Ali Zare

Bio: Ali Zare is an academic researcher from Deakin University. The author has contributed to research in topics: Diesel fuel & Diesel engine. The author has an hindex of 22, co-authored 108 publications receiving 1446 citations. Previous affiliations of Ali Zare include Shahid Beheshti University of Medical Sciences and Health Services & Michigan State University.

Potential energy savings by radiative cooling system for a building in tropical climate

Abstract: Nowadays, almost every building required a cooling system and most of them use active cooling, which normally operates using electricity generated from non-renewable fossil fuel. To achieve comfort, it is possible to utilize the natural environmental conditions to partially replace the active cooling energy requirements. This research attempts to investigate the correlation between the radiative cooling power and the temperature difference between the ambient and the sky. The potential of a radiative cooling system in Malaysia is evaluated as well. The radiative cooling system operates by using a flat-plate rooftop as a radiator to reject heat to the cooler nocturnal sky for cooling purposes. In addition, the radiative cooling potential is determined by using the climate data of 10 different locations in Malaysia. The study found that radiative cooling can save up to 11% of the power consumption for cooling purposes. This value is the same for all 10 locations in this country.

Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA

Abstract: The widespread use of multisensor technology and the emergence of big data sets have brought the necessity to develop more versatile tools to represent higher order data with multiple aspects and high dimensionality. Data in the form of multidimensional arrays, also referred to as tensors, arise in a variety of applications including chemometrics, hyperspectral imaging, high-resolution videos, neuroimaging, biometrics, and social network analysis. Early multiway data analysis approaches reformatted such tensor data as large vectors or matrices and then resorted to dimensionality reduction methods developed for classical two-way analysis such as principal component analysis (PCA). However, one cannot discover hidden components within multiway data using conventional PCA. To this end, tensor decomposition methods which are flexible in the choice of the constraints and that extract more general latent components have been proposed. In this paper, we review the major tensor decomposition methods with a focus on problems targeted by classical PCA. In particular, we present tensor methods that aim to solve three important challenges typically addressed by PCA: dimensionality reduction, i.e., low-rank tensor approximation; supervised learning, i.e., learning linear subspaces for feature extraction; and robust low-rank tensor recovery. We also provide experimental results to compare different tensor models for both dimensionality reduction and supervised learning applications.

Boundary Layer Theory

Abstract: The boundary layer equations for plane, incompressible, and steady flow are $$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

Secondary Organic Aerosol Formation from Anthropogenic Air Pollution: Rapid and Higher than Expected

Abstract: [1] The atmospheric chemistry of volatile organic compounds (VOCs) in urban areas results in the formation of ‘photochemical smog’, including secondary organic aerosol (SOA). State-of-the-art SOA models parameterize the results of simulation chamber experiments that bracket the conditions found in the polluted urban atmosphere. Here we show that in the real urban atmosphere reactive anthropogenic VOCs (AVOCs) produce much larger amounts of SOA than these models predict, even shortly after sunrise. Contrary to current belief, a significant fraction of the excess SOA is formed from first-generation AVOC oxidation products. Global models deem AVOCs a very minor contributor to SOA compared to biogenic VOCs (BVOCs). If our results are extrapolated to other urban areas, AVOCs could be responsible for additional 3–25 Tg yr−1 SOA production globally, and cause up to −0.1 W m−2 additional top-of-the-atmosphere radiative cooling.

The genetic architecture of type 2 diabetes

Abstract: The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.