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A drone delivery system, characterized by its low energy consumption, high efficiency, and extensive coverage capability, has been adopted as an effective solution to overcome the limitations of traditional ground transportation. However, due to strong interactions between urban structures and wind, the wind environment in the low-altitude airspace of urban areas poses significant safety risks for drone operations, a challenge that remains unresolved. To mitigate these risks, this study presents a methodology for precisely defining the no-fly zones (NFZs) for drone operations using computational fluid dynamics (CFD) simulations. Three hazardous indices—safe, deviation, and unsafe—are proposed to indicate the drone operation status. High-resolution CFD models of urban wind environments in a real city area are coupled with meteorological wind data to provide statistical results for the three indices. The Reynolds-averaged Navier–Stokes turbulence model is employed to simulate two wind environments, standard wind and strong wind, under 36 incoming flow directions. Considering eight flight orientations of drone operating in horizontal planes at various heights, a set of maps for the occurrence probability of the three hazardous indices is provided. These maps can be utilized to determine safe areas, identify no-fly zones corresponding to high occurrence probabilities of deviation and unsafe indices, and establish efficient flight paths for drone operations. [ABSTRACT FROM AUTHOR]

Urban cooling becomes a priority in urban planning and design practices. Limited by the slow running speed and prescriptive nature, existing computational tools such as simulation and optimization are yet to be fully integrated in the design decision-making process. This paper describes the Machine Learning-Enhanced Design Optimizer (MLEDO), a novel workflow in search of optimal design option for urban cooling. A physics-based simulation model was developed to assess the cooling performances of a large database of urban design variations. The database was used to train an Artificial Neural Network model, which was then linked with a Genetic Algorithm to rapidly identify optimal design options. The MLEDO workflow was evaluated using a new development urban site against a traditional Simulation-based Genetic Algorithm Design Optimizer (SGADO) as well as human designers. MLEDO outperformed the latter two in terms of efficiency and the performance of the optimal design options. It can also quantify the importance of design parameters in their contribution to cooling performances, which can be used to enhance the understanding of human designers and inform design revisions. MLEDO has the potential to be further developed into a software tool in support of early-stage urban design. [ABSTRACT FROM AUTHOR]

Unit-to-unit variation among the production chambers is a long-lasting challenge for Fault Detection and Classification (FDC) development in the semiconductor industry. Currently, various methods are applied for knowledge transfer among chambers and generalized FDC model development. However, the existing methods cannot give a quantitative or qualitative measure for cross-chamber data transferability evaluation. This research proposes a novel methodology for data transferability evaluation and important sensor screening, which can serve as a data quality evaluation tool for any FDC model. In this research, firstly, Time Series Alignment Kernel (TSAK) is incorporated into Multidomain Discriminant Analysis (MDA) algorithm to achieve sensor-based domain generalization. Then, domain-invariant features are directly extracted for sensor visualization. After that, Fisher’s criterion ratios of the labeled good wafer samples and defective ones are computed based on the domain-invariant features of each sensor to quantitatively estimate how easy it is to transfer knowledge of each sensor among chambers, i.e., data transferability evaluation. Lastly, the proposed method develops a Recursive Feature Elimination (RFE)-based sensor selection algorithm to qualitatively analyze the importance of each sensor channel and identify the critical sensor subset. In this study, validation of the proposed method is based on two open-source datasets from real production lines. [ABSTRACT FROM AUTHOR]

Gliomas are the most common malignant brain tumors. High-grade gliomas, represented by glioblastoma multiforme (GBM), have a poor prognosis and are prone to recurrence. The standard treatment strategy is tumor removal combined with radiotherapy and chemotherapy, such as temozolomide (TMZ). However, even after conventional treatment, they still have a high recurrence rate, resulting in an increasing demand for effective anti-glioma drugs. Drug repurposing is a method of reusing drugs that have already been widely approved for new indication. It has the advantages of reduced research cost, safety, and increased efficiency. Disulfiram (DSF), originally approved for alcohol dependence, has been repurposed for adjuvant chemotherapy in glioma. This article reviews the drug repurposing method and the progress of research on disulfiram reuse for glioma treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhong, Shengyu Liu, Xin Shi, Zhang, Li, Liu, Li, Tao, Zheng, Sheng, Ye, Xing, Zhai, Ren, Wu and Bao.)