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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.
In 2015, the United Nations adopted the Sustainable Development Goals (SDGs), a collection of 17 global objectives to promote economic, social and ecological development in all parts of the world. While the academic discussion on the contribution of companies to the Sustainable Development Goals has recently gained momentum, the role of business-to-business (B2B) partnerships in reaching the SDGs is underexplored, particularly when it comes to North-South relationships. With our research, we aim to fill this gap in the literature by investigating sales partnerships between German manufacturers and their distributors in African markets. Based on a qualitative analysis of 28 interviews with managers of German and African companies, we show that long-term partnerships and job creation, technology transfer, training as well as high standards are significant contributions of companies to achieve the SDGs. While several SDGs such as goals 4,6,13,16 and 17 are addressed by B2B partnership, we also discuss approaches on how the firms’ engagement could be further leveraged and expanded.
This book examines the implementation of the Belt and Road Initiative (BRI) in East Africa. The BRI is considered China's central geopolitical and geo-economic project in the era of President Xi Jinping. Through this work, the author aims to contribute to filling some research gaps, such as the lack of depth in studies of individual BRI projects and the underconsideration of processing narratives in participating countries. The guiding question is the extent to which the BRI is a political or hegemonic project of the CCP-directed state-civil society complex in East Africa. To answer these questions, databases of international organizations and policy documents are analyzed. In addition, the author conducts a qualitative content analysis of newspaper articles from local media houses in the countries of Ethiopia, Kenya, and Tanzania to examine three infrastructure projects. The work illustrates that the BRI contributes to increasing connectivity in East Africa. At the same time, the compression of economic relations and the implementation of infrastructure projects in East Africa lead to numerous consequences and contour a hegemonic project.
The Belt and Road Initiative (BRI) has reinforced China’s business engagement in Sub-Saharan Africa (SSA). While previous international business research focused on the internationalization and investments of Chinese companies, this viewpoint uncovers how both local African and international non-Chinese Small and Medium Sized Enterprises (SMEs) may benefit from and participate in the BRI. A focus is laid on the infrastructure sector accounting for the highest investments since the inception of the BRI in 2013. In a conceptual way, the motives of SMEs to participate in infrastructure project business in the context of the BRI are explored. Investigating the challenges of two large transport infrastructure projects, the business potentials for SMEs become visible. It is argued that SMEs find business potentials particularly as investors, sub-contractors and project management experts in the BRI in Sub-Saharan Africa.
Automatic classification of rotating machinery defects using Machine Learning (ML) algorithms
(2020)
Electric machines and motors have been the subject of enormous development. New concepts in design and control allow expanding their applications in different fields. The vast amount of data have been collected almost in any domain of interest. They can be static; that is to say, they represent real-world processes at a fixed point of time. Vibration analysis and vibration monitoring, including how to detect and monitor anomalies in vibration data are widely used techniques for predictive maintenance in high-speed rotating machines. However, accurately identifying the presence of a bearing fault can be challenging in practice, especially when the failure is still at its incipient stage, and the signal-to-noise ratio of the monitored signal is small. The main objective of this work is to design a system that will analyze the vibration signals of a rotating machine, based on recorded data from sensors, in the time/frequency domain. As a consequence of such substantial interest, there has been a dramatic increase of interest in applying Machine Learning (ML) algorithms to this task. An ML system will be used to classify and detect abnormal behavior and recognize the different levels of machine operation modes. The proposed solution can be deployed as predictive maintenance for Industry 4.0.
Our paper gives first answers on a fundamental question: how can the design of architectures of intelligent digital systems and services be accomplished methodologically? Intelligent systems and services are the goals of many current digitalization efforts today and part of massive digital transformation efforts based on digital technologies. Digital systems and services are the foundation of digital platforms and ecosystems. Digtalization disrupts existing businesses, technologies, and economies and promotes the architecture of open environments. This has a strong impact on new value-added opportunities and the development of intelligent digital systems and services. Digital technologies such as artificial intelligence, the Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, and social enterprise networks systems are important enablers of digitalization. The current publication presents our research on the architecture of intelligent digital ecosystems and products and services influenced by the service-dominant logic. We present original methodological extensions and a new reference model for digital architectures with an integral service and value perspective to model intelligent systems and services that effectively align digital strategies and architectures with artificial intelligence as main elements to support intelligent digitalization.
Enterprises are currently transforming their strategy, processes, and their information systems to extend their degree of digitalization. The potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, artificial intelligence, big data with analytics, mobile systems, collaboration networks, and cyber physical systems both drives and enables new business designs. Digitalization deeply disrupts existing businesses, technologies and economies and fosters the architecture of digital environments with many rather small and distributed structures. This has a strong impact for new value producing opportunities and architecting digital services and products guiding their design through exploiting a Service-Dominant Logic. The main result of the book chapter extends methods for integral digital strategies with value-oriented models for digital products and services which are defined in the framework of a multi-perspective digital enterprise architecture reference model.
