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In the upcoming years, huge benefits are expected from Artificial Intelligence (AI). However, there are also risks involved in the technology, such as accidents of autonomous vehicles or discrimination by AI-based recruitment systems. This study aims to investigate public perception of these risks, focusing on realistic risks of Narrow AI, i.e., the type of AI that is already productive today. Based on perceived risk theory, several risk scenarios are examined using data from an exploratory survey. This research shows that AI is perceived positively overall. The participants, however, do evaluate AI critically when being confronted with specific risk scenarios. Furthermore, a strong positive relationship between knowledge about AI and perceived risk could be shown. This study contributes to knowledge by advancing our understanding of the awareness and evaluation of the risks by consumers and has important implications for product development, marketing and society.
The situation in the markets is changing rapidly and competition in the business sector is increasing rapidly. As a result, corporate marketing decisions are based on creating greater value for the consumer, which creates competitiveness and provides an advantage in competing for future customer loyalty. The purpose of this study is to determine whether there is a link between marketing communication tools and consumer perceived value in pursuit of consumer loyalty. Qualitative (observational research) and quantitative (a questionnaire survey) research methods were used to investigate the problem empirically. The observational research elucidated the value provided to the consumer by the research objects through marketing communication tools, supplementing the key questions for the quantitative study. Correlation and regression analysis were used in the study, with the results showing a statistically significant relationship between marketing communication tools and consumer perceived value in terms of user loyalty. It has also been determined that the greatest and strongest relationship in consumer value creation through marketing communication tools is the appropriate, mutually coordinated and complementary use of a package of marketing communication tools to achieve synergies that create the preconditions for increasing consumer loyalty in a competitive market.
Access to clinical information during interventions is an important aspect to support the surgeon and his team in the OR. The OR-Pad research project aims at displaying clinically relevant information close to the patient during surgery. With the OR-Pad system, the surgeon shall be able to access case-specific information, displayed on a sterile-packaged, portable display device. Therefore, information shall be prepared before surgery and also be available afterwards. The project follows an user-centered design process. Within the third iteration, the interaction concept was finalized, resulting in an application that can be used in two modes, mobile and intraoperative, to support the surgeon before/after and during surgery, respectively. By supporting the surgeon perioperatively, it is expected to improve the information situation in the OR and thereby the quality of surgical results. Based on this concept, the system architecture was designed in detail, using a client-server architecture. Components, communication interfaces, exchanged data, and intended standards for data exchange of the OR-Pad system including connecting systems were conceived. Expert interviews by using a clickable prototype were conducted to evaluate the concepts.
Introduction
Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed.
Objective
This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection.
Methods
Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions.
Results
The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods.
Conclusions
The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.
Towards Automated Surgical Documentation using automatically generated checklists from BPMN models
(2021)
The documentation of surgeries is usually created from memory only after the operation, which is an additional effort for the surgeon and afflicted with the possibility of imprecisely, shortend reports. The display of process steps in the form of checklists and the automatic creation of surgical documentation from the completed process steps could serve as a reminder, standardize the surgical procedure and save time for the surgeon. Based on two works from Reutlingen University, which implemented the creation of dynamic checklists from Business Process Modelling Notation (BPMN) models and the storage of times at which a process step was completed, a prototype was developed for an android tablet, to expand the dynamic checklists by functions such as uploading photos and files, manual user entries, the interception of foreseeable deviations from the normal course of operations and the automatic creation of OR documentation.
Purpose
Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research.
Methods
Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods.
Results
The developed Slicer-DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches.
Conclusions
Developed Slicer-DeepSeg allows the development of novel AI-assisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg.
Die Bereitstellung klinischer Informationen im Operationssaal ist ein wichtiger Aspekt zur Unterstützung des chirurgischen Teams. Die roboter-assistierte Ösophagusresektion ist ein besonders komplexer Eingriff, der Potenzial zur workflowbasierten Unterstützung bietet. Wir präsentieren erste Ergebnisse der Entwicklung eines Checklisten-Tools mit der zugrundeliegenden Modellierung des chirurgischen Workflows und Informationsbedarf der Chirurgen. Das Checklisten-Tool zeigt hierfür die durchzuführenden Schritte chronologisch an und stellt zusätzliche Informationen kontextadaptiert bereit. Eine automatische Dokumentation von Start- und Endzeiten einzelner OP-Phasen und Schritte soll zukünftige Prozessanalysen der Operation ermöglichen.
