Little consensus has emerged about how organizational performance should be defined and measured. Most studies have used traditional approaches to give their own perspective about organizational performance and effectiveness, but none have recently tried to encompass these different views into one unified model. In the present paper, Chelladurai's systems view of organizations is used to integrate the dimensions of organizational performance highlighted by previous studies on non-profit sport organizations. These organizational performance dimensions are highlighted and categorized into macro-dimensions (e.g., financial resources acquisition, size, internal atmosphere, organizational operating, financial independence, achieving elite sport success and mass sport participation). Relationships between these macro-dimensions are analyzed. A multidimensional framework is developed which gives an overview of which dimensions constitute organizational performance in non-profit sport organizations and of how to measure them. Further research directions and management implications are discussed.
LINK
Introduction: Different models of frameworks for dietetic care are used in Europe. There is a substantial need for a consistent framework to compare research results and to cooperate on an international level. Therefore, one of the goals of the EU-funded project IMPECD was the development of a unified framework Dietetic Care Process (DCP) in order to foster a shared understanding of process-driven dietetic counselling. Materials and Methods:: Based on a literature review and in-depth analysis of different frameworks an iterative and incremental development process of finding solutions for decision-making within the consortium consisting of dietetic experts from 5 European HEI was passed. The developed DCP model was integrated in an online training course including 9 clinical cases (MOOC) to train students. The draft versions and the concluding final version DCP model were evaluated and re-evaluated by teachers and 25 students at two Intensive Study Programmes. Results:: The DCP model consists of five distinct, interrelated steps which the consortium agreed on: Dietetic Assessment, Dietetic Diagnosis, Planning Dietetic Intervention, Implementing Dietetic Intervention, Dietetic Outcome Evaluation. A standardized scheme was developed to define the process steps: dedication, central statement, aim and principles, and operationalization. Discussion:: Existing different process models were analyzed to create a new and consistent concept of a unified framework DCP. The variety within the European countries represented by the consortium proved to be both a challenge in decision-making and an opportunity to integrate multinational perspectives and intensify the scientific discourse. The development of a standardized scheme with precise definitions is a prerequisite for planning study designs in health services research. Besides, clarification is essential for establishing process-guided work in practice. The evaluated MOOC is now implemented in study programmes used by 5 European HEI in order to keep approaches and process-driven action comparable. The MOOC promotes the exchange of ideas between future professionals on an international level.
DOCUMENT
This paper generalizes existing BRDF fitting algorithms presented in the literature that aims to find a mapping of the parameters of an arbitrary source material model to the parameters of a target material model. A material model in this context is a function that maps a list of parameters, such as roughness or specular color, to a BRDF. Our conversion function approximates the original model as close as possible under a chosen similarity metric, either in physical reflectivities or perceptually, and calculates the error with respect to this conversion. Our conversion function imposes no constraints other than that the dimensionality of the represented BRDFs match.
DOCUMENT
Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
