Search

This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion. The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data cubes. Independent variables including altitude, slope, and distance from barren, crop, greenery, roads, and urban areas for 1998, 2008, and 2018 were considered as covariates for the simulation of built-up land expansion in Tehran and Karaj regions in Iran. The proposed method was compared with the random forest (RF) algorithm as the baseline model. Evaluation using the area under the total operating characteristic indicated the superiority of our modified U-Net (0.87) over the RF (0.82) algorithm. Furthermore, evaluation using the percent correct metric indicated that our proposed model is capable of learning neighborhood effects effectively leading to simulate built-up land expansion accurately, independent from applying a cellular automata (CA) model. Therefore, the modified U-Net independent from the CA which can consider the neighborhood effects is recommended for the simulation of built-up land expansion precisely. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic posed significant risks to children worldwide. This study aimed to assess the COVID-19 protection status of children and explored the relationship between household socio-economic status and COVID-19 morbidity and preventive measures, including vaccination and mask-wearing, in two cities in Iran. Method: A population-based cross-sectional study was conducted from July to October 2022 among 7 to 18-year-old children and their families in Tehran and Karaj. A total of 3,022 samples were selected using stratified multistage cluster sampling. Data were collected through interviews with children and adults, using questionnaires and was analyzed with Stata software version 14. Results: The analysis focused on 2,878 children with a median age of 12. Over half (54%) reported that the pandemic negatively affected their family's financial status, with 45% describing its impact on children's needs as negative or very negative. Just under 50% of respondents consistently wore masks during the study period, and around 54% had received at least one dose of the COVID-19 vaccine. Reasons for not getting vaccinated included concerns about side effects, ineligibility for the target age group, and overcrowding at vaccination sites. The odds of not getting vaccinated were significantly lower for children aged 15–18, with boys more likely to refuse vaccination than girls. Conclusion: The financial impact of the pandemic in Iran affected families' ability to meet their children's needs. Moreover, low vaccination acceptance rates increased children's vulnerability to health problems and contributed to COVID-19 infections. Efforts should be made to increase vaccination acceptance, particularly among immigrant populations. [ABSTRACT FROM AUTHOR]

In this survey, the symptomatic leaves of Clivia miniata were collected from a greenhouse in Karaj city of Iran. The isolation and morphological investigation showed Scytalidium-like fungus associated with leaf blight symptom. The phylogenetic analysis of the internal transcribed spacer along with partial sequences of rDNA large subunit and translation elongation factor 1-α (tef-1α) genomic regions confirmed the identification of the recovered isolate as Neoscytalidium dimidiatum. The pycnidial morph of the fungus didn't observe both in vitro and in vivo. The pathogenicity test on C. miniata and C. nobilis was also conducted to fulfill the Koch's postulates. To our Knowledges, this is the first report of N. dimidiatum causing leaf blight disease on C. miniata and C. nobilis worldwide, as well as these host plants are new for N. dimidiatum in the world. [ABSTRACT FROM AUTHOR]

Groundwater resources play a crucial role in most arid/semi-arid regions such as Karaj plain, Iran. Excavation of wells and exploiting water resources of aquifers have long been known as ordinary solutions to supply water demands for drinking, agricultural and industrial purposes. In many agricultural areas such as the above-mentioned region, extraction wells have been utilized for both drinking and agricultural consumptions, while measures taken for water quality monitoring and protecting public health are seriously limited. On the other hand, most of the shared extraction wells in the region used for drinking purpose have been located near the agricultural lands and they are highly under the risk of getting polluted by Agricultural pesticides. The current paper firstly intends to demonstrate the results obtained from Drinking water Quality Index (DWQI) as well as Irrigation Water Quality Index (IWQI) and secondly determines probable conflicts that may be aroused in ranking of water wells using these two methods Subsequently, Multi Criteria Decision Making (MCDM) techniques such as Ordered Weighted Averaging (OWA), Compromise Programing (CP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were employed to decrease effects of the conflicts. It was clarified that MCDMs, to some extent, alleviated contradictions in wells’ ranks −determined by DWQI/IWQI- and authenticated this procedure as an appropriate method for water quality ranking in agricultural societies. [ABSTRACT FROM AUTHOR]

The present study aims at predicting future land use/land cover (LULC) and quantifying and mapping the ecosystem services (ESs) of water yield, outdoor recreation opportunity and food production in current (here, 2017) and future landscapes in Northern Iran, using the InVEST, Recreation Opportunity Spectrum (ROS) and yield models. To that end, two LULC scenarios known as business as usual (BAU) and protection-based (PB) plan were applied for 2028, using the Markov Artificial Neural Network and Multi-objective land allocation (MOLA) models. The results show that rapid urbanisation, caused by the expansion of human settlements and industrial areas, has led to a decline in the ESs in the region. Compared to the ESs in 2017, the service of water yield increases as urban expansion increases, whereas food production and recreation services decrease as urban expansion increases, under the BAU scenario. On the other hand, in the PB scenario, relatively better conditions can be observed for all three ESs. Considering that the ecological structures of this region have been severely affected by rapid urban expansion, the results of this research will be useful for maintaining the existing ESs and can greatly affect planning and decision-making regarding future development towards urban sustainability. [ABSTRACT FROM AUTHOR]

