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Introduction. Dengue Hemorrhagic Fever (DHF) is still a public health problem even in the era of the COVID-19 pandemic in 2020, including in Indonesia. This study aimed to analyze the incidence of DHF based on the integration of climatic factors, including rainfall, humidity, air temperature, and duration of sunlight and their distribution. Materials and Methods. This was an ecological time series study with secondary data from the Surabaya City Health Office covering the incidence of DHF and larva-free rate and climate data on rainfall, humidity, air temperature, and duration of sunlight obtained from the Meteorology and Geophysics Agency (BMKG). Silver station in Surabaya, the distribution of dengue incidence during 2018-2020. Results and Discussion. The results showed that humidity was correlated with the larvae-free rate. Meanwhile, the larva-free rate did not correlate with the number of DHF cases. DHF control is estimated due to the correlation of climatic factors and the incidence of DHF, control of vectors and disease agents, control of transmission media, and exposure to the community. Conclusions. The integration of DHF control can be used for early precautions in the era of the COVID-19 pandemic by control-ling DHF early in the period from January to June in Surabaya. It is concluded that humidity can affect the dengue outbreak and it can be used as an early warning system and travel warning regarding the relative risk of DHF outbreak.Copyright © the Author(s), 2023.
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The pandemic of the COVID-19, has brought a great impact to the education and teaching, so the teaching can only be carried out online, in order to ensure the monitoring and management of teaching quality during teaching. This project import the latest information intelligent teaching system, multi-directional to ensure the monitoring and control of teaching quality, the deep integration of 'Rain Classroom' technology and teaching in this project, So that the interaction information between teachers and students can be recorded in real time during the online teaching, and organized, analyzed, stored also;Import the online teaching platform, collected the students records that finished homework during the epidemic;Import the online virtual experiment platform, online to finished the remote experimental operation of students. Thus, the trinity builds a three-dimensional teaching quality assurance system to escort high-quality big data course teaching during the epidemic. © 2023 IEEE.
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Insights on the indirect effects of the COVID-19 pandemic are critical for designing and implementing policies to alleviate the food security burden it may have caused, and for bolstering rural communities against similar macroeconomic shocks in the future. Yet estimating the causal effects of the pandemic is difficult due to its ubiquitous nature and entanglement with other shocks. In this descriptive study, we combine high-resolution satellite imagery to control for plot-level rainfall with household socio-economic panel data from 2014, 2016, 2019 and 2020, to differentiate the effect of the pandemic from climatic shocks on food security in Morogoro, Tanzania. We find evidence of decreased incomes, increased prices of staple foods, and increased food insecurity in 2020 relative to previous years, and link these changes to the pandemic by asking households about their perceptions of COVID-19. Respondents overwhelmingly attribute economic hardships to the pandemic, with perceived impacts differing by asset level.
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One of the leading causes of the increase in the intensity of dengue fever transmission is thought to be climate change. Examining panel data from January 2000 to December 2021, this study discovered the nonlinear relationship between climate variables and dengue fever cases in Bangladesh. To determine this relationship, in this study, the monthly total rainfall in different years has been divided into two thresholds: (90 to 360 mm) and (<90 or >360 mm), and the daily average temperature in different months of the different years has been divided into four thresholds: (16degreeC to <=20degreeC), (>20degreeC to <=25degreeC), (>25degreeC to <=28degreeC), and (>28degreeC to <=30degreeC). Then, quasi-Poisson and zero-inflated Poisson regression models were applied to assess the relationship. This study found a positive correlation between temperature and dengue incidence and furthermore discovered that, among those four average temperature thresholds, the total number of dengue cases is maximum if the average temperature falls into the threshold (>28degreeC to <=30degreeC) and minimum if the average temperature falls into the threshold (16degreeC to <=20degreeC). This study also discovered that between the two thresholds of monthly total rainfall, the risk of a dengue fever outbreak is approximately two times higher when the monthly total rainfall falls into the thresholds (90 mm to 360 mm) compared to the other threshold. This study concluded that dengue fever incidence rates would be significantly more affected by climate change in regions with warmer temperatures. The number of dengue cases rises rapidly when the temperature rises in the context of moderate to low rainfall. This study highlights the significance of establishing potential temperature and rainfall thresholds for using risk prediction and public health programs to prevent and control dengue fever.Copyright © 2023 Shamima Hossain.
