Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Refine
Year of publication
Document Type
- Article (132)
- Part of a Book (1)
- conference proceeding (article) (1)
- conference talk (1)
- conference proceeding (volume) (1)
- Other (1)
Language
- English (137) (remove)
Is part of the Bibliography
- no (137)
Keywords
- Shotcrete (4)
- 3D volumetry (3)
- Artificial Intelligence (3)
- Durability (3)
- Maschinelles Lernen (3)
- Medical Image Computing (3)
- Photoacoustic spectroscopy (3)
- Acetone breath analysis (2)
- Acetone detection (2)
- AnyBody (2)
Institute
- Fakultät Maschinenbau (47)
- Fakultät Informatik und Mathematik (39)
- Regensburg Center of Biomedical Engineering - RCBE (23)
- Fakultät Elektro- und Informationstechnik (20)
- Regensburg Center of Health Sciences and Technology - RCHST (16)
- Fakultät Bauingenieurwesen (15)
- Regensburg Medical Image Computing (ReMIC) (15)
- Labor Biomechanik (LBM) (13)
- Fakultät Angewandte Natur- und Kulturwissenschaften (12)
- Labor Intelligente Materialien und Strukturen (7)
Begutachtungsstatus
- peer-reviewed (127)
Eye tracking is becoming more and more important as a research method within the field of software engineering (SE). Existing meta-analyses focus on the design or conduct of SE eye tracking studies rather than the analysis phase. This article attempts to fill this gap; it presents a systematic literature review of eye tracking studies in the field of SE—focusing mainly on the data analysis methods used. From the IEEE Xplore and ACM digital libraries we gather 125 papers up to the first quarter of 2024. Detailed evaluation provides information on the number of papers that use specific methods of analysis (i.e., descriptive or inferential statistics, and gaze visualization) or settings (e.g., sample size, technical setup, and selected aspects of research design). With the data obtained we can infer the popularity of specific analysis methods in the field. Those results enable efficient work on data analysis tools or education of aspiring researchers and can serve as basis for standardization or guidelines within the community—providing for methods to include as well as current inconsistencies.
In order to determine the influence of the ondulations in fabrics on the damping properties of fiber-reinforced plastics, the structural dynamic properties of fabric- and unidirectionally reinforced plastics are investigated. The free decay behavior of flat beam-like specimens is investigated under fixed-free boundary conditions. As the material damping is consistently higher in fabric-reinforced specimens compared to unidirectionally reinforced ones, a contribution of an additionally acting mesomechanic kinematic in fabric weaves is implied. Based on a degree of ondulation, it is possible to classify the enhancement of the material damping and determine the corresponding energy dissipation. The study provides valuable quantitative relations of the additional damping effect due to the mesomechanic kinematic. Compared to the unidirectionally reinforced material, plain weave enhances the material damping by 37…52% at ˜OPL=0.0133 , whereas twill weave 2/2 enhances it by 31…40% at ˜OT2=0.0098 . The consideration of the findings contributes to a deeper understanding of the visco-elastic dynamic behavior of fabric-reinforced plastics and allows further applications in research, development, and industry.
The chemical reaction between CO2 and a blended Portland cement concrete, referred to as carbonation, can lead to reduced performance, particularly when concrete is exposed to elevated levels of CO2 (i.e., accelerated carbonation conditions). When slight changes in concrete mix designs or testing conditions are adopted, conflicting carbonation results are often reported. The RILEM TC 281-CCC ‘Carbonation of Concrete with Supplementary Cementitious Materials’ has conducted a critical analysis of the standardised testing methodologies that are currently applied to determine carbonation resistance of concrete in different regions. There are at least 17 different standards or recommendations being actively used for this purpose, with significant differences in sample curing, pre-conditioning, carbonation exposure conditions, and methods used for determination of carbonation depth after exposure. These differences strongly influence the carbonation depths recorded and the carbonation coefficient values calculated. Considering the importance of accurately determining carbonation potential of concrete, not just for predicting their durability performance, but also for determining the amount of CO2 that concrete can re-absorb during or after its service life, it is imperative to recognise the applicability and limitations of the results obtained from different tests. This will enable researchers and practitioners to adopt the most appropriate testing methodologies to evaluate carbonation resistance, depending on the purpose of the conclusions derived from such testing (e. g. materials selection, service life prediction, CO2 capture potential).
