scholarly journals Impact of Daily Weather on COVID-19 outbreak in India

2020 ◽  
Author(s):  
Amitesh Gupta ◽  
Biswajeet Pradhan
Keyword(s):  
Relative Humidity ◽  
Rank Correlation ◽  
Weather Conditions ◽  
Dew Point ◽  
Support Vector ◽  
Spearman’S Rank Correlation ◽  
Daily Data ◽  
Diurnal Range ◽  
Weather Parameters ◽  
The Impact

AbstractThe COVID-19 pandemic has outspread obstreperously in India. As of June 04, 2020, more than 2 lakh cases have been confirmed with a death rate of 2.81%. It has been noticed that, out of each 1000 tests, 53 result positively infected. In order to investigate the impact of weather conditions on daily transmission occurring in India, daily data of Maximum (TMax), Minimum (TMin), Mean (TMean) and Dew Point Temperature (TDew), Diurnal Temperature range (TRange), Average Relative Humidity, Range in Relative Humidity, and Wind Speed (WS) over 9 most affected cities are analysed in several time frames: weather of that day, 7, 10, 12, 14, 16 days before transmission. Spearman’s rank correlation (r) shows significant but low correlation with most of the weather parameters, however, comparatively better association exists on 14 days lag. Diurnal range in Temperature and Relative Humidity shows non-significant correlation. Analysis shows, COVID-19 cases likely to be increased with increasing air temperature, however role of humidity is not clear. Among weather parameters, Minimum Temperature was relatively better correlate than other. 80% of the total confirmed cases were registered when TMax, TMean, TMin, TRange, TDew, and WS on 12-16 days ago vary within a range of 33.6-41.3° C, 29.8-36.5° C, 24.8-30.4° C, 7.5-15.2° C, 18.7-23.6° C, and 4.2-5.75 m/s respectively, hence, it gives an idea of susceptible weather conditions for such transmission in India. Using Support Vector Machine based regression, the daily cases are profoundly estimated with more than 80% accuracy, which indicate that coronavirus transmission can’t be well linearly correlated with any single weather parameters, rather multivariate non-linear approach must be employed. Accounting lag of 12-16 days, the association found to be excellent, thus depict that there is an incubation period of 14 ± 02 days for coronavirus transmission in Indian scenario.

scholarly journals Estimating the Impact of Daily Weather on the Temporal Pattern of COVID-19 Outbreak in India

2020 ◽  
Vol 4 (3) ◽  
pp. 523-534 ◽  
Author(s):  
Amitesh Gupta ◽  
Biswajeet Pradhan ◽  
Khairul Nizam Abdul Maulud
Keyword(s):  
Relative Humidity ◽  
Temporal Pattern ◽  
Dew Point ◽  
Support Vector ◽  
Daily Maximum ◽  
Daily Weather ◽  
Linear Approach ◽  
Diurnal Range ◽  
The Impact ◽  
Spearman’S Correlation

Abstract The COVID-19 pandemic has spread obstreperously in India. The increase in daily confirmed cases accelerated significantly from ~ 5 additional new cases (ANC)/day during early March up to ~ 249 ANC/day during early June. An abrupt change in this temporal pattern was noticed during mid-April, from which can be inferred a much reduced impact of the nationwide lockdown in India. Daily maximum (TMax), minimum (TMin), mean (TMean) and dew point temperature (TDew), wind speed (WS), relative humidity, and diurnal range in temperature and relative humidity during March 01 to June 04, 2020 over 9 major affected cities are analyzed to look into the impact of daily weather on COVID-19 infections on that day and 7, 10, 12, 14, 16 days before those cases were detected (i.e., on the likely transmission days). Spearman’s correlation exhibits significantly lower association with WS, TMax, TMin, TMean, TDew, but is comparatively better with a lag of 14 days. Support Vector regression successfully estimated the count of confirmed cases (R2 > 0.8) at a lag of 12–16 days, thus reflecting a probable incubation period of 14 ± 02 days in India. Approximately 75% of total cases were registered when TMax, TMean, TMin, TDew, and WS at 12–16 days previously were varying within the range of 33.6–41.3 °C, 29.8–36.5 °C, 24.8–30.4 °C, 18.7–23.6 °C, and 4.2–5.75 m/s, respectively. Thus, we conclude that coronavirus transmission is not well correlated (linearly) with any individual weather parameter; rather, transmission is susceptible to a certain weather pattern. Hence multivariate non-linear approach must be employed instead.