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Due to the impact of the COVID-19, online teaching has become a common teaching method at present, and in-depth research on online teaching methods is of great practical significance. In the computer network course, we organize the online teaching process according to the BOPPPS model combined with online teaching tools such as DingTalk and Rain Classroom. We use Rain Classroom to seize the pre-class preview and use DingTalk to achieve participatory online interactive teaching and complete homework correction and online Q&A. The results of the questionnaire show that the above-mentioned teaching organization method can enable students to actively participate in interactive teaching, and students' approval of online teaching is relatively high. © 2022 IEEE.
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One constraint of Thai soybean production is the volume of seeds used for cultivation in the dry season or after the rice season. PhuPha Man district, KhonKaenprovince, in Northeast Thailand, faces the same problem and can be solved by producing seeds during the rainy season for use in the dry season. The collaborative brainstorming to participatory action development employed in this community-based soybean production project involves four steps: (1) needs assessment, (2) planning, (3) implementation, and (4) evaluation. From 2019-2020, a total of 40 farmers jointly participated in this project: 20 from the Non-Korm Sub-district and 20 from the Sawab Sub-district. They agreed to implement three missions: (1) Farmer group management. A Community-based Soybean Seed Production Center (CSSPC) was established in each area. Each CSSPC was responsible for determining the structure and role of management, including the implementation of disciplines. (2) Seed production management. During its implementation, a shortage of rainfall and drought occurred from September to October 2019, causing soybean production to decline. Moreover, product harvesting and project evaluation took place at the site during the COVID-19 pandemic from January to May 2020, influencing the ability of farmers and facilitators to work together on group activities which required delicate management of the monitoring, control, production, exchange, and learning to solve problems. (3) Seed purchasing and distribution management. Rainfall shortage and drought influenced the ability of the farmers to produce the required soybean seeds. Consequently, the CSSPC did not purchase the seeds and manage their distribution.Copyright © 2023, Anka Publishers. All rights reserved.
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The effects of 9 precipitation events in Suzhou City in Anhui Province, China, on the air quality index (AQI), PM2.5, and dry deposition flux of PCDD/Fs (polydibenzo-p-dioxins and polydibenzofurans) were investigated. A total of 7 precipitation events were positive contributes to the reduction of AQI;among them, the AQI were between 23 and 216, with an average of 75, the PM2.5 concentrations were between 5.0 and 169 mu g m-3, with an average of 25 mu g m-3, while the total-PCDD/F-TEQ dry deposition flux ranged from 149 to 1034 pg WHO2005-TEQ m-2 day-1 and averaged 315 pg WHO2005-TEQ m-2 day-1. By comparing the average AQI and PM2.5, respectively, during and after rainfall with that before rainfall, the results indicated that the average reduction fractions of AQI were 26% and 44%, respectively, while those of PM2.5 were 58% and 43%. In addition, the effect of precipitation on the average reduction fraction of total PCDD/F-TEQ dry deposition flux was 31%. However, in the other 2 AQI elevation events, the AQI were between 23 and 100, and averaged 51;when comparing the average AQI and PM2.5 concentrations, during and after the rain with that before the rain, the increases in AQI were 42% and 49%, respectively, while the increases in PM2.5 concentration were 26% and 29%, respectively. The above results show that, on the whole, rain and snow improved the air quality. This is because rainwater removes particles or dissolved gaseous pollutants from the atmosphere and brings aerosols to the ground. However, in some cases, the increase of source emissions and atmospheric vertical convection, the effect of precipitation or air humidity increased the AQI and elevated the concentration of PM2.5, and dry deposition flux of PCDD/Fs. The results of this study provide useful information for both scientific communities and air quality management.