Lehre und Lernen unterliegt einem stetigen Wandel, wobei Interaktion als ein zentrales Element der Motivationssteigerung im Lernkontext angesehen wird. Der vorliegende Beitrag zeigt verschiedene Ansätze zur Gestaltung von interaktivem und kollaborativem Lehren und Lernen in einem virtuellen Klassenzimmer auf und stellt ein Beispiel für die Umsetzung und den Einsatz eines solchen Systems vor. Die Mehrwerte und Erfolgsfaktoren, die sich beim Einsatz virtueller Klassenzimmer und deren Gestaltung in Form einer interaktiven blended-learning Umgebung ergeben, werden dargestellt und diskutiert. Mit dem System Accelerator wird eine CSILT (Computer Supported Interactive Learning and Teaching)-Umgebung vorgestellt, in der diese Faktoren zum Einsatz kommen.
Avatars are in use when interacting in virtual environments in different contexts, in collaborative work, as well as in gaming and also in virtual meetings with friends. Therefore it is important to understand how the relationship between user and avatar works. In this study, an online survey is used to determine how the perception of an avatar changes in different contexts by relating it to existing avatar relationship typologies. Additionally, it is determined whether in each context a realistic, abstract or comic-like representation is preferred by the participants. One result was a preference of low poly representations in the work context, which are associated with the perception of the avatar as a tool. In the context of meeting friends, a realistic representation is perceived as more appropriate, which is perceived as an accurate self-representation. In the gaming context, the results are less clear, which can be attributed to different gaming preferences. Here, unlike in the other contexts, a comic-like representation is also perceived as appropriate, which is associated with the perception of the avatar as a friend. A symbiotic user-avatar relationship is not directly related to any form of representation, but always lies in the midfield, which is attributed to the fact that it represents a whole spectrum between other categories.
The disruptive potential of digital transformation (DT) has been widely discussed in scholarly literature and practitioner-oriented discourses. The management control (MC) function is an important corporate function, as it provides transparency on the economic situation of a firm. DT challenges MC in a two-fold and reciprocal nature as it (i) changes the MC function itself as well as (ii) the entire firm and its business models, which needs to be accompanied by the MC function. Given the complexity and variety of phenomena within the developments in the context of DT, a comprehensive management approach is essential. Surprisingly, there exist few convincing approaches, which support a comprehensive management of the DT. The objectives of this paper are therefore to discuss the impact of DT on MC, as well as, to develop a framework to control DT of an organization from a MC perspective. Based on a literature review and conceptual research, our study contributes to knowledge by proposing an initial, preliminary conceptual framework to manage DT, from a MC perspective. The framework highlights important dimensions that should be considered in the management of DT, for example related to processes and MC instruments.
The main aim of presented in this manuscript research is to compare the results of objective and subjective measurement of sleep quality for older adults (65+) in the home environment. A total amount of 73 nights was evaluated in this study. Placing under the mattress device was used to obtain objective measurement data, and a common question on perceived sleep quality was asked to collect the subjective sleep quality level. The achieved results confirm the correlation between objective and subjective measurement of sleep quality with the average standard deviation equal to 2 of 10 possible quality points.
Health monitoring in a home environment can have broader use since it may provide continuous control of health parameters with relatively minor intrusiveness into regular life. This work aims to verify if it is possible to replace the typical in some sleep medicine areas subjective questioning by an objective measurement using electronic devices. For this purpose, a study was conducted with ten subjects, in which objective and subjective measurement of relevant sleep parameters took place. The results of both measurement methods were evaluated and analyzed. The results showed that while for some measures, such as Total Time in Bed, there is a high agreement between objective and subjective measurements, for others, such as sleep quality, there are significant differences. For this reason, currently, a combination of both measurement methods may be beneficial and provide the most detailed results, while a partial replacement can already reduce the number of questions at the subjective measurement by measurement through electronic devices.
The present work proposes the use of modern ICT technologies such as smartphones, NFCs, internet, and web technologies, to help patients in carrying out their therapies. The implemented system provides a calendar with a reminder of the assumptions, ensures the drug identification through NFC, allows remote assistance from healthcare staff and family members to check and manage the therapy in real-time. The system also provides centralized information on the patient's therapeutic situation, helpful in choosing new compatible therapies.
Background: One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.
Objective: This paper aims to review the feasibility of blockchain technology for telemedicine.
Methods: The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).
Results: Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).
Conclusions: These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.
Preliminary results of homomorphic deconvolution application to surface EMG signals during walking
(2021)
Homomorphic deconvolution is applied to sEMG signals recorded during walking. Gastrocnemius lateralis and tibialis anterior signals were acquired according to SENIAM recommendation. MUAP parameters like amplitude and scale were estimated, whilst the MUAP shape parameter was fixed. This features a useful time-frequency representation of sEMG signal. Estimation of scale MUAP parameter was verified extracting the mean frequency of filtered EMG signal, extracted from the scale parameter estimated with two different MUAP shape values.