scholarly journals A Quantitative Assessment of Pre-Operative MRI Reports in Glioma Patients: Report Metrics and IDH Prediction Ability

2021 ◽  
Vol 10 ◽  
Author(s):  
Hang Cao ◽  
E. Zeynep Erson-Omay ◽  
Murat Günel ◽  
Jennifer Moliterno ◽  
Robert K. Fulbright
Keyword(s):  
High Performance ◽  
Prediction Models ◽  
Rank Correlation ◽  
Support Vector ◽  
Wild Type ◽  
K Nearest Neighbors ◽  
Prediction Ability ◽  
Spearman’S Rank Correlation ◽  
Negative Findings ◽  
T1 Contrast

ObjectivesTo measure the metrics of glioma pre-operative MRI reports and build IDH prediction models.MethodsPre-operative MRI reports of 144 glioma patients in a single institution were collected retrospectively. Words were transformed to lowercase letters. White spaces, punctuations, and stop words were removed. Stemming was performed. A word cloud method applied to processed text matrix visualized language behavior. Spearman’s rank correlation assessed the correlation between the subjective descriptions of the enhancement pattern. The T1-contrast images associated with enhancement descriptions were selected. The keywords associated with IDH status were evaluated by χ2 value ranking. Random forest, k-nearest neighbors and Support Vector Machine algorithms were used to train models based on report features and age. All statistical analysis used two-tailed test with significance at p <.05.ResultsLonger word counts occurred in reports of older patients, higher grade gliomas, and wild type IDH gliomas. We identified 30 glioma enhancement descriptions, eight of which were commonly used: peripheral, heterogeneous, irregular, nodular, thick, rim, large, and ring. Five of eight patterns were correlated. IDH mutant tumors were characterized by words related to normal, symmetric or negative findings. IDH wild type tumors were characterized words by related to pathological MR findings like enhancement, necrosis and FLAIR foci. An integrated KNN model based on report features and age demonstrated high-performance (AUC: 0.89, 95% CI: 0.88–0.90).ConclusionReport length depended on age, glioma grade, and IDH status. Description of glioma enhancement was varied. Report descriptions differed for IDH wild and mutant gliomas. Report features can be used to predict glioma IDH status.

scholarly journals Flood Prediction and Warning System using Dam Data Monitoring

2020 ◽  
Vol 9 (9) ◽  
pp. 294-298
Keyword(s):  
Machine Learning ◽  
Flood Hazard ◽  
Warning System ◽  
Support Vector ◽  
Flood Prediction ◽  
Loss Of Life ◽  
Related Data ◽  
Dam Management ◽  
Weather Parameters ◽  
The Impact

Flood is one of the most devastating natural calamities affecting parts of the state from past few years. The recurring calamity necessitates an efficient early warning system since anticipation and preparedness play a key role in mitigating the impact. Though heavy and erratic rainfall has been marked as one of the main reasons for flood in several places, flood witnessed by various regions of Kerala was the result of sudden opening of reservoirs indicating poor dam management. The unforeseen flow of water often provided less time for evacuation. Prediction thus plays key role in avoiding loss of life and property, followed by such calamities. The vast benefits and potentials offered by Machine Learning makes it the most promising approach. The developed system is a model by taking Malampuzha Dam as reference. Support Vector Machine (SVM) is used as machine learning method for prediction and is programmed in python. The idea has been to create early flood prediction and warning system by monitoring different weather parameters and dam-related data. The feature vectors include current live storage, current reservoir level, rainfall and relative humidity from the period 2016-2019. Based on the analysis of these parameters, the open/closure of shutters of the dam is predicted. Release of shutters has varied impacts in the nearby regions and is measured by succeeding prediction, by mapping regions on grounds of level warning to be issued. Warning is issued through Flask-based server, by identifying vulnerable areas based on flood hazard reference for regions. The dam status prediction model delivered highest prediction accuracy of 99.14% and associated levels of warning has been generated in the development server, thus preventing unexpected release.

