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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.
The 17 SDGs, as agreed upon by the international community, are designed to be implemented across all levels of human activity. Alongside the level of international politics, this also includes the local levels, national politics, wider society, and the economic sphere. Many channels are called on to further implementation, including the transfer of technology to developing and emerging countries. As the patent holders, this must include the active participation of companies. While the literature examines the important role of technology transfer in North-South business-to-business (B2B) partnerships, studies on the technology transfer between European and African companies are scarce. Therefore, in this study we use original data from 26 interviews conducted with managers engaged in sales partnerships between German manufacturers and their distributors in African markets to examine the existence and forms of technology transfer. We find that training and marketing excellence are the predominant forms of technology transfer and based on that suggest a refinement of established frameworks on B2B technology transfer.
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.
Unternehmen sind derzeit dabei, ihre Strategie, ihre Prozesse und ihre Informationssysteme zu verändern, um ihren Digitalisierungsgrad zu erhöhen. Das Potenzial des Internets und verwandter digitaler Technologien wie Internet der Dinge, Services Computing, Cloud Computing, künstliche Intelligenz, Big Data mit Analysen, mobile Systeme, Kollaborationsnetzwerke und cyber-physikalische Systeme treibt neue Geschäftsmodelle an und ermöglicht sie. Die Digitalisierung führt zu einer tiefgreifenden Umwälzung bestehender Unternehmen, Technologien und Volkswirtschaften und fördert die Architektur digitaler Umgebungen mit vielen eher kleinen und verteilten Strukturen. Dies hat starke Auswirkungen auf neue Wertschöpfungsmöglichkeiten und die Gestaltung digitaler Dienste und Produkte, die durch die Nutzung einer service-dominanten Logik gesteuert werden. Das Hauptergebnis des Buchkapitels erweitert Methoden für integrale digitale Strategien um wertorientierte Modelle für digitale Produkte und Dienstleistungen, die im Rahmen eines multiperspektivischen digitalen Unternehmensarchitektur-Referenzmodells definiert werden.
In diesem Kapitel wird eine Einführung in die sich abzeichnenden Trends bei der Gestaltung der digitalen Transformation gegeben, wobei der Schwerpunkt auf digitalen Produkten, intelligenten Diensten und damit verbundenen Systemen sowie auf Methoden, Modellen und Architekturen liegt. Das primäre Ziel dieses Buches ist es, einige der neuesten Forschungsergebnisse auf diesem Gebiet hervorzuheben. Wir stellen eine Reihe von Kurzbeschreibungen der im Buch enthaltenen Kapitel zur Verfügung.
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.
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively.
Recent advances in artificial intelligence have enabled promising applications in neurosurgery that can enhance patient outcomes and minimize risks. This paper presents a novel system that utilizes AI to aid neurosurgeons in precisely identifying and localizing brain tumors. The system was trained on a dataset of brain MRI scans and utilized deep learning algorithms for segmentation and classification. Evaluation of the system on a separate set of brain MRI scans demonstrated an average Dice similarity coefficient of 0.87. The system was also evaluated through a user experience test involving the Department of Neurosurgery at the University Hospital Ulm, with results showing significant improvements in accuracy, efficiency, and reduced cognitive load and stress levels. Additionally, the system has demonstrated adaptability to various surgical scenarios and provides personalized guidance to users. These findings indicate the potential for AI to enhance the quality of neurosurgical interventions and improve patient outcomes. Future work will explore integrating this system with robotic surgical tools for minimally invasive surgeries.
Intracranial brain tumors are one of the ten most common malignant cancers and account for substantial morbidity and mortality. The largest histological category of primary brain tumors is the gliomas which occur with an ultimate heterogeneous appearance and can be challenging to discern radiologically from other brain lesions. Neurosurgery is mostly the standard of care for newly diagnosed glioma patients and may be followed by radiation therapy and adjuvant temozolomide chemotherapy.
However, brain tumor surgery faces fundamental challenges in achieving maximal tumor removal while avoiding postoperative neurologic deficits. Two of these neurosurgical challenges are presented as follows. First, manual glioma delineation, including its sub-regions, is considered difficult due to its infiltrative nature and the presence of heterogeneous contrast enhancement. Second, the brain deforms its shape, called “brain shift,” in response to surgical manipulation, swelling due to osmotic drugs, and anesthesia, which limits the utility of pre-operative imaging data for guiding the surgery.
Image-guided systems provide physicians with invaluable insight into anatomical or pathological targets based on modern imaging modalities such as magnetic resonance imaging (MRI) and Ultrasound (US). The image-guided toolkits are mainly computer-based systems, employing computer vision methods to facilitate the performance of peri-operative surgical procedures. However, surgeons still need to mentally fuse the surgical plan from pre-operative images with real-time information while manipulating the surgical instruments inside the body and monitoring target delivery. Hence, the need for image guidance during neurosurgical procedures has always been a significant concern for physicians.
This research aims to develop a novel peri-operative image-guided neurosurgery (IGN) system, namely DeepIGN, that can achieve the expected outcomes of brain tumor surgery, thus maximizing the overall survival rate and minimizing post-operative neurologic morbidity. In the scope of this thesis, novel methods are first proposed for the core parts of the DeepIGN system of brain tumor segmentation in MRI and multimodal pre-operative MRI to the intra-operative US (iUS) image registration using the recent developments in deep learning. Then, the output prediction of the employed deep learning networks is further interpreted and examined by providing human-understandable explainable maps. Finally, open-source packages have been developed and integrated into widely endorsed software, which is responsible for integrating information from tracking systems, image visualization, image fusion, and displaying real-time updates of the instruments relative to the patient domain.
The components of DeepIGN have been validated in the laboratory and evaluated in the simulated operating room. For the segmentation module, DeepSeg, a generic decoupled deep learning framework for automatic glioma delineation in brain MRI, achieved an accuracy of 0.84 in terms of the dice coefficient for the gross tumor volume. Performance improvements were observed when employing advancements in deep learning approaches such as 3D convolutions over all slices, region-based training, on-the-fly data augmentation techniques, and ensemble methods.
To compensate for brain shift, an automated, fast, and accurate deformable approach, iRegNet, is proposed for registering pre-operative MRI to iUS volumes as part of the multimodal registration module. Extensive experiments have been conducted on two multi-location databases: the BITE and the RESECT. Two expert neurosurgeons conducted additional qualitative validation of this study through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that the proposed iRegNet is fast and achieves state-of-the-art accuracies. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images, as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.
For the explainability module, the NeuroXAI framework is proposed to increase the trust of medical experts in applying AI techniques and deep neural networks. The NeuroXAI includes seven explanation methods providing visualization maps to help make deep learning models transparent. Experimental findings showed that the proposed XAI framework achieves good performance in extracting both local and global contexts in addition to generating explainable saliency maps to help understand the prediction of the deep network. Further, visualization maps are obtained to realize the flow of information in the internal layers of the encoder-decoder network and understand the contribution of MRI modalities in the final prediction. The explainability process could provide medical professionals with additional information about tumor segmentation results and therefore aid in understanding how the deep learning model is capable of processing MRI data successfully.
Furthermore, an interactive neurosurgical display has been developed for interventional guidance, which supports the available commercial hardware such as iUS navigation devices and instrument tracking systems. The clinical environment and technical requirements of the integrated multi-modality DeepIGN system were established with the ability to incorporate: (1) pre-operative MRI data and associated 3D volume reconstructions, (2) real-time iUS data, and (3) positional instrument tracking. This system's accuracy was tested using a custom agar phantom model, and its use in a pre-clinical operating room is simulated. The results of the clinical simulation confirmed that system assembly was straightforward, achievable in a clinically acceptable time of 15 min, and performed with a clinically acceptable level of accuracy.
In this thesis, a multimodality IGN system has been developed using the recent advances in deep learning to accurately guide neurosurgeons, incorporating pre- and intra-operative patient image data and interventional devices into the surgical procedure. DeepIGN is developed as open-source research software to accelerate research in the field, enable ease of sharing between multiple research groups, and continuous developments by the community. The experimental results hold great promise for applying deep learning models to assist interventional procedures - a crucial step towards improving the surgical treatment of brain tumors and the corresponding long-term post-operative outcomes.
In this paper, it aims to model wind speed time series at multiple sites. The five-parameter Johnson distribution is deployed to relate the wind speed at each site to a Gaussian time series, and the resultant m-dimensional Gaussian stochastic vector process Z(t) is employed to model the temporal-spatial correlation of wind speeds at m different sites. In general, it is computationally tedious to obtain the autocorrelation functions (ACFs) and cross-correlation functions (CCFs) of Z(t), which are different to those of wind speed times series. In order to circumvent this correlation distortion problem, the rank ACF and rank CCF are introduced to characterize the temporal-spatial correlation of wind speeds, whereby the ACFs and CCFs of Z(t) can be analytically obtained. Then, Fourier transformation is implemented to establish the cross-spectral density matrix of Z(t), and an analytical approach is proposed to generate samples of wind speeds at m different sites. Finally, simulation experiments are performed to check the proposed methods, and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds, and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.
