Change point detection for compositional multivariate data

Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault detection in industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem of change point detection on compositional multivariate data (each sample is a probability mass function), which is a practically important sub-class of general multivariate data. While the problem of change-point detection is well studied in univariate setting, and there are few viable implementations for a general multivariate data, the existing methods do not perform well on compositional data. In this paper, we propose a parametric approach for change point detection in compositional data. Moreover, using simple transformations on data, we extend our approach to handle any general multivariate data. Experimentally, we show that our method performs significantly better on compositional data and is competitive on general data compared to the available state of the art implementations.

     

Green synthesis of copper oxide nanoparticles: Characterization and applications for environmental and biomedical fields

In recent years, there has been an increasing interest in the development of plant-based nanoparticles due to their numerous benefits over conventional physio-chemical methods, including sustainability and environmental safety. Green synthesis, a process that produces safe and sustainable goods without the use of harsh chemicals or other harmful processes, is gaining popularity. The current study focuses on the green synthesis of copper oxide nanoparticles using Piper nigrum leaf extracts, their characterization, and applications. The synthesis of nanoparticles was confirmed by changes in colour, further endorsed by UV–visible spectroscopy. Copper oxide (CuO) nanoparticles were characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). CuO nanoparticle sizes ranged between 58.23 and 69.89 nm and were spherical in shape. FTIR results indicated a functional group capped on the nanoparticle surface. The antibacterial activity of the copper oxide nanoparticles was tested, and they exhibited the significant decrease in bacterial concentration and the largest zone of inhibition, making them an efficient disinfectant. Antimicrobial activity against Bacillus subtilis and Escherichia coli was observed. Furthermore, the synthesized CuO nanoparticles exhibited a high affinity for safranin dyes and demonstrated maximum removal efficiency. This makes them an effective agent for removing dyes in wastewater from industries such as clothing manufacturing. Safranin dye was successfully removed with an efficiency of 78% using nanoparticles. In conclusion, the green synthesis of copper oxide nanoparticles using plant extracts presents an eco-friendly and sustainable approach for producing nanoparticles with a wide range of potential applications.     

Investigation of auto-ignition in turbulent methanol spray flames using Large Eddy Simulation

Large Eddy Simulation (LES) of a recently developed series of auto-igniting methanol spray flames in a vitiated coflow are presented in the paper. The Eulerian stochastic field method and a reduced chemical mechanism involving 18 reactive species are employed to characterise the turbulent reactive gas phase. A fully coupled Lagrangian particle method is applied to represent the dispersed phase. The gas phase and droplet statistics show good overall agreement with the measurements, confirming that droplet dispersion and evaporation are adequately represented. Comparison of snapshots with images of OH and CH₂O from laser induced fluorescence (LIF) highlight, that the employed methodology is also able to capture a range of key features that characterise this complex flame series. This includes the correct lift-off behaviour, the formation of OH that mark the initiation of auto-ignition kernels upstream of the flame base and the presence of “most reactive mixtures” at lean conditions. This agreement with experimental data enables the exploration of mechanisms of auto-ignition in sprays.     

Analysis of couple stress nanofluid flow under convective condition in the temperature-dependent fluid properties and Lorentz forces

In recent years, a great deal of interest has been generated in modern micro- and nanotechnologies for micro/nano-electronic devices. These technologies are increasingly utilizing sophisticated fluid media to enhance performance. Among the new trends is the simultaneous adoption of nanofluids and biological micro-organisms. Motivated by bio-nanofluid vertical channel oxygenators in medical engineering, in the current work, a mathematical model is developed to examine the flow of mixed convective couple-stress nanofluids in a vertical channel with a transverse magnetic field, fluid viscosity that changes with temperature, and thermal conductivity. The non-Newtonian model follows Brownian motion and heat spread by nanoparticles in a fluid under coupled stress. Highly linked, nonlinear regulating equations are translated into nondimensional equations using relevant variables. The governing equations are then turned into a form with no dimensions. The Keller-box technique, a second-order finite difference method for solving second-order equations, is used to solve them numerically. On the other hand, the effects of different non-Newtonian flow parameters, such as the couple stress fluid parameter, the magnetic parameter, the variable fluid viscosity, the variable thermal conductivity parameters, the Brinkman number, the nanofluid and buoyancy parameters, and the rate of chemical reaction parameter, are carefully studied. The velocity, temperature, and concentration fields are calculated over a wide range of possible values for the relevant parameters.     