scholarly journals Impact of climatic variables on the population of Trichoderma sp. in the rhizosphere of black pepper

2021 ◽  
Vol 22 (1&2) ◽  
pp. 45-48
Author(s):  
Manju Mohan ◽  
Surendra Gopal
Keyword(s):  
Relative Humidity ◽  
Rhizosphere Soil ◽  
Black Pepper ◽  
Soil Samples ◽  
Climatic Variables ◽  
Monthly Interval ◽  
Trichoderma Sp ◽  
Weather Parameters ◽  
One Year ◽  
The Impact

The present study was undertaken with an objective to determine the impact of climatic variables on the population of Trichoderma sp. in the rhizosphere of black pepper. Rhizosphere soil samples were obtained from pepper plantation at a monthly interval for one year. Trichoderma sp. population were assessed at monthly interval along with weather parameters from  July, 2015  to June,2016. The number of Trichoderma sp. were maximum in July and lowest in June. The correlation between weather parameters on the population of Trichoderma sp. revealed that the population increased with an increase in rainfall and relative humidity, whereas it decreased by an increase in temperature. The results of the present studies showed that climatic variables affect the population of Trichoderma sp. in the rhizosphere of black pepper. However, further studies are needed to confirm it.

scholarly journals Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 282
Author(s):  
Di Wu ◽  
Wanying Zhang ◽  
Heming Jia ◽  
Xin Leng
Keyword(s):  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Search Algorithm ◽  
Rank Correlation ◽  
Population Diversity ◽  
The Other ◽  
Support Vector ◽  
Local Optimum ◽  
Original Algorithm ◽  
Spearman’S Rank Correlation

Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced Chimp Optimization Algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman’s rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search Algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.

scholarly journals Effect of Lockdown amid COVID-19 Pandemic on Weather Parameters of Mid Hill Region of Jammu District of J&K, UT

2020 ◽  
pp. 53-77
Author(s):  
Mahinder Singh ◽  
Vishaw Vikas ◽  
Charu Sharma ◽  
Rohit Sharma
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Soil Temperature ◽  
Rain Gauge ◽  
Mean Value ◽  
Night Temperature ◽  
Hill Region ◽  
Normal Mean ◽  
Weather Parameters ◽  
The Impact

Aim: A study was conducted in mid hill region of Jammu district, J&K to analyze the impact lockdown amid covid-19 pandemic on weather parameters so as to define it as a tool to mitigate the pace of climate change. Methodology: Day and night temperature readings were recorded fortnightly during 22nd March to 10th June 2020 from maximum and minimum thermometer, relative humidity from dry and wet bulb thermometers in stevenson screen, rainfall values from ordinary rain gauge,  evaporation readings from pan evaporimeter and soil temperature at different depth from soil thermometers. Results: After analyzing the data statistically using “Descriptive statistics” in MS-Excel 2010, it was observed that after the implementation of lockdown and with the beginning of unlock down the change in day temperature was -8.07% from normal mean value, night temperature was -4.44% from normal mean value, rainfall pattern was 30.00% more from normal mean value, Relative Humidity (morning) pattern was 6.94% more from normal mean value, relative humidity (evening) pattern was 20.94% more from normal mean value, evaporation pattern was 7.66% more from normal mean value. The average change in soil temperature in morning at 5 cm, 10 cm and 20 cm depth was -3.46%, -3.84% and -7.23% as compared to year 2019 (22nd March to 10th June 2019) mean value and the change in soil temperature in evening at same depths was -7.69%, -6.31% and -4.14% from year 2019 (22nd March to 10th June 2019). Conclusion: With the variable significant pattern observed in almost all parameters, it can be concluded that lockdown might be an effective tool in mitigating pace of climate change in future.

