Novel algorithms for the network lifetime problem in wireless settings

One of the major concerns in wireless ad-hoc networks design is energy efficiency. Wireless devices are typically equipped with a limited energy supply sufficient only for a limited amount of time which is reversely proportional to the transmission power of the device. The network lifetime is defined as the time the first device runs out of its initial energy charge. In this paper we study the maximum network lifetime problem for broadcast and data gathering in wireless settings. We provide polynomial time approximation algorithms, with guaranteed performance bounds while considering omnidirectional and unidirectional transmissions. We also consider an extended variant of the maximum lifetime problem, which simultaneously satisfies additional constraints, such as bounded hop-diameter and degree of the routing tree, and minimizing the total energy used in a single transmission. Finally, we evaluate the performance of some of our algorithms through simulations.     

Cytotoxic T Cell Expression of Leukocyte-Associated Immunoglobulin-Like Receptor-1 (LAIR-1) in Viral Hepatitis C-Mediated Hepatocellular Carcinoma

Virus-related hepatocellular carcinoma (HCC) pathogenesis involves liver inflammation, therefore, despite successful treatment, hepatitis C virus (HCV) may progress to HCC from initiated liver cirrhosis. Cytotoxic T cells (Tcs) are known to be involved in HCV-related cirrhotic complications and HCC pathogenesis. The inhibitory checkpoint leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) is expressed on Tcs. Therefore, we aimed to determine whether the Tc expression level of LAIR-1 is associated with HCC progression and to evaluate LAIR-1 expression as a noninvasive biomarker for HCC progression in the context of liver cirrhosis related to HCV genotype 4 (G4) in Egyptian patients’ peripheral venous blood liquid biopsy. A total of 64 patients with HCC and 37 patients with liver cirrhosis were enrolled in this case-controlled study, and their LAIR-1 expression on Tc related to the progression of liver cirrhosis was examined and compared to that of the apparently healthy control group (n = 20). LAIR-1 expression was analyzed using flow cytometry. Results: The HCC group had significantly higher LAIR-1 expression on Tc and percentage of Tc positive for LAIR-1 (LAIR-1+Tc%) than the HCV G4-related liver cirrhosis group. LAIR-1+Tc% was correlated with the HCC surrogate tumor marker AFP (r = 0.367, p = 0.001) and insulin resistance and inflammation prognostic ratios/indices. A receiver operating characteristic (ROC) curve revealed that adding LAIR-1+Tc% to AFP can distinguish HCC transformation in the Egyptian patients’ cohort. Upregulated LAIR-1 expression on Tc could be a potential screening noninvasive molecular marker for chronic inflammatory HCV G4 related liver cirrhosis. Moreover, LAIR-1 expression on Tc may be one of the players involved in the progression of liver cirrhosis to HCC.     

A Hybrid Virtual Cloud Learning Model during the COVID-19 Pandemic

The COVID-19 pandemic has affected the educational systems worldwide, leading to the near-total closures of schools, universities, and colleges. Universities need to adapt to changes to face this crisis without negatively affecting students’ performance. Accordingly, the purpose of this study is to identify and help solve to critical challenges and factors that influence the e-learning system for Computer Maintenance courses during the COVID-19 pandemic. The paper examines the effect of a hybrid modeling approach that uses Cloud Computing Services (CCS) and Virtual Reality (VR) in a Virtual Cloud Learning Environment (VCLE) system. The VCLE system provides students with various utilities and educational services such as presentation slides/text, data sharing, assignments, quizzes/tests, and chatrooms. In addition, learning through VR enables the students to simulate physical presence, and they respond well to VR environments that are closer to reality as they feel that they are an integral part of the environment. Also, the research presents a rubric assessment that the students can use to reflect on the skills they used during the course. The research findings offer useful suggestions for enabling students to become acquainted with the proposed system’s usage, especially during the COVID-19 pandemic, and for improving student achievement more than the traditional methods of learning.     

Effect of impeller on sinking and floating behavior of suspending particle materials in stirred tank: A computational fluid dynamics and factorial design study

The present study focuses upon the effect of the impeller on sinking and floating behavior of suspending particles in stirred tank reactor, employing computational fluid dynamics (CFD) simulation where factorial design is used to investigate the main and interaction effects of design parameters on the particle distribution performance of four typical impeller designs. Factorial design results show the effect of diameter and width of the impeller and off-bottom clearance on sinking particles is different from that of floating particles and regression equations for sinking particles and floating particles are achieved separately. Meanwhile, optimal equations which quantitatively reveal the effect of impeller factors on suspension quality and energy input is established for impeller improvement. Besides the development of computational models, the combination of CFD simulation with factorial design method provides a useful approach to gain insight into the suspension behavior of sinking and floating particles, also it guides to optimize the impeller design.     

An Algorithm for Perspective Viewing of Objects Represented by Octrees

A new algorithm is presented for viewing three-dimensional objects, represented by an octree, from an arbitrary location. The algorithm generates aperspective view of the objects while eliminating hidden surfaces. The viewer can be located anywhere inside or outside the objects. The algorithm presented in this short notefixes an artifact that is generated by a previously published algorithm due to Meagher when the viewer is located in certain regions in space. The new algorithm traverses the octree in a back-to-front order and recursively chooses correct orders for visiting the sons of non-leaf nodes.     

Improvement of the magnesium battery electrolyte properties through gamma irradiation of nano polymer electrolytes doped with magnesium oxide nanoparticles

Nanocomposite polymer electrolytes (NCPE) were prepared using nano polyethylene oxide PEO doped with Magnesium (Mg) salts. Gamma irradiation was utilized to improve the PEO‐Mg salts particle sizes. Consequently, Magnesium Oxide (MgO) nanoparticles were prepared by green synthesis and incorporated into PEO‐Mg salts to improve their properties toward magnesium battery electrolyte applications. The prepared samples were examined before and after exposures to the radiation doses. Dynamic light scattering (DLS) indicated the particles size of the synthesized nano polymer‐Mg salts and MgO nanoparticles. Fourier transform infra‐red (FTIR) spectroscopic measurements, transmission electron microscopy (TEM), electrical conductivity, electrochemical properties, and thermal stability of the samples were determined. FTIR indicated the interaction between PEO with Mg salts and MgO nanoparticles which confirmed the structure. The TEM results showed a spherical nanoparticles of MgO and a good dispersion of MgO in PEO matrix. It was found that the irradiation dose 70 kGy gave the best results for the nano polymer‐Mg salts (13 nm). The electrical conductivity (σ) evaluated for NCPE, was more than three orders of magnitude of pure PEO. The liquid NCPE of 20 mL MgO NPs at 100 kGy exhibited a maximum conductivity of 3.63 × 10^–3 Scm^?1 at room temperature. The increase in temperature caused a slight effect on conductivity, 4.85 × 10^–3 Scm^?1 at temperature 250°C, at the same concentration. While un‐irradiated sample of 30 mL MgO NPs (σ) reached to 3.8 × 10^?3 Scm^?1 then became 5.03 × 10^?3 Scm^?1 by increasing temperature. From the cyclic voltammetry results, the polymer electrolytes containing MgO filler, 20 and 30 mL, for irradiated and un‐irradiated samples, respectively exhibited wider electrochemical stability window than the others due to the appearance of Mg deposition/desolution peak in CV curve showed that magnesium effectively migrating through electrolytes. Thermogravimetric analysis (TGA) was enhanced by adding Mg salts electrolyte and also MgO nanoparticles to PEO. J. VINYL ADDIT. TECHNOL., 25:243–254, 2019. © 2018 Society of Plastics Engineers     

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN’s basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.