Normal breathing during sleep is essential for people’s health and well-being. Therefore, it is crucial to diagnose apnoea events at an early stage and apply appropriate therapy. Detection of sleep apnoea is a central goal of the system design described in this article. To develop a correctly functioning system, it is first necessary to define the requirements outlined in this manuscript clearly. Furthermore, the selection of appropriate technology for the measurement of respiration is of great importance. Therefore, after performing initial literature research, we have analysed in detail three different methods and made a selection of a proper one according to determined requirements. After considering all the advantages and disadvantages of the three approaches, we decided to use the impedance measurement-based one. As a next step, an initial conceptual design of the algorithm for detecting apnoea events was created. As a result, we developed an activity diagram on which the main system components and data flows are visually represented.
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.
Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.
We present the literature review of non-invasive sound acquisition devices and techniques.
The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.
Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
The energy sector in Germany, as in many other countries, is undergoing a major transformation. To achieve the climate targets, numerous measures to implement smart energy and resource efficiency are necessary. Therefore, energy companies are experiencing increasing pressure from politics and society to transform their business areas in a sustainable manner and implement smart and sustainable business models. Consequently, numerous resources are expected to flow into the development and implementation of new business models. But often these efforts remain unsuccessful in practice. There is a large amount of literature on barriers and drivers of smart and sustainable business models in the energy sector. But what are the factors that companies struggle with most when developing and implementing new business models in practice? To answer this question, the results of a systematic literature review were evaluated by conducting semi-structured interviews with experts of the German energy sector. Six categories of transformation barriers were identified: Organizational, Financial, Legal, Partner-Network, Societal and Technological barriers. To overcome these barriers, recommendations for action and key success factors are outlined by the experts interviewed. The interview study validates key barriers and drivers in terms of their significance in practice in the German energy sector and makes recommendations to advance the smart and sustainable transformation of the energy sector.
A hybrid deep registration of MR scans to interventional ultrasound for neurosurgical guidance
(2021)
Despite the recent advances in image-guided neurosurgery, reliable and accurate estimation of the brain shift still remains one of the key challenges. In this paper, we propose an automated multimodal deformable registration method using hybrid learning-based and classical approaches to improve neurosurgical procedures. Initially, the moving and fixed images are aligned using classical affine transformation (MINC toolkit), and then the result is provided to the convolutional neural network, which predicts the deformation field using backpropagation. Subsequently, the moving image is transformed using the resultant deformation into a moved image. Our model was evaluated on two publicly available datasets: the retrospective evaluation of cerebral tumors (RESECT) and brain images of tumors for evaluation (BITE). The mean target registration errors have been reduced from 5.35 ± 4.29 to 0.99 ± 0.22 mm in the RESECT and from 4.18 ± 1.91 to 1.68 ± 0.65 mm in the BITE. Experimental results showed that our method improved the state-of-the-art in terms of both accuracy and runtime speed (170 ms on average). Hence, the proposed method provides a fast runtime for 3D MRI to intra-operative US pair in a GPU-based implementation, which shows a promise for its applicability in assisting the neurosurgical procedures compensating for brain shift.
Bausparverträge sind kombinierte Spar- und Finanzierungsinstrumente, die für die breite Bevölkerung ausgelegt sind. Im Jahr 2020 umfasste der Bestand an Bausparverträgen in Deutschland ca. 25 Mio. Verträge. Ein wesentlicher Teil der Attraktivität des Bausparvertrags für Kunden liegt in der hohen Flexibilität dieser Finanzprodukte, die im Vertragsablauf eine flexible Anpassung an individuelle Finanzierungsbedingungen ermöglicht. In der Sparphase sind das insbesondere Möglichkeiten zur Erhöhung, Ermäßigung und Teilung der Verträge sowie zur relativ flexiblen Anpassung der Sparrate. Bei einem zuteilungsreifen Vertrag kann die Sparphase innerhalb bestimmter zeitlicher Grenzen fortgesetzt werden. In der Darlehensphase sind flexible Sondertilgungen jederzeit und ohne Vorfälligkeitsentschädigung möglich.
Die Vielzahl eingebetteter Optionen beeinflussen sich wechselseitig und müssen in ihrer Wirkungsweise immer gesamthaft betrachtet und gesteuert werden. Die empirische Erfahrung der letzten Jahrzehnte zeigt bezüglich der Optionsausübung ein Kundenverhalten, das sich zwar an finanzmathematischen Überlegungen orientiert, aber nicht vollständig finanzrational abläuft.
