Spatial analysis of myocardial infarction in Iran: National report from the Iranian myocardial infarction registry

Background: Myocardial infarction (MI) is a leading cause of mortality and morbidity in Iran. No spatial analysis of MI has been conducted to date. The present study was conducted to determine the pattern of MI incidence and to identify the associated factors in Iran by province. Materials and Methods: This study has two parts. One part is prospective and hospital-based, and the other part is an ecological study. In this study, the data of 20,750 new MI cases registered in Iranian Myocardial Infarction Registry in 2012 were used. For spatial analysis in global and local, spatial autocorrelation, Moran's I, Getis-Ord, and logistic regression models were used. Data were analyzed by Stata software and ArcGIS 9.3. Results: Based on autocorrelation coefficient, a specific pattern was observed in the distribution of MI incidence in different provinces (Moran's I:0.75, P < 0.001). Spatial pattern of incidence was approximately the same in men and women. MI incidence was clustering in six provinces (North Khorasan, Yazd, Kerman, Semnan, Golestan, and Mazandaran). Out of the associated factors with clustered MI in six provinces, temperature, humidity, hypertension, smoking, and body mass index (BMI) could be mentioned. Hypertension, smoking, and BMI contributed to clustering with, respectively, 2.36, 1.31, and 1.31 odds ratio. Conclusion: Addressing the place-based pattern of incidence and clarifying their epidemiologic dimension, including spatial analysis, has not yet been implemented in Iran. Report on MI incidence rate by place and formal borders is useful and is used in the planning and prioritization in different levels of health system

Spatial Analysis of Irans’ Rural Settlement

The analysis of the pattern of the arrangement of settlements in the Iran It is effective in creating and developing population centers. This research in the area of spatial planning and relying on quantitative-analytical method, has analyzed the pattern of spatial arrangement of rural settlements based on environmental factors.Therefore, it analyzes the spatial pattern of rural settlements. Morphology of the space of rural settlements in Iran according to environmental factors such as; Elevation, slope, aspect, convex and concave surfaces, land surface temperature (LST), precipitation, relative humidity have been analyzed. And by analyzing and combining these factors, raster data models and new concepts have been invented and explained. After thresholding and coding the raster data model, the spatial relationships between them have been processed and Iran's Geomorphic systems have been extracted. Then, the pattern of spatial syntax of rural settlements and the potential of environmental civilization in each of the Geomorphic systems and subsystems have been analyzed and studied. Also, the potential of environmental civilization with morphological components including Elevation, slope and aspect was calculated and analyzed. The results of this study show that the pattern of spatial syntax of rural settlements has been in interaction with Geomorphic systems and morphological components. Also, the analysis of the land spatial analysis can play an important and effective role in achieving the strategic goals of the Iranian residential system, within the framework of the principles of fundamental spatial planning

Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches

© 2019 The Author(s). Background: Recent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT to eradicate leptospirosis, it remains a public health problem in this province. Modelling and Prediction of this disease may play an important role in reduction of the prevalence. Methods: This study aims to model and predict the spatial distribution of leptospirosis utilizing Geographically Weighted Regression (GWR), Generalized Linear Model (GLM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) as capable approaches. Five environmental parameters of precipitation, temperature, humidity, elevation and vegetation are used for modelling and predicting of the disease. Data of 2009 and 2010 are used for training, and 2011 for testing and evaluating the models. Results: Results indicate that utilized approaches in this study can model and predict leptospirosis with high significance level. To evaluate the efficiency of the approaches, MSE (GWR = 0.050, SVM = 0.137, GLM = 0.118 and ANN = 0.137), MAE (0.012, 0.063, 0.052 and 0.063), MRE (0.011, 0.018, 0.017 and 0.018) and R2 (0.85, 0.80, 0.78 and 0.75) are used. Conclusion: Results indicate the practical usefulness of approaches for spatial modelling and predicting leptospirosis. The efficiency of models is as follow: GWR > SVM > GLM > ANN. In addition, temperature and humidity are investigated as the most influential parameters. Moreover, the suitable habitat of leptospirosis is mostly within the central rural districts of the province

