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The purpose of this paper is to improve risk assessment processes in airline flight operations by introducing a dynamic risk assessment method.

Fuzzy logic and Bayesian network are used together to form a dynamic structure in the analysis. One of the most challenging factors of the analyses in aviation is to get quantitative data. In this study, the fuzzy data quantification technique is used to perform dynamic risk assessment. Dynamic structure in the analysis is obtained by transforming the bow-tie model into a Bayesian network equivalent.

In this study, the probability of top-event from fault tree analysis is calculated as 1.51 × 10⁻⁶. Effectiveness of the model is measured by comparing the analysis with the safety performance indicator data that reflects past performance of the airlines. If two data are compared with each other, they are at the same order of value, with small difference (0.6 × 10⁻⁷).

This study proposes a dynamic model to be used in risk assessment processes in airline flight operations. A dynamic model for safety analysis provides real-time, autonomous and faster risk assessment. Moreover, it can help in the decision-making process and reduce airline response time to undesired states, which means that the proposed model can contribute to the efficiency of the risk management process in airline flight operations.

With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

The aim of this paper is to provide up‐to‐date discussion of the types of factors used to assess sexual offenders risk.

The current status of the factors used to assess risk in sexual offenders is examined.

Risk factors broadly fall into two categories: static factors (i.e. generally unchangeable information such as previous offence history) from which a number of actuarial scales have been developed; and dynamic factors (i.e. psychological dispositions) that are typically identified in treatment. It is suggested that these risk factors are artefacts of the same behavioural and psychological vulnerabilities at different stages of assessment, with static factors acting as markers for underlying dispositions, while dynamic factors are the underlying dispositions.

The paper discusses in some detail the status of age as a risk factor, where even though it is typically considered a static risk factor in a number of actuarial scales (allowance typically being made if individuals are over/under 25), there is a dynamic element (i.e. change with age or the passage of time) to this aspect of assessment.

This paper may be useful to practitioners working in the field, in terms of providing a useful heuristic framework for risk conceptualisation.

A co-produced clinical practice that aims to improve outcomes through a partnership with service users is becoming increasingly important in intellectual disability (ID) services, yet these approaches are under-evaluated in forensic settings. This study aims to explore and compare the feasibility of two approaches to co-production in the completion of dynamic risk assessments and management plans in a secure setting.

A convenience sample of adults admitted to a secure specialist forensic ID service (N = 54) completed the short dynamic risk scale (SDRS) and drafted risk management plans under one of two conditions. In the first condition, participants rated the SDRS and risk management plan first, separately from the multidisciplinary team (MDT). In the second condition, participants and MDTs rated the SDRS and risk management plan together.

In total, 35 (65%) participants rated their risk assessments and 25 (47%) completed their risk management plans. Participants who rated their risk assessments separately from the MDT were significantly more likely to complete the SDRS (p = 0.025) and draft their risk management plans (p = 0.003). When rated separately, MDT scorers recorded significantly higher total SDRS scores compared to participants (p = 0.009). A series of Mann-Whitney U tests revealed significant differences between MDT and participant ratings on questions that required greater skills in abstraction and social reasoning, as well as sexual behaviour and self-harm.

Detained participants with an ID will engage in their dynamic risk assessment and management plan processes. The study demonstrates the impact of different co-production methodologies on engagement and highlights areas for future research pertaining to co-production.

The classification of criminal acts as violent or nonviolent should be a keystone of actuarial predictors of violent recidivism, as it affects their outcome measure and scoring of criminal history, thus influencing many decisions about sentencing, release and treatment allocation. Examination of existing actuarial and clinical violence risk assessment tools and research studies reveals considerable variation in the classifications used. This paper aims to use large samples to develop an alternative, empirically grounded classification that can be used to improve actuarial predictive scores within the offender assessment system (OASys), the tool used by the National Offender Management Service of England and Wales to assess static and dynamic risk.

Two analytical steps are implemented. First, to identify offences that frequently involve violent acts, 230,334 OASys cases are analyzed for indicators of violent content. Second, the ability of dynamic and static risk factors to predict reoffending for various offence types is investigated, analyzing 26,619 OASys cases that have official recidivism data.

The resulting empirical classification of violent offences adds public order, criminal damage, threats/harassment, robbery/aggravated burglary and weapon possession offences to the central group of homicide and assault offences. The need to assess risk of sexual recidivism separately is discussed.

This study has successfully produced an offence classification for use in a new predictor of violent recidivism. The use of empirical methods to select these offences helps to maximise predictive validity.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

Offenders with intellectual disability (ID) have been largely neglected in past forensic literature on assessment of dynamic risk factors. The purpose of this paper is to evaluate the predictive validity of the Short-Term Assessment of Risk and Treatability (START), in a sample of males with IDs in a low-secure hospital (n=28).

A prospective analysis was conducted, with START scores as the predictor variables, and the number of recorded aversive incidents as the outcome measure.

Receiver operating characteristic analysis demonstrated that total START risk scores had a significant high predictive accuracy for incidents of physical aggression to others (area under the curve (AUC)=0.710, p<0.001) and property damage/theft (AUC=0.730, p<0.001), over a 30-day period, reducing to medium predictive validity over a 90-day period. Medium predictive validity was also identified for incidents of verbal aggression, suicide, self-harm, and stalking and intimidation. START strength scores were also predictive of overt aggression (AUC=0.716), possible reasons for this are explored.

The small sample size limits the generalisability of the findings, and further research is required.

The paper offers preliminary support for the use of the START with ID offenders in low-secure settings. Given the lack of validation of any previous dynamic risk assessment tools, multi-disciplinary teams in such settings now have the option to use a tool which has potentially good validity with an ID population.

This study represents the first attempt to examine the predictive validity of the START with ID offenders, and a step forward in the understanding of dynamic risk factors for violence in this population. The significant predictive relationship with incidents of physical aggression and property damage offers clinicians a preliminary evidence base supporting its use in low-secure settings.

The purpose of this study was to explore the predictive validity of the revised Problem Identification Checklist (PIC‐R) in predicting inpatient and community violence using a retrospective design. The Historical Scale (H‐Scale) of the HCR‐20 was employed to control for static risk factors. The predictive accuracy between predictors and outcome measures was evaluated using Receiver Operating Characteristics (ROC) analysis. The PIC‐R significantly predicted inpatient violence (AUC range 0.77‐0.92) over a 12‐month follow‐up period but did not predict community violence. Conversely, the H‐Scale significantly predicted community violence (AUC 0.82) but did not predict inpatient violence over a 12‐month follow‐up period. The findings offer preliminary validation for the predictive accuracy of the PIC‐R for violence in a UK inpatient population. Additionally, the findings suggest that short‐term risk of violence within a psychiatric inpatient population may be more related to dynamic and clinical risk variables rather than to static ones.

The assessment of risk of recidivism in sexual offenders is fundamental to clinical practice. It is widely accepted that, compared with actuarial measures of risk, unaided clinical judgment has generally been found to be of low reliability. Consequently, the literature has shown a surge in actuarial measures. However, a major difficulty in assessing risk in sex offenders is the low base rate, leading to an increased likelihood of making a false positive predictive error. To overcome this, risk assessment studies are increasingly using the receiver operating characteristic (ROC), which displays the relationship between level of risk and decision choice. This note summarises the methodological issues in measuring predictive accuracy in assessing risk of re‐offending in sexual offenders, and identifies from the literature both static and dynamic risk factors associated with sexual offence recidivism.

This study provides a safety prewarning mechanism, which includes a comprehensive risk assessment model and a safety prewarning system. The comprehensive risk assessment model is capable of assessing nine safety indicators, which can be categorised into workers’ behaviour, environment and machine-related safety indicators, and the model is embedded in the safety prewarning system. The safety prewarning system can automatically extract safety information from surveillance cameras based on computer vision, assess risks based on the embedded comprehensive risk assessment model, categorise risks into five levels and provide timely suggestions.

Firstly, the comprehensive risk assessment model is constructed by adopting grey multihierarchical analysis method. The method combines the Analytic Hierarchy Process (AHP) and the grey clustering evaluation in the grey theory. Expert knowledge, obtained through the questionnaire approach, contributes to set weights of risk indicators and evaluate risks. Secondly, a safety prewarning system is developed, including data acquisition layer, data processing layer and prewarning layer. Computer vision is applied in the system to automatically extract real-time safety information from the surveillance cameras. The safety information is then processed through the comprehensive risk assessment model and categorized into five risk levels. A case study is presented to verify the proposed mechanism.

Through a case study, the result shows that the proposed mechanism is capable of analyzing integrated human-machine-environment risk, timely categorising risks into five risk levels and providing potential suggestions.

The comprehensive risk assessment model is capable of assessing nine risk indicators, identifying three types of entities, workers, environment and machine on the construction site, presenting the integrated risk based on nine indicators. The proposed mechanism, which adopts expert knowledge through Building Information Modeling (BIM) safety simulation and extracts safety information based on computer vision, can perform a dynamic real-time risk analysis, categorize risks into five risk levels and provide potential suggestions to corresponding risk owners. The proposed mechanism can allow the project manager to take timely actions.