The digital transformation is today’s dominant business transformation having a strong influence on how digital services and products are designed in a service-dominant way. A popular underlying theory of value creation and economic exchange that is known as the service-dominant (S-D) logic can be connected to many successful digital business models. However, S-D logic by itself is abstract. Companies cannot directly use it as an instrument for business model innovation and design in an easy way. To address this a comprehensive ideation method based on S-D logic is proposed, called service-dominant design (SDD). SDD is aimed at supporting firms in the transition to a service- and value-oriented perspective. The method provides a simplified way to structure the ideation process based on four model components. Each component consists of practical implications, auxiliary questions and visualization techniques that were derived from a literature review, a use case evaluation of digital mobility and a focus group discussion. SDD represents a first step of having a toolset that can support established companies in the process of service- and value-orientation as part of their digital transformation efforts.
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.
Due to digitalization, constant technological progress and ever shorter product life cycles, enterprises are currently facing major challenges. In order to succeed in the market, business models have to be adapted more often and more quickly to changing market conditions than they used to be. Fast adaptability, also called agility, is a decisive competitive factor in today’s world. Because of the ever-growing IT part of products and the fact that they are manufactured using IT, changing the business model has a major impact on the enterprise architecture (EA). However, developing EAs is a very complex task, because many stakeholders with conflicting interests are involved in the decision-making process. Therefore, a lot of collaboration is required. To support organizations in developing their EA, this article introduces a novel integrative method that systematically integrates stakeholder interests into decision-making activities. By using the method, collaboration between stakeholders involved is improved by identifying points of contact between them. Furthermore, standardized activities make decision-making more transparent and comparable without limiting creativity.
Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and services.
In this paper we describe an interactive web-based tool for visual analysis of Formula 1 data. A calendar-like representation provides an overview of all races on a yearly basis, either in absolute or normalized time. After selecting a dedicated race more details about this race can be explored. Furthermore it is possible to compare up to three different races. Beside visualizing details on dedicated races it is also possible to analyse driver and team performance over time. A user study was applied to get feedback about the usage of the application and decide between different visualization options.
Context
Microservices as a lightweight and decentralized architectural style with fine-grained services promise several beneficial characteristics for sustainable long-term software evolution. Success stories from early adopters like Netflix, Amazon, or Spotify have demonstrated that it is possible to achieve a high degree of flexibility and evolvability with these systems. However, the described advantageous characteristics offer no concrete guidance and little is known about evolvability assurance processes for microservices in industry as well as challenges in this area. Insights into the current state of practice are a very important prerequisite for relevant research in this field.
Objective
We therefore wanted to explore how practitioners structure the evolvability assurance processes for microservices, what tools, metrics, and patterns they use, and what challenges they perceive for the evolvability of their systems.
Method
We first conducted 17 semi-structured interviews and discussed 14 different microservice-based systems and their assurance processes with software professionals from 10 companies. Afterwards, we performed a systematic grey literature review (GLR) and used the created interview coding system to analyze 295 practitioner online resources.
Results
The combined analysis revealed the importance of finding a sensible balance between decentralization and standardization. Guidelines like architectural principles were seen as valuable to ensure a base consistency for evolvability and specialized test automation was a prevalent theme. Source code quality was the primary target for the usage of tools and metrics for our interview participants, while testing tools and productivity metrics were the focus of our GLR resources. In both studies, practitioners did not mention architectural or service-oriented tools and metrics, even though the most crucial challenges like Service Cutting or Microservices Integration were of an architectural nature.
Conclusions
Practitioners relied on guidelines, standardization, or patterns like Event-Driven Messaging to partially address some reported evolvability challenges. However, specialized techniques, tools, and metrics are needed to support industry with the continuous evaluation of service granularity and dependencies. Future microservices research in the areas of maintenance, evolution, and technical debt should take our findings and the reported industry sentiments into account.
In recent years, artificial intelligence (AI) has increasingly become a relevant technology for many companies. While there are a number of studies that highlight challenges and success factors in the adoption of AI, there is a lack of guidance for firms on how to approach the topic in a holistic and strategic way. The aim of this study is therefore to develop a conceptual framework for corporate AI strategy. To address this aim, a systematic literature review of a wide spectrum of AI-related research is conducted, and the results are analyzed based on an inductive coding approach. An important conclusion is that companies should consider diverse aspects when formulating an AI strategy, ranging from technological questions to corporate culture and human resources. This study contributes to knowledge by proposing a novel, comprehensive framework to foster the understanding of crucial aspects that need to be considered when using the emerging technology of AI in a corporate context.
This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen’s κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.
