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Many technical improvements have recently been made in the field of road safety, as accidents have been increasing at an alarming rate, and one of the major causes of such accidents is a driver’s lack of attention. To lower the incidence of accidents and keep safe, technological innovations should be made. One way to accomplish this is with IoT-based lane detection systems, which function by recognizing the lane borders on the road and then prompting the turning of the road. Because of the various road conditions that one can encounter when driving, lane detection is a difficult problem. An image processing-based method for lane detection has been proposed in this paper. In this regard, each frame from the video is extracted and image processing techniques are applied for the detection of lanes. The frame which is extracted from the video is then subjected to a Gaussian filter for the removal of noise. Subsequently, color masking has been used to process the frame to detect only the road lanes, whose edges are obtained by applying the canny edge detection algorithm. Afterward, the Hough transform has been applied to the region of interest to extrapolate the lines. Finally, the path is plotted along the lines, and turns are predicted by using the concept of vanishing points.

The UK Government has announced its plans to bring forward the deadline for phasing out all petrol and diesel vehicles from 2040 to 2030, 10 years earlier than planned. This is a radical acceleration in the transition to electric mobility. The need to draw up coherent and robust UK regulatory structures for managing the end-of-life consequences of this transition is now more urgent than ever. This article explores the potential role of extended producer responsibility (EPR) in facilitating the safe and sustainable management of electric vehicle (EV) batteries at their end of life. It outlines the current EV battery problem from the perspective of end-of-life management, before exploring the utility of EPR in achieving a circular economy approach and reviewing the current EPR frameworks that would apply to this waste stream once a battery is no longer powerful enough to drive an EV. We conclude that current EPR frameworks for battery management are neither sufficiently clear nor suitably robust to ensure safe and sustainable electric lithium ion battery management and suggest how these could be remodelled to achieve better outcomes in this area.

Purpose With the UK’s accelerating plans to transition to electric mobility, this paper aims to highlight the need for policies to prepare for appropriate management of electric vehicle (EV) lithium-ion batteries (LIBs) as they reach the end of their life. Design/methodology/approach This is a regulatory review based on projections of EV LIBs coming off the market and associated problems of waste management together with the development of a servitisation model. Findings Circular economy in EV LIBs is unlikely to shape itself because LIB recycling is challenging and still in development. LIB volumes are insufficient for recycling to be currently profitable, and a circular economy here will need to be driven by regulatory intervention. Ignoring the problem carries potentially high environmental and health costs. This paper offers potential solutions through new EV ownership models to facilitate a circular economy. Research limitations/implications The authors suggest a new EV ownership model. However, despite environmental benefits, re-shaping the fundamentals of market economies can have disruptive effects on current markets. Therefore, further exploration of this topic is needed. Also, the data presented is based on future projections of EV markets, battery lifespan, etc., which are uncertain at present. These are to be taken as estimates only. Originality/value The paper proposes regulatory interventions or incentives to fundamentally change consumer ideas of property ownership for EVs, so that EV automotive batteries remain the property of the manufacturer even when the consumer owns the car.

Linear thermal instability of a horizontal nanofluid layer considering thermally non-equilibrium effect by applying magnetic field is investigated. Analysis is done with more practical boundary conditions on volume fraction of nanoparticles i.e. zero nanoparticle flux is considered across free-free boundaries. A two-temperature model has been introduced in order to consider the influence of thermally non-equilibrium phases (fluid and particle). Local thermal non-equilibrium (LTNE) means that there is a difference in temperature between the two phases that cannot be neglected. LTNE model has acquainted three parameters modified thermal diffusivity ratio, modified thermal capacity ratio and Nield number in addition to the Chandrasekhar number which was introduced due to magnetic field. Results are interpreted analytically as well as graphically. It is found that system stability enhances by enhancing the values of Chandrasekhar number, modified thermal diffusivity ratio, modified thermal capacity ratio and falls with the rise in Nield number, Lewis number, concentration Rayleigh number and modified diffusivity ratio. Critical wave number tends to its local thermal equilibrium (LTE) when there is no interaction among the particle and fluid phases and also when both the phases act as a single phase due to having identical temperatures. Further, critical values of two different nanomaterials (alumina and copper) immersed in base fluid water is compared. It is found that with in thermally non-equilibrium conditions among the phases along with revised boundary conditions copper–water nanofluid is having higher critical values (critical wave number as well as critical Rayleigh number) than alumina-water nanofluid in the presence of magnetic field. Thus, stability of copper nanomaterials is more pronounced as compared to alumina nanomaterials under the above said conditions.

