This study sought to develop a typology model of temporary facility constraints that can be used practically in construction site layout planning (CSLP) automation models. A triangulated methodology (literature review, in-depth interviews, and actual case studies) was used to identify constraints of 11 temporary facilities that are considered mainly in CSLP and to classify them into six constraint types (i.e., dimensional, regional, relocation, non-overlap, inter-facility distance, and visibility constraints) and seven subtypes. In addition, this study proposed computational modeling methods that would allow a computer to judge whether or not the constraints are met by a created construction site layout. This study contributes to CSLP theory by providing a typology model of temporary facilities that can be used in the model-based verification of the created construction site layout and constraint processing condition in CSLP optimization models. This would also help provide efficient, safe, and eco-friendly construction site management, while refraining from experience- and intuition-centered CSLP practices.
As one of the fields of Artificial Intelligence (AI), Optical Character Recognition (OCR) systems have wide application in both industrial production and daily life. Conventional OCR systems are commonly designed and implement data computation on the basis of microprocessors; the performance of the processor relates to the effect of the computation. However, due to the “Memory-wall” problem and Von Neumann bottlenecks, the drawbacks of traditional processor-based computing for OCR systems are gradually becoming apparent. In this paper, an approach based on the Memory-Centric Computing and “Memory-Tree” algorithm has been proposed to perform hardware optimization of traditional OCR systems. The proposed algorithm was first designed in software implementation using C/C++ and OpenCV to verify the feasibility of the idea and then the RTL conversion of the algorithm was done using the Xilinx Vitis High Level Synthesis (HLS) tool to implement the hardware. This work chose Xilinx Alveo U50 FPGA Accelerator to complete the hardware design, which can be connected to the x86 CPU in the PC by PCIe to form heterogeneous computing. The results of the hardware implementation show that the system this work designed can recognize characters of English capital letters and numbers within 34.24 us. The power of FPGA is 18.59 W, which saves 77.87% of energy consumption compared to the 84 W of the processor in PC.
Calystegia dahurica (Herb.) Choisy is a natural product that has not been studied for efficacy or active ingredients. The purpose of this study is to investigate the activation effect of natural killer cells using a natural extract composition based on Calystegia dahurica (Herb.) Choisy extract (CDCE). We evaluated the activity of natural killer cells in natural products using PBMCs from healthy participants. All natural products were extracted with 50% ethanol. Based on the results of the cell viability assay, PBMCs of healthy participants were treated with extracts at various concentrations. Then, analysis was performed using flow cytometry to measure the cd107a surface expression of natural killer cells. As a result, treatment with a single extract of PBMCs increased the expression of cd107a in a concentration-dependent. Furthermore, it was confirmed that the treatment of the extract composition showed the highest expression of cd107a. In conclusion, it is expected that the extract composition containing CDCE according to this study can be used for prevention or treatment of cancer cells, tumor cells, and immune diseases.
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