Approximate analytical solutions for self-similar flows of a dusty gas with variable energy

Approximate analytical solutions are obtained for the unsteady one dimensional self-similar flow behind a strong shock wave propagating in a mixture of a gas and small solid particles at rest. It is assumed that variable energy is deposited at the shock and the total energy of the flow behind the shock is assumed to be varying with shock radius obeying a power law. The initial density of the medium is taken to be a constant. A simple integral method is employed to study the adiabatic and isothermal flows. The effect of the parameter characterizing the variation of total energy of the flow, on solutions of the flow field is investigated in detail. It as also shown that the similarity solutions of perfect gas with variable energy can be obtained as a particular case of this problem by taking the initial volume fraction of solid particles in the mixture as zero. Approximate solutions are compared with the numerical solutions and found a good agreement between them.     

Exercise therapy for depression in middle-aged and older adults: Predictors of early dropout and treatment failure.

Psychosocial factors predicting treatment dropout or failure to benefit from treatment were identified in a randomized trial of exercise therapy and pharmacotherapy for major depression. One hundred fifty-six men and women over age 50 diagnosed with major depressive disorder were assigned to a 16-week program of aerobic exercise, medication (sertraline), or a combination of exercise and medication. Thirty-two patients (21%) failed to complete the program and were considered treatment "dropouts." At the end of 16 weeks, 83 patients (53%) were in remission; the remaining patients not in remission were considered treatment "failures." Baseline levels of self-reported anxiety and life satisfaction were the best predictors of both patient dropout and treatment success or failure across all treatment conditions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

CoSec-RPL: detection of copycat attacks in RPL based 6LoWPANs using outlier analysis

Telecommunication Systems - The IPv6 routing protocol for low-power and lossy networks (RPL) is the standard routing protocol for IPv6 based low-power wireless personal area networks (6LoWPANs). In...     

LES of intermittency in a turbulent round jet with different inlet conditions

Large eddy simulation (LES) is a promising technique for accurate prediction of turbulent free shear flows in a wide range of applications. Here the LES technique has been applied to study the intermittency in a high Reynolds number turbulent jet with and without a bluff body. The objective of this work is to study the turbulence intermittency of velocity and scalar fields and its variation with respect to different inlet conditions. Probability density function distributions (pdf) of instantaneous mixture fraction and velocity have been created from which the intermittency has been calculated. The time averaged statistical results for a round jet are first discussed and comparisons of velocity and passive scalar fields between LES calculations and experimental measurements are seen to be good. The calculated probability density distributions show changes from a Gaussian to a delta function with increased radial distance from the jet centreline. The effect of introducing a bluff body into the core flow at the inlet changes the structure of pdfs, but the variation from Gaussian to delta distribution is similar to the jet case. However, the radial variation of the intermittency indicates differences between the results with and without a bluff body at axial locations due the recirculation zone created by the bluff body.     

Dynamically balanced optimal gaits of a ditch-crossing biped robot

This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot.     

Detecting outliers in categorical data through rough clustering

Outlier detection is an important data mining task with many contemporary applications. Clustering based methods for outlier detection try to identify the data objects that deviate from the normal data. However, the uncertainty regarding the cluster membership of an outlier object has to be handled appropriately during the clustering process. Additionally, carrying out the clustering process on data described using categorical attributes is challenging, due to the difficulty in defining requisite methods and measures dealing with such data. Addressing these issues, a novel algorithm for clustering categorical data aimed at outlier detection is proposed here by modifying the standard \(k\)-modes algorithm. The uncertainty regarding the clustering process is addressed by considering a soft computing approach based on rough sets. Accordingly, the modified clustering algorithm incorporates the lower and upper approximation properties of rough sets. The efficacy of the proposed rough \(k\)-modes clustering algorithm for outlier detection is demonstrated using various benchmark categorical data sets.     

Study of non-self similar flows behind shock waves in a dusty gas

Non-self similar solutions for plane, cylindrical and spherical unsteady flows of dusty gas (mixture of gas and small solid particles) behind shock waves of arbitrary strength initiated by the instantaneous release of finite energy and propagating into a uniform medium at rest are investigated. Two methods based upon the assumptions, namely, (i) power law density and (ii) linear velocity, are employed to study the effects of the parameters characterising the initial volume fraction $\text{Z}{}_{\mathrm{o}}$, mass concentration of the solid particles $\text{K}{}_{\mathrm{p}}$ in the dusty gas and the ratio of the density of the solid particles to that of the initial density of the gas $\text{G}$ on shock trajectory and the flow field behind the shock. Comparative study of the results obtained by the two assumptions is also made. A simple formula is derived to study the effect of the solid particles on the shock strength in the strong shock regime.     

Concurrent optimal allocation of geometric and process tolerances based on the present worth of quality loss using evolutionary optimisation techniques

Concurrent tolerancing becomes an optimisation problem to find out the optimum allocation of the process tolerances in the given design function constraints. In traditional optimisation methods, finding out the optimum solution for this advanced tolerance design problem is complex. The proposed algorithms (elitist non-dominated sorting genetic algorithm) and differential evolution extensively do better than the previous algorithms for attaining the optimum result. The aim of this paper is to suggest a model for optimal tolerance allocation by considering both tolerance cost and the present worth of quality loss such that the total manufacturing cost/loss is minimised. The suggested model takes into account the time value of money for quality loss and product degradation over time and consists of two new parameters: the planning horizon and the product user’s discount rate. From the outcome of this study, a longer planning horizon results in an increase in both tolerance cost and quality loss; however, a larger value of discount rate gives up a decrease in both tolerance cost and quality loss. Finally, a practical example is brought into reveal the effectiveness of the suggested method.     

Machine Learning Based Intrusion Detection Systems for IoT Applications

Internet of Things (IoT) and its applications are the most popular research areas at present. The characteristics of IoT on one side make it easily applicable to real-life applications, whereas on the other side expose it to cyber threats. Denial of Service (DoS) is one of the most catastrophic attacks against IoT. In this paper, we investigate the prospects of using machine learning classification algorithms for securing IoT against DoS attacks. A comprehensive study is carried on the classifiers which can advance the development of anomaly-based intrusion detection systems (IDSs). Performance assessment of classifiers is done in terms of prominent metrics and validation methods. Popular datasets CIDDS-001, UNSW-NB15, and NSL-KDD are used for benchmarking classifiers. Friedman and Nemenyi tests are employed to analyze the significant differences among classifiers statistically. In addition, Raspberry Pi is used to evaluate the response time of classifiers on IoT specific hardware. We also discuss a methodology for selecting the best classifier as per application requirements. The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier’s performance.

     

Mechanical behavior and wear characteristics of fine grained ZE41 magnesium alloy

ZE41 magnesium alloy was successfully produced by friction stir processing and grain refinement was achieved from a starting size of 107 μm±6.7 μm to 3.5 μm±1.5 μm. MgZn intermetallic which was appeared as network like structure at the grain boundaries before friction stir processing was greatly affected due to the severe plastic deformation and broken as small particles as observed from the microstructural studies. Higher hardness (≈30 %) was measured for the fine grained ZE41 magnesium alloy compared with the base alloy due to the grain refinement. From the tensile tests, yield strength and ultimate tensile strength was significantly increased at the cost of decreased ductility reflected in lower strain for the fine grained ZE41 compared with the base alloy. Wear studies showed higher coefficient of friction and lower mass loss for the grain refined ZE41 magnesium alloy. From the results, it can be understood that the grain refinement achieved by friction stir processing has a profound influence on enhancing the mechanical and tribological properties of ZE41 magnesium alloy.