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This study aims to compare the performance of data classifying for COVID-19 patients. In this study, the patients' data acquired from the department of disease control (1,608,923 patients) are collected. They are patients records from January 2020 to October 2021. The study focus on three main data classification techniques: Random forest;Neural Network;and Naïve Bayes. The authors study the comparative performance of the techniques. We apply the split test method to evaluate the performance of data prediction. The data are divided into two parts: training data. The results show that Random Forest has an accuracy of 93.51%. Neural network has an accuracy of 93.02%. Naive Bayes has an accuracy of 27.54%. This presents the Random Forest with the highest accuracy Figure for screening of COVID-19 patients © 2022 IEEE.
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The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.Significance StatementDuring the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.
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The analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) gene copy numbers in wastewater samples can provide quantitative information on Coronavirus Disease-19 (COVID-19) cases within a sewer catchment. However, many wastewater-based epidemiology (WBE) studies have neglected virus decay during the wastewater transportation process in sewers while back-calculating COVID-19 prevalence. Among various sewer condition parameters, wastewater temperature and dilution by fresh/saltwater infiltration may result in a significant change to the virus decay, in terms of both infectivity and Ribonucleic Acid (RNA). This paper reviewed the literature to identify and discuss the effects of temperature and water types (i.e., wastewater, freshwater, and seawater) on coronavirus decay based on the decay rate constants that were collected from published papers. To evaluate the importance of virus decay, a sensitivity analysis was then conducted with decay rates of SARS-CoV-2 RNA based on a WBE back-calculation equation. Finally, the decay rates of coronavirus in wastewater were also compared with those of other viruses to further understand the difference among virus species. The decay of SARS-CoV-2 RNA was found to be less impacted by temperature variation than viable coronaviruses. Nevertheless, WBE back-calculation was still sensitive to the RNA decay rates increased by warm wastewater (i.e., over 26 °C), which could lead to a two-times higher relative variance in estimated COVID-19 prevalence, considering the wastewater temperature variation between 4 and 37 °C in a sewer catchment with a 12-h hydraulic retention time. Comparatively, the sensitivity of the WBE estimation to the enveloped SARS-CoV-2 was greater than nonenveloped enteric viruses, which were less easily degradable in wastewater. In addition, wastewater dilution by stormwater inflow and accompanied cold weather might alleviate the decay of coronavirus infectivity, thus increasing the potential risk of COVID-19 transmission through wastewater. Overall, this paper aims to better understand the impact of in-sewer processes on coronavirus decay and its potential implications for WBE. The outcome could quantitatively inform WBE and improve awareness of the increased risk of COVID-19 infection via wastewater during heavy rainfall events. Given the identified scarcity of data available for coronavirus decay in salt water or with chemical additions, future research on the fate of SARS-CoV-2 subjected to chemical dosing for sewer or wastewater treatment plant operations is recommended. © 2023 by the authors.
