Aerial Drone-based System for Wildfire Monitoring and Suppression

Wildfire, also known as forest fire or bushfire, being an uncontrolled fire crossing an area of combustible vegetation, has become an inherent natural feature of the landscape in many regions of the world. From local to global scales, wildfire has caused substantial social, economic and environmental consequences. Given the hazardous nature of wildfire, developing automated and safe means to monitor and fight the wildfire is of special interest. Unmanned aerial vehicles (UAVs), equipped with appropriate sensors and fire retardants, are available to remotely monitor and fight the area undergoing wildfires, thus helping fire brigades in mitigating the influence of wildfires. This thesis is dedicated to utilizing UAVs to provide automated surveillance, tracking and fire suppression services on an active wildfire event. Considering the requirement of collecting the latest information of a region prone to wildfires, we presented a strategy to deploy the estimated minimum number of UAVs over the target space with nonuniform importance, such that they can persistently monitor the target space to provide a complete area coverage whilst keeping a desired frequency of visits to areas of interest within a predefined time period. Considering the existence of occlusions on partial segments of the sensed wildfire boundary, we processed both contour and flame surface features of wildfires with a proposed numerical algorithm to quickly estimate the occluded wildfire boundary. To provide real-time situational awareness of the propagated wildfire boundary, according to the prior knowledge of the whole wildfire boundary is available or not, we used the principle of vector field to design a model-based guidance law and a model-free guidance law. The former is derived from the radial basis function approximated wildfire boundary while the later is based on the distance between the UAV and the sensed wildfire boundary. Both vector field based guidance laws can drive the UAV to converge to and patrol along the dynamic wildfire boundary. To effectively mitigate the impacts of wildfires, we analyzed the advancement based activeness of the wildfire boundary with a signal prominence based algorithm, and designed a preferential firefighting strategy to guide the UAV to suppress fires along the highly active segments of the wildfire boundary

A new fourth-order family for solving nonlinear problems and its dynamics

In this manuscript, a new parametric class of iterative methods for solving nonlinear systems of equations is proposed. Its fourth-order of convergence is proved and a dynamical analysis on low-degree polynomials is made in order to choose those elements of the family with better conditions of stability. These results are checked by solving the nonlinear system that arises from the partial differential equation of molecular interaction.This research was supported by Ministerio de Ciencia y Tecnologia MTM2011-28636-C02-{01, 02} and Universitat Politecnica de Valencia SP20120474.Cordero Barbero, A.; Feng, L.; Magrenan, A.; Torregrosa Sánchez, JR. (2015). A new fourth-order family for solving nonlinear problems and its dynamics. Journal of Mathematical Chemistry. 53(3):893-910. https://doi.org/10.1007/s10910-014-0464-4S893910533R.C. Rach, J.S. Duan, A.M. Wazwaz, Solving coupled Lane–Emden boundary value problems in catalytic diffusion reactions by the Adomian decomposition method. J. 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A Survey of Deep Learning-Based Object Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.Comment: 30 pages,12 figure

Acute myocardial infarction after inactivated COVID-19 vaccination: a case report and literature review

A number of vaccines have been developed and deployed globally to restrain the spreading of the coronavirus disease 2019 (COVID-19). The adverse effect following vaccination is an important consideration. Acute myocardial infarction (AMI) is a kind of rare adverse event after COVID-19 vaccination. Herein, we present a case of an 83-year-old male who suffered cold sweat ten minutes after the first inactivated COVID-19 vaccination and AMI one day later. The emergency coronary angiography showed coronary thrombosis and underlying stenosis in his coronary artery. Type II Kounis syndrome might be a potential mechanism, which is manifested as coronary thrombosis secondary to allergic reactions in patients with underlying asymptomatic coronary heart disease. We also summarize the reported AMI cases post COVID-19 vaccination, as well as overview and discuss the proposed mechanisms of AMI after COVID-19 vaccination, thus providing insights for clinicians to be aware of the possibility of AMI following COVID-19 vaccination and potential underlying mechanisms

Has the Reform of Nontradable Shares Raised Prices?: An Event-Study Analysis

This study examines the abnormal stock returns of pilot companies to determine if investors believed that reform of nontradable shares, which began on April 29, 2005, would lead to higher stock prices. Employing event-study analysis, we find that the pilot companies have positive significant abnormal returns. The average abnormal return of the first batch is higher than that of the second batch, the average abnormal return on the Shenzhen Stock Exchange is higher than that of the Shanghai Stock Exchange, the average abnormal return on the Small and medium Enterprise board is higher than that of the main board, and companies with high-compensation packages have higher average abnormal returns than do companies with low-compensation packages. Our results suggest that investors generally viewed nontradable share reform as positive news.abnormal return, China, event study, nontradable share reform,

Attribution of the Extreme Drought in the Horn of Africa during Short-Rains of 2016 and Long-Rains of 2017

The Horn of Africa (HOA) is one of the most drought-prone regions in the world with many arid and semiarid areas, and even some extremely arid areas. Primarily affected by the marine continental climate systems, this zone is really sensitive to global warming. Drought is the main type of natural disaster affecting this region, which triggers famine, civil conflict, and even deterioration of food security. The present study examined changes in droughts in the HOA during 1979–2019 based on Standardized Precipitation Evapotranspiration Index (SPEI). Results show that frequency, duration, and intensity of droughts exhibited an increasing trend over the past decades. Moreover, in October to December (called locate “short rains”) 2016 and March to May (called locate “long rains”) 2017, the HOA experienced the most severe drought. Based on Generalized Extreme Value (GEV) fitting, the 2016/2017 SPEI index corresponds to a drought that occurs every 250 years in the observational records. However, considering the precipitation in short rains of 2016 (long rains of 2017) was only 27.1% (11.8%) less than normal, it is hard to explain the formation of this extreme drought only from precipitation anomalies. Further statistical result shows that the evaporation in 2016/2017 corresponds to a 1-in-131 years event in the observed records. The abnormally high temperature (1.02 °C higher than normal) as well as the greatest potential evaporation since 1979 are the more important causes for the formation of drought. Thus, the extreme drought in 2016/2017, probably caused by the combined effect of dry condition and high temperature simultaneously, and the latter, played a leading role. In other words, droughts can be exacerbated by the co-occurrence of extreme high temperature. With continuous warming caused by anthropogenic activities in the next decades, the Horn of Africa may be a hotspot of the compound droughts and, therefore, it is especially important to considering the combined impacts from less precipitation and high temperature when predicting the future drought trend and making adaptation measures