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Article ; Online: Quantitative spatiotemporal impact of dynamic population density changes on the COVID-19 pandemic in China’s mainland

Guangyuan Zhang / Stefan Poslad / Yonglei Fan / Xiaoping Rui

Geo-spatial Information Science, Vol 26, Iss 4, Pp 642-

2023 Volume 663

Abstract: ABSTRACTThe coronavirus disease 2019 (COVID-19) and its mutant viruses are still wreaking global havoc over the last two years, but the impact of human activity on the transmission of the pandemic is difficult to ascertain. Estimating human dynamic ... ...

Abstract	ABSTRACTThe coronavirus disease 2019 (COVID-19) and its mutant viruses are still wreaking global havoc over the last two years, but the impact of human activity on the transmission of the pandemic is difficult to ascertain. Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread, which can help in maintaining urban health within a county and between counties within a country. This distribution can be computed using the Volunteered Geographic Information (VGI) of the citizens in conjunction with other variables, such as climatic conditions, and used to analyze how human’s daily density distribution quantitatively affects COVID-19 transmission. Based on the estimated population density, when the population density increases daily by 1 person/km2 in a county or prefectural-level administrative unit with an average size of 26,000 km2, the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days, which is the illness onset time for a new COVID-19 case. After 14 days, which is the maximum incubation period of the COVID-19 virus, there would be 5 new confirmed cases and 0.092 death cases. However, in neighboring regions, there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities. The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic. Additionally, the direct and indirect effects of the impact are estimated using spatial panel models. The models that control the unobserved factors improve the reliability of the estimation, as validated by random experiments and the use of the Baidu migration dataset.
Keywords	COVID-19 ; Geographic Information Systems (GIS) ; panel data ; Spatial Durbin Model (SDM) ; Mathematical geography. Cartography ; GA1-1776 ; Geodesy ; QB275-343
Subject code	910
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: DFA-UNet

Yan Zhang / Kefeng Li / Guangyuan Zhang / Zhenfang Zhu / Peng Wang

Applied Sciences, Vol 13, Iss 1, p

Efficient Railroad Image Segmentation

2023 Volume 662

Abstract: In computer vision technology, image segmentation is a significant technological advancement for the current problems of high-speed railroad image scene changes, low segmentation accuracy, and serious information loss. We propose a segmentation algorithm, ...

Abstract	In computer vision technology, image segmentation is a significant technological advancement for the current problems of high-speed railroad image scene changes, low segmentation accuracy, and serious information loss. We propose a segmentation algorithm, DFA-UNet, based on an improved U-Net network architecture. The model uses the same encoder–decoder structure as U-Net. To be able to extract image features efficiently and further integrate the weights of each channel feature, we propose to embed the DFA attention module in the encoder part of the model for the adaptive adjustment of feature map weights. We evaluated the performance of the model on the RailSem19 dataset. The results showed that our model showed improvements of 2.48%, 0.22%, 3.31%, 0.97%, and 2.2% in mIoU, F1-score, Accuracy, Precision, and Recall, respectively, compared with U-Net. The model can effectively achieve the segmentation of railroad images.
Keywords	deep learning ; image segmentation ; U-Net ; depthwise separable convolution ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006 ; 004
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Granular Content Distribution for IoT Remote Sensing Data Supporting Privacy Preservation

Xiaoshuai Zhang / Guangyuan Zhang / Xingru Huang / Stefan Poslad

Remote Sensing, Vol 14, Iss 5574, p

2022 Volume 5574

Abstract: Facilitated by the Internet of Things (IoT) and diverse IoT devices, remote sensing data are evolving into the multimedia era with an expanding data scale. Massive remote sensing data are collected by IoT devices to monitor environments and human ... ...

