Search results for: Pooria Sekhavatfar

Authors: Hadiseh Hosamirudsari, Farhad Afsarikordehmahin, Pooria Sekhavatfar

Abstract:

Concomitant cortical blindness and deafness that follow varicella zoster virus (VZV) infection is rare. We describe a case of ophthalmic zoster that caused cortical blindness and deafness after central nervous system (CNS) involvement. A 42-year old, HIV infected woman has developed progressive blurry vision and deafness, 4 weeks after ophthalmic zoster. A physical examination and positive VZV polymerase chain reaction (PCR) of cerebrospinal fluid (CSF) suggested VZV encephalitis. Complication of VZV encephalitis is considered as the cause of blindness and deafness. In neurological deficit patient especially with a history of herpes zoster, VZV infection should be regarded as the responsible agent in inflammatory disorders of nervous system. The immunocompromised state of patient (including HIV) is as important an agent as VZV infection in developing the disease.

Keywords: blindness, deafness, hiv, VZV encephalitis

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Authors: Pooria Pasbakhsh, Rangika T. De Silva, Vahdat Vahedi, Hanafi Ismail

Abstract:

The influence of three different types of halloysite nanotubes (HNTs) with different dimensions, namely as camel lake (CLA), Jarrahdale (JA) and Matauri Bay (MB), on their reinforcing ability of ethylene propylene dine monomer (EPDM) were investigated by varying the HNTs loading (from 0-15 phr). Mechanical properties of the nanocomposites improved with addition of all three HNTs, but CLA based nanocomposites exhibited a significant enhancement compared to the other HNTs. For instance, tensile properties of EPDM nanocomposites increased by 120%, 256% and 340% for MB, JA, and CLA, respectively with addition of 15 phr of HNTs. This could be due to the higher aspect ratio and higher surface area of CLA compared to others. Scanning electron microscopy (SEM) of nanocomposites at 15 phr of HNT loadings showed low amounts of pulled-out nanotubes which confirmed the presence of more embedded nanotubes inside the EPDM matrix, as well as aggregates within the fracture surface of EPDM/HNT nanocomposites.

Keywords: aspect ratio, halloysite nanotubes (HNTs), mechanical properties, rubber/clay nanocomposites

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Authors: Seyed Habib Shojaei, Reza Eivazi, Mir Sajad Shojaei, Alireza Akbari, Pooria Mazloom, Seyede Mitra Sadati, Mir Zeinalabedin Shojaei, Farnaz Farbakhsh

Abstract:

To study the genetic diversity in rapeseeds and agronomic traits, an experiment was conducted using multivariate statistical methods at Agricultural Research Station of Kashmar in 2012-2013.In this experiment, ten genotypes of rapeseed in a Randomized Complete Block designs with three replications were evaluated. The following traits were studied: seed yield, number of days to the fifty percent of flowering, plant height, number of pods on main stem, length of the pod, seed yield per plant, number of seed in pod, harvest index, weight of 100 seeds, number of pods on lateral branch, number of lateral branches. In analyzing the variance, differences between cultivars were significant. The average comparative revealed that the most valuable variety was Licord regarding to the traits while the least valuable variety was Opera. In stepwise regression, harvest index, grain yield per plant and number of pods per lateral branches were entering to model. Correlation analysis showed that the grain yield with the number of pods per lateral branches and seed yield per plant have positive and significant correlation. In the factor analysis, the first five components explained more than 83% of the variance in the data. In the first factor, seed yield and the number of pods per lateral branches were of the highest importance. The traits, seed yield per plant, and pod per main stem were of a great significance in the second factor. Moreover, in the third factor, plant height and the number of lateral branches were more important. In the fourth factor, plant height and one hundred seeds weight were of the highest variance. Finally, days to fifty percent of flowering and one hundred seeds weight were more important in fifth factor.

Keywords: rapeseed, variance analysis, regression, factor analysis

Procedia PDF Downloads 220

Authors: Pooria Norouzi, Nicholas Tsang, Adam van der Goes, Joseph Yu, Douglas Zheng, Sirine Maleej

Abstract:

In this study, virtual meters will be designed and used for energy balance measurements of an air handling unit (AHU). The method aims to replace traditional physical sensors in heating, ventilation, and air conditioning (HVAC) systems with simulated virtual meters. Due to the inability to manage and monitor these systems, many HVAC systems have a high level of inefficiency and energy wastage. Virtual meters are implemented and applied in an actual HVAC system, and the result confirms the practicality of mathematical sensors for alternative energy measurement. While most residential buildings and offices are commonly not equipped with advanced sensors, adding, exploiting, and monitoring sensors and measurement devices in the existing systems can cost thousands of dollars. The first purpose of this study is to provide an energy consumption rate based on available sensors and without any physical energy meters. It proves the performance of virtual meters in HVAC systems as reliable measurement devices. To demonstrate this concept, mathematical models are created for AHU-07, located in building NE01 of the British Columbia Institute of Technology (BCIT) Burnaby campus. The models will be created and integrated with the system’s historical data and physical spot measurements. The actual measurements will be investigated to prove the models' accuracy. Based on preliminary analysis, the resulting mathematical models are successful in plotting energy consumption patterns, and it is concluded confidently that the results of the virtual meter will be close to the results that physical meters could achieve. In the second part of this study, the use of virtual meters is further assisted by artificial intelligence (AI) in the HVAC systems of building to improve energy management and efficiency. By the data mining approach, virtual meters’ data is recorded as historical data, and HVAC system energy consumption prediction is also implemented in order to harness great energy savings and manage the demand and supply chain effectively. Energy prediction can lead to energy-saving strategies and considerations that can open a window in predictive control in order to reach lower energy consumption. To solve these challenges, the energy prediction could optimize the HVAC system and automates energy consumption to capture savings. This study also investigates AI solutions possibility for autonomous HVAC efficiency that will allow quick and efficient response to energy consumption and cost spikes in the energy market.

Keywords: virtual meters, HVAC, artificial intelligence, energy consumption prediction

Procedia PDF Downloads 71