Sharif Digital Repository

Mooring lines may be damaged under severe conditions during the life of an offshore platform. In the event of a mooring failure, it is possible to break remaining mooring lines in a severe condition, which ultimately leads to the total structural failure; Therefore, the post-failure analysis is very important for the mooring system. In this study, the Amir-Kabir semi-submersible platform has been modeled which is installed in 700 m’ depth of the Caspian Sea, and random waves have been generated towards the structure in the form of the JONSWAP wave spectrum according to the conditions of the Caspian Sea. For analysis, four different mooring configurations have been considered. Then, in each... 

Purpose: The purpose of this paper is to estimate long-run elasticities for housing prices in Tehran's (capital of Iran) 20 different zones relative to several explanatory variables available for use such as land price, total substructure area, material price, etc. Moreover, another goal of this paper is to propose a new approach to deal with problems which arise due to a lack of proper data. Design/methodology/approach: The data set is gathered from "The Municipality of Tehran" and "The Central Bank of Islamic Republic of Iran (CBI)". One-way fixed effects and one-way random effects approaches (which are panel data approaches) are applied to model housing price forecasting function in... 

A downscaling model capable of explaining the temporal and spatial variability of regional hydroclimatic variables is essential for reliable climate change studies and impact assessments. This study proposes a novel statistical approach based on generalized hierarchical linear model (GHLM) to downscale precipitation from the outputs of general circulation models (GCMs) at multiple sites. GHLM partitions the total variance of precipitation into within- and between-site variability allowing for transferring information between sites to develop a regional downscaling model. The methodology is demonstrated by downscaling precipitation using the outputs of eight GCMs in Lake Urmia basin in... 

This paper describes the construction of a silver-based LSPR biosensor for endotoxin detection. We used GLAD method to procure reproducible silver nanocolumns. In this work, the silver nanostructures were considerably stabilized by a SAM of MPA, and the limit of detection of biosensor was measured to be 340 pg/ml for endotoxin E. coli. Considering endotoxin B. abortus as the second type of endotoxin contamination in our target samples (HBs-ag produced in Institute Pasteur, Iran), we investigated selectivity of the biosensor in various experiments. We showed that this biosensor can selectively detect both types of endotoxins compared to other biological species. Overall, this study proposes... 

General Circulation Models (GCMs) are advanced tools for impact assessment and climate change studies. Previous studies show that the performance of the GCMs in simulating climate variables varies significantly over different regions. This study intends to evaluate the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs in simulating temperature and precipitation over Iran. Simulations from 37 GCMs and observations from the Climatic Research Unit (CRU) were obtained for the period of 1901–2005. Six measures of performance including mean bias, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), linear correlation coefficient (r), Kolmogorov-Smirnov... 

General Circulation Models (GCMs) are advanced tools for impact assessment and climate change studies. Previous studies show that the performance of the GCMs in simulating climate variables varies significantly over different regions. This study intends to evaluate the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs in simulating temperature and precipitation over Iran. Simulations from 37 GCMs and observations from the Climatic Research Unit (CRU) were obtained for the period of 1901–2005. Six measures of performance including mean bias, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), linear correlation coefficient (r), Kolmogorov-Smirnov... 

Fuel cells belong to an avant-garde technology family for a wide variety of applications including micro-power, transportation power, stationary power for buildings and other distributed generation applications. The first objective of this contribution is to find a suitable reduced model of a Solid Oxide Fuel Cell (SOFC). The derived reduced model is then used to design a state estimator. In the first step, the distributed model of the SOFC that is derived using the first principle balance equations is solved by the method of lines. Since this model is too complex and sluggish for real-time applications, a representation of this model with lower number of states and good accuracy is needed.... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects... 

The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the movement of the market. This paper proposes a hybrid artificial intelligence model for stock exchange index forecasting, the model is a combination of genetic algorithms and feedforward neural networks. Actually it evolves neural network weights by using genetic algorithms. We also employ preprocessing... 

Multiple Attribute Decision Making (MADM) addresses the problem of choosing an optimum choice containing the highest degree of satisfaction from a set of alternatives which are characterized in terms of their attributes. In order to make a decision or choose a best alternative, a decision maker (DM) is often asked to provide his/her preferences either on alternatives or on the relative weights of attributes or on both of them. In this paper some basic principles from data envelopment analysis (DEA) is used in order to extract the necessary information for solving an MADM problem. We will introduce a comprehensive yet efficient approach for accountable and understandable MADM. For obtaining... 

