Disaster-survivable cloud-network mapping

Cloud-computing services are provided to consumers through a network of servers and network equipment. Cloud-network (CN) providers virtualize resources [e.g., virtual machine (VM) and virtual network (VN)] for efficient and secure resource allocation. Disasters are one of the worst threats for CNs as they can cause massive disruptions and CN disconnection. A disaster may also induce post-disaster correlated, cascading failures which can disconnect more CNs. Survivable virtual-network embedding (SVNE) approaches have been studied to protect VNs against single physical-link/-node and dual physical-link failures in communication infrastructure, but massive disruptions due to a disaster and their consequences can make SVNE approaches insufficient to guarantee cloud-computing survivability. In this work, we study the problem of survivable CN mapping from disaster. We consider risk assessment, VM backup location, and post-disaster survivability to reduce the risk of failure and probability of CN disconnection and the penalty paid by operators due to loss of capacity. We formulate the proposed approach as an integer linear program and study two scenarios: a natural disaster, e.g., earthquake and a human-made disaster, e.g., weapons-of-mass-destruction attack. Our illustrative examples show that our approach reduces the risk of CN disconnection and penalty up to 90 % compared with a baseline CN mapping approach and increases the CN survivability up to 100 % in both scenarios.     

A framework for mining interesting high utility patterns with a strong frequency affinity

High utility pattern (HUP) mining is one of the most important research issues in data mining. Although HUP mining extracts important knowledge from databases, it requires long calculations and multiple database scans. Therefore, HUP mining is often unsuitable for real-time data processing schemes such as data streams. Furthermore, many HUPs may be unimportant due to the poor correlations among the items inside of them. Hence,the fast discovery of fewer but more important HUPs would be very useful in many practical domains. In this paper, we propose a novel framework to introduce a very useful measure, called frequency affinity, among the items in a HUP and the concept of interesting HUP with a strong frequency affinity for the fast discovery of more applicable knowledge. Moreover, we propose a new tree structure, utility tree based on frequency affinity (UTFA), and a novel algorithm, high utility interesting pattern mining (HUIPM), for single-pass mining of HUIPs from a database. Our approach mines fewer but more valuable HUPs, significantly reduces the overall runtime of existing HUP mining algorithms and is applicable to real-time data processing. Extensive performance analyses show that the proposed HUIPM algorithm is very efficient and scalable for interesting HUP mining with a strong frequency affinity.     

Effective periodic pattern mining in time series databases

The goal of analyzing a time series database is to find whether and how frequent a periodic pattern is repeated within the series. Periodic pattern mining is the problem that regards temporal regularity. However, most of the existing algorithms have a major limitation in mining interesting patterns of users interest, that is, they can mine patterns of specific length with all the events sequentially one after another in exact positions within this pattern. Though there are certain scenarios where a pattern can be flexible, that is, it may be interesting and can be mined by neglecting any number of unimportant events in between important events with variable length of the pattern. Moreover, existing algorithms can detect only specific type of periodicity in various time series databases and require the interaction from user to determine periodicity. In this paper, we have proposed an algorithm for the periodic pattern mining in time series databases which does not rely on the user for the period value or period type of the pattern and can detect all types of periodic patterns at the same time, indeed these flexibilities are missing in existing algorithms. The proposed algorithm facilitates the user to generate different kinds of patterns by skipping intermediate events in a time series database and find out the periodicity of the patterns within the database. It is an improvement over the generating pattern using suffix tree, because suffix tree based algorithms have weakness in this particular area of pattern generation. Comparing with the existing algorithms, the proposed algorithm improves generating different kinds of interesting patterns and detects whether the generated pattern is periodic or not. We have tested the performance of our algorithm on both synthetic and real life data from different domains and found a large number of interesting event sequences which were missing in existing algorithms and the proposed algorithm was efficient enough in generating and detecting periodicity of flexible patterns on both types of data.     

Synthesis and rheological properties of an associative star polymer in aqueous solutions

Rheological properties of aqueous solutions and hydrogels formed by an amphiphilic star block copolymer, poly(acrylic acid)-block-polystyrene (PAA₅₄-b-PS₆)₄, were investigated as a function of the polymer concentration (C_p), temperature, and added salt concentration. The water-soluble polymer synthesised by atom transfer radical polymerization (ATRP) was found to form hydrogels at room temperature at polymer concentrations, C_p, over 22 g/L due to the interpolymer hydrophobic association of the PS blocks. Increasing C_p leads to stronger elastic networks at room temperature that show a gel-to-solution transition with increasing temperature. Increase of ionic strength decreases the moduli compared with the pure hydrogel but did not affect the gel-sol transition temperature significantly. Small-angle X-ray experiments showed two distinct scattering correlation peaks for samples above the gelling C_p, which indicates the aggregates formed due to hydrophobic association. Upon heating the intensity of the scattering correlation peaks was found to decrease indicating the loss of the network structure due to thermal motion.     

