Chemical Stability of Curcumin: Structure and Activity Relationship (SAR) Study

Over the past decades, numerous studies have shown that curcumin has potent biological activities. As a potential chemopreventing agent, curcumin was demonstrated to exert anti-cancer effects in both in vitro and in vivo studies. However, low bioavailability of curcumin limited human clinical trials and its application to be formulated as therapeutics. In this thesis, we will summarize the anti-cancer effects of curcumin in animal studies and clinical trials. In addition, an SAR study will be introduced to elucidate the mechanism of curcumin degradation at physiological pH. We synthesized various curcumin analogues and compared their stability in phosphate buffer using HPLC and colorimetry assay. The results not only demonstrated that the -OH group and the methoxy group play a critical role in stability of curcumin in physiological environment, but also support the proposed mechanism of phenolic radical formation by which curcumin degrades to its major product bicyclopentadione

Towards a deep-learning-based framework of sentinel-2 imagery for automated active fire detection

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019-2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km(2) (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans

SGM3D: Stereo Guided Monocular 3D Object Detection

Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image. It poses a great challenge due to its ill-posed property which is critically lack of depth information in the 2D image plane. While there exist approaches leveraging off-the-shelve depth estimation or relying on LiDAR sensors to mitigate this problem, the dependence on the additional depth model or expensive equipment severely limits their scalability to generic 3D perception. In this paper, we propose a stereo-guided monocular 3D object detection framework, dubbed SGM3D, adapting the robust 3D features learned from stereo inputs to enhance the feature for monocular detection. We innovatively present a multi-granularity domain adaptation (MG-DA) mechanism to exploit the network's ability to generate stereo-mimicking features given only on monocular cues. Coarse BEV feature-level, as well as the fine anchor-level domain adaptation, are both leveraged for guidance in the monocular domain.In addition, we introduce an IoU matching-based alignment (IoU-MA) method for object-level domain adaptation between the stereo and monocular predictions to alleviate the mismatches while adopting the MG-DA. Extensive experiments demonstrate state-of-the-art results on KITTI and Lyft datasets.Comment: 8 pages, 5 figure

InSAR reveals land deformation at Guangzhou and Foshan, China between 2011 and 2017 with COSMO-SkyMed data

Subsidence from groundwater extraction and underground tunnel excavation has been known for more than a decade in Guangzhou and Foshan, but past studies have only monitored the subsidence patterns as far as 2011 using InSAR. In this study, the deformation occurring during the most recent time-period between 2011 and 2017 has been measured using COSMO-SkyMed (CSK) to understand if changes in temporal and spatial patterns of subsidence rates occurred. Using InSAR time-series analysis (TS-InSAR), we found that significant surface displacement rates occurred in the study area varying from -35 mm/year (subsidence) to 10 mm/year (uplift). The 2011-2017 TS-InSAR results were compared to two separate TS-InSAR analyses (2011-2013, and 2013-2017). Our CSK TS-InSAR results are in broad agreement with previous ENVISAT results and levelling data, strengthening our conclusion that localised subsidence phenomena occurs at different locations in Guangzhou and Foshan. A comparison between temporal and spatial patterns of deformations from our TS-InSAR measurements and different land use types in Guangzhou shows that there is no clear relationship between them. Many local scale deformation zones have been identified related to different phenomena. The majority of deformations is related to excessive groundwater extraction for agricultural and industrial purposes but subsidence in areas of subway construction also occurred. Furthermore, a detailed analysis on the sinkhole collapse in early 2018 has been conducted, suggesting that surface loading may be a controlling factor of the subsidence, especially along the road and highway. Roads and highways with similar subsidence phenomenon are identified. Continuous monitoring of the deforming areas identified by our analysis is important to measure the magnitude and spatial pattern of the evolving deformations in order to minimise the risk and hazards of land subsidence