This article is a maiden and naive attempt to formulate, analyse and investigate the Rayleigh–Taylor (RT) instability of two superimposed horizontal layers of nanofluids having different densities. Conservation equations are formulated and linearised by keeping in mind that density of the base fluids as well as nanoparticles is not constant. Linearised perturbed equations are sorted out by using the technique of normal modes and a dispersion relation incorporating the effects of surface tension, Atwood number and volume fraction of nanoparticles is obtained. Stable and unstable modes of RT instability are scrutinised using Routh–Hurtwitz criterion in the presence/absence of nanoparticles and presented graphically. Numerical calculations have been performed to explore the effect of surface tension, Atwood number and volume fraction of the nanoparticles. It is observed that in the presence/absence of nanoparticles, surface tension has a significant impact on stabilising the unstable mode of RT instability whereas Atwood number and volume fraction of nanoparticles hasten this instability. The graphical representations of these numerical investigations confirm the very explanation of RT instability under the effect of different parameters that have significant impact on the intensity of growth rate.

Pedagogical Agents are human-like, life-like animated characters that help the learner learn through multimedia learning environments presented on computer screen and exhibit their behaviour according to the learner’s abilities. This study mainly focuses on the design, architecture, role and functionalities that pedagogical agent executes along with the variety of multi-agents. This review also discusses the available tutoring systems for learning-disabled learners. This research highlights the wide-spanning focus areas for future developmental research on pedagogical agents.

This article focuses on the hidden insights about the Rayleigh-Taylor instability of two superimposed horizontal layers of nanofluids having different densities in the presence of rotation factor. Conservation equations are subjected to linear perturbations and further analyzed by using the Normal Mode technique. A dispersion relation incorporating the effects of surface tension, Atwood number, rotation factor and volume fraction of nanoparticles is obtained. Using Routh-Hurtwitz criterion the stable and unstable modes of Rayleigh-Taylor instability are discussed in the presence/absence of nanoparticles and presented through graphs. It is observed that in the absence/presence of nanoparticles, surface tension helps to stabilize the system and Atwood number has a destabilizing impact without the consideration of rotation factor. But if rotation parameter is considered (in the absence/presence of nanoparticles) then surface tension destabilizes the system while Atwood number has a stabilization effect (for a particular range of wave number). The volume fraction of nanoparticles destabilizes the system in the absence of rotation but in the presence of rotation the stability of the system is significantly stimulated by the nanoparticles.

Nature inspired algorithms have become popular for discovering classification rules due to their ability to effectively handle large and complex search spaces. However, nature inspired algorithms have to compute the fitness of individuals (candidate classification rules) over successive generations repeatedly. Each fitness computation requires scanning the training data. Since the database scan is computationally expensive operation, the execution time for nature inspired algorithms for discovering classification rules grows unreasonably faster for bulky datasets. This paper proposes a novel fitness computation framework for nature inspire algorithms by using a list structure. The indices of instances, covered by every possible attribute-value pair with respect to each class in the training data, are stored in the suggested list structure. The list is prepared only once in advance and stores all information to compute the fitness of any rule that may come up in the life time of the nature inspired algorithm. The existence of the pre-maintained list eliminates the need of scanning training data again and again for fitness computation. We have conducted experiments on 12 datasets from UCI machine learning repository. The results show that the suggested fitness computational framework brings significant speed gain without compromising predictive accuracy. Although, a Genetic Algorithm is used for classification rule discovery as the nature inspired algorithm in this paper, the fitness computation framework is general and can be used with any other nature inspired algorithm.

