Reducing Packet Overhead in Mobile IPv6

Common Mobile IPv6 mechanisms, Bidirectional tunneling and Route optimization, show inefficient packet overhead when both nodes are mobile. Researchers have proposed methods to reduce packet overhead regarding to maintain compatible with standard mechanisms. In this paper, three mechanisms in Mobile IPv6 are discussed to show their efficiency and performance. Following discussion, a new mechanism called Improved Tunneling-based Route Optimization is proposed and due to performance analysis, it is shown that proposed mechanism has less overhead comparing to common mechanisms. Analytical results indicate that Improved Tunneling-based Route Optimization transmits more payloads due to send packets with less overhead

A New Label-Free Technique for Analysing Evaporation Induced Self-Assembly of Viral Nanoparticles Based on Enhanced Dark-Field Optical Imaging

Nanoparticle self-assembly is a complex phenomenon, the control of which is complicated by the lack of appropriate tools and techniques for monitoring the phenomenon with adequate resolution in real-time. In this work, a label-free technique based on dark-field microscopy was developed to investigate the self-assembly of nanoparticles. A bio-nanoparticle with complex shape (T4 bacteriophage) that self-assembles on glass substrates upon drying was developed. The fluid flow regime during the drying process, as well as the final self-assembled structures, were studied using dark-field microscopy, while phage diffusion was analysed by tracking of the phage nanoparticles in the bulk solutions. The concentrations of T4 phage nanoparticles and salt ions were identified as the main parameters influencing the fluid flow, particle motion and, consequently, the resulting self-assembled structure. This work demonstrates the utility of enhanced dark-field microscopy as a label-free technique for the observation of drying-induced self-assembly of bacteriophage T4. This technique provides the ability to track the nano-sized particles in different matrices and serves as a strong tool for monitoring self-assembled structures and bottom-up assembly of nano-sized building blocks in real-time

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained