基于Partial New Causality的因果脑网络情绪识别

为了研究情绪产生过程中脑区以及通道之间的因果作用,在部分格兰杰与新型因果关系的基础上,提出一种用于研究时间序列之间因果关系的部分新型因果关系(PNC)方法。在不同情绪下选取脑区内的8个通道,用PNC计算脑区内通道之间的因果连接关系,根据连接关系构建因果网络;对因果网络中节点的信息流向和介数属性进行分析,将PNC因果网络和Granger因果网络节点之间的因果连接视为一种特征送入SVM中训练分类。实验结果表明,基于PNC因果网络和Granger因果网络的平均识别精度分别为76.4%和68.5%,PNC可用于计算时间序列之间的因果关系。

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

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.

Not in Control,but Liable?Attributing Human Responsibility for Fully Automated Vehicle Accidents

Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.

Research on the Collaborative Governance of Social Responsibility in Online Audiovisual Enterprises

This paper aims to analyze the present conditions of the social responsibility ecosystem in online audiovisual enterprises in the digital age. It focuses on the governance of social responsibility in these enterprises and conducts an in-depth analysis of the problems and influencing factors related to the social responsibility aberrations of online audiovisual enterprises. Drawing upon social responsibility theory and collaborative governance theory, this research constructs a social responsibility guidance and governance system guided by the public, supported by the voluntary fulfillment of responsibilities by online audiovisual enterprises, and based on the collaborative participation of diverse stakeholders. It explores and optimizes the implementation pathways of this system, providing theoretical support and practical guidance for promoting the sustainable development of online audiovisual enterprises. Furthermore, it aims to contribute to the creation of a harmonious Internet ecosystem.

Complete response of gallbladder cancer treated with gemcitabine and cisplatin chemotherapy combined with durvalumab:A case report and review of literature

BACKGROUND Gallbladder cancer(GBC)is the most common and aggressive subtype of biliary tract cancer(BTC)and has a poor prognosis.A newly developed regimen of gemcitabine,cisplatin,and durvalumab shows promise for the treatment of advanced BTC.However,the efficacy of this treatment for GBC remains unclear.CASE SUMMARY In this report,we present a case in which the triple-drug regimen exhibited marked effectiveness in treating locally advanced GBC,thus leading to a long-term survival benefit.A 68-year-old man was diagnosed with locally advanced GBC,which rendered him ineligible for curative surgery.Following three cycles of therapy,a partial response was observed.After one year of combined therapy,a clinical complete response was successfully achieved.Subsequent maintenance therapy with durvalumab monotherapy resulted in a disease-free survival of 9 months for the patient.The patient experienced tolerable toxicities of reversible grade 2 nausea and fatigue.Tolerable adverse events were observed in the patient throughout the entirety of the treatment.CONCLUSION The combination of gemcitabine and cisplatin chemotherapy with durvalumab was proven to be an effective treatment approach for advanced GBC,with manageable adverse events.Further research is warranted to substantiate the effectiveness of the combined regimen in the context of GBC.

Practice and Exploration of Ideological and Political Education for Postgraduate Students Under the Mentor Responsibility System

This study aims to address the problems existing in the ideological and political education of postgraduate students,and combines the implementation of the mentor responsibility system to explore the role orientation,work content,and practical effects of mentors in the ideological and political education of postgraduate students.Through questionnaire surveys,interviews,and other research methods,the current situation of ideological and political education of postgraduate students under the mentor responsibility system is analyzed,and corresponding optimization strategies are proposed.

The Correlation between Corporate Social Responsibility(CSR)and Chinese Family Enterprises in China

It is imperative to aggressively advocate for and transmit the ideas of corporate social responsibility as Chinese family enterprises transition into the phase of corporate inheritance.This study conducts a thorough examination of existing literature to elucidate the concept and model of western corporate social responsibility.It also analyses the current state of Chinese corporate social responsibility and highlights the fulfilment of corporate social responsibility by private enterprises.Furthermore,it specifically investigates the significance of family enterprises in promoting and preserving the culture of corporate responsibility.Lastly,it delves into the correlation between corporate social responsibility and family enterprises.This study presents the process of transmission and inheritance of Chinese family firms,focusing on the influence of Chinese traditional culture,the original purpose of enterprise development,and enterprise image.