Unreinforced masonry (URM) buildings are rather popular around the globe due to low construction costs, even though they can be prone to substantial damage caused by even moderate earthquakes. Numerous URM buildings that have experienced damages from past earthquakes require to be upgraded or at least return to their undamaged state in order to be able to withstand future earthquakes. In many cases, reconstruction is not the best choice because of financial and time restrictions. As such, repair/retrofit is the best choice, assuring the post-earthquake serviceability. Furthermore, seismic repair/retrofit can be a cost-efficient method to avoid reconstruction complexities and expenses. In... 

In this paper, we combine a novel Sequential Graph Coloring Heuristic Algorithm (SGCHA) with a non-systematic method based on a cultural algorithm to solve the graph coloring problem (GCP). The GCP involves finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. In our solving approach, we first use an estimator which is implemented with SGCHA to predict the minimum colors. Then, in the non-systematic part which has been designed using cultural algorithms, we improve the prediction. Various components of the cultural algorithm have been implemented to solve the GCP with a self adaptive behavior in an efficient manner. As a result... 

A new air shower experiment of the Alborz Observatory, Alborz-I, located at the Sharif University of Technology, Iran, will be constructed in near future. An area of about 30×40 m2 will be covered by 20 plastic scintillation detectors (each with an area of 50×50 cm2). A series of experiments have been performed to optimize the height of light enclosures of the detectors for this array and the results have been compared to an extended code simulation of these detectors. Operational parameters of the detector obtained by this code are cross checked by the Geant4 simulation. There is a good agreement between the extended-code and Geant4 simulations. We also present further discussions on the... 

Meteorological droughts due to the concurrent occurrence of low-precipitation and high-temperature events can lead to severe negative impacts on agriculture, economy, ecosystem, and society. This study proposes a novel framework to characterize such drought conditions based on the joint variability of precipitation–temperature, particularly under climate change. Generalized hierarchical linear model is used to downscale precipitation and temperature at multiple stations from the outputs of nine General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bivariate drought index called Precipitation–Temperature Deciles Index (PTDI) is developed using... 

Meteorological droughts due to the concurrent occurrence of low-precipitation and high-temperature events can lead to severe negative impacts on agriculture, economy, ecosystem, and society. This study proposes a novel framework to characterize such drought conditions based on the joint variability of precipitation–temperature, particularly under climate change. Generalized hierarchical linear model is used to downscale precipitation and temperature at multiple stations from the outputs of nine General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bivariate drought index called Precipitation–Temperature Deciles Index (PTDI) is developed using... 

The reliability of data driven prognostics algorithms severely depends on the volume of data. Therefore in case of limited data availability, life estimations usually are not acceptable; because the quantity of run to failure data is not sufficient to train prognostics model efficiently. To board this problem, a life clustering prognostics (LCP) framework is proposed. LCP regenerates the train data at different ages and outcomes to increment of the training data volume. So, the method is useful for limited data conditions. In this research, initially LCP performance is studied in normal situation is; successively robustness of the framework under limited data conditions is considered. For... 

Meteorological droughts due to the concurrent occurrence of low-precipitation and high-temperature events can lead to severe negative impacts on agriculture, economy, ecosystem, and society. This study proposes a novel framework to characterize such drought conditions based on the joint variability of precipitation–temperature, particularly under climate change. Generalized hierarchical linear model is used to downscale precipitation and temperature at multiple stations from the outputs of nine General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bivariate drought index called Precipitation–Temperature Deciles Index (PTDI) is developed using... 

Accurate forecasting of outpatient visits aids in decision-making and planning for the future and is the foundation for greater and better utilization of resources and increased levels of outpatient care. It provides the ability to better manage the ways in which outpatient's needs and aspirations are planned and delivered. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani type fuzzy rule based system to forecast outpatient visits with high accuracy. The hybrid model uses genetic algorithm for evolving knowledge base of fuzzy system. Actually it extracts useful patterns of information with a descriptive rule induction approach based on Genetic Fuzzy Systems... 

Accurate forecasts of tourist arrivals and study of the tourist arrival patterns are essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani-type fuzzy rule-based system to forecast tourist arrivals with high accuracy. The hybrid model uses genetic algorithm for learning rule base and tuning data base of fuzzy system. Actually it extracts useful information patterns with a descriptive rule induction approach based...