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In the engineering domain, representing real-world objects using a body of data, called a digital twin, which is frequently updated by “live” measurements, has shown various advantages over tradi- tional modelling and simulation techniques. Consequently, urban planners have a strong interest in digital twin technology, since it provides them with a laboratory for experimenting with data before making far-reaching decisions. Realizing these decisions involves the work of professionals in the architecture, engineering and construction (AEC) domain who nowadays collaborate via the methodology of building information modeling (BIM). At the same time, the citizen plays an integral role both in the data acquisition phase, while also being a beneficiary of the improved resource management strategies. In this paper, we present a prototype for a “digital energy twin” platform we designed in cooperation with the city of Regensburg. We show how our extensible platform de- sign can satisfy the various requirements of multiple user groups through a series of data processing solutions and visualizations, in- dicating valuable design and implementation guidelines for future projects. In particular, we focus on two example use cases concern- ing building electricity monitoring and BIM. By implementing a flexible data processing architecture we can involve citizens in the data acquisition process, meeting the demands of modern users regarding maximum transparency in the handling of their data.
In the realm of parallel computing, optimization plays a pivotal role in achieving efficient and scalable solutions. In this work, we present the parallelization of a hybrid genetic search for solving the Capacitated Vehicle Routing Problem with Pickup and Delivery (CVRPPD).It leverages the synergy between genetic algorithms and parallel computing to address the complex optimization problem. This hybrid algorithm combines a customized version of local search with a genetic algorithm to compute an effective solution. Our implementation makes use of the Message Passing Interface (MPI) for data distribution and parallel execution. In addition, we run multi-threaded processes on NVIDIA graphical processors using the CUDA technology, which further increases the computation speed and consequently minimizes the runtime. Parallelization also allows the best-improvement strategy to be used instead of the rst-improvement strategy while maintaining the same runtime. We store the resulting routes in a bus route database which we created as the basis of an extensive library of optimal routes for our specifc use case of optimizing bus routes in a rural area. The experimental results on real road data show that the parallel implementation of the Hybrid Genetic Search (HGS) achieves significant improvements in runtime over the sequential implementation above a certain problem size. We believe that our implementation of the parallel hybrid genetic search method can have a great in influence on optimization strategies in parallel computing and can also be applied to other subproblems of the VRP.
In educational research, non-personalized learning content increases learners' cognitive load, causing them to lower their performance and sometimes drop out of the course. Personalizing learning content with learners’ unique characteristics, like learning styles, personality traits, and learning strategies, is being suggested to improve learners’ success. Several theories exist for assessing learners’ unique characteristics. By the end of 2020, 71 learning style theories have been formulated, and research has shown that combining multiple learning style theories to recommend learning paths yields better results. As of the end of 2022, there is no single research that demonstrates a relationship between the Index of Learning Styles (ILS) based Felder-Silverman learning style model (FSLSM) dimensions, Big Five (BFI-10) based personality traits, and the Learning strategies in studying (LIST-K) based learning strategies factors for personalizing learning content.
In this paper, an innovative approach is proposed to estimate the relationship between these theories and map the corresponding learning elements to create personalized learning paths. Respective questionnaires were distributed to 297 higher education students for data collection. A three-step approach was formulated to estimate the relationship between the models. First, a literature search was conducted to find existing studies. Then, an expert interview was carried out with a group of one software engineering education research professor, three doctoral students, and two master’s students. Finally, the correlations between the students' questionnaire responses were calculated. To achieve this, a Bayesian Network was built with expert knowledge from the three-step approach, and the weights were learned from collected data. The probability of individual FSLSM learning style dimensions was estimated for a new test sample. Based on the literature, the learning elements were mapped to the respective FSLSM learning style dimensions and were initiated as learning paths to the learners.
The next steps are proposed to extend this framework and dynamically recommend learning paths in real time. In addition, the individual levels of learning style dimensions, personality traits, and learning strategies can be considered to improve the recommendations. Further, using probabilities for mapping learning elements to learning styles can increase the chance of initiating multiple learning paths for an individual learner.
This paper presents the results of a data collection with the LIST-K questionnaire. This questionnaire measures students’ learning strategies and shows which strategies are particularly dominant or rather weak.
Learning strategies have long been a major area of research in educational science and psychology. In these disciplines, learning strategies are understood as intentional behaviors and cognitive skills that learners employ to effectively complete learning tasks, by selecting, acquiring, organizing, and integrating information into their existing knowledge for long-term retention.
The LIST-K, developed by Klingsieck in 2018, was chosen for accessing learning strategies due to its thematic suitability, widespread use, and test economy. It covers a total of four main categories (i.e., cognitive strategies, metacognitive strategies, management of internal resources, and management of external resources), each of which are subdivided into further subscales. With a total of 39 items answered via a 5-step Likert scale, the LIST-K can cover the topic relatively comprehensively and at the same time be completed in a reasonable amount of time of approximately 10 minutes.
The LIST-K was used as part of a combined data collection along with other questionnaires on their personal data, their preferences regarding certain learning elements, their learning style (i.e. the ILS), and personality (i.e. the BFI-10). A total of 207 students from different study programs participated via an online survey created using the survey tool "LimeSurvey". Participation in the study was voluntary, anonymously, and in compliance with the GDPR.
Overall, the results of the LIST-K show that students are willing to work intensively on relevant topics intensively and to perform beyond the requirements of the course seeking additional learning material. At the same time, however, it is apparent that the organization of their own learning process could still be improved. For example, students start repeating content too late (mean=2.70; SD=0.92) and do not set goals for themselves and do not create a learning plan (mean=3.19; SD=0.90). They also learn without a schedule (mean=2.23; SD=0.97) and miss opportunities to learn together with other students (mean=3.17; SD=0.94).
The findings of the data collection will be used to create an AI-based adaptive learning management system that will create individualized learning paths for students in their respective courses. From the results of the LIST-K, it appears that the adaptive learning management system should primarily support organizational aspects of student learning. Even small impulses (an individual schedule of when to learn what or a hierarchical structuring of the learning material) could help students to complete their courses more successfully and improve their learning.
Eye tracking has proven to be a powerful tool in a variety of empirical research areas; hence, it is steadily gaining attention. Driven by the expanding frontiers of Artificial Intelligence and its potential for data analysis, eye tracking technology offers promising applications in diverse fields, from usability research to cognitive research. The education sector in particular can benefit from the increased use of eye tracking technology - both indirectly, for example by studying the differences in gaze patterns between experts and novices to identify promising strategies, and directly by using the technology itself to teach in future classrooms.
As with any empirical method, the results depend directly on the quality of the data collected. That raises the question of which parameters educators or researchers can influence to maximize the data quality of an eye tracker. This is the starting point of the present work: In an empirical study of eye tracking as an (educational) technology, we systematically examine factors that influence the data quality, such as illumination, sampling frequency, and head orientation - parameters that can be varied without much additional effort in everyday classroom or research use - using two human subjects, an artificial face, and the Tobii Pro Spectrum.
We rely on metrics derived from the raw gaze data, such as accuracy or precision, to measure data quality. The obtained results derive practical advice for educators and researchers, such as using the lowest sampling frequency appropriate for a certain purpose. Thereby, this research fills a gap in the current understanding of eye tracker performance and, by offering best practices, enables researchers or teachers to produce data of the highest quality possible and therefore best results when using eye trackers in laboratories or future classrooms.
Universities are faced with a rising number of dropouts in recent years. This is largely due to students' limited capability of finding individual learning paths through various course materials. However, a possible solution to this problem is the introduction of adaptive learning management systems, which recommend tailored learning paths to students – based on their individual learning styles. For the classification of learning styles, the most commonly used methods are questionnaires and learning analytics. Nevertheless, both methods are prone to errors: questionnaires may give superficial answers due to lack of time or motivation, while learning analytics do not reflect offline learning behavior. This paper proposes an alternative approach to classify students' learning styles by integrating eye tracking in combination with Machine Learning (ML) algorithms.
Incorporating eye tracking technology into the classification process eliminates the potential problems arising from questionnaires or learning analytics by providing a more objective and detailed analysis of the subject's behavior. Moreover, this approach allows for a deeper understanding of subconscious processes and provides valuable insights into the individualized learning preferences of students.
In order to demonstrate this approach, an eye tracking study is conducted with 117 participants using the Tobii Pro Fusion. Using qualitative and quantitative analyses, certain patterns in the subjects' gaze behavior are assigned to their learning styles given by the validated Index of Learning Styles (ILS) questionnaire.
In short, this paper presents an innovative solution to the challenges associated with classifying students' learning styles. By combining eye tracking data with ML algorithms, an accurate and insightful understanding of students' individual learning paths can be achieved, ultimately leading to improved educational outcomes and reduced dropout rates.
Moving Object Databases are designed to store and process database objects with attributes that can change over time. Simple examples are moving points, that change position over time, a bit more complex are moving regions, that can also change shape. The spatial and spatiotemporal object types in current moving objects databases are limited to two dimensions. This work strives to extend the set of spatial moving object types into the third and even higher dimensions while preserving a consistent family of operations for it. A robust algorithm for the interpolation of two regions to a moving region of any dimensionality is developed, as well as the fundamental ideas for several other operations.