Abstract	Facilitated by the Internet of Things (IoT) and diverse IoT devices, remote sensing data are evolving into the multimedia era with an expanding data scale. Massive remote sensing data are collected by IoT devices to monitor environments and human activities. Because IoT devices are involved in the data collection, there are probably private data contained in the collected remote sensing data, such as the device owner information and the precise location. Therefore, when data analysts, researchers, and other stakeholders require remote sensing data from numerous IoT devices for different analyses and investigations, how to distribute massive remote sensing data efficiently and regulate different people to view different parts of the distributed remote sensing data is a challenge to be addressed. Many general solutions rely on granular access control for content distribution but do not consider the low computational efficiency caused by the huge file size of the remote sensing data or certain IoT devices only have a constrained computational performance. Therefore, we propose a new granular content distribution scheme, which is more lightweight and practical for the distribution of multimedia remote sensing data with the consideration of the large data size to avoid complicated operations to the data. Furthermore, a dual data integrity check (hash summary and watermark) designed in our scheme can detect tampering or forgery from encrypted remote sensing data before decrypting it and validate it again after decryption. The security analyses and experimental results manifest that our new scheme can maintain high computational efficiency and block tampering and forgery during the granular content distribution for IoT remote sensing data.
Keywords	content distribution ; privacy ; remote sensing data ; IoT ; access control ; data management ; Science ; Q
Subject code	600
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Supply-demand balance and spatial distribution optimization of primary care facilities in highland cities from a resilience perspective

Yang Yu / Rui Zhou / Liyuan Qian / Xian Yang / Liuyang Dong / Guangyuan Zhang

Frontiers in Public Health, Vol

A study of Lhasa, China

2023 Volume 11

Abstract: IntroductionThe development of urban resilience, which is fundamentally a balance between the supply capacity of primary care resources and the demand from urban residents, includes an appropriate architecture of primary care facilities. Resilient city ... ...

Abstract	IntroductionThe development of urban resilience, which is fundamentally a balance between the supply capacity of primary care resources and the demand from urban residents, includes an appropriate architecture of primary care facilities. Resilient city construction in highland areas is hampered by the physical environment and transportation constraints and frequently encounters issues like poor accessibility and unequal distribution of primary care facilities.MethodsTo optimize the supply and demand of primary care resources in highland cities and effectively improve the resilience of urban public health, this paper assesses the distribution of primary care facilities within the built-up area of Lhasa (China) through a spatial network analysis method based on GIS, combined with population distribution data, and employs a location-allocation model to optimize the distribution.ResultsFirstly, the overall supply of primary care exceeds the overall demand, but the facilities' service area can only accommodate 59% of the residences. Secondly, there is a clear spatial variation in the accessibility of primary care facilities, and the time cost of healthcare is too high in some residences. Thirdly, the supply-demand relationship of primary care facilities is unbalanced, with both over-saturated and over-deficient areas.DiscussionAfter distribution optimization, the coverage and accessibility of primary care facilities have increased significantly, and the spatial imbalance of supply and demand has been alleviated. This paper proposes a research method to evaluate and optimize the spatial distribution of primary care facilities from multiple perspectives based on the resilience theory. The results of the study and visualization analysis methods can be used as an invaluable reference for planning the distribution of urban healthcare facilities and urban resilience construction in highland areas and other underdeveloped areas.
Keywords	resilient city ; public health ; spatial analysis ; urban planning ; highland area ; Public aspects of medicine ; RA1-1270
Subject code	910
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Ameliorating the sensitivities, thermal and combustion properties of RDX by in situ self-assembly TA-Pb/Cu shells to RDX surface

Guanchao Lan / Guangyuan Zhang / Jinjie Shen / Guoliang Jin / Jianlong Wang / Jing Li

Arabian Journal of Chemistry, Vol 16, Iss 3, Pp 104497- (2023)

2023

Abstract: In order to ameliorate the sensitivities, thermal and combustion properties of cyclotrimethylenetrinitramine (RDX), tannic acid (TA) is used to react with lead and copper via in situ self-assembly to coat RDX for preparing RDX@TA-Pb/Cu microcapsules. The ...

