Computer Vision: Advances in Image and Video Analysis

The topic of computer vision has emerged as one that is fast developing, altering how we examine and comprehend pictures and movies. Image and video analysis has significantly advanced in recent years, opening the door for applications in a variety of industries including healthcare, robotics, surveillance, and autonomous systems. An overview of current developments in computer vision methods, algorithms, and techniques used in image and video analysis is given in this abstract. In conclusion, there have been tremendous improvements in image and video analysis in the field of computer vision. Recurrent neural networks (RNNs) and CNNs are two examples of deep learning approaches that have been used to enhance accuracy, resilience, and efficiency in a variety of applications. A richer comprehension of visual material has resulted from the integration of spatial and temporal data with semantic analysis. These developments have enormous potential for use in a variety of fields, influencing the direction of computer vision and its social effects

Evaluating the most common mutation in BRCAI and BRCA2 genes in women who had mothers with brest cancer and controls

Background and purpose: Breast Cancer is one of the health problems in every population. The aim of this study was to determine the frequency of BRCA1 and BRCA2 common mutations in women whose mothers were diagnosed with breast cancer. Materials and methods: A case�control study was performed in 109 females (less than 40 years of age) who had mothers with breast cancer. For scanning of genomic mutations in BRCA1 and BRCA2, genes mutation analysis was done in BRCA1 (exon2, 20) and BRCA2 (exon11) using Real Time PCR test. We also studied 109 healthy controls without family history of breast cancer. Results: No any mutation was found in this population. Conclusion: This study showed no mutation in affected and control group. Therefore, other mutations and genes may have a role in breast cancer pathogenesis in our population. © 2016, Mazandaran University of Medical Sciences. All rights reserved

Graphene -- Based Nanocomposites as Highly Efficient Thermal Interface Materials

We found that an optimized mixture of graphene and multilayer graphene - produced by the high-yield inexpensive liquid-phase-exfoliation technique - can lead to an extremely strong enhancement of the cross-plane thermal conductivity K of the composite. The "laser flash" measurements revealed a record-high enhancement of K by 2300 % in the graphene-based polymer at the filler loading fraction f =10 vol. %. It was determined that a relatively high concentration of single-layer and bilayer graphene flakes (~10-15%) present simultaneously with thicker multilayers of large lateral size (~ 1 micrometer) were essential for the observed unusual K enhancement. The thermal conductivity of a commercial thermal grease was increased from an initial value of ~5.8 W/mK to K=14 W/mK at the small loading f=2%, which preserved all mechanical properties of the hybrid. Our modeling results suggest that graphene - multilayer graphene nanocomposite used as the thermal interface material outperforms those with carbon nanotubes or metal nanoparticles owing to graphene's aspect ratio and lower Kapitza resistance at the graphene - matrix interface.Comment: 4 figure

Effect of Covalent Functionalisation on Thermal Transport Across Graphene-Polymer Interfaces

This paper is concerned with the interfacial thermal resistance for polymer composites reinforced by various covalently functionalised graphene. By using molecular dynamics simulations, the obtained results show that the covalent functionalisation in graphene plays a significant role in reducing the graphene-paraffin interfacial thermal resistance. This reduction is dependent on the coverage and type of functional groups. Among the various functional groups, butyl is found to be the most effective in reducing the interfacial thermal resistance, followed by methyl, phenyl and formyl. The other functional groups under consideration such as carboxyl, hydroxyl and amines are found to produce negligible reduction in the interfacial thermal resistance. For multilayer graphene with a layer number up to four, the interfacial thermal resistance is insensitive to the layer number. The effects of the different functional groups and the layer number on the interfacial thermal resistance are also elaborated using the vibrational density of states of the graphene and the paraffin matrix. The present findings provide useful guidelines in the application of functionalised graphene for practical thermal management.Comment: 8 figure

A review on the applications of micro-/mini-channels for battery thermal management

This review of the literature explores the potentials of liquid micro-/mini-channels to reduce operating temperatures and make temperature distributions more uniform in batteries. First, a classification and an overview of the various methods of battery thermal management are presented. Then, different types of lithium-ion batteries and their advantages and disadvantages are introduced, and the components of batteries are described in detail. The studies conducted on the performance of micro-/mini-channels for cooling all types of rectangular and cylindrical batteries are reviewed, and the key finding of these studies is presented. It is shown that, in general, using counterflow configuration creates a rather uniform temperature distribution in the battery cell and keeps the maximum temperature difference below 5 ∘C. The lowest battery maximum temperature is obtained for parallel and counterflow configurations in the straight and U-turn channels, respectively. In a parallel configuration, the peak point of the battery temperature is in the outlet area. However, in the counter-flow configuration, it occurs in the central region of the battery module. The survey of the literature further reveals that proper channel paths and flow configurations keep the battery maximum temperature within the safe range of 25∘C<Tmax<40∘C. For such flow configurations, the pressure drop remains minimally affected

A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran

This paper presents an innovative hybrid approach for the estimation of the solar global radiation. New prediction equations were developed for the global radiation using an integrated search method of genetic programming (GP) and simulated annealing (SA), called GP/SA. The solar radiation was formulated in terms of several climatological and meteorological parameters. Comprehensive databases containing monthly data collected for 6 years in two cities of Iran were used to develop GP/SA-based models. Separate models were established for each city. The generalization of the models was verified using a separate testing database. A sensitivity analysis was conducted to investigate the contribution of the parameters affecting the solar radiation. The derived models make accurate predictions of the solar global radiation and notably outperform the existing models

Computer Vision: Advances in Image and Video Analysis

The topic of computer vision has emerged as one that is fast developing, altering how we examine and comprehend pictures and movies. Image and video analysis has significantly advanced in recent years, opening the door for applications in a variety of industries including healthcare, robotics, surveillance, and autonomous systems. An overview of current developments in computer vision methods, algorithms, and techniques used in image and video analysis is given in this abstract. In conclusion, there have been tremendous improvements in image and video analysis in the field of computer vision. Recurrent neural networks (RNNs) and CNNs are two examples of deep learning approaches that have been used to enhance accuracy, resilience, and efficiency in a variety of applications. A richer comprehension of visual material has resulted from the integration of spatial and temporal data with semantic analysis. These developments have enormous potential for use in a variety of fields, influencing the direction of computer vision and its social effects