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We analyze economics PhDs’ collaborations in peer-reviewed journals from 1990 to 2014 and investigate such collaborations’ quality in relation to each co-author’s research quality, field and specialization. We find that a greater overlap between co-authors’ previous research fields is significantly related to a greater publication success of co-authors’ joint work and this is robust to alternative specifications. Co-authors that engage in a distant collaboration are significantly more likely to have a large research overlap, but this significance is lost when co-authors’ social networks are accounted for. High quality collaboration is more likely to emerge as a result of an interaction between specialists and generalists with overlapping fields of expertise. Regarding interactions across subfields of economics (interdisciplinarity), it is more likely conducted by co- authors who already have interdisciplinary portfolios, than by co-authors who are specialized or starred in different subfields.
New business opportunities appeared using the potential of the Internet and related digital technologies, like the Internet of Things, services computing, artificial intelligence, cloud, edge, and fog computing, social networks, big data with analytics, mobile systems, collaboration networks, and cyber-physical systems. Companies are transforming their strategy and product base, as well as their culture, processes and information systems to adopt digital transformation or to approach for digital leadership. Digitalization fosters the development of IT environments with many rather small and distributed structures, like the Internet of Things, Microservices, or other micro-granular elements. Digitalization has a substantial impact for architecting the open and complex world of highly distributed digital servcies and products, as part of a new digital enterprise architecture, which structure and direct service-dominant digital products and services. The present research paper investigates mechanisms for supporting the evolution of digital enterprise architectures with user-friendly methods and instruments of interaction, visualization, and intelligent decision management during the exploration of multiple and interconnected perspectives by an architecture management cockpit.
Artificial Intelligence-based Assistants AIAs are spreading quickly both in homes and offices. They already have left their original habitats of "intelligent speakers" providing easy access to music collections. The initiated a multitude of new devices and are already populating devices such as TV sets. Characteristic for the intelligent digital assistants is the formation of platforms around their core functionality. Thus, AIS capabilities of the assistants are used to offer new services and create new interfaces for business processes. There are positive network effects between the assistants and the services as well as within the services. Therefore, many companies see the need to get involved in the field of digital assistants but lack a framework to align their initiatives with their corporate strategies. In order to lay the foundation for a comprehensive method, we are therefore investigating intelligent digital assistants. Based on this analysis, we are developing a framework of strategic opportunities and challenges.
The general conclusion of climate change studies is the necessity of eliminating net CO2 emissions in general and from the electric power systems in particular by 2050. The share of renewable energy is increasing worldwide, but due to the intermittent nature of wind and solar power, a lack of system flexibility is already hampering the further integration of renewable energy in some countries. In this study, we analyze if and how combinations of carbon pricing and power-to-gas (PtG) generation in the form of green power-to-hydrogen followed by methanation (which we refer to as PtG throughout) using captured CO2 emissions can provide transitions to deep decarbonization of energy systems. To this end, we focus on the economics of deep decarbonization of the European electricity system with the help of an energy system model. In different scenario analyses, we find that a CO2 price of 160 €/t (by 2050) is on its own not sufficient to decarbonize the electricity sector, but that a CO2 price path of 125 (by 2040) up to 160 €/t (by 2050), combined with PtG technologies, can lead to an economically feasible decarbonization of the European electricity system by 2050. These results are robust to higher than anticipated PtG costs.
Vehicles have been so far improved in terms of energy-efficiency and safety mainly by optimising the engine and the power train. However, there are opportunities to increase energy-efficiency and safety by adapting the individual driving behaviour in the given driving situation. In this paper, an improved rule match algorithm is introduced, which is used in the expert system of a human-centred driving system. The goal of the driving system is to optimise the driving behaviour in terms of energy-efficiency and safety by giving recommendations to the driver. The improved rule match algorithm checks the incoming information against the driving rules to recognise any breakings of a driving rule. The needed information is obtained by monitoring the driver, the current driving situation as well as the car, using in-vehicle sensors and serial-bus systems. On the basis of the detected broken driving rules, the expert system will create individual recommendations in terms of energy-efficiency and safety, which will allow eliminating bad driving habits, while considering the driver needs.