Enterprise architecture (EA) is useful for promoting digital transformation in global companies and information societies. In this paper, the authors investigated and analyzed the process for digital transformation in global companies, together with related work in using and applying an enterprise architecture framework for the digital era named the adaptive integrated digital architecture framework (AIDAF). Moreover, they position the AIDAF framework for processing digital transformation in global companies. Based on this analysis, the authors propose and describe a new enterprise architecture process for promoting digital transformation in global companies. Furthermore, the authors propose an adaptive EA cycle-based architecture board framework on digital platforms, while verifying them with case studies in global companies. Finally, the authors clarify the challenges and critical success factors of the process and framework for digital transformation with architecture board reviews in the adaptive EA cycle to assist EA practitioners with its implementation.
Enterprise architecture (EA) is useful for effectively structuring digital platforms with digital transformation in information societies. Moreover, digital platforms in the healthcare industry accelerate and increase the efficiency of drug discovery and development processes. However, there is the lack of knowledge concerning relationships between EA and digital platforms, in spite of the needs of it. In this paper, we investigated and analyzed the process of drug design and development within the healthcare industry, together with related work in using an enterprise architecture framework for the digital era named the Adaptive Integrated Digital Architecture Framework (AIDAF), specifically supporting the design of digital platforms there. Based on this analysis, we evaluate a method and propose a new reference architecture for promoting digital platforms in the healthcare industry, with future specific aspects of them making effective use of Artificial Intelligence (AI). The practical and theoretical contributions include: (1) Streamlined processes through digital platforms in organizations. (2) Informal knowledge supply and sharing among organizational members through digital platforms. (3) Efficiency and effectiveness in planning production and business for drug development. The findings indicate that EA with digital platforms using the AIDAF contribute to digital transformation with effectiveness for new drugs in the healthcare industry.
Theory and practice of implementing a successful enterprise IoT strategy in the industry 4.0 era
(2021)
Since the arrival of the internet and affordable access to technologies, digital technologies have occupied a growing place in industries, propelling us towards a 4th industrial revolution: Industry 4.0. In today’s era of digital upheaval, enterprises are increasingly undergoing transformations that are leading to their digitalization. The traditional manufacturing industry is in the throes of a digital transformation that is accelerated by exponentially growing technologies (e.g., intelligent robots, Internet of Things, sensors, 3D printing). Around the world, enterprises are in a frantic race to implement solutions based on IoT to improve their productivity, innovation, and reduce costs and improve their markets on the international scene. Considering the immense transformative potential that IoTs and big data have to bring to the industrial sector, the adoption of IoT in all industrial systems is a challenge to remain competitive and thus transform the industry into a smart factory. This paper presents the description of the innovation and digitalization process, following the Industry 4.0 paradigm to implement a successful enterprise IoT strategy.
Enterprises and information societies confront crucial challenges currently, while Industry 4.0 becomes important in the global manufacturing industry and Society 5.0 should contribute to a supersmart society, especially for healthcare. Physical activity monitoring digital platforms are architected to improve the healthcare status of patients with diabetes and other lifestyle-related diseases. Furthermore, digital platforms are expected to generate profits for health technology companies and help control costs in the healthcare ecosystem. However, current digital enterprise architecture approaches are not well-established, and the potentials have not yet been realized. Design thinking approach and agile software development methodologies can overcome these limitations, beginning with proof of concept and pilot projects and then scaling to the production environment. In this paper, we describe how that the adaptive integrated digital architecture framework (AIDAF) for Design Thinking approach is proposed and verified in a case of a university hospital in the Americas. In addition, challenges and future activities for this area are discussed that cover the directions for Society 5.0.
The Internet of Things (IoT) is coined by many different standards, protocols, and data formats that are often not compatible to each other. Thus, the integration of different heterogeneous (IoT) components into a uniform IoT setup can be a time-consuming manual task. This lacking interoperability between IoT components has been addressed with different approaches in the past. However, only very few of these approaches rely on Machine Learning techniques. In this work, we present a new way towards IoT interoperability based on Deep Reinforcement Learning (DRL). In detail, we demonstrate that DRL algorithms, which use network architectures inspired by Natural Language Processing (NLP), can be applied to learn to control an environment by merely taking raw JSON or XML structures, which reflect the current state of the environment, as input. Applied to IoT setups, where the current state of a component is often reflected by features embedded into JSON or XML structures and exchanged via messages, our NLP DRL approach eliminates the need for feature engineering and manually written code for pre-processing of data, feature extraction, and decision making.
