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Institute
The volume of program source code created, reused, and maintained worldwide is rapidly increasing, yet code comprehension remains a limiting productivity factor. For developers and maintainers, well known common software design patterns and the abstractions they offer can help support program comprehension. However, manual pattern documentation techniques in code and code-related assets such as comments, documents, or models are not necessarily consistent or dependable and are cost-prohibitive. To address this situation, we propose the Hybrid Design Pattern Detection (HyDPD), a generalized approach for detecting patterns that is programming-language-agnostic and combines graph analysis (GA) and Machine Learning (ML) to automate the detection of design patterns via source code analysis. Our realization demonstrates its feasibility. An evaluation compared each technique and their combination for three common patterns across a set of 75 single pattern Java and C# public sample pattern projects. The GA component was also used to detect the 23 Gang of Four design patterns across 258 sample C# and Java projects as well as in a large Java project. Performance and scalability were measured. The results show the advantages and potential of a hybrid approach for combining GA with artificial neural networks (ANN) for automated design pattern detection, providing compensating advantages such as reduced false negatives and improved F1 scores.
Leveraging Augmented Reality to Support Context-Aware Tasks in Alignment with Business Processes
(2021)
The seamless inclusion of Augmented Reality (AR) with Business Process Management Systems (BPMSs) for Smart Factory and Industry 4.0 processes remains a challenge. Towards this end, this paper contributes an approach integrating context-aware AR into intelligent business processes to support and guide manufacturing personnel tasks and enable live task assignment optimization and support task execution quality. Our realization extends two BPMSs (Camunda and AristaFlow) and various AR devices. Various AR capabilities are demonstrated via a simulated industrial case study.
DEKXTROSE: An Education 4.0 Mobile Learning Approach and Object-Aware App Based on a Knowledge Nexus
(2020)
The exponential growth in knowledge coupled with the decreasing knowledge half-life creates a challenging situation for educational programs - particularly those preparing software engineers for their very dynamic high-technology field. Teachers in high technology education areas are challenged in selecting and making relevant knowledge intuitively accessible to students, especially with regard the highly dynamic digital and software technologies. This paper contributes a knowledge nexus-based multimedia approach aligned with Higher Education 4.0 for creating learning apps on mobile devices that support multiple didactic models, leverage intrinsic curiosity and motivation, support gamification, and enable digital collaboration. Object recognition is used to trigger learning paths, and various didactic methods are supported via workflow-like learning flows to support group or team-based learning. A prototype app was realized to demonstrate its feasibility and an empirical evaluation in software engineering shows the didactic potential and advantages of the approach, which can be readily generalized and applied to the arts, sciences, etc.
Purpose: The aim is to be able to advise patients on the choice of sports and exercises regarding the effects on the intraocular pressure.
Methods: The search engines Google Scholar and PubMed were used to search for suitable studies. The studies were summarized, and the most important data were collected in one table for each study. The effect on the IOP was extracted or, if not given in the article, calculated by the difference of means of the IOP after or during exercise, and the baseline IOP before, whenever these values were available.
Findings: A total of 47 studies out of the years 1990 to 2020 that investigated the influence on the IOP of the most popular sports actively practiced in Germany were reviewed and summarized: twelve for running, sixteen for fitness/ weight training, one for swimming/diving, twelve for cycling, four for hiking, and two for yoga.
Conclusions: Throughout all studies and sports it was seen that physical fitness stabilized the IOP. Higher
intensity of exercise led to higher fluctuations of the IOP. Moderate endurance training keeps the IOP fluctuations low and may lead to a lower baseline IOP if practiced on a regular base. Fitness and weight training lead to fluctuations of the IOP in a pronounced manner when performed at moderate and high intensity. Therefore, only a moderate training can be recommended if there is need to keep the IOP stable. Isometric exercise is not recommended as it provokes a rise of the IOP even when performed with light loads. The Valsalva Maneuver should always be avoided as it leads to additional fluctuations of the IOP. Also, the IOP behaved more stable during resistance training when higher fitness was present.
