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Business Processes (BPs) can use Internet of Things (IoT) devices to make decisions based on information about the environmental context and even to actuate on it. These IoT-aware BPs, when applied to critical systems, have to deal with different types of IoT device failures and their consequences. To address such scenarios, decision-makers can take advantage of risk assessment techniques to analyze the risk associated with BPs. Current proposals analyze risk at the process level without providing a more fine-grained perspective. This paper presents a risk analysis methodology that calculates the risk level of BPs following a (de)composition approach using the risk-related information of BP elements. Therefore, it supports decision-makers in identifying the groups of tasks that contribute the most to the overall BP risk. In addition, it provides information for analyzing the worst-case scenarios at different levels of BP granularity. This information allows process designers to adjust and improve BPs to meet the risk acceptance criteria.

The evaluation of auto insurance risks is a fundamental task for financial institutions, crucial for setting equitable premiums and managing risks effectively. Traditional machine learning algorithms often struggle to capture the intricate relationships necessary for accurate risk assessment. In contrast, deep learning methods, while capable of processing complex data structures, lack the ability to model feature interactions and provide interpretability, which are essential for transparent decision-making in the insurance industry. To address these challenges, we introduce the Actuarial Transformer (AT)–a pioneering model that leverages the self-attention mechanism of the Transformer architecture to meticulously map feature interactions. The AT integrates advanced residual models with tree-based methods, enhancing its predictive accuracy. Additionally, it incorporates the SHAP (SHapley Additive exPlanations) model, which uses Shapley values from cooperative game theory to ensure interpretability and transparency in its risk assessments. Our empirical analysis, conducted on a representative dataset of auto insurance risks, demonstrates the AT’s superior performance in risk prediction. The SHAP analysis further validates the model’s ability to prioritize features logically, providing clear insights into the decision-making process. The AT not only improves the precision of auto insurance risk evaluations but also enhances the interpretability of these evaluations, making it a valuable tool for both industry practitioners and clients.

The discovery of security vulnerabilities and their mitigation in networked systems managed by Software-Defined Networking (SDN) are fundamental for ensuring their normal operation. The main goal of this survey was to investigate the literature on preventing system security vulnerabilities instead of detecting ongoing cyber-attacks as quickly as possible. Thus, in our opinion, organizations should fortify their systems’ security by identifying and eliminating any new security vulnerabilities before they can be successfully exploited. We comprehensively discuss different vulnerability detection approaches based on important comparison parameters such as vulnerability assessment, the SDN controller used, automation capability, system risk indicators, passive scanning and active probing of system vulnerabilities. The paper also analyzes relevant literature considering the mitigation mechanisms for discovered vulnerabilities such as the proposed SDN controller, automation capability, solution adaptation to system operational changes, risk indicators, and the solution’s impact on network quality metrics like latency and throughput. Despite the strengths of the surveyed work, we have also identified promising open issues that need further consideration by scholars, industry participants, and policymakers. We concluded that the majority of analyzed literature contributions are largely reactive in their implementation against running network threats. This suggests a new research domain for applying SDN in the automatic detection of security vulnerabilities and their proactive mitigation before external cyber-attackers can exploit them.

The nuclear event risk (NER) is an important and disputed factor that should be reasonably considered when planning the pathway of nuclear power development (NPD) to assess the benefits and risks of developing nuclear power more objectively. This paper aims to explore the impact of nuclear events on NPD pathway planning. The influence of nuclear events is quantified as a monetary risk component, and an optimization model that incorporates the NER in the objective function is proposed. To optimize the pathway of NPD in the lowcarbon transition course of power supply structure evolution, a simulation model is built to deduce alternative NPD pathways and corresponding power supply evolution scenarios under the constraint of an exogenously assigned carbon emission pathway (CEP); moreover, a method is proposed to describe the CEP by superimposing the maximum carbon emission space and each carbon emission reduction (CER) component, and various CER components are clustered considering the emission reduction characteristics and resource endowments of different power generation technologies. A case study is conducted to explore the impact of NER and its risk valuation uncertainty on NPD pathway planning. The method presented in this paper allows the impact of nuclear events on NPD pathway planning to be quantified and improves the level of coordinated optimization of benefits and risks.

