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Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train The CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labelled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.
On the design of an urban data and modeling platform and its application to urban district analyses
(2020)
An integrated urban platform is the essential software infrastructure for smart, sustainable and resilitent city planning, operation and maintenance. Today such platforms are mostly designed to handle and analyze large and heterogeneous urban data sets from very different domains. Modeling and optimization functionalities are usually not part of the software concepts. However, such functionalities are considered crucial by the authors to develop transformation scenarios and to optimized smart city operation. An urban platform needs to handle multiple scales in the time and spatial domain, ranging from long term population and land use change to hourly or sub-hourly matching of renewable energy supply and urban energy demand.
Entrepreneurship education is becoming increasingly important in higher education and also drives the development of innovative teaching formats, which can increase student engagement. It does, however, need greater international focus to become more attractive for both domestic and international students. This paper presents the examination and course design of two case studies, which promote entrepreneurship education for domestic and international students. These examples show that entrepreneurship courses are attractive due to their focus on interdisciplinarity, experience-based learning, and project-based work. Following a design-based research approach, this paper provides a practical contribution by offering a detailed overview of course design principles, classroom practice and presents reflections and learnings from an iterative development process.
With on-demand access to compute resources, pay-per-use, and elasticity, the cloud evolved into an attractive execution environment for High Performance Computing (HPC). Whereas elasticity, which is often referred to as the most beneficial cloud-specific property, has been heavily used in the context of interactive (multi-tier) applications, elasticity-related research in the HPC domain is still in its infancy. Existing parallel computing theory as well as traditional metrics to analytically evaluate parallel systems do not comprehensively consider elasticity, i.e., the ability to control the number of processing units at runtime. To address these issues, we introduce a conceptual framework to understand elasticity in the context of parallel systems, define the term elastic parallel system, and discuss novel metrics for both elasticity control at runtime as well as the ex post performance evaluation of elastic parallel systems. Based on the conceptual framework, we provide an in depth analysis of existing research in the field to describe the state-of-the art and compile our findings into a research agenda for future research on elastic parallel systems.
This paper explores the application of People Analytics in
recruiting professors for universities of applied sciences. Using data-driven personas, the research project aims to identify and communicate the different paths and connections leading candidates to a professorship. The authors introduce the concept of personas, describe the underlying data source and derive an example for the current project.
A behavior marker for measuring non-technical skills of software professionals : an empirical study
(2015)
Managers recognize that software development teams need to be developed. Although technical skills are necessary, non-technical (NT) skills are equally, if not more, necessary for project success. Currently, there are no proven tools to measure the NT skills of software developers or software development teams. Behavioral markers (observable behaviors that have positive or negative impacts on individual or team performance) are successfully used by airline and medical industries to measure NT skill performance. This research developed and validated a behavior marker tool rated video clips of software development teams. The initial results show that the behavior marker tool can be reliably used with minimal training.
Context: Organizations increasingly develop software in a distributed manner. The cloud provides an environment to create and maintain software-based products and services. Currently, it is unknown which software processes are suited for cloud-based development and what their effects in specific contexts are.
Objective: We aim at better understanding the software process applied to distributed software development using the cloud as development environment. We further aim at providing an instrument which helps project managers comparing different solution approaches and to adapt team processes to improve future project activities and outcomes.
Method: We provide a simulation model which helps analyzing different project parameters and their impact on projects performed in the cloud. To evaluate the simulation model, we conduct different analyses using a Scrumban process and data from a project executed in Finland and Spain. An extra adaptation of the simulation model for Scrum and Kanban was used to evaluate the suitability of the simulation model to cover further process models.
Results: A comparison of the real project data with the results obtaind from the different simulation runs shows the simulation producing results close to the real data, and we could successfully replicate a distributed software project. Furthermore, we could show that the simulation model is suitable to address further process models.
Conclusion: The simulator helps reproducing activities, developers, and events in the project, and it helps analyzing potential tradeoffs, e.g., regarding throughput, total time, project size, team size and work-in-progress limits. Furthermore, the simulation model supports project managers selecting the most suitable planning alternative thus supporting decision-making processes.
Predictive maintenance information systems: the underlying conditions and technological aspects
(2020)
Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.
Erfolg durch Kooperation
(2009)
Die für Deutschland verfügbaren Studien zur Digitalen Transformation in klein- und mittelständischen Unternehmen (KMU) sind sich weitgehend einig. KMU tun sich mit dem Thema Digitalisierung schwer. Der vorliegende Beitrag diskutiert, weshalb KMU an der Digitalen Transformation scheitern und was dagegen getan werden kann.