Finding the optimal join order (JO) is one of the most important problems in query optimisation, and has been extensively considered in research and practise. As it involves huge search spaces, approximation approaches and heuristics are commonly used, which explore a reduced solution space at the cost of solution quality. To explore even large JO search spaces, we may consider special-purpose software, such as mixed-integer linear programming (MILP) solvers, which have successfully solved JO problems. However, even mature solvers cannot overcome the limitations of conventional hardware prompted by the end of Moore’s law. We consider quantum-inspired digital annealing hardware, which takes inspiration from quantum processing units (QPUs). Unlike QPUs, which likely remain limited in size and reliability in the near and mid-term future, the digital annealer (DA) can solve large instances of mathematically encoded optimisation problems today. We derive a novel, native encoding for the JO problem tailored to this class of machines that substantially improves over known MILP and quantum-based encodings, and reduces encoding size over the state-of-the-art. By augmenting the computation with a novel readout method, we derive valid join orders for each solution obtained by the (probabilistically operating) DA. Most importantly and despite an extremely large solution space, our approach scales to practically relevant dimensions of around 50 relations and improves result quality over conventionally employed approaches, adding a novel alternative to solving the long-standing JO problem.
Control Oriented Mathematical Modeling of a Bidirectional DC-DC Converter - Part 1: Buck Mode
(2023)
Parallel connection of different batteries equipped with bidirectional DC-DC converters offers an increase of the total storage capacity, the provision of higher currents and an improvement of reliability and system availability. To share the load current among the DC-DC converters while maintaining the safe operating range of the batteries, appropriate controllers are needed. The basis for the design of these control approaches requires knowledge of both the static and dynamic characteristics of the DC-DC converter used. In this paper, the small signal analysis of a DC-DC converter in buck mode is shown using the circuit averaging technique. The paper gives an overview of all required transfer functions:. The control and line to output transfer functions for CCM and DCM relevant for average current mode control as well as for voltage control are derived and their poles and zeros are determined. This provides the basis for stability consideration, analysis of the overall control structure and controller design.
This paper examines the conceptualization of sustainability in the context of information and communication technology (ICT) research. Through an inductive text analysis of sixteen literature reviews spanning from 2014 to 2023, key themes and concepts are identified, highlighting the complex relationship between ICT and sustainability. ICT is perceived both as an enabler and a problem for sustainability. Furthermore, the terminology and concept of sustainability in the context of ICT remain unclear. The emergence of digitalization as a novel socio-technical phenomenon poses additional challenges for conceptual alignment. While a holistic view of sustainability in ICT is desired, business and social implications receive less attention. The paper summarizes and discusses the developments in research on this topic over the past decade.
In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.
In the field of software engineering, graph-based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout.
In a distributed system, functionally equivalent nodes work together to form a system with improved availability, reliability and fault tolerance. Thereby, the purpose is to achieve a common control objective. As multiple components cooperate to accomplish tasks, coordination between them is required. Electing a node as the temporary leader can be a possible solution to perform coordination. This work presents a self-stabilizing algorithm for the election of a leader in dynamically reconfigurable bus topology-based broadcast systems with a message and time complexity of O(1). The election is performed dynamically, i.e., not only when the leader node fails, and is criterion-based. The criterion used is a performance related value which evaluates the properties of the node regarding the ability to perform the tasks of the leader. The increased demands on the leader are taken into account and a re-election is started when the criterion value drops below a predefined level. The goal here is to distribute the load more evenly and to reduce the probability of failure due to overload of individual nodes. For improved system availability and reduced fault rates, a management level consisting of leader, assistant and co-assistant is introduced. This reduces the number of required messages and the duration in case of non-initial election. For further reduction of required messages to uniquely determine a leader, the CAN protocol is exploited. The proposed algorithm selects a node with an improved failure rate and a reduced message and hence time complexity while satisfying the safety and termination constraints. The operation of the algorithm is validated using a hardware test setup.
Auch kleine und mittlere Unternehmen (KMUs) benötigen zunehmend ein effektives Informationstechnologie- (IT)-Management, um wettbewerbsfähig zu bleiben. Im Vergleich zu großen Unternehmen verfügen KMUs jedoch oft nicht über die Ressourcen, die Arbeitgeberattraktivität oder den Bedarf, um einen Chief Information Officer (CIO) in Vollzeit zu beschäftigen. Um diese Lücke zu schließen, hat eine wachsende Zahl von Expertinnen und Experten weltweit damit begonnen, CIO-Dienste in Teilzeit anzubieten. Auf diese Weise erhalten KMUs Zugang zu erfahrenen und kompetenten IT-Führungskräften zu einem Bruchteil der Kosten und ohne langfristige Verpflichtungen. Während diese so genannten „Fractional CIOs“ in der Praxis bereits einen Mehrwert schaffen, gibt es noch kaum wissenschaftliche Untersuchungen zu diesem neuen Phänomen. In einem größeren Forschungsprojekt mit insgesamt 62 Fractional CIOs aus 10 Ländern wurden daher eine Definition, Typen verschiedenartiger Engagements und Erfolgsfaktoren abgeleitet. Die vorliegende Studie fasst die Ergebnisse zusammen und setzt sie in Bezug zum deutschen Markt, indem sie drei Fractional CIOs/CTOs aus Deutschland befragt. Es zeigt sich, dass die folgenden vier Engagement-Typen von Fractional CIOs für KMUs in verschiedenen Situationen von Nutzen sind: Strategisches IT-Management, Restrukturierung, Skalierung und Hands-on Support. Darüber hinaus zeigt die Studie, dass Vertrauen, die Unterstützung durch das Top-Management-Team und die Integrität des Fractional CIOs Schlüsselfaktoren für den Erfolg von Fractional CIO-Engagements sind. Für den deutschen Markt werden die Ergebnisse durch drei befragte Fractional CIOs/CTOs weitgehend bestätigt. Die Fractional CIOs/CTOs können zwar keine genauen Gründe für die geringe Akzeptanz der Rolle nennen, betonen aber ihr Wertpotenzial für den deutschen Markt.
The average tenure of Chief Information Officers (CIOs) has increased over the past few years. Nevertheless, the average tenure of CIOs is shorter than that of Chief Executive Officers (CEOs). While most studies on tenure and background are based on data from US IT executives, studies on German CIOs are missing. This study analyzes the tenure of German CIOs as a proxy for management effectiveness and how certain factors influence it. An original and unique dataset of 384 IT executives from German companies is examined. The data include the size and industry sector of the companies, educational and professional backgrounds of the CIOs, and the CIOs' reporting lines. Data were analyzed using the chi-square test and Fisher's exact test. The German CIOs had a median tenure of 4.0 years. However, if we examine executives who are currently in office and executives with a completed term of office separately , the median tenure differs. The results also show that German CIOs do not have shorter tenures than German CEOs. When compared with US CIOs, the results depend on the values selected for comparison. In addition, the analysis shows that neither the size and industry sector of the companies nor the educational and professional backgrounds of the CIOs and managers of the CIO reports have a statistically significant influence on the tenure of IT executives. The factors examined in this study can be considered as preconditions for the CIO position. In the future, factors that play a role during tenure should be examined.
Im Rahmen eines Forschungsprojektes soll eine Plattform für die Vermittlung von Telekonsilen und die Bereitstellung einer Konsilakte an die Telematik-Infrastruktur (TI) angeschlossen werden. Um sowohl eine bestmögliche Skalierbarkeit als auch eine optimale Integrierbarkeit in bestehende Systeme und Anwendungen zu erreichen, wurde HL7 FHIR als syntaktischer Standard für das Reha-Konsil festgelegt. Dieses Dokument liefert einen systematischen Überblick über die notwendigen Schritte und Voraussetzungen, um diesen Anschluss zu bewerkstelligen.
An immense diversity in bottle types requires high accuracy during sorting for recycling purposes by breweries. This extremely complex and time-consuming procedure can result in enormous additional costs for them. This paper presents transfer learning-based algorithms for classifying beer bottle brands using camera images, applicable in individual sorting solutions for different use cases. The problem is tackled using customised EfficientNet, InceptionResNet and VGG models along with an augmented dataset. In addition, a detailed analysis of different model and parameter combinations is performed, enabling tailor-made technologies for specific conditions and resource limitations. In accompanying validations and subsequent tests, a test accuracy of 100% in the recognition of beer brands could be achieved, proving the proposed method fully contributes to the solution of the problem.
Preface QDSM
(2023)
The first international workshop on Quantum Data Science and anagement (QDSM), co-located with VLDB 2023, is centered around addressing the possibilities of quantum computing for data science and data management. Quantum computing is a relatively new and emerging field that is believed to have huge computational potential in the future. In the QDSM workshop, we want to provide a venue for discussing and publishing novel results of applying quantum computing to hard data science and data management problems. These problems include join order optimization, designing efficient quantum feature maps, studying possibilities of solving linear programs with quantum algorithms, and divergent index tuning with quantum machine learning. Besides, we include a short and visionary survey on quantum computing for databases. Theworkshop provides a platform for active discussion on these and related topics.
