Selenium Speciation Studies from Soan-Sakesar Valley,Salt Range,Pakistan

Abstract

A regional study of Selenium (Se) speciation in surface waters, groundwaters, and saturation extracts from Soan-Sakesar valley Salt-Range, Pakistan was started in March 1994 and completed in October 1996. Groundwaters, streams, and springs had median concentrations of: total Se 48, 404.5, and 82 μg/l; Se^+VI 40, 350 and 51 μg/l; Se^+IV 8, 48 and 29 μg/l; Se^-II 0, 6.5 and 2.0 μg/l, respectively. The concentration of Se exceeded the recommended water quality guidelines for drinking and irrigation water of 10 and 20 μg/l, respectively. In saturation extracts, median total Se, Se^+VI, Se^+IV and Se^-II were 190, 146, 37 and 7.0 μg/l, respectively. Uchhali, Khabbaki and Jahlar lake water samples had the mean concentration of: total Se 2103, 670 and 297 μg/l; Se^+VI 1777, 470, and 233 μg/l; Se^+IV 291, 166 and 39 μg/l; Se^-II 35, 34 and 25 μg/l, respectively. The Sakesar limestone Formation of Eocene age rich in shales and fossils (median Se concentration 7.2 μg/kg) are considered to be the source of selenium that have enriched soils of Soan-Sakesar valley. Selenim speciation results show the abundance of selenate in all the environmental samples. A linear relationship between Se^+VI and pH (R²= 0.84, 0.58, 0.88, 0.82, and 0.91; significant at the 0.05 level) for groundwater, saturation extract, stream, spring, and lake waters were noted, suggesting that the Se^+VI in the samples is highly associated with pH. Linear relationships were established between Se^+VI and Mg, NO₃, and SO₄ for groundwater (R²= 0.24, 0.16, and 0.64; significant at the 0.01 level) and surface water (R²= 0.96, 0.14, and 0.91; significant at the 0.001 level). This suggests the high concentrations of Mg, NO₃ and SO₄, which strongly adsorb and compete with selenium for surface site, increases the Se^+VI fraction. The results show that the higher percentage of selenate on other species in the analyzed samples is favored by higher pH and oxidizing environments of the valley.     

ELF-Miner: using structural knowledge and data mining methods to detect new (Linux) malicious executables

Linux malware can pose a significant threat—its (Linux) penetration is exponentially increasing—because little is known or understood about Linux OS vulnerabilities. We believe that now is the right time to devise non-signature based zero-day (previously unknown) malware detection strategies before Linux intruders take us by surprise. Therefore, in this paper, we first do a forensic analysis of Linux executable and linkable format (ELF) files. Our forensic analysis provides insight into different features that have the potential to discriminate malicious executables from benign ones. As a result, we can select a features’ set of 383 features that are extracted from an ELF headers. We quantify the classification potential of features using information gain and then remove redundant features by employing preprocessing filters. Finally, we do an extensive evaluation among classical rule-based machine learning classifiers—RIPPER, PART, C4.5 Rules, and decision tree J48—and bio-inspired classifiers—cAnt Miner, UCS, XCS, and GAssist—to select the best classifier for our system. We have evaluated our approach on an available collection of 709 Linux malware samples from vx heavens and offensive computing. Our experiments show that ELF-Miner provides more than 99% detection accuracy with less than 0.1% false alarm rate.     

A correlation-based ant miner for classification rule discovery

In recent years, a few sequential covering algorithms for classification rule discovery based on the ant colony optimization meta-heuristic (ACO) have been proposed. This paper proposes a new ACO-based classification algorithm called AntMiner-C. Its main feature is a heuristic function based on the correlation among the attributes. Other highlights include the manner in which class labels are assigned to the rules prior to their discovery, a strategy for dynamically stopping the addition of terms in a rule’s antecedent part, and a strategy for pruning redundant rules from the rule set. We study the performance of our proposed approach for twelve commonly used data sets and compare it with the original AntMiner algorithm, decision tree builder C4.5, Ripper, logistic regression technique, and a SVM. Experimental results show that the accuracy rate obtained by AntMiner-C is better than that of the compared algorithms. However, the average number of rules and average terms per rule are higher.     