Abstract	In order to ameliorate the sensitivities, thermal and combustion properties of cyclotrimethylenetrinitramine (RDX), tannic acid (TA) is used to react with lead and copper via in situ self-assembly to coat RDX for preparing RDX@TA-Pb/Cu microcapsules. The structures of RDX@TA-Pb/Cu microcapsules are characterized by X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and Fourier-transform infrared spectra (FT-IR). The surface topography of RDX@TA-Pb/Cu microcapsules are characterized by scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS). The mechanical sensitivities and explosion points of RDX@TA-Pb/Cu microcapsules are measured to study the influence of TA-Pb/Cu shells on mechanical and thermal safeties of RDX. The non-isothermal properties of RDX@TA-Pb/Cu microcapsules are characterized by differential scanning calorimetry (DSC). The catalytic effects of TA-Pb/Cu shells on RDX are characterized by accelerating rate calorimeter (ARC). The residues of RDX@TA-Pb/Cu microcapsules after combustion in air are collected and characterized by SEM and XRD to further study the catalytic effect of TA-Pb/Cu shells. The study results show that a 150 nm TA-Pb/Cu shells are uniformly coated on RDX surfaces. The chemical structure of RDX maintains constant during in situ self-assembly coating process. The mechanical and thermal safeties of RDX are enhanced after coating with TA-Pb/Cu shells. The decomposition and combustion property of RDX can be catalyzed by TA-Pb/Cu, and the catalytic effects of in situ self-assembly coating are better than that of physical mixing. The RDX@TA-Pb/Cu microcapsules can be used in RDX based composite modified double base (CMDB) propellants.
Keywords	RDX@TA-Pb/Cu ; In situ self-assembly coating ; Catalytic ; Chemistry ; QD1-999
Subject code	620 ; 540
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Preparation of HMX@DHBA-Pb and HMX@NTO-Pb composites via in situ deposition

Guanchao Lan / Guanghui Gu / Yuchuan Wang / Guangyuan Zhang / Jianlong Wang / Jing Li

Arabian Journal of Chemistry, Vol 16, Iss 8, Pp 104915- (2023)

A way to achieve surface catalysis of HMX

2023

Abstract: In order to ameliorate the combustion properties of 1,3,5,7-tetranitro-1,3,5,7-tetrazacyclooctane (HMX) based composite modified double base (CMDB) propellants, HMX particles with many gullies are first prepared, and then 2,4-dihydroxybenzoic acid (DHBA) ...

Abstract	In order to ameliorate the combustion properties of 1,3,5,7-tetranitro-1,3,5,7-tetrazacyclooctane (HMX) based composite modified double base (CMDB) propellants, HMX particles with many gullies are first prepared, and then 2,4-dihydroxybenzoic acid (DHBA) and 1,2,4-triazol-5-one (NTO) are used to react with lead via in situ deposition to coat HMX for preparing HMX@DHBA-Pb and HMX@NTO-Pb composites. The structures and properties of HMX@DHBA-Pb and HMX@NTO-Pb composites are characterized in detail. The characterization results show that DHBA-Pb and NTO-Pb shells are uniformly coated on HMX surfaces. The chemical structure of HMX maintains constant during in situ deposition coating process. The mechanical and thermal safeties of HMX are enhanced after coating with DHBA-Pb and NTO-Pb shells. The introduce of DHBA-Pb and NTO-Pb can catalyze the decomposition and combustion of HMX core, and the catalytic effects of in situ coating are better than that of physical mixing.
Keywords	HMX ; Composites ; In situ deposition ; Catalytic ; Chemistry ; QD1-999
Subject code	620
Language	English
Publishing date	2023-08-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An Intelligent UAV Path-Planning Method Based on the Theory of the Three-Dimensional Subdivision of Earth Space

Guoyi Sun / Qian Xu / Guangyuan Zhang / Tengteng Qu / Chengqi Cheng / Haojiang Deng

ISPRS International Journal of Geo-Information, Vol 12, Iss 10, p

2023 Volume 397

Abstract: With the rapid development of the big data era, Unmanned Aerial Vehicles (UAVs) are being increasingly adopted for various complex environments. This has imposed new requirements for UAV path planning. How to efficiently organize, manage, and express all ...

