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The number of electronic vehicles increase steadily while the space for extending the charging infrastructure is limited. In particular in urban areas, where parking spaces in attractive areas are famous, opportunities to setup new charging stations is very limited. This leads to an overload of some very attractive charging stations and an underutilization of less attractive ones. Against this background, the paper at hand presents the design of an e-vehicle reservation system that aims at distributing the utilization of the charging infrastructure, particularly in urban areas. By applying a design science approach, the requirements for a reservation-based utilization approach are elicited and a model for a suitable distribution approach and its instantiation are developed. The artefact is evaluated by simulating the distribution effects based on data of real charging station utilizations.
We introduce a new way to measure the forecast effort that analysts devote to their earnings forecasts by measuring the analyst's general effort for all covered firms. While the commonly applied effort measure is based on analyst behaviour for one firm, our measure considers analyst behaviour for all covered firms. Our general effort measure captures additional information about analyst effort and thus can identify accurate forecasts. We emphasise the importance of investigating analyst behaviour in a larger context and argue that analysts who generally devote substantial forecast effort are also likely to devote substantial effort to a specific firm, even if this effort might not be captured by a firm-specific measure. Empirical results reveal that analysts who devote higher general forecast effort issue more accurate forecasts. Additional investigations show that analysts' career prospects improve with higher general forecast effort. Our measure improves on existing methods as it has higher explanatory power regarding differences in forecast accuracy than the commonly applied effort measure. Additionally, it can address research questions that cannot be examined with a firm-specific measure. It provides a simple but comprehensive way to identify accurate analysts.
Mouse nongenotoxic hepatocarcinogens phenobarbital (PB) and chlordane induce hepatomegaly characterized by hypertrophy and hyperplasia. Increased cell proliferation is implicated in the mechanism of tumor induction. The relevance of these tumors to human health is unclear. The xenoreceptors, constitutive androstane receptors (CARs), and pregnane X receptor (PXR) play key roles in these processes. Novel “humanized” and knockout models for both receptors were developed to investigate potential species differences in hepatomegaly. The effects of PB (80 mg/kg/4 days) and chlordane (10 mg/kg/4 days) were investigated in double humanized PXR and CAR (huPXR/huCAR), double knockout PXR and CAR (PXRKO/CARKO), and wild-type (WT) C57BL/6J mice. In WT mice, both compounds caused increased liver weight, hepatocellular hypertrophy, and cell proliferation. Both compounds caused alterations to a number of cell cycle genes consistent with induction of cell proliferation in WT mice. However, these gene expression changes did not occur in PXRKO/CARKO or huPXR/huCAR mice. Liver hypertrophy without hyperplasia was demonstrated in the huPXR/huCAR animals in response to both compounds. Induction of the CAR and PXR target genes, Cyp2b10 and Cyp3a11, was observed in both WT and huPXR/huCAR mouse lines following treatment with PB or chlordane. In the PXRKO/CARKO mice, neither liver growth nor induction of Cyp2b10 and Cyp3a11 was seen following PB or chlordane treatment, indicating that these effects are CAR/PXR dependent. These data suggest that the human receptors are able to support the chemically induced hypertrophic responses but not the hyperplastic (cell proliferation) responses. At this time, we cannot be certain that hCAR and hPXR when expressed in the mouse can function exactly as the genes do when they are expressed in human cells. However, all parameters investigated to date suggest that much of their functionality is maintained.
Digital Shadows as the aggregation, linkage and abstraction of data relating to physical objects are a central vision for the future of production. However, the majority of current research takes a technocentric approach, in which the human actors in production play a minor role. Here, the authors present an alternative anthropocentric perspective that highlights the potential and main challenges of extending the concept of Digital Shadows to humans. Following future research methodology, three prospections that illustrate use cases for Human Digital Shadows across organizational and hierarchical levels are developed: human-robot collaboration for manual work, decision support and work organization, as well as human resource management. Potentials and challenges are identified using separate SWOT analyses for the three prospections and common themes are emphasized in a concluding discussion.
While bringing new opportunities, the Industry 4.0 movement also imposes new challenges to the manufacturing industry and all its stakeholders. In this competitive environment, a skilled and engaged workforce is a key to success. Gamification can generate valuable feedbacks for improving employees’ engagement and performance. Currently, Gamification in workspaces focuses on computer-based assignments and training, while tasks that require manual labor are rarely considered. This research provides an overview of Enterprise Gamification approaches and evaluates the challenges. Based on that, a skill-based Gamification framework for manual tasks is proposed, and a case study in the Industry 4.0 model factory is shown.
Like all preceding transformations of the manufacturing industry, the large-scale usage of production data will reshape the role of humans within the sociotechnical production ecosystem. To ensure that this transformation creates work systems in which employees are empowered, productive, healthy, and motivated, the transformation must be guided by principles of and research on human-centered work design. Specifically, measures must be taken at all levels of work design, ranging from (1) the work tasks to (2) the working conditions to (3) the organizational level and (4) the supra-organizational level. We present selected research across all four levels that showcase the opportunities and requirements that surface when striving for human-centered work design for the Internet of Production (IoP). (1) On the work task level, we illustrate the user-centered design of human-robot collaboration (HRC) and process planning in the composite industry as well as user-centered design factors for cognitive assistance systems. (2) On the working conditions level, we present a newly developed framework for the classification of HRC workplaces. (3) Moving to the organizational level, we show how corporate data can be used to facilitate best practice sharing in production networks, and we discuss the implications of the IoP for new leadership models. Finally, (4) on the supra-organizational level, we examine overarching ethical dimensions, investigating, e.g., how the new work contexts affect our understanding of responsibility and normative values such as autonomy and privacy. Overall, these interdisciplinary research perspectives highlight the importance and necessary scope of considering the human factor in the IoP.
Humanized UGT2 and CYP3A transchromosomic rats for improved prediction of human drug metabolism
(2019)