Recent advances in the manufacture of quantum computers attract much attention over a wide range of fields, as early-stage quantum processing units (QPU) have become accessible. While contemporary quantum machines are very limited in size and capabilities, mature QPUs are speculated to eventually excel at optimisation problems. This makes them an attractive technology for database problems, many of which are based on complex optimisation problems with large solution spaces. Yet, the use of quantum approaches on database problems remains largely unexplored. In this paper, we address the long-standing join ordering problem, one of the most extensively researched database problems. Rather than running arbitrary code, QPUs require specific mathematical problem encodings. An encoding for the join ordering problem was recently proposed, allowing first small-scale queries to be optimised on quantum hardware. However, it is based on a faithful transformation of a mixed integer linear programming (MILP) formulation for JO, and inherits all limitations of the MILP method. Most strikingly, the existing encoding only considers a solution space with left-deep join trees, which tend to yield larger costs than general, bushy join trees. We propose a novel QUBO encoding for the join ordering problem. Rather than transforming existing formulations, we
construct a native encoding tailored to quantum systems, which allows us to process general bushy join trees. This makes the
full potential of QPUs available for solving join order optimisation problems.
Inverse problems are inherently ill-posed and therefore require regularization techniques to achieve a stable solution. While traditional variational methods have wellestablished theoretical foundations, recent advances in machine learning based approaches have shown remarkable practical performance. However, the theoretical foundations of learning-based methods in the context of regularization are still underexplored. In this paper, we propose a general framework that addresses the current gap between learning-based methods and regularization strategies. In particular, our approach emphasizes the crucial role of data consistency in the solution of inverse problems and introduces the concept of data-proximal null-space networks as a key component for their solution. We provide a complete convergence analysis by extending the concept of regularizing null-space networks with data proximity in the visual part. We present numerical results for limited-view computed tomography to illustrate the validity of our framework.
In Anbetracht der hohen Zahl an zu ertüchtigenden Bauwerken in Deutschland bei gleichzeitig zunehmendem Fachkräftemangel in der Baubranche ist es wichtig, effiziente Planungs- und Bauprozesse zu schaffen. Digitale und automatisierte Arbeitsmethoden auf Basis von digitalen Bauwerksmodellen beginnen sich daher zu etablieren. Für viele Bestandsbauwerke werden diese Methoden aber nur zögerlich angewendet, da das als Grundlage benötigte digitale Bauwerksmodell erst aufwendig erstellt werden müsste. Moderne Techniken der künstlichen Intelligenz (KI) wie Machine- oder Deep Learning bewegen im Moment viele Forschungs- und Wirtschaftsbereiche. Ihr Einsatz kann auch in der Bauwirtschaft Lösungen für komplexe oder repetitive Aufgaben bieten.
In dieser Arbeit wird ein Tool konzipiert und evaluiert, welches in der Lage ist, mithilfe von KI-Techniken aus Bestandsplänen dreidimensionale (3D) Bauwerksmodelle zu rekonstruieren. Bei der Konzeptionierung hat sich eine der großen Herausforderung beim Einsatz von Machine Learning herauskristallisiert. Um KI-Systeme überwacht trainieren zu können, werden sog. gelabelte Trainingsdaten benötigt. In diesem konkreten Fall Positionspläne, bei denen in einer maschinenlesbaren Form hinterlegt ist, welche Art von Bauteilen an welcher Stelle des Planes dargestellt sind. Damit kann eine KI lernen, selbstständig unbekannte Pläne zu analysieren und darin Bauteile zu klassifizieren. Diese Daten können entweder unter großem Aufwand manuell erstellt werden oder man greift auf synthetische Daten zurück. Synthetische Daten sind künstlich erschaffene Daten, die von ihrer Struktur und ihren Eigenschaften her echte Daten imitieren. Da dabei der Erzeugungsprozess gesteuert wird, lassen sich die benötigten Labels gleichzeitig miterzeugen. Auf diese Weise entsteht sog. Ground Truth Data, also Daten, bei denen man sich sicher sein kann, dass sie korrekt gelabelt sind. Für diese Arbeit wurde eine Datenpipeline umgesetzt, die in der Lage ist, synthetische Daten speziell für die Zwecke der Baubranche zu generieren. Mithilfe des Applications Programming Interface von Autodesk Revit wurde eine C#-Anwendung programmiert, die zufallsgesteuert Modelle erzeugt und anhand dieser Modelle realistische Pläne ableitet. Parallel dazu werden mithilfe der im Modell hinterlegten Bauteilinformationen die für die KI benötigten gelabelten Daten erzeugt. Erste Testläufe mit dem Objekterkennungsframework YOLOv5 waren vielversprechend. Der gewählte Ansatz hat sich somit als flexibel und skalierbar erwiesen, um damit KI-Systeme für bauspezifische Aufgaben - wie die Bauteilerkennung auf Plänen - zu trainieren.
Ziel der Studie:
Ziel der Studie ist die Messung des Stands der Digitalisierung und die mit einer Anbindung an die Telematikinfrastruktur verbundenen Chancen und Herausforderungen für Rehabilitationseinrichtungen.
Methodik:
Teilstandardisierte Online-Befragung bei Trägern von Rehabilitationseinrichtungen in Bayern (n=33). Der Fragebogen mit 36 Fragen beinhaltet eine leicht veränderte Skala auf Basis des „Electronic Medical Record Adoption Model (EMRAM)“.
Ergebnisse:
Der Digitalisierungsgrad wurde in 70 Prozent der Rehabilitationseinrichtungen mit Stufe 0 angegeben (Stufenmodell bis 7). Die Übermittlung patientenbezogener Daten (Eingang und Ausgang) erfolgt häufig analog, wohingegen die Verarbeitung innerhalb der Einrichtung in vielen Fällen bereits überwiegend digital ist. Beim Anschluss an die Telematikinfrastruktur wird hoher Aufwand bei der Installation, aber auch der Schulung des Personals und der Anpassung der Arbeitsorganisation gesehen.
Schlussfolgerung:
Durch Änderung der gesetzlich-finanziellen Lage in Deutschland eröffnen sich für Rehabilitationseinrichtungen neue Möglichkeiten einer verstärkten Digitalisierung. Hürden hängen mit Anforderungen an IT-Sicherheit, Schulung des Personals und sowie dem ebenfalls geringen Digitalisierungsstand bei Krankenhäusern und Ärzt*innen sowie Patient*innen zusammen, die eine digitale Datenübermittlung erschweren.
The Internet of Things (IoT) is an emerging computing paradigm providing new approaches to collect and analyze environmental data. However, as specific challenges arose, the paradigm of Edge Computing with its potential solution capabilities came into place. The combination of both paradigms is currently highly discussed in industry and research. This paper aims to contribute to this field by conducting a systematic literature review to examine the differences and relation between IoT and Edge Computing on a meta-level. It first investigates conceptual backgrounds, use cases, and implementation types. After that, the differences between the paradigms are highlighted. It becomes clear that the significant distinction is in the architectural composition. However, the scientific consensus reveals that both paradigms have a common historical background, and Edge Computing is perceived as the next step in the evolution of IoT. Furthermore, Edge Computing-based systems can address common IoT challenges identified in the two paradigms’ problem-solution space. Ultimately, there is a need for further research in security, edge intelligence, and standardization, with Edge Computing frameworks able to address these in practice.
Lean IT
(2023)
Companies have applied Lean Management and its methods in their production functions for several decades. They also increasingly use Lean Management to improve service delivery, for example, in their IT organizations, which is referred to as “Lean IT”. Lean IT finds widespread recognition in business practice, but corresponding academic research is still scarce. The paper at hand intends to shed light on the current perspectives of Lean IT from an academic point and a practitioner point of view. The paper applies an innovative quantitative approach of literature analysis using semantic entity annotator and a keyword analysis to systematically identify and compare topics academics and practitioners deem relevant in context of Lean IT. We analyze practitioner media and scholarly articles published from January 2014 to June 2019. The analysis shows that research does not seem to adequately address the topics that are highly relevant for practitioners when it comes to Lean IT, e.g., issues pertinent to Automation, DevOps, role of the CIO, IT Service Management or Scrum in context of Lean IT are under-researched. Our analysis further shows that interest in Lean IT as a field is rising in both groups. Our study can help to guide further research activities.
Business process improvement (BPI) is of high priority for practitioners. But especially the most value-adding phase in a BPI project, namely the “act of improvement”, is insufficiently supported despite the many existing methods and techniques. Until now, it is largely unclear as to what degree existing BPI techniques support each other and are interrelated with one another. Thus, the purpose of this paper is to investigate the functional interdependencies between BPI techniques to get a better understanding for the beneficial synergies between the BPI techniques and to provide a basis for purposefully combining them within projects. Based on the functional interdependencies, a graphical “Functional Interdependency Map” is developed and its usability demonstrated in an experiment. The paper is valuable for academics and practitioners alike because the impact of BPI on organizational performance is high.
MongoDB kompakt
(2023)
Die Dokumentendatenbank MongoDB ist das am weitesten verbreitete NoSQL-Datenbanksystem. Dieses kompakte Werk präsentiert MongoDB von der Installation bis zur Administrierung in großen Rechenclustern. Sie erfahren, wie Sie JSON-Dokumente in Kollektionen einfügen, suchen, ändern und löschen, wie man Replikation und Sharding effektiv einsetzt und wie Sie komplexe Analysen mithilfe der Aggregation-Pipeline durchführen können.
