Research on Risk Identification and Industrial Governance of Digital Education Products Based on Data Annotation Technology

The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”.

Research on strategic risk identification method of equipment system development based on system dynamics

Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of strategic risk of equip-ment system development is analyzed and classified.Based on this,a causal loop diagram of strategic risk of equipment sys-tem development based on system dynamics is established.The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis,risk consequences ana-lysis,risk feedback loop identification and corresponding pre-control measures,and achieves a good risk identification effect.

Research of Risk Identification and Prevention of Underground Pressure Pipelines Damage Caused by External Disturbance

External disturbance is an important cause of underground pressure pipeline damage,which leads to accidents,and it is crucial to study the risk of damage caused by external disturbance and come up with proper prevention and control measures.We reviewed literature on risk identification of underground pressure pipelines damage due to external disturbance was conducted,and a list of risk factors was formed.Based on the list of risk factors,fault tree analysis was carried out on underground pressure pipelines damage caused by external disturbances,and risk prevention and control measures were proposed through the calculation of minimum cut sets,minimum path sets,and structural importance,in hopes of providing reference for the normal operation of underground pressure pipelines.

Intelligent risk identification of gas drilling based on nonlinear classification network

During the transient process of gas drilling conditions,the monitoring data often has obvious nonlinear fluctuation features,which leads to large classification errors and time delays in the commonly used intelligent classification models.Combined with the structural features of data samples obtained from monitoring while drilling,this paper uses convolution algorithm to extract the correlation features of multiple monitoring while drilling parameters changing with time,and applies RBF network with nonlinear classification ability to classify the features.In the training process,the loss function component based on distance mean square error is used to effectively adjust the best clustering center in RBF.Many field applications show that,the recognition accuracy of the above nonlinear classification network model for gas production,water production and drill sticking is 97.32%,95.25%and 93.78%.Compared with the traditional convolutional neural network(CNN)model,the network structure not only improves the classification accuracy of conditions in the transition stage of conditions,but also greatly advances the time points of risk identification,especially for the three common risk identification points of gas production,water production and drill sticking,which are advanced by 56,16 and 8 s.It has won valuable time for the site to take correct risk disposal measures in time,and fully demonstrated the applicability of nonlinear classification neural network in oil and gas field exploration and development.

Risk identification and risk mitigation during metro station construction by enlarging shield tunnel combined with cut-and-cover method

Constructing a metro station by enlarging shield tunnels combined with a mining/cut-and-cover method provides a new method to solve the contradictions of construction time limits of shield tunnels and stations. As a new-style construction method, there are several specific risks involved in the construction process. Based on the test section of Sanyuanqiao station on Beijing metro line 10, and combined with the existing methods of risk identification at present, including a review of world-wide operational experience of similar projects, the study of generic guidance on hazards associated with the type of work being undertaken, and discussions with qualified and experienced staff from the project team, etc., the specific risks during the construction process of the metro station constructed by enlarging shield tunnels combined with the cut-and-cover method are identified. The results show that the specific risks mainly come from three construction processes which include constructing upper enclosure structures, excavating the soil between shield tunnels and demolishing shield segments. Then relevant risk mitigation measures are put forward. The results can provide references for scheme improvement and a comprehensive risk assessment of the new-style construction method.

Risk Identification for the Ecological Environment of Flood Disasters Based on the Life Cycle

Aiming at the complexity of ecological environment risk of flood disaster, the life cycle of flood disaster is defined based on the relevant literatures, which is divided into incubation period, development period, outbreak period, recession period and extinction period. The characteristics of each period are analyzed. Further, the main risks for ecological environment faced at each stage under the life cycle of flood disaster are studied systematically to form the systematic ecological environment risk system for flood disaster.

A New Sustainability Controlling Approach for Urban Water Systems： Multidimensional Risk Identification and System Analysis

There are many pressures on urban water systems in today＇s highly dynamic world. These include the diverse impacts that are summarized under the term ＂Global Change＂. At the same time, high demands on water utilities to act sustainably do exist. For this purpose, the collaborative project ＂Sustainability Controlling for Urban Water Systems＂ （NaCoSi） introduces an innovative approach with which sustainability risks can be identified and controlled. The sustainability controlling is based on common process-oriented management systems. The starting point is a system of sustainability objectives, which were developed together with practice partners from the German water sector. A method for multidimensional risk identification is introduced to identify sustainability risks. Complex networks of cause-effect relationships are disaggregated into unbranched linear causal chains, which are managed as records in a risk database. The subsequent analysis of the risk database allows the examination of cross-linked risks. Severe risk factors, vulnerable processes and sustainability objectives can thereby be identified and subsequently analyzed. The sustainability controlling was successfully tested and improved by an iterative process of case studies in cooperation with practice partners. The results of the case studies demonstrate the benefit of the project＇s interdisciplinary approach and the applicability of the sustainability controlling.