Abstract	With the rapid development of the big data era, Unmanned Aerial Vehicles (UAVs) are being increasingly adopted for various complex environments. This has imposed new requirements for UAV path planning. How to efficiently organize, manage, and express all kinds of data in complex scenes and intelligently carry out fast and efficient path planning for UAVs are new challenges brought about by UAV application requirements. However, traditional path-planning methods lack the ability to effectively integrate and organize multivariate data in dynamic and complicated airspace environments. To address these challenges, this paper leverages the theory of the three-dimensional subdivision of earth space and proposes a novel environment-modeling approach based on airspace grids. In this approach, we carried out the grid-based modeling and storage of the UAV flight airspace environment and built a stable and intelligent deep-reinforcement-learning grid model to solve the problem of the passage cost of UAV path planning in the real world. Finally, we designed multiple sets of experiments to verify the efficiency of the global subdivision coding system as an environmental organization framework for path planning compared to a longitude–latitude system and to demonstrate the superiority of the improved deep-reinforcement-learning model in specific scenarios.
Keywords	deep reinforcement learning ; UAV path planning ; GeoSOT ; Geography (General) ; G1-922
Subject code	629
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Identification of a Novel Defined Immune-Autophagy-Related Gene Signature Associated With Clinical and Prognostic Features of Kidney Renal Clear Cell Carcinoma

Guangyuan Zhang / Lei Zhang / Si Sun / Ming Chen

Frontiers in Molecular Biosciences, Vol

2021 Volume 8

Abstract: Background: As a common cancer of the urinary system in adults, renal clear cell carcinoma is metastatic in 30% of patients, and 1–2 years after diagnosis, 60% of patients die. At present, the rapid development of tumor immunology and autophagy had ... ...

Abstract	Background: As a common cancer of the urinary system in adults, renal clear cell carcinoma is metastatic in 30% of patients, and 1–2 years after diagnosis, 60% of patients die. At present, the rapid development of tumor immunology and autophagy had brought new directions to the treatment of renal cancer. Therefore, it was extremely urgent to find potential targets and prognostic biomarkers for immunotherapy combined with autophagy.Methods: Through GSE168845, immune-related genes, autophagy-related genes, and immune-autophagy-related differentially expressed genes (IAR-DEGs) were identified. Independent prognostic value of IAR-DEGs was determined by differential expression analysis, prognostic analysis, and univariate and multivariate Cox regression analyses. Then, the lasso Cox regression model was established to evaluate the correlation of IAR-DEGs with the immune score, immune checkpoint, iron death, methylation, and one-class logistic regression (OCLR) score.Results: In this study, it was found that CANX, BID, NAMPT, and BIRC5 were immune-autophagy-related genes with independent prognostic value, and the risk prognostic model based on them was well constructed. Further analysis showed that CANX, BID, NAMPT, and BIRC5 were significantly correlated with the immune score, immune checkpoint, iron death, methylation, and OCLR score. Further experimental results were consistent with the bioinformatics analysis.Conclusion: CANX, BID, NAMPT, and BIRC5 were potential targets and effective prognostic biomarkers for immunotherapy combined with autophagy in kidney renal clear cell carcinoma.
Keywords	immune-autophagy ; kidney renal clear cell carcinoma ; prognosis ; biomarkers ; autophagy ; Biology (General) ; QH301-705.5
Subject code	616
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Frontiers Media S.A.
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Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: IL-17C neutralization protects the kidney against acute injury and chronic injuryResearch in context

Fangfei Zhang / Jianyong Yin / Li Liu / Shuiying Liu / Guangyuan Zhang / Yiwei Kong / Yajun Wang / Niansong Wang / Xiangmei Chen / Feng Wang

EBioMedicine, Vol 92, Iss , Pp 104607- (2023)

2023

Abstract: Summary: Background: Interleukin-17C (IL-17C), a member of the IL-17 cytokine family, plays a pathogenic role in kidney diseases. Our previous studies have shown that pre-administration of IL-17C neutralizing antibody attenuated acute kidney injury (AKI, ...