Outcome aware ranking in value creation networks

In this paper, we consider a natural ranking problem that arises in settings in which a community of people (or agents) are engaged in regular interactions with an end goal of creating value. Examples of such scenarios are academic collaboration networks, creative collaborations, and interactions between agents of a service delivery organization. For instance, consider a service delivery organization which essentially resolves a sequence of service requests from its customers by deploying its agents to resolve the requests. Typically, resolving a request requires interaction between multiple agents and results in an outcome (or value). The outcome could be success or failure of problem resolution or an index of customer satisfaction. For this scenario, the ranking of the agents of the network should take into account two aspects: importance of the agents in the network structure that arises as a result of interactions and the value generated by the interactions involving the respective agents. Such a ranking can be used for several purposes such as identifying influential agents of the interaction network, effective and efficient spreading of messages in the network. In this paper, we formally model the above ranking problem and develop a novel algorithm for computing the ranking. The key aspect of our approach is creating special nodes in the interaction network corresponding to the outcomes and endowing them independent, external status. The algorithm then iteratively spreads the external status of the outcomes to the agents based on their interactions and the outcome of those interactions. This results in an eigenvector like formulation, which results in a method requiring computing the inverse of a matrix rather than the eigenvector. We present several theoretical characterizations of our algorithmic approach. We present experimental results on the public domain real-life datasets from the Internet Movie Database and a dataset constructed by retrieving impact and citation ratings for papers listed in the DBLP database.     

Completely biodegradable banana fiber-wheat gluten composites for dielectric applications

Biodegradable composites with desirable dielectric constants were obtained using banana fibers as reinforcement and wheat gluten as matrix. Dielectric materials play a critical role in many integrated circuits and microelectronics. Most dielectric materials are made from ceramics and synthetic polymers which are heavy, expensive, have limited range of dielectric values and also not biodegradable. Unlike previous reports that have developed partially biodegradable composites for dielectric applications, we have used protein and a natural fiber as matrix and reinforcement, respectively resulting in composites that are completely biodegradable. In addition, the thickness of the composites was varied which creates air gaps and influences the dielectric properties. Mechanical and electrical properties of the composites were studied at ambient and higher temperatures. Flexural and tensile properties were significantly influenced by the ratio of matrix and reinforcement and thickness of the composites. Dielectric values obtained ranged from 2 to 49 depending on the frequency. Dielectric constant, dielectric loss and dissipation factor decreased whereas conductivity increased with increasing frequency for all the samples. Biodegradable composites suitable for various electronic applications can be obtained with desired dielectric values by varying the composite fabrication conditions.     

The effect of variable viscosity on the flow and heat transfer of a viscous Agwater and Cu-water nanofluids

A numerical study is carried out to study the effects of the temperature dependent viscosity on the flow and heat transfer of a nanofluid over a flat surface in the presence of viscous dissipation. The governing nonlinear partial differential equations are transformed into nonlinear ordinary differential equations, and are solved numerically by the Keller-box method. The numerical results indicate that the effect of nanoparticle volume fraction is to increase the heat transfer and hence enhance the thermal boundary layer thickness. This is true even in the presence of variable viscosity and the viscous dissipation. Furthermore, the results obtained for heat transfer characteristics with nanoparticles reveal many interesting behaviors that warrant further study on the effects of the "nano-solid-particles".     

Combined effects of homogeneous and heterogeneous reactions on peristalsis of Ree-Eyring liquid: Application in hemodynamic flow

This research examines the influence of homogeneous and heterogeneous chemical reactions on the peristaltic flow via an inclined permeable channel. The current investigation emphasizes on modeling the flow of blood in narrow arteries by taking convective and wall properties into account. The Ree-Eyring non-Newtonian model is used to govern the fluid flow due to its significance in understanding the behavior of dilatant, pseudoplastic, and viscous liquids. The variation in variable viscosity and thermal conductivity is considered for analyzing the complex rheological behavior of blood. The similarity transformations are used in the process of nondimensionalization. The series solution procedure is adopted to solve the governing nonlinear differential equations. The expressions for velocity, temperature, concentration, and trapped bolus are obtained. The computational results are analyzed with the help of graphs for shear thickening, shear thinning, and Newtonian fluid models. One of the significant findings of the current model is that an introduction of variable liquid properties improves the temperature and velocity profiles for Newtonian and pseudoplastic fluid models. Compared with the other theoretical models developed, the rheological and flow properties of various biological fluids can be derived from the model used in the present investigation.