Risk Identification Method Research Articles

Hybrid Text Representation for Explainable Suicide Risk Identification on Social Media

Social media data that characterize users can provide mental health signals, including suicide risks. Existing methods for suicide risk identification on social media have demonstrated promising results; however, the limitation of existing methods is that they are unable to capture low-and high-level features with complex structured data on social media and are incapable of explaining the predicted labels. Explainable models are more useful when translated, so we aimed to evaluate a novel method that would produce explainable models. This article presents a hybrid text representation method that integrates word and document-level text representations to explain suicide risk identification on social media. The proposed method is then fed to a transformer-based encoder with ordinal classification to determine suicide risk. Our results show that our method outperforms state-of-the-art baselines with an FScore of 0.79 (an absolute increase of 15%) on a public suicide dataset. Our method shows that an explainable model can perform at a comparable level to the best nonexplainable models but has advantages if translated for use in clinical and public health practice.

УПРАВЛЕНИЕ РИСКАМИ В ОРГАНИЗАЦИИ: МЕТОДЫ ИДЕНТИФИКАЦИИ

The article is devoted to methods of risk identification in organization management. This article presents effective methods of risk analysis designed to ensure long-term business stability and sustainability, and helps to develop an integrated approach to risk management that combines several methods of analysis for more complete and accurate identification of potential threats, which makes it useful for both management researchers and practicing managers.

A risk identification method for abnormal accounting data based on weighted random forest

A risk identification method for abnormal accounting data based on weighted random forest

Potential Risk Identification of Agricultural Nonpoint Source Pollution: A Case Study of Yichang City, Hubei Province

Potential risk identification of agricultural nonpoint source pollution (ANPSP) is essential for pollution control and sustainable agriculture. Herein, we propose a novel method for potential risk identification of ANPSP via a comprehensive analysis of risk sources and sink factors. A potential risk assessment index system (PRAIS) was established. The proposed method was used to systematically evaluate the potential risk level of ANPSP of Yichang City, Hubei Province. The potential risk of ANPSP in Yichang City was 18.86%. High-risk areas account for 4.95% and have characteristics such as high nitrogen and phosphorus application rates, large soil erosion factors, and low vegetation coverage. Compared with the identification results of the Diffuse Pollution estimation with the Remote Sensing (DPeRS) model, the area difference of the same risk level calculated by the PRAIS was reduced by 33.9% on average. This indicates that PRAIS has the same level of accuracy as the DPeRS model in identifying potential risks of ANPSP. Thus, a rapid and efficient identification system of potential risks of regional ANPSP was achieved.

Fusing multi-source quality statistical data for construction risk assessment and warning based on deep learning

In the context where accidents and fatalities in the construction industry remain persistently high, the assessment and early warning of construction risks become critically imperative. However, existing research has not fully leveraged the multi-source construction quality statistical data available, leading to a hindrance in formulating data-driven effective risk warning strategies. Considering the urgent need for optimized risk identification and assessment methods, this study introduced a novel risk assessment and warning strategy for comprehensive processing of multi-source construction practical quality monitoring statistical data based on cloud model-optimized Dempster-Shafer (D-S) evidence theory. This method employed 4M1E for monitoring data categorization and integrated a deep learning prediction model based on Long Short-Term Memory (LSTM) for forecasting potential quality monitoring data. Subsequently, the combination of the cloud model and d-S evidence theory facilitated the transformation of LSTM-predicted multi-source quality data between quantitative and qualitative, enabling comprehensive risk assessment and warning considerations. The findings revealed that the prediction results and the actual data were both at the highest level, indicating a high likelihood of a collapse incident. Lastly, the developed BIM-integrated platform offered a dynamic approach to risk assessment and real-time control, serving as a potential reference for warning risks in construction. This study is a pioneering study to assess and predict risk by cohesively integrating various methods and realizing a practical BIM platform. This study not only provides new measures to mitigate risks in construction but also advocates for the wider integration of multi-source quality statistical data into risk management.

