterculia gum.And the Roussel Uclaf Causality Assessment Method score was updated from 5 to 7,which was consistent with probable drug-induced liver injury.This is the first report of Sterculia-induced liver injury.Clinicians need to be aware of the potential hepatotoxicity of Sterculia.

Characteristics,causality,and suicidal behavior:a qualitative study of family members with suicide history in Wonogiri,Indonesia

Objective:This study explored the characteristics,causality,and suicidal behavior among family members with suicide history in Wonogiri,Central Java,Indonesia.Methods:Qualitative semistructured interviews were conducted with 15 family members who had made suicide attempts.The interviews directly explored the relationships among characteristics,causality,and suicidal behavior.The research data were analyzed using the Miles and Huberman approach.Results:The results indicate that there are relationships among characteristics,causality,and suicidal behaviors.The characteristics contributing to suicide attempts were male gender,age(adolescence and old age),lack of religious activities,introvert nature,low economic status,chronic diseases,unemployment,and a history of family members with suicide attempts.The causality of suicide was joblessness,economic crisis,unemployment,family conditions,personality disorder,depression,sickness,and suicide ideation.Conclusions:The findings highlight the relationships among characteristics,causality,and suicidal behaviors.Suicide occurs when individuals have some problems that they cannot solve because of the lack of family support.The causal factors were related to one another,which caused the suicidal behavior.One of the most effective suicide prevention strategies is educating the community on how to identify suicidal signs and increase social supports.

Stock prices and economic activity nexus in OECD countries:new evidence from an asymmetric panel Granger causality test in the frequency domain

This study investigates the stock price–economic activity nexus in 12 member countries of the Organization for Economic Cooperation and Development(OECD)by employing monthly data over the period 1981:1–2018:3.For this purpose,the study uses Granger causality in the frequency domain in the panel setting by decomposing the symmetric and asymmetric fluctuations.This methodology determines whether the predictive power of interested variables is concentrated on quickly,moderately,or slowly fluctuating components.Our findings show that the stock prices have predictive power for future long-term economic activity in the panel setting.However,economic activity has more reliable information for stock prices for negative components.Additionally,empirical findings for asymmetric shocks are not fully consistent with those of symmetric ones.Besides,the country-specific results provide different causal linkages across members and frequencies.These findings may provide valuable information for policymakers to design proper and effective policies in OECD countries regarding the stock market and economic activity nexus.

Empirical study of the dilemma regarding preliminary proof of causality and methods used by victims in China's environmental health litigation

Environmental health incidents have occurred frequently in recent years;in China,however,litigation has not been effective in protecting the rights and interests of victims due to their heavy burden of proof.In order to mitigate the burden of proof for victims,the environmental judicature in China introduced the rule of inversion of burden of proof;this means that the victim only assumes the preliminary burden of proof,but in practice it has not been sufficient.Victims in environmental health litigation still face many difficulties in proving causality.Based on the statistical analysis of judicial big data,this paper demonstrates three dilemmas faced by victims.First,the preliminary burden of proof of causality has a high standard.Second,the victim's ability to produce evidence is low.Third,the casual relationship between pollution behaviors and health damage is difficult to identify.When examining the possible legal instruments,there are three aspects to the causes of these dilemmas:theory,judicial practice,and society.The theoretical aspect manifests as the differences between the rule of inversion of burden of proof and the presumption of causality;the practical aspect manifests as the lack of an identification system for personal injury;and the social aspect manifests as the victim's lack of economic,scientific,technological,and information resources,as well as the absence of assistance from government sectors and social organizations.Combining Chinese law with judicial practice,this paper proposes a solution to the dilemma of victims needing to prove causality in environmental health litigation.On the basis of clarifying the theoretical differences between the inversion of burden of proof and the presumption of causality,China should adhere to the rule of inversion of burden of proof,establish an identification system for personal injuries caused by environmental pollution,and use methods such as epidemiological causality,indirect counterevidence,and prima facie bewies.Furthermore,the full functions of the government and social organizations must be brought into play.This will not only help victims demonstrate causality,it can also help victims achieve equitable relief.

Causality between bank’s major activities and economic growth:evidences from Pakistan

Background:Banking is an important sector of Pakistan’s economy.It is general consideration that bank’s major activities saving and lending have positive impact on economic growth.So the aim of this study is to investigate this consideration and also investigate that either growth led deposits and credits,or deposit and credits led growth means the purpose of this study is to investigate the direction of this relationship.Methods:Johansen test of Co-integration and Granger Causality is employed by using time series data of Pakistan from 1961 to 2013.Results:The results show that two major activities of banking sector that are saving and lending don’t have any long run or short run causality towards economic growth so the general consideration of positive impact of these activities proved wrong in case of Pakistan.However there is unidirectional causality running from GDP growth to credit provided by banking sector which shows that economic prosperity or economic growth will have a major impact on lending activities of banks meaning that demand following hypothesis is true for Pakistan in case of GDP and Bank’s credit or we can say that growth led Bank’s credit in Pakistan.Conclusions:Hence Government and central bank should make policies by keeping this fact in consideration that bank’s two major activities that are saving and lending does not have impact on GDP growth.There might be other factors which influence economic growth of Pakistan more than banking sector these activities,which can be bank’s profitability,human resource,technology,infrastructure and other sectors of the economy.However GDP growth affects bank’s lending activities so during high economic growth year central bank and private bank’s management should introduce easy loans for businesses and industries and during poor economic growth years personal loan’s new schemes should be introduce by banks.

Insurance market density and economic growth in Eurozone countries:the granger causality approach

Background:This study examines the relationship between insurance market density(IMD)and economic growth.Methods:We employed Granger causality technique in 19 Eurozone countries for the period 1980-2014.We use three different indicators of IMD,namely life insurance density,non-life insurance density,and total insurance density.We particularly emphasize on whether Granger causality runs between IMD and economic growth both ways,one way,or not at all.Results:Our empirical result recognizes the presence of both unidirectional and bidirectional causality between insurance market density and economic growth.However,these results are mostly non-uniform across Eurozone countries.Conclusions:This study holds important policy implications-economic policies should recognize the differences in the insurance market density and economic growth in order to maintain sustainable economic growth in the Eurozone.