This study uses holistic models of image perception to analyze and interpret eye movements during a code review. 23 participants (15 novices and 8 experts) take part in the experiment. The subjects’ task is to review six short code examples in C programming language and identify possible errors. During the experiment, their eye movements are recorded by an SMI 250 REDmobile. Additional data is collected through questionnaires and retrospective interviews. The results implicate that holistic models of image perception provide a suitable theoretical background for the analysis and interpretation of eye movements during code reviews. The assumptions of these models are particularly evident for expert programmers. Their approach can be divided into different phases with characteristic eye movement patterns. It is best described as switching between scans of the code example (global viewing) and the detailed examination of errors (focal viewing).
Nowadays, learning management systems are widely employed in all educational institutions to instruct students as a result of the increasing in online usage. Today’s learning management systems provide learning paths without personalizing them to the characteristics of the learner. Therefore, research these days is concentrated on employing AI-based strategies to personalize the systems. However, there are many different AI algorithms, making it challenging to determine which ones are most suited for taking into account the many different features of learner data and learning contents. This paper conducts a systematic literature review in order to discuss the AI-based methods that are frequently used to identify learner characteristics, organize the learning contents, recommend learning paths, and highlight their advantages and disadvantages.
Nowadays, most database lectures are performed with an accompanying visual presentation that further illustrates the conveyed facts. Conventional presentation software allows dynamic elements up to a certain level, for example revealing or changing parts of the slide step by step, or even an interaction with the viewers by means of polls or similar mechanisms. Recently, HTML-and browser-based frameworks for presentations have emerged, which allow an even higher degree of flexibility due to the manifold possibilities of HTML5 and JavaScript. This paper presents an approach of how to interactively modify parts of a slide during the presentation, like SQL-based queries or program code snippets, and show the results pretty-printed on the corresponding slide in real-time. This enables the lecturer to easily show more examples, and answer and illustrate side questions, which they did not prepare in advance.
Typical Alexa skills and other add-ons for voice assistants need to be custom developed for their one specific use case. This paper presents an approach to map arbitrary data sources (databases, APIs, services) to the relational model by using SQL/MED and to transform voice-based queries into SQL. The key challenges for such a universal skill are to correctly map the natural-language question into a SQL query on the correct source table in the federated database and to convert the result set back to a compact and well-understandable answer.
As humans, we tend to use models to describe reality. Modeling languages provide the formal frameworks for creating such models. Usually, the graphical design of individual model elements is based on subjective decisions; their suitability is determined at most by the prevalence of the modeling language. With other words: there is no objective way to compare different designs of model elements. The present paper addresses this issue: it introduces a systematic approach for evaluating the elements of graph-based modeling languages comprising 14 criteria – derived from standards, usability analyses, or the design theories ‘Physics of Notations’ and ‘Cognitive Dimensions of Notations’. The criteria come with measurement procedures and evaluation schemes based on reasoning, eye tracking, and questioning. The developed approach is demonstrated with a specific use case: three distinct sets of node elements for causal graphs are evaluated in an eye tracking study with 41 subjects.
The dropout rate at universities has been very high for years. Thereby, the inexperience and lack of knowledge of students in dealing with individual learning paths in various courses of study plays a decisive role. Adaptive learning management systems are suitable countermeasures, in which learners’ learning styles are classified using questionnaires or computationally intensive algorithms before a learning path is suggested accordingly. In this paper, a study design for student learning style classification using eye tracking is presented. Furthermore, qualitative and quantitative analyses clarify certain relationships between students’ eye movements and learning styles. With the help of classification based on eye tracking, the filling out of questionnaires or the integration of computationally or cost-intensive algorithms can be made redundant in the future.
This paper assesses the relation between personality, demographics, and learning style. Hence, data is collected from 200 participants using 1) the BFI-10 to obtain the participant’s expression of personality traits according to the five-factor model, 2) the ILS to determine the participant’s learning style according to Felder and Silverman, and 3) a demographic questionnaire. From the obtained data, we train and evaluate a Bayesian network. Using Bayesian statistics, we show that age and gender slightly influence personality and that demographics as well as personality have at least a minor effect on learning styles. We also discuss the limitations and future work of the presented approach.
This study examines how Klingsieck’s LIST-K questionnaire [22] can be shortened and adapted to the requirements of an online learning management system. In a study with 213 participants, the questionnaire is subjected to an exploitative factor analysis. In a next step, the results are evaluated in terms of their reliability. This process creates a modified factor structure for the LIST-K, comprising a total of eight factors. The reliability of the modified questionnaire is at an α of .770. The shortened version of the LIST-K questionnaire is currently being used on an experimental basis in different courses.
C is one of the most widely used programming languages - MISRA C is one of the most known sets of coding guidelines for C. This paper examines the usefulness and comprehensibility of the MISRA C:2012 guidelines in an eye tracking study. There, subjects encounter non-compliant code in four different code review settings: with no additional reference, with an actual MISRA C guideline, with a case-specific interpretation of a MISRA C guideline, and with a compliant version of the code. The data collected was analyzed not only in terms of the four presentation styles, but also by dividing the subjects into experience levels based on their semesters of study or years of work experience. Regarding the difference between actual and interpreted guidelines, we found that for interpreted guidelines the error detection rate is higher whereas the duration and frequency of visits to the guideline itself are mainly lower. This suggest that the actual guidelines are less useful and more difficult to understand. The former is contradicted by the subjects’ opinions: when surveyed, they rated the usefulness of the actual guidelines higher.
As machine learning becomes ever more popular, the question of how to enable it on ever more platforms becomes more important. This thesis explores the extension of SQL and extension databases for machine learning.
For this purpose, a framework for extending the Exasol database for machine learning is created. This framework uses the existing support for scripting languages and the in-database file system of the Exasol database to integrate existing machine-learning libraries into SQL. Currently, only Scikit-Learn was integrated.
The focuses of our framework are simplicity, usability, smooth integration in SQL, and expressive power, while runtime efficiency and speed are only secondary focuses. Further benefits of the framework are reduction of communication overhead, increased data security, simplification of data synchronization, and the usage of core database strengths. Compared to other in-database machine learning approaches, Apache MADlib and Oracle Machine Learning, our framework most likely has inferior speed and efficiency, while having the advantage of using well-integrated and tested libraries.
This thesis also provides an overview of related work on extending SQL and databases for machine learning. Furthermore, future directions for our framework are discussed.
The created framework is freely available at https://github.com/christoph-grossmann/Exasol_DB_ML_Framework.
Computing a sample mean of time series under dynamic time warping is NP-hard. Consequently, there is an ongoing research effort to devise efficient heuristics. The majority of heuristics have been developed for the constrained sample mean problem that assumes a solution of predefined length. In contrast, research on the unconstrained sample mean problem is underdeveloped. In this article, we propose a generic average-compress (AC) algorithm to address the unconstrained problem. The algorithm alternates between averaging (A-step) and compression (C-step). The A-step takes an initial guess as input and returns an approximation of a sample mean. Then the C-step reduces the length of the approximate solution. The compressed approximation serves as initial guess of the A-step in the next iteration. The purpose of the C-step is to direct the algorithm to more promising solutions of shorter length. The proposed algorithm is generic in the sense that any averaging and any compression method can be used. Experimental results show that the AC algorithm substantially outperforms current state-of-the-art algorithms for time series averaging.
The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and suffer from significant imperfections, which prevents achieving quantum advantage. To step towards practical utility, one approach is to apply hardware-software co-design methods. This can involve tailoring problem formulations and algorithms to the quantum execution environment, but also entails the possibility of adapting physical properties of the QPU to specific applications. In this work, we follow the latter path, and investigate how key figures— circuit depth and gate count—required to solve four cornerstone NP-complete problems vary with tailored hardware properties. Our results reveal that achieving near-optimal performance and properties does not necessarily require optimal quantum hardware, but can be satisfied with much simpler structures that can potentially be realised for many hardware approaches.m Using statistical analysis techniques, we additionally identify an underlying general model that applies to all subject problems. This suggests that our results may be universally applicable to other algorithms and problem domains, and tailored QPUs can find utility outside their initially envisaged problem domains.
The substantial possible improvements nonetheless highlight the importance of QPU tailoring to progress towards practical deployment and scalability of quantum software.
Quantum computing is a relatively new paradigm that has raised considerable interest in physics and computer science in general but has so far received little attention in software engineering and architecture. Hybrid applications that consist of both quantum and classical components require the development of appropriate quantum software architectures. However, given that quantum software engineering (QSE) in general is a new research area, quantum software architecture–a subresearch area in QSE is also understudied. The goal of this chapter is to provide a list of research challenges and opportunities for such architectures. In addition, to make the content understandable to a broader computer science audience, we provide a brief overview of quantum computing and explain the essential technical foundations.