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

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

Risk identification approaches and the number of risks identified: the use of work breakdown structure and business process

Losses and damages that any company or organization experience is usually caused by some unidentified risks that hit them unprepared. Some companies even must close their business or discontinue their operation due to the huge negative impact of the risk that hit them. Knowing the risks that an organization face is important. Risk needs to be identified properly. Though there had been some approaches a person can use in identifying risk, most of those approaches are complex, require several people involved, are too subjective, and are not able to identify unseen risks. Therefore, the purpose of this study is to know which risk identification method can identify more risks when knowledge of the context is controlled. The study made use of experimental research design with the use of covariance analysis in analyzing the data. With a 95% level of confidence, it was found that risk identification methods affect the number of risks identified. The business process approach seems to identify more risks than the work breakdown structure approach and placebo. The step-by-step procedures in doing the business process approach and work breakdown structure approach are also introduced in this study.

Adaptive identification method of new energy grid operation risk based on linear decision function

The current conventional risk identification method of new energy grid operation mainly achieves risk identification by mining equipment status information, which leads to poor identification effect due to the lack of effective extraction of abnormal data features. In this regard, a linear decision function-based adaptive risk identification method for new energy grid operation is proposed. The data flow model is constructed by combining the linear decision function, and the features of interval abnormal data and fluctuating abnormal data are extracted. A sliding window model is constructed, and the unsupervised model is used to realize the effective update of new energy grid operation data. In the experiments, the proposed method is verified for recognition accuracy. The experimental results show that when the proposed method is used to identify the risk of grid operation, the effective data mis-deletion rate of the algorithm is low and has a more desirable identification effect.

A Novel Financial Risk Identification and Management Method of Commercial Bank with Emerging Technology

Chinese financial market environment has also undergone a series of changes. How banks can identify and effectively prevent and control financial risks has become the focus of their research. For this reason, this paper makes a careful study on the management ideas of bank financial risk, first introduce the relevant content and characteristics of bank risk identification and management, explaining the new requirements and challenges brought about by the bank's financial risk management under the current background, and focus on analyzing the relatively representative issues in the financial risk management of banks in various regions, finally, it puts forward the strategies and improvement ideas on how to improve the quality and efficiency of bank risk management.

Risk identification and assessment methods of offshore platform equipment and operations

Risk management is crucial for the safety of offshore platforms. With advancements in safety management, much data on hidden dangers has been generated. The data contains a wealth of risk information, which realistically reflects the current state of safety on offshore platforms. However, this data is unstructured natural language, which cannot be analyzed statistically or queried with structured fields. Moreover, the manual identification of risk factors from hidden danger data is time-consuming and subject to subjective biases. A method for risk identification and assessment for offshore platform equipment and operations based on text mining of hidden danger data is proposed. From a big data perspective, a framework for expressing risk factors is established, which completes the transition from an unstructured text about the hidden danger to a structured expression of risk information. An automated method for risk identification is developed that combines machine learning, deep learning, and natural language processing techniques. The identified risk factors are used to build the Bayesian network model for data-driven risk assessment. Data from China’s Bohai oil field is used to demonstrate the effectiveness of this method. The result shows that it improves the identification of risk factors and the automation of the assessment.

Maneuver Decision-Making Method for Ship Collision Avoidance in Chengshantou Traffic Separation Scheme Waters

A maneuvering decision-making model based on time series rolling and feedback compensation methods is proposed to solve the problem of high traffic risk in Chengshantou traffic separation scheme (TSS) waters. Firstly, a digital traffic environment model suitable for the TSS waters is proposed. Secondly, a navigation risk identification method in these waters is constructed based on the digitized traffic environment and situation identification model in the Chengshantou TSS waters. Thirdly, considering the requirements of the rules and good seamanship, minimum course altering is obtained by combining the collision avoidance mechanism. Lastly, a maneuvering decision-making model in the TSS waters based on time series rolling and feedback compensation methods is developed. The simulation results show that the ship can correctly identify the collision risk and appropriately obtain maneuvering decisions, and can resume the planned route under the premise of ensuring safety. When the target ships alter course or change speed, the ship can also make adaptive maneuvering decisions. In summary, the proposed method meets the requirement of safe navigation in Chengshantou waters and provides a theoretical basis for the realization of intelligent navigation in waters similar to TSS.

