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Menopause is the permanent cessation of menstruation occurring naturally in women's aging. The most frequent symptoms associated with menopausal phases are mucosal dryness, increased weight and body fat, and changes in sleep patterns. Oral symptoms in menopause derived from saliva flow reduction can lead to dry mouth, ulcers, and alterations of taste and swallowing patterns. However, the oral health phenotype of postmenopausal women has not been characterized. The aim of the study was to determine postmenopausal women's oral phenotype, including medical history, lifestyle, and oral assessment through artificial intelligence algorithms. We enrolled 100 postmenopausal women attending the Dental School of the University of Seville were included in the study. We collected an extensive questionnaire, including lifestyle, medication, and medical history. We used an unsupervised k-means algorithm to cluster the data following standard features for data analysis. Our results showed the main oral symptoms in our postmenopausal cohort were reduced salivary flow and periodontal disease. Relying on the classical assessment of the collected data, we might have a biased evaluation of postmenopausal women. Then, we used artificial intelligence analysis to evaluate our data obtaining the main features and providing a reduced feature defining the oral health phenotype. We found 6 clusters with similar features, including medication affecting salivation or smoking as essential features to obtain different phenotypes. Thus, we could obtain main features considering differential oral health phenotypes of postmenopausal women with an integrative approach providing new tools to assess the women in the dental clinic.
Human pose estimation (HPE) is integral to scene understanding in numerous safety-critical domains involving human-machine interaction, such as autonomous driving or semi-automated work environments. Avoiding costly mistakes is synonymous with anticipating failure in model predictions, which necessitates meta-judgments on the accuracy of the applied models. Here, we propose a straightforward human pose regression framework to examine the behavior of two established methods for simultaneous aleatoric and epistemic uncertainty estimation: maximum a-posteriori (MAP) estimation with Monte-Carlo variational inference and deep evidential regression (DER). First, we evaluate both approaches on the quality of their predicted variances and whether these truly capture the expected model error. The initial assessment indicates that both methods exhibit the overconfidence issue common in deep probabilistic models. This observation motivates our implementation of an additional recalibration step to extract reliable confidence intervals. We then take a closer look at deep evidential regression, which, to our knowledge, is applied comprehensively for the first time to the HPE problem. Experimental results indicate that DER behaves as expected in challenging and adverse conditions commonly occurring in HPE and that the predicted uncertainties match their purported aleatoric and epistemic sources. Notably, DER achieves smooth uncertainty estimates without the need for a costly sampling step, making it an attractive candidate for uncertainty estimation on resource-limited platforms.
Evaluation of a contactless accelerometer sensor system for heart rate monitoring during sleep
(2024)
The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain.
The article pleads for Education for Sustainable Development (ESD) in the textile and fashion sector and shows possibilities how this can be implemented from elementary school to higher education and vocational training. It begins by highlighting the non-sustainable practices and deficits that can be found in the fashion and textile sector worldwide and explains the sustainability goals in the context of the UN Roadmap ESD for 2030. In order to raise the awareness for sustainability and implement these goals, education is needed. The article introduces the concept of ESD as a guiding principle with the core element design competence, implemented by the interdisciplinary method of Design Thinking (DT). In order to successfully teach the ESD-relevant design competence, various didactic principles are required. It can be shown that they are very similar to the principles and phases of DT. Within a research project DT and its potential for implementing ESD has been investigated in teaching-learning situations at elementary schools as well as in an interdisciplinary seminar for student teachers. These findings have been transferred to the EU project Fashion DIET, which pursues the goal of implementing ESD in the textile and fashion sector. By means of an online pilot workshop, the methods and principles of DT were presented and explained to lecturers, teachers and educators, who gave their feedback on the potential of DT as a method to implement ESD as a guiding principle in their curricula.
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.
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.
AI-based prediction and recommender systems are widely used in various industry sectors. However, general acceptance of AI-enabled systems is still widely uninvestigated. Therefore, firstly we conducted a survey with 559 respondents. Findings suggested that AI-enabled systems should be fair, transparent, consider personality traits and perform tasks efficiently. Secondly, we developed a system for the Facial Beauty Prediction (FBP) benchmark that automatically evaluates facial attractiveness. As our previous experiments have proven, these results are usually highly correlated with human ratings. Consequently they also reflect human bias in annotations. An upcoming challenge for scientists is to provide training data and AI algorithms that can withstand distorted information. In this work, we introduce AntiDiscriminationNet (ADN), a superior attractiveness prediction network. We propose a new method to generate an unbiased convolutional neural network (CNN) to improve the fairn ess of machine learning in facial dataset. To train unbiased networks we generate synthetic images and weight training data for anti-discrimination assessments towards different ethnicities. Additionally, we introduce an approach with entropy penalty terms to reduce the bias of our CNN. Our research provides insights in how to train and build fair machine learning models for facial image analysis by minimising implicit biases. Our AntiDiscriminationNet finally outperforms all competitors in the FBP benchmark by achieving a Pearson correlation coefficient of PCC = 0.9601.
