peer reviewed
Refine
Document Type
- Article (137)
- Conference Proceeding (32)
- Book (1)
Language
- English (133)
- German (35)
- Multiple languages (2)
Is part of the Bibliography
- yes (170)
Keywords
Institute
- Maschinenbau und Werkstofftechnik (59)
- Wirtschaftswissenschaften (38)
- Chemie (24)
- Elektronik und Informatik (19)
- Optik und Mechatronik (14)
Today’s Industry 4.0 Smart Factories involve complicated and highly automated processes. Nevertheless, certain crucial activities such as machine maintenance remain that require human involvement. For such activities, many factors have to be taken into account, like worker safety or worker qualification. This adds to the complexity of selection and assignment of optimal human resources to the processes and overall coordination. Contemporary Business Process Management (BPM) Systems only provide limited facilities regarding activity resource assignment. To overcome these, this contribution pro- poses a BPM-integrated approach that applies fuzzy sets and rule processing for activity assignment. Our findings suggest that our approach has the potential for improved work distribution and cost savings for Industry 4.0 production processes. Furthermore, the scalability of the approach provides efficient performance even with a large number of concurrent activity assignment requests and can be applied to complex production scenarios with minimal effort.
Although production processes in Industry 4.0 set- tings are highly automated, many complicated tasks, such as machine maintenance, continue to be executed by human workers. While smart factories can provide these workers with some digitalization support via Augmented Reality (AR) devices, these AR tasks depend on many contextual factors, such as live data feeds from machines in view, or current work safety conditions. Although currently feasible, these localized contextual factors are mostly not well-integrated into the global production process, which can result in various problems such as suboptimal task assignment, over-exposure of workers to hazards such as noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) were not particularly designed to consider and integrate context-aware factors during planning and execution. This paper describes the AR-Process Framework (ARPF) for extending a BPMS to support context-integrated modeling and execution of processes with AR tasks in industrial use cases. Our realization shows how the ARPF can be easily integrated with prevalent BPMS. Our evaluation findings from a simulation scenario indicate that ARPF can improve Industry 4.0 processes with regard to AR task execution quality and cost savings.
The volume of program source code created, reused, and maintained worldwide is rapidly increasing, yet code comprehension remains a limiting productivity factor. For developers and maintainers, well known common software design patterns and the abstractions they offer can help support program comprehension. However, manual pattern documentation techniques in code and code-related assets such as comments, documents, or models are not necessarily consistent or dependable and are cost-prohibitive. To address this situation, we propose the Hybrid Design Pattern Detection (HyDPD), a generalized approach for detecting patterns that is programming-language-agnostic and combines graph analysis (GA) and Machine Learning (ML) to automate the detection of design patterns via source code analysis. Our realization demonstrates its feasibility. An evaluation compared each technique and their combination for three common patterns across a set of 75 single pattern Java and C# public sample pattern projects. The GA component was also used to detect the 23 Gang of Four design patterns across 258 sample C# and Java projects as well as in a large Java project. Performance and scalability were measured. The results show the advantages and potential of a hybrid approach for combining GA with artificial neural networks (ANN) for automated design pattern detection, providing compensating advantages such as reduced false negatives and improved F1 scores.
The over-expression and aggregation of α-synuclein (αSyn) are linked to the onset and pathology of Parkinson's disease. Native monomeric αSyn exists in an intrinsically disordered ensemble of interconverting conformations, which has made its therapeutic targeting by small molecules highly challenging. Nonetheless, here we successfully target the monomeric structural ensemble of αSyn and thereby identify novel drug-like small molecules that impact multiple pathogenic processes. Using a surface plasmon resonance high-throughput screen, in which monomeric αSyn is incubated with microchips arrayed with tethered compounds, we identified novel αSyn interacting drug-like compounds. Because these small molecules could impact a variety of αSyn forms present in the ensemble, we tested representative hits for impact on multiple αSyn malfunctions in vitro and in cells including aggregation and perturbation of vesicular dynamics. We thereby identified a compound that inhibits αSyn misfolding and is neuroprotective, multiple compounds that restore phagocytosis impaired by αSyn overexpression, and a compound blocking cellular transmission of αSyn. Our studies demonstrate that drug-like small molecules that interact with native αSyn can impact a variety of its pathological processes. Thus, targeting the intrinsically disordered ensemble of αSyn offers a unique approach to the development of small molecule research tools and therapeutics for Parkinson's disease.
Taar1 is a G protein-coupled receptor (GPCR) confined to primary cilia of rodent thyroid epithelial cells. Taar1-deficient mouse thyroid follicles feature luminal accumulation of thyroglobulin suggesting that Taar1 acts as a regulator of extra- and pericellular thyroglobulin processing, which is mediated by cysteine cathepsin proteases present at the apical plasma membrane of rodent thyrocytes. Here, by immunostaining and confocal laser scanning microscopy, we demonstrated co-localization of cathepsin L, but only little cathepsin B, with Taar1 at primary cilia of rat thyrocytes, the FRT cells. Because proteases were shown to affect half-lives of certain receptors, we determined the effect of cathepsin activity inhibition on sub-cellular localization of Taar1 in FRT cells, whereupon Taar1 localization altered such that it was retained in compartments of the secretory pathway. Since the same effect on Taar1 localization was observed in both cathepsin B and L inhibitor-treated cells, the interaction of cathepsin activities and sub-cellular localization of Taar1 was thought to be indirect. Indeed, we observed that cathepsin inhibition resulted in a lack of primary cilia from FRT cells. Next, we proved that primary cilia are a necessity for Taar1 trafficking to reach the plasma membrane of FRT cells, since the disruption of primary cilia by treatment with β-cyclodextrin resulted in Taar1 retention in compartments of the secretory pathway. Furthermore, in less well-polarized rat thyrocytes, namely in FRTL-5 cells lacking primary cilia, Taar1 was mainly confined to the compartments of the secretory pathway. We conclude that Taar1 localization in polarized thyroid epithelial cells requires the presence of primary cilia, which is dependent on the proteolytic activity of cysteine cathepsins B and L.