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Cybercrime is an online crime committing fraud, stealing identities, violating privacy or hacking the personal information. A high level of information security in banking can be attained through striving to achieve an integrity, confidentiality, availability, assurance, and accountability. This Pandemic situation (COVID-19) paved the way for the customers to avoid traditional ways of banking and adapt to digital transactions. This banking digitalization increases in the utilization of cashless transactions like digital money (Cryptocurrency). Cyber security is imperative to preserve sensitive information, therefore, Blockchain technology has been adapted to provide security. Transactions done via Blockchain are tested through every block, which makes transactions secure and helps the banking system to work faster. The proposed algorithm WFB is used to estimate the average queue rate and avoid unwanted block generation. Then the trapezoidal fuzzy technique optimizes the allocation of blocks. An objective of this investigation is to enhance the security in banking systems from Cybercrimes by verifying Rain Drop Service (RDS) and Fingerprint Biometric without the need of any central authority. Once the service is completed, the service is a dropout and the following new service will be provided (Hence the name RDS). For the strong authentication scheme to fight against bank fraud, RSA encryption technique has been implemented successfully. Therefore, Blockchain technology increases the need for cyber security as a part of design architecture which intends to detect the stemming attacks in real time instead of repairing the damage. [ FROM AUTHOR] Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Air pollution causes respiratory ailments and drives climate change. Air quality is driven by emissions from various sources, weather patterns, and transport of pollutants. Satellite analysis of pollutants in the atmosphere can provide temporally consistent and spatially wide measurements. In this study, the monthly concentrations of Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Carbon Monoxide (CO), and Ozone (O3) from the Sentinel-5 Tropospheric Monitoring Instrument (TROPOMI) were analyzed in four major cities in the Philippines, representing different climate types. Satellite-based measurements of land surface temperature and rainfall were used to investigate meteorological effects to air pollutants. Seasonal patterns were observed in the time series of NO2, O3 and CO alongside rainfall and LST. During the dry season, high LST and low precipitation is observed to be associated with increase in NO2, O3, and CO concentrations. On the other hand, wet seasons show decreases in concentrations of air pollutants, consistent with the washout effect. The NO2 average concentration in NCR is 1.9, 2.1, 2.3 times higher than in Metro Cebu, Davao City, and Legazpi City, respectively. In contrast, SO2 average concentration is highest in Legazpi City due to the nearby active volcano by a maximum factor of 1.8 compared to other cities. In addition, air quality changes brought about by community quarantines were examined since the onset of the COVID-19 crisis. Transition from the pre-quarantine period to the first lockdown shows sudden decrease by 28% in satellite-based retrievals of NO2 in NCR, mainly due to reduced anthropogenic emissions. As tiers of community quarantines were introduced, an increase in pollutant concentrations was observed, returning to pre-pandemic air quality as the guidelines ease physical and economic restrictions. Monitoring and analyzing the patterns in concentration of air pollutants in relation to meteorological and anthropogenic drivers can help in the air quality management in the country. © 43rd Asian Conference on Remote Sensing, ACRS 2022.
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To help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand for bike-sharing. First, we consider time, built environment, and weather. We use a multigraph convolution network (GCN) to model the built environment, utilize a long short-term memory (LSTM) network to extract temporal features, and utilize a fully connected network (FCN) to model weather influence. We construct SGCNPM which can effectively fuse GCN, LSTM, and FCN, thus creating a prediction method considering the influence of multiple factors. The results of the real case in Tianjin, China, show that the proposed model can perform well in improving prediction accuracy. Also, we analyze the influence of factors on model prediction results in different periods.
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Water clarity is a key parameter for assessing changes of aquatic environment. Coastal waters are complex and variable, remote sensing of water clarity for it is often limited by low spatial resolution. The Sentinel-2 Multi-Spectral Instrument (MSI) imagery with a resolution of up to 10 m are employed to solve the problem from 2017 to 2021. Distribution and characteristics of Secchi disk depth (SDD) in Jiaozhou Bay (JZB) are analyzed. Subtle changes in localized small areas are discovered, and main factors affecting the changes are explored. Among natural factors, precipitation and wind play dominant roles in variation in SDD. Human activities have a significant influence on transparency, among which fishery farming has the greatest impact. This is clearly evidenced by the significant improvement of SDD in JZB due to the sharp decrease in human activities caused by coronavirus disease 2019 (COVID-19).Copyright © 2022 The Authors
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In general, support for rainy day travel is known to be imperative during long times. Rainy weather has significant risk for tourists to terribly reduce a satisfaction level of their travel. However, its solution is not fully developed. In Post-Covid-19 environment, support for tourism in an actual field could become imperative again. In the present paper, we put one assumption that 'a tourist has already made his/her travel plan for a sunny day'. By the way, there exists a social approach: 'potential-of-interest maps for mobile tourist information services'. It shows the amounts of the numbers of the photographs in social photograph sharing system 'Flickr' by color and intensity on a map. This paper modifies it for support of rainy day travel planning. Concretely, we propose the following three menus in our system: 1) a menu to show 'potential-of-interest maps' per a degree of rainfall amount, 2) a menu to show only travel spot which is robust to rainy weather based on its static characteristics, and 3) a menu to show only travel spots within a specified distance range from a basic point, taking into account decrease of behavior range. With these three menus, we try to support a tourist to change his/her travel plan efficiently even if weather suddenly becomes rain. In actual, we have evaluated our pilot system by the following two method: (1) evaluation experiment with some subjects, and (2) interviews to tourism professionals. Both of their results shows that our system would be useful in order to support for a tourist to change his/her travel plan efficiently when weather has suddenly become rain. © 2023 IEEE.