Detecting the adherence of driving rules in an energy-efficient, safe and adaptive driving system
(2016)
An adaptive and rule-based driving system is being developed that tries to improve the driving behavior in terms of the energy-efficiency and safety by giving recommendations. Therefore, the driving system has to monitor the adherence of driving rules by matching the rules to the driving behavior. However, existing rule matching algorithms are not sufficient, as the data within a driving system is changing frequently. In this paper a rule matching algorithm is introduced that is able to handle frequently changing data within the context of the driving system. 15 journeys were used to evaluate the performance of the rule matching algorithms. The results showed that the introduced algorithm outperforms existing algorithms in the context of the driving system. Thus, the introduced algorithm is suited for matching frequently changing data against rules with a higher performance, why it will be used in the driving system for the detection of broken energy-efficiency of safety-relevant driving rules.
Palladium-doped silica materials with SiCH3 groups were fabricated by sol-gel method under various calcination atmospheres and membranes were made thereof by coating process. The results showed that air atmosphere can lead to the partial oxidation of metallic Pd0 to PdO while N2 and H2 atmospheres can effectively prevent metallic Pd0 from being oxidized. H2 atmosphere is proved to be a more prominent way to slow down the decomposition of organic SiCH3 group than N2 and air atmospheres. The surface area, micropore volume and porosity of palladium-doped silica membrane material calcined in H2 atmosphere are much higher than those calcined in N2 atmosphere. Compared with N2 atmosphere, the palladium-doped silica membranes calcined in H2 atmosphere showed higher H2 permeability and H2/CO2 selectivity before and after the steam exposure. The apparent activation energy of H2 permeation through the palladium-doped silica membrane calcined under H2 atmosphere (2.51 ± 0.05 kJ/mol) was slightly lower than that calcined under N2 atmosphere (2.84 ± 0.04 kJ/mol). Calcination atmosphere plays some role in membrane performance, which has greater influence on the permeance than on the gas permselectivity. Calcination under H2 atmosphere is well conducive to improve the gas permeance and H2 permselectivity of palladium-doped silica membrane.
Introducing continuous experimentation in large software-intensive product and service organisations
(2017)
Software development in highly dynamic environments imposes high risks to development organizations. One such risk is that the developed software may be of only little or no value to customers, wasting the invested development efforts.Continuous experiment ation, as an experiment-driven development approach, may reduce such development risks by iteratively testing product and service assumptions that are critical to the success of the software. Although several experiment-driven development approaches are available, there is little guidance available on how to introduce continuous experimentation into an organization. This article presents a multiple-case study that aims at better understanding the process of introducing continuous experimentation into an organization with an already established development process. The results from the study show that companies are open to adopting such an approach and learning throughout the introduction process. Several benefits were obtained, such as reduced development efforts, deeper customer insights, and better support for development decisions. Challenges included complex stakeholder structures, difficulties in defining success criteria, and building experimen- tation skills. Our findings indicate that organizational factors may limit the benefits of experimentation. Moreover, introducing continuous experimentation requires fundamental changes in how companies operate, and a systematic introduction process can increase the chances of a successful start.
Powder coating of engineered wood panels such as medium density fibreboards (MDF) is gaining industrial interest due to ecological and economic advantages of powder coating technology. For transferring powder coating technology to temperature-sensitive substrates like MDF, a thorough understanding of the melting, flowing and curing behaviour of the used low-bake resins is required. In the present study, thermo-analysis in combination with iso-conversional kinetic data analysis as well as rheometry is applied to characterise the properties of an epoxy-based powder coating. Neat resin and cured powder coating films are examined in order to define an ideal production window within which the resin is preferably applied and processed to yield satisfactory surface performance on the one hand and without exposing the carrier MDF too high a temperature load on the other hand to prevent the panel from deteriorating in mechanical strength. In order to produce powder coated films of high surface gloss – a feature that has not yet successfully been realized on MDF with powder coatings – a new curing technology, in-mould surface finishing, has been applied.
Using predictive maintenance, more efficient processes can be implemented, leading to fewer maintenance costs and increased availability. The development of a predictive maintenance solution currently requires high efforts in time and capacity as well as often interdisciplinary cooperation. This paper presents a standardized model to describe a predictive maintenance use case. The description model is used to collect, present, and document the required information for the implementation of predictive maintenance use cases by and for different stakeholders. Based on this model, predictive maintenance solutions can be introduced more efficiently. The method is validated across departments in the automotive sector.
