The Depth-Depended Fungal Diversity and Non-depth-Depended Aroma Profiles of Pit Mud for Strong-Flavor Baijiu

Microorganisms in pit mud are the essential factor determining the style of strong flavor Baijiu. The spatial distribution characteristics of fungal communities and aroma in the pit mud for strong flavor Baijiu from Xinjiang, China, were investigated using Illumina MiSeq high-throughput sequencing and electronic nose technology. A total of 138 fungal genera affiliated with 10 fungal phyla were identified from 27 pit mud samples; of these, Saccharomycopsis, Aspergillus, and Apiotrichum were the core fungal communities, and Aspergillus and Apiotrichum were the hubs that maintain the structural stability of fungal communities in pit mud. The fungal richness and diversity, as well as aroma of pit mud, showed no significant spatial heterogeneity, but divergences in pit mud at different depths were mainly in pH, total acid, and high abundance fungi. Moisture, NH4+, and lactate were the main physicochemical factors involved in the maintenance of fungal stability and quality in pit mud, whereas pH had only a weak effect on fungi in pit mud. In addition, the fungal communities of pit mud were not significantly associated with the aroma. The results of this study provide a foundation for exploring the functional microorganisms and dissecting the brewing mechanism of strong flavor Baijiu in Xinjiang, and also contributes to the improvement of pit mud quality by bioaugmentation and controlling environmental physicochemical factors.

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

Principal component analysis of rainwater composition at BAPMoN stations in India

Data from Indian BAPMoN stations were analyzed using the Principal Component Analysis (PCA) by examining broadly the temporal and spatial distribution characteristics of the ions, from mineral and gaseous sources, observed in rainwater samples collected over the Indian BAPMoN stations over along period (1976-87), The results show that the pH of rainwater can be generally explained In terms of the concentration of SO.-2 , NO3 -1, CI-l, Ca+2 and Na+1 ion~, However, other mechanisms could determine the overall nature of the Interactions, These mechanisms have become more clear by performing principal component analysis.

Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors

House price is closely associated with the development of the national economy and people’s daily life. Understanding the spatial distribution characteristics and influencing factors of the house price is of great practical significance. Although a lot of attention has been paid to modeling the house price from structure and location attributes, limited work has considered the impact of visual attributes. Intuitively, a better visual environment may raise the surrounding house price. When aggregating multiple factors that influence house price, the multiscale geographically weighted regression (MGWR) provides a suitable solution. Specifically, the MGWR assigns each factor a bandwidth to model the spatial heterogeneity, e.g., a factor may have different influences at different places. In this paper, we introduce the visual environment factors into the MGWR method. In detail, we extract ten visual elements, e.g., sky, vegetation, road, from the Baidu street view (BSV) images, using a deep learning framework. We further define six visual environment factors to investigate their influence on house price. Based on the data from two representative Chinese cities, i.e., Beijing and Chongqing, we reveal the influence degree and spatial scale difference of six visual indexes on the house price in two cities. Results show that: (1) the influence intensity of our proposed six visual environment factors on the house price in different regions of the city can be identified, and the green view index (GVI) is the most important visual environmental factor; and (2) the influence of these view indexes changes significantly or even reversely depends on different areas.

Mechanism and Evolution of Soil Organic Carbon Coupling with Rocky Desertification in South China Karst

