The Mineral Biochar Alters the Biochemical and Microbial Properties of the Soil and the Grain Yield of Hordeum vulgare L. under Drought Stress

Biochar improves soil physical, biochemical, and microbial properties, leading to the amelioration of soil fertility, which, in turn, results in better growth and yield in crop plants. The current study aimed to evaluate whether using different levels of biochar can enhance soil characteristics and plant attributes. Accordingly, an experimental study was conducted in 2022 using a randomized complete block design with four replications (n = 4) in the experimental glasshouse of the University of Zanjan, in which two regimes of irrigation (D0, full irrigation as the control; D1, water scarcity was applied immediately after the flowering stage for two weeks) and four levels of natural mineral biochar (0% as the control treatment, 0.25, 0.5, and 1% of soil weight) were applied. The results indicated that drought substantially decreased the organic carbon content of the soil and the grain yield while increasing the available phosphorous, soil carbohydrate content, and microbial biomass of the soil. Biochar could considerably alter the means of the studied soil quality parameters and the barley grain yield. Adding biochar could be considered a valid strategy to increase the resistance of plants to drought

TEHRAN AIR POLLUTION MODELING USING LONG-SHORT TERM MEMORY ALGORITHM: AN UNCERTAINTY ANALYSIS

Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 μm or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 ± 9.8 μg/m3) and predicted data (56.6 ± 13.3 μg/m3) compared with those of the IDW in the test (47.7 ± 22.43 μg/m3) and predicted set (62.18 ± 26.1 μg/m3). However, in PIs, IDW ([0, 0.7] μg/m3) compared with the OK ([0, 0.5] μg/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 μg/m3 and a standard deviation of 9.8 μg/m3 and PIs between [2.7 ± 4.8, 14.9 ± 15] μg/m3

GROUNDWATER LEVEL PREDICTION USING DEEP RECURRENT NEURAL NETWORKS AND UNCERTAINTY ASSESSMENT

Groundwater is one of the most important sources of regional water supply for humans. In recent years, several factors have contributed to a significant decline in groundwater levels (GWL) in certain regions. As a result of climate change, such as temperature increase, rainfall decrease, and changes in relative humidity, it is necessary to investigate and model the effects of these factors on GWL. Although a number of researches have been conducted on GWL modeling with machine learning (ML) and deep learning (DL) algorithms, only a limited number of studies have reported model uncertainty. In this paper, GWL modeling of some piezometric wells has been conducted by considering the effects of the meteorological parameters with Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The models were trained on one piezometric well data and predictions were executed on six other wells. To perform an uncertainty assessment, the models were run 10 times and their means were calculated. Subsequently, their standard deviations were considered to evaluate the outcomes. In addition, the prediction power of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and R-Squared (R2). Finally, for all the six wells that did not participate in the training phase, the prediction functions of the trained models were run 10 times and their accuracy was assessed. The results indicate that LSTM (R2=95.6895, RMSE=0.4744 m, NRMSE=0.0558, MAE=0.3383 m) had a better performance compared to that of GRU (R2=95.2433, RMSE=0.4984 m, NRMSE=0.0586, MAE=0.3658 m) on the GWL modeling

The burden of disease and injury in Iran 2003

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to estimate the burden of disease and injury in Iran for the year 2003, using Disability-Adjusted Life Years (DALYs) at the national level and for six selected provinces.</p> <p>Methods</p> <p>Methods developed by the World Health Organization for National Burden of Disease (NBD) studies were applied to estimate disease and injury incidence for the calculation of Years of Life Lost due to premature mortality (YLL), Years Lived with Disability (YLD), and DALYs. The following adjustments of the NBD methodology were made in this study: a revised list with 213 disease and injury causes, development of new and more specific disease modeling templates for cancers and injuries, and adjustment for dependent comorbidity. We compared the results with World Health Organization (WHO) estimates for Eastern Mediterranean Region, sub-region B in 2002.</p> <p>Results</p> <p>We estimated that in the year 2003, there were 21,572 DALYs due to all diseases and injuries per 100,000 Iranian people of all ages and both sexes. From this total number of DALYs, 62% were due to disability premature deaths (YLD) and 38% were due to premature deaths (YLL); 58% were due to noncommunicable diseases, 28% – to injuries, and 14% – to communicable, maternal, perinatal, and nutritional conditions. Fifty-three percent of the total number of 14.349 million DALYs in Iran were in males, with 36.5% of the total due to intentional and unintentional injuries, 15% due to mental and behavioral disorders, and 10% due to circulatory system diseases; and 47% of DALYs were in females, with 18% of the total due to mental and behavioral disorders, 18% due to intentional and unintentional injuries, and 12% due to circulatory system diseases. The disease and injury causes leading to the highest number of DALYs in males were road traffic accidents (1.071 million), natural disasters (548 thousand), opioid use (510 thousand), and ischemic heart disease (434 thousand). The leading causes of DALYs in females were ischemic heart disease (438 thousand), major depressive disorder (420 thousand), natural disasters (419 thousand), and road traffic accidents (235 thousand). The burden of disease at the province level showed marked variability. DALY estimates by Iran's NBD study were higher than those for EMR-B by WHO.</p> <p>Conclusion</p> <p>The health and disease profile in Iran has made the transition from the dominance of communicable diseases to that of noncommunicable diseases and road traffic injuries. NBD results are to be used in health program planning, research, and resource allocation and generation policies and practices.</p

