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DNA ‘barcoding’ has contributed greatly to resolving species limits in rapidly diverging tropical insect groups. Here, we explored species diversity in the widespread, cryptic Neotropical butterfly genus Hermeuptychia by generating 601 new sequences of the cytochrome c oxidase I (COI) barcode, tripling available information. Our dataset focused in particular on Ecuador, a biogeographically pivotal country, with additional sequences from Brazil, Costa Rica, French Guiana, Guatemala, Mexico, Panama and Peru. We examined the performance of two phylogeny-based approaches for objectively delineating species, Generalized Mixed Yule Coalescent (GMYC) and Poisson Tree Processes (PTP), as well as a clustering-based approach, Automatic Barcode Gap Discovery (ABGD), on the combined dataset, including our data and 302 published sequences. In general, GMYC and PTP tended to cluster or split likely species as assessed from morphology, depending on model settings, whereas ABGD was less sensitive and produced a more plausible classification. Numerous sequences formed well-supported clades, putative species, that were unrepresented in previously published datasets. The average diversity across all methods was 45 species, in contrast to the 11 species recognized in the current taxonomy. The resulting COI dataset, in combination with ongoing genomic and morphological research, should significantly clarify our understanding of Hermeuptychia species diversity.

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.

© 2018 Springer Science+Business Media, LLC, part of Springer Nature Modern-day logistics companies require increasingly shorter lead-times in order to cater for the increasing popularity of on-demand services. There is consequently an urgent need for fast scheduling algorithms to provide high quality, real-time implementable solutions. In this work we model a spare part delivery problem for an on-demand logistics company, as a variant of vehicle routing problem. For large delivery networks, the optimisation solution technique of column generation has been employed successfully in a variety of vehicle routing settings and is often used in combination with exact methods for solving problems with a large number of variables. Challenges may arise when the pricing subproblem is difficult to solve in a realistic period due to complex constraints or a large number of variables. The problem may become intractable when the network structure varies daily or is known with less certainty over longer period. In such instances, a high quality heuristic solution may be more preferable than an exact solution with excessive run time. We propose an improved version of column generation approach integrating an efficient genetic algorithm to obtain fast and high-quality solutions for a sustainable spare parts delivery problem. More specifically, we propose to retain the traditional column generation iterative framework, with master problem solved exactly, but with pricing subproblem solved using a metaheuristic. Computational results on a real dataset indicate that this approach yields improved solutions compared to the current best-case business-as-usual costs. It also substantially decreases the computational time; allowing for high-quality, tractable solutions to be obtained in few minutes. We propose to strike a balance between the practical and efficient solution aspects of metaheuristic algorithms, and the exact decomposition and iterative aspect of the column generation solution technique.

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 c...

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 are combined based on the 3D distribution of tumor positions in the past 10 s 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 were 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

Background HIV 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. Methods Ninety-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. Results The 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%. Conclusion This 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.

Waterborne parasites that infect both humans and animals are common causes of diarrhoeal illness, but the relative importance of transmission between humans and animals and vice versa remains poorly understood. Transmission of infection from animals to humans via environmental reservoirs, such as water sources, has attracted attention as a potential source of endemic and epidemic infections, but existing mathematical models of waterborne disease transmission have limitations for studying this phenomenon, as they only consider contamination of environmental reservoirs by humans. This paper develops a mathematical model that represents the transmission of waterborne parasites within and between both animal and human populations. It also improves upon existing models by including animal contamination of water sources explicitly. Linear stability analysis and simulation results, using realistic parameter values to describe Giardia transmission in rural Australia, show that endemic infection of an animal host with zoonotic protozoa can result in endemic infection in human hosts, even in the absence of person-to-person transmission. These results imply that zoonotic transmission via environmental reservoirs is important.

To achieve greater transit-time reduction and improvement in reliability of transport services, there is an increasing need to assist transport planners in understanding the value of punctuality; i.e. the potential improvements, not only to service quality and the consumer but also to the actual profitability of the service. In order for this to be achieved, it is important to understand the network-specific aspects that affect both the ability to decrease transit-time, and the associated cost-benefit of doing so. In this paper, we outline a framework for evaluating the effectiveness of proposed changes to average transit-time, so as to determine the optimal choice of average arrival time subject to desired punctuality levels whilst simultaneously minimizing operational costs. We model the service transit-time variability using a truncated probability density function, and simultaneously compare the trade-off between potential gains and increased service costs, for several commonly employed cost-benefit functions of general form. We formulate this problem as a constrained optimization problem to determine the optimal choice of average transit time, so as to increase the level of service punctuality, whilst simultaneously ensuring a minimum level of cost-benefit to the service operator.

The authors consider a general transportation network where travelers are categorized into classes according to their value-of-time (VOTs). The travel demand of each class in each origin-destination (OD) pair is a known decreasing function of the generalized travel cost to reflect different values of the trips. The authors give an economic interpretation of elastic demand with user heterogeneity. The authors further consider the first-best pricing problem with elastic demand with heterogeneous users. The authors show that such a nonnegative toll always exists, and finding such a toll is simple in that only a convex optimization problem needs to be solved.

Sydney Harbour Bridge is a key transport infrastructure that connects North Sydney and Sydney Central Business District (CBD). To alleviate the congestion on Sydney Harbour Bridge, NSW Roads and Maritime Services imposes a time of day tolling between $2.5 and $4 on the southbound traffic to Sydney CBD. This study develops mathematical models for formulating the toll pricing problem on Sydney Harbour Bridge considering that different travellers may have different value-of-times (VOTs). The models examine quantitatively the effect of different toll levels on the efficiency (in terms of the total generalized travel time and generalized travel cost of all travellers) and equity (in terms of the ratio of generalized travel cost among different traveller classes). The proposed models can serve as a useful decision-support tool for NSW Roads and Maritime Services.