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Open Access
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"Machen Sie doch mal mehr PR und Werbung für Ihre Schule": Kommunikationscontrolling in Schulen
(2015)
Henry Fords Bonmot zur Werbeerfolgskontrolle ist sicherlich der bekannteste Satz im Sektor des Kommunikationscontrollings: „Die Hälfte unserer Werbegelder werfen wir zum Fenster raus. Ich weiß nur nicht, welche Hälfte das ist.“ Diese kritische Würdigung von Kommunikationsleistungen ist auch heute noch immer wieder Thema und gerade im Umfeld von Schule, wo diese Prozesse noch keine sehr lange Tradition haben, Teil der internen und externen Diskussion. Die Steuerung von Kommunikationsprozessen erfordert jedoch nicht nur die Quantifizierung von Kommunikationsleistungen, sondern eine Einbettung in die gesamte Marketingstrategie und in die Bewertung einzelner Marketingbereiche und der dort entwickelten Marketingziele.
Featherweight Generic Go (FGG) is a minimal core calculus modeling the essential features of the programming language Go. It includes support for overloaded methods, interface types, structural subtyping and generics. The most straightforward semantic description of the dynamic behavior of FGG programs is to resolve method calls based on runtime type information of the receiver.
This article shows a different approach by defining a type-directed translation from FGG to an untyped lambda-calculus. The translation of an FGG program provides evidence for the availability of methods as additional dictionary parameters, similar to the dictionary-passing approach known from Haskell type classes. Then, method calls can be resolved by a simple lookup of the method definition in the dictionary.
Every program in the image of the translation has the same dynamic semantics as its source FGG program. The proof of this result is based on a syntactic, step-indexed logical relation. The step-index ensures a well-founded definition of the relation in the presence of recursive interface types and recursive methods.
The Advanced Innovation Design Approach is a holistic methodology for enhancing innovative and competitive capability of industrial companies. AIDA can be considered as an open mindset, an individually adaptable range of strongest innovation techniques such as comprehensive front-end innovation process, advanced innovation methods, best tools and methods of the TRIZ methodology, organizational measures for accelerating innovation, IT-solutions for Computer-Aided Innovation, and other innovation methods, elaborated in the recent decade in the industry and academia
Recent advances in spiked shoe design, characterized by increased longitudinal stiffness, thicker midsole foams, and reconfigured geometry are considered to improve sprint performance. However, so far there is no empirical data on the effects of advanced spikes technology on maximal sprinting speed (MSS) published yet. Consequently, we assessed MSS via ‘flying 30m’ sprints of 44 trained male (PR: 10.32 s - 12.08 s) and female (PR: 11.56 s - 14.18 s) athletes, wearing both traditional and advanced spikes in a randomized, repeated measures design. The results revealed a statistically significant increase in MSS by 1.21% on average when using advanced spikes technology. Notably, 87% of participants showed improved MSS with the use of advanced spikes. A cluster analysis unveiled that athletes with higher MSS may benefit to a greater extent. However, individual responses varied widely, suggesting the influence of multiple factors that need detailed exploration. Therefore, coaches and athletes are advised to interpret the promising performance enhancements cautiously and evaluate the appropriateness of the advanced spike technology for their athletes critically.
Financing trade and development sustainably will be crucial for Africa. Enhanced collaboration between multilateral development banks, development finance institutions and ECAs could greatly enhance intra-regional trade. Furthermore, setting up a ‘level playing field’ on the continent will allow governments to make strategic interventions for successful export credits and trade finance solutions, fostering growth through trade. African trade is already showing signs of rebounding from the coronavirus- induced recession. Through concerted, co-operative and continent-wide efforts, drawing on the knowledge and resources of all types of institutions and policy experts, Africa will continue to grow confidently and quickly into its increasingly important role as an engine of economic growth and global trade.
Objective: This paper deals with the design and the optimization of mechatronic devices.
Introduction: Comparing with existing works, the design approach presented in this paper aims to integrate optimization in the design phase of complex mechatronic systems in order to increase the efficiency of this method.
Methods: To solve this problem, a novel mechatronic system design approach has been developed in order to take the multidisciplinary aspect and to consider optimization as a tool that can be used within the embodiment design process to build mechatronic solutions from a set of solution concepts designed with innovative or routine design methods.
Conclusions: This approach has then been applied to the design and optimization of a wind turbine system that can be implemented to autonomously supply a mountain cottage.
The communication technologies for automatic me-ter reading (smart metering) and for energy production and distribution networks (smart grid) have the potential to be one of the first really highly scaled machine-to-machine-(M2M)-applications. During the last years two very promising devel-opments around the wireless part of smart grid communication were initialized, which possibly have an impact on the markets far beyond Europe and far beyond energy automation. Besides the specifications of the Open Metering System (OMS) Group, the German Federal Office for Information Security (Bundesamt für Sicherheit in der Informationstechnik, BSI) has designed a protection profile (PP) and a technical directive (TR) for the communication unit of an intelligent measurement sys-tem (smart meter gateway), which were released in March 2013. This design uses state-of-the-art technologies and prescribes their implementation in real-life systems. At first sight the expenditures for the prescribed solutions seem to be significant. But in the long run, this path is inevitable and comes with strategic advantages.