Production processes in Industry 4.0 settings are usually highly automated. However, many complicated tasks, such as machine maintenance, must be executed by human workers. In current smart factories, such tasks can be supported by Augmented Reality (AR) devices. These AR tasks rely on high numbers of contextual factors like live data from machines or work safety conditions and are mostly not well integrated into the global production process. This can lead to various problems like suboptimal task assignment, over-exposure of workers to hazards like noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) are not capable of readily taking such factors into account. There- fore, this contribution proposes a novel approach for context- integrated modeling and execution of processes with AR tasks. Our practical evaluations show that our AR Process Framework can be easily integrated with prevalent BPMS. Furthermore, we have created a comprehensive simulation scenario and our findings suggest that the application of this system can lead to various benefits, like better quality of AR task execution and cost savings regarding the overall Industry 4.0 processes.
Industry 4.0 production comprises complicated highly automated processes. However, human activities are also a crucial component of these processes, e.g., for machine main- tenance. Task assignment of human resources in this domain is challenging, as many factors have to be taken into account to ensure effective and efficient activity execution and satisfy special conditions (like worker safety). To overcome the limita- tions of current Business Process Management (BPM) Systems regarding activity resource assignment, this contribution provides a BPM-integrated approach that applies fuzzy sets for activity assignment. Our findings suggest that this approach can be easily applied to complex production scenarios, while providing efficient performance even with a large number of concurrent activity assignment requests. Additionally, our evaluation shows its potential for improved work distribution which can lead to cost savings in Industry 4.0 production processes.
Strategy development is one of the crucial factors for a firm's performance. For it
to be developed, a strategic analysis has to be conducted first. It enables
companies to gain a deeper understanding of their internal and external
environment. In the present work, the specialty coffee market is closely analyzed through a strategic analysis. The focus of this study is the young company Tikuna, a coffee producer that aims to enter the German market. In this context, Tikuna's possible entry into the German market and the companies competitive capacities are analyzed. In order to implement the different tools of the analysis, extensive literature research, as well as one expert interview and a survey were conducted.
It was found that Tikuna possesses all characteristics to enter the German
market. However, due to the lack of a differentiation factor in Tikuna's value
proposition, its competitive capacity is limited to a short period of time. In this
sense, different recommendations are given in order to ensure long term success
in the market. The central one being that Tikuna has to use its main strength and
bring innovation to the market.
Forming complex parts out of high and ultra-high strength aluminium alloys has proved to be more challenging in comparison to the currently used deep drawing steels. Nevertheless, aluminium alloys show a limited formability in contrast with, for example, deep drawing steels. Novel processes like Warm-forming, W-Temper or Hotforming, offer the potential to produce light and highly integrated one-piece components from such aluminium alloys at elevated temperatures. When considering aluminium alloys of the 7000 group, which can reach strength values (UTS) of about 600 MPa, crash components such as side impact bars would offer a suitable field of application.
Forming at elevated temperatures, in particular with the Hotforming process, offers high potential in the production of complex structural components on the one hand and in the use of existing press hardening equipment on the other. To date, the material behaviour of aluminium alloys in the 7000 group, applied in such processes and in the later final state after forming, is not sufficiently known.
Therefore, in this study, systematic investigations on the formability and the final strength during and after forming at elevated temperature of the EN AW-7075 aluminium wrought alloy from different suppliers are conducted. In general, material- and damage/ failure models were created and implemented into simulation in order to make predictions. Characterisation of the plastic material properties on the basis of various tensile specimens as shear-, notched-, tensile- and Erichsen tests are carried out to adapt the complex material- and failure models such as Barlat YLD2000 and GISSMO to the experimental values using a parameter optimisation. These were made for the material conditions during forming, i.e. after solution heat treatment, the final condition after artificial ageing at 180°C for 20 minutes, which corresponds to the cathodic dip coating, and the T6 condition, which is the highest strength condition.