Geo-tagged image based on Voronoi and TIN module

The current increasing use of Geo-tagged data is creating new demand for web mapping service applications. Until now, most mapping service web pages have already used Geo-tagged elements, such as Google Map, Microsoft’s Bing Map and Nokia’s Here Map. It has also been noticed that the number of researchers interested in Geo-tagged Map has increased recent years. Some are concerned with establishing local lexicons for retrieving data accurately, while others want to know more about how to retrieval geo-tagged images efficiently. There are also many researchers interested in how to represent geo-tagged images. However, the challenge of representing images is that since all the online data sets are unstructured, it is necessary to find sophisticated algorithms to represent large image data sets as graphs or networks, where each image is a vertex and is connected to surrounding other images, thus the whole dataset can be easily deal with. In this paper, two most popular algorithms, triangular irregular network and voronoi structure are discussed and applied as basic structures to display images (there is no such research until now) in both 2D plan maps and 3D sphere from Google Earth. It is no double that there are significant differences between these two platforms. More specifically, 2D structure has highly developed, while 3D structures are less mature. In this paper, the limitations and potential improvements to these algorithms will be described, and future work will be demonstrated. Results from the evaluation questionnaire indicate that the program interface and colour choice are favoured by most volunteers, while the efficiency and cross-browser compatibility have major problems and need to be refined in the future. The assessment of preference of effects demonstrated that the “lomo” approval is inferior to the other five results for producing a 2D plan map

Mapping Earth Surface Deformation using New Time Series Satellite Radar Interferometry

Land subsidence is an environmental, geological phenomenon that often refers to gradual settling or rapid sinking of the ground surface as a result of subsurface movement of earth materials. Satellite-based interferometric synthetic aperture radar (InSAR) has been proved to be an excellent technique for monitoring the subsidence at various temporal and spatial scales. Differential Interferometric Synthetic Aperture Radar (DInSAR) method has been used to observe such events over the past three decades. However, its result can be affected by spatial/temporal decorrelation and atmospheric disturbance. In recent decade, Time Series InSAR (TS-InSAR) was proposed to minimise these biases by taking advantage of the principle of temporal and spatial statistical analysis. Nevertheless, TS-InSAR has issues due to the tropospheric stratification in high elevation regions and insufficient measurement pixels over rapid subsiding zones. This dissertation mainly focused on optimisation of the TSInSAR-based technique for land subsidence measuring induced by the extraction of natural resources, such as coal, coalbed methane (CBM) and groundwater. Firstly, TS-InSAR has the problem dealing with the rapid surface subsidence and consequently gaps would appear in such areas. A new method has been proposed to fill these gaps by integrating DInSAR and TS-InSAR. Secondly, ALOS-1 PALSAR and ENVISAT ASAR based TS-InSAR has been conducted to monitor the subsidence over underground mining regions. Nevertheless, the result of the counterpart ENVISAT failed to produce reasonable outcome due to the underground mining effect. An approach has been developed and implemented to address this issue through an IDW (Inverse Distance Weighted)-based integration method. Thirdly, TS-InSAR was being exploited to monitor groundwater and CBM extraction induced subsidence in Beijing Municipality and Liulin County, respectively, by taking both tropospheric stratification and turbulence into consideration. Good correlations were observed between InSAR and levelling derived measurements. Indeed, by applying several established TS-InSAR techniques to different areas, and these significant findings from the TS-InSAR analysis have led to new insights into the processes causing the deformation

DInSAR Monitoring of Surface Subsidence by Fusing Sentinel-1A and -1B Data to Improve Time Resolution in a Mining Area

Monitoring large gradient ground deformation due to temporal and spatial image decoherence has long been a challenge. We attempted an improvement using Sentinel-1A and Sentinel-1B C-band data fusion methods based on the variation law of subsidence velocity of ground leveling monitoring points. This approach improved the temporal resolution from 12 to 6 days. Using a mine in Datong, Shanxi Province, China as an example, 13 scenes of Sentinel-1A data and 12 scenes of Sentinel-1B data were fused and compared with ground-measured data. The results obtained were closer to the measured values than those obtained by single data set (Sentinel-1A or -1B only). Simultaneously, 61 scenes of Sentinel-1A data and 12 scenes of Sentinel-1B data were used to calculate the subsidence of the mining area over two years. The subsidence map was consistent with the actual leveling trend, which reflected the dynamic change of surface subsidence range in the mining area. This study provides an approach using DInSAR technology to monitor large gradient deformation in mining areas and provides an effective method to monitor of surface dynamic deformation

Geodetic Monitoring for Land Deformation

Land deformation is a pervasive hazard that could lead to serious problems, for example, increasing risk of flooding in coastal areas, damaging buildings and infrastructures, destructing groundwater systems, generating tension cracks on land, and reactivating faults, to name only a few [...