Sharif Digital Repository

Purpose: This study aims to systematically review the economic complexity literature to advance the knowledge on its contribution to building regional competitiveness. Design/methodology/approach: In this study, we did a systematic review of 111 relevant papers. In this regard, we did a thematic analysis on all the collected papers, which led to a two-level processed approach. In the first level, the contributions of the reviewed articles have been classified into three main streams. In the second level, the findings under each contribution category are analyzed and explained. This approach led to a thematic network demonstrating economic complexity and the dynamics of regional... 

Data aggregation is an effective technique which is introduced to conserve energy by reducing packet transmissions in wireless sensor networks (WSNs). In addition, it is possible to consume less energy by using the spatial correlation and redundancy of data in dense networks to form clusters of nodes sensing similar values and, in turn, transmit one data packet per cluster. In this paper, we propose a Data-Aware Clustering and Aggregation scheme (DACA) to manage the energy constraint in a query-driven WSN. The DACA selects cluster head nodes by forming a new factor as a function of three parameters including the residual energy, the data value, and the number of neighbors at each node.... 

The development of energy regeneration capability in electric vehicles can extend their driving range making them a competent alternative for conventional internal combustion engine vehicles. In this study, a novel energy regeneration technique, called a two-boost method, for electric vehicles driven by a brushless DC (BLDC) motor, a widely used motor in vehicular technology, is proposed. Based on this technique, the BLDC motor driver, which is selected to be a three-phase inverter, is converted into two simultaneous boost converters during energy regeneration periods in order to transfer energy from the BLDC motor into the battery and provide the braking force. Also, this method is compared... 

Hardening and operational measures in electrical distribution systems (EDSs) aim at improving the resilience of EDS in case of natural disasters. This paper proposes a novel two-stage framework for establishing optimal restoration paths for supplying critical loads (CLs) in the aftermath of a natural disaster that will improve the resilience of EDSs. To this end, in the first stage, an algorithm is introduced to find all possible candidate paths between available distributed energy resources (DERs) and CLs, the output of which is applied to the second stage, as the inputs. Subsequently, in the second stage, the problem of finding the optimal restoration paths is modeled as a mixed integer... 

Secure operation of protective intelligent electronic devices (IEDs) has been recognized as a crucial issue for power grids. By gaining access to substation IEDs, intruders can severely disrupt the operation of protection systems. This paper develops an analytical reliability assessment framework for quantifying the impacts of the hypothesized integrity attacks against protection systems. Petri net models are used to simulate possible intrusion scenarios into substation networks. The cyber network model is constructed from firewall, intrusion prevention system (IPS), and password models, which are three types of defense mechanisms for protecting substation networks. In this paper, two main... 

Objectives Several studies have examined the prevalence of depression in Iran, mainly before and during the COVID-19 pandemic. There is a lack of information regarding the rate of depression in the post-covid era. Therefore, this study aims to investigate the prevalence of depression among people aged ≥15 years in Iran and find the associated demographic and socio-economic factors in the post-COVID era. Methods This is a descriptive-analytical population-based study that was conducted from February to April 2023 on 2,892 Iranian people ≥15 years, who were selected using proportional stratified sampling method. The patient health questionnaire (PHQ-2) was administered through telephone... 

Synthetic CT (sCT) generation from MR images is yet one of the major challenges in the context of MR-guided radiation planning as well as quantitative PET/MR imaging. Deep convolutional neural networks have recently gained special interest in large range of medical imaging applications including segmentation and image synthesis. In this study, a novel deep convolutional neural network (DCNN) model is presented for synthetic CT generation from single T1-weighted MR image. The proposed method has the merit of highly accelerated convergence rate suitable for applications where the number of training da-taset is limited while highly robust model is required. This algorithm exploits a Visual... 

Purpose: Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in radiation therapy, MRI-guided radiation treatment planning is limited by the fact that MRI does not directly provide the electron density map required for absorbed dose calculation. In this work, a new deep convolutional neural network model with efficient learning capability, suitable for applications where the number of training subjects is limited, is proposed to generate accurate synthetic computed tomography (sCT) images from MRI. Methods: This efficient convolutional neural network (eCNN) is built upon a combination of the SegNet architecture (a 13-layer encoder-decoder structure similar to the... 

This article proposes a new three-stage stochastic framework for dealing with predictable two-phase natural disasters in distribution systems. This framework is a multiobjective optimization, in which the amount of curtailed energy, the number of switching actions, and the vulnerability of operational components are selected as the main criteria for decision-making process. The optimization problem is formulated in the form of a stochastic mixed-integer linear programming (MILP) problem. In this article, a windstorm event followed by flooding is analyzed as a two-phase natural disaster. In this regard, the uncertainties associated with gust-wind speed, floodwater depths, and load demands are... 

Extreme weather events can devastate parts of power grids. Thus, the appropriate post-disaster reaction is a crucial duty for power utilities. To address this important concern, a new two-stage framework is proposed in this paper. Stage I optimally coordinates disaster mutual assistance between affected and supporting utilities. To this end, distance between the damaged and supporting utilities, extent of damage, and repair resources are taken into consideration as decision criteria. Then, a novel formulation in the form of mixed integer linear programming (MILP) is developed for mutual aid management problem. The results of stage I are used as inputs to stage II. A new multi-horizon... 

