Sharif Digital Repository

A Markov model is constructed for studying the counterparty risk in a CDS contract. The "wrong-way risk" in this model is accounted for by the possibility of the common default of the reference name and of the counterparty. A dynamic copula property as well as affine model specifications make pricing and calibration very efficient. We also consider the issue of dynamically hedging the CVA with a rolling CDS written on the counterparty. Numerical results are presented to show the adequacy of the behavior of CVA in the model with stylized features  

This paper considers a resource allocation problem in device-to-device (D2D) communications sharing the same frequency spectrum. In particular, the cellular users (CUs) utilize non-orthogonal multiple access (NOMA) while D2D users (DUs) adopt the orthogonal frequency division multiple access (OFDMA). A multi-objective optimization problem (MOOP) is formulated, which jointly maximizes the sum rate of DUs and CUs in uplink communications while taking into account the maximum transmit power budget and minimum data rate requirement for DUs and CUs. This MOOP is handled by the weighted Tchebycheff method, which converts it into a single-objective optimization (SOOP). Then, the monotonic... 

In this paper, we investigate the impact of mobility on the spread of COVID-19 in Tehran, Iran. We have performed a time series analysis between the indicators of public transit use and inter-city trips on the number of infected people. Our results showed a significant relationship between the number of infected people and mobility variables with both short-term and long-term lags. The long-term effect of mobility showed to have a consistent lag correlation with the weekly number of new COVID-19 positive cases. In our statistical analysis, we also investigated key non-transportation variables. For instance, the mandatory use of masks in public transit resulted in observing a 10% decrease in... 

Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions. This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive... 

Cyber-physical systems contain networked embedded systems. Such systems may implement cryptographic algorithms for processing and/or communication. Therefore, they can be prone to side-channel attacks. Differential power analysis is one of such attacks, which is considered among the most serious threats against cryptographic devices. Various metrics have been proposed to evaluate the resistance of different implementations against these attacks. Some of these metrics need side-channel attacks to be conducted and depend on the considered power model. Due to the vast variety of proposed side-channel attacks and power models, comprehensively evaluating a design under these metrics is commonly... 

Through this work, a novel mathematical-based approach was proposed to develop reliable models for calculation of PVT properties of crude oils at various reservoir conditions. For this purpose, a new soft computing approach namely Least Square Support Vector Machine (LSSVM) modeling optimized with Coupled Simulated Annealing (CSA) optimization technique was implemented. The constructed models are evaluated by carrying out extensive experimental data reported in open literature. Results obtained by the proposed models were compared with the corresponding experimental values. Moreover, in-depth comparative studies have been carried out between these models and all other predictive models. The... 

An important aspect of corrosion prediction for oil/gas wells and pipelines is to obtain a realistic estimate of the corrosion rate. Corrosion rate prediction involves developing a predictive model that utilizes commonly available operational parameters, existing lab/field data, and theoretical models to obtain realistic assessments of corrosion rates. This study presents a new model to predict corrosion rates by using artificial neural network (ANN) systems. The values of pH, velocity, temperature, and partial pressure of the CO2 are input variables of the network and the rate of corrosion has been set as the network output. Among the 718 data sets, 503 of the data were implemented to find...