Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people’s lives and socio-economic development. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman’s rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspots and cluster/outlier areas were observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemics at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain

Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion

Deep learning is the subject of increasing research for fruit tree detection. Previously developed deep-learning-based models are either too large to perform real-time tasks or too small to extract good enough features. Moreover, there has been scarce research on the detection of pomelo trees. This paper proposes a pomelo tree-detection method that introduces the attention mechanism and a Ghost module into the lightweight model network, as well as a feature-fusion module to improve the feature-extraction ability and reduce computation. The proposed method was experimentally validated and showed better detection performance and fewer parameters than some state-of-the-art target-detection algorithms. The results indicate that our method is more suitable for pomelo tree detection

Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion

Deep learning is the subject of increasing research for fruit tree detection. Previously developed deep-learning-based models are either too large to perform real-time tasks or too small to extract good enough features. Moreover, there has been scarce research on the detection of pomelo trees. This paper proposes a pomelo tree-detection method that introduces the attention mechanism and a Ghost module into the lightweight model network, as well as a feature-fusion module to improve the feature-extraction ability and reduce computation. The proposed method was experimentally validated and showed better detection performance and fewer parameters than some state-of-the-art target-detection algorithms. The results indicate that our method is more suitable for pomelo tree detection

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain

The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China

The effect of urbanization on the urban thermal environment (UTE) has attracted increasing research attention for its significant relationship to local climatic change and habitat comfort. Using quantitative thermal remote sensing and spatial statistics methods, here we analyze four Landsat TM/ETM+ images of Guangzhou in South China acquired respectively on 13 October 1990, 2 January 2000, 23 November 2005, and 2 January 2009, to investigate the spatiotemporal variations in the land surface temperature (LST) over five land use/land cover (LULC) types and over different urban/rural zones. The emphases of this study are placed on the urban heat island (UHI) intensity and the relationships among LST, the normalized difference built-up index (NDBI), and the normalized difference vegetation index (NDVI). Results show that: (1) the UHI effect existed obviously over the period from 1990 to 2009 and high temperature anomalies were closely associated with built-up land and densely populated and heavily industrialized districts; (2) the UHI intensities represented by the mean LST difference between the urbandowntown area and the suburban area were on average 0.88, 0.49, 0.90 and 1.16 K on the four dates, at the 99.99% confidence level; and (3) LST is related positively with NDBI and negatively with NDVI. The spatiotemporal variation of UTE of Guangzhou could be attributed to rapid urbanization, especially to the expanding built-up and developing land, declining vegetation coverage, and strengthening of anthropogenic and industrial activities which generate increasing amounts of waste heat. This study provides useful information for understanding the local climatic and environment changes that occur during rapid urbanization.</p

Efficacy of adjuvant TACE on the prognosis of patients with HCC after hepatectomy: a multicenter propensity score matching from China

Abstract Background The survival benefit of adjuvant transarterial chemoembolization (TACE) in patients with hepatectomy for hepatocellular carcinoma (HCC) after hepatectomy remains controversial. We aimed to investigate the survival efficacy of adjuvant TACE after hepatectomy for HCC. Methods 1491 patients with HCC who underwent hepatectomy between January 2018 and September 2021 at four medical centers in China were retrospectively analyzed, including 782 patients who received adjuvant TACE and 709 patients who did not receive adjuvant TACE. Propensity score matching (PSM) (1:1) was performed to minimize selection bias, which balanced the clinical characteristics of the two groups. Results A total of 1254 patients were enrolled after PSM, including 627 patients who received adjuvant TACE and 627 patients who did not receive adjuvant TACE. Patients who received adjuvant TACE had higher disease-free survival (DFS, 1- ,2-, and 3-year: 78%-68%-62% vs. 69%-57%-50%, p < 0.001) and overall survival (OS, 1- ,2-, and 3-year: 96%-88%-80% vs. 90%-77%-66%, p < 0.001) than those who did not receive adjuvant TACE (Median DFS was 39 months). Among the different levels of risk factors affecting prognosis [AFP, Lymphocyte-to-monocyte ratio, Maximum tumor diameter, Number of tumors, Child-Pugh classification, Liver cirrhosis, Vascular invasion (imaging), Microvascular invasion, Satellite nodules, Differentiation, Chinese liver cancer stage II-IIIa], the majority of patients who received adjuvant TACE had higher DFS or OS than those who did not receive adjuvant TACE. More patients who received adjuvant TACE accepted subsequent antitumor therapy such as liver transplantation, re-hepatectomy and local ablation after tumor recurrence, while more patients who did not receive adjuvant TACE accepted subsequent antitumor therapy with TACE after tumor recurrence (All p < 0.05). Conclusions Adjuvant TACE may be a potential way to monitor early tumor recurrence and improve postoperative survival in patients with HCC