Die Coronapandemie hat Deutschland seit dem Frühjahr 2020 fest im Griff. Eine zentrale Maßnahme zur Verlangsamung der Ausbreitung des Coronavirus war von Beginn an die Schließung von Schulen. In einer ersten Studie wurden die Lernzeitverluste durch die Corona-bedingten Schulschließungen im Frühjahr 2020 quantifiziert (Wößmann, Freundl, Lergetporer, Grewenig, Werner & Zierow, 2020). Es zeigte sich, dass sich die Lernzeit der Schülerinnen und Schüler durch die Schulschließungen halbiert hatte und die Verluste bei leistungsschwächeren Schülerinnen und Schülern besonders groß waren. Im Frühjahr 2020 wurde die Verringerung der Lernzeit von den Schulen nicht kompensiert: Nur ein kleiner Teil der Schülerinnen und Schüler hatte in dieser Phase regelmäßigen Distanzunterricht und täglichen Kontakt mit Lehrkräften. Während der Sommer- und Herbstmonate seit der Phase der ersten Schulschließungen hatten Schulverwaltung, Schulen und Lehrkräfte Zeit, sich auf Distanzunterricht und digitale Lehrmethoden umzustellen, um Lernausfällen während etwaiger erneuter Schulschließungen entgegenzuwirken. Inwiefern dies dazu geführt hat, dass die Schülerinnen und Schüler während der Schulschließungen Anfang 2021 tatsächlich mehr Zeit mit Lernen verbracht haben als im Frühjahr 2020, ist jedoch bislang weitgehend unbekannt.
Um zu erfahren, mit welchen Aktivitäten die Schulkinder die Zeit der Schulschließungen Anfang 2021 verbracht haben, wurde erneut eine deutschlandweite Umfrage durchgeführt, diesmal unter mehr als 2.000 Eltern von Schulkindern. Die Ergebnisse liefern umfassende Einblicke in den Alltag von Schulkindern, Eltern und Schulen während der Schulschließungen Anfang 2021. Sie zeigen, wie viele Stunden die Schulkinder in dieser Phase mit Lernen und anderen kreativen und passiven Tätigkeiten verbracht haben, welche konkreten Maßnahmen die Schulen ergriffen haben, um den Schulbetrieb aufrechtzuerhalten, wie effektiv das Lernen zu Hause war, und wie die Eltern das häusliche Lernumfeld einschätzen. Dabei vergleichen wir die Aktivitäten während der Schulschließungen Anfang 2021 mit den Aktivitäten während der ersten Corona-bedingten Schulschließungen im Frühjahr 2020 sowie mit den Aktivitäten vor Corona (vgl. Wößmann et al., 2020). Wir berichten zudem Ergebnisse zum sozio-emotionalen Wohlbefinden der Kinder nach einem Jahr Coronapandemie und zu den Einschätzungen der Eltern, welche breiteren Auswirkungen die Schulschließungen auf verschiedene Lebensbereiche ihrer Kinder haben. Die Befragung liefert somit neue empirische Erkenntnisse über mögliche Folgen der Corona-Krise für den Bildungserfolg von Kindern in Deutschland. Dabei untersuchen wir auch, inwiefern sich die Auswirkungen der Schulschließungen zwischen leistungsstärkeren und -schwächeren Schülerinnen und Schülern sowie zwischen Akademikerkindern und Nicht-Akademikerkindern unterscheiden.
The dawn of the 21st Century has witnessed a tremendous increase in trade pacts among nations, resulting in renewed hopes for sustainable enterprise development in emerging economies worldwide. Ghana and other sub-Saharan African (SSA) countries have signed onto several North-South and South-South free trade agreements with the hope of strengthening their presence in the international trade arena, and to promote economic growth in SSA. For over two decades, however, very little has changed, and many have dashed their high hopes as enterprises continue to struggle in SSA. Not even the African Continental Free Trade Agreement (AfCFTA) could renew the hopes of sceptics. Several studies opined that enterprises in SSA could improve their domestic and international competitiveness by establishing mutually beneficial partnerships with their counterparts from the Global North and South. This study delved into the issues that affect North-South and South-South business collaborations and recommends key success factors that could help promote mutually beneficial cross-border business partnerships. The research includes both literature and empirical information on the key success factors of business partnerships between African enterprises as well as between African enterprises and firms from the Global North. We approached the study qualitatively using a phenomenological research design. Research participants included important stakeholders in Africa and Europe's international trade and sustainable enterprise development ecosystem. The study identified several challenges with the current business collaborations and recommended new ways of making such partnerships more beneficial.