Although huge number of features and instances give more information about the data yet it presents irrelevant and superfluous (noisy) features and instances. Therefore, there is a need to remove such type of features and instances for efficient classification. The more appropriate term used for simultaneous reduction of features and instances, called dual selection, leads to faster classification without sacrificing much for the classifier’s performance. In the current paper, we suggest a parallel genetic algorithm (PGA) for dual selection that allows an efficient selection of features as well as instances. The paper encapsulates experimental analysis on ten medium- and large-sized datasets. The results confirm the superiority of proposed PGA in terms of higher accuracy along with considerable reduction rates.

Authentication is among the initial steps of network formulation. Lightweight authentication processes are preferred for resource-constrained devices. These processes are efficient in terms of computational and communicational costs but inappropriate in providing required security rating. This work explores the feasibility of elliptic curve-based cryptography (ECC) for authentication processes. Three lightweight authentication protocols, which use ECC are identified and analysed for hierarchical network. These protocols are simulated and validated using lightweight cryptography primitives and protocols. In results, it is observed that out of three protocols protocol 1 performs better for 75 and 1000 nodes networks. In comparative analysis, a minimum of 14.1% and maximum of 24.7% throughput improvement is observed. Further, delay is reduced by 15.6% (minimum) and 31.3% (maximum) for protocol 1 as compared to protocol 2 and protocol 3.

Learner Model is the base for providing the adaptivity in the Intelligent Tutoring Systems (ITS) as learner performance data and information are stored in the learner model. Recently, there has been a rapid progress in education delivery through web due to the advancement in internet technology. With the limitations such as lack of adaptive support and presentations reasoned that research has been expanded in the domain of ITS. The aim of the paper is to present an adaptive framework of the learner model using learner characteristics that helps to provide the adaptive presentation and feedback to the prospective learner. Adaptive learner model has three component such as Learner Characteristics Model, Learner Classification Model, and Learner Adaptation Model. Learner Characteristics Model includes the characteristics of learner such as learning style, knowledge levels, and cognitive and meta-cognitive skills. Learner Classification Model classifies the learner into groups based on his/her learning style, levels of knowledge, and performance data and implemented through artificial intelligence technique. The Learner Adaptation Model recommends the tutoring strategy which best suits the learner to provide the adaptation.

Optimum peace maintenance amongst monitored communities is needed for sustainable and social development of the communities and society. The maintenance of peace with the application of correct strategies should be able to deliver maximum degree of trust and strength of relations among the communities. This study presents STES, an expert system that utilizes a biologically inspired novel optimization technique of semiotics that imitates the behavior of predators in marking their territories with their odours and physical marks, known as territorial predator scent marking strategies (TPSMS), to obtain appropriate strategies for maintenance of peace. The current study presents the use of game theory and deterministic finite automata (DFA) theory to model and design a strategic rule set for the Expert System to take decisions for maintenance of optimum peace amongst the communities. A DFA is obtained using various peace states as the set of states, and the alphabet set is constructed by modelling a two-person Prisoner’s Dilemma game over TPSMS. A context free grammar is then obtained by employing top-down parsing, which would suggest the proper rules to be included in the rule set of the Expert System for the maintenance of peace and bringing out sustainable development. A key objective in such decision making therefore would be to select the semiotical peace maintenance strategy in a manner that their applicability configuration ensures a high degree of cooperation within often intensely conflicting communities, over a long term resource use scenario. Key words: Peace, semiotics, prisoner’s dilemma game, deterministic finite automata (DFA), context free grammar, top-down parsing, territorial predator scent marking strategy.

