Evaluating the potential for zero discharge from reverse osmosis desalination using integrated processes : a review

Desalination processes used worldwide produce a large amount of waste concentrate, the disposal of which can have significant environmental impacts. As such, there has been research carried out into the development of zero liquid discharge technologies which recognise that the waste concentrate streams contain valuable salts, minerals and water. These technologies include the proprietary SALPROC systems, as well as other integrated systems that use a variety of different technologies for the extraction of salts and minerals from waste concentrates. Research has also been conducted on using forward osmosis as a means of treating the waste concentrate in order to produce additional product water and thus reduce the volume of waste concentrate. This article provides a review of these technologies and evaluates the potential for achieving zero liquid discharge by combining these technologies with conventional desalination technologies into integrated processes.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Zero-day malware detection based on supervised learning algorithms of API call signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k−Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO – PolyKernel, SMO – Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.

The L(2,1)-labelling problem for cubic Cayley graphs on dihedral groups

A k-L(2,1)-labelling of a graph G is a mapping f:V(G)→{0,1,2,…,k} such that |f(u)−f(v)|≥2 if uv∈E(G) and f(u)≠f(v) if u,v are distance two apart. The smallest positive integer k such that G admits a k-L(2,1)-labelling is called the λ-number of G. In this paper we study this quantity for cubic Cayley graphs (other than the prism graphs) on dihedral groups, which are called brick product graphs or honeycomb toroidal graphs. We prove that the λ-number of such a graph is between 5 and 7, and moreover we give a characterisation of such graphs with λ-number 5.

Zero permission android applications - attacks and defenses

Google advertises the Android permission framework as one of the core security features present on its innovative and flexible mobile platform. The permissions are a means to control access to restricted AP/s and system resources. However, there are Android applications which do not request permissions at all.In this paper, we analyze the repercussions of installing an Android application that does not include any permission and the types of sensitive information that can be accessed by such an application. We found that even app/icaaons with no permissions are able to access sensitive information (such the device ID) and transmit it to third-parties.

Power graphs : a survey

This article gives a survey of all results on the power graphs of groups and semigroups obtained in the literature. Various conjectures due to other authors, questions and open problems are also included.

Stability analysis of grid-connected photovoltaic systems using a linear zero dynamic controller

This paper proposed a new linear zero dynamic controller (LZDC) for

Incidence semirings of graphs and visible bases

We consider the incidence semirings of graphs and prove that every incidence semiring has convenient visible bases for its right ideals and for its left ideals, and that these visible bases can be used to determine the weights of all right ideals that have maximum weight and all left ideals that have maximum weight. ©2013 Australian Mathematical Publishing Association Inc..

Enhancement of aerobic granulation by zero-valent iron in sequencing batch airlift reactor

This study elucidates the enhancement of aerobic granulation by zero-valent iron (ZVI). A reactor augmented with ZVI had a start-up time of aerobic granulation (43 days) that was notably less than that for a reactor without augmentation (64 days). The former reactor also had better removal efficiencies for chemical oxygen demand and ammonium. Moreover, the mature granules augmented with ZVI had better physical characteristics and produced more extracellular polymeric substances (especially of protein). Three-dimensional-excitation emission matrix fluorescence showed that ZVI enhanced organic material diversity. Additionally, ZVI enhanced the diversity of the microbial community. Fe(2+) dissolution from ZVI helped reduce the start-up time of aerobic granulation and increased the extracellular polymeric substance content. Conclusively, the use of ZVI effectively enhanced aerobic granulation.

Stabilizing high-dimensional prediction models using feature graphs

We investigate feature stability in the context of clinical prognosis derived from high-dimensional electronic medical records. To reduce variance in the selected features that are predictive, we introduce Laplacian-based regularization into a regression model. The Laplacian is derived on a feature graph that captures both the temporal and hierarchic relations between hospital events, diseases, and interventions. Using a cohort of patients with heart failure, we demonstrate better feature stability and goodness-of-fit through feature graph stabilization.

Zero knowledge grouping proof protocol for RFID EPC C1G2 Tags

In this paper, we propose a novel zero knowledge grouping proof protocol for RFID Systems. Over the years, several protocols have been proposed in this area but they are either found to be vulnerable to certain attacks or do not comply with the EPC Class 1 Gen 2 (C1G2) standard because they use hash functions or other complex encryption schemes. Also, the unique design requirements of grouping proofs have not been fully addressed by many. Our protocol addresses these important security and design gaps in grouping proofs. We present a novel approach based on pseudo random squares and quadratic residuosity to realize a zero knowledge system. Tag operations are limited to functions such as modulo (MOD), exclusive-or (XOR) and 128 bit Pseudo Random Number Generators (PRNG). These can be easily implemented on passive tags and hence achieves compliance with the EPC Global standard while meeting the security requirements.