Quantum computers promise considerable speedups over classical approaches, which has raised interest from many disciplines. Since any currently available implementations suffer from noise and imperfections, achieving concrete speedups for meaningful problem sizes remains a major challenge. Yet, imperfections and noise may remain present in quantum computing for a long while. Such limitations play no role in classical software computing, and software engineers are typically not well accustomed to considering such imperfections, albeit they substantially influence core properties of software and systems. In this paper, we show how to model imperfections with an approach tailored to (quantum) software engineers. We intuitively illustrate, using numerical simulations, how imperfections influence core properties of quantum algorithms on NISQ systems, and show possible options for tailoring future NISQ machines to improve system performance in a co-design approach. Our results are obtained from a software framework that we provide in form of an easy-to-use reproduction package. It does not require computer scientists to acquire deep physical knowledge on noise, yet provide tangible and intuitively accessible means of interpreting the influence of noise on common software quality and performance indicators.
Design propositions for nudging in healthcare: Adoption of national electronic health recordsystems
(2023)
Objectives: Electronic health records (EHRs) are considered important for improving efficiency and reducing costs of ahealthcare system. However, the adoption of EHR systems differs among countries and so does the way the decision to par-ticipate in EHRs is presented. Nudging is a concept that deals with influencing human behaviour within the research streamof behavioural economics. In this paper, we focus on the effects of the choice architecture on the decision for the adoption ofnational EHRs. Our study aims to link influences on human behaviour through nudging with the adoption of EHRs to inves-tigate how choice architects can facilitate the adoption of national information systems.
Methods: We employ a qualitative explorative research design, namely the case study method. Using theoretical sampling,we selected four cases (i.e., countries) for our study: Estonia, Austria, the Netherlands, and Germany. We collected and ana-lyzed data from various primary and secondary sources: ethnographic observation, interviews, scientific papers, homepages,press releases, newspaper articles, technical specifications, publications from governmental bodies, and formal studies.
Results: The findings from our European case studies show that designing for EHR adoption should encompass choice archi-tecture elements (i.e., defaults), technical elements (i.e., choice granularity and access transparency), and institutional ele-ments (i.e., regulations for data protection, information campaigns, and financial incentives) in combination.
Conclusions: Our findings provide insights on the design of the adoption environments of large-scale, national EHR systems.Future research could estimate the magnitude of effects of the determinants.
We present an industrial end-user perspective on the current state of quantum computing hardware for one specific technological approach, the neutral atom platform. Our aim is to assist developers in understanding the impact of the specific properties of these devices on the effectiveness of algorithm execution. Based on discussions with different vendors and recent literature, we discuss the performance data of the neutral atom platform. Specifically, we focus on the physical qubit architecture, which affects state preparation, qubit-to-qubit connectivity, gate fidelities, native gate instruction set, and individual qubit stability. These factors determine both the quantum-part execution time and the end-to-end wall clock time relevant for end-users, but also the ability to perform fault-tolerant quantum computation in the future. We end with an overview of which applications have been shown to be well suited for the peculiar properties of neutral atom-based quantum computers.
In a variety of tomographic applications, data cannot be fully acquired, leading to severely underdetermined image reconstruction. Conventional methods result in reconstructions with significant artifacts. In order to remove these artifacts, regularization methods have to be applied that incorporate additional information. An important example is TV reconstruction which is well known to efficiently compensate for missing data and well reduces reconstruction artifacts. At the same time, however, tomographic data is also contaminated by noise, which poses an additional challenge. The use of a single regularizer within a variational regularization framework must therefore account for both the missing data and the noise. However, a single regularizer may not be ideal for both tasks. For example, the TV regularizer is a poor choice for noise reduction over different scales, in which case ℓ1 curvelet regularization methods work well. To address this issue, in this paper we introduce a novel variational regularization framework that combines the advantages of two different regularizers. The basic idea of our framework is to perform reconstruction in two stages, where the first stage mainly aims at accurate reconstruction in the presence of noise, and the second stage aims at artifact reduction. Both reconstruction stages are connected by a data proximity condition. The proposed method is implemented and tested for limited-view CT using a combined curvelet-TV approach. We define and implement a curvelet transform adapted to the limited view problem and demonstrate the advantages of our approach in a series of numerical experiments in this context.
We study samples with full and partial occlusion causing streak artifacts, and propose two mod-ifications of filtered backprojection for artifact removal. Data is obtained by the SPring-8 synchrotron using a monochromatic parallel-beam scan [1]. Thresholding in the sinogram segments the metal, resulting in edges on which we apply 1) a smooth transition, or 2) a Dirichlet boundary condition.
The transfer of knowledge from client to service provider poses major challenges in information systems (IS) offshoring projects. Knowledge transfer directly affects IS offshoring success. Therefore, associated challenges must be overcome. Our study examines the determinants of success and failure of knowledge transfer in IS offshoring projects based on a ranking-type Delphi study. We questioned 32 experts from Germany, each with more than ten years of experience in near- or offshore initiatives to seek a consensus among them. We identified 19 success and 20 failure determinants. These determinants are ranked in order of importance using best-worst scaling. Aspects of closer cooperation are critical for effective knowledge transfer. This includes regular collaboration, willingness to help and support, and mutual trust. In contrast, critical determinants of failure are concerned with fears and fluctuation of human resources. Hidden ambiguities or knowledge gaps, an unwillingness and disability to share knowledge, and high fluctuation of human resources negatively impact knowledge transfer.
Organizations are under increasing pressure to develop applications within budget and time at high quality. Therefore, multiple organizations adopt Low Code Development Platforms (LCDP) to develop applications faster and cheaper compared to traditional application development. However, current research on LCDP adoption lacks empirical grounding as well as a deeper understanding of the importance of adoption drivers and inhibitors. We conducted semi-structured interviews and a Delphi study with seventeen experts to address these gaps. As a result, we identified twelve drivers and nineteen inhibitors for adopting LCDPs. We show that the experts have a consensus on the most and the least important drivers and inhibitors for LCDP adoption. Yet, the ranking of the drivers and inhibitors between the most and least important is highly context dependent. For some drivers and inhibitors, the experts’ ranking is similar to academic literature, whereas, for others, it differs. In conclusion, the study at hand empirically validates drivers and inhibitors for LCDP adoption, adds six new drivers and six new inhibitors to the body of knowledge, and analyses the importance of these factors.
In this paper, we present a new approach to determine the estimated time of arrival (ETA) for bus routes using (Deep) Graph Convolutional Networks (DGCNs). In addition we use the same DGCN to detect detours within a route. In our application, a classification of routes and their underlying graph structure is performed using Graph Learning. Our model leads to a fast prediction and avoids solving the vehicle routing problem (VRP) through expensive computations. Moreover, we describe how to predict travel time for all routes using the same DGCN Model. This method makes it possible not to use a more computationally intensive approximation algorithm when determining long travel times with many intermediate stops, but to use our network for an early estimate of the quality of a route. Long travel times, in our case result from the use of a call-bus system, which must distribute many passengers among several vehicles and can take them to places without a regular stop. For a case study, the rural town of Roding in Bavaria is used. Our training data for this area results from an approximation algorithm that we implemented to optimize routes, and to generate an archive of routes of varying quality simultaneously.
In the context of production and factory planning, the expansion of the factory must already be taken into account during initial planning. This results in an increase in planning complexity, as the involved planners have to know the expansion stages of the factory in the different time periods and have to evaluate concept modifications across all time periods. This paper presents an idea for a planning tool, which takes expansion stages into consideration. The data model contains all relevant information to generate a simulation model of the factory in an almost automated way. The aim is to enable factory planners to quickly investigate concept changes with the help of simulation, for example, to identify bottlenecks.
One major goal of green-field factory planning is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements in the factory and should therefore be considered in the factory planning. This research article proposes a simulation approach for a factory simulation in which raw material replenishment is integrated to evaluate the space requirements for raw materials.
To evaluate the performance of a ropeway in an urban environment, simulations of the dynamic passenger transport characteristics are required. Therefore, a modular simulation model for urban ropeway networks was developed, which can be flexibly adapted to any city and passenger volume. This simulation model was used to analyze the ropeway network concept of the German city Regensburg and to determine the expected operating conditions. The passenger volume, different types of persons, their occurrence probability and their destination distribution is depending on the location and daytime and can be defined for each individual station. In an initial analysis, the number of passengers currently occurring in bus traffic were projected onto the ropeway network. To enable climate-friendly and efficient operation, different strategies were developed to significantly reduce the number of gondolas. The best fitting strategies resulted in significant cost savings while passenger comfort, as represented by queue time, remained unchanged.
Simulation Based Approach for Reconfiguration and Ramp up Scenario Analysis in Factory Planning
(2022)
Structural changes in production entail a potential economic risk for manufacturing companies. It is necessary to identify a suitable strategy for the reconfiguration process and to continue to meet the demand during the change in the factory structure and ramp-up phase. A simulation offers the possibility to analyze different ramp-up scenarios for the factory structure and to select a suitable concept for the reconfiguration process. A discrete event simulation approach is presented that can be used to evaluate variants of structural changes and serves as a basis for deciding on a reconfiguration strategy. This approach is demonstrated using a specific production step of a plant producing hydrogen electrolyzers, the results and generalized conclusions are discussed.
