Mobile App of Food Combinations- Medley for Mom

“There were variations in the types of dietary intervention and the effect of specific nutrient components. There was no heterogeneity for dietary intervention components, but considerate heterogeneity for all dietary interventions combined.” With the development of the society and the improvement of living conditions, people’s eating habit has become much more diverse. However, some food combinations could potentially bring discomfort, such as indigestion, constipation and so on. Due to the individual differences of the human digestive system structures, improper food combinations lead to various harms to different groups of people, among which pregnant women suffer the most. “There is little that extols the health benefits for both women and their babies of taking a health-based approach to eating during pregnancy. In the medical model, the woman is seen as essentially a barrier to fetal care, and her food choices understood primarily in terms of fetal risk.” Women during pregnancy, due to the endocrine effects, become more sensitive than before, which requires special attention to the diet during this period. The aim of this research is to design a mobile phone application with a friendly interface and simple operation to provide healthy food collocation guidance for pregnant women in real time, so they can avoid potential danger from daily eating

NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing

Neural Architecture Search (NAS) is an automated architecture engineering method for deep learning design automation, which serves as an alternative to the manual and error-prone process of model development, selection, evaluation and performance estimation. However, one major obstacle of NAS is the extremely demanding computation resource requirements and time-consuming iterations particularly when the dataset scales. In this paper, targeting at the emerging vision transformer (ViT), we present NasHD, a hyperdimensional computing based supervised learning model to rank the performance given the architectures and configurations. Different from other learning based methods, NasHD is faster thanks to the high parallel processing of HDC architecture. We also evaluated two HDC encoding schemes: Gram-based and Record-based of NasHD on their performance and efficiency. On the VIMER-UFO benchmark dataset of 8 applications from a diverse range of domains, NasHD Record can rank the performance of nearly 100K vision transformer models with about 1 minute while still achieving comparable results with sophisticated models

Energy Efficiency Comparison of Hybrid Powertrain Systems for Fuel-Cell-Based Electric Vehicles

Fuel cell electric vehicles have great superiorities in endurance mileage, charging speed and climate tolerance compared to battery electric vehicles. However, a supercapacitor or battery bank is required to maintain a fast-dynamic response, which leads to several hybridization structures for fuel-cell-based electric vehicles due to the unique characteristics of each device, and their performances are also differing. The purpose of this paper is to provide a comprehensive comparison of hybrid powertrain systems for three types of powertrains: fuel cell/supercapacitor passive hybrid, fuel cell/supercapacitor semi-active hybrid, and fuel cell/battery semi-active hybrid. Each powertrain component model is developed from the real components wherever possible, and Honda FCX Clarity fuel cell vehicle is studied as the benchmark. The powertrain energy efficiency under Worldwide harmonized Light vehicles Test Cycle (WLTC) is analyzed and evaluated. The simulation results show that three powertrains have the same energy consumption, and fuel cell/supercapacitor passive hybrid powertrain increases the system efficiency by 2% and 4% in propulsion and regenerative braking, respectively. By contrast, the other two powertrain topologies have similar performance in terms of energy efficiency