Tehran Air Pollution Modeling Using Long-Short Term Memory Algorithm: An Uncertainty Analysis

Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 μm or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 ± 9.8 μg/m3) and predicted data (56.6 ± 13.3 μg/m3) compared with those of the IDW in the test (47.7 ± 22.43 μg/m3) and predicted set (62.18 ± 26.1 μg/m3). However, in PIs, IDW ([0, 0.7] μg/m3) compared with the OK ([0, 0.5] μg/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 μg/m3 and a standard deviation of 9.8 μg/m3 and PIs between [2.7 ± 4.8, 14.9 ± 15] μg/m3

Algebraic Hyper-Structures Associated to Nash Equilibrium Point and Applications

In this paper, we generalize some concepts of the game theory such as Nash equilibrium point, saddle point and existence theorems on hyper-structures. Based on new definitions and theorems, we obtain some important results in the game theory. A few suitable examples have been given for better understanding.</p

Algebraic Hyper-Structures Associated to Nash Equilibrium Point and Applications

In this paper, we generalize some concepts of the game theory such as Nash equilibrium point, saddle point and existence theorems on hyper-structures. Based on new definitions and theorems, we obtain some important results in the game theory. A few suitable examples have been given for better understanding.</p

EARTHQUAKE VULNERABILITY ASSESSMENT FOR HOSPITAL BUILDINGS USING A GIS-BASED GROUP MULTI CRITERIA DECISION MAKING APPROACH: A CASE STUDY OF TEHRAN, IRAN

Nowadays, urban areas are threatened by a number of natural hazards such as flood, landslide and earthquake. They can cause huge damages to buildings and human beings which necessitates disaster mitigation and preparation. One of the most important steps in disaster management is to understand all impacts and effects of disaster on urban facilities. Given that hospitals take care of vulnerable people reaction of hospital buildings against earthquake is vital. In this research, the vulnerability of hospital buildings against earthquake is analysed. The vulnerability of buildings is related to a number of criteria including age of building, number of floors, the quality of materials and intensity of the earthquake. Therefore, the problem of seismic vulnerability assessment is a multi-criteria assessment problem and multi criteria decision making methods can be used to address the problem. In this paper a group multi criteria decision making model is applied because using only one expert’s judgments can cause biased vulnerability maps. Sugeno integral which is able to take into account the interaction among criteria is employed to assess the vulnerability degree of buildings. Fuzzy capacities which are similar to layer weights in weighted linear averaging operator are calculated using particle swarm optimization. Then, calculated fuzzy capacities are included into the model to compute a vulnerability degree for each hospital

I‌N‌V‌E‌S‌T‌I‌G‌A‌T‌I‌O‌N O‌F T‌H‌E B‌E‌H‌A‌V‌I‌O‌R O‌F S‌T‌A‌B‌I‌L‌I‌Z‌E‌D S‌I‌L‌T‌Y S‌A‌N‌D W‌I‌T‌H C‌O‌L‌L‌O‌I‌D‌A‌L S‌I‌L‌I‌C‌A A‌G‌A‌I‌N‌S‌T L‌I‌Q‌U‌E‌F‌A‌C‌T‌I‌O‌N

Chemical injection is a useful geotechnical procedure for stabilizing soils and also making them impermeable. For characterizing the soil stabilized by colloidal silica, the clean and silty sand samples with silt values of 20 and 40 percent, in both unstabilized and stabilized conditions, were prepared with different concentrations of the stabilizer from 5 to 30 percent, and the effects of colloidal silica on its behavior were evaluated under cyclic loading. 30 cyclic triaxial tests were performed on various samples. A 100 kPa confining pressure was used in these tests. All 15 experimental samples were loaded at cyclic stress ratios of 0.2 and 0.4 with a frequency of 1 Hz and up to 200 sinusoidal cycles. In this research, for the stabilized and unstabilized samples, the double amplitude of axial strain of five percent or the pore water pressure ratio of one, whichever occurs earlier, was considered as the liquefaction criterion. By performing cyclic triaxial tests, it was observed that by stabilizing clean and silty sand with colloidal silica, liquefaction phenomenon is postponed. Thus, adding even low concentration of colloidal silica such as 5 percent can prevent liquefaction of soil at the low level of dynamic loads (such as cyclic stress of 0.2). By adding colloidal silica, the double amplitude of axial strain and the pore water pressure ratio were reduced in cyclic loading. For example, in silty sand with a silt content of 40%, by increasing the stabilizing concentration from 10% to 30%, the pore water pressure ratio reduced from 1 (the state of full liquefaction) in 10 cycles to about 0.1 in 100 cycles, and also the double amplitude of axial strain decreased from 5% in 10 cycles to about 0.7% in 100 loading cycles. Gelatinization of colloidal silica between soil grains causes elastic behavior for the soil sample and prevents permanent deformation between soil grains. Reducing permanent deformation in undrained conditions reduces the development of excess pore water pressure in the soil during cyclic loading. The choice of colloidal silica concentration to prevent the liquefaction of sand and silty sand in a specific area depends on the cyclic stress ratio in that area; thus, at a cyclic stress ratio of 0.2, colloidal silica concentration of five percent is sufficient. However, at a cyclic stress ratio of 0.4 (higher level of dynamic loads), colloidal silica with a concentration of 20% or more should be used