Mankind has always been confronted with limited knowledge about Nature and many people devoted their lives and intellects to the very question: How to gain knowledge, confirm ideas and theories, and understand the world we live in? Religion was one fundamental base for explanations and existential questions. Philosophy and Science were the other. Nowadays, science and technology dominate our daily lives, religion and metaphysics have lost their former dominance, while our thoughts and factual knowledge are increasingly governed by the digitised versions of conversation and discussion, filtered and guided by the algorithms of social networks. Today, "artificial intelligence" is a phenomenon in information technology which takes over everyday routines. A new ideal, a new promise, and at the same time used as a stratagem by political and economic systems to guide and manipulate our thinking, values, and behaviour. But nothing is so new that one could not find its precursors in earlier times. Science and mathematics has kindled many brilliant ideas. Can we describe the world by numbers, find truth and explanation in mathematical structures? This essay is devoted to early ideas by the medieval philosopher Ramón Llull, the great rationalist G.W. Leibniz, and early approaches to combinations, number theory. and information processing. Llull's thinking machine and subsequent endeavours to find methods of mechanical reasoning and inference paved the way to modern-day concepts of artificial intelligence.
Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research
(2023)
In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms havethe proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the optimization of join order problem for databases.
Ascertaining reproducibility of scientific experiments is receiving increased attention across disciplines. We argue that the necessary skills are important beyond pure scientific utility, and that they should be taught as part of software engineering (SWE) education. They serve a dual purpose: Apart from acquiring the coveted badges assigned to reproducible research, reproducibility engineering is a lifetime skill for a professional industrial career in computer science.
SWE curricula seem an ideal fit for conveying such capabilities, yet they require some extensions, especially given that even at flagship conferences like ICSE, only slightly more than one-third of the technical papers (at the 2021 edition) receive recognition for artefact reusability. Knowledge and capabilities in setting up engineering environments that allow for reproducing artefacts and results over decades (a standard requirement in many traditional engineering disciplines), writing semi-literate commit messages that document crucial steps of a decision-making process and that are tightly coupled with code, or sustainably taming dynamic, quickly changing software dependencies, to name a few: They all contribute to solving the scientific reproducibility crisis, and enable software engineers to build sustainable, long-term maintainable, software-intensive, industrial systems. We propose to teach these skills at the undergraduate level, on par with traditional SWE topics.
Sicherheit in der Cloud
(2022)
Finite state machines (FSMs) are an appealing mechanism for simple practical computations: They lend themselves to very effcient and deterministic implementation, are easy to understand, and allow for formally proving many properties of interest. Unfortunately, their computational power is deemed insuffcient for many tasks, and their usefulness has been further hampered by the state space explosion problem and other issues when naïvely trying to scale them to sizes large enough for many real–life applications.
This paper expounds on theory and implementation of multiple coupled fnite state machines (McFSMs), a novel mechanism that combines benefits of FSMs with near Turing-complete, practical computing power, and that was designed from the ground up to support static analysis and reasoning. We develop an elaborate category–theoretical foundation based on non–deterministic Mealy machines, which gives a suitable algebraic description for novel ways of blending di#erent computing models. Our experience is based on a domain specific language and an integrated development environment that can compile McFSM models to multiple target languages, applying it to use-cases based on industrial scenarios. We discuss properties and advantages of McFSMs, explain how the mechanism can interact with real–world systems and existing code without sacrificing provability, determinism or performance.
We discuss how McFSMs can be used to replace and improve on commonly employed programming patterns, and show how their effcient handling of large state spaces enables them to be used as core building blocks for distributed, safety critical, and real–time systems of industrial complexity, which contributes to the longdesired goal of providing executable specifications.
Computer-based automation in industrial appliances led to a growing number of logically dependent, but physically separated embedded control units per appliance. Many of those components are safety-critical systems, and require adherence to safety standards, which is inconsonant with the relentless demand for features in those appliances. Features lead to a growing amount of control units per appliance, and to a increasing complexity of the overall software stack, being unfavourable for safety certifications. Modern CPUs provide means to revise traditional separation of concerns design primitives: the consolidation of systems, which yields new engineering challenges that concern the entire software and system stack.
Multi-core CPUs favour economic consolidation of formerly separated systems with one efficient single hardware unit. Nonetheless, the system architecture must provide means to guarantee the freedom from interference between domains of different criticality. System consolidation demands for architectural and engineering strategies to fulfil requirements (e.g., real-time or certifiability criteria) in safety-critical environments.
In parallel, there is an ongoing trend to substitute ordinary proprietary base platform software components by mature OSS variants for economic and engineering reasons. There are fundamental differences of processual properties in development processes of OSS and proprietary software. OSS in safety-critical systems requires development process assessment techniques to build an evidence-based fundament for certification efforts that is based upon empirical software engineering methods.
In this thesis, I will approach from both sides: the software and system engineering perspective. In the first part of this thesis, I focus on the assessment of OSS components: I develop software engineering techniques that allow to quantify characteristics of distributed OSS development processes. I show that ex-post analyses of software development processes can be used to serve as a foundation for certification efforts, as it is required for safety-critical systems.
In the second part of this thesis, I present a system architecture based on OSS components that allows for consolidation of mixed-criticality systems on a single platform. Therefore, I exploit virtualisation extensions of modern CPUs to strictly isolate domains of different criticality. The proposed architecture shall eradicate any remaining hypervisor activity in order to preserve realtime capabilities of the hardware by design, while guaranteeing strict isolation across domains.
Internal transport systems are an essential part of intralogistics in production and distribution facilities. These are characterized by a variety of technologies as well as a multitude of interactions with other processes, such as warehouse, picking, and production processes. Therefore, resource planning and control of these systems is complex, especially for discontinuous conveyors. In this task, users can be supported by Digital Twins for decision-making, as they are suitable for investigating both future system states and possible actions. However, relevant use cases that are generally applicable across sectors as well as a generic system architecture for Digital Twins for resource planning and process control of in-plant transport systems have not yet been sufficiently investigated. In this paper, use cases are presented, relevant functions defined, and, finally, a generic functional and a logical reference architecture described. This is conducted with the design science in information systems research method together with a Systems Engineering approach. The use cases are determined at industrial partners of the research project TwInTraSys, which explores Digital Twins for the planning and control of internal transport systems. They are generalized and, thus, also applicable to other production and distribution facilities in different sectors. Further, the reference architecture can provide a basis for the successful implementation of the Digital Twin.
Die Konformitätsanalyse ist eine Technik der statischen Code-Analyse (SCA) zur Software-Qualitätssicherung. Ihr Kernproblem ist, dass Werkzeuge nicht aus bereits eingetretenen Fehlern automatisiert dazulernen. Zur Lösung wurde in dieser Arbeit das maschinelle Lernen (ML) evaluiert, indem ein wissenschaftlich fundierter und praktisch erprobter Ansatz zur unüberwachten Lerntechnik angewandt und das Ergebnis analysiert wurde. Es wurde festgestellt, dass zur Anwendung auf verschiedene Programmiersprachen nur ein sprachspezifisches API Mining-Tool notwendig ist. Ein derartiges Tool durchsucht in parallelisierter Form Codezeilen und normalisiert sie für maschinelle Lernprozesse. Dieses System wurde für die Programmiersprache C# implementiert, da viele Industrieprojekte in dieser Sprache entwickelt werden. Zur funktionalen Validierung wurde in einer Fallstudie gezeigt, dass Regeln mit einem positiven Effekt auf Software-Qualität gelernt wurden. Konkret wurde der Wartungsaufwand eines Code-Smells in einem Beispielprojekt durch das Auslagern einer gelernten Assoziation in eine gemeinsame Methode um den Faktor 30 reduziert. Die Laufzeit des Algorithmus wurde empirisch in acht open-source Repositorys evaluiert. Durch Parallelisierung kann eine durchschnittliche Laufzeitverbesserung von 45,16% erwartet werden. Allerdings wurden bei der Anwendung auch Grenzen deutlich: Viele Assoziationen sind nutzlos, die Regelbewertung ist von einem subjektiven Faktor abhängig und die Wirtschaftlichkeit des Tools ist deshalb nicht transparent. Dennoch belegt diese Arbeit, dass ein ML-basiertes SCA-Tool als ergänzende Qualitätssicherungsmaßnahme im Software-Engineering möglich ist.
Digitale Transformation in Echtzeit: Die Ziele von morgen basierend auf dem Datenmodell von gestern
(2022)
Die Digitale Transformation fordert Unternehmen aller Couleur. Ironischer Weise sind es gerade die bisher verwendeten IT-Systeme mit ihren starren Strukturen, die Unternehmen in Ihrer digitalen Trans-formation oft ausbremsen. Auch wenn die Soft-warehersteller längst reagiert haben und neue, flexib-lere Versionen ihrer Produkte anbieten, so ist ein größerer Softwarewechsel immer noch eine Heraus-forderung für Unternehmen und ein Schritt der wohl-überlegt und geplant sein will. In dieser Arbeit wird deshalb ein Vorgehen vorge-stellt, um mittels In-Memory Technologie und Vir-tualisierung zumindest die wichtigsten Ergebnisse der Transformation bereits auf den bestehenden Da-tenmodellen in Echtzeit zu generieren. Dadurch wird genug Zeit gewonnen, um die eigentliche Transfor-mation der IT-Landschaft geplant und mit der not-wendigen Sorgfalt durchzuführen.
