Analysis of energy-tax for multipath routing in wireless sensor networks

Recently, multipath routing in wireless sensor networks (WSN) has got immense research interest due to its capability of providing increased robustness, reliability, throughput, and security. However, a theoretical analysis on the energy consumption behavior of multipath routing has not yet been studied. In this paper, we present a general framework for analyzing the energy consumption overhead (i.e., energy tax) resulting from multipath routing protocol in WSN. The framework includes a baseline routing model, a network model, and two energy consumption schemes for sensor nodes, namely, periodic listening and selective wake-up schemes. It exploits the influence of node density, link failure rates, number of multiple paths, and transmission environment on the energy consumption. Scaling laws of energy-tax due to routing and data traffic are derived through analysis, which provide energy profiles of single-path and multipath routing and serve as a guideline for designing energy-efficient protocols for WSN. The crossover points of relative energy taxes, paid by single-path and multipath routing, reception, and transmission, are obtained. Finally, the scaling laws are validated and performance comparisons are depicted for a reference network via numerical results.     

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.     

Measurement and Analysis of Thermophysical Properties of Diorites in the Temperature Range from 253 to 333 K

Thermal conductivity and thermal diffusivity are simultaneously measured for a collection of diorite samples taken from Shewa-Shahbaz Garhi volcanic complex near Mardan, Pakistan by using the transient plane source (TPS) technique. The temperature dependence of the transport properties of these samples is studied in the temperature range from 253 to 333 K. Different relationships for the temperature dependence of the thermal conductivity and thermal diffusivity are tested. The samples are also characterized by their chemical composition, density, porosity, and specific gravity at room temperature and atmospheric pressure. Theoretical calculation of the specific gravity parameter based on the chemical composition is in good agreement with the experimental observation. No correlation was found for the temperature dependence of the thermal transport behavior on porosity, chemical composition, and density.     

Rapid and multimodal <Emphasis Type="Italic">in vivo</Emphasis> bioimaging of cancer cells through <Emphasis Type="Italic">in situ</Emphasis> biosynthesis of Zn&;Fe nanoclusters

Early diagnosis remains highly important for efficient cancer treatment,and hence,there is significant interest in the development of effective imaging strategies.This work reports a new multimodal bioimaging method for accurate and rapid diagnosis of cancer cells by introducing aqueous Fe2+ and Zn2+ ions into cancer cells (i.e.,HeLa,U87,and HepG2 cancer cells).We found that the biocompatible metal ions Fe2+ and Zn2+ forced the cancer cells to spontaneously synthesize fluorescent ZnO nanoclusters and magnetic Fe3O4 nanoclusters.These clusters could then be used for multimodal cancer imaging by combining fluorescence imaging with magnetic resonance imaging and computed tomography imaging.Meanwhile,for normal cells (i.e.,L02) and tissues,neither fluorescence nor any other obvious difference could be detected between preand post-injection.This multimodal bioimaging strategy based on the in situ biosynthesized Zn&Fe oxide nanoclusters might therefore be useful for early cancer diagnosis and therapy.     

Comparisons of the Microstructure and Magnetic Properties of Anisotropic NdFeB Magnets Prepared by Hot Pressing and Spark Plasma Sintering

Journal of Superconductivity and Novel Magnetism - NdFeB hot-pressed and hot-deformed magnets were prepared by hot-pressed (HP) sintering and spark plasma sintering (SPS). The effects of sintering...     

Magnetic Properties and Microstructural Modifications of Sm-Co-Hf Alloy Ribbons by B Addition

Journal of Superconductivity and Novel Magnetism - Microstructure optimization is highly desirable for improving the magnetic properties of permanent magnetic materials. To optimize the...     

Squeezed MHD tangent hyperbolic fluid flow across a sensor surface

In the present numerical study, the combined effect of temperature-dependent thermal conductivity, linear thermal radiation, and magnetic effect on shear-thinning tangent hyperbolic fluid past a sensor surface has been studied. After converting the modelled partial differential equations into ordinary differential equations by using similarity transformation, the system of equations is tackled with the aid of the shooting method. The influence of important parameters on the fluid motion and energy distribution is displayed graphically and analyzed in detail. The presented simulations depict that a significant rise in fluid velocity is noticed for an enhancement in the magnetic parameter while an opposite trend is observed for the temperature distribution. Moreover, the skin friction coefficient decreases as the squeezed flow index is increased.     

Simultaneously achievement of high recoverable energy density and efficiency in sodium niobate-based ceramics

Journal of Materials Science: Materials in Electronics - Single lead-free Na0.73Bi0.09(Nb1???xTax)O3 (x?=?0, 0.10, 0.20, 0.30, and 0.40) ceramic phases were processed...     

Hysteresis of the water retention curves of geosynthetic clay liners in the high suction range

This paper examines two needle-punched geosynthetic clay liners' water retention behaviour at high suction ranges using the vapour equilibrium technique where super-saturated salt solutions controlled the relative humidity. This study shows that the bentonite form and its mineralogy affect the absorption/desorption of GCLs and their corresponding water retention curves. In particular, a granular bentonite-based GCL was found to absorb more and release less water than a powdered bentonite-based GCL due to its higher montmorillonite content and larger pores. The water retention curves of both GCLs exhibited very little hysteretic behaviour at high suction. Repeated wetting-drying cycles shifted the WRCs of both GCLs slightly downward with minimal impact on their degree of hysteresis.     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.