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Context. Asteroid and comet surfaces are exposed to a complex environment that includes low gravity, high temperature gradients, and a bombardment of micrometeorites and cosmic rays. Surface material exposed to this environment evolves in a specific way depending on various factors such as the bodiesa-size, heliocentric distance, and composition. Fractures in boulders, as seen on asteroid Ryugu, can help to determine and constrain the dominant processes eroding small-body surface materials. It is also possible to estimate fracture growth timescales based on the abundance and length of fractures in boulders. Aims. We analyse the number, orientation, and length of fractures on asteroid Ryugu to establish the relation between the fractures and the processes that may have formed them. We also compare our results to similar investigations conducted on other small bodies and estimate the timescale of fracture growth. Methods. 198 high-resolution Hayabusa2 images of asteroid Ryugu suitable for our fracture analysis were selected and map-projected. Within these images, fractures in boulders were manually mapped using the QGIS software. The fracture coordinates were extracted and the fracturesa-orientation and length were computed for 1521 identified fractures. Results. Fractures in boulders on asteroid Ryugu are found to be preferentially north-south aligned, suggesting a formation through thermal erosion. Modeling the fracture length indicates a fracture growth timescale of 30 000 to 40 000 yr, slightly younger than ages found previously for asteroid Bennu. The errors in these ages, due to uncertainties about the thermophysical parameters used in this model, are substantial (âà  à  33 000 yr +250 000 yr). However, even with these large errors, the model suggests that thermal fracturing is a geologically fast process. These times are not too dissimilar to those quoted in the literature for Ryugu and Bennu, since similar thermophysical material parameters for Ryugu and Ben, Validerad;2024;Nivå 2;2024-04-08 (marisr);Full text license: CC BY

In order to facilitate the prediction of some physical properties, we propose several simple formulas based on two parameters only, the metallic valence and metallic atomic radii. Knowing the composition, for single-phase alloys, the average parameters can be calculated by the rule of mixture. The input parameters can be obtained from tabulated databases. Adopting from the literature the results of Coulomb crystal model for metals and single-phase high-entropy alloys, we have derived formulas for the shear modulus (G) and the cohesion energy (Ecoh). Based on these parameters separately, we set up two formulas to estimate the hardness in the case of pure metals. For single-phase (solid-solution) HEAs, by simplifying the Maresca and Curtin model, we obtained a formula for estimating the hardness, which takes into account the atomic misfit in addition to G. The maximal hardness for single-phase HEA is approximately 600 kg/mm2 and is obtained for a composition with a valence electron concentration of approximately 6 divided by 7., QC 20240626

Nature provides a rich source of information for the design of novel materials; yet there remain significant challenges in the design and manufacture of materials that replicate the form, function, and sustainability of biological solutions. Here, we identify key challenges and promising approaches to the development of materials informed by biology. These challenges fall into two main areas; the first relates to harnessing biological information for materials innovation, including key differences between biological and synthetic materials, and the relationship between structure and function. We propose an approach to materials innovation that capitalizes on biodiversity, together with high-throughput characterization of biological material architectures and properties, linked to environmental and ecological context. The second area relates to the design and manufacture of bioinformed materials, including the physical scale of material architectures and manufacturing scale up. We suggest ways to address these challenges and promising prospects for a bioinformed approach to materials innovation.

In human-centered product design and development, understanding the users is essential. Empathizing with the user can help designers gain deeper insights into the user experience and their needs. However, a few studies have captured empathy real time during user interactions. Accordingly, the degree to which empathy occurs and enhances user understanding remains unclear. To narrow this gap, a study was performed exploring the use of video-based facial expression analysis during user interviews, as a means to capture empathy related to understanding vehicle driving experiences under challenging conditions. Mimicry and synchrony have been shown to be predictors of empathy in cognitive psychology. In this study, we adapted this method to study 46 user-designer interviews. The results show that the user and designer exhibited mimicry in their facial expressions, which thereby indicated that affective empathy can be captured via simple video facial recognition. However, we found that the user's facial expressions might not represent their actual emotional tone, which can mislead the designer, and they achieve false empathy. Further, we did not find a link between the observed mimicry of facial expressions and the understanding of mental contents, which indicated that the affective and some cognitive parts of user empathy may not be directly connected. Further studies are needed to understand how facial expression analysis can further be used to study and advance empathic design.

Low-pressure die cast (LPDC) is widely used in high performance, precision aluminum alloy automobile wheel castings, where defects such as porosity voids are not permitted. The quality of LPDC parts is highly influenced by the casting process conditions. A need exists to optimize the process variables to improve the part quality against difficult defects such as gas and shrinkage porosity. To do this, process variable measurements need to be studied against occurrence rates of defects. In this paper, industry 4.0 cloud-based systems are used to extract data. With these data, supervised machine learning classification models are proposed to identify conditions that predict defectives in a real foundry Aluminum LPDC process. The root cause analysis is difficult, because the rate of defectives in this process occurred in small percentages and against many potential process measurement variables. A model based on the XGBoost classification algorithm was used to map the complex relationship between process conditions and the creation of defective wheel rims. Data were collected from a particular LPDC machine and die mold over three shifts and six continuous days. Porosity defect occurrence rates could be predicted using 36 features from 13 process variables collected from a considerably small sample (1077 wheels) which was highly skewed (62 defectives) with 87% accuracy for good parts and 74% accuracy for parts with porosity defects. This work was helpful in assisting process parameter tuning on new product pre-series production to lower defectives.