Regional Study of the Anomalous Change in Apparent Resistivity Before the Tangshan Earthquake (M = 7.8, 1976) in China

1985 ◽  
pp. 901-920
Author(s):  
J. Qian
Keyword(s):  
Apparent Resistivity ◽  
Regional Study ◽  
Tangshan Earthquake ◽  
Anomalous Change

REGIONAL STUDY ON THE FEED-IN-TARIFF MECHANISM OF THE PHOTOVOLTAIC INDUSTRY IN CHINA

2014 ◽  
Vol 13 (5) ◽  
pp. 1241-1249
Author(s):  
Hong Li ◽  
Juanrui Lou ◽  
Tingting Zhang
Keyword(s):  
Regional Study ◽  
Feed In Tariff

Forward modelling of circular loop source and calculation of whole area apparent resistivity based on TEM

2018 ◽  
Vol 35 (2) ◽  
pp. 183-198 ◽  
Author(s):  
Jun ZHANG ◽  
Yanging WU ◽  
Hongchun YI
Keyword(s):  
Apparent Resistivity ◽  
Forward Modelling ◽  
Circular Loop

scholarly journals Influence of the Water Source on the Carbon Footprint of Irrigated Agriculture: A Regional Study in South-Eastern Spain

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 351
Author(s):  
Bernardo Martin-Gorriz ◽  
Victoriano Martínez-Alvarez ◽  
José Francisco Maestre-Valero ◽  
Belén Gallego-Elvira
Keyword(s):  
Carbon Footprint ◽  
Carbon Balance ◽  
Ghg Emissions ◽  
Water Source ◽  
Policy Implications ◽  
Irrigated Agriculture ◽  
Regional Study ◽  
South Eastern ◽  
Hypothetical Scenario ◽  
Co2 Sink

Curbing greenhouse gas (GHG) emissions to combat climate change is a major global challenge. Although irrigated agriculture consumes considerable energy that generates GHG emissions, the biomass produced also represents an important CO2 sink, which can counterbalance the emissions. The source of the water supply considerably influences the irrigation energy consumption and, consequently, the resulting carbon footprint. This study evaluates the potential impact on the carbon footprint of partially and fully replacing the conventional supply from Tagus–Segura water transfer (TSWT) with desalinated seawater (DSW) in the irrigation districts of the Segura River basin (south-eastern Spain). The results provide evidence that the crop GHG emissions depend largely on the water source and, consequently, its carbon footprint. In this sense, in the hypothetical scenario of the TSWT being completely replaced with DSW, GHG emissions may increase by up to 50% and the carbon balance could be reduced by 41%. However, even in this unfavourable situation, irrigated agriculture in the study area could still act as a CO2 sink with a negative total and specific carbon balance of −707,276 t CO2/year and −8.10 t CO2/ha-year, respectively. This study provides significant policy implications for understanding the water–energy–food nexus in water-scarce regions.

scholarly journals Transfer Learning of a Deep Learning Model for Exploring Tourists’ Urban Image Using Geotagged Photos

2021 ◽  
Vol 10 (3) ◽  
pp. 137
Author(s):  
Youngok Kang ◽  
Nahye Cho ◽  
Jiyoung Yoon ◽  
Soyeon Park ◽  
Jiyeon Kim
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Field Studies ◽  
Learning Model ◽  
Learning Technologies ◽  
Regional Study ◽  
Processing Technologies ◽  
Final Model ◽  
Urban Image ◽  
Deep Learning Model

Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists’ urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist’ photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists’ urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists’ photos.

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