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Effective control of plant disease remains a key challenge. Eradication attempts often involve removal of host plants within a certain radius of detection, targeting asymptomatic infection. Here we develop and test potentially more effective, epidemiologically motivated, control strategies, using a mathematical model previously fitted to the spread of citrus canker in Florida. We test risk-based control, which preferentially removes hosts expected to cause a high number of infections in the remaining host population. Removals then depend on past patterns of pathogen spread and host removal, which might be nontransparent to affected stake-holders. This motivates a variable radius strategy, which approximates risk-based control via removal radii that vary by location, but which are fixed in advance of any epidemic. Risk-based control outperforms variable radius control, which in turn outperforms constant radius removal. This result is robust to changes in disease spread parameters and initial patterns of susceptible host plants. However, efficiency degrades if epidemiological parameters are incorrectly characterised. Risk-based control including additional epidemiology can be used to improve disease management, but it requires good prior knowledge for optimal performance. This focuses attention on gaining maximal information from past epidemics, on understanding model transferability between locations and on adaptive management strategies that change over time.

Background The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk‐based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed. Methods Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk‐based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI). Results Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk‐based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation. Conclusions In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk‐based SQC strategy development and improvement measure implementation. Flowchart of Westgard sigma rules with run sizes (cited from website http://www.clinet.com.cn/sigmapv/#sgm4)

Purpose In developing countries, how risk management technologies influence management accounting and control (MAC) practices is under-researched. By drawing on insights from institutional studies, this study aims to examine the multiple institutional pressures surrounding an entity and influencing its risk-based management control (RBC) system – that is, how RBC appears in an emerging market attributed to institutional multiplicity. Design/methodology/approach The authors used qualitative case study research methods to collect empirical evidence from a privately owned Egyptian insurance company. Findings The authors observed that in the transformation to risk-based controls, especially in socio-political settings such as Egypt, changes in MAC systems were consistent with the shifts in the institutional context. Along with changes in the institutional environment, the case company sought to configure its MAC system to be more risk-based to achieve its strategic goals effectively and maintain its sustainability. Originality/value This research provides a fuller view of risk-based management controls based on the social, professional and political perspectives central to the examined institutional environment. Moreover, unlike early studies that reported resistance to RBC, this case reveals the institutional dynamics contributing to the successful implementation of RBC in an emerging market.

Kuwait relies on imported food to supply more than 90% of its food consumption, and food import control is essential in the area to protect public health and food security. The Food and Drugs risk food control system provided by the FAO/WHO Risk-Based Imported Food Control Manual (2015) is used in this paper to assess the imported food control system in Kuwait. The descriptive qualitative paradigm was used, which incorporated documentary analysis of the related legislation and a structured evaluation checklist and semi-structured interviews with 15 major officials of the Public Authority for Food and Nutrition (PAFN). The assessment involved control processes applied to imported foods, legal and institutional environment and facilitating services. Findings reveal good performance on the legal system, the administrative system and the post-border inspection processes, which demonstrates a well-developed enforcement-based system. Nevertheless, significant loopholes are still present in risk-based food classification, pre-border responses, inter-agency coordination, and strategic management of financial and human resources. Compliance on an overall basis was 68 on a documentary scale and 62 on a perceptions scale. The results highlight the necessity of specific reforms to enhance preventive, risk-based control and make the food import system of Kuwait to match the international standards.

Healthcare systems are increasingly vulnerable to security threats due to their reliance on digital platforms. Traditional access control models like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) have limitations in mitigating evolving risks in these systems. Despite their unique features, these models face limitations in mitigating evolving risks in healthcare systems. Traditional models are primarily oriented towards allocating permissions according to predetermined roles or policies, which results in challenges in effectively adapting to the dynamic complexities of modern healthcare ecosystems. Therefore, this paper proposes a novel risk-aware RBAC and ABAC access control model to enhance the flexibility, adaptability and security issues associated with healthcare systems. The proposed model integrates RBAC for role-based categorization, ABAC for fine-grained control based on user attributes and environmental factors, and Risk-Based Access Control (RiBAC) for dynamic risk assessment. The proposed model dynamically adjusts access permissions based on risk values, ensuring accurate and adaptable access control decisions. The experimental results demonstrate the feasibility and effectiveness of the proposed model in granting access to authorized users while denying access to unauthorized users. Through a series of 43 experiments that simulate various scenarios of access control operations in the healthcare system, the proposed model demonstrates significant improvement in the accuracy, precision, and recall of access control decisions compared to traditional models. The proposed model’s ability to dynamically assess risk and adjust access permissions based on contextual factors significantly enhances its ability to mitigate threats and protect sensitive medical data.