Several studies analyzed existing Web APIs against the constraints of REST to estimate the degree of REST compliance among state-of-the-art APIs. These studies revealed that only a small number of Web APIs are truly RESTful. Moreover, identified mismatches between theoretical REST concepts and practical implementations lead us to believe that practitioners perceive many rules and best practices aligned with these REST concepts differently in terms of their importance and impact on software quality. We therefore conducted a Delphi study in which we confronted eight Web API experts from industry with a catalog of 82 REST API design rules. For each rule, we let them rate its importance and software quality impact. As consensus, our experts rated 28 rules with high, 17 with medium, and 37 with low importance. Moreover, they perceived usability, maintainability, and compatibility as the most impacted quality attributes. The detailed analysis revealed that the experts saw rules for reaching Richardson maturity level 2 as critical, while reaching level 3 was less important. As the acquired consensus data may serve as valuable input for designing a tool-supported approach for the automatic quality evaluation of RESTful APIs, we briefly discuss requirements for such an approach and comment on the applicability of the most important rules.
Science-based analysis for climate action: how HSBC Bank uses the En-ROADS climate policy simulation
(2021)
In 2018, the Intergovernmental Panel on Climate Change (IPCC, 2018) found that rapid decarbonization and net negative greenhouse gas (GHG) emissions by mid-century are required to "hold the increase in global average temperature to well below 2°C above pre-industrial levels and pursue efforts to limit the temperature increase to 1.5°C," as stipulated by the Paris Agreement (UNFCCC, 2015, p. 2). Meeting these goals reduces physical climate-related risks from, for example, sea-level rise, ocean acidification, extreme weather, water shortages, declining crop yields, and other impacts. These impacts threaten our economy, security, health, and lives.
At the same time, policies to mitigate these harms by rapidly reducing GHG emissions can create transition risks for businesses - for example, stranded assets and loss of market value for fossil fuel producers and firms dependent on fossil energy (Carney, 2019). Rapid decarbonization requires an unprecedented energy transition (IEA, 2021a) driven by and affecting economic players including businesses, asset managers, and investors in all sectors and all countries (Kriegler et al., 2014).
However, GHG emissions are not falling rapidly enough to meet the goals of the Paris Agreement (Holz et al., 2018). The UNFCCC, 2021 found that the emissions reductions pledged by all nations as of early 2021 "fall far short of what is required, demonstrating the need for Parties to further strengthen their mitigation commitments under the Paris Agreement" (2021, p. 5). Businesses are faring no better. Despite high-profile calls to action from influential firms such as BlackRock (Fink, 2018, 2021), corporate action to meet climate goals has thus far fallen short (e.g. the Right, 2019 analysis of the German DAX 30 companies' emissions targets by NGO "right."). Instead of implementing climate strategies that might mitigate the risks, managers are often caught up in "firefighting" and capability traps that erode the resources needed for ambitious climate action (Sterman, 2015). Firms may also exaggerate environmental accomplishments, leading to greenwashing (Lyon and Maxwell, 2011); implement policies that are vague, rely on unproven offsets, or are not climate neutral (e.g. Sterman et al., 2018); or simply take no action at all (Delmas and Burbano, 2011; Sterman, 2015).
Adding to the confusion are difficulties evaluating the effectiveness of different climate policies. Misperceptions include wait-and-see approaches (Dutt and Gonzalez, 2012; Sterman, 2008), underestimating time delays and ignoring the unintended consequences of policies (Sterman, 2008), and beliefs in "silver bullet" solutions (Gilbert, 2009; Kriegler et al., 2013; Shackley and Dütschke, 2012). These beliefs arise in part because the climate–energy system is a high-dimensional dynamic system characterized by long time delays, multiple feedback loops, and nonlinearities (Sterman, 2011), while even simple systems are difficult for people to understand (Booth Sweeney and Sterman, 2000; Cronin et al., 2009; Kapmeier et al., 2017). Although senior executives might receive briefings on climate change, simply providing more information does not necessarily lead to more effective action (Pearce et al., 2015; Sterman, 2011).
Alternatively, interactive approaches to learning about climate change and policies to mitigate it can trigger climate action (Creutzig and Kapmeier, 2020). Decision-makers require tools and methods grounded in science that enable them to learn for themselves how a low-carbon economy can be achieved and how climate policies condition physical and transition risks. The system dynamics climate–energy simulation En-ROADS (Energy-Rapid Overview and Decision Support; Jones et al., 2019b), codeveloped by the climate think-tank Climate Interactive and the MIT Sloan Sustainability Initiative, provides such a tool.
Here we show how En-ROADS helps HSBC Bank U.S.A., the American subsidiary of U.K.-based multinational financial services company HSBC Holdings plc, focus its global sustainability strategy on activities with higher impact and relevance, communicate and implement the strategy, understand transition risks, and better align the strategy with global climate goals. We show how the versatility and interactivity of En-ROADS increases its reach throughout the organization. Finally, we discuss challenges and lessons learned that may be helpful to other organizations.
Many modern DBMS architectures require transferring data from storage to process it afterwards. Given the continuously increasing amounts of data, data transfers quickly become a scalability limiting factor. Near-Data Processing and smart/computational storage emerge as promising trends allowing for decoupled in-situ operation execution, data transfer reduction and better bandwidth utilization. However, not every operation is suitable for an in-situ execution and a careful placement and optimization is needed.
