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The impact of climate change and global warming makes it imperative to seek sustainable solutions for the built environment. To facilitate the design of future sustainable buildings, wind tunnel tests are conducted in this study to investigate the flow characteristics and wind energy potential over a flat building roof with different edge configurations. Specifically, this study addresses the effect of parapet walls and roof edge-mounted solar panels on the wind flow over a flat-roof tall building. The results show that parapet walls generally slow down the wind speed and increase turbulence intensity as well as skewness angle, which compromises the efficiency of traditional turbine-based wind energy harvesting. On the other hand, the presence of solar panels on the roof edge (or on the top of the parapet wall) further alters flow separation and has the potential to enhance wind energy harvesting over the roof, especially for the solar panel inclined at 30°. In addition to providing valuable data for validating computational fluid dynamics (CFD) simulations, this study could also help to guide the design of wind energy harvesting devices on the building roof and explore the promising synergy with solar panels. 

Aerodynamic shape optimization is very useful for enhancing the performance of wind-sensitive structures. However, shape parameterization, as the first step in the pipeline of aerodynamic shape optimization, still heavily depends on empirical judgment. If not done properly, the resulting small design space may fail to cover many promising shapes, and hence hinder realizing the full potential of aerodynamic shape optimization. To this end, developing a novel shape parameterization scheme that can reflect real-world complexities while being simple enough for the subsequent optimization process is important. This study proposes a machine learning-based scheme that can automatically learn a low-dimensional latent representation of complex aerodynamic shapes for bluff-body wind-sensitive structures. The resulting latent representation (as design variables for aerodynamic shape optimization) is composed of both discrete and continuous variables, which are embedded in a hierarchy structure. In addition to being intuitive and interpretable, the mixed discrete and continuous variables with the hierarchy structure allow stakeholders to narrow the search space selectively based on their interests. As a proof-of-concept study, shape parameterization examples of tall building cross sections are used to demonstrate the promising features of the proposed scheme and guide future investigations on data-driven parameterization for aerodynamic shape optimization of wind-sensitive structures. 

Abstract Polydimethylsiloxane (PDMS)—the simplest and most common silicone compound—exemplifies the central characteristics of its class and has attracted tremendous research attention. The development of PDMS‐based materials is a vivid reflection of the modern industry. In recent years, PDMS has stood out as the material of choice for various emerging technologies. The rapid improvement in bulk modification strategies and multifunctional surfaces has enabled a whole new generation of PDMS‐based materials and devices, facilitating, and even transforming enormous applications, including flexible electronics, superwetting surfaces, soft actuators, wearable and implantable sensors, biomedicals, and autonomous robotics. This paper reviews the latest advances in the field of PDMS‐based functional materials, with a focus on the added functionality and their use as programmable materials for smart devices. Recent breakthroughs regarding instant crosslinking and additive manufacturing are featured, and exciting opportunities for future research are highlighted. This review provides a quick entrance to this rapidly evolving field and will help guide the rational design of next‐generation soft materials and devices. 

Abstract Anthropogenic environmental changes are known to affect the Earth's ecosystems. However, how these changes influence assembly trajectories of the impacted communities remains a largely open question.In this study, we investigated the effect of elevated nitrogen (N) deposition and increased precipitation on plant taxonomic and phylogenetic β‐diversity in a 9‐year field experiment in the temperate semi‐arid steppe of Inner Mongolia, China.We found that both N and water addition significantly increased taxonomic β‐diversity, whereas N, not water, addition significantly increased phylogenetic β‐diversity. After the differences in local species diversity were controlled using null models, the standard effect size of taxonomic β‐diversity still increased with both N and water addition, whereas water, not N, addition, significantly reduced the standard effect size of phylogenetic β‐diversity. The increased phylogenetic convergence observed in the water addition treatment was associated with colonizing species in each water addition plot being more closely related to species in other replicate plots of the same treatment. Species colonization in this treatment was found to be trait‐based, with leaf nitrogen concentration being the key functional trait.Synthesis.Our analyses demonstrate that anthropogenic environmental changes may affect the assembly trajectories of plant communities at both taxonomic and phylogenetic scales. Our results also suggest that while stochastic processes may cause communities to diverge in species composition, deterministic process could still drive communities to converge in phylogenetic community structure.