Corporate Social Responsibility and Shipping Supply Chain Risks

This study aims to investigate whether Corporate Social Responsibility(CSR)activities reduce supply chain disruptions by examining the impact of the Suez Canal obstruction on the Ever Given container ship in March 2021.This study conclude that the more responsible companies have higher returns and are less affected by this event than the less responsible companies;the less responsible companies have lower returns.The companies with better CSR have a lower impact on their supply chains when faced with disruptions in the supply chain.

CAUSALITY DIAGRAM BASED SAFETY ANALYSIS OF MICRO TURBOJET ENGINE

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.

Study on Environmental Protection during Agricultural Eco-tourism Development in Guangzhou City——From the Perspective of Perfecting Environmental Responsibility System of Tourism Company

By taking Guangzhou City for example,a series of environmental problems brought by agricultural ecological tourism were analyzed from the perspective of environmental degradation caused by that tourism company failed to take environmental responsibility.Based on economic development and environmental protection,some suggestions on environmental protection were proposed from the perspective of perfecting environmental responsibility of tourism company in order to realize win-win of economic development and environmental protection.

Way to Push Ahead Corporate Social Responsibility Report System in China

According to the analysis and statistics of relevant institution, it is a prominent problem that CSR reports generally become a mere formality. Through the analysis on the causes, it finds that it is inevitable for the phenomenon. Thus, we proposed to improve the cultivation mechanism of corporate responsibility by ex- panding and advancing the subject and the object, as well as the contents, of fi- nancial analysis in order to push ahead with CSR report in China.

基于Time-Causality模型的供热用气量预测分析

目前关于时间序列预测的特征选择一直是研究的热点,但很少有学者分析多时间尺度下不同特征对预测的差异。提出基于Granger关系的Time-Causality预测模型,利用Granger关系进行特征选择,引入时间维度作为输入维度,并利用LSTM模型进行实验,在多时间尺度下分析预测供热用气量的特征。实验结果表明:Time-Causality模型能筛选到更有助于用气量预测的特征;从不同的时间尺度预测,所选取的特征不同;每个特征的预测作用也可能会随时间尺度的变化而变化。这为长期和短期预测提供理论和实践支持。

Correction of Corporation Value Assessment Model Based on Corporate Social Responsibility

The performance of corporate social responsibility is conducive to the con- tinuous improvement of their profitability, and promotes the upgrading of corporation value. However, it is difficult to confirm, calculate and check the costs and benefits brought by the implementation of corporate social responsibility under the current ac- counting theory system, so it is difficult to estimate whether the fulfillment of corpo- rate social responsibility has any effects on the corporation value assessment. Therefore, based on corporate social responsibility, the correction mode of corpora- tion value assessment is put forward.

基于改进causality graph的分布式可伸缩事件关联机制

传统事件关联技术无法有效满足分布式网络管理的需求,本文提出一种分布式可伸缩事件关联机制,采用先分布再集中的关联模式与自适应可伸缩的关联关系。定义了本地关联和网络关联两个过程,首先由设备进行本地关联,而后各地关联结果汇总到管理平台进行网络关联;将事件的关联关系与管理任务的关联关系相结合,根据管理任务在设备端的动态配置情况构建自适应可伸缩的关联关系,并支持对逻辑事件的推理。同时,在改进Causality Graph算法的基础上提出了实现该机制的相关算法。原型系统的应用实例验证了机制的有效性和优越性。

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.

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

Systematic analysis of corporate environmental responsibility:elements,structure,function,and principles

Corporate Environmental Responsibility(CER) is the subsystem of Corporate Social Responsibility(CSR),which continues to increase prominence in the global economy.Nowadays,CER becomes more important in corporate sustainable development.Based on Carroll's pyramid mode of CSR,this article explores the systematic feature of CER.From a systematic perspective,it sorts out its elements,structure,function,and principles of CER.And the four levels of economic,legal,ethical,and charitable structure are emphasized.With these considerations interpreted into practice,success will be achieved in potential cost savings,technological innovation,increased public acceptance,and better relations with governments.

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.

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.