Presence of superfluous and undesirable features and instances influences the performance of classification algorithm in a negative manner. The task of removing these redundant features and instances is considered important to arrive at accurate and comprehensible classification models. It is all the true about KNN classifier which suffers from curse of dimensionality problem and higher computation complexity in incidence of large number of features and instances. The problem of instance and feature selection is well conceived as a multi-objective problem. In this paper, we apply a CHC (cross generational, heterogeneous recombination, elitist selection and cataclysmic mutation operator) approach in multi-objective environment for selecting representative features and instances simultaneously. The hybridized form of multi-objective genetic algorithm (NSGAII) and CHC algorithm has proved its appropriateness for the simultaneous selection of instances and features. The effectiveness of the proposed algorithm is confirmed by comparing it with other standard algorithms. The proposed approach achieves higher accuracies and competitive reduction rates.

Data reduction has always been an important field of research to enhance the performance of data mining algorithms. Instance selection, a data reduction technique, relates to selecting a subset of informative and non-redundant examples from data. This paper deals with the problem of instance selection in a multi-objective perspective and, hence, proposes a multi-objective cross-generational elitist selection, heterogeneous recombination, and cataclysmic mutation (CHC) for discovering a set of Pareto-optimal solutions. The suggested MOCHC algorithm integrates the concept of non-dominating sorting with CHC. The algorithm has been employed to eight datasets available from UCI machine learning repository. The MOCHC has been successful in finding a range of multiple optimal solutions instead of yielding a single solution. These solutions provide a user with several choices of reduced datasets. Further, the solutions may be combined into a single instance subset by exploiting the promising characteristics across the potentially good solutions based on some user-defined criteria.

Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the feature subset under consideration. Hence, Multi-Objective Genetic Algorithms (MOGAs) are a natural choice for this problem. In this paper, we propose a hybrid approach (a wrapper guided by filter approach) for feature selection which employs a MOGA at filter phase and a simple GA at the wrapper phase. The MOGA at filter phase provides a non-dominated set of feature subsets optimized on several criteria as input to the wrapper phase. Now, Genetic Algorithm at wrapper phase does the classifier dependent optimization. We have used support vector machine (SVM) as the classification algorithm in the wrapper phase. The proposed hybrid approach has been validated on ten datasets from UCI Machine learning repository. A comparison is presented in terms of predictive accuracy, feature subset size and running time among the pure filter, pure wrapper, an earlier hybrid approach based on genetic algorithm and the proposed approach.

Cooperative learning strategies have occupied a prominent place among language learning methodologies. t his study quantitatively validates the effect of the Cooperative Learning approach-Cooperative Integrated Reading Composition (CIRC) on the Writing Composition achievement in English among seventh graders. Cooperative Learning was compared with conventional teaching-learning classroom structure using an experimental design. 140 students of seventh class were randomly selected out of which 70 students formed Experimental Group (E) and 70 students formed Control Group (C). Writing Composition achievement test in English and Instructional Material were developed by the investigators themselves. Whole teaching-learning process was carried out for eight weeks only. at the end of the experiment, Writing Composition achievement test in English was given to the subjects. Data were analyzed by using the t-test. the findings revealed that Experimental Group outscored significantly Control Group on post-test showing the obvious supremacy of Co-operative Learning technique (CIRC) over Conventional Method of teaching. It was concluded that co-operative learning is an effective approach which needs to be incorporated in teaching Writing Composition to improve writing skill.

Summary The conservation equations for binary nanofluid system are solved to get eigenvalue equation. Valid approximations are used to simplify the complex expressions for analytical results. Oscillatory motions are not possible and hence mode of convection is invariably through stationary mode. Numerical computations are carried out for water based nanofluids to analyze solutal effects on the stability of the system using the software Mathematica. Higher conductivity and density of copper makes it more stable than alumina.

