Operational disruptions impact a supply chain's ability to match supply and demand. To remain competitive, supply chains need to be resilient and thus capable of rapidly and effectively recovering from operational disruptions. Supply chain resilience is inherently multidimensional, as it spans across multiple tiers, and thus is difficult to quantify. Extant research has measured the transient response through a single-dimension or single-organisation as a proxy for operational resilience. Whilst this greatly simplifies the analysis, it is also potentially misleading, as an erroneous selection of metric (s) may lead to an inaccurate evaluation of the transient response. This research extends the understanding of operational resilience via quantitative evaluation of multiple transient response measures across multiple tiers; the objective being to construct a multidimensional, multi-echelon operational supply chain resilience metric. The study utilises disruptions as experimental inputs for a serial supply chain simulation model; results are obtained for individual measurements of the transient response across multiple supply chain tiers. Analysis indicates that individual dimensions of resilience can adequately explain the transient response at the single-firm level, whilst aggregation of multiple resilience dimensions across multiple tiers has greater capacity to holistically capture the performance response to supply chain disruptions.

The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement is combined based on the 3D distribution of tumor positions in the past 10 seconds and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error was found to range from 1.6–2.9 mm, 0.6–1.5 mm, and 2.6–5.8 mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5 mm reduction in the 95th-percentile SI error for patients with >10 mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude <5 mm was 96.5% for markerless tumor tracking and 84.1% for the standard of care. The feasibility of markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method.

For reasons of tractability, the airline scheduling problem has traditionally been sequentially decomposed into various stages (eg. schedule generation, fleet assignment, aircraft routing, and crew pairing), with the decisions from one stage imposed upon the decision making process in subsequent stages. Whilst this approach greatly simplifies the solution process, it unfortunately fails to capture the many dependencies between the various stages, most notably between those of aircraft routing and crew pairing, and how these dependencies affect the propagation of delays through the flight network. As delays are commonly transferred between late running aircraft and crew, it is important that aircraft routing and crew pairing decisions are made together. The propagated delay may then be accurately estimated to minimize the overall propagated delay for the network and produce a robust solution for both aircraft and crew. In this paper we introduce a new approach to accurately calculate and minimize the cost of propagated delay, in a framework that integrates aircraft routing and crew pairing.

BackgroundHIV physicians have limited time for cognitive screening. Here we developed an extra-brief, clinically based tool for predicting HIV-associated neurocognitive impairment (HAND) in order to determine which HIV-positive individuals require a more comprehensive neurological/ neuropsychological (NP) assessment. MethodsNinety-seven HIV-positive individuals with advanced disease recruited in an HIV out-patient clinic received standard NP testing. A screening algorithm was developed using support vector machines, an optimized prediction procedure for classifying individuals into two groups (here NP-impaired and NP-normal) based on a set of predictors. ResultsThe final algorithm utilized age, current CD4 cell count, past central nervous system HIV-related diseases and current treatment duration and required approximately 3 min to complete, with a good overall prediction accuracy of 78% (against the gold standard; NP-impairment status derived from standard NP testing) and a good specificity of 70%. ConclusionThis noncognitive-based algorithm should prove useful to identify HIV-infected patients with advanced disease at high risk of HAND who require more formal assessment. We propose staged guidelines, using the algorithm, for improved HAND therapeutic management. Future larger, international studies are planned to test the predictive effect of nadir CD4 cell count, hepatitis C virus infection, gender, ethnicity and HIV viral clade. We recommend the use of this first version for HIV-infected Caucasian men with advanced disease.Keywords: antiretroviral therapy, diagnosis algorithm, HIV/AIDS, HIV-associated neurocognitive disorder, mathematical model Accepted 21 January 2010 IntroductionThe clinical management of HIV-positive persons is increasingly complicated in the era of combination antiretroviral therapy (CART). One aspect of management that requires extensive training relates to the early identification of neurocognitive complications of HIV infection. In many countries the general practitioner or the HIV physician is often the primary patient's interlocutor [1]. Without specific screening using procedures that are still relatively lengthy or require specific training, especially for interpretation [2], physicians may sometimes overlook patients in need of further neurological evaluation.In the CART era, the prevalence of neurocognitive impairment remains high (up to 50% [3]) and HIVassociated neurocognitive disorder (HAND) has shifted towards a milder clinical presentation [4]. Such a mild clinical presentation can escape detection without formal neurological assessment and neuropsychological testing [5]. HAND, even in its mild form, is independently predictive of death [6] The development of the screening algorithm was based on support vector machine (SVM) methodology. Because the aim of our study was to provide a simplified algorithm from a complex set of predictors, SVM was the most appropriate procedure [18]. The SVM has been shown to be extremely robust in solving prediction problems while handling la...

