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Digitalization and enterprise architecture management: a perspective on benefits and challenges
(2023)
Many companies digitally transform their business models, processes, and services. They have also been using Enterprise Architecture Management approaches for a long time to synchronize corporate strategy and information technology. Such digitalization projects bring different challenges for Enterprise Architecture Management. Without understanding and addressing them, Enterprise Architecture Management projects will fail or not deliver the expected value. Since existing research has not yet addressed these challenges, they were investigated based on a qualitative expert study with leading industry experts from Europe. Furthermore, potential benefits of digitalization projects for Enterprise Architecture Management were researched. Our results provide a theoretical framework consisting of five identified challenges, triggers and a number of benefits. Furthermore, we discuss in what ways digitalization and EAM is a promising topic for future research.
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.
Assistant platforms
(2023)
Many assistant systems have evolved toward assistant platforms. These platforms combine a range of resources from various actors via a declarative and generative interface. Among the examples are voice-oriented assistant platforms like Alexa and Siri, as well as text-oriented assistant platforms like ChatGPT and Bard. They have emerged as valuable tools for handling tasks without requiring deeper domain expertise and have received large attention with the present advances in generative artificial intelligence. In view of their growing popularity, this Fundamental outlines the key characteristics and capabilities that define assistant platforms. The former comprise a multi-platform architecture, a declarative interface, and a multi-platform ecosystem, while the latter include capabilities for composition, integration, prediction, and generativity. Based on this framework, a research agenda is proposed along the capabilities and affordances for assistant platforms.
Silicon neurons represent different levels of biological details and accuracies as a trade-off between complexity and power consumption. With respect to this trade-off and high similarity to neuron behaviour models, relaxation-type oscillator circuits often yield a good compromise to emulate neurons. In this chapter, two exemplified relaxation-type silicon neurons are presented that emulate neural behaviour with energy consumption under the scale of nJ/spike. The first proposed fully CMOS relaxation SiN is based on mathematical Izhikevich model and can mimic a broad range of physiologically observable spike patterns. The results of kinds of biologically plausible output patterns and coupling process of two SiNs are presented in 0.35 μm CMOS technology. The second type is a novel ultra-low-frequency hybrid CMOS-memristive SiN based on relaxation oscillators and analog memristive devices. The hybrid SiN directly emulates neuron behaviour in the range of physiological spiking frequencies (less than 100 Hz). The relaxation oscillator is implemented and fabricated in 0.13 μm CMOS technology. An autonomous neuronal synchronization process is demonstrated with two relaxation oscillators coupled by an analog memristive device in the measurement to emulate the synchronous behaviour between spiking neurons.
Context
Web APIs are one of the most used ways to expose application functionality on the Web, and their understandability is important for efficiently using the provided resources. While many API design rules exist, empirical evidence for the effectiveness of most rules is lacking.
Objective
We therefore wanted to study 1) the impact of RESTful API design rules on understandability, 2) if rule violations are also perceived as more difficult to understand, and 3) if demographic attributes like REST-related experience have an influence on this.
Method
We conducted a controlled Web-based experiment with 105 participants, from both industry and academia and with different levels of experience. Based on a hybrid between a crossover and a between-subjects design, we studied 12 design rules using API snippets in two complementary versions: one that adhered to a rule and one that was a violation of this rule. Participants answered comprehension questions and rated the perceived difficulty.
Results
For 11 of the 12 rules, we found that violation performed significantly worse than rule for the comprehension tasks. Regarding the subjective ratings, we found significant differences for 9 of the 12 rules, meaning that most violations were subjectively rated as more difficult to understand. Demographics played no role in the comprehension performance for violation.
Conclusions
Our results provide first empirical evidence for the importance of following design rules to improve the understandability of Web APIs, which is important for researchers, practitioners, and educators.
Purpose
For the modeling, execution, and control of complex, non-standardized intraoperative processes, a modeling language is needed that reflects the variability of interventions. As the established Business Process Model and Notation (BPMN) reaches its limits in terms of flexibility, the Case Management Model and Notation (CMMN) was considered as it addresses weakly structured processes.