Digital twins deployed in production are important in practice and interesting for research. Currently, mostly structured data coming from e.g., sensors and timestamps of related stations, are integrated into Digital Twins. However, semi- and unstructured data are also important to display the current status of a digital twin (e.g., of a machinery or produced good). Process Mining and Text Mining in combination can be used to support the use of log file data to understand the current state of the process as well as highlight issues. Therefore, issue related reactions can be taken more quickly, targeted and cost oriented. Applying a design science research approach; here a prototype as an artefact based on derived requirements is developed. This prototype helps to understand and to clarify the possibilities of Process Mining and Text Mining based on log data for production related Digital Twins. Contributions for practice and research are described. Furthermore, limitations of the research and future opportunities are pointed out.
The fifth mobile communications generation (5G) can lead to a substantial change in companies enabling the full capability of wireless industrial communication. 5G with its key features of providing Enhanced Mobile Broadband, Ultra-Reliable and Low-Latency Communication, and Massive Machine Type Communication will support the implementation of Industry 4.0 applications. In particular, the possibility to set-up Non-Public Networks provides the opportunity of 5G communication in factories and ensures sole access to the 5G infrastructure offering new opportunities for companies to implement innovative mobile applications. Currently there exist various concepts, ideas, and projects for 5G applications in an industrial environment. However, the global rollout of 5G systems is a continuous process based on various stages defined by the global initiative 3rd Generation Partnership Project that develops and specifies the 5G telecommunication standard. Accordingly, some services are currently still far from their final performance capability or not yet implemented. Additionally, research lacks in clarifying the general suitability of 5G regarding frequently mentioned 5G use cases. This paper aims to identify relevant 5G use cases for intralogistics and evaluates their technical requirements regarding their practical feasibility throughout the upcoming 5G specifications.
Mobile monitoring of outpatients during cancer therapy becomes possible through technological advancements. This study leveraged a new remote patient monitoring app for in-between systemic therapy sessions. Patients’ evaluation showed that the handling is feasible. Clinical implementation must consider an adaptive development cycle for reliable operations.
This article proposes several modified quasi Z-source dc/dc boost converters. These can achieve soft-switching by using a clamp-switch network comprised of an active switch and a diode in parallel with a capacitor connected across one of the inductors of the Z-source network. In this way, ringing at the transistor switching node is mitigated, and the voltage at the turn-on of the transistor is reduced. Even a zero voltage switching (ZVS) of the main transistor is possible if the capacitor in the clamp-switch network is adequately chosen. The proposed circuit structure and operating mode are described and validated through simulations and measurements on a low-power prototype.
Non-fungible tokens (NFTs) are unique digital assets that have recently gained significant popularity, particularly in the digital art sector. The success of NFTs and other blockchain-based innovations depends on their ac-acceptance and use by consumers. This study aims to understand the impact of moral values on the acceptance of NFTs. Based on a quantitative survey with over 800 complete responses, the analysis shows that moral aspects of NFTs are indeed important for potential users. However, there is an attitude-behavior gap, as the positive impact of moral values on the intention to use NFTs is not reflected in the actual current usage of NFTs by the respondents. This study contributes to knowledge by providing new empirical data on the acceptance of NFTs and highlighting the role of moral values on the acceptance decision.
The volume includes papers presented at the International KES Conference on Human Centred Intelligent Systems 2023 (KES HCIS 2023), held in Rome, Italy on June 14–16, 2023. This book highlights new trends and challenges in intelligent systems, which play an important part in the digital transformation of many areas of science and practice. It includes papers offering a deeper understanding of the human-centred perspective on artificial intelligence, of intelligent value co-creation, ethics, value-oriented digital models, transparency, and intelligent digital architectures and engineering to support digital services and intelligent systems, the transformation of structures in digital businesses and intelligent systems based on human practices, as well as the study of interaction and the co-adaptation of humans and systems.
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.
In today’s education, healthcare, and manufacturing sectors, organizations and information societies are discussing new enhancements to corporate structure and process efficiency using digital platforms. These enhancements can be achieved using digital tools. Industry 5.0 and Society 5.0 give several potentials for businesses to enhance the adaptability and efficacy of their industrial processes, paving the door for developing new business models facilitated by digital platforms. Society 5.0 can contribute to a super-intelligent society that includes the healthcare industry. In the past decade, the Internet of Things, Big Data Analytics, Neural Networks, Deep Learning, and Artificial Intelligence (AI) have revolutionized our approach to various job sectors, from manufacturing and finance to consumer products. AI is developing quickly and efficiently. We have heard of the latest artificial intelligence chatbot, ChatGPT. OpenAI created this, which has taken the internet by storm. We tested the effectiveness of a considerable language model referred to as ChatGPT on four critical questions concerning “Society 5.0”, “Healthcare 5.0”, “Industry,” and “Future Education” from the perspectives of Age 5.0.