Attachment theorists have described the parent–child attachment relationship as a foundation for the emergence and development of children’s capacity for emotion regulation and coping with stress. The purpose of this review was to summarize the existing research addressing this issue. We identified 23 studies that employed validated assessments of attachment, which were not based on self-report questionnaires, and separated the summary into findings for toddlers/preschool, children, and adolescents. Although most associations were weak and only a minority of the multiple possible associations tested was supported in each study, all studies (but one) reported at least one significant association between attachment and emotion regulation or coping. The evidence pointed to the regulatory and coping problems of toddlers showing signs of ambivalent attachment or the benefits of secure (relative to insecure) attachment for toddlers, children, and adolescents. Toddlers who showed signs of avoidant attachment relied more on self-related regulation (or less social-oriented regulation and coping), but it was not clear whether these responses were maladaptive. There was little information available regarding associations of ambivalent attachment with school-age children’s or adolescents’ emotion regulation. There were also few studies that assessed disorganized attachment.

For reasons of tractability, the airline scheduling problem has traditionally been sequentially decomposed into various stages (e.g. schedule generation, fleet assignment, aircraft routing, and crew pairing), with the decisions from one stage imposed upon the decision-making process in subsequent stages. Whilst this approach greatly simplifies the solution process, it unfortunately fails to capture many dependencies between the various stages, most notably between those of aircraft routing and crew pairing, and how these dependencies affect the propagation of delays through the flight network. In Dunbar et al. (2012) [9] we introduced a new algorithm to accurately calculate and minimize the cost of propagated delay, in a framework that integrates aircraft routing and crew pairing. In this paper we extend the approach of Dunbar et al. (2012) [9] by proposing two new algorithms that achieve further improvements in delay propagation reduction via the incorporation of stochastic delay information. We additionally propose a heuristic, used in conjunction with these two approaches, capable of re-timing an incumbent aircraft and crew schedule to further minimize the cost of delay propagation. These algorithms provide promising results when applied to a real-world airline network and motivate our final integrated aircraft routing, crew pairing and re-timing approach which provides a substantially significant reduction in delay propagation. Gary FroylandSchool of Mathematics and Statistics, University of New South Wales, Sydney NSW 2052, Australia. Cheng-Lung WuSchool of Aviation, University of New South Wales, Sydney NSW 2052, Australia. AbstractFor reasons of tractability, the airline scheduling problem has traditionally been sequentially decomposed into various stages (eg. schedule generation, fleet assignment, aircraft routing, and crew pairing), with the decisions from one stage imposed upon the decisionmaking process in subsequent stages. Whilst this approach greatly simplifies the solution process, it unfortunately fails to capture the many dependencies between the various stages, most notably between those of aircraft routing and crew pairing, and how these dependencies affect the propagation of delays through the flight network. In Dunbar et al. [9] we introduced a new algorithm to accurately calculate and minimize the cost of propagated delay, in a framework that integrates aircraft routing and crew pairing. In this paper we extend the approach of [9] by proposing two new algorithms that achieve further improvements in delay propagation reduction via the incorporation of stochastic delay information. We additionally propose a heuristic, used in conjunction with these two approaches, capable of re-timing an incumbent aircraft and crew schedule to further minimize the cost of delay propagation. These algorithms provide promising results when applied to a real-world airline network and motivate our final integrated aircraft routing, crew pairing and re-timing approach which provides a substantially signif...

Hydrological infrastructure components such as pumps, floodgates, and flood gauges are invaluable assets for mitigating flooding, which threatens millions of lives and damages property worth billions of dollars in coastal mega-cities around the world. By improving the understanding of how these hydrological infrastructure components are both spatially and topologically connected through waterways (rivers, canals, streams, etc.) within coastal mega-cities, more precise decisions can be made regarding the most appropriate hydrological infrastructure components required to mitigate flooding during emergency conditions. This paper explores the use of graph theory to create a spatio-topological model of a real world hydrological infrastructure network for one of the most representative coastal mega-cities-Jakarta, Indonesia. The network is modeled as a directed multigraph, with hydrological infrastructure represented as network nodes and waterways as edges. The article demonstrates how the network model can be used as a real-time decision support tool for responding to flooding events by alerting decision makers to the occurrence of rising water levels in any given area and, suggesting the most appropriate infrastructure components to engage in order to prevent a given area from flooding.

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