Digital Transformation - Towards flexible human-centric enterprises - Kehrbusch, Burkhard; Engels, Gregor (Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective)

Optimization of optical waveguide antennas for directive emission of light - Farheen, Henna; Leuteritz, Till; Linden, Stefan; Myroshnychenko, Viktor; Förstner, Jens (Journal of the Optical Society of America B)
Optical traveling wave antennas offer unique opportunities to control and selectively guide light into a specific direction, which renders them excellent candidates for optical communication and sensing. These applications require state-of-the-art engineering to reach optimized functionalities such as high directivity and radiation efficiency, low sidelobe levels, broadband and tunable capabilities, and compact design. In this work, we report on the numerical optimization of the directivity of optical traveling wave antennas made from low-loss dielectric materials using full-wave numerical simulations in conjunction with the particle swarm optimization algorithm. The antennas are composed of a reflector and a director deposited on a glass substrate, and an emitter placed in the feed gap between them serves as an internal source of excitation. In particular, we analyze antennas with rectangular- and horn-shaped directors made of either hafnium dioxide or silicon. The optimized antennas produce highly directional emissions due to the presence of two dominant guided TE modes in the director in addition to leaky modes. These guided modes dominate the far-field emission pattern and govern the direction of the main lobe emission, which predominately originates from the end facet of the director. Our work also provides a comprehensive analysis of the modes, radiation patterns, parametric influences, and bandwidths of the antennas, which highlights their robust nature.
Keywords: tet_topic_opticalantenna

Utilizing Geographic Information Systems for Condition-Based Maintenance on the Energy Distribution Grid - zur Heiden, Philipp; Priefer, Jennifer; Beverungen, Daniel (Proceedings of the 55th Hawaii International Conference on System Sciences)

Context in Design Science Research: Taxonomy and Framework - Herwix, Alexander; zur Heiden, Philipp (Proceedings of the 55th Hawaii International Conference on System Sciences)

A Renaissance of Context in Design Science Research - zur Heiden, Philipp; Beverungen, Daniel (Proceedings of the 55th Hawaii International Conference on System Sciences)

Introduction to the Data-driven Services in Manufacturing Minitrack - Exploring Management, Engineering, and Organizational Transformation - Koldewey, Christian; Dumitrescu, Roman; Rabe, Martin (Proceedings of the 55th Hawaii International Conference on System Sciences)

Visual Interpretability of Image-based Real Estate Appraisal - Kucklick, Jan-Peter (55th Annual Hawaii International Conference on System Sciences (HICSS-55))
Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%.
Keywords: Explainable Artificial Intelligence (XAI); Regression Activation Maps; Real Estate Appraisal; Convolutional Block Attention Module; Computer Vision

Using Geolocated Text to Quantify Location in Real Estate Appraisal - Heuwinkel, Tim; Kucklick, Jan-Peter; Müller, Oliver (55th Annual Hawaii International Conference on System Sciences (HICSS-55))
Accurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania.
Keywords: Real Estate Appraisal; Text Regression; Natural Language Processing (NLP); Location Intelligence; Wikipedia

Capacity investments in a competitive energy market - Block, Lukas; Westbrock, Bastian
We study the abilities of competitive markets to produce sufficient energy capacities to meet a fixed energy demand. Renewable energy producers with stochastic outputs and no variable costs compete against conventional energy producers with deterministic, pollutant outputs and increasing marginal costs. We find that either market forces are strong enough to serve the entire demand, or they are too weak such that the market fails and nothing is produced. This crucially depends on the relative cost of renewable energy investments, such that relatively cheap renewable energy causes the market to fail. Welfare analyses show that with increasing levels of conventional energy pollution the ability of the market to produce an ecient outcome further declines. As a policy implication, our findings refute the use of a strategic reserve as a blackout backstop solution. Instead, a capacity mechanism consisting of a tax-and-subsidy scheme can align the market outcome with the eficient solution for all pollution levels and relative costs of renewable energy capacities.
Keywords: Renewable versus conventional energy; capacity mechanisms; strategic reserves; capacity payments

Situational Business Model Developer: A Tool-support for Situation-speciﬁc Business Model Development - Gottschalk, Sebastian; Yigitbas, Enes; Nowosad, Alexander; Engels, Gregor (Proceedings of the 17th International Conference on Wirtschaftsinformatik)
The development of business models is a challenging task that can be supported with software tools. Here, existing approaches and tools do not focus on the company’s situation in which the development takes place (e.g., ﬁnancial resources, product type). To tackle this challenge, we used design science research to develop a situation-speciﬁc business model development approach that contains three stages: First, existing knowledge in terms of tasks to do (e.g., analyze competitive advantage), and decisions to be made (e.g., social media marketing) are stored in repositories. Second, the knowledge is used to compose a development method based on the company’s situation. Third, the development method is enacted to develop a business model. This demonstration paper presents a tool-support called Situational Business Model Developer that supports all stages of our approach. We release the tool under open-source and evaluate it with a case study on developing business models for mobile apps.
Keywords: Business Model Development; Situational Method Engineering; Tool Support