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The potential to exploit 2.223 MeV capture γ rays for the position sensitive detection of neutrons as a proxy for water in spent nuclear fuel is described. Previously, we demonstrated from a modelling perspective that the extent to which water might be dispersed in spent nuclear fuel following interim storage in ponds might be implied via this proxy, as opposed to direct detection of the neutron(s) itself. Position-sensitive neutron detection via secondary gamma-ray production can be advantageous for the detection of media that attenuate neutrons whilst being relatively transparent to γ rays, especially, for example, in situations where hydrogenous liquids might enhance criticality risk in heterogeneous waste mixtures. However, precisely mapping hydrogen concentration based on neutron attenuation directly can be impeded by the indirect nature of most neutron-based interactions whilst the coincident signatures of neutrons associated with spontaneous fission that are de rigueur for safeguards assay are not always viable. In this work, an experimental testbed exploiting hydrogenous foams, for example, Nylon-12, as analogues for dispersed water in fuel-containing materials, and 316L stainless steel metal foam as analogues for the fuel-containing materials was developed. These materials were used to simulate the emission and transmission of γ rays in a mix of fuel and water. The results show that 2.223 MeV γ rays are a viable indicator of detecting water in spent nuclear fuel.

Cyber security risk management plays an important role for today’s businesses due to the rapidly changing threat landscape and the existence of evolving sophisticated cyber attacks. It is necessary for organisations, of any size, but in particular those that are associated with a critical infrastructure, to understand the risks, so that suitable controls can be taken for the overall business continuity and critical service delivery. There are a number of works that aim to develop systematic processes for risk assessment and management. However, the existing works have limited input from threat intelligence properties and evolving attack trends, resulting in limited contextual information related to cyber security risks. This creates a challenge, especially in the context of critical infrastructures, since attacks have evolved from technical to socio-technical and protecting against them requires such contextual information. This research proposes a novel integrated cyber security risk management (i-CSRM) framework that responds to that challenge by supporting systematic identification of critical assets through the use of a decision support mechanism built on fuzzy set theory, by predicting risk types through machine learning techniques, and by assessing the effectiveness of existing controls. The framework is composed of a language, a process, and it is supported by an automated tool. The paper also reports on the evaluation of our work to a real case study of a critical infrastructure. The results reveal that using the fuzzy set theory in assessing assets' criticality, our work supports stakeholders towards an effective risk management by assessing each asset's criticality. Furthermore, the results have demonstrated the machine learning classifiers’ exemplary performance to predict different risk types including denial of service, cyber espionage and crimeware.

The entropy-oriented approach called security- or cybersecurity-informed safety (SIS or CSIS, respectively) is discussed and developed in order to analyse and evaluate the safety and dependability of autonomous transport systems (ATSs) such as unmanned aerial vehicles (UAVs), unmanned maritime vehicles (UMVs), and satellites. This approach allows for extending and integrating the known techniques FMECA (Failure Modes, Effects, and Criticality Analysis) and IMECA (Intrusion MECA), as well as developing the new SISMECA (SIS-based Intrusion Modes, Effects, and Criticality Analysis) technique. The ontology model and templates for SISMECA implementation are suggested. The methodology of safety assessment is based on (i) the application and enhancement of SISMECA considering the particularities of various ATSs and roles of actors (regulators, developers, operators, customers); (ii) the development of a set of scenarios describing the operation of ATS in conditions of cyberattacks and physical influences; (iii) AI contribution to system protection for the analysed domains; (iv) scenario-based development and analysis of user stories related to different cyber-attacks, as well as ways to protect ATSs from them via AI means/platforms; (v) profiling of AI platform requirements by use of characteristics based on AI quality model, risk-based assessment of cyberattack criticality, and efficiency of countermeasures which actors can implement. Examples of the application of SISMECA assessment are presented and discussed.