To study the spatial distribution characteristics of soil organic carbon (SOC) coupled with rocky desertification, 1212 soil samples from 152 soil profiles were sampled from different karst landforms, including karst low hills/virgin forest (KLH) in Libo County, a karst peak-cluster depression (KPCD) in Xingyi County, a karst canyon (KC) in Guanling County, a karst plateau basin (KPB) in Puding County and a karst trough valley (KTV) in Yinjiang County. The spatial distribution characteristics of the responses of SOC, SOC density (SOCD), rocky desertification and soil bulk density (SBD) to different influencing factors were analyzed. The relationships among SOC, SOCD, rocky desertification and SBD were analyzed using Pearson correlation analysis. The SOC storage capacity was characterized by using SOCD, and then the SOC storage capacity in different evolution stages of karst landforms was assessed. The SOC contents of KLH, KPCD, KC, KPB and KTV ranged from 6.16 to 38.20 g·kg−1, 7.42 to 27.08 g·kg−1, 6.28 to 35.17 g·kg−1, 4.62 to 23.79 g·kg−1 and 5.24 to 37.85 g·kg−1, respectively, and their average SOCD values (0–100 cm) were 7.37, 10.79, 7.06, 8.51 and 7.84 kg·m−2, respectively. The karst landforms as ordered by SOC storage capacity were KPCD > KPB > KLH > KTV > KC. The SOC content was negatively correlated with the SBD; light rocky desertification may lead to SOC accumulation. The rocky desertification degree and SBD were closely associated with slope position and gradient. Rocky desertification first increased, then decreased from mountain foot to summit, and increased with increasing slope gradient. However, the SBD decreased from mountain foot to summit and with increasing slope gradient. The SOC contents on the northern aspect of the mountains were generally higher than the other aspects. In summary, rock outcrops controlled the SOC contents in the studied regions. The slope position, gradient and aspect influenced the composition and distribution of vegetation, which influenced the evolution of rocky desertification. Therefore, these factors indirectly affected the SOC content. Additionally, the SOCD decreased with increasing rocky desertification. During the different evolution stages of karst landforms, the SOC storage capacity first decreases, then increases.

An Anti-Jamming Method against Two-Dimensional Deception Jamming by Spatial Location Feature Recognition

Interrupted sampling repeater jamming (ISRJ) is an effective method for implementing deception jamming on chirp radars. By means of frequency-shifting jamming processing of the target echo signal and pulse compression during image processing, a group of false targets will appear in different spatial locations around the true target. Extracting the features of these false targets is complex and limited to existing countering methods against ISRJ. This paper proposes an anti-jamming method to identify the spatial location characteristics of two-dimensional deception false targets. By adjusting the parameters of the radar transmitted signal, the method simultaneously transmits the anti-jamming signal and carries out false target identification and elimination in the range and azimuth dimensions. Eventually, the optimal signal parameter design of the anti-jamming signal is obtained by comparing different anti-jamming strategies in the range dimension. The validity of the proposed method is proved by deducing the mathematical model between the spatial distribution characteristics of the false targets and the radar transmitted signal parameters and demonstrated by simulations.

Analysis of Spatial–Temporal Characteristics of Industrial Land Supply Scale in Relation to Industrial Structure in China

Taking China’s industrial land transfer data as the data source, this study quantitatively analyzes the transfer structure and spatial distribution of China’s industrial land from 2010 to 2019. By constructing the information entropy and the equilibrium degree model of industrial land-use structure, this study evaluates the transfer characteristics of industrial land of different functional types in various provinces of China, analyzes the scale advantages of various types of transferred industrial land by using the land transfer scale advantage index, and summarizes the spatial distribution characteristics of different types of industrial land transfer in China through the spatial center of gravity analysis and cold/hot spot regional distribution mapping. The following results were obtained. (1) There are significant differences in the transfer scale of industrial land among provinces in China. The transfer scale of Eastern and Central China is large, whereas that of Western China is small. (2) From the perspective of land-use structure, the transfer scale of industrial land in the central and western regions is more balanced than that in the east. (3) From the gravity center distribution of the standard deviation ellipse, the land transfer direction of the energy industry, and the mining industry, and other types of industries is more significant than that of the culture and sports hygiene industries, modern manufacturing industry, and high-tech industry. (4) From the analysis of cold and hot spots, the mining industry, the energy industry, and other types of industries in the western region with rich mineral resources are the hot spots of industrial land transfer, and the southeast coast is the cold spot; the eastern coastal area is a hot area for land transfer of modern manufacturing, the high-tech industry, and the culture and sports hygiene industries. The results reveal the regional differences and spatial distribution characteristics of industrial transfer in China and provide a reference for authorities to formulate industrial planning and industrial land collection, storage, and transfer plans.