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  1. The rooftop is a default location for photovoltaic solar panels and is often not enough to offset increasing building energy consumption. The vertical surface of urban buildings offers a prime location to harness solar energy. The overall goal of this research is to evaluate power production potentials and multi-functionalities of a 3D building integrated photovoltaic (BIPV) facade system. The traditional BIPV which is laminated with window glass obscures the view-out and limits daylight penetration. Unlike the traditional system, the 3D solar module was configured to reflect the sun path geometry to maximize year-round solar exposure and energy production. In addition, the 3D BIPV façade offers multiple functionalities – solar regulations, daylighting penetration, and view-out, resulting in energy savings from heating, cooling, and artificial lighting load. Its ability to produce solar energy offsets building energy consumption and contributes to net-zero-energy buildings. Both solar simulations and physical prototyping were carried out to investigate the promises and challenges of the 3D BIPV façade system compared to a traditional BIPV system. With climate emergency on the rise and the need for clean, sustainable energy becoming ever more pressing, the 3D BIPV façade in this paper offers a creative approach to tackling the problems of power production, building energy savings, and user health and wellbeing. 
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    Free, publicly-accessible full text available December 1, 2024
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  5. This paper investigates the mechanical behaviour of a bi-layered panel containing many particles in one layer and demonstrates the size effect of particles on the deflection. The inclusion-based boundary element method (iBEM) considers a fully bounded bi-material system. The fundamental solution for two-jointed half spaces has been used to acquire elastic fields resulting from source fields over inclusions and boundary-avoiding multi-domain integral along the interface. Eshelby’s equivalent inclusion method is used to simulate the material mismatch with a continuously distributed eigenstrain field over the equivalent inclusion. The eigenstrain is expanded at the centre of the inclusion, which provides tailorable accuracy based on the order of the polynomial of the eigenstrain. As a single-domain approach, the iBEM algorithm is particularly suitable for conducting virtual experiments of bi-layered composites with many defects or reinforcements for both local analysis and homogenization purposes. The maximum deflection of solar panel coupons is studied under uniform vertical loading merged with inhomogeneities of different material properties, dimensions and volume fractions. The size of defects or reinforcements plays a significant role in the deflection of the panel, even with the same volume fraction, as the substrate is relatively thin. 
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  6. In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training. In this paper, we investigate how these benefits of Mixup training rely on properties of the data in the context of classification. For minimizing the original empirical risk, we compute a closed form for the Mixup-optimal classification, which allows us to construct a simple dataset on which minimizing the Mixup loss can provably lead to learning a classifier that does not minimize the empirical loss on the data. On the other hand, we also give sufficient conditions for Mixup training to also minimize the original empirical risk. For generalization, we characterize the margin of a Mixup classifier, and use this to understand why the decision boundary of a Mixup classifier can adapt better to the full structure of the training data when compared to standard training. In contrast, we also show that, for a large class of linear models and linearly separable datasets, Mixup training leads to learning the same classifier as standard training. 
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