In this paper we present an NDP-aware cost model. It has been implemented in MySQL and evaluated with nKV. We make several observations underscoring the need for optimization.
Near-Data Processing is a promising approach to overcome the limitations of slow I/O interfaces in the quest to analyze the ever-growing amount of data stored in database systems. Next to CPUs, FPGAs will play an important role for the realization of functional units operating close to data stored in non-volatile memories such as Flash.It is essential that the NDP-device understands formats and layouts of the persistent data, to perform operations in-situ. To this end, carefully optimized format parsers and layout accessors are needed. However, designing such FPGA-based Near-Data Processing accelerators requires significant effort and expertise. To make FPGA-based Near-Data Processing accessible to non-FPGA experts, we will present a framework for the automatic generation of FPGA-based accelerators capable of data filtering and transformation for key-value stores based on simple data-format specifications.The evaluation shows that our framework is able to generate accelerators that are almost identical in performance compared to the manually optimized designs of prior work, while requiring little to no FPGA-specific knowledge and additionally providing improved flexibility and more powerful functionality.
In this paper, we propose a radical new approach for scale-out distributed DBMSs. Instead of hard-baking an architectural model, such as a shared-nothing architecture, into the distributed DBMS design, we aim for a new class of so-called architecture-less DBMSs. The main idea is that an architecture-less DBMS can mimic any architecture on a per-query basis on-the-fly without any additional overhead for reconfiguration. Our initial results show that our architecture-less DBMS AnyDB can provide significant speedup across varying workloads compared to a traditional DBMS implementing a static architecture.
We examine the role of communication from users on dropout from digital learning systems to answer the following questions: (1) how does the sentiment within qualitative signals (user comments) affect dropout rates? (2) does the variance in the proportion of positive and negative sentiments affect dropout rates? (3) how do quantitative signals (e.g. likes) moderate the effect of the qualitative signals? and (4) how does the effect of qualitative signals on dropout rates change across early and late stages of learning? Our hypotheses draws from learning theory and self-regulation theory, and were tested using data of 447 learning videos across 32 series of online tutorials, spanning 12 different fields of learning. The findings indicate a main effect of negative sentiment on dropout rates but no effect of positive sentiment on preventing dropout behaviour. This main effect is stronger in the early stages of learning and weakens at later stages. We also observe an effect of the extent of variance of positive and negative sentiments on dropout behaviour. The effects are negatively moderated by quantitative signals. Overall, making commenting more broad-based rather than polarised can be a useful strategy in managing learning, transferring knowledge, and building consensus.
The increasing urban population growth leads to challenges in cities in many aspects: Urbanisation problems such as excessive environmental pollution or increasing urban traffic demand new and innovative solutions. In this context, the concept of smart cities is discussed. An enabling element of the smart city concept is applying information technology (IT) to improve administrative efficiency and quality of life while reducing costs and resource consumption and ensuring greater citizen participation in administrative and urban development issues. While these smart city services are technologically studied and implemented, government officials, citizens or businesses are often unaware of the large variety of smart city service solutions. Therefore, this work deals with developing a smart city services catalogue that documents best practice services to create a platform that brings citizens, city government, and businesses together. Although the concept of IT service catalogues is not new and guidelines and recommendations for the design and development of service catalogues already exist in the corporate context, there is little work on smart city service catalogues. Therefore, approaches from agile software development and pattern research were adapted to develop the smart city service catalogue platform in this work.
Context: Many companies are facing an increasingly dynamic and uncertain market environment, making traditional product roadmapping practices no longer sufficiently applicable. As a result, many companies need to adapt their product roadmapping practices for continuing to operate successfully in today’s dynamic market environment. However, transforming product roadmapping practices is a difficult process for organizations. Existing literature offers little help on how to accomplish such a process.
Objective: The objective of this paper is to present a product roadmap transformation approach for organizations to help them identify appropriate improvement actions for their roadmapping practices using an analysis of their current practices.
Method: Based on an existing assessment procedure for evaluating product roadmapping practices, the first version of a product roadmap transformation approach was developed in workshops with company experts. The approach was then given to eleven practitioners and their perceptions of the approach were gathered through interviews.
Results: The result of the study is a transformation approach consisting of a process describing what steps are necessary to adapt the currently applied product roadmapping practice to a dynamic and uncertain market environment. It also includes recommendations on how to select areas for improvement and two empirically based mapping tables. The interviews with the practitioners revealed that the product roadmap transformation approach was perceived as comprehensible, useful, and applicable. Nevertheless, we identified potential for improvements, such as a clearer presentation of some processes and the need for more improvement options in the mapping tables. In addition, minor usability issues were identified.