Fused filament fabrication (FFF) is a widely used additive manufacturing process for producing functional components and prototypes. The FFF process involves depositing melted material layer-by-layer to build up 3D physical parts. The quality of the final product depends on several factors, including the component density and tensile strength, which are typically determined through destructive testing methods. X-ray microtomography (XCT) can be used to investigate the pore sizes and distribution. These approaches are time-consuming, costly, and wasteful, making it unsuitable for high-volume manufacturing. In this paper, a new method for non-destructive determination of component density and estimation of the tensile strength in FFF processes is proposed. This method involves the use of gradual error detection by sensors and convolutional neural networks. To validate this approach, a series of experiments has been conducted. Component density and tensile strength of the printed specimens with varying extrusion factor were measured using traditional destructive testing methods and XCT. The cumulative error detection method was used to predict the same properties without destroying the specimens. The predicted values were then compared with the measured values, and it was observed that the method accurately predicted the component density and tensile strength of the tested parts. This approach has several advantages over traditional destructive testing methods. The method is faster, cheaper, and more environmentally friendly since it does not require the destruction of the product. Moreover, it facilitates the testing of each individual part instead of assuming the same properties for components from one series. Additionally, it can provide real-time feedback on the quality of the product during the manufacturing process, allowing for adjustments to be made as needed. The advancement of this approach points toward a future trend in non-destructive testing methodologies, potentially revolutionizing quality assurance processes not only for consumer goods but various industries such as electronics or automotive industry. Moreover, its broader applications extend beyond FFF to encompass other additive manufacturing techniques such as selective laser sintering (SLS), or electron beam melting (EBM). A comparison between the old destructive testing methods and this innovative non-destructive approach underscores the possible fundamental change toward more efficient and sustainable manufacturing practices. This approach has the potential to significantly reduce the time and cost associated with traditional destructive testing methods while ensuring the quality of FFF-manufactured products.
Abstract
Motivation
Recognition of specific molecules by proteins is a fundamental cellular mechanism and relevant for many applications. Being able to modify binding is a key interest and can be achieved by repurposing established interaction motifs. We were specifically interested in a methodology for the design of peptide binding modules. By leveraging interaction data from known protein structures, we plan to accelerate the design of novel protein or peptide binders.
Results
We developed ATLIGATOR—a computational method to support the analysis and design of a protein’s interaction with a single side chain. Our program enables the building of interaction atlases based on structures from the PDB. From these atlases pocket definitions are extracted that can be searched for frequent interactions. These searches can reveal similarities in unrelated proteins as we show here for one example. Such frequent interactions can then be grafted onto a new protein scaffold as a starting point of the design process. The ATLIGATOR tool is made accessible through a python API as well as a CLI with python scripts.
Availability and implementation
Source code can be downloaded at github (https://www.github.com/Hoecker-Lab/atligator), installed from PyPI (‘atligator’) and is implemented in Python 3.
The chemical reaction between CO2 and a blended Portland cement concrete, referred to as carbonation, can lead to reduced performance, particularly when concrete is exposed to elevated levels of CO2 (i.e., accelerated carbonation conditions). When slight changes in concrete mix designs or testing conditions are adopted, conflicting carbonation results are often reported. The RILEM TC 281-CCC ‘Carbonation of Concrete with Supplementary Cementitious Materials’ has conducted a critical analysis of the standardised testing methodologies that are currently applied to determine carbonation resistance of concrete in different regions. There are at least 17 different standards or recommendations being actively used for this purpose, with significant differences in sample curing, pre-conditioning, carbonation exposure conditions, and methods used for determination of carbonation depth after exposure. These differences strongly influence the carbonation depths recorded and the carbonation coefficient values calculated. Considering the importance of accurately determining carbonation potential of concrete, not just for predicting their durability performance, but also for determining the amount of CO2 that concrete can re-absorb during or after its service life, it is imperative to recognise the applicability and limitations of the results obtained from different tests. This will enable researchers and practitioners to adopt the most appropriate testing methodologies to evaluate carbonation resistance, depending on the purpose of the conclusions derived from such testing (e. g. materials selection, service life prediction, CO2 capture potential).
