Cost-Efficient Data Backup for Data Center Networks against {\epsilon}-Time Early Warning Disaster

Data backup in data center networks (DCNs) is critical to minimize the data loss under disaster. This paper considers the cost-efficient data backup for DCNs against a disaster with $\varepsilon$ early warning time. Given geo-distributed DCNs and such a $\varepsilon$-time early warning disaster, we investigate the issue of how to back up the data in DCN nodes under risk to other safe DCN nodes within the $\varepsilon$ early warning time constraint, which is significant because it is an emergency data protection scheme against a predictable disaster and also help DCN operators to build a complete backup scheme, i.e., regular backup and emergency backup. Specifically, an Integer Linear Program (ILP)-based theoretical framework is proposed to identify the optimal selections of backup DCN nodes and data transmission paths, such that the overall data backup cost is minimized. Extensive numerical results are also provided to illustrate the proposed framework for DCN data backup

Some new conjugate orthogonal Latin squares

AbstractWe present some new conjugate orthogonal Latin squares which are obtained from a direct method of construction of the starter-adder type. Combining these new constructions with earlier results of K. T. Phelps and the first author, it is shown that a (3, 2, 1)- (or (1, 3, 2)-) conjugate orthogonal Latin square of order v exists for all positive integers v ≠ 2, 6. It is also shown that a (3, 2, 1)- (or (1, 3, 2)-) conjugate orthogonal idempotent Latin square of order v exists for all positive integers v ≠ 2, 3, 6 with one possible exception v = 12, and this result can be used to enlarge the spectrum of a certain class of Mendelsohn designs and provide better results for problems on embedding

Determination of the Ignorable Boundary Condition and Standard Sample for A Novel in-situ Dynamic Mechanical Analysis Method on Soft Matter

An in-situ Dynamic Mechanical Analysis (DMA) method for soft matter developed by our group [Wu. et.al. 2022] encounters the problem of irregular samples, which significantly vary in shape and size in practice, therefore a standard sample "large enough" to ignore the boundary and size effects is necessary to determine the baseline of test and build the correspondence between this new method to classical mechanical tests. In this work, we use finite element analysis to approach the optimal size of a brick sample where the stress on the boundaries in three spatial directions are ignorable, and certified the results by testing a series of silicone gel samples on the in-situ DMA device. The stress-strain of tensile and compression are characterized. The material properties of gel are chosen to be close to the biological soft tissue. The size of 40mm(L)*40mm(W)*20mm(H) is determined to be the optimal result.Comment: 7 pages, 7 figure

Fabrication and Spectral Properties of Wood-Based Luminescent Nanocomposites

Pressure impregnation pretreatment is a conventional method to fabricate wood-based nanocomposites. In this paper, the wood-based luminescent nanocomposites were fabricated with the method and its spectral properties were investigated. The results show that it is feasible to fabricate wood-based luminescent nanocomposites using microwave modified wood and nanophosphor powders. The luminescent strength is in positive correlation with the amount of phosphor powders dispersed in urea-formaldehyde resin. Phosphors absorb UV and blue light efficiently in the range of 400–470 nm and show a broad band of bluish-green emission centered at 500 nm, which makes them good candidates for potential blue-green luminescent materials

A Coarse-to-fine Framework for Automated Kidney and Kidney Tumor Segmentation from Volumetric CT Images

Automatic semantic segmentation of kidney and kidney tumor is a promising tool for the treatment of kidney cancer. Due to the wide variety in kidney and kidney tumor morphology, it is still a great challenge to complete accurate segmentation of kidney and kidney tumor. We propose a new framework based on our previous work accepted by MICCAI2019, which is a coarse-to-fine segmentation framework to realize accurate and fast segmentation of kidney and kidney tumor

Blocking Probability of f -Cast Optical Banyan Networks on Vertical Stacking

Abstract-Vertical stacking of banyan networks has been an attractive architecture to construct optical switching networks due to its small depth, absolute signal loss uniformity and good fault tolerance property. Recently, F.K.Hwang extended the study of banyan-based networks to the general f -cast case, which covers the unicast (f = 1) and multicast (f = N ) as special cases. In this paper, we study the blocking probability of f -cast optical banyan networks under crosstalk-free constraint. It is expected that the proposed probability model can be used to dimension such an f -cast network and achieve a graceful tradeoff between hardware cost and blocking probability