The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for the known quantum algorithms. We present the first quantum implementation of join ordering, which is one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes that can be processed with current quantum annealers. However, we also confirm that limits of early-stage technology are quickly reached.
Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future quantum computers, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and possible problem size.
In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.
We evaluate the applicability of quantum computing on two fundamental query optimization problems, join order optimization and multi query optimization (MQO). We analyze the problem dimensions that can be solved on current gate-based quantum systems and quantum annealers, the two currently commercially available architectures.
First, we evaluate the use of gate-based systems on MQO, previously solved with quantum annealing. We show that, contrary to classical computing, a different architecture requires involved adaptations. We moreover propose a multi-step reformulation for join ordering problems to make them solvable on current quantum systems. Finally, we systematically evaluate our contributions for gate-based quantum systems and quantum annealers. Doing so, we identify the scope of current limitations, as well as the future potential of quantum computing technologies for database systems.
This paper addresses the problem of properly placing a given task in the manipulator workspace by a heuristic and numeric approach. Thus, the task is placed relatively to the manipulator for each element of the discretized workspace and the required joint torques are determined. The results are are by a torque-based optimization criterion. The modularity of this approach ensures general applicability on various systems and tasks while the high computational effort is treated by GPU parallelization. The method is presented for a given 6DOF manipulator and a highly dynamic trajectory. The resulting interactive map of the manipulator workspace gives an overview of the task dependent dynamic performance, detailed evaluation of certain solutions will show the dexterity of the proposed approach.
The design of the NoSQL schema has a direct impact on the scalability of web applications. Especially for developers with little experience in NoSQL stores, the risks inherent in poor schema design can be incalculable. Worse yet, the issues will only manifest once the application has been deployed, and the growing user base causes highly concurrent writes. In this paper, we present a model checking approach to reveal scalability bottlenecks in NoSQL schemas. Our approach draws on formal methods from tree automata theory to perform a conservative static analysis on both the schema and the expected write-behavior of users. We demonstrate the impact of schema-inherent bottlenecks for a popular NoSQL store, and show how concurrent writes can ultimately lead to a considerable share of failed transactions.
The modular addition is a popular building block when designing lightweight ciphers. While algorithms mainly based on the addition can reach very high performance, masking their implementations results in a huge penalty. Since efficient protection against side-channel attacks is a requirement in lots of use cases, we focus on optimizing the Boolean masking of the modular addition. Contrary to recent related work, we target evolving a masked full adder instead of parts of a parallel prefix adder. We study how techniques typically found in neural network evolution and genetic algorithms can be adapted in order to help in evolving an efficiently masked adder. We customize a well-known neuroevolution algorithm, develop an optimized masked adder with our new approach and implement the ChaCha20 cipher on an ARM Cortex-M3 controller. We compare the performance of the protected neuroevolved implementation to solutions found by traditional search methods. Moreover, the leakage of our new solution is validated by a t-test conducted with a leakage simulator. We present under which circumstances our masked implementation outperforms related work and prove the feasibility of successfully using neuroevolution when searching for complex Boolean networks.
In this work, we present our benchmarking results for the ten finalist ciphers of the Lightweight Cryptography (LWC) project initiated by National Institute of Standards and Technology (NIST). We evaluate the speed and code size of various software implementations on five different platforms featuring four different architectures. Moreover, we benchmark the dynamic memory utilization of the remaining NIST LWC algorithms on one 32-bit ARM controller. We describe our test cases and methodology and provide some information regarding the design and properties of the finalists before showing and discussing our results. Altogether, we evaluated almost 300 implementations of the 3rd round candidates and pick the most appropriate and best (primary) implementation of each cipher for our comparisons. We include a variant of AES-GCM in our benchmarking in order to be able to compare the state-of-the-art to the novel LWC ciphers. Our research gives an overview over the performance of the latest software implementations of the NIST LWC finalists and shows under which circumstances which candidate is performing the best in our individual test cases. Additionally, we make all benchmarking results, the code for our test framework and every tested implementation available to the public to ensure a transparent testing process.
EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition
(2015)
Background:
Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field.
New method:
EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis.
Results:
EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox.
Comparison with existing methods:
EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal.
Conclusions:
EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.
Virtualization has come a long way since its beginnings in the 1960s. Nowadays, Virtual Machine Monitor (VMM) - or hypervisor-based virtualization of servers is the de facto standard in data centers and a building block of the cloud hype. In recent years, virtualization has also been adopted to embedded devices such as avionics systems and mobile phones. The first mass deployment of embedded virtualization can probably be seen in video game consoles, though. However, it is still not employed by automotive electronics. This is despite the fact that with the upcoming domain controller architecture, virtualization can yield benefits beyond a mere consolidation of a multitude of Electronic Control Units (ECUs) into a few Domain Controller Units (DCUs). This paper presents merits of automotive virtualization, especially as a foundation for DCUs.
Today, ubiquitous mobile devices have not only arrived but entered the safety critical domain. There, systems are about to be controlled where human health or even human life is put at risk. For example, in automation systems first ideas surface to control parts of the system via a COTS smartphone. Another example is the idea to control the autonomous parking function of a car via a COTS smartphone too. As beneficial and convenient these ideas are on the first thought, on the second thought, dangers of these approaches become obvious. Especially in case of failures the system’s safety has to be maintained. The open question is how to achieve this mandatory requirement with COTS components, e.g. smartphones that are not developed following the development process necessary for safetycritical systems. This paper presents a concept to reliably detect human interaction while activating safety critical functions via COTS mobile devices. Thus a means is provided to detect erroneous activation requests for the safetycritical function.
We present two methods that combine image reconstruction and edge detection in computed tomography (CT) scans. Our first method is as an extension of the prominent filtered backprojection algorithm. In our second method we employ ℓ1-regularization for stable calculation of the gradient. As opposed to the first method, we show that this approach is able to compensate for undersampled CT data.
We present a paradigm for characterization of artifacts in limited data tomography problems. In particular, we use this paradigm to characterize artifacts that are generated in reconstructions from limited angle data with generalized Radon transforms and general filtered backprojection type operators. In order to find when visible singularities are imaged, we calculate the symbol of our reconstruction operator as a pseudodifferential operator.
The performance of cognitive models often depends on the settings of specific model parameters, such as the rate of memory decay or the speed of motor responses. The systematic exploration of a model’s parameter space can yield relevant insights into model behavior and can also be used to improve the fit of a model to human data. However, exhaustive parameter space searches quickly run into a combinatorial explosion as the number of parameters investigated increases. Taking an established instance-based learning task as example, we show
how simulation using parallel computing and derivative-free optimization methods can be applied to investigate the effects
of different parameter settings. We find that both global optimization methods involving genetic algorithms as well as local methods yield satisfactory results in this case. Furthermore, we show how a model implemented in a specific cognitive architecture (ACT-R) can be mathematically reformulated to prepare the application of derivative-based optimization methods which promise further efficiency gains for quantitative analysis.
PURPOSE
Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography.
METHODS
In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization.
RESULTS
Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection.
CONCLUSIONS
The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.
Differential phase contrast imaging (DPCI) enables the visualization of soft tissue contrast using X-rays. In this work we introduce a reconstruction framework based on curvelet expansion and sparse regularization for DPCI. We will show that curvelets provide a suitable data representation for DPCI reconstruction that allows preservation of edges as well as an exact analytic representation of the system matrix. As a first evaluation, we show results using simulated phantom data
This thesis is devoted to the problem of tomographic reconstruction at limited angular range. In the first part, we prove a characterization of filtered backprojection reconstructions from limited angle data. Moreover, we develop a strategy for artifact reduction and stabilization. In the second part, we introduce a new edge-preserving reconstruction algorithm for limited angle tomography and analyze this algorithm mathematically. Some numerical experiments are also presented.
We propose a new framework for limited angle tomographic reconstruction. Our approach is based on the observation that for a given acquisition geometry only a few (visible) structures of the object can be reconstructed reliably using a limited angle data set. By formulating this problem in the curvelet domain, we can characterize those curvelet coefficients which correspond to visible structures in the image domain. The integration of this information into the formulation of the reconstruction problem leads to a considerable dimensionality reduction and yields a speedup of the corresponding reconstruction algorithms.
In order to better understand the mechanisms of gas transport during High Frequency Oscillatory Ventilation (HFOV) Magnetic Resonance Imaging (MRI) with contrast gases and numerical flow simulations based on Computational Fluid Dynamics(CFD) methods are performed. Validation of these new techniques is conducted by comparing the results obtained with simplified models of the trachea and a first lung bifurcation as well as in a cast model of the upper central airways with results achieved from conventional fluid mechanical measurement techniques like e.g. Laser Doppler Anemometry (LDA). Further it is demonstrated that MRI of experimental HFOV is feasible and that Hyperpolarized 3He allows for imaging the gas re-distribution inside the lung. Finally, numerical results of oscillatory flow in a 3rd generation model of the lung as well as the impact of endotracheal tubes on the flow regime development in a trachea model are presented.