Enterprises and societies currently face essential challenges, and digital transformation can contribute to their resolution. Enterprise architecture (EA) is useful for promoting digital transformation in global companies and information societies covering ecosystem partners. The advancement of new business models can be promoted with digital platforms and architectures for Industry 4.0 and Society 5.0. Therefore, products from the sector of healthcare, manufacturing and energy, etc. can increase in value. The adaptive integrated digital architecture framework (AIDAF) for Industry 4.0 and the design thinking approach is expected to promote and implement the digital platforms and digital products for healthcare, manufacturing and energy communities more efficiently. In this paper, we propose various cases of digital transformation where digital platforms and products are designed and evaluated for digital IT, digital manufacturing and digital healthcare with Industry 4.0 and Society 5.0. The vision of AIDAF applications to perform digital transformation in global companies is explained and referenced, extended toward the digitalized ecosystems such as Society 5.0 and Industry 4.0.
Recent work on database application development platforms has sought to include a declarative formulation of a conceptual data model in the application code, using annotations or attributes. Some recent work has used metadata to include the details of such formulations in the physical database, and this approach brings significant advantages in that the model can be enforced across a range of applications for a single database. In previous work, we have discussed the advantages for enterprise integration of typed graph data models (TGM), which can play a similar role in graphical databases, leveraging the existing support for the unified modelling language UML. Ideally, the integration of systems designed with different models, for example, graphical and relational database, should also be supported. In this work, we implement this approach, using metadata in a relational database management system (DBMS).
The Fifteenth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2023), held between March 13 – 17, 2023, continued a series of international events covering a large spectrum of topics related to advances in fundamentals on databases, evolution of relation between databases and other domains, data base technologies and content processing, as well as specifics in applications domains databases.
Advances in different technologies and domains related to databases triggered substantial improvements for content processing, information indexing, and data, process and knowledge mining. The push came from Web services, artificial intelligence, and agent technologies, as well as from the generalization of the XML adoption.
High-speed communications and computations, large storage capacities, and load-balancing for distributed databases access allow new approaches for content processing with incomplete patterns, advanced ranking algorithms and advanced indexing methods.
Evolution on e-business, ehealth and telemedicine, bioinformatics, finance and marketing, geographical positioning systems put pressure on database communities to push the ‘de facto’ methods to support new requirements in terms of scalability, privacy, performance, indexing, and heterogeneity of both content and technology.
Acting like a startup - using corporate startup structures to manage the digital transformation
(2023)
Digital transformation is proving to be a significant challenge for firms and companies when it comes to maintaining their market position. It is evident that many companies are struggling to find their particular way through this transformation. A corporate startup structure is one way to find a suitable solution quickly. Therefore, we are presenting a model for corporate startup activities, which we will instantiate in an appropriate tool to support the management of corporate startups by their parent firms. We have derived the first requirements and design principles from a comprehensive problem analysis and literature study. In addition to this,we are presenting a first artifact, which should realize the design principles by implementing a practical tool. Forming a cooperation with an automotive firm has enabled us to gain access to real-world data for the design and evaluation of the artifact.
The basis for developing future products in the automotive industry is finding creative and innovative solutions. Ideas can be found by means of creativity methods that support product developers throughout the creative process. Product developers are provided with a variety of different and new methods. This leads to a “method jungle” in which it is difficult for product developers to find the most suitable path. The successful use of methods in product development goes hand in hand with the acceptance and implementation of the methods. Despite the added value, only a low use is observed in the development process. The field of Creativity Support Tools also offers a wide variety of different tools that support the creativity process. Although a chasm exists between the many CSTs that are developed and what creative practitioners actually use. Therefore, previous studies iteratively developed a user-centered tool called “IDEA” that tries to provide a tool that responds to users' needs. The question arises how the developed tool IDEA performs in “real life setting” regarding its UX and usability as well as the creativity method acceptance and level of mental workload.
There are indicators we are entering a new era for MTM research, by moving beyond the structural approach that has characterized MTM research to date, to focus on important and under-researched issues, such as the nature of employees’ experiences in an MTM context. Although team research suggests that the experiences of members impact team functioning, these lines of reasoning have not, until recently, made their way to MTM research. To overcome this limitation, this symposium showcases five papers that use a variety of theoretical perspectives, research designs (i.e., qualitative, quantitative), contexts (e.g., healthcare, automotive manufacturer, online panels), methodologies, and analytical methods (i.e., meta-analysis, content/thematic analysis). The symposium focuses on surfacing and advancing unanswered questions that extend theory and can offer fruitful directions for MTM research by examining critical individual and team level outcomes (e.g., individual/team performance, individual counterproductive and organizational citizenship behavior, individual learning, individual turnover intentions, organizational commitment) in the experiences of MTM employees across their teams (e.g., goals, functions, roles). We hope to provide a forum to advance unanswered questions that offer fruitful directions for MTM research.
Different network architectures are being used to build remote laboratories. Historically, it has been difficult to integrate industrial control systems with higher level IT systems like enterprise resource planning (ERP), manufacturing execution systems (MES), and manufacturing operations management (MOM). Getting these systems to communicate with one another has proven to be relatively difficult due to the absence of shared protocols between them. The Open Platform Communications United Architecture (OPC-UA) protocol was introduced as a remedy for this issue and is gaining popularity, but what if open-source protocols that are widely used in the IT industry could be used instead? This paper presents the development of an IT-Architecture for a cyber-physical industrial control systems laboratory that enables a seamless interconnection and integration of its elements. The architecture utilises Node-Red technology. Node-RED is an open-source programming platform developed by IBM that is focused on making it simple to link physical components, APIs, and web services. This cyber-physical laboratory is for learning principles of an industrial cascaded process control factory. Finally, this text will also discuss future work relating to digital twin (DT). A coupled tank system is selected as a teaching factory to illustrate a range of fluid control application in a typical chemical process factory.
In the era of digital transformation, the notion of software quality transcends its traditional boundaries, necessitating an expansion to encompass the realms of value creation for customers and the business. Merely optimizing technical aspects of software quality can result in diminishing returns. Product discovery techniques can be seen as a powerful mechanism for crafting products that align with an expanded concept of quality - one that incorporates value creation. Previous research has shown that companies struggle to determine appropriate product discovery techniques for generating, validating, and prioritizing ideas for new products or features to ensure they meet the needs and desires of the customers and the business. For this reason, we conducted a grey literature review to identify various techniques for product discovery. First, the article provides an overview of different techniques and assesses how frequently they are mentioned in the literature review. Second, we mapped these techniques to an existing product discovery process from previous research to provide concrete guidelines for establishing product discovery in their organizations. The analysis shows, among other things, the increasing importance of techniques to structure the problem exploration process and the product strategy process. The results are interpreted regarding the importance of the techniques to practical applications and recognizable trends.
Framework for integrating intelligent product structures into a flexible manufacturing system
(2023)
Increasing individualisation of products with a high variety and shorter product lifecycles result in smaller lot sizes, increasing order numbers, and rising data and information processing for manufacturing companies. To cope with these trends, integrated management of the products and manufacturing information is necessary through a “product-driven” manufacturing system. Intelligent products that are integrated as an active element within the controlling and planning of the manufacturing process can represent flexibility advantages for the system. However, there are still challenges regarding system integration and evaluation of product intel-ligence structures. In light of these trends, this paper proposes a conceptual frame-work for defining, analysing, and evaluating intelligent products using the example of an assembly system. This paper begins with a classification of the existing problems in the assembly and a definition of the intelligence level. In contrast to previous approaches, the analysis of products is expanded to five dimensions. Based on this, a structured evaluation method for a use case is presented. The structure of solving the assembly problem is provided by the use case-specific ontology model. Results are presented in terms of an assignment of different application areas, linking the problem with the target intelligence class and, depending on the intelligence class of the product, suggesting requirements for implementation. The conceptual frame-work is evaluated by utilising a case study in a learning factory. Here, the model-mix assembly is controlled actively by the workpiece carrier in terms of transferring the variant-specific work instructions to the operator and the collaborative robot (cobot) at the workstations. The resulting system thus enables better exploitation of the poten-tials through less frequent errors and shorter search times. Such an implementation has demonstrated that the intelligent workpiece carrier represents an additional part for realising a cyber-physical production system (CPPS).
The members of the European TRIZ Campus (ETC) have been learning from and working together with many honorable members of MATRIZ Official for many years and feel very connected to the official International TRIZ Association.
To further spread the TRIZ methodology and TRIZ teaching in the European area in the past 12 months the ETC has put a lot of thought in how making TRIZ accessible to a broader audi-ence and getting more professionals in touch with the methodology was one of the focal points.
To this end, we have developed new formats such as the "Trainer Day" to support trainers on their way into practice. We have drawn up detailed quality guidelines for the teaching of the TRIZ methodology, which are intended to provide orientation for the design of training classes and docu-mentation. We strive for exchange with representatives of "neighbouring" methods such as Six sigma, Lean, DFMA and Design Thinking to indicate synergies and added value among methods and approaches of different kinds. We are testing formats for community building, in order to connect users of all places more strongly with the TRIZ methodology through communication and information of-fers. If TRIZ users feel alone in their organizations, the exchange outside their organi-zation helps them to keep up with the TRIZ methodology. Moreover, the ETC strives to increase the ability to communicate the benefits of TRIZ-usage inside organizations. We discuss, how to reach teachers and students of all age, to make them the unique way of inventive thinking accessible.
In our paper we want to give other MATRIZ Official members insights and share our experi-ences and best practices with our fellow MO members.