Enterprises are presently transforming their strategy, culture, processes, and their information systems to become more digital. The digital transformation deeply disrupts existing enterprises and economies. Digitization fosters the development of IT systems with many rather small and distributed structures, like Internet of Things or mobile systems. Since years a lot of new business opportunities appeared using the potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, collaboration networks, and cyber physical systems. This has a strong impact for architecting digital services and products. The change from a closed-world modeling perspective to more flexible open-world composition and evolution of system architectures defines the moving context for adaptable systems, which are essential to enable the digital transformation. In this paper, we are focusing on a decision-oriented architectural composition approach to support the transformation for digital services and products.
The digitization of our society changes the way we live, work, learn, communicate, and collaborate. This defines the strategical context for composing resilient enterprise architectures for micro-granular digital services and products. The change from a closed-world modeling perspective to more flexible open-world composition and evolution of system architectures defines the moving context for adaptable systems, which are essential to enable the digital transformation. Enterprises are presently transforming their strategy and culture together with their processes and information systems to become more digital. The digital transformation deeply disrupts existing enterprises and economies. Since years a lot of new business opportunities appeared using the potential of the Internet and related digital technologies, like Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, collaboration networks, and cyber physical systems. Digitization fosters the development of IT systems with many rather small and distributed structures, like Internet of Things or mobile systems. In this paper, we are focusing on the continuous bottom-up integration of micro-granular architectures for a huge amount of dynamically growing systems and services, like Internet of Things and Microservices, as part of a new digital enterprise architecture. To integrate micro-granular architecture models to living architectural model versions we are extending more traditional enterprise architecture reference models with state of art elements for agile architectural engineering to support the digitalization of services with related products, and their processes.
This chapter presents an introduction to the emerging trends for architecting the digital transformation having a strong focus on digital products, intelligent services, and related systems together with methods, models and architectures. The primary aim of this book is to highlight some of the most recent research results in the field. We are providing a focused set of brief descriptions of the chapters included in the book.
Current advances in Artificial Intelligence (AI) combined with other digitalization efforts are changing the role of technology in service ecosystems. Human-centered intelligent systems and services are the target of many current digitalization efforts and part of a massive digital transformation based on digital technologies. Artificial intelligence, in particular, is having a powerful impact on new opportunities for shared value creation and the development of smart service ecosystems. Motivated by experiences and observations from digitalization projects, this paper presents new methodological experiences from academia and practice on a joint view of digital strategy and architecture of intelligent service ecosystems and explores the impact of digitalization based on real case study results. Digital enterprise architecture models serve as an integral representation of business, information, and technology perspectives of intelligent service-based enterprise systems to support management and development. This paper focuses on the novel aspect of closely aligned digital strategy and architecture models for intelligent service ecosystems and highlights the fundamental business mechanism of AI-based value creation, the corresponding digital architecture, and management models. We present key strategy-oriented architecture model perspectives for intelligent systems.
Purpose
Artificial intelligence (AI), in particular deep learning (DL), has achieved remarkable results for medical image analysis in several applications. Yet the lack of human-like explanations of such systems is considered the principal restriction before utilizing these methods in clinical practice (Yang, Ye, & Xia, 2022).
Methods
Explainable Artificial Intelligence (XAI) provides a human-explainable and interpretable description of the “black-box” nature of DL (Gulum, Trombley, & Kantardzic, 2021). An effective XAI diagnosis generator, namely NeuroXAI (refer to Fig. 1), has been developed to extract 3D explanations from convolutional neural networks (CNN) models of brain gliomas (Zeineldin et al., 2022). By providing visual justification maps, NeuroXAI can help make DL models transparent and thus increase the trust of medical experts.
Results
NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e. image classification and segmentation using magnetic resonance imaging (MRI). Visual attention maps of multiple XAI methods have been generated and compared for both applications, which could help to provide transparency about the performance of DL systems.
Conclusion
NeuroXAI helps to understand the prediction process of 3D CNN networks for brain glioma using human-understandable explanations. Results revealed that the investigated DL models behave in a logical human-like manner and can improve the analytical process of the MRI images systematically. Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist medical professionals in the detection and diagnosis of brain tumors. NeuroXAI code is publicly accessible at https://github.com/razeineldin/NeuroXAI
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.
