Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the los...Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.展开更多
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 fro...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.展开更多
An improved safety analysis based on the causality diagram for the complex system of micro aero-engines is presented.The study is examined by using the causality diagram in analytical failure cases due to rupture or p...An improved safety analysis based on the causality diagram for the complex system of micro aero-engines is presented.The study is examined by using the causality diagram in analytical failure cases due to rupture or pentration in the receiver of micro turbojet engine casing,and the comparisons are also made with the results from the traditional fault tree analysis.Experimental results show two main advantages：（1）Quantitative analysis which is more reliable for the failure analysis in jet engines can be produced by the causality diagram analysis;（2）Graphical representation of causality diagram is easier to apply in real test cases and more effective for the safety assessment.展开更多
As physicalisms of various kinds have faced difficulties in recent years, the time has come to explore possible alternatives, one of which is yinyang ontology. A yinyang theorist is expected to provide a plausible acc...As physicalisms of various kinds have faced difficulties in recent years, the time has come to explore possible alternatives, one of which is yinyang ontology. A yinyang theorist is expected to provide a plausible account of causation to replace the traditional notion of causation. The present paper is critical of the Humean tradition, which understands the relata of causal relations in terms of passive materiality so that humans use referential terms to describe causal relations constructively. But an alternative notion of reference is available according to which causal relata are active processors of the information with which they interact. On this latter view, humans use referential language to describe the structure in which the relata interrelate themselves so that the structure can be understood hermeneutically. Reference on this view is naturalized. In this article, I advance two arguments for this thesis, one concerning the informationality of states and the other related to the essentiality of properties.展开更多
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 pa...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.展开更多
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 s...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.展开更多
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 eva...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展开更多
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 t...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.展开更多
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 neuro...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.展开更多
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 Aut...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.展开更多
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 ...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.展开更多
This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the em...This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the employment data from China Statistic Yearbook for the years from 1982 to 2003. Co-integration test showed that 1% increase in export value and import value of services created respectively 0.205% and 0.068 7% more job opportunities in the service sector. Both export and import of services impacted positively on employment in service industry, and export did more than import. However, in the short run, the impacts of services export and import on employment in service industry were both very small, though positive; and the impacts of employment in service industry on both export and import of services were very big, but not stable. Granger causality test indicated that employment in service industry was a Granger cause of services export. The findings highlight the importance of facilitating services import and reducing import barriers, and suggest that the competitiveness of China's labor- intensive services trade can be exploited to boost services export and help employment in service sector, and that the structure of services trade should be optimized by shifting from labor-intensive to knowledge-and technology-intensive services thus to enhance China's competitiveness of services export.展开更多
Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper...Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper, and the Minimal Cut Set, the Minimal Path Set and the Importance were introduced to develop the methodology. These concepts are employed to analyze the influence each event has on the top event ? the gas explosion, so as to find out about the defects of the system and accordingly help to work out the emphasis of the precautionary work and some preventive measures as well. The results of the safety analysis are in accordance with the practical requirements; therefore the preventive measures are certain to work effectively. In brief, according to the research CD is so effective in the safety analysis and the safety assessment that it can be a qualitative and quantitative method to predict the accident as well as offer some effective measures for the investigation, the prevention and the control of the accident.展开更多
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 ben...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. 展开更多
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 ca...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.展开更多
Market efficiency is based on efficient market hypothesis （EMH）. EMH claims that market totally contains the available information. In case of EMH, valid investors who take position will not gain abnormal profits. I...Market efficiency is based on efficient market hypothesis （EMH）. EMH claims that market totally contains the available information. In case of EMH, valid investors who take position will not gain abnormal profits. If the efficiency can not be established, that is, if markets are not efficient, investors will have the opportunity of abnormal profits. This paper investigates the causality relations to determine validity of EMH among G7 （Canada, France, Germany, Italy, Japan, United Kingdom, and United States） countries＇ stock exchange markets for the period from July 2003 to October 2014. To find out whether the variables cause each other or not provides knowledge about the market efficiency. The implication of this analysis is twofold. One implication is that if the markets are informationally efficient, the possibility of abnormal returns through arbitrage is ruled out and investors can reduce the risk of their investment for the same expected returns, if they establish portfolios that consist of both markets rather than consisting of only one market. Based on this, Hacker-Hatemi-J. bootstrap causality test that is newer and has many advantages contrary to other tests was used. Results showed that EMH is valid among each G7 countries＇ stock exchange markets. Also portfolio diversification benefits exist among these markets.展开更多
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 an...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.展开更多
This review paper focuses on the application of the Granger causality technique to the study of the causes of recent global warming (a case of climatic attribution). A concise but comprehensive review is performed and...This review paper focuses on the application of the Granger causality technique to the study of the causes of recent global warming (a case of climatic attribution). A concise but comprehensive review is performed and particular attention is paid to the direct role of anthropogenic and natural forcings, and to the influence of patterns of natural variability. By analyzing both in-sample and out-of-sample results, clear evidences are obtained (e.g., the major role of greenhousegases radiative forcing in driving temperature, a recent causal decoupling between solar irradiance and temperature itself) together with interesting prospects of further research.展开更多
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 provide...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.展开更多
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 contributin...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.展开更多
基金Project supported by the Key National Natural Science Foundation of China(Grant No.62136005)the National Natural Science Foundation of China(Grant Nos.61922087,61906201,and 62006238)。
文摘Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.