The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential

Hywind-Scotland is a wind farm in Scotland that for many reasons is at the leading edge of technology and is located at a paradigmatic study area for offshore wind energy assessment. The objective of this paper is to compute the Capacity Factor ( CF ) changes and instantaneous power generation changes due to seasonal and hourly fluctuations in air density. For that reason, the novel ERA5 reanalysis is used as a source of temperature, pressure, and wind speed data. Seasonal results for winter show that CF values increase by 3% due to low temperatures and denser air, with economical profit consequences of tens of thousands (US$). Hourly results show variations of 7% in air density and of 26% in power generation via FAST simulations, emphasizing the need to include air density in short-term wind energy studying.This work was financially supported by the Spanish Government through the MINECO project CGL2016-76561-R, (MINECO/ERDF, UE) and the University of the Basque Country (UPV/EHU, GIU 17/002). ERA5 hindcast data were downloaded at no cost from the Copernicus Climate Data Store. All the calculations and plots were made using R: https://www.r-project.org

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Spatio-Temporal Variations of Reference Evapotranspiration in Western Iran

Optimal management of water resources requires accurate determination of water balance components in each region and Evapotranspiration is one of the most important components of water balance. The purpose of this study was to investigate the spatiotemporal variability of reference evapotranspiration in Lorestan province- western Iran country using the Man-Kendall test and GIS then assess the effect of different climatic parameters on ET0 using multivariate regression. Lorestan province with a 28064 km area in western Iran has 9 synoptic stations including Khorramabad, Boroujerd, Aligouderz, Azna, Doroud, Koohdasht, Poldokhter, Aleshatar, and Noorabad. In this study, meteorological data were used for 9 synoptic stations of the Lorestan in a period from 2001 to 2017. The results showed that at most of the stations and most months, the changing trend was decreasing. The annual decrease in Azna station with Z=-2.73 at 99% level, and in the stations of Aligodarz, Kohdasht, and Doroud with Z equal to -2.27, -2.35, and -2.2, respectively at 95% was significant. The spatial distribution of ET0 showed that the maximum amount of ET0 occurred in the south of Lorestan Province, and decreased from south to north and west to east of the study area. These results indicate the influence of latitude and altitude on the spatial distribution of ET0. The impact of different parameters showed the greatest effect of maximum temperature and wind speed on ET0

Meteorological Trend Analysis in Western Rajasthan (India) using Geographical Information System and Statistical Techniques

The present study focuses on the long term trends of meteorological parameters like precipitation, temperature, solar radiation, wind direction etc. of Udaipur district, Rajasthan which is mainly located in semi-arid zone in India. Meteorological parameters have been taken for this study to find out the annual variability using Mann-Kendall test and Sen’s slope estimator. Yearly long term trend has been identified for thirty one years of data. There are both increasing and decreasing trends of meteorological parameters obtained by this MK test, suggesting overall significant changes in the study area.   Keywords: Trend analysis, Meteorological parameters, Mann-Kendall test, Sen’s slope.

Delineation of dew formation zones in Iran using long-term model simulations and cluster analysis

Dew is a non-conventional source of water that has been gaining interest over the last two decades, especially in arid and semi-arid regions. In this study, we performed a long-term (1979-2018) energy balance model simulation to estimate dew formation potential in Iran aiming to identify dew formation zones and to investigate the impacts of long-term variation in meteorological parameters on dew formation. The annual average of dew occurrence in Iran was similar to 102 d, with the lowest number of dewy days in summer (similar to 7 d) and the highest in winter (similar to 45 d). The average daily dew yield was in the range of 0.03-0.14 Lm(-2) and the maximum was in the range of 0.29-0.52 Lm(-2). Six dew formation zones were identified based on cluster analysis of the time series of the simulated dew yield. The distribution of dew formation zones in Iran was closely aligned with topography and sources of moisture. Therefore, the coastal zones in the north and south of Iran (i.e., Caspian Sea and Oman Sea), showed the highest dew formation potential, with 53 and 34 Lm(-2) yr(-2), whereas the dry interior regions (i.e., central Iran and the Lut Desert), with the average of 12-18 Lm(-2) yr(-2), had the lowest potential for dew formation. Dew yield estimation is very sensitive to the choice of the heat transfer coefficient. The uncertainty analysis of the heat transfer coefficient using eight different parameterizations revealed that the parameterization used in this study the Richards (2004) formulation - gives estimates that are similar to the average of all methods and are neither much lower nor much higher than the majority of other parameterizations and the largest differences occur for the very low values of daily dew yield. Trend analysis results revealed a significant (p < 0:05) negative trend in the yearly dew yield in most parts of Iran during the last 4 decades (1979-2018). Such a negative trend in dew formation is likely due to an increase in air temperature and a decrease in relative humidity and cloudiness over the 40 years.Peer reviewe