Uncertain Future Dwelling: Emergent Interiors of the Metaverse

Contemporarily, a flood of digital interior architectural imagery has emerged of spaces developed for the Metaverse, a forthcoming immersive 3D virtual world. These spaces are not bound by the conventions of architectural practice nor the demands of the physical world, providing an opportunity for design exploration and innovation in the future of interiors and positing challenges to core architectural concepts that have accompanied traditional practice. This research offers a visual analysis of aesthetic trends and new typologies present in the interior architectural spaces designed for the Metaverse. The analysis features a curated and collaged collection of works from ten creators of Metaverse spaces, categorised to examine the impact of digital architectural spaces that increasingly detach from the needs of physical dwellings. The research reveals commanding visual trends in Metaverse interior imagery that challenge traditional notions of interiority and dwelling and finds aesthetic signifiers of belonging in spaces that Augé (1995/2009) would neatly classified as an empirical ‘non-place.’ Positioned as a form of heterotopia in a realm where architecture is being designed for the purely visual, we posit that the less recognisable these spaces become, the more potential they hold for innovation in both the Metaverse and in dialogue with real-world interior architecture

The EIFS distribution for anodized and pre-corroded 7010-T7651 under constant amplitude loading

This paper reports results from SICAS, an experimental programme to evaluate the effectiveness of the equivalent initial flaw size (EIFS) approach in managing the structural integrity effects of pitting corrosion. Fatigue crack growth and life tests were conducted on anodized and pre-corroded 7010-T7651. The corrosion pits that initiated fatigue were then measured using the SEM. These data were analysed statistically to identify the pit geometric parameter(s) that influenced fatigue life. Projected pit area had the strongest effect, while pit depth and pit width were each statistically insignificant. The EIFS distribution for corroded 7010-T7651 was then calculated. Examination of the probability distribution of the ratio of EIFS area to pit area allowed the derivation of a scatter factor that gave safely conservative fatigue life predictions for the corroded material

An investigation of the native oxide of aluminum alloy 7475-T7651 using XPS, AES, TEM, EELS, GDOES, RBS.

The native oxide on the rolled aerospace aluminum alloy 7475-T7651 was characterized using a variety of different techniques, including X-ray Photoelectron Spectrometry (XPS), Auger Electron Spectrometry (AES), Transmission Electron Microscopy (TEM), Electron Energy Loss Spectrometry (EELS), Glow Discharge Optical Emission Spectrometry (GDOES), and Rutherford Backscattered Spectrometry (RBS). All techniques revealed that the native oxide layer is magnesium-rich and is probably a mixture of magnesium and aluminum&ndash;magnesium oxides.1 The oxide layer was found to be of nonuniform thickness due to the rolling process involved during the manufacture of this sheet alloy; this complicates analysis using techniques which have poor spatial resolution. Direct thickness measurement from cross-sectional TEM reveals an oxide thickness which varies between 125 and 500 nm. This large variation in thickness was also evident from GDOES and AES depth profiles as well asthe RBS data. Both XPS and RBS also show evidence for the presence of heavy metals in the oxide.<br /

High Speed Image Segmentation using a Binary Neural Network.

In the very near future large amounts of Remotely Sensed data will become available on a daily basis. Unfortunately, it is not clear if the processing methods are available to deal with this data in a timely fashion. This paper describes research towards an approach which will allow a user to perform a rapid pre-search of large amounts of image data for regions of interest based on texture. The method is based on a novel neural network architecture (ADAM) that is designed primarily for speed of operation by making use of computationally simple pre-processing and only uses Boolean operations in the weights of the network. To facilitate interactive use of the network, it is capable of rapid training. The paper outlines the neural network, its application to RS data in comparison with other methods, and briefly describes a fast hardware implementation of the network. 1 Introduction The advent of new satellites producing tens of megabytes of image-data per day has placed a challenge on th..