Studies of the estimation of reference evapotranspiration (ET0) in Iran are mostly related to areas with humid and semiarid climates and less related to arid areas. On the other hand, few studies in arid regions have reported high root-mean square error (RMSE) values. However, these regions make an important contribution to agricultural production, and thus, water management of these regions is crucial. It motivated the implementation of such a study in an arid environment of Karaj, Iran, as a case study, in order to estimate daily ET0 with as much accuracy as possible. To achieve this purpose, the performance of 21 known models estimating ET0 , including 9 empirical models and 12 machine learning (ML) models, were evaluated. The method provided by the food and agriculture organization (FAO) known as FAO-56 Penman-Monteith (FPM) was regarded as the main reference method for measuring ET0. A new climate data set related to the period of 2005–2020 (April–September) was used to calibrate and cross-validate models. In fact, this study intended to develop an approach for simulating day-to-day variations in ET0 in arid environments by benefitting minimal weather data (i.e., temperature, humidity, and wind speed) for practical purposes because most regions suffer from a lack of weather data, especially radiation data. Therefore, the performance of the best models calibrated in Karaj station was also validated based on data recorded in a research field with a similar climate during 2020–2021. The cross-validated results showed that the deep-learning (DL) model had the lowest RMSE as well as the highest R2 compared with other models. As averaged over all months in Karaj, the DL model exhibited a RMSE 64% less than the best-calibrated empirical model (i.e., Valiantzas-VTS), which is a solar radiation–based model. Furthermore, the improvements arising from using the DL model were more considerable in the extremes than in the middle values. The findings of the field research also demonstrated that the approach developed in the current study would be beneficial at locations other than the calibration station. This approach requires few inputs, is independent of solar radiation data, and also has the lowest RMSE compared with that of other studies. For future studies, the combination of the daily ET0 estimator developed in this study with a recently developed empirical approach to estimate crop coefficients is suggested to manage irrigation on croplands if arid regions. [ABSTRACT FROM AUTHOR]

Larvae of Charletonia nazeleae Karimi Iravanlou, Kamali & Talebi were previously collected from Karaj, Alborz province, Iran, parasitizing two different acridid grasshopper species. Here, we present new morphological and meristic data based on collecting new mite specimens during surveys to Zagros Mountains, Fars province, Iran and a correction of the previous description by reconsidering the type specimen and augmenting the original data. Also, two new hosts for this species are identified. [ABSTRACT FROM AUTHOR]

This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. [Display omitted] • A Forward Feature Selection - Random Forest (FFS-RF) model describes urban growth. • FFS-RF found temporal non-stationarity of drivers in Iran's Tehran-Karaj region. • The Total Operating Characteristic evaluated the transition index map (TIM). • FFS-RF produced a TIM that has predictive power no better than a baseline TIM. • FFS-RF and Cellular Automata extrapolated urban growth to the year 2060. [ABSTRACT FROM AUTHOR]

TBM has been used extensively in civil engineering activities and plays an important role in tunnelling projects. Hence, the penetration rate of these machines plays a crucial role in the success of their application. Therefore, in order to predict the TBM penetration rate in this study in several water conveyance tunnels including the tunnels of Karaj, Ghomrood, Golab, Nosoud and Sabzkooh, four intelligence techniques including multiple linear and nonlinear regression analysis, Gene Expression Programming (GEP) method, and Support Vector Machine (SVM) were applied. The obtained values of R² and RMSE included 0.43 and 3.08 for linear regression, 0.68 and 2.3 for nonlinear regression, 0.74 and 2.09 for GEP method and 0.97 and 0.6 for SVM method, respectively which were utilized to predict TBM penetration rate. By investigating the tunnels database, the results indicated that the SVM method had the most accurate prediction of penetration rate (in terms of R² and RMSE) and the maximum amount of R² and the minimum amount of RMSE among all predictive modelings. Finally, respecting the amount of R² and RMSE, the other methods like GEP method, nonlinear regression, and linear regression are listed to have the required accuracy in predicting penetration rate. [ABSTRACT FROM AUTHOR]

Vegetative and reproductive characteristics, fruit yield, and biochemical compounds of six bitter melon cultivars (Iranshahr, Mestisa, No. 486, Local Japanese, Isfahan, and Ilocano) were evaluated under Karaj conditions in Iran. The phytochemical properties of the cultivars were evaluated using both shade-dried and freeze-dried samples at three fruit developmental stages (unripe, semi-ripe, and ripe). There were significant differences in the vegetative and reproductive characteristics among cultivars, where cv. No. 486 was superior to most vegetative attributes. The fruit yield of cultivars varied from 2.98–5.22 kg/plant. The number of days to male and female flower appearance ranged from 19.00–25.33 and from 25–33 days, respectively. The leaf charantin content was in the range of 4.83–11.08 μg/g. Fruit charantin content varied with developmental stage, drying method, and cultivar. The highest charantin content (13.84 ± 3.55 µg/g) was observed at the semi-ripe fruit stage, and it was much higher in the freeze-dried samples than the shade-dried samples. Cultivar No. 486 had the highest (15.43 ± 2.4 µg/g) charantin content, whereas the lowest charantin content (8.51 ± 1.15 µg/g) was recorded in cultivar cv. Local Japanese. The highest total phenol content (25.17 ± 2.27 mg GAE/g) was recorded in freeze-dried samples of ripe fruits of cv. No. 486, whereas the lowest phenol content was detected in the shade-dried samples of semi-ripe fruits of Isfahan. cv. Flavonoid content was higher with the shade-drying method, irrespective of cultivar. In conclusion, considering the fruit yield and active biological compounds in the studied cultivars, cv. No. 486 should be grown commercially because of its higher yield and production of other secondary metabolites. [ABSTRACT FROM AUTHOR]