Most current access control models are rigid, as they are designed using static policies that always give the same outcome in different circumstances. In addition, they cannot adapt to environmental changes and unpredicted situations. With dynamic systems such as the Internet of Things (IoT) with billions of things that are distributed everywhere, these access control models are obsolete. Hence, dynamic access control models are required. These models utilize not only access policies but also contextual and real-time information to determine the access decision. One of these dynamic models is the risk-based access control model. This model estimates the security risk value related to the access request dynamically to determine the access decision. Recently, the risk-based access control model has attracted the attention of several organizations and researchers to provide more flexibility in accessing system resources. Therefore, this paper provides a systematic review and examination of the state-of-the-art of the risk-based access control model to provide a detailed understanding of the topic. Based on the selected search strategy, 44 articles (of 1044 articles) were chosen for a closer examination. Out of these articles, the contributions of the selected articles were summarized. In addition, the risk factors used to build the risk-based access control model were extracted and analyzed. Besides, the risk estimation techniques used to evaluate the risks of access control operations were identified.

Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.

Border control systems at Europe’s Schengen (and worldwide) borders are necessary to mitigate cross-border threats, but are perceived as free-traveling bottlenecks. Today’s applicable European regulations demand rule-based control schemes and do not allow risk-based elements. A policy shift towards risk-based border control has been considered in several studies and research (including HEU projects). However, there is a lack of scientific evidence on how they compare with existing rule-based schemes. This paper aims to fill that gap. The simulation allows design of a realistic border control system. The passenger flow is modeled via travelers with good and bad intents. The border control system includes decision-making elements to classify travelers into risk groups. System elements including operators and their interaction were modeled in terms of statistical distributions based on the subject matter experts’ input. The performance is estimated across security effectiveness, resource usage, passenger flow, and traveler experience. Assessment of a set of simulations reveals better scalability of risk-based systems in terms of resource usage and passenger flow. The potential factors to improve the detection rate of the border control process are also studied. Despite having several benefits, the model demonstrates that social acceptance of the risk-based system is the limiting factor for increased scalability.

The risk-based access control model is one of the dynamic models that use the security risk as a criterion to decide the access decision for each access request. This model permits or denies access requests dynamically based on the estimated risk value. The essential stage of implementing this model is the risk estimation process. This process is based on estimating the possibility of information leakage and the value of that information. Several researchers utilized different methods for risk estimation but most of these methods were based on qualitative measures, which cannot suit the access control context that needs numeric and precise risk values to decide either granting or denying access. Therefore, this paper presents a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for risk estimation in the risk-based access control model for the Internet of Things (IoT). The proposed ANFIS model was implemented and evaluated against access control scenarios of smart homes. The results demonstrated that the proposed ANFIS model provides an efficient and accurate risk estimation technique that can adapt to the changing conditions of the IoT environment. To validate the applicability and effectiveness of the proposed ANFIS model in smart homes, ten IoT security experts were interviewed. The results of the interviews illustrated that all experts confirmed that the proposed ANFIS model provides accurate and realistic results with a 0.713 in Cronbach’s alpha coefficient which indicates that the results are consistent and reliable. Compared to existing work, the proposed ANFIS model provides an efficient processing time as it reduces the processing time from 57.385 to 10.875 Sec per 1000 access requests, which demonstrates that the proposed model provides effective and accurate risk evaluation in a timely manner.

Long-term effects of paratuberculosis on within-herd prevalence and on-farm economy of implementing risk-based control strategies were compared with alternative strategies by using a herd-simulation model. Closing transmission routes is essential for effective control of paratuberculosis. However, many farmers lack the resources to carry out these procedures for all cows in the herd. When using risk-based control strategies 1) all cows are tested quarterly with a milk ELISA, 2) specific cows with a high risk of being infectious are identified, and 3) the farmer can focus only on these infectious animals to close infection routes. In this way the workload can be reduced, making these control strategies more feasible. This study evaluates potential long-term effects of the risk-based approach compared with non-risk-based strategies by simulations conducted with the herd-simulation model PTB-Simherd. Seven control strategies were simulated in herds with initial true herd prevalences of 5, 25, and 50%, respectively. The results predicted the risk-based control strategies to be very efficient and comparable to the best whole-herd strategies in reducing the within-herd prevalence of paratuberculosis with considerably less labor. If infection routes are closed efficiently, prevalence can be reduced to 10% of initial prevalence within 5 to 7 yr. Test-and-cull strategies without closing infection routes were found, by simulation, to be ineffective in reducing prevalence and were not cost-effective methods. The profitability of the various control strategies depends on hourly wages and time spent per cow/calving. Furthermore, simulations show that immediate culling of highly infectious cows is only necessary and cost-effective if infection routes from these cows are not efficiently closed. The risk-based control strategies are recommended in the Danish voluntary control program “Operation Paratuberculosis,” which was initiated in February 2006 and now includes 1,220 dairy farmers in Denmark.