A preliminary discussion on a framework for health geological survey and evaluation

While the Healthy China strategy is being implemented, the China Geological Survey has proposed and deployed a health geological survey project, starting a new journey of health geological survey and evaluation to support and serve the construction of Healthy China. However, there are still many urgent problems to be solved in this project, such as the survey vision, the framework of the evaluation indicators, the risk identification method, and the service scopes of the potential achievements. Therefore, in this paper, a framework for health geological survey and evaluation was proposed, which includes regional health geological survey zoning and local ecological risk identification. At regional scale, the framework of indicators, grading standard, score calculation, zoning method of health geological survey, identification of beneficial and harmful health geological factors, and application of achievements were proposed. These contents were presented based on the core evaluation indicators (e.g., the chemical composition, physicochemical properties, topography, and climate of the crustal surface environment caused by geological evolution processes) and the optional evaluation indicators (e.g., sanitary condition, economic level and geo-environment). At local scale, the selection of evaluation area, workflow, risk identification and the application of achievements were proposed to avoid the ecological risks of harmful trace elements and develop beneficial health geological resources.

Retracted: Financial Transaction Risk Identification Method Based on Boosting-SVM Algorithm

Retracted: Financial Transaction Risk Identification Method Based on Boosting-SVM Algorithm

Supply Chain Risk Management in a Digital Era: Evidence from SMEs of Clothing Retailers in Australia

With the increased globalisation and disruptions faced by businesses in this digital era and the occurrence of natural disasters such as floods and disease outbreaks in the world, supply chain risks and management of those risks are major challenges for businesses, especially for SMEs of clothing retailers in Australia. This study, hence, is carried out using an exploratory case study research method, and the data have been collected through semi-structured face-to-face interviews with key informants from managerial levels of 20 Australian SMEs of clothing retailing businesses to identify various supply chain risks and their management processes. This study finds five supply chain risks, namely supply risk, demand risk, financial risk, environmental risk, and operational risk, that the SMEs of clothing retailers mostly face in the supply chain. This study also finds that most of the investigated retailers lack a formal risk identification approach, though they informally use the reactive and proactive methods of risk identification. Furthermore, the assessment methods are not well established in most of the participating firms, and supplier monitoring receives more attention compared to their own performance to deal with their supply chain risks. This study contributes to the body of knowledge by being one of the first empirical studies to explore the SMEs of clothing retailers’ supply chain risks and their management processes in the Australian business context, which can add value in guiding supply chain design decisions for SMEs in other sectors.

Risk Assessment Study of Academic Talent Data Management Platform Development Project

With the continuous promotion of talent informatization, to improve the talent development index system for social needs and talent growth needs, academic evaluation and talent data management type platforms have gradually emerged and rapidly developed, and have been more widely used. However, there are many risks in the process of developing such emerging data platforms, which seriously affect the final benefit of the project. Therefore, this reaseach take the Global Unified Artifact Distributed Autonomous project as an example and apply the FMEA method for risk identification and risk assessment. The results show that the technical risks are the most serious and the schedule risks are the least serious. At the same time, the paper proposes strategies for optimizing the risk management of software projects to help the projects be carried out correctly and orderly.

Risk identification of expressway project management based on WBS method

With the rapid development of science and technology and the acceleration of people's life rhythm, the emergence of expressway is of great significance to the progress of our country. Based on the characteristics of expressway construction, the identification of risks is particularly important. Based on WBS method and combined with FMEA analysis method, this paper proposes a risk identification method for expressway. Taking Xiyu expressway as an example, the specific application of the method to Tuhe super large bridge is carried out. After the project is decomposed, the risk source is identified, and the experts score the project, calculate the risk priority number, and finally put forward the corresponding control method.

RISK MANAGEMENT ASPECTS FOR QUALITY IN PHARMACEUTICAL DEVELOPMENT

Currently, for the pharmaceutical development of both original and generic drugs, a systematic scientifically based approach is used to create model formulations and dosage form technology. The use of this approach is expressed in the paradigm of Quality-by-Design (QbD) and the application of quality risk management (QRM) methods. The use of these concepts and their deep integration with each other is due to the presence of uncertainty properties in the manifestation of risks that have different degrees of severity of consequences, the probability of occurrence and their detectability. In the case of the use of original pharmaceutical substances, innovative methods or dosage forms, uncertainty increases, and with them the need to use methods for assessing, analyzing, controlling, accepting and communicating risks. The purpose of this article is to analyze the possibilities for introducing the QRM system into the pharmaceutical development process, identify the project field and critical indicators of quality and process, analyze the features and systematize the initial risk assessment methods, in particular risk identification. The article demonstrates possible methods for assessing and managing risks in pharmaceutical development using solid dosage forms as an example, in particular, in terms of such key characteristics as polymorphism, particle size, solubility of pharmaceutical substances, their interaction with excipients, etc. In addition, when considering risk identification methods such as: brainstorming, Delphi method, interviews, documentation review, application of checklists and construction of cause-and-effect diagrams, etc. an assessment of the advantages and disadvantages of the approaches and the main problems characteristic of all QRM methods was carried out.

Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors.

Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk. Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD, were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods. Overall, 180 males and 165 females were included in this study with a mean age of approximately 76years and 97 (28%) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95% CI: 0.56-0.84), which improved to 0.71 (0.57-0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83-0.96) with a sensitivity of 0.83 and specificity of 0.74. These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.

Research on Financial Risk Path Identification of Chinese Energy Enterprises Based on ISM-MICMAC Model

As an important resource commodity related to the global economic lifeline, energy is the material basis for the survival and development of human society. With its identity as a major energy importer and consumer, China must ensure the stability of production and operation of energy enterprises, of which the identification and control of financial risks is of vital importance. Therefore, this paper first uses multiple risk identification methods to identify 9 external and 8 internal key risk factors affecting energy corporate finance in both external and internal dimensions, respectively. Then the ISM and MICMAC methods were used to construct a progressive multi-level structural model of the interactions of key risk factors, and the risk factors were classified by combining the driving power and dependence power of each key risk factor to reveal the interactions among the risk factors. The results showed that 5 partition levels were extracted from 17 risk factors according to ISM model and MICMAC analysis placed all factors into dependent quadrant and independent quadrant. This study reveals the internal relationship between risk factors and is of great significance to the financial risk identification and management of Chinese energy enterprises.

Assessment of the Explosion Accident Risk in Non-Coal Mining by Hasse Diagram Technique

The aim of is paper is to address the problem of identifying critical factors in the analysis of non-coal mine explosion accidents as well as to improve the rationality and accuracy of the risk analysis results. Hence, we developed a risk identification method for non-coal mine explosion accidents, combining the Systems-Theoretic Accident Model and Process (STAMP) and the Rank-order Centroid (ROC) method based on the Poset decision-making theory. The proposed method was applied to identify risk in engineering cases. Findings showed that four main dangerous events (out of twelve identified ones) were the primary culprits of related accidents, which were the events “Blasters without licenses and illegal operation” at the basic level, the event of “the confusion about the safety management system of non-coal mine companies” at the control level, and the event of “the failure about the emergency management departments” and “public security departments” at the supervision level. The approximate values of the average rank of the four events are 11.56, 10.4, 4.33, 4.33. The results of risk identification of non-coal mine explosion accidents based on Poset were consistent with the results obtained by the method used in the case study. This study extends the methods for identifying risks of non-coal mine explosion accidents and facilitates the formulation of effective preventive measures.