Distant 10 years results of laparoscopic operative treatment in patients with varicocele

Open Medicine ◽  
2009 ◽  
Vol 4 (2) ◽  
pp. 203-207
Author(s):  
Zbigniew Jabłonowski ◽  
Adam Grzegorczyk ◽  
Robert Kȩdzierski ◽  
Eugeniusz Miȩkoś ◽  
Marek Sosnowski
Keyword(s):  
Pregnancy Rate ◽  
Operative Treatment ◽  
Rank Correlation ◽  
Testicular Artery ◽  
Artery Ligation ◽  
Scrotal Pain ◽  
Spearman’S Rank Correlation ◽  
Smoking Addiction ◽  
The Impact ◽  
Varicocele Repair

AbstractVaricocele has been regarded a curable cause of infertility for dozens of years. The impact of varicocele treatment in terms of increase in pregnancy rates is a debated issue. We evaluate data from a 10-year cohort of results from laparoscopic operative treatment of varicocele patients according to pregnancy rate, complication rate, and satisfaction with varicocele repair. Ninety seven patients were treated by means of laparoscopy between 1993 and 1996. Ten years after operation questionnaires were sent to all patients. Answers were obtained from 49/97 pts. (50,5%). Details connected with marital status, pregnancy rate, addictions and scrotal pain discomfort were collected. Statistical analysis was performed using chi-square independence test and Spearman’s rank correlation coefficient. After 10 years, 75.5% who answered the questionnaire were fully satisfied with the results of treatment, 12.3% pts of patients were partially satisfied; 63.3% of patients fathered 1 to 3 children. Married patients and those who fathered children were found to be the most satisfied with the operation. We also found the statistically significant negative dependency between smoking addiction and number of children. There were no major complications intra- or postoperatively. No harmful consequences of testicular artery ligation were found. In conclusion, long-time 10 years follow-up enables better estimation of the results of laparoscopic treatment in patients with varicocele. Pregnancy rate may depend not only on varicocele repair but on smoking addiction as well. There is still no evidence of harmful consequences due to testicular artery ligation after varicocele repair

scholarly journals The impact of the quality of life perception on the self-esteem of physically active adults

2017 ◽  
Vol 39 (1) ◽  
pp. 51
Author(s):  
Daniel Vicentini de Oliveira ◽  
José Roberto Andrade do Nascimento Júnior ◽  
Renan Codonhato ◽  
Thayna Da Silva Zamboni ◽  
Adriele Tarini dos Santos ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Rank Correlation ◽  
Self Esteem ◽  
The Self ◽  
Physically Active ◽  
Spearman’S Rank Correlation ◽  
Spearman’S Rank Correlation Coefficient ◽  
The Impact ◽  
Active Adults

This study aimed at investigating the impact of the quality of life perception on the self-esteem of physically active adults. A total of 63 male and female swimming practitioners (38.13 ± 11.72) were evaluated. A socio-demographic questionnaire, WHOQOL-Bref Scale, and the Rosenberg Self-Esteem Scale were used as tools. For data analysis the descriptive statistics, Kolmogorov-Smirnov Test, Mann-Whitney U Test, Spearman’s Rank Correlation Coefficient, and the Univariate Multiple Regression were used. No significant differences were found either for the quality of life or the self-esteem between sexes; there was a significant positive correlation (p < 0.05) among the physical (r = 0.37), psychological (r = 0.36) and environmental (r = 0.30) domains with self-esteem. The regression model explained 20% of the self-esteem variability, with moderate and significant pathways of the physical (β = 0.23) and psychological (β = 0.23) domains, whereas the environmental domain did not show a significant predictive relation (p = 0.988) with self-esteem. It is concluded that a higher quality of life perception may result in a higher self-esteem for physically active adults. 

scholarly journals The Effect of Pre-Analytical Conditions on Blood Metabolomics in Epidemiological Studies

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 64 ◽  
Author(s):  
Diana Santos Ferreira ◽  
Hannah Maple ◽  
Matt Goodwin ◽  
Judith Brand ◽  
Vikki Yip ◽  
...  
Keyword(s):  
Rank Correlation ◽  
Correlation Coefficients ◽  
Epidemiological Studies ◽  
Processing Conditions ◽  
Linear Regression Models ◽  
Nmr Analysis ◽  
Serum Samples ◽  
Spearman’S Rank Correlation ◽  
Epidemiology Studies ◽  
The Impact

Serum and plasma are commonly used in metabolomic-epidemiology studies. Their metabolome is susceptible to differences in pre-analytical conditions and the impact of this is unclear. Participant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic resonance (NMR) metabolomics platform (n = 151 traits). Correlations and differences in mean of metabolite concentrations were compared between reference (pre-storage: 4 °C, 1.5 h; post-storage: no buffer addition delay or NMR analysis delay) and four pre-storage blood processing conditions, where samples were incubated at (i) 4 °C, 24 h; (ii) 4 °C, 48 h; (iii) 21 °C, 24 h; and (iv) 21 °C, 48 h, before centrifugation; and two post-storage sample processing conditions in which samples thawed overnight (i) then left for 24 h before addition of sodium buffer followed by immediate NMR analysis; and (ii) addition of sodium buffer, then left for 24 h before NMR profiling. We used multilevel linear regression models and Spearman’s rank correlation coefficients to analyse the data. Most metabolic traits had high rank correlation and minimal differences in mean concentrations between samples subjected to reference and the different conditions tested, that may commonly occur in studies. However, glycolysis metabolites, histidine, acetate and diacylglycerol concentrations may be compromised and this could bias results in association/causal analyses.

scholarly journals Assessment of Human Productivity Drivers for Construction Labor through Importance Rating and Risk Mapping

2020 ◽  
Vol 12 (20) ◽  
pp. 8614
Author(s):  
Murat Gunduz ◽  
Abdulrahman Abu-Hijleh
Keyword(s):  
Labor Productivity ◽  
Construction Projects ◽  
Rank Correlation ◽  
Weather Conditions ◽  
Risk Mapping ◽  
Importance Rating ◽  
Factors Affecting ◽  
Importance Index ◽  
The Impact ◽  
Productivity Factors

Labor constitutes a significant portion of the overall cost of a construction project, where labor productivity is often the main driver of the cost. Although studies on labor productivity factors exist, their frequency of occurrence in terms of their ranking remains unexplored. This study differs from other studies in the literature by introducing the frequency component to the productivity factors, a more realistic ranking of the factors by adjusting the importance by frequency (frequency adjusted importance index) and risk mapping of the factors. Moreover, this study is the first to apply risk mapping on labor productivity drivers. The aim of this paper is to identify the project factors affecting the labor productivity in construction projects and to rank these factors considering the perception of the industry on project performance. A literature review of past relevant studies was performed to identify and draft a list of factors affecting labor productivity in construction projects. Thirty-seven labor productivity factors were presented in a questionnaire to investigate the impact and frequency of their occurrence in construction projects. A 9-point scale structured questionnaire was constructed to measure the importance and the frequency of the factors and to evaluate the ranking for different categories. The frequency adjusted importance index (FAII), Spearman’s rank correlation, and risk mapping were used to study and analyze the 105 completed responses. The participants rated the following factors as the five most significant labor productivity-influencing factors: (1) poor labor supervision, (2) delays in payments, (3) poor work environment, (4) lowly skilled labor, and (5) bad weather conditions.

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