Advancing mental health diagnostics: AI-based method for depression detection in patient interviews
(2023)
In this paper, we present a novel artificial intelligence (AI) application for depression detection, using advanced transformer networks to analyse clinical interviews. By incorporating simulated data to enhance traditional datasets, we overcome limitations in data protection and privacy, consequently improving the model’s performance. Our methodology employs BERT-based models, GPT-3.5, and ChatGPT-4, demonstrating state-of-the-art results in detecting depression from linguistic patterns and contextual information that significantly outperform previous approaches. Utilising the DAIC-WOZ and Extended-DAIC datasets, our study showcases the potential of the proposed application in revolutionising mental health care through early depression detection and intervention. Empirical results from various experiments highlight the efficacy of our approach and its suitability for real-world implementation. Furthermore, we acknowledge the ethical, legal, and social implications of AI in mental health diagnostics. Ultimately, our study underscores the transformative potential of AI in mental health diagnostics, paving the way for innovative solutions that can facilitate early intervention and improve patient outcomes.
Transforming our food system is important to achieving global climate neutrality and food security. Germany has set a national target of reaching a 30% share in organic farming to support the goal. When looking at the transformation process from conventional to organic farming, it becomes apparent that measures need to be taken to reach this anticipated goal. A particular emphasis of this work is placed on finding a digital solution and process improvements to ensure longevity and efficiency. Interviews with actors along the farm-to-fork value chain were conducted to identify central barriers and drivers of organic transformation. The results of the interviews show firstly, that three subsystems need to be distinguished when talking about the farm-to-fork value chain: (1) farmers, (2) intermediaries, and (3) the canteen system. Although all three subsystems can be combined to form a coherent value chain, they rarely act and communicate beyond the boundaries of their subsystem. Secondly, we were able to allocate primary barriers and drivers to each of the subsystems, highlighting the need to include all three in the transformation process and aim for a comprehensive digital solution. This work explores the potential of a network-based platform to improve the current practice of rigid and strictly hierarchical value chains. We focus on deriving user requirements from the interviews to describe the necessary functionality of the platform to address the identified barriers and exploit existing drivers.
Applications often need to be deployed in different variants due to different customer requirements. However, since modern applications often need to be deployed using multiple deployment technologies in combination, such as Ansible and Terraform, the deployment variability must be considered in a holistic way. To tackle this, we previously developed Variability4TOSCA and the prototype OpenTOSCA Vintner, which is a TOSCA preprocessing and management layer that implements Variability4TOSCA. In this demonstration, we present a detailed case study that shows how to model a deployment using Variability4TOSCA, how to resolve the variability using Vintner, and how the result can be deployed.
Motivation
In order to enable context-aware behavior of surgical assistance systems, the acquisition of various information about the current intraoperative situation is crucial. To achieve this, the complex task of situation recognition can be delegated to a specialized system. Consequently, a standardized interface is required for the seamless transfer of the recognized contextual information to the assistance systems, enabling them to adapt accordingly.
Methods
Our group analyzed four medical interface standards to determine their suitability for exchanging intraoperative contextual information. The assessment was based on a harmonized data and service model derived from the requirements of expected context-aware use cases. The Digital Imaging and Communications in Medicine (DICOM) and IEEE 11073 for Service-oriented Device Connectivity (SDC) were identified as the most appropriate standards.
Results
We specified how DICOM Unified Procedure Steps (UPS), can be used to effectively communicate contextual information. We proposed the inclusion of attributes to formalize different granularity levels of the surgical workflow.
Conclusions
DICOM UPS SOP classes can be used for the exchange of intraoperative contextual information between a situation recognition system and surgical assistance systems. This can pave the way for vendor-independent context awareness in the OR, leading to targeted assistance of the surgical team and an improvement of the surgical workflow.
The increase in product variance and shorter product lifecycles result in higher production ramp-up frequencies and promote the usage of mixed-model lines. The ramp-up is considered a critical step in the product life cycle and in the automotive industry phases of the ramp-up are often executed on separated production lines (pilot lines) or factories (pilot plants) to verify processes and to qualify employees without affecting the production of other products in the mixed-model line. The required financial funds for planning and maintaining dedicated pilot lines prevent small and medium-sized enterprises (SMEs) from the application. Hence, SMEs require different tools for piloting and training during the production ramp-up. Learning islands on which employees can be trained through induced and autonomous learning propose a solution. In this work, a concept for the development and application which contains the required organization, activities, and materials is developed through expert interviews. The results of a case study application with a medium-sized automotive manufacturer show that learning islands are a viable tool for employee qualification and process verification during the ramp-up of mixed-model lines.
Because of a high product and technology complexity, companies involve external partners in their research and development (R&D) processes. Interorganizational projects result, which represent temporary organizations. In these projects heterogenous organizations work closely together. Since project work is always teamwork, these projects face due to their characteristic’s major challenges on an organizational, relational, and content-related collaboration level. Thus, this paper raises the following research question: “How can a project team be supported on an organizational, relational, and content-related level in an interorganizational new product development setting?” To answer this research question, an explorative expert study was set up with two digital workshops using the interactive presentation tool Mentimeter. The results show that a cooperative innovation culture could support project teams on an organizational and relational level in the future in minimizing predominant problems. Moreover, it supports project teams for example in a functional communication. Furthermore, 18 values of a cooperative innovation culture result which are for example openness and transparency, risk and failure tolerance or respect. On a content-related level the results show that an adaptable tool which promotes creativity and collaboration method as well as content-related input support could be beneficial for problem-solving in an interorganizational new product development setting in the future. Because the tool can guide product developers through the process with suitable creativity and collaboration methods, can give content-related input and can enable interactive interchange on a table-top. Future research could mainly focus on the connection of the cooperative innovation culture and the tool since these potentially influence each other.
