In tourism and recreation management it is still common practice to apply traditional input-output (IO) economic impact models, despite their well-known limitations. In this study the authors analyse the usefulness of applying a non-linear input-output (NLIO) model, in which price-induced input substitution is accounted for. For large changes in final demand, a NLIO model is more useful than a traditional IO model, leading to higher or lower impacts. For small changes in final demand input substitution is less likely. In that case the application of the NLIO may lead to the same results as a traditional IO model. To analyse changes of subsidies, a traditional IO model is not an option. A more flexible model, such as the NLIO, is required. The NLIO model forces researchers to make choices about capacity constraints, factor mobility and the substitution elasticity, which can be difficult but create flexibility and allow for more realism.
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In tourism management, traditional input-output models are often applied to calculate economic impacts, including employment impacts. These models imply that increases in output are translated into proportional increases in labour, indicating constant labour productivity. In non-linear input- output (NLIO) models, final demand changes lead to substitution. This causes changes in labour productivity, even though one unit of labour ceteris paribus still produces the same output. Final demand changes can, however, also lead to employees working longer, harder and/or more efficiently. The goal of this article is to include this type of 'real' labour productivity change into an NLIO model. To do this, the authors introduce factor augmenting technical change (FATC) and a differentiation between core and peripheral labour. An NLIO model with and without FATC is used to calculate the regional economic impacts of a 10% final demand increase in tourism in the province of Zeeland in the Netherlands. Accounting for real productivity changes leads to smaller increase in the use of labour, as productivity increases allow output to be produced using fewer inputs.
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We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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This study explores the evaluation of research pathways of self-management health innovations from discovery to implementation in the context of practice-based research. The aim is to understand how a new process model for evaluating practice-based research provides insights into the implementation success of innovations. Data were collected from nine research projects in the Netherlands. Through document analysis and semi-structured interviews, we analysed how the projects start, evolve, and contribute to the healthcare practice. Building on previous research evaluation approaches to monitor knowledge utilization, we developed a Research Pathway Model. The model’s process character enables us to include and evaluate the incremental work required throughout the lifespan of an innovation project and it helps to foreground that innovation continues during implementation in real-life settings. We found that in each research project, pathways are followed that include activities to explore a new solution, deliver a prototype and contribute to theory. Only three projects explored the solution in real life and included activities to create the necessary changes for the solutions to be adopted. These three projects were associated with successful implementation. The exploration of the solution in a real-life environment in which users test a prototype in their own context seems to be a necessary research activity for the successful implementation of self-management health innovations.
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Learning by Design (LBD) is a project-based inquiry approach for interdisciplinary teaching that uses design contexts to learn skills and conceptual knowledge. Research around the year 2000 showed that LBD students achieved high skill performances but disappointing conceptual learning gains. A series of exploratory studies, previous to the study in this paper, indicated how to enhance concept learning. Small-scale tested modifications, based on explicit teaching and scaffolding, were promising and revealed improved conceptual learning gains. The pretest-posttest design study discussed in this paper confirms this improvement quantitatively by comparing the conceptual learning gains for students exposed to the modified approach (n = 110) and traditional approach (n = 77). Further modifications, which resulted in a remodified approach tested with 127 students, show a further improvement through reduced fragmentation of the task and addressed science. Overall, the remodified approach (FITS model: Focus - Investigation - Technological design - Synergy) enriches technology education by stimulating an empirical and conceptual way of creating design solutions.
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The Smart Current Limiter is a switching DC to DC converter that provides a digitally pre-set input current control for inrush limiting and power management. Being able to digitally adjust the current level in combination with external feedback can be used for control systems like temperature control in high power DC appliances. Traditionally inrush current limiting is done using a passive resistance whose resistance changes depending on the current level. Bypassing this inrush limiting resister with a Mosfet improves efficiency and controllability, but footprint and losses remain large. A switched current mode controlled inrush limiter can limit inrush currents and even control the amount of current passing to the application. This enables power management and inrush current limitation in a single device. To reduce footprint and costs a balance between losses and cost-price on one side and electromagnetic interference on the other side is sought and an optimum switching frequency is chosen. To reduce cost and copper usage, switching happens on a high frequency of 300kHz. This increases the switching losses but greatly reduces the inductor size and cost compared to switching supplies running on lower frequencies. Additional filter circuits like snubbers are necessary to keep the control signals and therefore the output current stable.
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Educational institutions in higher education encounter different thresholds when scaling up to institution-wide learning analytics. This doctoral research focuses on designing a model of capabilities that institutions need to develop in order to remove these barriers and thus maximise the benefits of learning analytics.
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Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation of XAI is not straightforward, as it requires addressing both technical and social aspects. Previous studies on XAI primarily focused on either technical or social aspects and lacked a practical perspective. This study aims to empirically examine the XAI related aspects faced by developers, users, and managers of AI systems during the development process of the AI system. To this end, a multiple case study was conducted in two Dutch financial services companies using four use cases. Our findings reveal a wide range of aspects that must be considered during XAI implementation, which we grouped and integrated into a conceptual model. This model helps practitioners to make informed decisions when developing XAI. We argue that the diversity of aspects to consider necessitates an XAI “by design” approach, especially in high-risk use cases in industries where the stakes are high such as finance, public services, and healthcare. As such, the conceptual model offers a taxonomy for method engineering of XAI related methods, techniques, and tools.
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A B S T R A C T Background: Approximately 4 years ago a new concept of learning in practice called the ‘Learning and Innovation Network (LIN)’ was introduced in The Netherlands. To develop a definition of the LIN, to identify working elements of the LIN in order to provide a preliminary framework for evaluation, a concept analysis was conducted. Method: For the concept analysis, we adopted the method of Walker and Avant. We searched for relevant publications in the EBSCO host portal, grey literature and snowball searches, as well as Google internet searches and dictionary consults. Results: Compared to other forms of workplace learning, the LIN is in the centre of the research, education and practice triangle. The most important attributes of the LIN are social learning, innovation, daily practice, reflection and co-production. Often described antecedents are societal developments, such as increasing complexity of work, and time and space to learn. Frequently identified consequences are an attractive workplace, advancements of expertise of care professionals, innovations that endorse daily practice, improvement of quality of care and the integration of education and practice. Conclusions: Based on the results of the concept analysis, we describe the LIN as ‘a group of care professionals, students and an education representatives who come together in clinical practice and are all part of a learning and innovation community in nursing. They work together on practice-based projects in which they combine best practices, research evidence and client perspectives in order to innovate and improve quality of care and in which an integration of education, research and practice takes place’. We transferred the outcomes of the concept analysis to an input-throughput-output model that can be used as a preliminary framework for future research.
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Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
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