Electrical and magnetic properties of lithium borate glass containing iron

Journal of Materials Science Letters -     

Effect of the addition of Fe2O3 and heat treatment duration on the magnetic susceptibility of V2O5-P2O5-B2O3 glass system

The effect of the addition of Fe₂O₃ and heat treatment duration on the magnetic susceptibility of vanadium borophosphate glass were studied. The magnetic susceptibility of glass samples was found to increase with increasing Fe₂O₃ content, which may be explained by the formation of the FeO₆ group and the change of Fe²⁺ to Fe³⁺ which has higher paramagnetic properties. No detectable changes in the magnetic susceptibility with heat treatment for the samples containing 0.0, 0.5 and 1.0 mol% Fe₂O₃ was observed. The magnetic susceptibility for the heat treated samples containing 2.5, 5.0 and 7.5 mol% Fe₂O₃ decreases sharply with increasing duration of heat treatment up to 6 h and then remains almost constant. The sharp decrease in magnetic susceptibility of 2.5 mol% Fe₂O₃ is attributed to the increase in the number of ferrous ions. The sharp decrease for samples containing 5.0 and 7.5 mol% Fe₂O₃ is attributed to the increase in the number of Fe³⁺ in tetrahedral co-ordination. The rate of crystallization owing to the heat treatment was calculated and was found to increase with increasing iron oxide content. The geometry of crystallization was found to be in three-, two-and one-dimension(s) for samples containing 2.5, 5.0 and 7.5 mol% Fe₂O₃, respectively.     

Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization

A mobile ad hoc network (MANET) is dynamic in nature and is composed of wirelessly connected nodes that perform hop-by-hop routing without the help of any fixed infrastructure. One of the important requirements of a MANET is the efficiency of energy, which increases the lifetime of the network. Several techniques have been proposed by researchers to achieve this goal and one of them is clustering in MANETs that can help in providing an energy-efficient solution. Clustering involves the selection of cluster-heads (CHs) for each cluster and fewer CHs result in greater energy efficiency as these nodes drain more power than noncluster-heads. In the literature, several techniques are available for clustering by using optimization and evolutionary techniques that provide a single solution at a time. In this paper, we propose a multi-objective solution by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in an ad hoc network as well as energy dissipation in nodes in order to provide an energy-efficient solution and reduce the network traffic. In the proposed solution, inter-cluster and intra-cluster traffic is managed by the cluster-heads. The proposed algorithm takes into consideration the degree of nodes, transmission power, and battery power consumption of the mobile nodes. The main advantage of this method is that it provides a set of solutions at a time. These solutions are achieved through optimal Pareto front. We compare the results of the proposed approach with two other well-known clustering techniques; WCA and CLPSO-based clustering by using different performance metrics. We perform extensive simulations to show that the proposed approach is an effective approach for clustering in mobile ad hoc networks environment and performs better than the other two approaches.     

An architecture modeling framework for probabilistic prediction

In the design phase of business and IT system development, it is desirable to predict the properties of the system-to-be. A number of formalisms to assess qualities such as performance, reliability and security have therefore previously been proposed. However, existing prediction systems do not allow the modeler to express uncertainty with respect to the design of the considered system. Yet, in contemporary business, the high rate of change in the environment leads to uncertainties about present and future characteristics of the system, so significant that ignoring them becomes problematic. In this paper, we propose a formalism, the Predictive, Probabilistic Architecture Modeling Framework (P²AMF), capable of advanced and probabilistically sound reasoning about business and IT architecture models, given in the form of Unified Modeling Language class and object diagrams. The proposed formalism is based on the Object Constraint Language (OCL). To OCL, P²AMF adds a probabilistic inference mechanism. The paper introduces P²AMF, describes its use for system property prediction and assessment and proposes an algorithm for probabilistic inference.     

Enhanced halftone-based secure and improved visual cryptography scheme for colour/binary Images

Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can be seen through naked eye. In this paper, a novel enhanced halftoning-based VC scheme is proposed that works for both binary and color images. Fake share is generated by the combination of random black and white pixels. The proposed algorithm consists of 3 stages i.e., detection, encryption, and decryption. Halftoning, Encryption, (2, 2) visual cryptography and the novel idea of fake share, make it even more secure and improved. As a result, it facilitates the original restored image to the authentic user, however, the one who enters the wrong password gets the combination of fake share with any real share. Both colored and black images can be processed with minimal capacity using the proposed scheme.

     

Energy and load balancing routing protocol for IoT

Due to the limitation of node energy resources, the management of energy consumption is one of the most important problems of the internet of things (IoT). Therefore, many studies have tried to optimize and manage energy consumption by focusing on different techniques. Although each of these studies has improved and optimized energy consumption, there are many important problems, including maintaining traffic balance and energy consumption of network nodes. Therefore, a new method is necessary to maintain the load and energy balancing of network nodes. Therefore, this paper introduces energy and load balancing routing protocol for IoT (ELBRP) based on the development of the RPL routing protocol and the efficiency of data distribution technique. The ELBRP performance has three steps. In the first step, along with the process of sending DODAG information object (DIO) messages, the status of network nodes is evaluated. In the second step, the DODAG communication graph is formed according to the ELBRP. In the third step, data transmission is done according to the distribution technique with the goal of balancing traffic and energy. The simulation results using cooja simulator showed the superiority of ELBRP in improving energy consumption and successful delivery ratio, reducing delay and increasing the network lifetime compared to the similar methods.     

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.     

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.