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Vascularization is essential for realizing thick and functional tissue constructs that can be utilized for in vitro study platforms and in vivo grafts. The vasculature enables the transport of nutrients, oxygen, and wastes and is also indispensable to organ functional units such as the nephron filtration unit, the blood–air barrier, and the blood–brain barrier. This review aims to discuss the latest progress of organ-like vascularized constructs with specific functionalities and realizations even though they are not yet ready to be used as organ substitutes. First, the human vascular system is briefly introduced and related design considerations for engineering vascularized tissues are discussed. Second, up-to-date creation technologies for vascularized tissues are summarized and classified into the engineering and cellular self-assembly approaches. Third, recent applications ranging from in vitro tissue models, including generic vessel models, tumor models, and different human organ models such as heart, kidneys, liver, lungs, and brain, to prevascularized in vivo grafts for implantation and anastomosis are discussed in detail. The specific design considerations for the aforementioned applications are summarized and future perspectives regarding future clinical applications and commercialization are provided. 

For the pulping process in a pulp & paper plant that uses wood as a raw material, it is important to have real-time knowledge about the moisture content of the woodchips so that the process can be optimized and/or controlled correspondingly to achieve satisfactory product quality while minimizing the consumption of energy and chemicals. Both destructive and non-destructive methods have been developed for estimating moisture content in woodchips, but these methods are often lab-based that cannot be implemented online, or too fragile to stand the harsh manufacturing environment. To address these limitations, we propose a non-destructive and economic approach based on 5 GHz Wi-Fi and use channel state information (CSI) to estimate the moisture content in woodchips. In addition, we propose to use statistics pattern analysis (SPA) to extract features from raw CSI data of amplitude and phase difference. The extracted features are then used for classification model building using linear discriminant analysis (LDA) and subspace discriminant (SD) classification. The woodchip moisture classification results are validated using the oven drying method.