Intelligent Tutoring Systems (ITSs) are increasingly used in modern education to automatically give students individual feedback on their performance. The advantage for students is fast individual feedback on their answers to asked questions, while lecturers benefit from considerable time savings and easy delivery of educational material. Of course, it is important that the provided feedback is as effective as direct feedback from the lecturer. However, in digital teaching, lecturers cannot assess the student’s knowledge precisely but can only provide information on which questions were answered correctly and incorrectly. Therefore, this paper presents a concept for integrating ITS elements into the gamified e-learning platform IT-REX so that the feedback quality can be improved to support students in the best possible way.
Gamification has been increasingly applied to software engineering education in the past. The approaches vary from applying game elements on a conceptual phase in the course to using specific tools to engage the students more and support their learning goals. However, existing tools usually have game elements, such as quizzes or challenges, but do not provide a more computer game-like experience. Therefore, we try to raise the level of gamified learning experience to another level by proposing Gamify-IT. Gamify-IT is a Unity- and web-based game platform intended to help students learn software engineering. It follows an immersive role-play game characteristic where the students explore a world, find and solve minigames and clear dungeons with SE tasks. Lecturers can configure the worlds, e.g., to add content hints. Furthermore, they can add and configure minigames and dungeons to include exercises in a fully gamified way. Thereby, they customize their course in Gamify-IT to adapt the world very precisely to other materials such as lectures or exercises. Results of an evaluation of our initial prototype show that (i) students like to engage with the platform, (ii) students are motivated to learn when using Gamify-IT, and (iii) the minigames support students in understanding the learning objectives.
Analog integrated circuit sizing still relies heavily on human expert knowledge as previous automation approaches have not found wide-spread acceptance in industry. One strand, the optimization-based automation, is often discarded due to inflated constraining setups, infeasible results or excessive run times. To address these deficits, this work proposes a alternative optimization flow featuring a designer’s intuition for feasible design spaces through integration of expert knowledge based on the gm/ID-method. Moreover, the extensive run times of simulation-based optimization flows are overcome by incorporating computationally efficient machine learning methods. Neural network surrogate models predicting eleven performance parameters increase the evaluation speed by 3 400× on average compared to a simulator. Additionally, they enable the use of optimization algorithms dependent on automatic differentiation, that would otherwise be unavailable in this field. First, an up to 4× more efficient way for sampling training data based on the aforementioned space is detailed. After presenting the architecture and training effort regarding the surrogate models, they are employed as part of the objective function for sizing three operational amplifiers with three different optimization algorithms. Additionally, the benefits of using the gm/ID-method become evident when considering technology migration, as previously found solutions may be reused for other technologies.
Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.
Ecuador, traditionally an agricultural based economy, has a great potential for valorizing their industrial residues. This study, presents a techno-economic analysis for applying a novel biomass oxidation method to produce formic and acetic acids from coffee husk residues in Machala, Ecuador. The analysis determined that the time of return of investment was lower than 5 years, making this project economically feasible, when producing approx. 1000 tons of formic acid per year, which is enough for supplying the Ecuadorian market. This production, would reduce imports costs and develop the chemical industry in the country.
This research evaluates current measurement scales for ambidexterity and proposes a new approach for the measurement of this important construct. We argue that current measurement approaches may be unsuitable to capture the concept of ambidexterity. Through a systematic scale development process, we derive a measurement scale with dual items that simultaneously refer to both dimensions, exploitation and exploration, thus reflecting the true nature of ambidexterity. An extensive pre-test with 39 executives suggests that our scale is suitable for capturing ambidexterity. Our measurement model enhances conceptual clarity of ambidexterity and can serve as a base for future investigations of the concept.
Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence.
In a recently developed study programme at Reutlingen University, which focuses on practical orientations, an innovative product with solid company references is to be defined and realised by student teams. On the basis of this product, all subjects of the business engineering study programme “Sustainable Production and Business” are taught. By focusing on three main paths of future skills that have been developed by NextSkills to analyse upcoming social changes, global challenges and fields of work that are innovation-driven and agile, the new study programme aims to create responsible leaders who will shape global businesses respectfully. Thereby, different TRIZ tools help to support students in developing their own products with a focus on sustainability and pay off on the future skills enhancement. Further, students get to know TRIZ tools in an unbiased way, unburdened by too much theory, and are thus continuously supported in the progressing product development process that accompanies their studies. Hence, students perceive TRIZ on the one hand as a method to develop sustainable products and, on the other hand, to find sustainable solutions for everyday problems. The knowledge and positive experiences gained in this way should then arouse curiosity for the TRIZ class at the end of the study programme. The students can graduate with a TRIZ Level 1 certificate. Thereby, as many students as possible are introduced to the TRIZ methods, and the TRIZ tool is spread widely.
