Water shortages are a worldwide problem. Virtual water and the water footprint link water resources, human beings and agricultural products, and are effective tools to alleviate water-resources stress. The production of agricultural products consumes a large amount of water, and food is the most basic consumer good for human survival, so it is very necessary to study the water footprint of residents’ food consumption, which is also the weak point of current research on virtual water and the water footprint. This paper aimed to conduct a comprehensive analysis on the water footprint of food consumption in China from the perspectives of urban and rural residents, per capita water footprint, water footprint structure and food consumption structure. The results revealed that the average water footprint of residents’ food consumption was 605.12 billion m3/year, basically showing an upward trend. Guangdong residents had the highest water footprint for food consumption due to the highest population and higher consumption of water-intensive foodstuffs such as grain and meat in their diet. The water footprint of Xizang residents’ food consumption was the lowest followed by Ningxia and Qinghai due to having the least population. The water footprint of food consumption consumed by urban residents was on the rise while that consumed by rural residents was on the decline in China, which was consistent with the changing trend of population. On the whole, the rural population consumed more virtual water embedded in food than the urban population. From the water footprint structure point, the contribution rate of the green water footprint is the largest, reaching 69.36%. The second is the gray water footprint and then the blue water footprint, accounting for 18.71% and 11.93%, respectively. From the perspective of the food consumption structure, grain and pig, beef and mutton consumption contributed significantly to the total water footprint of residents’ food consumption, contributing 37.5% and 22.56%, respectively. The study is helpful for water management and water allocation in rural and urban areas, improving agricultural technology to reduce the gray water footprint and optimizing food consumption structure, such as reducing the consumption of grain and meat.
With the deregulation of electricity markets, short-term load forecasting (STLF) has gained importance for the operation of power systems. However, an effective STLF model is hard to achieve as the load is affected by various factors. Here we present a STLF method based on similar day approach to predict the electricity usage 24 h ahead and by employing long short-term memory (LSTM) and wavelet transform to further improve the forecasting accuracy. Compared with other methods, the proposed method achieves higher accuracy, and brings out the significance of using similar day's load, wavelet transform, and LSTM network.
Minutiae-based matching is the main method of fingerprint recognition. Anil K. Jain brought forward a method (FingerCode) which use Gabor filter to extract the texture feature of fingerprint and match it. In order to get a faster algorithm of fingerprint identification, the properties of the real part of Gabor filter are analyzed and the Gabor filter algorithm is accelerated in special conditions and is validated by experiment, which decreases the computational complexity from O(n2) to O(n).
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