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The article focuses on the current, urgent, and much discussed global issue of climate change, the impacts of which are expansive and involve a wide range of expertise. The base forms the evaluation of a sample of European Union member states using the quantification of threats and intensity of two key factors. The main objective of this article is to evaluate EU countries the INFORM assessment tool and to highlight the link between the effects of climate change (environmental, social, and economical) as quantified by respective threats posed by emission volume and poverty. In the present research, we relied on the new INFORM Risk Index assessment indicator because it represents a completely new but also globally applicable, reliable, and transparent tool to understand the risk of humanitarian crises and disasters. The significant results of the performed quantitative analysis suggest that security risk, poverty, and pollution levels operate as closely linked areas. It can be expected that recent changes (the COVID-19 pandemic, state of war) will mean that these influences will increase in severity.
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Schools are the key units for infectious disease prevention and control. Students are the key groups in prevention and control of infectious diseases. The establishment of links between schools and out campus hospitals is an important measure for epidemic prevention and control of Corona Virus Disease 2019 (COVID-19). How to use reasonable human resources, financial resources and material resources to make rational decisions and respond quickly to the best cost performance is the practical problem facing all schools. Therefore, the accessibility of Wuhan medical services to schools is analyzed through GIS, and the resilience of hospitals is analyzed by simulating short-term heavy rainfall weather. The results show that large hospitals in Wuhan are mostly concentrated in the city center, which can effectively resist the impact of sudden epidemic and disperse the risk to the periphery of the city. The buffer zone of large hospitals in Wuhan can effectively cover most schools and can respond quickly, but some hospitals have low accessibility in extreme weather. It is necessary to further improve the distribution of medical resources in various regions and improve emergency transportation planning. © 2022 ACM.
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Wastewater-based epidemiology has proven to be a supportive tool to better comprehend the dynamics of the COVID-19 pandemic. As the disease moves into endemic stage, the surveillance at wastewater sub-catchments such as pump station and manholes is providing a novel mechanism to examine the reemergence and to take measures that can prevent the spread. However, there is still a lack of understanding when it comes to wastewater-based epidemiology implementation at the smaller intra-city level for better granularity in data, and dilution effect of rain precipitation at pump stations. For this study, grab samples were collected from six areas of Seattle between March-October 2021. These sampling sites comprised five manholes and one pump station with population ranging from 2580 to 39,502 per manhole/pump station. The wastewater samples were analyzed for SARS-CoV-2 RNA concentrations, and we also obtained the daily COVID-19 cases (from individual clinical testing) for each corresponding sewershed, which ranged from 1 to 12 and the daily incidence varied between 3 and 64 per 100,000 of population. Rain precipitation lowered viral RNA levels and sensitivity of viral detection but wastewater total ammonia (NH4+-N) and phosphate (PO43--P) were shown as potential chemical indicators to calibrate/level out the dilution effect. These chemicals showed the potential in improving the wastewater surveillance capacity of COVID-19.