Many examples tell us that organizations’ well-being should be laid upon risk assessments. Typical risk assessments, on the other hand, which produce a list of risk scenarios, more often than not, do not result in enough information for mitigations when it comes to resource limitations such as financial, time, or in this case: delays. Multi-attribute risk assessments give a solid foundation to develop systematic quantitative risk measurements that organizations can use for security mitigations. This study proposes a multi-level and multi-attribute risk assessment method that can be used to prepare mitigations in organization-wide or specific sections of a company. This study presents the evaluation process and results from a case study of a container terminal to evaluate delay risks in its dwelling time processes and to prepare requirements on mitigations based on the risk projections. The proposed method in this study mainly operates within the risk evaluation stage in the risk management lifecycle to complement other methods such as FMEA, FTA, and DEMATEL, which mainly operate within the risk identification and risk analysis stages of risk management. The evaluations in this case study address the efficiency of business process risk factors in a container terminal, not the safety-related risk factors. The proposed method takes event logs from information systems as input to evaluate the risk performances by computing the attributes of the event data. The method also offers risk projections to be treated as a reference to plan for mitigations and determine the likelihood of risk factors. The evaluation results from this case study have provided insights for operation managers and management to perceive and address risks within TPS Surabaya container terminal’s business processes. The proposed method is called Improved Multi-Attribute Risk Assessment (IMARA.)

Voltage sag insurance (VSI) is an effective measure to reduce the voltage sag loss and risk for enterprises with insufficient mitigation confidence or economic ability. However, due to the lack of understanding of voltage sag risk (VSR), it is difficult for power utilities to identify the enterprises with VSI demands, and it is also unable to realize differentiated premium according to different enterprises' situations. To solve the above problems, this paper proposes an improved voltage sag premium model based on VSR. Firstly, the VSR factors are analyzed, such as the hazard, the vulnerability, the exposure. The process immune time is introduced to describe the vulnerability. Based on the traditional consideration of only the ability of users to resist voltage sag, the ability of users to recover from voltage sag is further considered. Then, the risk evaluation model of voltage sag is established. The evaluation results can help power utilities to judge whether enterprises have the demand of VSI. Finally, the improved premium model of VSI based on VSR is constructed to ensure the differentiation and rationalization of the premium. The correctness and rationality of this method are proved by ten manufacturing enterprises in China by analyzing the VSR and comparing the premium before and after the improvement. [ABSTRACT FROM AUTHOR]

Electricity retailer is the most critical player in the restructured electricity market. Retailer's electricity procurement problem is a big challenge for them. Electricity retailer can purchase their energy from various options such as pool market, forward contracts, and etc. A relatively new way with a lesser investigation is the demand response exchange market. In this paper, the risk-constrained stochastic power procurement problem of electricity retailers is formulated by modeling the uncertainty of pool-market price and consumers' electricity demand. The Downside risk constraints (DRC) risk-evaluation method is used to obtain risk-based power procurement scheduling of electricity retailers. By using the proposed method, conservative electricity retailer can experience a scenario-independent strategy over the stochastic optimization. In other words, the proposed risk strategy is such that impose more cost for electricity retailer while having an equal cost in all scenarios. Based on the obtained results, the operation cost of electricity retailers in all scenarios is closed to about $4827570, which is a relatively zero-risk strategy due to equality overall scenarios. Besides, results are compared in two cases to demonstrate the advantages of the proposed risk-evaluation method. Also, the Pareto front between risk-in-cost and expected cost can introduce an optimal risk-strategy for electricity retailers in the presence of uncertainties. Finally, the risk-averse strategy of electricity retailer is proposed to obtain the retailer's optimal conservative schedule during power procurement in the presence of uncertainties.

In this paper, we dealt with some problems of operation and maintenance in wind farms. We focused on the main critical aspects of any maintenance strategy, which must include the identification of the plant elements to inspect as well as the planning of the possible actions aimed at minimizing production losses. At the same time, any maintenance strategy must take into account the possible costs. In fact, those decisions can be made based on risk-based methods. We designed a risk-based maintenance approach to plan inspection tasks to be assigned to service robots in wind power plants. A supervisory control and data acquisition (SCADA) system is employed to collect and manage suitable data (power, wind velocity, and related machine events), and the risk is evaluated on a daily basis over the data collected. The evaluation of the risk is strictly related to the healthiness of the power plant itself. Then, the tasks are created and scheduled based on a certain priority, which is strictly correlated with the evaluated risk. For the analysis of our approach, we used the real data collected on a wind power plant in Greece over 396 days. The power plant is capable to produce an overall power of 7.2 MW, and it is composed of eight wind turbines of 900 KW per each. We observed that, out of 396 days, 50 days presented machine events leading to a related risk evaluation for which our approach will produce 258 inspection tasks. From this analysis, we conclude that the application of the risk-based methodology paired with the exploitation of permanent robots on the field could result in a 225-MWh reduction of the plant's lost production, in other words, an increase of production of 45.6%.