Effect of vertical magnetic field on thermal convection of a porous nanofluid layer is considered using Darcy's law. System of nanofluid layer in which nanoparticle concentration on the top is higher than that at the bottom is considered. Normal mode technique and single term Galerkin method is employed to investigate the stability and derive the eigen value problem. Brownian motion and thermophoretic forces are introduced due to the presence of nanoparticles. The mode of instability is invariably through stationary convection. The oscillatory instability may be possible when nanoparticles concentrate near the bottom of the layer, so that two buoyancy forces act in opposite directions. The value of the critical Rayleigh number is decreased by a substantial amount due to the presence of nanoparticles. The critical Rayleigh numbers and the critical wave numbers for alumina-water nanofluid and copper-water nanofluid for different values of Chandrasekhar number and porosityare found. Alumina-water nanofluid is found to exhibit higher stability as compared to copper-water nanofluid in the presence/absence of magnetic field and/or porous medium. Effect of nanoparticle volume fraction and temperature difference on stability of the system is investigated taking permissible values of various parameters. Magnetic field, Brownian to thermal diffusivity ratio and temperature difference is found to stabilize the nanofluid layer, appreciably, whereas the effect of volume fraction of nanoparticles, density ratio and porosity is to destabilize the layer.

This article examines the judicial approach to emotional harm claims from a medical perspective. Legal rules in this area are already recognised as being illogical and incoherent. Psychological and psychiatric research illustrate that they also conflict with empirical findings. By basing claims on erroneous criteria, courts may deny liability in meritorious cases, and impose liability in possibly less deserving claims. This not only brings the law into disrepute, but also reinforces the stigma that surrounds mental illness, and does disservice to an already misunderstood and vulnerable section of people in society. The article examines the evidence for the threshold requirement that distress must qualify for a psychiatric diagnosis to be actionable, and for the Alcock secondary victim criteria. It contends that these legal rules are based in misconceptions about mental illness and trauma, and suggests an alternative approach that is more principled, yet also addresses policy concerns about excessive liability.

The paper presents a linear stability analysis for the onset of convection in a nanofluid layer with magnetic field. The nanofluid layer incorporates the effect of Brownian motion along with thermophoresis. System of nanofluid layer in which nanoparticles concentrate near the bottom of the layer has been considered. The density gradient caused by such a bottom heavy nanoparticle distribution competes with the density variation caused by heating from the bottom as a result of which the mode of instability is oscillatory rather than stationary convection. It is established that the instability is almost purely a phenomenon due to buoyancy coupled with the conservation of nanoparticles. It is independent of the contribution of Brownian motion and thermophoresis to the thermal energy equation. Rather, the Brownian motion and thermophoresis enter to produce their effects directly into the equation expressing the conservation of nanoparticles so that the temperature and particle density are coupled in a particular way, and that results in the thermal and concentration buoyancy effects being coupled in the same way. Both stationary and oscillatory convection are investigated using normal mode technique. It is found that instability sets in as oscillatory motions rather than stationary convection. The effects of the Lewis number (Le), Concentration Rayleigh number (Rn), Modified diffusivity ratio (NA), magnetic field (Q) on the stability of the system has been investigated. Magnetic field is found to stabilize the nanofluid layer for both the cases: the stationary convection and the oscillatory motions. The effect of various parameters on thermal Rayleigh number has been presented graphically.

The growing concern for education and innovative technologies has led to a new dimension of learning. The paper proposes a new framework NormATel using the concept of Normative Multi-Agent system, Activity Theory and e-Learning. The basic idea of the paper is to make e-learning more user specific using the concept of Web 3.0, like its ability to work on two separate modes: single user learning and community based learning. The proposed framework is verified using Deontic Logic.