Modern component-based architectural styles, e.g., microservices, enable developing the components independently from each other. However, this independence can result in problems when it comes to managing issues, such as bugs, as developer teams can freely choose their technology stacks, such as issue management systems (IMSs), e.g., Jira, GitHub, or Redmine. In the case of a microservice architecture, if an issue of a downstream microservice depends on an issue of an upstream microservice, this must be both identified and communicated, and the downstream service’s issues should link to its causing issue. However, agile project management today requires efficient communication, which is why more and more teams are communicating through comments in the issues themselves. Unfortunately, IMSs are not integrated with each other, thus, semantically linking these issues is not supported, and identifying such issue dependencies from different IMSs is time-consuming and requires manual searching in multiple IMS technologies. This results in many context switches and prevents developers from being focused and getting things done. Therefore, in this paper, we present a concept for seamlessly integrating different IMS technologies into each other and providing a better architectural context. The concept is based on augmenting the websites of issue management systems through a browser extension. We validate the approach with a prototypical implementation for the Chrome browser. For evaluation, we conducted expert interviews, which approved that the presented approach provides significant advantages for managing issues of agile microservice architectures.
Application systems often need to be deployed in different variants if requirements that influence their implementation, hosting, and configuration differ between customers. Therefore, deployment technologies, such as Ansible or Terraform, support a certain degree of variability modeling. Besides, modern application systems typically consist of various software components deployed using multiple deployment technologies that only support their proprietary, non-interoperable variability modeling concepts. The Variable Deployment Metamodel (VDMM) manages the deployment variability across heterogeneous deployment technologies based on a single variable deployment model. However, VDMM currently only supports modeling conditional components and their relations which is sometimes too coarse-grained since it requires modeling entire components, including their implementation and deployment configuration for each different component variant. Therefore, we extend VDMM by a more fine-grained approach for managing the variability of component implementations and their deployment configurations, e.g., if a cheap version of a SaaS deployment provides only a community edition of the software and not the enterprise edition, which has additional analytical reporting functionalities built-in. We show that our extended VDMM can be used to realize variable deployments across different individual deployment technologies using a case study and our prototype OpenTOSCA Vintner.
Most Question-answering (QA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose TFCSG, an unsupervised similar question retrieval approach that leverages pre-trained language models and multi-task learning. Firstly, topic keywords in question sentences are extracted sequentially based on a latent topic-filtering algorithm to construct unsupervised training corpus data. Then, the multi-task learning method is used to build the question retrieval model. There are three tasks designed. The first is a short sentence contrastive learning task. The second is the question sentence and its corresponding topic sequence similarity judgment task. The third is using question sentences to generate their corresponding topic sequence task. The three tasks are used to train the language model in parallel. Finally, similar questions are obtained by calculating the cosine similarity between sentence vectors. The comparison experiment on public question datasets that TFCSG outperforms the comparative unsupervised baseline method. And there is no need for manual marking, which greatly saves human resources.
The fifth generation of mobile communication (5G) is a wireless technology developed to provide reliable, fast data transmission for industrial applications, such as autonomous mobile robots and connect cyber-physical systems using Internet of Things (IoT) sensors. In this context, private 5G networks enable the full performance of industrial applications built on dedicated 5G infrastructures. However, emerging wireless communication technologies such as 5G are a complex and challenging topic for training in learning factories, often lacking physical or visual interaction. Therefore, this paper aims to develop a real-time performance monitoring system of private 5G networks and different industrial 5G devices to visualise the performance and impact factors influencing 5G for students and future connectivity experts. Additionally, this paper presents the first long-term measurements of private 5G networks and shows the performance gap between the actual and targeted performance of private 5G networks.
The market for indoor positioning systems for a variety of applications has grown strongly in recent years. A wide range of systems is available, varying considerably in terms of accuracy, price and technology used. The suitability of the systems is highly dependent on the intended application. This paper presents a concept to use a single low-cost PTZ camera in combination with fiducial markers for indoor position and orientation determination. The intended use case is to capture a plant layout consisting of position, orientation and unique identity of individual facilities. Important factors to consider for the selection of a camera have been identified and the transformation of the marker pose in camera coordinates into a selectable plant coordinate system is described. The concept is illustrated by an exemplary practical implementation and its results.