Saving energy and road safety became important in the last decades, hence several driving assistant systems were developed that help to improve the driving behaviour. However, these driving systems cover the area of either energy-efficiency or safety. Furthermore, they do not consider the reaction of the driver to a shown recommendation and the driver stress level. In this paper, the decision process of showing a recommendation to the driver in an energy-efficient and safety relevant driving system is presented. The decision process considers the driver's reaction to a shown recommendation and the driver stress in order to increase the user acceptance and the road safety. The results of the evaluation showed that the driving system was able to show recommendations when needed, while suppressing recommendations when the driver ignored a recommendation repeatedly or when the driver was in stress.
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.
This review gives a short overview of the physical processes involved in the formation of the polyelectrolyte multilayers (PEMs) and their destruction. These two processes are vital for the formation of PEMs with desired physical and chemical structures, and for loading them with active substances and their spatial controlled release. It includes a survey of the physical and chemical properties that are key points for controlling film nanostructure in relation to biological processes and different possibilities for controlling cell behavior by means of film composition, bioactivity, mechanical properties, and three-dimensional organization.
Within the scope of the present cumulative doctoral thesis six scientific papers were published which illustrates that modern reaction model-free (=isoconversional) kinetic analysis (ICKA) methods represents a universal and effective tool for the controlled processing of thermosetting materials. In order to demonstrate the universal applicability of ICKA methods, the thermal cure of different thermosetting materials having a very broad range of chemical composition (melamine-formaldehyde resins, epoxy resins, polyester-epoxy resins, and acrylate/epoxy resins) were analyzed and mathematically modelled. Some of the materials were based on renewable resources (an epoxy resin was made from hempseed oil; linseed oil was modified into an acrylate/epoxy resin). With the aid of ICKA methods not only single-step but also complex multi-step reactions were modelled precisely. The analyzed thermosetting materials were combined with wood, wood-based products, paper, and plant fibers which are processed to various final products. Some of the thermosetting materials were applied as coating (in form of impregnated décor papers or powder and wet coatings respectively) on wood substrates and the epoxy resin from hempseed oil was mixed with plant fibers and processed into bio-based composites for lightweight applications. From the final products mechanical, thermal, and surface properties were determined. The activation energy as function of cure conversion derived from ICKA methods was utilized to predict accurately the thermal curing over the course of time for arbitrary cure conditions. Furthermore the cure models were used to establish correlations between the cross-linking during processing into products and the properties of the final products. Therewith it was possible to derive the process time and temperature that guarantee optimal cross-linking as well as optimal product properties
CODE RED FOR HUMANITY. The alarm bells are deafening, and the evidence is irrefutable: greenhouse-gas emissions from fossil-fuel burning and deforestation are choking our planet and putting billions of people at immediate risk. Global heating is affecting every region on Earth, with many of the changes becoming irreversible. (Guterres 2021)
The digitalisation ongoing in households and sustainability-related challenges are multifaceted and complex. The introducing quote of the United Nations Secretary-General refers to the latest report of the Intergovernmental Panel on Climate Change (IPCC), emphasising the urgency to act – now. As of today, becoming a sustainable population is still a distant destination. As outlined in the previous chapters, the challenges associated with that transformation remain huge, complex, and largely unsolved. Recent dramas such as the power incident in Texas (2021), the floods in Germany (2021), or the drought in sub-Saharan Africa (2020s) – are just a few of the uncountable issues stirring up the debate about fossil-fuel abandonment and the timing of climate neutrality. Business research can actually be accused of referring to the persistent focus on gains and growth, despite early warnings for society at large (e.g., Meadows et al., 1972; Kölsch & Veit, 1981; Veit & Thatcher, 2023). However, academic researchers, corporations, and society are now waking up, as shown by the climate change conference. In fact, it appears that the information systems (IS) discipline just began tackling mammoth challenges around climate change within the last decade (Melville, 2010; Watson et al., 2010). The central discussion in emerging work revolves around the role and use of digital technologies on the path to a healthy planet. But while early studies have focused on organisational settings (e.g., Gholami et al., 2016; Seidel et al., 2013), increasingly research addresses private settings (e.g., Wunderlich et al., 2019).
Rats are commonly used in medical research as they enable a high grade of standardization. The exhalome of ventilated rats has not as yet been investigated using an ion mobility spectrometer coupled with a multi-capillary column (MCC-IMS). As a first step, a rat model has to be established to measure potential biomarkers in the exhale with long-term settings, allowing constant and continuous analysis of exhaled air in time series. Therefore, eight animals were anaesthetized, prepared and ventilated for 1 h. A total of 73 peaks were directly detected with the IMS chromatogram. Thirty five of them were assigned to the ventilator system and 38 to the animals. Peak intensity varied within three measurements. The intensity of analytes of individual rats varied by a factor of up to 18. This new model will also enable continuous measurements of volatile organic compounds (VOCs) from rat's breath in long-term experiments. It is hoped that, in the future, variability and progression of VOCs can be monitored in different models of diseases using this set-up.