Methods
To analyze the suitability of the modeling languages, BPMN and CMMN models of a Robot-Assisted Minimally Invasive Esophagectomy and Cochlea Implantation were derived and integrated into a situation recognition workflow. Test cases were used to contrast the differences and compare the advantages and disadvantages of the models concerning modeling, execution, and control. Furthermore, the impact on transferability was investigated.
Results
Compared to BPMN, CMMN allows flexibility for modeling intraoperative processes while remaining understandable. Although more effort and process knowledge are needed for execution and control within a situation recognition system, CMMN enables better transferability of the models and therefore the system. Concluding, CMMN should be chosen as a supplement to BPMN for flexible process parts that can only be covered insufficiently by BPMN, or otherwise as a replacement for the entire process.
Conclusion
CMMN offers the flexibility for variable, weakly structured process parts, and is thus suitable for surgical interventions. A combination of both notations could allow optimal use of their advantages and support the transferability of the situation recognition system.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
Flame-retardant finishing of cotton fabrics using DOPO functionalized alkoxy- and amido alkoxysilane
(2023)
In the present study, DOPO-based alkoxysilane (DOPO-ETES) and amido alkoxysilane (DOPO-AmdPTES) were synthesized by one-step and without by-products as halogen-free flame retardants. The flame retardants were applied on cotton fabric utilizing sol–gel method and pad-dry-cure finishing process. The flame retardancy, the thermal stability and the combustion ehaviour of treated cotton were evaluated by surface and bottom edge ignition flame test (according to EN ISO 15025), thermogravimetric analysis (TGA) and micro-scale combustion calorimeter (MCC). Unlike CO/DOPO-ETES sample, cotton treated with DOPO-AmdPTES nanosols exhibits self-extinguishing ehaviour with high char residue, an improvement of the LOI value and a significant reduction of the PHRR, HRC and THR compared to pristine cotton. Cotton finished with DOPO-AmdPTES reveals a semi-durability after ten laundering cycles keeping the flame-retardant properties unchanged. According to the results obtained from TGA-FTIR, Py-GC/MS and XPS, the major activity of flame retardant occurs in the condensed phase via catalytic induced char formation as physical barrier along with the activity in the gas phase derived mainly from the dilution effect. The early degradation of CO/DOPO-AmdPTES compared to CO/DOPO-ETES, triggered by the cleavage of the weak bond between P and C=O, as the DFT study indicated, provides the beneficial effect of this flame retardant on the fire resistance of cellulose.
Gender pay gaps are commonly studied in populations with already completed educational careers. We focus on an earlier stage by investigating the gender pay gap among university students working alongside their studies. With data from five cohorts of a large-scale student survey from Germany, we use regression and wage decomposition techniques to describe gender pay gaps and potential explanations. We find that female students earn about 6% less on average than male students, which reduces to 4.1% when accounting for a rich set of explanatory variables. The largest explanatory factor is the type of jobs male and female students pursue.
Turning students into Industry 4.0 entrepreneurs: design and evaluation of a tailored study program
(2022)
Startups in the field of Industry 4.0 could be a huge driver of innovation for many industry sectors such as manufacturing. However, there is a lack of education programs to ensure a sufficient number of well-trained founders and thus a supply of such startups. Therefore, this study presents the design, implementation, and evaluation of a university course tailored to the characteristics of Industry 4.0 entrepreneurship. Educational design-based research was applied with a focus on content and teaching concept. The study program was first implemented in 2021 at a German university of applied sciences with 25 students, of which 22 participated in the evaluation. The evaluation of the study program was conducted with a pretest–posttest-design targeting three areas: (1) knowledge about the application domain, (2) entrepreneurial intention and (3) psychological characteristics. The entrepreneurial intention was measured based on the theory of planned behavior. For measuring psychological characteristics, personality traits associated with entrepreneurship were used. Considering the study context and the limited external validity of the study, the following can be identified in particular: The results show that a university course can improve participants' knowledge of this particular area. In addition, perceived behavioral control of starting an Industry 4.0 startup was enhanced. However, the results showed no significant effects on psychological characteristics.