Integrated Satellite System for Fire Detection and Prioritization

Several studies have shown the relevance of satellite systems in detecting, monitoring, and characterizing fire events as support to fire management activities. On the other hand, up to now, only a few satellite-based platforms provide immediately and easily usable information about events in progress, in terms of both hotspots, which identify and localize active fires, and the danger conditions of the affected area. However, this kind of information is usually provided through separated layers, without any synthetic indicator which, indeed, could be helpful, if timely provided, for planning the priority of the intervention of firefighting resources in case of concurrent fires. In this study, we try to fill these gaps by presenting an Integrated Satellite System (ISS) for fire detection and prioritization, mainly based on the Robust Satellite Techniques (RST), and the Fire Danger Dynamic Index (FDDI), an original re-structuration of the Índice Combinado de Risco de Incêndio Florestal (ICRIF), for the first time presented here. The system, using Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data, provides near real-time integrated information about both the fire presence and danger over the affected area. These satellite-based products are generated in common formats, ready to be ingested in Geographic Information System (GIS) technologies. Results shown and discussed here, on the occasion of concurrent winter and summer fires in Italy, in agreement with information from independent sources, demonstrate that the ISS system, operating at a regional/national scale, may provide an important contribution to fire prioritization. This may result in the mitigation of fire impact in populated areas, infrastructures, and the environment.

Surveillance of SARS-CoV-2 antibodies of patients in the local affected area during Wuhan lockdown

Background Serosurveillance is crucial in estimating the range of SARS-CoV-2 infections, predicting the possibility of another wave, and deciding on a vaccination strategy. To understand the herd immunity after the COVID-19 pandemic, the seroprevalence was measured in 3062 individuals with or without COVID-19 from the clinic. Methods The levels of SARS-CoV-2 antibody IgM and IgG were measured by the immuno-colloidal gold method. A fusion fragment of nucleocapsid and spike protein was detected by a qualitative test kit with sensitivity (89%) and specificity (98%). Results The seroprevalence rate for IgM and IgG in all outpatients was 2.81% and 7.51%, respectively. The sex-related prevalence rate of IgG was significantly higher (P < 0.05) in women than men. The highest positive rate of IgM was observed in individuals < 20 years of age (3.57%), while the highest seroprevalence for IgG was observed in persons > 60 years of age (8.61%). Positive rates of IgM and IgG in the convalescent patients were 31.82% and 77.27%, respectively, which was significantly higher than individuals with suspected syndromes or individuals without any clinical signs (P < 0.01). Seroprevalence for IgG in medical staff was markedly higher than those in residents. No significant difference of seroprevalence was found among patients with different comorbidities (P > 0.05). Conclusions The low positive rate of the SARS-CoV-2 IgM and nucleic acid (NA) test indicated that the SARS-CoV-2 outbreak is subsiding after 3 months, and the possibility of reintroduction of the virus from an unidentified natural reservoir is low. Seroprevalence provides information for humoral immunity and vaccine in the future.

Topical Therapy of Skin Scabs in Psoriasis with Salicylic Acid: Case Report

The objective was to evaluate the keratolytic and anti-inflammatory action of salicylic acid in the affected area in the case reported. Methods. Clinical photography a digital camera was used Olympus SP570UZ with master software 2.0. The images obtained both in the initial phase without treatment and at the 72 hours of treatment were transferred and stored on a 4-core Sansung computer. The auto-dial adjustment option was selected. In this way, the camera selects the optimal way to take the photo shot. Analysis of the affected skin with scabs. Image J software was used to measure the area selected for the study in both cases. This measurement was made in pixels for the photographic region under study. Results. The image j software program measures the areas in pixels, to decrease in measurement error there was no calibration. That is, the measurement of the areas in pixels was maintained. The percentage ratios of the affected or non-affected areas for both left and right legs are presented in Tables. It is possible to observe the reduction of the affected area. The doubtful areas represent for the left leg 5.21% and for the right leg 30.08% after treatment. Only a clearly visible area with crusts and scabs of 1.60% was observed after treatment.

Detection of Windthrown Tree Stems on UAV-Orthomosaics Using U-Net Convolutional Networks

The increasing number of severe storm events is threatening European forests. Besides the primary damages directly caused by storms, there are secondary damages such as bark beetle outbreaks and tertiary damages due to negative effects on the market. These subsequent damages can be minimized if a detailed overview of the affected area and the amount of damaged wood can be obtained quickly and included in the planning of clearance measures. The present work utilizes UAV-orthophotos and an adaptation of the U-Net architecture for the semantic segmentation and localization of windthrown stems. The network was pre-trained with generic datasets, randomly combining stems and background samples in a copy–paste augmentation, and afterwards trained with a specific dataset of a particular windthrow. The models pre-trained with generic datasets containing 10, 50 and 100 augmentations per annotated windthrown stems achieved F1-scores of 73.9% (S1Mod10), 74.3% (S1Mod50) and 75.6% (S1Mod100), outperforming the baseline model (F1-score 72.6%), which was not pre-trained. These results emphasize the applicability of the method to correctly identify windthrown trees and suggest the collection of training samples from other tree species and windthrow areas to improve the ability to generalize. Further enhancements of the network architecture are considered to improve the classification performance and to minimize the calculative costs.

Sialadenitis associated with Sialolith- A Case Report

Sialadenitis is the inflammation of the major salivary glands. It is an insidious inflammatory disease of the major salivary glands which may lead to the development of fibrous masses. The most frequent cause of sialadenitis is the presence of a salivary duct calculi, the sialolith. Sialolithiasis is the presence of stones in the salivary gland. It presents with pain and tenderness in the affected area. A 56-year old male patient had reported in the department with pain and swelling in the right lower back region of the jaw since 7 days. On clinical, radiographic and histopathological examination, a case of sialolith was reported in the right submandibular gland along with sialadenitis.

Identification of Regional Drought Processes in North China Using MCI Analysis

Comprehensive identification of drought events is of great significance for monitoring and evaluating drought processes. Based on the date of daily precipitation, temperature and drought-affected area of 403 meteorological stations in North China from 1960 to 2019, the Comprehensive Drought Process Intensity Index (CDPII) has been developed by using the Meteorological-drought Composite Index (MCI) and regional drought process identification method, as well as the EIDR theory method. The regional drought processes in the past 60 years in North China, including Beijing, Tianjin, Hebei, Shanxi and Middle Inner Mongolia, were analyzed and identified. The result shows that the distribution characteristic of droughts with different intensities is as follows: The number of days of all annual-average mild droughts, moderate droughts and severe droughts was highest in Tianjin and that of extreme droughts was highest in Shanxi. The number of days of mild droughts was highest in May and lowest in January. The number of days of moderate droughts was highest in June. The number of days with mild and moderate drought showed an overall increasing trend, while the number of days with severe drought and above showed an overall decreasing trend (through a 95% significance test). The number of drought days was the highest in the 1990s. The annual frequency of drought is between 66.7% and 86.7%; the drought frequency in Hebei is the highest at 86.7%, followed by Beijing at 80%. There were 75 regional drought processes in North China from 1960 to 2019, and the correlation coefficient between process intensity and the drought-affected area was 0.55, which passed the 99% significance test. The comprehensive intensity of drought process from 27 April to 1 September 1972 was the strongest. From 18 May to 31 October 1965, the drought lasted 167 days. The overall drought intensity had a slight weakening trend in the past 60 years. A total of 75 regional drought processes occurred in North China, and the process intensity showed a trend of wavy decline with a determination coefficient (R2) of 0.079 (95% significance test). Overall, the regional drought process identification method and strength assessment result tally with the drought disaster, which can better identify the regional drought process. Furthermore, including the last days, the average intensity, average scope comprehensive strength, there are many angles to monitor and evaluate the drought and drought process. These provide a reference for drought control and decision-making.