Peer-to-Peer lending is developing quickly around the world as a new E-finance industry, especially in China. Yet fraudulence and business ceasing of Peer-to-Peer Lending Intermediaries (P2P-INTs) occur frequently, making P2P investors facing serious risk. This paper attempts to explore a bridge connecting managerial research with some most advanced natural language processing (NLP) technologies, and examines the risk assessing power of automatic learning text classifiers based on data of hazard status and top management team profile texts of the P2P-INTs. A risk evaluation model named MULTIPLE NLP Integrated Learning Text Classifier (MUN-LETCLA) based on five NLP techniques and meta-learning is proposed. Then risk classification power of the MUN-LETCLA and the single NLP models is assessed. The results show that the proposed model is effective in classifying low-risk and high-risk P2P-INTs. The NLP models can automatically detect the P2P-INTs risk from Top Management Team (TMT) members' working experience, educational background, and TMT composition with a precision level of more than 75%.

The artificial intelligence calculation method can effectively solve various nonlinear mapping relationships. The strength of these nonlinear solvers is exploited for the evaluation of power grid investment risk using back propagation (BP) neural network optimized by genetic algorithm. The mathematical model of the problem is constructed by selecting the transfer function of the neural network and defining the fitness function of genetic algorithm. BP neural network has good ability of self-learning, self-adaptation and generalization, which can overcome the drawbacks of traditional evaluation methods relying on experts' experience. For the characteristics of genetic algorithm global optimization, the genetic algorithm is used to optimize the weight and threshold of BP neural network, and BP neural network is trained to obtain the optimal evaluation model. The model fully exploits the local search ability of BP neural network and the global search ability of genetic algorithm. It has obtained good evaluation accuracy for the processing of multi-dimensional influence factor problem. And the model can be adapted to different power grids by changing the training data. However, the method cannot describe the specific relationship between each impact factor and the investment risk of the grid. The case study shows that the method can accurately and effectively evaluate power grid investment risk and improve the fault tolerance of the power grid investment risk evaluation.

Integration of heat and electricity supply improves the overall energy efficiency and system operational flexibility. The renewable powered heat-electricity energy system is a promising way to set up residential energy supply facilities in remote areas beyond the reach of power system infrastructures. However, the volatility of wind and solar energy brings about the risk of supply inadequacy. This article proposes a data-driven robust method to quantify two measures of such a risk in the stand-alone renewable powered heat-electricity energy system. The uncertainty of renewable generation is modeled through a family of ambiguous probability distributions around an empirical one based on the Wasserstein metric; then, the probability of heat and electricity load shedding during a short period and related penalty cost are discussed. Through a polyhedral characterization of renewable power feasible region, the load shedding probability under the Wasserstein ambiguity set comes down to a linear program. With a piecewise linear optimal value function of the penalty cost, its expectation under the worst case distribution in the Wasserstein ambiguity set also gives rise to a linear program. The proposed method requires moderate information on renewable generation and makes full use of available data, whereas sustains computational tractability. The evaluation result is robust against the inaccuracy of renewable power distributions. Case studies demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple attribute decision-making (MADM) problems for risk evaluation of enterprise human capital investment with interval-valued bipolar 2-tuple linguistic numbers (IVB2TLNs). Then, we propose some arithmetic and geometric aggregation operators with IVB2TLNs and analyze the characteristics of these operators. Furthermore, we present methods that apply these operators to solve the MADM problems with IVB2TLNs. Finally, a real-world example for the risk evaluation of enterprise human capital investment is used to illustrate the effectiveness of the proposed methodology.

