Highlights • Providing the first literature review of risk management models specifically for agribusiness supply chains. • Focusing on specific sources of uncertainty in agribusiness industries. • Providing new implications and further directions for developing the research in the context of agribusiness supply chain risk management. • Providing the first literature review of risk management models specifically for agribusiness supply chains.

Background and Purpose-Cerebral atrophy has been recently recognized as a key marker of disease progression in cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). The contribution of subcortical cerebral lesions in this process remains undetermined. The aim of this study was to investigate the relationships between cerebral volume and different types of subcortical MRI lesions in CADASIL. Methods-Demographic, clinical, and laboratory data from 147 patients with CADASIL recruited from a prospective cohort study were analyzed. Validated methods were used to determine the ratio of brain volume to intracranial cavity volume (brain parenchymal fraction [BPF]), volume of white matter hyperintensities, volume of lacunar lesions, number of cerebral microhemorrhages, and mean apparent diffusion coefficient. Associations between BPF, clinical scales, and the different subcortical MRI markers were tested. Results-BPF obtained in 129 patients was significantly associated with the Mattis dementia rating scale (PϽ0.0001), Mini-Mental State Examination (Pϭ0.002), and modified Rankin scale (PϽ0.0001) after adjustment for age and sex. Multiple linear regression modeling showed that BPF was independently associated with mean apparent diffusion coefficient (PϽ0.0001), volume of lacunar lesions (Pϭ0.004), and age (PϽ0.0001), accounting for 46% of the observed variance in BPF but not with volume of white matter hyperintensities or number of microhemorrhages. Conclusions-In association with age, mean apparent diffusion coefficient and volume of lacunar lesions are strong and independent MRI predictors of BPF, a key marker of cognitive and motor disability in CADASIL. These results suggest brain atrophy is related to remote and/or diffuse consequences of both lacunar lesions and widespread microstructural alterations within the brain outside lacunar lesions.

This paper demonstrates the fi rst steps towards self-healing composites that exploit a design philosophy inspired by the damage tolerance and self-repair functions of bone. Cracking in either fi bre reinforced polymers (FRP) or bone, if left unattended, can grow under subsequent cyclic stresses eventually leading to catastrophic failure of the structure. On detection of cracks, an FRP component must be repaired or completely replaced, whereas bone utilises a series of complex processes to repair such damage. Under normal circumstances, these processes allow the skeleton to continually perform over the lifespan of the organism, a highly desirable aspiration for engineering materials. A simple vasculature design incorporated into a FRP via a "lost wax" process was found to facilitate a self-healing function which resulted in an outstanding recovery ( ≥ 96%) in post-impact compression strength. The process involved infusion of a healing resin through the vascule channels. Resin egress from the backface damage, ultrasonic C-scan testing, and microscopic evaluation all provide evidence that suffi cient vascule-damage connectivity exists to confer a reliable and effi cient self-healing function.

Agribusiness supply chains involve more sources of uncertainty than typical manufacturing supply chains due to attributes such as long supply lead-times, seasonality, and perishability. Therefore, it is critical but challenging to mitigate risks in agribusiness supply chains. However, the extant literature includes limited quantitative research on robust and resilient strategies for agribusiness supply chain risk management, particularly when perishability is explicitly modeled. In this paper, we investigate the effectiveness of a mixed set of robust and resilient strategies for managing rare high-impact harvest time and yield disruptions. We develop a two-stage stochastic programming model, which integrates an exponential perishability function, to conduct our analysis. The model maximizes the expected profit by selecting optimal risk management strategies and making tactical supply chain planning decisions. The model is applied to a numerical case study of a real-world kiwifruit supply chain. The results suggest that a mixed combination of robust and resilient strategies are most effective for mitigating supply-side disruption risks. Furthermore, as perishability increases, risk management strategies provide a greater relative improvement in the expected profit.

No abstract

Choosing secure water resource management plans inevitably requires trade-offs between risks (for a variety of stakeholders), costs, and other impacts. We have previously argued that water resources planning should focus upon metrics of risk of water restrictions, accompanied by extensive simulation and scenario-based exploration of uncertainty. However, the results of optimization subject to risk constraints can be sensitive to the specification of tolerable risk, which may not be precisely or consistently defined by different stakeholders. In this paper, we recast the water resources planning problem as a multiobjective optimization problem to identify least cost schemes that satisfy a set of criteria for tolerable risk, where tolerable risk is defined in terms of the frequency of water use restrictions of different levels of severity. Our proposed method links a very large ensemble of climate model projections to a water resource system model and a multiobjective optimization algorithm to identify a Pareto optimal set of water resource management plans across a 25 years planning period. In a case study application to the London water supply system, we identify water resources management plans that, for a given financial cost, maximize performance with respect to one or more probabilistic criteria. This illustrates trade-offs between financial costs of plans and risk, and between risk criteria for four different severities of water use restrictions. Graphical representation of alternative sequences of investments in the Pareto set helps to identify water management options for which there is a robust case for including them in the plan.

No abstract

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.