The Risk Identification and Corresponding Strategy of Transnational Financial Accounting Outsourcing

Financial Accounting outsourcing in recent years has been the development of new outsourcing model with more and more enterprises management and CFOs of all ages. With the development of financial accounting outsourcing services, it will lead to economic, social and development prospects that are also widely recognized. Not only small and medium companies are keen to own non-core financial accounting processes outsourced to agencies, large group has the entire basis of accounting processes tend to be outsourced to a qualified and experienced outsourcing service. Group companies or large multinationals are gradually re-integration of the financial sector, the basic financial accounting processes separate from the daily work out for outsourcing to focus resources on the development of core competencies.

Automatic Risk Identification in Software Projects:an Approach based on Inductive Learning

Effective risk management is very important to increase the probability of success in software projects. Indeed, like other types of projects, software projects are also susceptible to various problems that can lead to the cancelation of their development or to the development of systems that do not meet the client’s requirements. One of the main active- ties of risk management is the risk identification, because the list of risks generated in this activity is used all along the risk control process. Thus, this work proposes the creation of an expert system which is capable of identifying risks in software projects by using the lessons inductively learned from similar software projects already developed. By using this proposed expert system, project managers and software developers must be able to avoid errors of the past.

Research on Risk Identification of Supply Chain Management in Pharmaceutical Wholesale Enterprises——Taking Enterprise A as an Example

Objective To study the current situation of supply chain management in pharmaceutical wholesale enterprises so that supply chain risks can be identified effectively.Methods Through the analysis of the actual situation of enterprise A,combining with the supply chain problem of the enterprise,the enterprise supply chain risks were found out.Results and Conclusion With the development of pharmaceutical circulation industry,the competition among drug wholesalers has become increasingly fierce.For enterprises,the supply chain risks are increasing,and the traditional upstream and downstream supply risks are transformed into multiple-dimensional risks.The goal of carrying out supply chain risk identification in pharmaceutical wholesale enterprises is to enable key enterprises to discover and understand different types of risks in time so as to improve the management of supply chain.

Risk identification and regulatory system design for the carbon market

Currently,the global carbon trading systems are fragmented and belong to different governments or are under the jurisdiction of different regions,resulting in a series of new problems,such as how to link dispersed trading systems,how to compare the emission reduction of various markets and other issues.Since the development of the international carbon market is relatively immature with uncertain life expectancy and volatility during its short history,and there is a lack of quantitative data on the long-term record,the market could provide few risk management tools.Meanwhile,with the launches of China's regional carbon trading pilots in seven provinces since 2013 and combined with the national voluntary emission trading system,carbon trading will become an important mechanism for China in achieving its emission reduction target.In the first stage,the carbon finance market is at least faced with mechanism design risks,market supply risks and compliance risks.Therefore,to secure the development of the carbon market and for public interest,relevant government departments of China should identify the risks facing the market and should make the basic principles and goals,such as ensuring effective trading and pricing mechanisms to avoid fraud and price manipulation,and balancing transparency and confidentiality of information.Consequently,the governments should develop a comprehensive carbon finance regulatory system covering regulatory legislation,regulatory institutions and their authorities,regulatory scope as well as regulatory objects.

Literature review of driving risk identification research based on bibliometric analysis

In order to understand the current research status and development direction of driving risk identification at home and abroad,relevant literatures in the field of driving risk identification from the China National Knowledge Infra-structure(CNKI)and Web of Science(WOS)in recent 12 years(2011-2022)were selected as research samples,and literature metrology tools VOSviewer and Citespace were used for visual analysis.The situation was analyzed from the aspects of chronological distribution,national cooperation network,distribution of domestic institutions,journal performance and keywords overview,literature coupling clustering and research hotspots.The results show that the number of published papers fluctuates year by year,and China,the United States and Germany have the largest number of published papers.The United States is at the center of international cooperation.The CNKI shows that universities in China such as Chang’an University and Chongqing Jiaotong University have published a large number of documents.According to the statistics of WOS,Accident Analysis&Prevention is the most widely published journal in the world.The average level of the journal is high and the quality of articles is better.Combining the research contents of CNKI and WOS,the main research directions can be clustered into five cluster themes by using the coupling function in VOSviewer,including driving risk assessment considering driver factors,the influence of driving environment on driving risk,driving risk assessment considering multi-source characteristic data,multiaspect research on driving risk and risk identification of non-traditional vehicles in specific scenarios.Human-machine co-driving,artificial intelligence,intelligent driving,risk identification and natural driving are the current research hotspots and the future research trends.

Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

Identification and Categorization of Climate Change Risks

The scientific evidence that climate is changing due to greenhouse gas emission is now incontestable, which may put many social, biological, and geophysical systems in the world at risk. In this paper, we first identified main risks induced from or aggravated by climate change. Then we categorized them applying a new risk categorization system brought forward by Renn in a framework of International Risk Governance Council. We proposed that ＂uncertainty＂ could be treated as the classification criteria. Based on this, we established a quantitative method with fuzzy set theory, in which ＂confidence＂ and ＂likelihood＂, the main quantitative terms for expressing uncertainties in IPCC, were used as the feature parameters to construct the fuzzy membership functions of four risk types. According to the maximum principle, most climate change risks identified were classified into the appropriate risk types. In the mean time, given that not all the quantitative terms are available, a qualitative approach was also adopted as a complementary classification method. Finally, we get the preliminary results of climate change risk categorization, which might iay the foundation for the future integrated risk management of climate change.

An intelligent identification method of safety risk while drilling in gas drilling

In view of the shortcomings of current intelligent drilling technology in drilling condition representation, sample collection, data processing and feature extraction, an intelligent identification method of safety risk while drilling was established. The correlation analysis method was used to determine correlation parameters indicating gas drilling safety risk. By collecting monitoring data in the safety risk period of more than 20 wells, a sample database of a variety of safety risks in gas drilling was established, and the number of samples was expanded by using the method of few-shot learning. According to the forms of gas drilling monitoring data samples, a two-layer convolution neural network architecture was designed, and multiple convolution cores of different sizes and weights were set to realize the vertical and horizontal convolution computations of samples to extract and learn the variation law and correlation characteristics of multiple monitoring parameters. Finally, based on the training results of neural network, samples of different kinds of safety risks were selected to enhance the recognition accuracy. Compared with the traditional BP(error back propagation) full-connected neural network architecture, this method can more deeply and effectively identify safety risk characteristics in gas drilling, and thus identify and predict risks in advance, which is conducive to avoid and quickly solve safety risks while drilling. Field application has proved that this method has an identification accuracy of various safety risks while drilling in the process of gas drilling of about 90% and is practical.

Risk Identification and Mitigation Strategies for Deepwater Oilfields Development

Deepwater oilfields will become main sources of the world's oil and gas production.It is characterized with high technology,huge investment,long duration,high risk and high profit.It is a huge system project,including exploration and appraising,field development plan(FDP)design,implementation,reservoir management and optimization.Actually,limited data,international environment and oil price will cause much uncertainty for FDP design and production management.Any unreasonable decision will cause huge loss.Thus,risk foreseeing and mitigation strategies become more important.This paper takes AKPO and EGINA as examples to analyze the main uncertainties,proposes mitigation strategies,and provides valuable experiences for the other deepwater oilfields development.

Risk identification and prioritization in banking projects of payment service provider companies:An empirical study

ldentifying risks and prioritizing is important for payment service provider(PSP)companies to get banking projects and gain more market share.However,studies regarding the identification of risks and causal relationships are insufficient in the lranian PSP industry and the industry is unique because of its characteristics.In this study,30 experts involved with PSP companies are employed as the research sample.Eleven key risks and 46 sub-risks are also ientified.Subsequently,the fuzzy decision-making trial and evaluation laboratory technique is applied to determine the effective and affected risks and the severity of their effects on each other.Finally,all risks are ranked.Due to the interal interrelationships of the main risks,the weight of each risk is calculated via the fuzy analytic network process.As the second-level risks have no significant interrelationships,they are ranked via the fuzzy analytical hierarchy process.Moreover,the best-worst method is used to ensure that the obtained rankings are reliable.This study identifies the risks affecting the loss of banking projects and determines the impacts of these risks on each.A sensitivity analysis is then conducted on the weights of the criteria,and the results are compared.

EPC Contract Settlement Risk Identification and Control in Indonesia

This thesis presents solutions to settlement risk identification and control in EPC contract performance in Indonesia from the perspective of international contractor.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Surface Mine System Simulation and Safety Risk Management

Modem surface mines, either mono-system or multi-systems, need a large fleet of equipment consisting of excavators, loaders, haulers and auxiliary machines. Presently, the complexity of the system, the interference between sub-systems and the lag in management skills has been a bottle neck for improving productivity of the system. Based on the fact that the traditional tools for safety analysis have been insufficient to evaluate systematically and dynamically the safety risks, this paper tries to create a virtual reality tool consisting of human, machine and mines, using Pro/E and the 3D MAX software in order to evaluate visually the operations of typical mining equipment, such as the bucket wheel excavator （BWE）, the shovel, the truck and the dragline. Within this virtual world, the behavior of the system, such as interaction, interference and potential risk can be replayed and reviewed visually. The objective of the study is to identify the critical safety issues of the system and to provide a convenient and powerful tool for safety training and safety management.