138,718 research outputs found

    Analysing and modelling train driver performance

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    Arguments for the importance of contextual factors in understanding human performance have been made extremely persuasive in the context of the process control industries. This paper puts these arguments into the context of the train driving task, drawing on an extensive analysis of driver performance with the Automatic Warning System (AWS). The paper summarises a number of constructs from applied psychological research which are thought to be important in understanding train driver performance. A “Situational Model” is offered as a framework for investigating driver performance. The model emphasises the importance of understanding the state of driver cognition at a specific time (“Now”) in a specific situation and a specific context

    An improved approach for flight readiness assessment

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    An improved methodology for quantitatively evaluating failure risk for a spaceflight system in order to assess flight readiness is presented. This methodology is of particular value when information relevant to failure prediction, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is limited. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomena are used in a prescribed probabilistic structure to generate a failure probability distribution that is modified by test and flight experience in a Bayesian statistical procedure. The probabilistic structure and statistical methodology are generally applicable to any failure mode for which quantitative engineering analysis can be employed to characterize the failure phenomenon and are particularly well suited for use under the constraints on information availability that are typical of such spaceflight systems as the Space Shuttle and planetary spacecraft

    A proposed psychological model of driving automation

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    This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation

    A preliminary investigation of trunk and wrist kinematics when using drivers with different shaft properties

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    It is unknown whether skilled golfers will modify their kinematics when using drivers of different shaft properties. This study aimed to firstly, determine if golf swing kinematics and swing parameters and related launch conditions differed when using modified drivers, then secondly, determine which kinematics were associated with clubhead speed. Twenty high level amateur male golfers (Mean ± SD: handicap = 1.9 ± 1.9 score) had their three-dimensional trunk and wrist kinematics collected for two driver trials. Swing parameters and related launch conditions were collected using a launch monitor. A one-way repeated measures ANOVA revealed significant (p ≤ 0.003) between-driver differences; specifically, faster trunk axial rotation velocity and an early wrist release for the low kick point driver. Launch angle was shown to be 2° lower for the high kick point driver. Regression models for both drivers explained a significant amount of variance (60 – 67%) in clubhead speed. Wrist kinematics were most associated with clubhead speed, indicating the importance of the wrists in producing clubhead speed regardless of driver shaft properties

    Maintaining standards in on-demand testing using item response theory

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    Regulatory research into on-demand testing

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    Inventory drivers in a pharmaceutical supply chain

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    In recent years, inventory reduction has been a key objective of pharmaceutical companies, especially within cost optimization initiatives. Pharmaceutical supply chains are characterized by volatile and unpredictable demands –especially in emergent markets-, high service levels, and complex, perishable finished-good portfolios, which makes keeping reasonable amounts of stock a true challenge. However, a one-way strategy towards zero-inventory is in reality inapplicable, due to the strategic nature and importance of the products being commercialised. Therefore, pharmaceutical supply chains are in need of new inventory strategies in order to remain competitive. Finished-goods inventory management in the pharmaceutical industry is closely related to the manufacturing systems and supply chain configurations that companies adopt. The factors considered in inventory management policies, however, do not always cover the full supply chain spectrum in which companies operate. This paper works under the pre-assumption that, in fact, there is a complex relationship between the inventory configurations that companies adopt and the factors behind them. The intention of this paper is to understand the factors driving high finished-goods inventory levels in pharmaceutical supply chains and assist supply chain managers in determining which of them can be influenced in order to reduce inventories to an optimal degree. Reasons for reducing inventory levels are found in high inventory holding and scrap related costs; in addition to lost sales for not being able to serve the customers with the adequate shelf life requirements. The thesis conducts a single case study research in a multi-national pharmaceutical company, which is used to examine typical inventory configurations and the factors affecting these configurations. This paper presents a framework that can assist supply chain managers in determining the most important inventory drivers in pharmaceutical supply chains. The findings in this study suggest that while external and downstream supply chain factors are recognized as being critical to pursue inventory optimization initiatives, pharmaceutical companies are oriented towards optimizing production processes and meeting regulatory requirements while still complying with high service levels, being internal factors the ones prevailing when making inventory management decisions. Furthermore, this paper investigates, through predictive modelling techniques, how various intrinsic and extrinsic factors influence the inventory configurations of the case study company. The study shows that inventory configurations are relatively unstable over time, especially in configurations that present high safety stock levels; and that production features and product characteristics are important explanatory factors behind high inventory levels. Regulatory requirements also play an important role in explaining the high strategic inventory levels that pharmaceutical companies hold

    Why continue sharing: determinants of behavior in collaborative economy services

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    La economía colaborativa está revolucionando la forma en que los consumidores utilizan los bienes y servicios. En nuestro estudio modificamos y ampliamos el modelo de confirmación de expectativas para determinar los factores motivacionales que impulsan la satisfacción y la intención de continuar usando los servicios de viajes colaborativos. Más importante aún, agregamos el valor social como un factor adicional. En este estudio fueron encuestados usuarios españoles experimentados de BlaBlaCar. La calidad del servicio, la utilidad percibida, la confianza y el valor social son determinantes de la satisfacción de los usuarios experimentados y, a través de ella, de la intención de continuar usando; mientras que no es el caso para el impacto ambiental ni para los beneficios económicos. Además, la confianza afecta directamente a la intención de continuar. Estos resultados tienen implicaciones gerenciales relevantes, mostrando que los usuarios de algunos servicios colaborativos están motivados por otros factores además de los económicos.The sharing economy is revolutionizing the way consumers use goods and services. In our study we modify and extend the expectation confirmation model to determine the motivational factors which drive the satisfaction and continue intention to use ridesharing services. Most importantly, we add social value as an additional factor to those previously studied in the literature. We apply our model in a survey among experienced Spanish users of BlaBlaCar. Service quality, perceived usefulness, trust and social value are determinants of satisfaction of experienced users and through it, of intention to continuance; while it is the case neither for environmental impact nor for economic benefits. Additionally, trust affects directly continuance. These results have relevant managerial implications, showing that users of some sharing services are motivated by other factors than purely economic
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