With the advent of smart manufacturing, companies are adapting their business processes and procedures to match concepts and requirements related to this industrial revolution. In this scenario, maintenance sector should evolve to keep pace with new technological trends by shifting from traditional approaches to a smart paradigm. Some consolidated methodologies rely on standard evaluation, like Risk Priority Number, to plan maintenance actions on machines and components. However, contemporary and smart approaches should be characterized by data-driven (quantitative knowledge) as well as operator’s experience (qualitative knowledge) in order to provide a whole understanding and visibility of the system health status. In this context and with a view of digital twin concepts (under the Industry 4.0 technology), a decision support methodology (or framework) plays an essential role by providing knowledge and assisting operators in the decision-making process. With this concept in mind, this paper focuses on integrating several and not alike fields of study (process mining, multicriteria decision-making (MCDM), and data fusion) to enable the assessment of a risk and criticality analysis to rank industrial machines in order to carry out/plan maintenance tasks on them. Therefore, merging those fields of study aims at accomplishing a dynamic, flexible, and real-time evaluation to perform a robust, accurate, and responsive analysis of the system. The results from different analysis scenarios show the influence of maintenance indicators on the machine ranking process, highlighting the feasibility and flexibility of the approach, as well as expanding the application in modern systems by optimizing quality of maintenance decisions.

Within maintenance management activities, engineers need to select maintenance strategies so to carry out the technical maintenance actions. A single equipment is composed of several components with different failure modes. There should be a maintenance strategy for each of them; while some of the components can be run-to-failure applying corrective maintenance, some others cannot afford a failure, and preventive or predictive strategies should be implemented. Selecting and assessing maintenance strategies is a complex task for which information from many sources should be retrieved. Information from a Failure Mode, Effects and Criticality Analysis, a cost–benefit-risk analysis, Computational Maintenance Management Systems, is often used by engineers to select and assess maintenance strategies. A selected strategy is often not evaluated over time to check its effectiveness. The strategy may need adjustments or substituted by a more efficient one, for example, a condition-based strategy substituting a time-based one. To facilitate maintenance strategies selection and assessment, the current study proposes an Ontology model for Maintenance Strategy Selection and Assessment (OMSSA). OMSSA serves as a formal terminology framework in maintenance strategies that can be used to develop smart computational agents that can help in the decision-making process for selecting and assessing maintenance strategies. To facilitate its future reuse and integration with other ontologies in the industrial domain, OMSSA builds following the state-of-the-art in ontology development by using a top-level domain-neutral ontology, the Basic Formal Ontology.

To assess patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations (ICU vs. non-ICU) by the Criticality Index-Dynamic (CI-D), with the goal of enhancing the CI-D. Retrospective structured chart review. All pediatric inpatients admitted from January` 1st 2018 - February 29th 2020 through the emergency department. Patient characteristics and care factors associated with correct (true positives, true negatives) and incorrect predictions (false positives, false negatives) of future care locations (ICU vs. non-ICU) by the CI-D were assessed. Of the 3,018, patients, 139 transitioned from non-ICU locations to ICU care; 482 were transferred from the ICU to non-ICU care locations, and 2,400 remained in non-ICU care locations. For the ICU Prediction group, the false negative patients were older, more frequently male, and had longer hospital and ICU lengths of stay compared to the true positive patients. The significant differences in the ICU Prediction group for false negative compared to the true positive patients included a less frequent: primary diagnosis of respiratory failure, use of high flow nasal canula, hourly cardio-respiratory vital signs prior to transfer to the ICU, and neurologic vital signs after transfer from the ICU. For the ICU Discharge prediction group, false positive patients were more frequently: younger, had a primary diagnosis of respiratory failure, more frequently received respiratory support after discharge from the ICU, and received less frequent neurological vital signs prior to transfer from the ICU. For the Non-transfer prediction category, demographics and clinical variables did not differ between the true negative and false positive prediction groups. We conducted the first comprehensive analysis via structured chart reviews of patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations by the machine learning algorithm, the CI-D, gaining insights into potential new predictor variables for inclusion in the model to improve future model iterations.