Motivated by the recent trend towards the usage of larger receptive fields for more context-aware neural networks in vision applications, we aim to investigate how large these receptive fields really need to be. To facilitate such study, several challenges need to be addressed, most importantly: (i) We need to provide an effective way for models to learn large filters (potentially as large as the input data) without increasing their memory consumption during training or inference, (ii) the study of filter sizes has to be decoupled from other effects such as the network width or number of learnable parameters, and (iii) the employed convolution operation should be a plug-and-play module that can replace any conventional convolution in a Convolutional Neural Network (CNN) and allow for an efficient implementation in current frameworks. To facilitate such models, we propose to learn not spatial but frequency representations of filter weights as neural implicit functions, such that even infinitely large filters can be parameterized by only a few learnable weights. The resulting neural implicit frequency CNNs are the first models to achieve results on par with the state-of-the-art on large image classification benchmarks while executing convolutions solely in the frequency domain and can be employed within any CNN architecture. They allow us to provide an extensive analysis of the learned receptive fields. Interestingly, our analysis shows that, although the proposed networks could learn very large convolution kernels, the learned filters practically translate into well-localized and relatively small convolution kernels in the spatial domain.
Die Energiewende ist ein elementares Thema, für Deutschland wie auch für viele andere Regionen weltweit. Bei der Bereitstellung effizienter und stabiler Verteilnetze stellen Kommunikationslösungen einen zentralen Baustein dar, um auf der Grundlage eines zeitnahen Monitorings koordinierte Regelalgorithmen zu realisieren. Dies gilt für alle Ebenen der Versorgung, wobei aus Sicht der Kommunikationstechnik die unterste Ebene der Verteilnetze am interessantesten ist: Hier sind die anspruchsvollsten Anforderungen im Hinblick auf die Kosten- und die Energieoptimierung der Kommunikationsknoten sowie die Administrierbarkeit, die Stabilität und die Skalierbarkeit der Gesamtlösung zu berücksichtigen. Das Steinbeis-Transferzentrum Embedded Design und Networking an der Hochschule Offenburg unter der Leitung von Prof. Dr.-Ing. Axel Sikora hat in verschiedenen Projekten mit renommierten Partnern umfangreiche Lösungen für diese sogenannte Primärkommunikation entwickelt.
Auswirkung eines Importstopps russischer Energieträger auf die Klimaschutzziele in Deutschland
(2022)
Ein Importstopp russischer Energieträger nach Deutschland wird derzeit vermehrt diskutiert. Wir wollen die Diskussion unterstützen, indem wir einen Weg zeigen, wie das Elektrizitätssystem in Deutschland kurzfristig mit geringen Energieimporten auskommt und welche Maßnahmen notwendig sind, um die Klimaschutzziele trotzdem einzuhalten. Die Ergebnisse eines solchen Energiewendeszenarios mit reduzierter Importabhängigkeit werden mit dem Energiesystemmodell MyPyPSA-Ger berechnet. Die wichtigsten Erkenntnisse sind, dass ein zügiger Ausbau Erneuerbarer Energien und von Speichertechnologien • die Abhängigkeit des deutschen Elektrizitätssystems von Energieimporten deutlich reduziert. • auch langfristig keine wesentlichen Importe der Energieträger Erdgas, Steinkohle und Mineralöl nach sich zieht. • über die Klimaziele der Bundesregierung hinaus das 1,5-Grad-Ziel im Elektrizitätssystem erreicht wird.
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
In the modern knowledge-based and digital economy, the value of knowledge is growing relative to other assets and new intellectual property is being created at an ever-increasing rate. Therefore, the ability to find non-trivial solutions, systematically generate new concepts, and create intellectual property rapidly become crucial to achieving competitive advantage and leveraging the intellectual potential of organizations.
Durch den Einsatz von Torschleieranlagen zwischen zwei Zonen mit unterschiedlichen Temperaturen kann der Luftaustausch aufgrund freier Konvektion verhindert werden. Der Einfluß unterschiedlicher Betriebsfälle auf den Energieverbrauch und die thermische Behaglichkeit wurde im Labor untersucht. Die Effizienz von Torschleieranlagen wurde zusätzlich im Praxisbetrieb beim Einsatz an einer Kühlzelle überprüft. Bei diesem Anwendungsfall steht nicht die thermische Behaglichkeit sondern der Energieverbrauch, die Gefahr der Kühlguterwärmung und die der Eisbildung vor dem Kühlzelleneingang im Vordergrund.
Beuys-Gespräch
(2022)
In sicherheitskritschen Systemen darf kein Stück Code im Produktionsbetrieb ablaufen, ohne vorher intensive Tests durchlaufen zu haben. Aber auch zur Qualitätssicherung muss Software getestet werden. Um die Codeüberdeckung zu prüfen, sind zusätzliche Prüf-Instruktionen im Quellcode erforderlich. Auf kleinen Systemen mit wenig RAM kann sich der Entwickler dann etwas einfallen lassen, damit das funktioniert.
Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the G are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.
The energy supply of Offenburg University of Applied Sciences (HS OG) was changed from separate generation to trigeneration in 2007/2008. Trigeneration was installed for supplying heat, cooling and electrical power at HS OG. In this paper, trigeneration process and its modes of operation along with the layout of the energy facility at HS OG were described. Special emphasis was given to the operation schemes and control strategies of the operation modes: winter mode, transition mode and summer mode. The components used in the energy facility were also outlined. Monitoring and data analysis of the energy system was carried out after the commissioning of trigeneration in the period from 2008 to 2011. Thus, valuable performance data was obtained.
CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision.