s. fallahi

Forest roads are essential infrastructures for accessing and managing forest units. Sustainable road management requires accurate spatial information from the road network. Various methods, differing in accuracy, precision, efficiency, and cost, are available for providing the location of roads. Given the remote location of forest roads, the lack of reference stations, potential inaccessibility to online networks, and conditions affecting positioning error, it is necessary to investigate the efficiency of existing positioning methods.

In this research, we studied the accuracy of PPK, RTK, online platforms such as Shamim, GPS, and mobile apps in positioning 34 points on 5 km of forest roads in watershed No. 30 in the Hyrcanian forest. The coordinates were first extended to the study area using the rapid static method. After post-processing and determining reference points, the exact coordinates of all ground control points on the road were obtained through the direct method. Then, the coordinates of all points were measured three times using various methods.

The health survey's statistical results showed that the highest mean differences were 2.53 m and 2.38 m for GPS and mobile apps, respectively. The least mean difference was 0.048 m, 0.049 m, and 0.092 m for PPK, RTK, and Shamim, respectively. The RMSE amount showed that the results from GPS and mobile apps had the least accuracy, and the methods PPK, RTK, and Shamim had the highest. Based on the one-sample T-test and Wilcoxon’s analysis, the amount of differences was not the same within each method through the 34 investigated points. Accuracy analysis indicated that GPS, with a distance mean of 1.14 m, had the least precision, and Shamim and RTK, with 0.0621 m and 0.063 m, were the most precise methods. The analysis of variance showed that the mobile app, Shamim, and PPK had the highest convergence.

The results of this research indicate that the PPK method was the most accurate, and the Shamim method was the most precise. Therefore, these methods can be used for purposes such as optimizing the forest road plan and longitudinal profile that require accurate location information.

COVID-19 has affected a worldwide population, causing more than a million deaths from the end of 2019 until now; so the aim of this study was to determine the symptoms of COVID-19 in the Iranian population through a teleconsultation-based service to better deal with it.

This study was a cross-sectional study that included 12125 individuals calling for COVID-19 screening and consultation from 2 March 2020 to 19 April 2020 with the census method. A telephone number was assigned for consultation with more than 70 nurses responding to first-level questions and more than 30 medical doctors responding to second-level questions. For statistical analysis, a chi-squared test and univariate logistic regression with SPSS 25 were used.

Cough was the most common complaint (41.3%), followed by shortness of breath (32.8%), and fever (31.5%). Confusion was the least common complaint (1.6%). Binary logistic regression revealed that men were at a higher risk of COVID-19 compared to women (OR:1.31, 95% CI 1.10-1.55, p=0.002). In addition, older age was a risk factor for COVID-19 (OR:1.02, 95% CI 1.02-1.03, p<0.001). Also, significant positive correlations were found between fever, chills, sore throat, shortness of breath, cough, body ache, and gastrointestinal symptoms with COVID-19 even after adjustment for gender and age.

Fever, cough, and shortness of breath were the most common complaints in individuals calling for COVID-19 teleconsultation. It’s suggested that in times of crisis, such as the Covid-19 pandemic, remote sensing can be done to raise public awareness and break the transmission chain.