This article proposes a new three-stage stochastic framework for dealing with predictable two-phase natural disasters in distribution systems. This framework is a multiobjective optimization, in which the amount of curtailed energy, the number of switching actions, and the vulnerability of operational components are selected as the main criteria for decision-making process. The optimization problem is formulated in the form of a stochastic mixed-integer linear programming (MILP) problem. In this article, a windstorm event followed by flooding is analyzed as a two-phase natural disaster. In this regard, the uncertainties associated with gust-wind speed, floodwater depths, and load demands are... 

In this investigation a theoretical model based on artificial neural network (ANN) has been developed to predict porosity percent and correlate the chemical composition and cooling rate to the amount of porosity in Al-Si casting alloys. In addition, the sensivity analysis was performed to investigate the importance of the effects of different alloying elements, composition, grain refiner, modifier and cooling rate on porosity formation behavior of Al-Si casting alloys. By comparing the predicted values with the experimental data, it is demonstrated that the well-trained feed forward back propagation ANN model with eight nodes in hidden layer is a powerful tool for prediction of porosity... 

In this investigation, a neural network model was used to predict mechanical properties of dual phase (DP) steels and sensivity analysis was performed to investigate the importance of the effects of pre-strain, deformation temperature, volume fraction and morphology of martensite on room temperature mechanical behavior of these steels. In order to train the neural network, dual-phase (DP) steels with different morphology and volume fractions of martensite were deformed between 2 and 8%, at high temperature range of 150-450 °C. The results of this investigation show that there is a good agreement between experimental and predicted values and the well-trained neural network has a great... 

This paper presents a new framework for island formation prior to windstorms, which considers tree-caused failures of distribution networks. In the proposed framework, both direct and indirect effects of windstorms on distribution lines are quantified. Thus, a novel discrete Markov chain model is proposed for representing the failure modes of trees in each time interval of windstorm duration. This model categorizes 'healthy', 'uprooted', 'stem breakage', and 'branch breakage' states of a tree. In addition, a new line-tree interaction model is presented for calculating tree-caused failure probability of overhead lines. The results of the proposed Markov model are taken as inputs by the... 

This paper proposes a two-stage stochastic framework for improving distribution system resilience against storms, in which the uncertainties associated with load demands, solar irradiance after a storm event, and maximum gust wind speed are considered. In the first stage, the available idle electric buses (EBs) are optimally allocated to the charging stations prior to storm arrival. In the second stage, critical loads are restored through island formation after the storm. In this framework, the solar-powered charging stations of EBs and the distributed generators (DGs) are part of the electric energy resources. The solar charging stations contribute to supplying the critical loads in two... 

Pore-scale investigation of hydrogen flow behavior in porous media is crucial for developing reliable hydrogen storage models. However, there is a lack of experimental pore-scale studies in the literature focusing on both injection and production processes. To address this gap, we developed a quasi-2D microfluidics system to investigate hydrogen-water flow dynamics, displacement, trapping mechanisms and hysteresis in cyclic hydrogen storage and production. The results are interpreted using in-situ wettability measurements, Euler characteristic calculation and sweep efficiency estimation. The behavior of hydrogen-brine system is also compared with that of CO2-water system. The results clearly... 

In this work, studies of underground gas storage (UGS) were performed on a partially depleted, naturally fractured gas reservoir through compositional simulation. Reservoir dynamic model was calibrated by history matching of about 20 years of researvoir production. Effects of fracture parameters, i.e. fracture shape factor, fracture permeability and porosity were studied. Results showed that distribution of fracture density affects flow and production of water, but not that of gas, through porous medium. However, due to high mobility of gas, the gas production and reservoir average pressure are insensitive to fracture shape factor. Also, it was found that uniform fracture permeability... 

In this research, the airflow pattern and particle dispersion in a contaminated full-scale cleanroom are investigated numerically using both Eulerian and Lagrangian approaches. Three different supply diffuser configurations namely (1) central, (2) horizontal and (3) vertical and three different exhaust grille configurations namely (4) vertical symmetric, (5) asymmetric and (6) horizontal symmetric are selected for the analysis. The presented results reveal that the supply/exhaust openings arrangement has a significant influence on the particulate contaminant dispersion in the cleanrooms. The comparison of the above different supply diffuser configurations shows that the vertical and... 

Understanding the plastic deformation and fracture behavior of Carbon nanotube (CNT)-reinforced aluminum composites is crucial for determining the factors controlling their strength and ductility. This article presents a novel approach by proposing a coupled micromechanical dislocation-based constitutive model combined with the Gurson-Tvergaard-Needleman (GTN) damage evolution model. The objective of this research is to accurately predict the load-displacement behavior of an AlMg6/CNT composite, which is manufactured through accumulative roll bonding (ARB), from yield to fracture. The study investigates the correlations between the number of ARB passes, which affects the dislocation density... 

Fractures identification is essential during exploration, drilling and well completion of naturally fractured reservoirs since they have a significant impact on flow contribution. There are different methods to characterize these systems based on formation properties and fluid flow behaviour such as logging and testing. Pressure-transient testing has long been recognized as a reservoir characterization tool. Although welltest analysis is a recommended technique for fracture evaluation, but its use is still not well understood. Analysis of pressure transient data provides dynamic reservoir properties such as average permeability, fracture storativity and fracture conductivity.An infusion of...