To evaluate a suitable friction coefficient for high temperature forming processes, anti-friction agents are screened, and the potential applicability evaluated by strip-drawing tests. Thereby, using an analytical relationship, friction coefficients are determined at room temperature and 180°C, which are used as corresponding friction model for the finite element forming simulation.
Crash simulations using the nonlinear finite element method (FEM) of side impact protection beams are used to demonstrate the weight saving potential of high and ultra-high strength aluminium alloys compared to a beam made of press hardened steel. A weight saving of about 20 % could be achieved with the same crash performance. This can be significantly increased to around 30 % - 40 % by using local reinforcements such as CFRP or GFRP (carbon/ glass fibre reinforced plastic) patch. For this reason, a novel process was developed which is based on the conventional Hotforming process with an integrated thermal direct joining step called “Extended Hotforming”.
Subsequently, a heatable forming tool for the production of a serial like sheet metal side impact beam was developed to validate the finite element simulation and to demonstrate the potential of the forming processes at elevated temperatures for aluminium sheet metal components.
As the amount of software source code increases, manual approaches for documentation or detection of software design patterns in source code become inefficient relative to the value. Furthermore, typical automatic pattern detection tools are limited to a single programming language. To address this, our Design Pattern Detection using Machine Learning (DPDML) offers a generalized and programming language agnostic approach for automated design pattern detection based on machine learning (ML). The focus of our evaluation was on ensuring DPDML can reasonably detect one design pattern in the structural, creational, and behavioral category for two popular programming languages (Java and C#). 60 unique Java and C# code projects were used to train the artificial neural network (ANN) and 15 projects were then used to test pattern detection. The results show the feasibility and potential for pursuing an ANN approach for automated design pattern detection.
Novel myopia control spectacle lenses induce peripheral contrast reduction via optical diffusion. It is suggested, that the contrast reduction alters retinal processes in the low-level neural circuity, leading to an inhibition of eye growth. The purpose of this thesis is to evaluate the influence of full-field contrast reduction on low-level neural processing of the retina, described by the edge contrast sensitivity.
Enterprise Architecture (EA) Frameworks (EAFs) have attempted to support comprehensive and cohesive modeling and documentation of the enterprise. However, these EAFs were not conceived for today’s rapidly digitalized enterprises and the associated IT complexity. A digitally-centric EAF is needed, freed from the past restrictive EAF paradigms and embracing the new potential in a data-centric world. This paper proposes an alternative EAF that is digital, holistic, and digitally sustainable - the Digital Diamond Framework. D2F is designed for responsive and agile enterprises, for aligning business plans and initiatives with the actual enterprise state, and addressing the needs of EA for digitized structure, order, modeling, and documentation. The feasibility of D2F is demonstrated with a prototype implementation of an EA tool that applies its principles, showing how the framework can be practically realized, while a case study based on ArchiSurance example and an initial performance and scalability characterization provide additional insights as to its viability.
Databases are becoming an ubiquitous and integral part of most software as the data era and the Internet of Everything unfolds. Alternative database types such as NoSQL grow in popularity and allow data to be stored and accessed more simply or in new ways. Thus, software developers, not just database specialists, are more likely to encounter and need to deal with databases. Virtual Reality (VR) technology has grown in popularity, yet its integration in the software development tool chain has been limited. One potential application area for VR technology that has not been sufficiently explored is database-model visualization. This paper describes Virtual Reality Immersion in Data Models (VRiDaM), a generic database-model approach for visualizing, navigating, and conveying database-model information interactively. It describes and explores both native VR and WebVR solution concepts, with prototypes showing the viability of the approach.
The direct ophthalmoscope is a retinal screening tool that has been in existence and development for more than 150 years, yet, the rapid influence of technological evolution in screening tools, has left the direct ophthalmoscope untouched. The main purpose of this master thesis is to determine if the direct ophthalmoscope has reached its maximum potential of development and, additionally, to determine if a further development, including a more electronic input, would be feasible.