Search for: All records

BRICKxAR (Multi 3D Models/M3D) prototype offers markerless, in-situ, and step-by-step, highly accurate Augmented Reality (AR) assembly instructions for large or small part assembly. The prototype employs multiple assembly phases of deep learning-trained 3D model-based AR registration coupled with a step count. This ensures object recognition and tracking persist while the model updates at each step, even if a part's location is not visible to the AR camera. The use of phases simplifies the complex assembly instructions. The testing and heuristic evaluation findings indicate that BRICKxAR (M3D) provides robust instructions for assembly, promising potential applicability at different scales and scenarios. 

Advances in computational technology provide opportunities to explore new methods to improve spatial abilities and the understanding of buildings in architecture education. The research employed BIMxAR, a Building Information Modeling-enabled AR educational tool with novel visualization features to support learning and understanding construction systems, materials configuration, and 3D section views of complex building structures. We validated the research through a test case based on a quasi-experimental research design, in which BIMxAR was used as an intervention. Two study groups were employed - non-AR and AR. The learning gain differences within and between the groups were not statistically significant, however, the AR group perceived significantly less workload and higher performance compared to the non-AR group. These findings suggest that the AR version is an easy, useful, and convenient learning tool. 

The Australia Telescope Large Area Survey (ATLAS) and the VLA survey in the XMM-LSS/VIDEO deep field provide deep (≈15 $\mu$ Jy beam−1) and high-resolution (≈4.5–8 arcsec) radio coverage of the three XMM-SERVS fields (W-CDF-S, ELAIS-S1, and XMM-LSS). These data cover a total sky area of 11.3 deg2 and contain ≈11 000 radio components. Furthermore, about 3 deg2 of the XMM-LSS field also has deeper MIGHTEE data that achieve a median RMS of 5.6 $\mu$ Jy beam−1 and detect more than 20 000 radio sources. We analyse all these radio data and find source counterparts at other wavebands utilizing deep optical and infrared (IR) surveys. The nature of these radio sources is studied using radio-band properties (spectral slope and morphology) and the IR–radio correlation. Radio AGNs are selected and compared with those selected using other methods (e.g. X-ray). We found 1656 new AGNs that were not selected using X-ray and/or MIR methods. We constrain the FIR-to-UV SEDs of radio AGNs using cigale and investigate the dependence of radio AGN fraction upon galaxy stellar mass and star formation rate.

 

Fabrics are an indispensable part of our everyday life. They provide us with protection, offer privacy and form an intimate expression of ourselves through their esthetics. Imparting functionality at the fiber level represents an intriguing path toward innovative fabrics with a hitherto unparalleled functionality and value. The fiber technology based on thermal drawing of a preform, which is identical in its materials and geometry to the final fiber, has emerged as a powerful platform for the production of exquisite fibers with prerequisite composition, geometric complexity and control over feature size. A ‘Moore's law’ for fibers is emerging, delivering higher forms of function that are important for a broad spectrum of practical applications in healthcare, sports, robotics, space exploration, etc. In this review, we survey progress in thermally drawn fibers and devices, and discuss their relevance to ‘smart’ fabrics. A new generation of fabrics that can see, hear and speak, sense, communicate, harvest and store energy, as well as store and process data is anticipated. We conclude with a critical analysis of existing challenges and opportunities currently faced by thermally drawn fibers and fabrics that are expected to become sophisticated platforms delivering value-added services for our society.

 

We perform X-ray spectral analyses to derive the characteristics (e.g., column density, X-ray luminosity) of ≈10,200 active galactic nuclei (AGNs) in the XMM-Spitzer Extragalactic Representative Volume Survey, which was designed to investigate the growth of supermassive black holes across a wide dynamic range of cosmic environments. Using physical torus models (e.g., Borus02) and a Bayesian approach, we uncover 22 representative Compton-thick (CT;N_H> 1.5 × 10²⁴cm⁻²) AGN candidates with good signal-to-noise ratios as well as a large sample of 136 heavily obscured AGNs. We also find an increasing CT fraction (f_CT) from low (z< 0.75) to high (z> 0.75) redshift. Our CT candidates tend to show hard X-ray spectral shapes and dust extinction in their spectral energy distribution fits, which may shed light on the connection between AGN obscuration and host-galaxy evolution.

 

Active dwarf galaxies are important because they contribute to the evolution of dwarf galaxies and can reveal their hosted massive black holes. However, the sample size of such sources beyond the local universe is still highly limited. In this work, we search for active dwarf galaxies in the recently completed XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS). XMM-SERVS is currently the largest medium-depth X-ray survey covering 13 deg²in three extragalactic fields, which all have well-characterized multiwavelength information. After considering several factors that may lead to misidentifications, we identify 73 active dwarf galaxies atz< 1, which constitutes the currently largest X-ray-selected sample beyond the local universe. Our sources are generally less obscured than predictions based on the massive-AGN (active galactic nucleus) X-ray luminosity function and have a low radio-excess fraction. We find that our sources reside in environments similar to those of inactive dwarf galaxies. We further quantify the accretion distribution of the dwarf-galaxy population after considering various selection effects and find that it decreases with X-ray luminosity, but redshift evolution cannot be statistically confirmed. Depending on how we define an AGN, the active fraction may or may not show a strong dependence on stellar mass. Their Eddington ratios and X-ray bolometric corrections significantly deviate from the expected relation, which is likely caused by several large underlying systematic biases when estimating the relevant parameters for dwarf galaxies. Throughout this work, we also highlight problems in reliably measuring photometric redshifts and overcoming strong selection effects for distant active dwarf galaxies.

 

There is a close relation between spatial thinking and mathematical problem-solving. This paper presents a newly developed educational Augmented Reality (AR) mobile application, BRICKxAR/T, to help students intuitively learn spatial transformations and the related mathematics through play. A pilot study with 7 undergraduate students evaluates students learning gain through a mental rotation and a math test on transformation matrices. The results show most students performed better with a higher score after learning with the app. Students found the app interesting to play and useful for learning geometric transformations and matrices.