How to prioritize your product roadmap when everything feels important: a grey literature review
(2021)
Context: A key factor in achieving product success is to identify what and in which order outputs must be launched in order to deliver the most value to the customer and the business. Therefore, a well-established process to discover and prioritize the content of the product roadmap in the right way is crucial for the success of a company. However, most companies prioritize their product roadmap items based on opinions of experts or the management. Additionally, increasing market dynamics, rapidly evolving technologies and fast changing customer behavior complicate the conduction of the prioritization process. Therefore, many companies are struggling to finding and establishing suitable techniques for prioritizing their product roadmap.
Objective: In order to gain a better understanding of the prioritization process in a dynamic and uncertain market environment, this paper aims to identify suitable techniques for the prioritization in such environments.
Method: We conducted a Grey Literature Review according to the guidelines of Garousi et al.
Results: 18 techniques for the prioritization of the product roadmap could be identified. 15 techniques are primarily used to prioritize outputs by considering factors such as the expected impact or effort. Two technique are most suitable for prioritizing risky assumptions that need to be validated and one technique focuses on the prioritization of outcomes. All techniques have in common that they should be conducted as cross-functional team activity in order to include different perspectives in the prioritization process.
Product roadmaps in the new mobility domain: state of the practice and industrial experiences
(2021)
Context: The New Mobility industry is a young market that includes high market dynamics and is therefore associated with a high degree of uncertainty. Traditional product roadmapping approaches such a detailed planning of features over a long-time horizon typically fail in such environments. For this reason, companies that are active in the field of New Mobility are faced with the challenge of keeping their product roadmaps reliable for stakeholders while at the same time being able to react flexibly to changing market requirements.
Objective: The goal of this paper is to identify the state of practice regarding product roadmapping of New Mobility companies. In addition, the related challenges within the product roadmapping process as well as the success factors to overcome these challenges will be highlighted.
Method: We conducted semi-structured expert interviews with 8 experts (7 German company and one Finnish company) from the field of New Mobility and performed a content analysis.
Results: Overall the results of the study showed that the participating companies are aware of the requirements that the New Mobility sector entails. Therefore, they exhibit a high level of maturity in terms of product roadmapping. Nevertheless, some aspects were revealed that pose specific challenges for the participating companies. One major challenge, for example, is that New Mobility in terms of public clients is often a tender business with non-negotiable product requirements. Thus, the product roadmap can be significantly influenced from the outside. As factors for a successful product roadmapping mainly soft factors such as trust between all people involved in the product development process and transparency throughout the entire roadmapping process were mentioned.
Context: The software-intensive business is characterized by increasing market dynamics, rapid technological changes, and fast-changing customer behaviors. Organizations face the challenge of moving away from traditional roadmap formats to an outcome-oriented approach that focuses on delivering value to the customer and the business. An important starting point and a prerequisite for creating such outcome-oriented roadmaps is the development of a product vision to which internal and external stakeholders can be aligned. However, the process of creating a product vision is little researched and understood.
Objective: The goal of this paper is to identify lessons-learned from product vision workshops, which were conducted to develop outcome-oriented product roadmaps.
Method: We conducted a multiple-case study consisting of two different product vision workshops in two different corporate contexts.
Results: Our results show that conducting product vision workshops helps to create a common understanding among all stakeholders about the future direction of the products. In addition, we identified key organizational aspects that contribute to the success of product vision workshops, including the participation of employees from functionally different departments.
Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. 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.
Context-aware systems to support actors in the operating room depending on the status of the intervention require knowledge about the current situation in the intra-operative area. In literature, solutions to achieve situation awareness already exist for specific use cases, but applicability and transferability to other conditions are less addressed. It is assumed that a unified solution that can be adapted to different processes and sensors would allow for greater flexibility, applicability, and thus transferability to different applications. To enable a flexible and intervention-independent system, this work proposes a concept for an adaptable situation recognition system. The system consists of four layers with several modular components for different functionalities. The feasibility is demonstrated via prototypical implementation and functional evaluation of a first basic framework prototype. Further development goal is the stepwise extension of the prototype.
Identifikation von Schlaf- und Wachzuständen durch die Auswertung von Atem- und Bewegungssignalen
(2021)
This research-oriented book presents key contributions on architecting the digital transformation. It includes the following main sections covering 20 chapters: · Digital Transformation · Digital Business · Digital Architecture · Decision Support · Digital Applications Focusing on digital architectures for smart digital products and services, it is a valuable resource for researchers, doctoral students, postgraduates, graduates, undergraduates, academics and practitioners interested in digital transformation.
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.