A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements
(2015)
Parkinson’s disease (PD) has affected millions of people world-wide, being its major problem the loss of movements and, consequently, the ability of working and locomotion. Although we can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this work, we present some results toward the automated diagnosis of PD by means of computer vision-based techniques in a dataset composed by dozens of patients, which is one of the main contributions of this work. The dataset is part of a joint research project that aims at extracting both visual and signal-based information from healthy and PD patients in order to go forward the early diagnosis of PD patients. The dataset is composed by handwriting clinical exams that are analyzed by means of image processing and machine learning techniques, being the preliminary results encouraging and promising. Additionally, a new quantitative feature to measure the amount of tremor of an individual’s handwritten trace called Mean Relative Tremor is also presented.
We consider the reconstruction problem for limited angle tomography using filtered backprojection (FBP) and lambda tomography. We use microlocal analysis to explain why the well-known streak artifacts are present at the end of the limited angular range. We explain how to mitigate the streaks and prove that our modified FBP and lambda operators are standard pseudodifferential operators, and so they do not add artifacts. We provide reconstructions to illustrate our mathematical results.
We investigate the reconstruction problem of limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, electron microscopy, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, e.g. filtered backprojection (FBP), do not perform well in such situations.
To stabilize the reconstruction procedure additional prior knowledge about the unknown object has to be integrated into the reconstruction process. In this work, we propose the use of the sparse regularization technique in combination with curvelets. We argue that this technique gives rise to an edge-preserving reconstruction. Moreover, we show that the dimension of the problem can be significantly reduced in the curvelet domain. To this end, we give a characterization of the kernel of the limited angle Radon transform in terms of curvelets and derive a characterization of solutions obtained through curvelet sparse regularization. In numerical experiments, we will show that the theoretical results directly translate into practice and that the proposed method outperforms classical reconstructions.
Artifacts in Incomplete Data Tomography with Applications to Photoacoustic Tomography and Sonar
(2015)
We develop a paradigm using microlocal analysis that allows one to characterize the visible and added singularities in a broad range of incomplete data tomography problems. We give precise characterizations for photoacoustic and thermoacoustic tomography and sonar, and provide artifact reduction strategies. In particular, our theorems show that it is better to arrange sonar detectors so that the boundary of the set of detectors does not have corners and is smooth. To illustrate our results, we provide reconstructions from synthetic spherical mean data as well as from experimental photoacoustic data.
We investigate the reconstruction problem for limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, such as filtered backprojection (FBP), do not perform well in such situations. To stabilize the inversion we propose the use of a sparse regularization technique in combination with curvelets. We argue that this technique has the ability to preserve edges. As our main result, we present a characterization of the kernel of the limited angle Radon transform in terms of curvelets. Moreover, we characterize reconstructions which are obtained via curvelet sparse regularizations at a limited angular range. As a result, we show that the dimension of the limited angle problem can be significantly reduced in the curvelet domain.
We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford–Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficiently. Our method does not require a priori knowledge on the gray levels nor on the number of segments of the reconstruction. Further, it avoids anisotropic artifacts such as geometric staircasing. We demonstrate the suitability of our method for joint image reconstruction and segmentation. We focus on Radon data, where we in particular consider limited data situations. For instance, our method is able to recover all segments of the Shepp–Logan phantom from seven angular views only. We illustrate the practical applicability on a real positron emission tomography dataset. As further applications, we consider spherical Radon data as well as blurred data.
Simultaneous EEG-fMRI provides an increasingly attractive research tool to investigate cognitive processes with high temporal and spatial resolution. However, artifacts in EEG data introduced by the MR scanner still remain a major obstacle. This study, employing commonly used artifact correction steps, shows that head motion, one overlooked major source of artifacts in EEG-fMRI data, can cause plausible EEG effects and EEG–BOLD correlations. Specifically, low-frequency EEG (< 20 Hz) is strongly correlated with in-scanner movement. Accordingly, minor head motion (< 0.2 mm) induces spurious effects in a twofold manner: Small differences in task-correlated motion elicit spurious low-frequency effects, and, as motion concurrently influences fMRI data, EEG–BOLD correlations closely match motion-fMRI correlations. We demonstrate these effects in a memory encoding experiment showing that obtained theta power (~ 3–7 Hz) effects and channel-level theta–BOLD correlations reflect motion in the scanner. These findings highlight an important caveat that needs to be addressed by future EEG-fMRI studies.
Background
The purpose of this study was to evaluate the impact of Cone Beam CT (CBCT) based setup correction on total dose distributions in fractionated frameless stereotactic radiation therapy of intracranial lesions.
Methods
Ten patients with intracranial lesions treated with 30 Gy in 6 fractions were included in this study. Treatment planning was performed with Oncentra® for a SynergyS® (Elekta Ltd, Crawley, UK) linear accelerator with XVI® Cone Beam CT, and HexaPOD™ couch top. Patients were immobilized by thermoplastic masks (BrainLab, Reuther). After initial patient setup with respect to lasers, a CBCT study was acquired and registered to the planning CT (PL-CT) study. Patient positioning was corrected according to the correction values (translational, rotational) calculated by the XVI® system. Afterwards a second CBCT study was acquired and registered to the PL-CT to confirm the accuracy of the corrections. An in-house developed software was used for rigid transformation of the PL-CT to the CBCT geometry, and dose calculations for each fraction were performed on the transformed CT. The total dose distribution was achieved by back-transformation and summation of the dose distributions of each fraction. Dose distributions based on PL-CT, CBCT (laser set-up), and final CBCT were compared to assess the influence of setup inaccuracies.
Results
The mean displacement vector, calculated over all treatments, was reduced from (4.3 ± 1.3) mm for laser based setup to (0.5 ± 0.2) mm if CBCT corrections were applied. The mean rotational errors around the medial-lateral, superior-inferior, anterior-posterior axis were reduced from (−0.1 ± 1.4)°, (0.1 ± 1.2)° and (−0.2 ± 1.0)°, to (0.04 ± 0.4)°, (0.01 ± 0.4)° and (0.02 ± 0.3)°. As a consequence the mean deviation between planned and delivered dose in the planning target volume (PTV) could be reduced from 12.3% to 0.4% for D95 and from 5.9% to 0.1% for Dav. Maximum deviation was reduced from 31.8% to 0.8% for D95, and from 20.4% to 0.1% for Dav.
Conclusion
Real dose distributions differ substantially from planned dose distributions, if setup is performed according to lasers only. Thermoplasic masks combined with a daily CBCT enabled a sufficient accuracy in dose distribution.
Re-irradiation of spinal column metastases by IMRT: Impact of setup errors on the dose distribution
(2013)
Background
This study investigates the impact of an automated image guided patient setup correction on the dose distribution for ten patients with in-field IMRT re-irradiation of vertebral metastases.
Methods
10 patients with spinal column metastases who had previously been treated with 3D-conformal radiotherapy (3D-CRT) were simulated to have an in-field recurrence. IMRT plans were generated for treatment of the vertebrae sparing the spinal cord. The dose distributions were compared for a patient setup based on skin marks only and a Cone Beam CT (CBCT) based setup with translational and rotational couch corrections using an automatic robotic image guided couch top (Elekta - HexaPOD™ IGuide® - system). The biological equivalent dose (BED) was calculated to evaluate and rank the effects of the automatic setup correction for the dose distribution of CTV and spinal cord.
Results
The mean absolute value (± standard deviation) over all patients and fractions of the translational error is 6.1 mm (±4 mm) and 2.7° (±1.1 mm) for the rotational error. The dose coverage of the 95% isodose for the CTV is considerable decreased for the uncorrected table setup. This is associated with an increasing of the spinal cord dose above the tolerance dose.
Conclusions
An automatic image guided table correction ensures the delivery of accurate dose distribution and reduces the risk of radiation induced myelopathy.
This paper introduces a novel chaotic flower pollination algorithm (CFPA) to solve a tardiness-constrained flow-shop scheduling problem with simultaneously loaded stations. This industrial manufacturing problem is modeled from a filter basket production line in Germany and has been generally solved using standard deterministic algorithms. This research develops a metaheuristic approach based on the highly efficient flower pollination algorithm coupled with different chaos maps for stochasticity. The objective function targeted is the tardiness constraint of the due dates. Fifteen different experiments with thirty scenarios are generated to mimic industrial conditions. The results are compared with the genetic algorithm and with the four standard benchmark priority rule-based deterministic algorithms of First In First Out, Raghu and Rajendran, Shortest Processing Time and Slack. From the obtained results and analysis of the relative difference, percentage relative difference and t tests, CFPA was found to be significantly better performing than the deterministic heuristics and the GA algorithm.
This paper briefly presents the challenges for order control and release of multi-zone order picking systems. On the one hand, the order control must ensure that all orders are processed on time, and on the other hand, the space requirements (buffer) and the utilisation of the zones must be considered.
Within the framework of a case study, different strategies for order release were developed. The paper shortly describes the ideas of the strategies and presents results of a case-based simulative evaluation of the strategies. The findings of the simulation study are the basis for the development of a digital twin for the operational control of multi-zone picking systems.