Cloud Computing, an emerging trend, in the e-learning sector has attracted number of service providers to the market in very less time, providing users with several applications at their disposal. However, while providing such service, not sufficient importance is given to the quality of the service, especially from the user's point of view. Hence it becomes necessary to monitor, track and quantify the QoS of the cloud computing e-learning applications in order to provide the right information to both the customers and the service providers. This information would help both the parties in terms of the comparison between the expectations and the capacity to meet them, but in this sector there is no standard model which defines the QoS parameters from the user's point of view. Thus, the need arises for developing a metrics model for enhancing the quality of service in cloud computing e-learning applications for higher education sector. In the current work, Quality of Service models are studied and comprehensive review of work done in this field is presented. Additionally an innovative QoS model for resolving this issue has been suggested.

To study the interdependent influence of rotation and magnetic field while acting simultaneously and keeping in mind applications of flow through porous medium in geophysics, especially in the study of earth's core and the presence of nano particles therein; the hydromagnetic stability of rotating nanofluid layer in porous medium is considered. Darcy-Model is incorporated for free-free boundaries. Method of superposition of basic modes and single term Galerkin approximation is used to get the eigen value equation. The mode of instability occurs through oscillatory motions instead of stationary convection. Numerical analysis for water based nanofluids with Cu and Ag nanoparticles is carried out using the software Mathematica. Ag-water nanofluid exhibits higher stability than Cu-water nanofluid in the presence of rotation and magnetic field in porous medium. Rotation parameter and magnetic field parameter are found to inhibit the onset of thermal convection while permeability assists the same.

The research on data reduction techniques has become important to enhance the efficacy and efficiency of data mining algorithms which may otherwise be compromised in the presence of a large number of irrelevant attributes and redundant instances. Data can be reduced by selecting either a subset of attributes or instances. Dual selection treats the problem of feature and instance selection together as a single optimisation problem. The problem of dual selection is relatively difficult as it involves an enormously large search space. In this paper, we propose a hybrid instance feature selection; HIFS-CHC method using heterogeneous recombination and cataclysmic mutation; CHC adaptive search genetic algorithm to solve the problem of dual selection. The proposed approach works in two stages. In the first stage, K-means clustering algorithm is used to reduce the search space. The second stage incorporates stratified prototype selection and CHC algorithm for data reduction. The clustering based hybrid scheme is experimentally tested on sixteen benchmark datasets and compared with the other similar data reduction algorithms with respect to the predictive accuracy, reduction rate and execution time. Experimental results show that the proposed method outperforms the other methods in terms of reduction rate and execution time while preserving the predictive accuracy almost at the same level.

Ant colony optimisation (ACO) algorithms have been used to discover accurate, and comprehensible classification rules. Discovering exceptions using ACO is an underexplored area of research. Most of the classification algorithms focus on discovering rules with high generality. Since exceptions have low support, these often get ignored as noise. This paper proposes an ant colony optimisation (ACO)-based algorithm to discover classification rules in if-then-unless framework, where the unless part contains exceptions. We have conducted experiments on ten datasets from the UCI machine learning repository. The suggested algorithm is found to be competitive with the two well-known ACO-based classification algorithms (Ant-Miner and cAnt-MinerPB) with respect to predictive accuracy and comprehensibility. The algorithm has been able to capture a number of exceptions across several datasets. The classification rules discovered with exceptions are accurate, semantically comprehensible and interesting. These rules provide an opportunity to amend one's decision in exceptional circumstances.

DNA microarray technology is a nascent technology having vast applications in the molecular biology. Despite of its many useful applications in disease diagnosis and drug discovery, analysing DNA microarray data has become a challenge for bio-analysts. Microarrays present curse of dimensionality problem and call for the development of new techniques to handle it. The problem of dimensionality reduction or feature/gene selection has been posed as a multi-objective problem in the literature and can be better solved using multi-objective meta-heuristics. In this paper, we have proposed a multi-objective ant colony optimisation (MOACO) algorithm for gene selection. The contribution of this paper is to obtain multiple non-dominated solutions instead of a single best solution. These multiple solutions enable a user to select a solution according to his/her preference or application domain. The predicted genes also called as bio-markers, can help in disease diagnosis and may direct the progress of drug efficacy. A comparative analysis of the proposed method has been done using some conventional feature selection techniques and a method based on multi-objective particle swarm optimisation (MOPSO) proposed by Mukhopadhyay and Mandal (2014). The results obtained confirm the superiority of MOACO-based approach over the others in terms of various performance metrics.