Evolution of Diagnostic Methods and Prevalence Detection of COVID-19: A Review

In clinical, research, and public health laboratories, many diagnostic methods are used to detect the coronavirus. Some tests directly detect infection by detecting viral RNA, while others detect the disease indirectly by detecting host antibodies. Several studies on SARS-CoV-2 diagnostic methods have found varying throughput, batching capacity, infrastructure requirements, analytical efficiency, and turnaround times ranging from minutes to hours. Serosurvey studies have been conducted for antibodies to understand, model, and forecast the prevalence of the disease in an area. While on the research and predictive modeling side, sampling and analysis of sewage have been conducted to determine the number of RNA copies and hence the prevalence. Certain studies indicate usefulness of GIS (Geographic Information System) for understanding the pervasiveness of COVID-19 in an area as well. The current chapter deals with the evolution of diagnostic techniques for COVID-19 and discusses use of specific techniques and appropriateness in certain specified conditions. It also focuses on understanding the methods used for assessing the prevalence of COVID-19 in a particular region to extract mitigative strategies from it, either by prediction or management of the affected area.

The Effects of Rainfall Patterns on Runoff, Sediment, and Nutrients Under Various Artificial Rainfall Experiments

Assessing the effects of rainfall patterns on runoff, sediment, nutrients under variation of rainfall pattern are significant in the quantification of sediment transported by overland flow. Previous experimental and field works studied that sediment transport is influenced by hydraulic properties of flow, physical properties of soil and surface characteristics. This study aims at determining the effect of rainfall patterns on surface runoff, sediment loss and nutrient loss. Experiments were carried out using four rainfall patterns, namely Pattern A (uniform-type: 8-8-8 l/min), Pattern B (increasing-type: 7-8-9 l/min), Pattern C (increasing-decreasing-type: 7-9-8 l/min) and Pattern D (decreasing-type: 9-8-7 l/min) with the changes of intensity every 30 minutes that gives total rainfall duration of 90 minutes for each pattern. The simulation was performed in three repetitions. The average total runoff produced was 668.65, 701.40, 699.10, and 722.63 liters, for rainfall patterns A, B, C, and D, respectively. The trend of runoff generated was influenced by the rainfall patterns, Pattern D generated the highest amount of runoff meanwhile Pattern A generated the lowest. For total suspended sediment concentrations, the mean value among every three repetitions of rainfall pattern resulted as 14,518.88, 13,732.73, 8,011.71 and 19,918.50 mg/l for patterns A, B, C, and D, respectively Pattern D contributed to the highest amount of sediment accumulated whereby Pattern C generated the lowest sediment despite the trend showed a different approach than the other 3 patterns. In nutrient concentrations, the determined total losses for ammonia nitrogen were 3.986, 2.891, 3.504, and 4.601g; nitrate nitrogen were 3.934, 2.665, 4.008, and 3.259g; phosphorus were 1.346, 0.222, 0.207, and 0.679g, for patterns A, B, C, and D, respectively. In general, rainfall pattern does have a significant impact on the trend of nutrient losses, where the trend shows that higher concentrations at the start and eventually lowered through the end, but Pattern D as compared to other patterns resulted in a more severe nutrient loss. For the affected area of the soil movement process, the calculated means of the affected area are 79.60, 68.70, 72.43, and 64.97% for patterns A, B, C, and D respectively. The lowest mean of the affected area is contributed by Pattern D and the highest by Pattern A.

The Impact of the COVID-19 Pandemic on Suicide Rate Trends in the Tsunami-Disaster-Affected Area Following the Great East Japan Earthquake

Background: People who experienced the Great East Japan Earthquake (GEJE) were expected to have additional levels of psychological burden resulting from the stressful conditions imposed during the coronavirus disease 2019 (COVID-19) pandemic; consequently, suicide rates may increase. Aim: We aimed to carry out continuous monitoring of suicide rates in the affected area following the GEJE under COVID-19 pandemic conditions. Method: This descriptive study monitored the suicide rates of the coastal area of Miyagi Prefecture, where disaster-related mental health activities have been continuing following severe damage caused by the tsunami disaster. An exponential smoothing time-series analysis that converted suicide rates into a smooth trend was conducted. Results: Although the suicide rate in the affected area was higher than the national average in February 2020, it showed a declining trend during the COVID-19 pandemic, while showing an increase trend in the national and non-affected areas. Limitations: Uncertainty about the direct reasons for suicide and the short time-scale observation are the limitations of this study. Conclusion: Although the national suicide rate increased, this was not the case for the affected area. Our findings may provide important lessons for suicide prevention during the COVID-19 pandemic, which needs careful regional monitoring of the state of suicide and of high-risk approaches such as disaster-related mental health activities.