Structural Studies of Anomalous Behavior in the Silica-Alumina Gel System

This paper compares the structure ofAl₂O₃-SiO₂ xerogels prepared with Al₂O₃ contents ranging from 21 to 75 wt% (13 to 64 mol%). The 47 wt% Al₂O₃ xerogel (Al/Si ≅ 1) exhibits anomalously low surface area (≅1 m²/g) and skeletal density compared with other Al₂O₃-Si₂ compositions. Based on the results of nitrogen adsorption/condensation, helium displacement, high-resolution transmission electron microscopy (HRTEM), small-angle X-ray scattering (SAXS), and small-angle neutron scattering (SANS), we attribute the low surface area and skeletal density (density of the solid phase which is inaccessable to helium) to the formation of closed micropores, whereas the higher surface area materials exhibit a slightly coarser texture comprising open pores with radii of ≅1 nm. X-ray diffraction (XRD) and ²⁹SI and ²⁷Al magic-angle spinning nuclear magnetic resonance (MASNMR) indicate no anomalous behavior in the 47% sample on molecular length scales. HRTEM indicates the presence of a small fraction of crystallites which is supported by the SAXS results, but it is unknown if this crystallinity is related to low surface area. Low-field ¹H NMR spin-lattice relaxation measurements show that the physical structure of all of the 'wet' gels is similar implying that pore closure occurs during drying. Consistent with this idea, gel surface area and density increased significantly when the pore fluid (water) was replaced with a lower surface tension fluid (formamide, dioxane, ethanol. water/surfactant).     

A Novel Approach for Mining High‐Utility Sequential Patterns in Sequence Databases

Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real‐world scenarios. In this paper, we propose a novel framework for mining high‐utility sequential patterns for more real‐life applicable information extraction from sequence databases with non‐binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high‐utility sequential patterns, we propose two new algorithms: UtilityLevel is a high‐utility sequential pattern mining with a level‐wise candidate generation approach, and UtilitySpan is a high‐utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high‐utility sequential patterns.     

Investigation of the phase separation of PNIPAM using infrared spectroscopy together with multivariate data analysis

The use of vibrational spectroscopy to investigate complex structural changes in polymers yields chemically rich data, but interpretation can be challenging and subtle but meaningful spectral changes may be missed through visual inspection alone. Multivariate analysis is an efficient approach to gain an oversight of small but systematic spectral differences anywhere within the spectra, providing further insight into structural changes and associated transformation mechanisms. In this study, the novel analytical approach of infrared spectroscopy combined with principal component analysis and Gaussian peak fitting was used to investigate the structural changes in aqueous solutions of a polymer, using poly(N-isopropyl acrylamide) (PNIPAM) in the atactic form and with controlled tacticity as a model system. Subtle spectral changes associated with the dehydration and phase separation upon heating included peak shifts, an area ratio change of the amide I band to the amide II band and formation of a new peak in the amide I band were efficiently detected. Dehydration and phase separation of PNIPAM occurred in two temperature ranges, one for the atactic and one for isotactic rich part, both involving a complex re-organization of the hydrogen bonds and change of the hydration layer. The changes agreed with existing results from other techniques, and new insights were gained into the effect of controlled tacticity on phase transformation behaviour. The study demonstrates that infrared spectroscopy combined with the multivariate analytical method principal component analysis and Gaussian peak fitting is an efficient approach to probing structural change in polymers during heating. The simplicity of the presented approach could find excellent use in analysing and understanding the molecular environment of a range of stimuli-responsive polymers, for instance block or grafted types of polymers, as well as those with controlled tacticity.     

Physiological and Nutraceutical Perspectives of Fructan

Functional and nutraceutical foods have captured the global market owing to trends and perceptions of consumers on the natural products and diet-health linkages. Health promoting potential of such foods has been attributed to the presence of essential bioactive moieties. Wheat, being staple food in many parts of the world, gained substantial attention of researchers particularly for the extraction of various functional components. Among these, fructan oligosaccharides in nature bestow quality of baked products and provide protection against various physiological disorders. Addition of fructan in various baked products enhances softness and color, especially in bread, and also imparts textural improvement. Moreover, fructans boost mineral absorption, hypocholesterolemic, and hypoglycemic perspectives, bifidogenic nature and controlling cancer insurgence. The benefits allied with fructan are mainly dose and time dependent. In this context, its industrial applications for vulnerable groups are increasing worldwide.