Abstract	Summary: Background: Interleukin-17C (IL-17C), a member of the IL-17 cytokine family, plays a pathogenic role in kidney diseases. Our previous studies have shown that pre-administration of IL-17C neutralizing antibody attenuated acute kidney injury (AKI, a common acute inflammation associated renal disease). In this study, we explored whether post-ischemia reperfusion (IR) of IL-17C blockade has therapeutic effects on AKI and whether IL-17C is involved in the pathogenesis of diabetic nephropathy (DN), a major type of chronic inflammation-associated kidney disease. Methods: 12-week-old male C57BL/6JGpt mice were treated with IL-17C neutralizing antibody or normal IgG control antibody at 3 h after reperfusion. Renal injury, inflammation, and oxidative stress were assessed. Additionally, we examined renal IL-17C expression in patients with DN and db/db mice and evaluated albuminuria, mesangial matrix accumulation and podocyte loss in db/db mice with IL-17C neutralization. Knockdown of NF-κB p65 using siRNA, and blocking Hypoxia-inducible factor-1α (HIF-1α) using YC-1 in mice and HIF-1α Decoy in HK2 cells were investigated to explore the possible signaling pathway involved in IL-17C regulation. Findings: We found that delayed IL-17C neutralization had similar reno-protective effects on renal ischemia-reperfusion injury (IRI). Additionally, renal IL-17C expression was increased in patients with DN and db/db mice, while IL-17C blockade significantly attenuated DN, accompanied with blunted albuminuria, mesangial matrix accumulation, and podocyte loss. Moreover, IL-17C neutralization significantly repressed the expression of downstream pro-inflammatory cytokines, inflammatory cell infiltration, and Th17/IL-17A activation both in mice with renal IRI and DN. Mechanistical studies demonstrated that hypoxia or high glucose-induced IL-17C up-regulation was predominantly mediated by NF-κB pathway. Interpretation: IL-17C participates in the pathogenesis of AKI and DN and inhibition of IL-17C shows potential as a therapeutic ...
Keywords	Acute kidney injury ; Ischemia/reperfusion injury ; Diabetic nephropathy ; IL-17C ; Medicine ; R ; Medicine (General) ; R5-920
Subject code	616
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Feature Merged Network for Oil Spill Detection Using SAR Images

Yonglei Fan / Xiaoping Rui / Guangyuan Zhang / Tian Yu / Xijie Xu / Stefan Poslad

Remote Sensing, Vol 13, Iss 3174, p

2021 Volume 3174

Abstract: The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic ... ...

Abstract	The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine oil spills can help control the spread of oil spill pollution over time and reduce the economic losses and environmental pollution caused by such spills. However, it is a significant challenge to distinguish between oil-spilled areas and oil-spill-like in SAR images. Semantic segmentation models based on deep learning have been used in this field to address this issue. In addition, this study is dedicated to improving the accuracy of the U-Shape Network (UNet) model in identifying oil spill areas and oil-spill-like areas and alleviating the overfitting problem of the model; a feature merge network (FMNet) is proposed for image segmentation. The global features of SAR image, which are high-frequency component in the frequency domain and represents the boundary between categories, are obtained by a threshold segmentation method. This can weaken the impact of spot noise in SAR image. Then high-dimensional features are extracted from the threshold segmentation results using convolution operation. These features are superimposed with to the down sampling and combined with the high-dimensional features of original image. The proposed model obtains more features, which allows the model to make more accurate decisions. The overall accuracy of the proposed method increased by 1.82% and reached 61.90% compared with the UNet. The recognition accuracy of oil spill areas and oil-spill-like areas increased by approximately 3% and reached 56.33%. The method proposed in this paper not only improves the recognition accuracy of the original model, but also alleviates the overfitting problem of the original model and provides a more effective monitoring method for marine oil spill monitoring. More importantly, the proposed method provides a design principle that opens up new development ideas for the optimization of other deep learning network models.
Keywords	SAR ; oil spill ; image segmentation ; deep learning ; UNet ; FMNet ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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