Metallurgical and tribological investigations of aluminium bronze bushes made by a novel centrifugal casting technique

Aluminium bronzes have been used as high strength, corrosion and wear resistance alloys for many decades. However, the alloy has a narrow solidification range and a complex nature which has confined its use to a limited field of applications. Published data on the wear characteristics and the metallurgical nature of the alloy system is limited, leaving wide possibilities for research. In the work described in this paper a study has been made of the metallurgical and tribological characteristics of aluminium bronzes conforming to BS 1400 AB2 specification. A novel centrifugal casting technique using a bonded sand lining integrated into a standard rotating die was adapted to cast a series of test bushes. Mechanical and microscopic aspects are studied and an attempt is made to correlate the physical properties with the tribological behaviour of the bushes. The results show that the test behaviour of the test bush was in some respects better than that of commercially produced bushes made by forging techniques. Detrimental embrittlement, due to ‘self annealing’ is significantly reduced by the adoption of the novel centrifugal casting technique referred to above.     

A nonlinear quantization scheme for two-layer HDR image encoding

Two-layer schemes provide an effective method of encoding high dynamic range images with backward compatibility. The first layer is the tone-mapped low dynamic range version of the original image, used for visualization. The residual information that cannot be preserved in the first layer is stored in the second layer, which itself is generally encoded as an image of a fixed bit-depth. Any further details that cannot be preserved in the second layer are discarded. In this paper, we present a nonlinear quantization algorithm that can significantly enhance the amount of details that can be preserved in the second layer, and therefore improve the encoding efficiency. The proposed technique can be incorporated in any existing two-layer encoding method and leads to significant improvement in their performance.     

Process optimization for efficient biomediated PHA production from animal-based waste streams

Conventional polymers are made of crude oil components through chemical polymerization. The aim of the project ANIMPOL is to produce biopolymers by converting lipids into polyhydroxyalkanoates (PHA) in a novel process scheme to reduce dependence on crude oil and decrease greenhouse gas emissions. PHA constitutes a group of biobased and biodegradable polyesters that may substitute fossil-based polymers in a wide range of applications. Waste streams from slaughtering cattle are used as substrate material. Lipids from rendering are used in this process scheme for biodiesel production. Slaughtering waste streams may also be hydrolyzed to achieve higher lipid yield. Biodiesel then is separated into a high- and low-quality fraction. High-quality biodiesel meets requirements for sale as fuel and low quality is used for PHA production as carbon source. Selected offal material is used for acid hydrolysis and serves as a source of organic nitrogen as well as carbon source for PHA-free biomass with high production rate in fermentation process. Nitrogen is a limiting factor to control PHA production during the fermentation process. It is available for bacterial growth from hydrolyzed waste streams as well as added separately as NH₄OH solution. Selected microbial strains are used to produce PHA from this substrate. The focus of the paper is about an overview of the whole process with the main focus on hydrolysis, to look for the possibility of using offal hydrolysis as an organic nitrogen substitute. The process design is optimized by minimizing waste streams and energy losses through cleaner production. Ecological evaluation of the process design will be done through footprint calculation according to Sustainable Process Index methodology.     

Hybrid model of content extraction

We present a hybrid model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features like link density and text distribution across the node to predict significance of the node towards overall content provided by the document. Once significance of the nodes is determined, the formatting characteristics like fonts, styles and the position of the nodes are evaluated to identify the nodes with similar formatting as compared to the significant nodes. The proposed hybrid model is derived from two different models, i.e., one is based on statistical features and other on formatting characteristics and achieved the best accuracy. We describe the validity of model with the help of experiments conducted on the standard data sets. The results revealed that the proposed model outperformed other existing content extraction models. We present a browser based implementation of the proposed model as proof of concept and compare the implementation strategy with various state of art implementations. We also discuss various applications of the proposed model with special emphasis on open source intelligence.     

Nanograin Mn-Zn ferrite smart cores to miniaturize electronic devices

Conventional ceramic and sol-gel auto combustion routes were adopted to develop Mn-Zn ferrite cores. To control high frequency (>500 kHz) losses, zirconia (0.2 wt%) and calcia (0.04 wt%) were added in Mn_0.57Zn_0.35Fe_2.08O₄. The results revealed that Mn-Zn ferrite smart cores synthesized by auto combustion process have superior properties than conventionally prepared cores. It is believed that the presence of unique properties such as nanograin microstructure, light weight and short height (thickness) dimensions have played their role to enhance the magnetic impedance of smart core to manifold. Fabricated smart core excellently performed in a test frequency band of 3-15 MHz.     

Parallel query execution over encrypted data in database-as-a-service (DaaS)

The Journal of Supercomputing - The main challenge in database-as-a-service is the security and privacy of data because service providers are not usually considered as trustworthy. So, the data...     

Distributed composition of complex event services in IoT network

The Journal of Supercomputing - In this paper, a distributed solution is presented for the composition of complex event services in the Internet of Things (IoT) environments. The composition of...