The goal of this article is to investigate the roles of individual behavior characteristics and Internet finance industry risk in the light of bank run theory for P2P. We know that risk evaluation is clearly important for peer-to-peer (P2P) lending platforms in China, as during the last two years, the industry has experienced thousands of platform crashes. Traditional approaches to evaluate enterprise risk are increasingly ineffective in this industry, due to the difficulty of assessing the real information. In addition, the Internet business model makes it possible to record new kinds of information. By applying a data-driven analytics method, we build an intelligent risk evaluation model for P2P platforms that have comparable targeting platforms. The case study shows that our risk evaluation method can generate early warning signals regarding platform or industry risk, which is able to provide effective supporting for P2P business in practice. [ABSTRACT FROM AUTHOR]

Continuous measurements of moisture content in transformer liquids open new diagnostic possibilities. [ABSTRACT FROM AUTHOR]

Vehicle driving safety is influenced by many factors, including drivers, vehicles, and road environments. The interactions among them are quite complex. Consequently, existing methods that evaluate driving safety perform inadequately because they only consider limited factors and their interactions. As such, it is difficult for kinematics-based and dynamics-based vehicle driving safety assistant systems to adapt to increasingly complex traffic environments. In this paper, we propose a new concept, i.e., the driving safety field. The concept makes use of field theory to represent risk factors owing to drivers, vehicles, road conditions, and other traffic factors. A unified model of the driving safety field is constructed, which includes the following three parts: 1) a potential field, which is determined by nonmoving objects on the roads, such as a stopped vehicle; 2) a kinetic field, which is determined by the moving objects on roads, such as vehicles and pedestrians; and 3) a behavior field, which is determined by the individual characteristics of drivers. Moreover, the applications of the model are proposed, and its application to a typical car-following scenario is illustrated, which evaluates the risks caused by multiple traffic factors. The driving safety field can reveal driver–vehicle–road interactions and their influences on driving safety, as well as predict driving safety trends owing to dynamic changes. In addition, the model can provide a new foundation for establishing driving safety measures and active vehicle control under complex traffic environments. [ABSTRACT FROM PUBLISHER]

In the field of compulsators, hybrid excitation concepts have been verified through the development of several hybrid excitation prototypes. Although there are some advantages associated with the hybrid excitation compulsator in comparison with the traditional electrical excitation topology compulsator, some drawbacks still exist, which may lead to demagnetization of the permanent magnets and therefore affect the performance and operational safety of such compulsators. For practical applications, the risk of demagnetization needs to be assessed, measurements need to be taken, and design parameters need to be refined. The problem is critical during discharge, with many factors associated with dictating the demagnetizing of these compulsators. In particular, the demagnetizing magnetic strength, temperature rise, and stress effects were analyzed. The temperature distribution in the compensation shield caused by eddy-current losses was simulated. During discharge of a permanent magnet, the location of temperature sources is important. The impact stress imposed on the permanent magnets was simulated and safety characteristics were evaluated. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method of risk assessment for lightning shielding flashover in transmission lines. This is used to improve the performance of lightning shielding failure which is a frequently serious problem. The method can be efficiently implemented in the optimization of a 3-D model. Three-dimensional laser scanning is used to derive the analytical 3-D model to high precision. This includes the data of the whole span line's structure and terrain parameters. In this study, the evaluation of lightning shielding flashover risk of each tower is calculated using the whole span calculation method associated with the laser scanning data and the given parameters of lightning activities. The evaluation results for the Gechen line in the zone near the Three Gorges area are presented based on the proposed method. Using a comparison of theoretical values and actual statistical data, this paper concludes that the proposed new method has better performance than the traditional method. [ABSTRACT FROM PUBLISHER]

This paper presents fuzzy-probabilistic modeling techniques for system component outage parameters and load curves. The fuzzy membership functions of systeiu component outage parameters are developed using statistical records, whereas the system load is modeled using a combined fuzzy and probabilistic representation. Based on the fuzzy-probabilistic models, a hybrid method of fuzzy set and Monte Carlo simulation for power system risk assessment is proposed to capture both randomness and fuzziness of loads and component outage parameters. An actual example using a regional system at the British Columbia Transmission Corpoation is given to demonstrate the application of the presented fuzzy-probabilistic models for system parameters and new system risk evaluation method. [ABSTRACT FROM AUTHOR]

This paper presents a risk evaluation based approach to the replacement strategy of aged HVDC components. It includes estimation of unavailability of individual HVDC components due to aging failures, calculation of capacity state probabilities of the HVDC system, quantified risk evaluation of the power system containing the HVDC link and benefit/cost analysis for different replacement strategies. The presented approach can be also applied to other system components. The replacement strategy for an aged submarine cable of the HVDC link in a power supply system at British Columbia Transmission Corporation has been analyzed as an example to demonstrate the actual application. The procedure of the analysis has been explained in detail in the example. [ABSTRACT FROM AUTHOR]