Railway operators are being challenged by increasing complexity and safeguarding the availability of passenger rolling stock, bringing maintenance and especially emerging technologies into the focus. This paper presents a model for selection and implementation of Industry 4.0 technologies in rolling stock maintenance. The model consists of different stages and considers the main components of rolling stock, the related appropriate maintenance strategies and Industry 4.0 technologies considering the maturity level of the railway operators. Relevant criteria and main prerequisites of the technologies were identified. The model proposes relevant activities and was validated by industry experts.
With the digital transformation, companies will experience a change that focuses on shaping the organization into an agile organizational form. In today's competitive and fast-moving business environment, it is necessary to react quickly to changing market conditions. Agility represents a promising option for overcoming these challenges. The path to an agile organization represents a development process that requires consideration of countless levels of the enterprise. This paper examines the impact of digital transformation on agile working practices and the benefits that can be achieved through technology. To enable a solution for today's so-called VUCA (Volatility, Uncertainty, Complexity und Ambiguity) world, agile ways of working can be applied project management requires adaptation. In the qualitative study, expert interviews were conducted and analyzed using the grounded theory method. As a result, a model can be presented that shows the influencing factors and potentials of agile management in the context of the digital transformation of medium-sized companies.
Nowadays CHP units are discussed for the production of electricity on demand rather than for generation of heat providing electricity as a by-product. By this means, CHP units are capable of satisfying a higher share of the electricity demand on-site and in this new role, CHP units are able to reduce the load on the power grid and to compensate for high fluctuations of solar and wind power.
Evidently, a novel control strategy for CHP units is required in order to shift the operation oriented at the heat demand to an operation led by the electricity demand. Nevertheless, the heat generated by the CHP unit needs to be utilized completely in any case, for maintaining energy as well as economic efficiency. Such a strategy has been developed at Reutlingen University, and it will be presented in the paper. Part of the strategy is an intelligent management for the thermal energy storage (TES) ensuring that the storage is at low level in terms of its heat content just before an electricity demand is calling the CHP unit into operation. Moreover, a proper forecast of both, heat and electricity demand, is incorporated and the requirements of the CHP unit in terms of maintenance and lifetime are considered by limiting the number of starts and stops per unit time and by maintaining a certain minimum length of the operation intervals.
All aspects of this novel control strategy are revealed in the paper, which has been implemented on a controller for further testing at two sites in the field. Results from these tests are given as well as results from a simulation model, which is able to evaluate the performance of the control strategy for an entire year.
Adoption of artificial intelligence (AI) has risen sharply in recent years but many firms are not successful in realising the expected benefits or even terminate projects before completion. While there are a number of previous studies that highlight challenges in AI projects, critical factors that lead to project failure are mostly unknown. The aim of this study is therefore to identify distinct factors that are critical for failure of AI projects. To address this, interviews with experts in the field of AI from different industries are conducted and the results are analyzed using qualitative analysis methods. The results show that both, organizational and technological issues can cause project failure. Our study contributes to knowledge by reviewing previously identified challenges in terms of their criticality for project failure based on new empirical data, as well as, by identifying previously unknown factors.
Automatic content creation system for augmented reality maintenance applications for legacy machines
(2024)
Augmented reality (AR) applications have great potential to assist maintenance workers in their operations. However, creating AR solutions is time-consuming and laborious, which limits its widespread adoption in the industry. It therefore often happens that even with the latest generation machines, instead of an AR solution, the user only receives an electronic manual for the equipment operation and maintenance. This is commonplace with legacy machines. For this reason, solutions are required that simplify the creation of such AR solutions. This paper presents an approach using an electronic manual as a basis to create fast and cost-effective AR solutions for maintenance. As part of the approach, an application was developed to automatically identify and subdivide the chapters of electronic manuals via the bookmarks in the table of contents. The contents are then automatically uploaded to a central server and indexed with a suitable marker to make the data retrievable. The prepared content can then be accessed for creating context-related AR instructions via the marker. The application is characterized by the fact that no developers or experts are required to prepare the information. In addition to complying with common design criteria, the clear presentation of the contents and the intuitive use of the system offer added value for the performance of maintenance tasks. Together, these two elements form a novel way to retrofit legacy machines with AR maintenance instructions. The practical validation of the system took place in a factory environment. For this purpose, the content was created for a filter change on a CNC milling machine. The results show that inexperienced users can extract appropriate content with the software application. Furthermore, it is shown that maintenance workers, can access the content with an AR application developed for the Microsoft HoloLens 2 and complete simple tasks provided in the manufacturer's electronic manual.
