Towards an integrated perspective on fleet asset management: engineering and governance considerations

The traditional engineering perspective on asset management concentrates on the operational performance the assets. This perspective aims at managing assets through their life-cycle, from technical specification, to acquisition, operation including maintenance, and disposal. However, the engineering perspective often takes for granted organizational-level factors. For example, a focus on performance at the asset level may lead to ignore performance measures at the business unit level. The governance perspective on asset management usually concentrates on organizational factors, and measures performance in financial terms. In doing so, the governance perspective tends to ignore the engineering considerations required for optimal asset performance. These two perspectives often take each other for granted. However experience demonstrates that an exclusive focus on one or the other may lead to sub-optimal performance. For example, the two perspectives have different time frames: engineering considers the long term asset life-cycle whereas the organizational time frame is based on a yearly financial calendar. Asset fleets provide a relevant and important context to investigate the interaction between engineering and governance views on asset management as fleets have distributed system characteristics. In this project we investigate how engineering and governance perspectives can be reconciled and integrated to enable optimal asset and organizational performance in the context of asset fleets

Greener workplace: understanding senior management's adoption decisions through the Theory of Planned Behaviour

Human Resources (HR) policies and practices have changed due to global environmental instability. These policies and practices are key factors for successful environmental management. Using the Theory of Planned Behaviour, this article aims to understand the critical factors which influence senior management's decision to adopt `green' HR practices. Data were collected from 210 organisations in Australia using two separate surveys. Survey one, which was addressed directly to HR managers and directors, contained questions relating to HR policies (the dependent variables), while survey two, which was addressed directly to CEOs and senior managers, contained questions about environmental-related attitudes, subjective norms and perceived control (the independent variables). Results indicated that senior management's environmental-related attitudes, subjective norms from stakeholders and perceived green resource readiness influenced their decision to adopt green HR initiatives. However, attitudes and green resource readiness in particular had greater impacts than subjective norms. Limitations, implications and future research are also outlined

Semantic Segmentation of Skin Lesions using a Small Data Set

Early detection of melanoma is difficult for the human eye but a crucial step towards reducing its death rate. Computerized detection of these melanoma and other skin lesions is necessary. The central research question in this paper is "How to segment skin lesion images using a neural network with low available data?". This question is divided into three sub questions regarding best performing network structure, training data and training method. First theory associated with these questions is discussed. Literature states that U-net CNN structures have excellent performances on the segmentation task, more training data increases network performance and utilizing transfer learning enables networks to generalize to new data better. To validate these findings in the literature two experiments are conducted. The first experiment trains a network on data sets of different size. The second experiment proposes twelve network structures and trains them on the same data set. The experimental results support the findings in the literature. The FCN16 and FCN32 networks perform best in the accuracy, intersection over union and mean BF1 Score metric. Concluding from these results the skin lesion segmentation network is a fully convolutional structure with a skip architecture and an encoder depth of either one or two. Weights of this network should be initialized using transfer learning from the pre trained VGG16 network. Training data should be cropped to reduce complexity and augmented during training to reduce the likelihood of overfitting