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Additive manufacturing (AM) technologies provide significant design freedom, which is highly desirable in today’s fast-paced product design processes. However, most of the parametric CAD software tools used today do not fully utilize this potential for freedom of form and design. Design mechanisms, such as topology optimization (TO), generative design (GD), and lattice structures, are available on the market to help designers minimize weight and material cost while maximizing the stiffness and flexibility of planned designs. This paper proposes a benchmarking approach for designers and engineers to select a suitable software tool for lattice structure generation for their specific applications. The approach includes preselecting software tools based on a weighted point evaluation of seven significant criteria. The tools are then evaluated based on key metrics such as computing time and file size of exported structures, as well as the following six distinctive attributes: usability, reliability, availability, performance, support, and cost. The evaluation process considers a total of 32 defined features. The investigation produced clear recommendations regarding overall performance, reliability, and user experience. The findings indicate that the option of a comprehensive support offering, as well as the initial and operational costs, are significant drivers in the decision-making process.

Handwriting learning disabilities, such as dysgraphia, have a serious negative impact on children’s academic results, daily life and overall well-being. Early detection of dysgraphia facilitates an early start of targeted intervention. Several studies have investigated dysgraphia detection using machine learning algorithms with a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated the fine grading of handwriting capabilities by predicting the SEMS score (between 0 and 12) with deep learning. Our approach provided a root-mean-square error of less than 1 with automatic instead of manual feature extraction and selection. Furthermore, the SensoGrip smart pen SensoGrip was used, i.e., a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

In recent years, significant advancements have been made in the field of design for additive manufacturing (DfAM). These advancements have focused on key aspects such as topology optimization (TO), generative design (GD), lattice structures, and AI-based algorithms. This paper presents a methodology for developing custom Grasshopper® algorithms to create strut-based, gradient, and conformal lattice structures. Two test geometries were devised and imported into Grasshopper®, and different lattice structures with varying settings, such as conformity, lattice topology, and strut diameter gradient and cell size gradient, were generated and manufactured. A series of experiments was conducted to assess the impact of input parameters on the formation of lattice structures, their performance in three-point bending tests, and their effect on functionality, applicability, and usability. The experimental investigation yielded clear findings regarding the usability and functionality of the proposed algorithm. However, the findings indicate that although the overall process is usable, improvements are required to streamline the algorithm in order to avoid geometry generation errors and to make it more user-friendly. This approach presents a low-cost, customizable alternative to commercial lattice generation tools, with direct integration in Rhino 8 and Grasshopper®.

Design for additive manufacturing (DfAM) has made significant advancements in recent years, with development focusing on pivotal aspects such as topology optimization (TO), generative design (GD), lattice structures, and AI-based algorithms. This paper puts forth a proposed methodology for the development of customizable bike saddle pads for manufacturing with AM. The approach entails the selection of appropriate AM processes and materials, the evaluation of material properties through compression testing, an initial saddle pressure mapping and bike fitting, the design and AM of bespoke saddle pads based on the initial measurements, and a validation pressure mapping and bike fitting. The investigation yielded clear findings regarding improvements in both pressure distribution and the change in pressure peaks, as well as an improvement in riding comfort. The findings indicate that although the overall process is innovative, improvements are required to streamline the measuring, modeling, and manufacturing workflow.

Activities in the field of automated driving have produced a variety of development tools and methodologies over the past decades. The requirements the systems have to fulfil and thus also the development guidelines are often documented in different kinds of catalogues (use-case catalogues, situation catalogues, scenario catalogues etc.). These catalogues cannot be directly applied for the development of partially and highly automated vehicle guidance concepts like conduct-by-wire (CbW) or H-mode. One reason is that up to now, no consistent terminology known to the authors yet exists for vehicle automation within the community. Moreover, as the aim of the two project groups CbW and H-mode is to make a comprehensive feasibility assessment of cooperative vehicle guidance, all interacting components of the overall system as well as all potential driving conditions a cooperative vehicle guidance system might have to cope with have to be analysed. This article focuses on two aspects. The first is a metaphor-based terminology discussion leading to a proposal for a fundamental ontology. The second aspect is an outlook on the different catalogues that use the new terminology and that have been developed. The methodology introduced here is a fundamental contribution towards simplifying communication and the exchange of findings.

Um dieser Herausforderung entgegentreten zu können, wurde „Drink Smart“, eine Active & Assisted Living- Lösung, entwickelt. Das zweite Ziel — ebenfalls unter Performanz fallend — war die Kommunikation des Drink Smart Systems mit einer elektronischen Pflegedokumentation bzw. das Angebot einer Stand-Alone-Lösung mithilfe einer Smartphone Applikation. Für die Evaluation der Projektziele wurden den Überkategorien Forschungsfragen zugeordnet, beispielsweise _ Welche technische Performanz erreicht das Drink Smart System im Alltag und in der Nutzung durch die Zielgruppen? _ Welche Funktionalitäten konnten bzw. konnten nicht umgesetzt werden? _ Welche technische Performanz erreicht das System in einer praxisnahen Anwendung? _ Welche Probleme können in der Gebrauchstauglichkeit während des Pre-Trails identifiziert werden? _ Welche Usability-Probleme bzw. Während der Pre-Trials fand eine heuristische Evaluation durch eine Expertin in Bezug auf die Usability sowie eine Testung auf Stabilität, Funktionalität und Einsatzsicherhit durch Projektmitarbeiter der Fachhochschule Campus Wien (FHCW) und der MIK — mobile, individuelle Krankenpflege -OG statt.

Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

Great advances have been made in sensitivity and acquisition speed on the Orbitrap mass analyzer, enabling increasingly deep proteome coverage. However, these advances have been mainly limited to the MS2 level, whereas ion beam sampling for the MS1 scans remains extremely inefficient. Here we report a data-acquisition method, termed BoxCar, in which filling multiple narrow mass-to-charge segments increases the mean ion injection time more than tenfold as compared to that of a standard full scan. In 1-h analyses, the method provided MS1-level evidence for more than 90% of the proteome of a human cancer cell line that had previously been identified in 24 fractions, and it quantified more than 6,200 proteins in ten of ten replicates. In mouse brain tissue, we detected more than 10,000 proteins in only 100 min, and sensitivity extended into the low-attomolar range.

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported 1 , advances in mass-spectrometry-based proteomics 2 have enabled increasingly comprehensive identification and quantification of the human proteome 3 – 6 . However, there have been few comparisons across species 7 , 8 , in stark contrast with genomics initiatives 9 . Here we use an advanced proteomics workflow—in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system—for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis . Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org . An advanced proteomics workflow is used to identify 340,000 proteins from 100 taxonomically diverse species, providing a comparative view of proteomes across the evolutionary range.