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The classification of waste with neural networks is already a topic in some scientific papers. An application in the embedded systems area with current AI processors to accelerate the inference has not yet been discussed. In this master work a prototype is created which classifies waste objects and automatically opens the appropriate container for the object. The area of application is in the public space.
For the classification a dataset with 25,681 images and 11 classes is created to re-train the Convolution Neuronal Networks EfficientNet-B0, MobileNet-v2 and NASNet-mobile. These Convolution Neuronal Networks run on the current Edge \acrshort{ai} processors from Google, Intel and Nvidia and are compared for performance, consumption and accuracy.
The master thesis evaluates the result of these comparisons and shows the advantages and disadvantages of the respective processors and the CNNs. For the prototype, the most suitable combination of hardware and AI architecture is used and exhibited at the university fair KasetFair2020. An opinion survey on the application of the machine is conducted.
Issues with professional conduct and discrimination against Lesbian, Gay, Bisexual, Transgender (LGBT+) people in health and social care, continue to exist in most EU countries and worldwide.
The project IENE9 titled: “Developing a culturally competent and compassionate LGBT+ curriculum in health and social care education” aims to enable teacher/trainers of theory and practice to enhance their skills regarding LGBT+ issues and develop teaching tools to support the inclusion of LGBT+ issues within health and social care curricula. The newly culturally competent and compassionate LGBT+ curriculum will be delivered though a Massive Open Online Course (MOOC) which is aimed at health and social care workers, professionals and learners across Europe and worldwide.
We have identified educational policies and guidelines at institutions teaching in health and social care, taken into account for developing the learning/teaching resources. The MOOC will be an innovative training model based on the Papadopoulos (2014) model for “Culturally Competent Compassion”. The module provides a logical and easy to follow structure based on its four constructs 'Culturally Aware and Compassionate Learning', 'Culturally Knowledgeable and Compassionate Learning', 'Culturally Sensitive and Compassionate Learning', 'Culturally Competent and Compassionate Learning'.
Specific training may result in better knowledge and skills of the health and social care workforce, which helps to reduce inequalities and communication with LGBT+ people, as well as diminishing the feelings of stigma or discrimination experienced.
Real-time measurements of the differences in inhaled and exhaled, unlabeled and fully deuterated acetone concentration levels, at rest and during exercise, have been conducted using proton transfer reaction mass spectrometry. A novel approach to continuously differentiate between the inhaled and exhaled breath acetone concentration signals is used. This leads to unprecedented fine grained data of inhaled and exhaled concentrations. The experimental results obtained are compared with those predicted using a simple three compartment model that theoretically describes the influence of inhaled concentrations on exhaled breath concentrations for volatile organic compounds with high blood:air partition coefficients, and hence is appropriate for acetone. An agreement between the predicted and observed concentrations is obtained. Our results highlight that the influence of the upper airways cannot be neglected for volatiles with high blood:air partition coefficients, i.e. highly water soluble volatiles.
Steigende Komplexität von Geschäftsmodellen und in der Niedrigzinsphase stets zulegende Renditeerwartungen an kapitalmarktorientierte Unternehmen führen dazu, dass sich deren Berichterstattung anpasst. Unter anderem ist eine verstärkte Verbreitung und Anwendung von Key Performance Indicators (alternative Führungsgrößen) in der Praxis zu beobachten. Wenigen, zwar prägnanten, aber eben nicht einheitlich definierten oder gar nach geltenden Rechnungslegungsnormen objektiv zu gewinnende Messgrößen kommt eine gesteigerte Bedeutung im externen Berichtswesen zu. Der Beitrag liefert eine Bestandesaufnahme der meist verwendeten Key Performance Indicators in der Praxis in Deutschland, in Österreich und in der Schweiz.
Business Analytics zählt zu den Zukunftsthemen im Controlling. In der Controllinglehre spielt Analytics bisher aber nur eine untergeordnete Rolle. Der Beitrag beschreibt ein innovatives Lehrprojekt, das Studierende im Masterstudium Accounting, Controlling & Finance an der FH Vorarlberg befähigt, controllingrelevante Fragestellungen im Kontext von Business Analytics eigenständig zu beantworten. Gleichzeitig erlernen die Studierenden den Umgang mit der Open-Source-Software R.
Blood flow and ventilatory flow strongly influence the concentrations of volatile organic compounds (VOCs) in exhaled breath. The physicochemical properties of a compound (e.g., water solubility) additionally determine if the concentration of the compound in breath reflects the alveolar concentration, the concentration in the upper airways, or a mixture of both. Mathematical modeling based on mass balance equations helps to understand how measured breath concentrations are related to their corresponding blood concentrations and physiological parameters, such as metabolic rates and endogenous production rates. In addition, the influence of inhaled compounds on their exhaled concentrations can be quantified and appropriate correction formulas can be derived. Isoprene and acetone, two endogenous VOCs with very different water solubility, have been modeled to explain the essential features of their behavior in breath. This chapter introduces the theory of physiological modeling of exhaled VOCs, with examples of isoprene and acetone.
The importance of Agent-Based Simulation (ABS) as scientific method to generate data for scientific models in general and for informed policy decisions in particular has been widely recognised. However, the important technique of code testing of implementations like unit testing has not generated much research interested so far. As a possible solution, in previous work we have explored the conceptual use of property-based testing. In this code testing method, model specifications and invariants are expressed directly in code and tested through automated and randomised test data generation. This paper expands on our previous work and explores how to use property-based testing on a technical level to encode and test specifications of ABS. As use case the simple agent-based SIR model is used, where it is shown how to test agent behaviour, transition probabilities and model invariants. The outcome are specifications expressed directly in code, which relate whole classes of random input to expected classes of output. During test execution, random test data is generated automatically, potentially covering the equivalent of thousands of unit tests, run within seconds on modern hardware. This makes property-based testing in the context of ABS strictly more powerful than unit testing, as it is a much more natural fit due to its stochastic nature.
With the emergence of the recent Industry 4.0 movement, data integration is now also being driven along the production line, made possible primarily by the use of established concepts of intelligent supply chains, such as the digital avatars. Digital avatars – sometimes also called Digital Twins or more broadly Cyber-Physical Systems (CPS) – are already successfully used in holistic systems for intelligent transport ecosystems, similar to the use of Big Data and artificial intelligence technologies interwoven with modern production and supply chains. The goal of this paper is to describe how data from interwoven, autonomous and intelligent supply chains can be integrated into the diverse data ecosystems of the Industry 4.0, influenced by a multitude of data exchange formats and varied data schemas. In this paper, we describe how a framework for supporting SMEs was established in the Lake Constance region and describe a demonstrator sprung from the framework. The demonstrator project’s goal is to exhibit and compare two different approaches towards optimisation of manufacturing lines. The first approach is based upon static optimisation of production demand, i.e. exact or heuristic algorithms are used to plan and optimise the assignment of orders to individual machines. In the second scenario, we use real-time situational awareness – implemented as digital avatar – to assign local intelligence to jobs and raw materials in order to compare the results to the traditional planning methods of scenario one. The results are generated using event-discrete simulation and are compared to common (heuristic) job scheduling algorithms.