We review the developments in life safety and the incorporation thereof in the design and assessment of structures over the last 50 years. Various measures of life safety are presented that have been developed according to the marginal life saving cost principle based on individual, societal and economic considerations. Target probabilities of failure, or target reliabilities, are central to modern structural design and assessment. These are derived either through back-calibration to existing practice or through life cycle cost minimisation, both of which yield comparable safety levels, and are underpinned by lower bounds from life safety. Life cycle cost minimisation is reviewed here, which considers all direct and indirect costs of failure including loss of life and limb, as well as the costs and efficiency of increasing reliability. We discuss the incorporation of life safety into reliability-based design and assessment through the concept of the Life Quality Index, which uses key societal indicators, namely, the GDP and life expectancy, and health economics as a basis for specifying minimum reliabilities for both new and existing structures. The current state of advancement of reliability- and risk-informed design, and recommendations for future developments in life safety are considered.
The success of surgical treatment for fractures hinges on various factors, notably accurate surgical indication. The process of developing and certifying a new osteosynthesis device is a lengthy and costly process that requires multiple cycles of review and validation. Current methods, however, often rely on predecessor standards rather than physiological loads in specific anatomical locations. This study aimed to determine actual loads experienced by an osteosynthesis plate, exemplified by a standard locking plate for the femoral shaft, utilizing finite elements analysis (FEA) and to obtain the bending moments for implant development standard tests. A protocol was developed, involving the creation and validation of a fractured femur model fixed with a locking plate, mechanical testing, and FEA. The model’s validation demonstrated exceptional accuracy in predicting deformations, and the FEA revealed peak stresses in the fracture bridging zone. Results of a parametric analysis indicate that larger fracture gaps significantly impact implant mechanical behavior, potentially compromising stability. This study underscores the critical need for realistic physiological conditions in implant evaluations, providing an innovative translational approach to identify internal loads and optimize implant designs. In conclusion, this research contributes to enhancing the understanding of implant performance under physiological conditions, promoting improved designs and evaluations in fracture treatments.
Context:
Causal probabilistic graph-based models have gained widespread utility, enabling the modeling of cause-and-effect relationships across diverse domains. With their rising adoption in new areas, such as safety analysis of complex systems, software engineering, and machine learning, the need for an integrated lifecycle framework akin to DevOps and MLOps has emerged. Currently, such a reference for organizations interested in employing causal engineering is missing. This lack of guidance hinders the incorporation and maturation of causal methods in the context of real-life applications.
Objective:
This work contextualizes causal model usage across different stages and stakeholders and outlines a holistic view of creating and maintaining them within the process landscape of an organization.
Method:
A novel lifecycle framework for causal model development and application called CausalOps is proposed. By defining key entities, dependencies, and intermediate artifacts generated during causal engineering, a consistent vocabulary and workflow model to guide organizations in adopting causal methods are established.
Results:
Based on the early adoption of the discussed methodology to a real-life problem within the automotive domain, an experience report underlining the practicability and challenges of the proposed approach is discussed.
Conclusion:
It is concluded that besides current technical advancements in various aspects of causal engineering, an overarching lifecycle framework that integrates these methods into organizational practices is missing. Although diverse skills from adjacent disciplines are widely available, guidance on how to transfer these assets into causality-driven practices still need to be addressed in the published literature. CausalOps’ aim is to set a baseline for the adoption of causal methods in practical applications within interested organizations and the causality community.