Risk management plays a vital role in tackling cyber threats within the cyber-physical system (CPS). It enables identifying critical assets, vulnerabilities and threats and determining suitable proactive control measures for the risk mitigation. However, due to the increased complexity of the CPS, cyber-attacks nowadays are more sophisticated and less predictable, which makes risk management task more challenging. This paper aims for an effective cybersecurity risk management (CSRM) practice using assets criticality, predication of risk types and evaluating the effectiveness of existing controls. We follow a number of techniques for the proposed unified approach including fuzzy set theory for the asset criticality, machine learning classifiers for the risk predication and comprehensive assessment model (CAM) for evaluating the effectiveness of the existing controls. The proposed approach considers relevant CSRM concepts such as asset, threat actor, attack pattern, tactic, technique and procedure (TTP), and controls and maps these concepts with the VERIS community dataset (VCDB) features for the risk predication. The experimental results reveal that using the fuzzy set theory in assessing assets criticality supports stakeholder for an effective risk management practice. Furthermore, the results have demonstrated the machine learning classifiers exemplary performance to predict different risk types including denial of service, cyber espionage and crimeware. An accurate prediction of risk can help organisations to determine the suitable controls in proactive manner to manage the risk.

PurposeAlthough being considered for adoption by stakeholders in container shipping, application of blockchain is hindered by different factors. This paper investigates the potential operational risks of blockchain-integrated container shipping systems as one of such barriers.Design/methodology/approachLiterature review is employed as the method of risk identification. Scientific articles, special institutional reports and publications of blockchain solution providers were included in an inclusive qualitative analysis. A directed acyclic graph (DAG) was constructed and analyzed based on network topological metrics.FindingsTwenty-eight potential risks and 47 connections were identified in three groups of initiative, transitional and sequel. The DAG analysis results reflect a relatively well-connected network of identified hazardous events (HEs), suggesting the pervasiveness of information risks and various multiple-event risk scenarios. The criticality of the connected systems' security and information accuracy are also indicated.Originality/valueThis paper indicates the changes of container shipping operational risk in the process of blockchain integration by using updated data. It creates awareness of the emerging risks, provides their insights and establishes the basis for further research.

To assess the single site performance of the Dynamic Criticality Index (CI-D) models developed from a multi-institutional database to predict future care. Secondarily, to assess future care-location predictions in a single institution when CI-D models are re-developed using single-site data with identical variables and modeling methods. Four CI-D models were assessed for predicting care locations >6-12 hours, >12-18 hours, >18-24 hours, and >24-30 hours in the future. Prognostic study comparing multi-institutional CI-D models' performance in a single-site electronic health record dataset to an institution-specific CI-D model developed using identical variables and modelling methods. The institution did not participate in the multi-institutional dataset. All pediatric inpatients admitted from January 1st 2018 -February 29th 2020 through the emergency department. The main outcome was inpatient care in routine or ICU care locations. A total of 29,037 pediatric hospital admissions were included, with 5,563 (19.2%) admitted directly to the ICU, 869 (3.0%) transferred from routine to ICU care, and 5,023 (17.3%) transferred from ICU to routine care. Patients had a median [IQR] age 68 months (15-157), 47.5% were female and 43.4% were black. The area under the receiver operating characteristic curve (AUROC) for the multi-institutional CI-D models applied to a single-site test dataset was 0.493-0.545 and area under the precision-recall curve (AUPRC) was 0.262-0.299. The single-site CI-D models applied to an independent single-site test dataset had an AUROC 0.906-0.944 and AUPRC range from 0.754-0.824. Accuracy at 0.95 sensitivity for those transferred from routine to ICU care was 72.6%-81.0%. Accuracy at 0.95 specificity was 58.2%-76.4% for patients who transferred from ICU to routine care. Models developed from multi-institutional datasets and intended for application to individual institutions should be assessed locally and may benefit from re-development with site-specific data prior to deployment.