Reputation, Learning and Project Choice in Frictional Economies

I introduce a dynamic model of learning and random meetings between a long-lived agent with unknown ability and heterogenous projects with observable types. There is incomplete yet symmetric information about the agent's ability. She needs to accept the contacting projects and create success to learn her type. Alternatively, lack of success during a match leads to a reputational loss followed from Bayesian learning, in that the reputation is interpreted as the posterior belief about the agent's ability. Developing a self-type learning framework with endogenous outside option, I find the optimal matching strategy of the agent, that determines what types of projects the agent with a certain level of reputation will accept. Comparing with a perfect information benchmark, I show learning incentives lead to larger matching sets in the optimum

Risk minimization and portfolio diversification

We consider the problem of minimizing capital at risk in the Black-Scholes setting. The portfolio problem is studied given the possibility that a correlation constraint between the portfolio and a financial index is imposed. The optimal portfolio is obtained in closed form. The effects of the correlation constraint are explored; it turns out that this portfolio constraint leads to a more diversified portfolio

Binary Mechanisms under Privacy-Preserving Noise

We study mechanism design for public-good provision under a noisy privacy-preserving transformation of individual agents' reported preferences. The setting is a standard binary model with transfers and quasi-linear utility. Agents report their preferences for the public good, which are randomly ``flipped,'' so that any individual report may be explained away as the outcome of noise. We study the tradeoffs between preserving the public decisions made in the presence of noise (noise sensitivity), pursuing efficiency, and mitigating the effect of noise on revenue

Robust sensor fault detection and isolation of gas turbine engines

An effective fault detection and isolation (FDI) technology can play a crucial role in improving the system availability, safety and reliability as well as reducing the risks of catastrophic failures. In this thesis, the robust sensor FDI problem of gas turbine engines is investigated and different novel techniques are developed to address the effects of parameter uncertainties, disturbances as well as process and measurement noise on the performance of FDI strategies. The efficiencies of proposed techniques are investigated through extensive simulation studies for the single spool gas turbine engine that is previously developed and validated using the GSP software. The gas turbine engine health degradation is considered in various forms in this thesis. First, it is considered as a part of the engine dynamics that is estimated off-line and updated periodically for the on-board engine model. Second, it is modeled as the time-varying norm-bounded parameter uncertainty that affects all the system state-space matrices and third as an unknown nonlinear dynamic that is approximated by the use of a dynamic recurrent neural network. In the first part of the thesis, we propose a hybrid Kalman filter (HKF) scheme that consists of a single nonlinear on-board engine model (OBEM) augmented with piecewise linear (PWL) models constituting as the multiple model (MM) based estimators to cover the entire engine operating regime. We have integrated the generalized likelihood ratio (GLR)-based method with our MM-based scheme to estimate the sensor fault severity under various single and concurrent fault scenarios. In order to ensure the reliability of our proposed HKF-based FDI scheme during the engine life cycle, it is assumed that the reference baselines are periodically updated for the OBEM health parameters. In the second part of the thesis, a novel robust sensor FDI strategy using the MM-based approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple PWL models. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (ARE) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The main objective is to propose a robust filter that satisfies the overall performance requirements and is not affected by system perturbations. The requirements include a quadratically stable filter that ensures bounded estimation error variances having predefined values. In the third part of the thesis, a novel hybrid approach is proposed to improve the robustness of FDI scheme with respect to different sources of uncertainties. For this purpose, a dynamic recurrent neural network (DRNN) is designed to approximate the gas turbine engine uncertainty due to the health degradations. The proposed DRNN is trained offline by using the extended Kalman filter (EKF) algorithm for an engine with different levels of uncertainty, but with healthy sensors. The convergence of EKF-based DRNN training algorithm is also investigated. Then, the trained DRNN with the fixed parameters and topology is integrated with our online model-based FDI algorithm to approximate the uncertainty terms of the real engine. In this part, the previously proposed HKF and RKF are integrated with the trained DRNN to construct the hybrid FDI structure

Evaluation of Metallo-β-Lactamase-Production and Carriage of bla-_VIM Genes in Pseudomonas aeruginosa Isolated from Burn Wound Infections in Isfahan

Background: Metallo-β-lactamase-production among Gram-negative bacteria, including Pseudomonas aeruginosa, has become a challenge for treatment of infections due to these resistant bacteria. Objectives: The aim of the current study was to evaluate the metallo-β-lactamase-production and carriage of bla-VIM genes among carbapenem-resistant P. aeruginosa isolated from burn wound infections. Patients and Methods: A cross-sectional study was conducted from September 2014 to July 2015. One hundred and fifty P. aeruginosa isolates were recovered from 600 patients with burn wound infections treated at Imam-Musa-Kazem Hospital in Isfahan city, Iran. Carbapenem-resistant P. aeruginosa isolates were screened by disk diffusion using CLSI guidelines. Metallo-β-lactamase-producing P. aeruginosa isolates were identified using an imipenem-EDTA double disk synergy test (EDTA-IMP DDST). For detection of MBL genes including bla-VIM-1 and bla-VIM-2, polymerase chain reaction (PCR) methods and sequencing were used. Results: Among the 150 P. aeruginosa isolates, 144 (96%) were resistant to imipenem by the disk diffusion method, all of which were identified as metallo-β-lactamase-producing P. aeruginosa isolates by EDTA-IMP DDST. Twenty-seven (18%) and 8 (5.5%) MBL-producing P. aeruginosa isolates harbored bla-VIM-1 and bla-VIM-2 genes, respectively. Conclusions: Our findings showed a high occurrence of metallo-β-lactamase production among P. aeruginosa isolates in burn patient infections in our region. Also, there are P. aeruginosa isolates carrying the bla-VIM-1 and bla-VIM-2 genes in Isfahan province

Hypersaline Lake Urmia: a potential hotspot for microbial genomic variation

Lake Urmia located in Iran is a hypersaline environment with a salinity of about 27% (w/v). Metagenomic analyses of water samples collected from six locations in the lake exhibited a microbial community dominated by representatives of the family Haloferacaceae (69.8%), mainly those affiliated to only two genera, Haloquadratum (59.3%) and Halonotius (9.1%). Similar to other hypersaline lakes, the bacterial community was dominated by Salinibacter ruber (23.3%). Genomic variation analysis by inspecting single nucleotide variations (SNVs) and insertions/deletions (INDELs) exhibited a high level of SNVs and insertions, most likely through transformation for abundant taxa in the Lake Urmia community. We suggest that the extreme conditions of Lake Urmia and specifically its high ionic concentrations could potentially increase the SNVs and insertions, which can consequently hamper the assembly and genome reconstruction from metagenomic reads of Lake Urmia

Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction

The World Health Organization (WHO) in 2016 considered m-health as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health”. WHO emphasizes the potential of this technology to increase its use in accessing health information and services as well as promoting positive changes in health behaviours and overall management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring through the collection of patient data remotely, has become an important component in m-health system. It is important that the integrity of the data collected is verified continuously through data authentication before storage. In this research work, we extracted heart rate variability (HRV) and decomposed the signals into sub-bands of detail and approximation coefficients. A comparison analysis is done after the classification of the extracted features to select the best sub-bands. An architectural framework and a used case for m-health data authentication is carried out using two sub-bands with the best performance from the HRV decomposition using 30 subjects’ data. The best sub-band achieved an equal error rate (EER) of 12.42%