algorithm

The existence of intelligent structures for the passive use of energy-regulating systems for energy consumption of buildings in today's era to optimize energy consumption, is one of the main concerns of users of existing buildings. Patterning of kinetic systems in nature is one of the best ways to reduce energy consumption in passive systems that will have the highest efficiency with the least amount of energy consumption. The aim of this research is modeling movable shading device that is compatible with Shiraz climate to optimize energy consumption. Sunflower has been selected as an inspiration source for this research and the plants movement mechanism has been analyzed.

Material & Methodlogy: 

The method of the study is simulation-modeling and kinetic facade performed parametrically in the Grasshopper and analysis of the objectives using the Ladybug plugins. The south facades shading device of a building in the Shiraz climate has been analyzed.   

Due to the use of intelligent materials, the lack of dynamic system in shading device movement, in addition to reduce energy consumption, the amount of radiation receiving during launch hours can be reduced and increased during radiation hours. In research, in addition to analyzing the structure of the shading device during radiation hours, the amount of sunlight entering the window sill was also examined to determine the optimal opening of the shading device during launch hours.

Modeled shading device can be used as a kinetic external sun shading device that can optimize energy consumption compatible with Shiraz climate.

Solar radiation is very important as one of the important variables in energy balance models and plant growth simulation. Although the measurement of this variable has a relatively long history in Iran, due to the high costs of measuring devices, there is no pyranometer in many existing stations in the country, and there are problems such as its recalibration, water, and dust accumulation that exists on the sensor. Even in meteorological stations that measure radiation, there are days when radiation data is not recorded or unrealistic values outside the expected range are observed due to equipment violations or other problems.This research investigated the performance of nine machine learning algorithms including linear regression (LR), Least Absolute Shrinkage and Selection Operator (Lasso), Elastic Net (EN), K-Nearest Neighbors (kNN), Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), Extra Trees (ET) and Gradient Boosting Machine (GBM) to estimate solar radiation in Yazd synoptic station between 2005 and 2021 with cross-validation method (kfold). The parameters of average temperature, minimum temperature, maximum temperature, sunny hours, relative humidity, and solar radiation are obtained from the National Meteorological Organization on a daily basis and extraterrestrial radiation variables, relative distance from the earth to the sun, solar inclination angle, and maximum sunny hours are calculated with existing relationships and were selected as input for the prediction models. The evaluation criteria for solar radiation estimation were MSE (Mean Square Error), MAPE (Mean Absolute Error), and determination Coefficient (R2).The results showed the coefficient of determination (R2) varies between 0.716 and 0.870 depending on the algorithm used in the training phase. In other words, in terms of the determination coefficient, all the used algorithms showed good results for predicting solar radiation. According to the results of all three criteria, it can be seen that the Support Vector Regression (SVR) algorithm has performed better than other algorithms. After the support vector regression (SVR) algorithm, the linear regression (LR) algorithm was ranked next with the MAPE of 5.04, the MSE of 1.13, and the R2 of 0.867. Also, the elastic pure regression algorithm (EN) with the highest mean absolute value of error (MAPE), the highest mean squared error (MSE), and the lowest coefficient of explanation (R2) ranked last among the nine used algorithms. After the data training phase, using the K-fold cross-validation method, the remaining 20% of the data were tested. As the results show, the MSE changes for nine machine learning algorithms are high and vary from about 3 to 9 Mj/m2/day. Its highest value was observed in the DT algorithm and its lowest value was observed in the support vector algorithm. The average value of the absolute value of the error was also in the range of 0.8 to 2.2 Mj/m2/day, and also the values of the R2 were different in the range of 0.7 to 0.9. In general, and according to the results of all three evaluation criteria, the support vector machine algorithm showed the best results in the data test stage as well as in the training stage.

Nowadays, based on the reports of the International Panel Climate Change (IPCC) there is no doubt that climate change has been occurring. All ecosystems on the earth have been concerned by the effects of climate change. Freshwater communities are particularly more vulnerable to the climate change because freshwaters are also exposed to numerous anthropogenic stressors such as hydrological, morphological, connectivity and, water quality pressures. The main objective of this study is to determine the effects of climate change on the Salmo trutta distribution under optimistic and pessimistic scenarios of 2050 and 2080. For this purpose, Species Distribution Modelling (SDM) method was used. For this purpose, data related to fish observation as well as environmental variables like elevation, slope, maximum air temperature, range temperature, precipitation and, maximum width were collected. Then, different models including GLM, GAM, GBM, RF, CTA, FDA, MARS, ANN and SRE as well as the Ensemble model (in order to reduce the uncertainty), were used to predict the potential distributions of considered species at the scale of the Lake Urmia basin and Iran. The results showed that Salmo trutta populations will decline sharply in the optimistic scenario in 2050. Whilst, in a similar scenario, populations of this species will disappear in 2080. In addition the populations of Salmo trutta would become extinct in the pessimistic scenario, including two-time scales in 2050 and 2080.

In August 2015 a distinguished collection of numerical analysts gathered at Oxford to celebrate the sixtieth birthday of Nick Trefethen FRS and consider the future of numerical analysis. Some of the plenary speakers provided short essays for Notices.

Assessment and controlling groundwater quality have an important role in planning and developing water resources. Therefore, the use of an efficient method can greatly increase accuracy and reduce costs in this field. In this study, 6 optimization algorithms Consist of Particle swarm optimization (PSO), Genetic algorithm (GA), Imperialist competitive algorithm (ICA), Fireflies algorithm(FA), Cultural algorithm(CA) and covariance matrix adaptation evolution strategy- Evolution strategies (CMA-ES) were used to train and optimize the parameters of the neural-fuzzy inference system model (ANFIS) to model the groundwater quality of Bafgh plain in Yazd province. At first, to select the best combination of input for an estimate of the electrical conductivity (EC), sodium adsorption (SAR) and total hardness (TH), Pearson and Spearman's methods were used to analyze the sensitivity and correlation of other parameters. then qualitative modeling is done with hybrid methods and the performance of the models was measured by correlation coefficients (R2), root mean square error (RMSE), and Nash-Sutcliffe Efficiency (NSE). The results showed that all six combined methods showed a very good performance in modeling groundwater parameters. Also, the ANFIS-FA model was one of the best models in all three modeling parts. So that the value of R2, RMSE and NSE for the test part in TH was, 0.99, 0.41, 0.99, for SAR, 0.98, 1.11,0.95 and for EC 0.99, 305.7 and 0.99. Other methods have also succeeded in modeling and predicting the desired parameters with proper precision. According to the accuracy of the calculations, these methods are suitable alternatives for the prediction of groundwater quality variables.

Dual-energy X-ray radiography is an important inspection technology in imaging large cargos for finding illegal materials such as explosives, narcotics, weapons, etc. A useful aspect of dual-energy X-ray radiography is the possibility of discriminating and identifying materials with different atomic numbers which can be obtained by imaging large cargos at two different X-ray energies (normally above 3 MeV). In the present work, first a dual-energy X-ray radiography system including X-ray sources, collimator, and detector array was simulated using MCNPX code. Then, the obtained data from this radiography system for four step wedges of graphite, aluminum, steel, and lead materials were gathered and material discrimination curves were obtained using four algorithms including log-log, R-T, r-θ, and α2-α1. The results show that among the proposed algorithms for material discrimination, R-T, r-θ, and α2-α1 algorithms have better performance and due to the less overlap of material discrimination curves for low sample thicknesses, R-T algorithm can be a better choice for obtaining material discrimination curves in dual-energy radiography systems.