文摘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.
文摘An improved safety analysis based on the causality diagram for the complex system of micro aero-engines is presented.The study is examined by using the causality diagram in analytical failure cases due to rupture or pentration in the receiver of micro turbojet engine casing,and the comparisons are also made with the results from the traditional fault tree analysis.Experimental results show two main advantages：（1）Quantitative analysis which is more reliable for the failure analysis in jet engines can be produced by the causality diagram analysis;（2）Graphical representation of causality diagram is easier to apply in real test cases and more effective for the safety assessment.
文摘As physicalisms of various kinds have faced difficulties in recent years, the time has come to explore possible alternatives, one of which is yinyang ontology. A yinyang theorist is expected to provide a plausible account of causation to replace the traditional notion of causation. The present paper is critical of the Humean tradition, which understands the relata of causal relations in terms of passive materiality so that humans use referential terms to describe causal relations constructively. But an alternative notion of reference is available according to which causal relata are active processors of the information with which they interact. On this latter view, humans use referential language to describe the structure in which the relata interrelate themselves so that the structure can be understood hermeneutically. Reference on this view is naturalized. In this article, I advance two arguments for this thesis, one concerning the informationality of states and the other related to the essentiality of properties.
文摘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.
基金Supported by Shandong Provincial Key Research and Development Program,No.2018CXGC1219City of Weihai Technique Extension Project,No.2016GNS023+1 种基金TaiShan Scholars Program of Shandong Province,No.tshw20120206TaiShan Industrial Experts Program,No.tscy20190612.
文摘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.
文摘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
基金supported by the National Natural Sciences Foundation of China(61473056,61533005,61522304,61603068,U1560102)
文摘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.
文摘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.
基金the financial support through the General Research Program under project number GRP-73-41.
文摘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.
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education,China
文摘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.
文摘This article describes a study by co-integration test and Granger causality test on the relationships between China's services trades and employment using the data of services trade from the WTO website and the employment data from China Statistic Yearbook for the years from 1982 to 2003. Co-integration test showed that 1% increase in export value and import value of services created respectively 0.205% and 0.068 7% more job opportunities in the service sector. Both export and import of services impacted positively on employment in service industry, and export did more than import. However, in the short run, the impacts of services export and import on employment in service industry were both very small, though positive; and the impacts of employment in service industry on both export and import of services were very big, but not stable. Granger causality test indicated that employment in service industry was a Granger cause of services export. The findings highlight the importance of facilitating services import and reducing import barriers, and suggest that the competitiveness of China's labor- intensive services trade can be exploited to boost services export and help employment in service sector, and that the structure of services trade should be optimized by shifting from labor-intensive to knowledge-and technology-intensive services thus to enhance China's competitiveness of services export.
基金Supported by the Natural Science Foundation of China (No. 59677009) the National Research Foundation for the Doctoral Program of Higher Education of China (No.99061116)
文摘Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper, and the Minimal Cut Set, the Minimal Path Set and the Importance were introduced to develop the methodology. These concepts are employed to analyze the influence each event has on the top event ? the gas explosion, so as to find out about the defects of the system and accordingly help to work out the emphasis of the precautionary work and some preventive measures as well. The results of the safety analysis are in accordance with the practical requirements; therefore the preventive measures are certain to work effectively. In brief, according to the research CD is so effective in the safety analysis and the safety assessment that it can be a qualitative and quantitative method to predict the accident as well as offer some effective measures for the investigation, the prevention and the control of the accident.
文摘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.
文摘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.
文摘Market efficiency is based on efficient market hypothesis （EMH）. EMH claims that market totally contains the available information. In case of EMH, valid investors who take position will not gain abnormal profits. If the efficiency can not be established, that is, if markets are not efficient, investors will have the opportunity of abnormal profits. This paper investigates the causality relations to determine validity of EMH among G7 （Canada, France, Germany, Italy, Japan, United Kingdom, and United States） countries＇ stock exchange markets for the period from July 2003 to October 2014. To find out whether the variables cause each other or not provides knowledge about the market efficiency. The implication of this analysis is twofold. One implication is that if the markets are informationally efficient, the possibility of abnormal returns through arbitrage is ruled out and investors can reduce the risk of their investment for the same expected returns, if they establish portfolios that consist of both markets rather than consisting of only one market. Based on this, Hacker-Hatemi-J. bootstrap causality test that is newer and has many advantages contrary to other tests was used. Results showed that EMH is valid among each G7 countries＇ stock exchange markets. Also portfolio diversification benefits exist among these markets.
文摘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.
文摘This review paper focuses on the application of the Granger causality technique to the study of the causes of recent global warming (a case of climatic attribution). A concise but comprehensive review is performed and particular attention is paid to the direct role of anthropogenic and natural forcings, and to the influence of patterns of natural variability. By analyzing both in-sample and out-of-sample results, clear evidences are obtained (e.g., the major role of greenhousegases radiative forcing in driving temperature, a recent causal decoupling between solar irradiance and temperature itself) together with interesting prospects of further research.
文摘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.
文摘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.