Rayleigh-Benard convection of a rotating nanofluid layer under local thermal non-equilibrium model in the presence of magnetic field is investigated. The impact of external applied forces rotation and magnetic field are exhibited by introducing Coriolis force term and Lorentz force term in the momentum equations along with the Maxwell's equations. For considering the impact of local thermal non-equilibrium between the fluid and particle phases; a two-temperature model has been considered. The problem is analysed by making use of the normal mode technique and one term approximation of Galerkin type weighted residual method. Due to thermal non-equilibrium three additional parameters Nield number, modified thermal capacity ratio, modified thermal diffusivity ratio and due to the presence of rotation and magnetic field Taylor number and Chandrasekhar number are introduced. The impact of these parameters on thermal Rayleigh number has been found analytically as well as numerically and presented graphically.

The impact of vertical magnetic field on the thermal instability of a horizontal porous nanofluid layer using Darcy model is considered for free-free boundaries. Brownian motion and thermophoretic forces are introduced due to the presence of nanoparticles and Lorentz's force term is added in the momentum equation along with the Maxwell's equations due to magnetic field. Normal mode technique and single term Galerkin approximation is employed to investigate the instability and derive the eigen value problem. It is found that the mode of instability is through oscillatory motions for bottom heavy suspension of nanoparticles. The reason for the existence of oscillatory motions is due to the occurrence of two opposite buoyancy forces i.e. density variation due to heating and density gradient of nanoparticles at the bottom of the layer. The thermal Rayleigh number increases with the increase of Chandrasekhar number and decreases with the increase of porosity. The effect of Lewis number, modified diffusivity ratio, concentration Rayleigh number and heat capacity ratio on the onset of thermal convection has been investigated analytically and presented graphically.

A good P2P file sharing system is usually expected to achieve the following design goals: scalability, routing efficiency and complex query support. In this paper, we propose such a system, called PSON, which can satisfy all the three requirements. PSON is essentially a semantic overlay network of logical nodes, in which queries are routed on the basis of semantics. A logical node is formed by a cluster of peers that are close to each other in the physical network. Each cluster selects a powerful peer as super peer to support routing in the overlay network. To facilitate routing, all the super peers (or logical nodes) are organized in the form of a balanced tree. By exploiting the concepts of hierarchy and semantics, PSON can support complex queries in a scalable and efficient way. In this paper, we will describe the system architecture, and examine the key component of PSON design, i.e., semantic overlay construction and routing. We also conduct simulations, and show that the query routing in PSON is very efficient (O(log(n)) in the case of exact query) .

In the present paper, hydromagnetic stability of alumina-water and copper oxide-water nanofluids is investigated for top heavy configuration of nanoparticles. Brownian motion and thermophoretic forces are introduced due to the presence of nanoparticles and Lorentz force term is added in the momentum equation due to the presence of magnetic field. The problem has application in geophysics due to earth's magnetic field and enhanced heat transfer characteristics of nanofluids. Computations are carried out within the frame work of normal mode technique and single term Galerkin approximation. An eigen value equation representing the relation between various nanofluid parameters, magnetic field, growth rate of disturbance and wave number is obtained. It is observed that the thermal Rayleigh number increases with the increase in Chandrasekhar number and decreases with the increase in volume fraction of nanoparticles. Nanofluids with Al2O3 nanoparticles exhibit higher stability than the nanofluids containing CuO nanoparticles. The mode of instability is found to be through stationary convection. It is expected since for oscillatory motions to exist two of the buoyancy forces must act in the opposite directions.