Knowledge-intensive organizations primarily rely on knowledge and expertise as key strategic resources. In light of economic, social, and health-related crises in recent years, such organizations increasingly need to operate in dynamic environments. However, examinations on dynamic capabilities specifically in knowledge-intensive organizations remain scarce. This is remarkable given the role that knowledge holds as an economic resource in developed countries. To provide an explanation of how knowledge-intensive organizations can prevail among competitors under dynamic conditions, the authors integrate two literature streams in a knowledge-intensive context: the knowledge-based view and the dynamic capabilities approach. The knowledge-based view focuses on the nature of organizational knowledge as a critical resource and illustrates specific properties of knowledge in contrast to traditional means of labor such as capital. The dynamic capabilities approach on the other hand is about a firm's ability to integrate, build, and reconfigure internal and external resources and can be drawn on to explain organizational success through adaptation to dynamic contexts. In this conceptual study, the authors propose a research model linking knowledge processes to organizational performance through two different paths: (1) Operational capabilities permit organizations to make their living in the present and refer to efficiency. (2) Dynamic capabilities allow organizations to change their resource base and, therefore, enable their long-term survival in dynamic environments by focusing on effectiveness. Additionally, the authors hypothesize a moderating effect of environmental dynamics on the relationship between dynamic capabilities and performance. The study offers a comprehensive overview on the interplay between dynamic capabilities and the knowledge-based view, offering valuable insights for both researchers and practitioners in the field.
Smart cities are considered data factories that generate an enormous amount of data from various sources. In fact data is the backbone of any smart services. Therefore, the strategic beneficial handling of this digital capital is crucial for cities. Some smart city pioneers have already written down their approach to data in the form of data strategies, but what should a city's data strategy include, and how can the goals and measures defined in the strategies be operationalized? This paper addresses these questions by looking closely at the data strategies of cities in Germany and the top three countries in the EU Digital Economy and Society Index. The in-depth analysis of 8 city data strategies has yielded 11 dimensions that cities should consider in their data strategy. These are relevance of data, principles, methods, data sharing, technology, data culture, data ethics, organizational structure, data security and privacy, collaborations, data literacy. In addition, data governance is a concept to put these 11 strategic dimensions into practice through standardization measures, training programs, and defining roles and responsibilities by developing a data catalog.
Organizational agility may be an antidote against threats from volatile, uncertain, complex, or ambiguous corporate environments. While agility has been extensively examined in manufacturing enterprises, comparably less is known about agility in knowledge-intensive organizations. As results may not be transferable, there is still some confusion about how agility in knowledge-intensive organizations can be characterized, what factors facilitate its development, what its organizational effects are, and what environmental conditions favor these effects. This study closes these gaps by presenting a systematic literature review on agility in knowledge-intensive organizations. A systematic literature search led to a sample of 37 relevant papers for our review. Integrating the knowledge-based view and a dynamic capabilities perspective, we (1) present different relevant conceptualizations of organizational agility, (2) discuss relevant knowledge management-related as well as information technology-related capabilities that support the development of organizational agility, and (3) shed light on the moderating role of environmental conditions in enhancing organizational agility and its effect on organizational performance. This academic paper adds value to theory by synthesizing existing research on agility in knowledge-intensive organizations. It furthermore may serve as a map for closing research gaps by proposing an extensive agenda for future research. Our study expands existing literature reviews on agility with its specific focus on a knowledge-intensive context and its integration of the research streams of knowledge management capabilities as well as information technology capabilities. It integrates relevant organizational knowledge management practices and the use of knowledge management systems to ensure superior performance effects. Our study can serve as a base for future examinations of organizational agility by illustrating fruitful topics for further examination as well as open questions. It may also provide value to practitioners by showing what factors favor the development of agility in knowledge-intensive organizations and what organizational effects can be achieved under which conditions.
Patterns are virtually simulated in 3D CAD programs before production to check the fit. However, achieving lifelike representations of human avatars, especially regarding soft tissue dynamics, remains challenging. This is mainly since conventional avatars in garment CAD programs are simulated with a continuous hard surface and not corresponding to the human physical and mechanical body properties of soft tissue. In the real world, the human body’s natural shape is affected by the contact pressure of tight-fitting textiles. To verify the fit of a simulated garment, the interactions between the individual body shape and the garment must be considered. This paper introduces an innovative approach to digitising the softness of human tissue using 4D scanning technology. The primary objective of this research is to explore the interactions between tissue softness and different compression levels of apparel, exerting pressure on the tissue to capture the changes in the natural shape. Therefore, to generate data and model an avatar with soft body physics, it is essential to capture the deform ability and elasticity of the soft tissue and map it into the modification options for a simulation. To aim this, various methods from different fields were researched and compared to evaluate 4D scanning as the most suitable method for capturing tissue deformability in vivo. In particular, it should be considered that the human body has different deformation capabilities depending on age, the amount of muscle and body fat. In addition, different tissue zones have different mechanical properties, so it is essential to identify and classify them to back up these properties for the simulation. It has been shown that by digitising the obtained data of the different defined applied pressure levels, a prediction of the deformation of the tissue of the exact person becomes possible. As technology advances and data sets grow, this approach has the potential to reshape how we verify fit digitally with soft avatars and leverage their realistic soft tissue properties for various practical purposes.