In recent years, machine learning algorithms have made a huge development in performance and applicability in industry and especially maintenance. Their application enables predictive maintenance and thus offers efficiency increases. However, a successful implementation of such solutions still requires high effort in data preparation to obtain the right information, interdisciplinarity in teams as well as a good communication to employees. Here, small and medium sized enterprises (SME) often lack in experience, competence and capacity. This paper presents a systematic and practice-oriented method for an implementation of machine learning solutions for predictive maintenance in SME, which has already been validated.
The isothermal curing of melamine resin is investigated by in-line infrared spectroscopy at different temperatures. The infrared spectra are decomposed into time courses of characteristic spectral patterns using Multivariate Curve Resolution (MCR). It was found that depending on the applied curing temperature, melamine films with different spectral fingerprints and correspondingly different chemical network structures are formed. The network structures of fully cured resin films are specific for the applied curing temperatures used and cannot simply be compensated by changes in the curing time. For industrial curing processes, this means that cure temperature is the main system determining factor at constant M:F ratio. However, different MF resin networks can be specifically obtained from one and the same melamine resin by suitable selection of the curing time and temperatures profiles to design resin functionality. The spectral fingerprints after short curing time as well as after long curing time reflect the fundamental differences in the thermoset networks that can be obtained with industrial short-cycle and multi-daylight presses.
The incudo-malleal joint (IMJ) in the human middle ear is a true diarthrodial joint and it has been known that the flexibility of this joint does not contribute to better middle-ear sound transmission. Previous studies have proposed that a gliding motion between the malleus and the incus at this joint prevents the transmission of large displacements of the malleus to the incus and stapes and thus contributes to the protection of the inner ear as an immediate response against large static pressure changes. However, dynamic behavior of this joint under static pressure changes has not been fully revealed. In this study, effects of the flexibility of the IMJ on middle-ear sound transmission under static pressure difference between the middle-ear cavity and the environment were investigated. Experiments were performed in human cadaveric temporal bones with static pressures in the range of +/- 2 kPa being applied to the ear canal (relative to middle-ear cavity). Vibrational motions of the umbo and the stapes footplate center in response to acoustic stimulation (0.2-8 kHz) were measured using a 3D-Laser Doppler vibrometer for (1) the natural IMJ and (2) the IMJ with experimentally-reduced flexibility. With the natural condition of the IMJ, vibrations of the umbo and the stapes footplate center under static pressure loads were attenuated at low frequencies below the middle-ear resonance frequency as observed in previous studies. After the flexibility of the IMJ was reduced, additional attenuations of vibrational motion were observed for the umbo under positive static pressures in the ear canal (EC) and the stapes footplate center under both positive and negative static EC pressures. The additional attenuation of vibration reached 4~7 dB for the umbo under positive static EC pressures and the stapes footplate center under negative EC pressures, and 7~11 dB for the stapes footplate center under positive EC pressures. The results of this study indicate an adaptive mechanism of the flexible IMJ in the human middle ear to changes of static EC pressure by reducing the attenuation of the middle-ear sound transmission. Such results are expected to be used for diagnosis of the IMJ stiffening and to be applied to design of middle-ear prostheses.
The Circular Economy aims to reintroduce the value of products back into the economic cycle at the same value chain level. While the activities of the Circular Economy are already well-defined, there exists a gap in how returned products are treated by the industry. This study aims to examine how a process should be designed to handle returned products in the context of the Circular Economy. To achieve this, a machine learning-based algorithm is used to classify data and extract relevant information throughout the product life cycle. The focus of this research is limited to land transportation systems within the Sharing Economy sector.
Development of an expert system to overpass citizens technological barriers on smart home and living
(2023)
Adopting new technologies can be overwhelming, even for people with experience in the field. For the general public, learning about new implementations, releases, brands, and enhancements can cause them to lose interest. There is a clear need to create point sources and platforms that provide helpful information about the novel and smart technologies, assisting users, technicians, and providers with products and technologies. The purpose of these platforms is twofold, as they can gather and share information on interests common to manufacturers and vendors. This paper presents the ”Finde-Dein-SmartHome” tool. Developed in association with the Smart Home & Living competence center [5] to help users learn about, understand, and purchase available technologies that meet their home automation needs. This tool aims to lower the usability barrier and guide potential customers to clear their doubts about privacy and pricing. Communities can use the information provided by this tool to identify market trends that could eventually lower costs for providers and incentivize access to innovative home technologies and devices supporting long-term care.