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).
We present the results of an extensive characterization of the performance and stability of a third-order continuous-time delta-sigma modulator with active coefficient error compensation. Using our previously published coefficient tuning technique, process variation induced R-C time-constant (TC) errors in the forward signal path can be compensated indirectly using continuously tunable DACs in the feedback path. To validate our technique experimentally with a range of real TC variations, we designed a modulator with discretely configurable integration capacitor arrays in a 0.35-μm CMOS process. We configured the capacitors of the fabricated device for a range of total TC variations from -28.4 % to +19.3 % and measured the signal-to-noise ratio (SNR) as a function of the input amplitude before and after compensating the variations electrically using the feedback DACs. The results show that our tuning technique is capable of restoring the desired nominal modulator performance over the entire parameter variation range, including the system’s nominal maximum stable amplitude (MSA).
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.
Global trade is plagued by slow and inefficient manual processes associated with physical documents. Firms are constantly looking for new ways to improve transparency and increase the resilience of their supply chains. This can be solved by the digitalisation of supply chains and the automation of document- and information-sharing processes. Blockchain is touted as a solution to these issues due to its unique combination of features, such as immutability, decentralisation and transparency. A lack of business cases that quantify the costs and benefits causes uncertainty regarding the truth of these claims. This paper explores how the costs and benefits of a blockchain-based solution for digitalising and automating documentation flows in cross-border supply chains compare to a conventional centralised relational database solution. The research described in this paper uses primary data collected through semi-structured interviews with industry experts, as well as secondary data from literature. Two models based on existing services were developed and the costs and benefits compared and then analysed using the Architecture Trade-off Analysis Method (ATAM) and the Analytic Network Process (ANP). Findings from the analysis show that a consortium blockchain solution like TradeLens is the favourable solution for digitalising and automating information flows in cross-border supply chains.
The efficient production and utilization of green hydrogen is vital to succeed in the global strive for a sustainable future. To provide the necessary amount of green hydrogen a high number of electrolyzers will be connected as decentralized power consumers to the grid. A large amount of decentralized renewable power sources will provide the energy. In such a system a control method is necessary to dispatch the available power most efficiently. In particular, the shutdown of renewable energy sources due to temporary overproduction must be avoided. This paper presents a decentralized tertiary control algorithm that provides a new decentralized control approach, thus creating a flexible, robust and easily scalable system. The operation of each grid participant within this grid connected microgrid is optimized for maximum financial profit, while minimizing the exchange of power with the mains grid and reducing the shutdown of renewable power sources.
Simulation eines dezentralen Regelungssystems zur netzdienlichen Erzeugung von grünem Wasserstoff
(2023)
Wasserstoff wird einen bedeutenden Beitrag zum Wandel von Industrie und Gesellschaft in eine klimaneutrale Zukunft leisten. Der Aufbau und die ökologisch und ökonomisch sinnvolle Nutzung einer Wasserstoffinfrastruktur sind hierbei die zentralen Herausforderungen. Ein notwendiger Baustein ist die effiziente Bereitstellung von grünem Strom und dem daraus produzierten grünen Wasserstoff. Der vorliegende Beitrag stellt ein dezentrales Regel- und Kommunikationssystem vor, mit dem Angebot und Nachfrage von grünem Strom und Wasserstoff in einem System aus dezentralen Akteuren in Einklang gebracht werden. In einer hierzu entwickelten Simulationsumgebung wird die Funktion und der Nutzen dieses dezentralen Ansatzes verdeutlicht.
Natural wood colors occur within a wide range from almost white (e.g., white poplar), various yellowish, reddish, and brownish hues to almost black (e.g., ebony). The intrinsic color of wood is basically defined by its chemical composition. However, other factors such as specific anatomical formations or physical properties further affect the optical impression. Starting with the chemical composition of wood and anatomical basics, wood color and its modifications are discussed in this chapter. The classic method of coloring or re-coloring wood-based material surfaces is the application of a coating containing appropriate dyes or pigments. Different concepts for wood coating and coloration are presented. Another method used dyes for coloration of the wood structure. As alternative techniques, physical methods, for example, drying, steaming, ammoniation, bleaching, enzyme treatment, as well as treatment with electromagnetic irradiation (e.g., UV), are explained in this chapter.