Internet of things for healthcare monitoring applications based on RFID clustering scheme

Wireless Networks - COVID-19 surprised the whole world by its quick and sudden spread. Coronavirus pushes all community sectors: government, industry, academia, and nonprofit organizations to take...     

Expectation Maximization Method for Effective Detection and Tracking of Object Using Machine Learning Technique for Secure Wireless Communication

Wireless Personal Communications - Object detection is one of the most important computer vision tasks that is used synonymous to object recognition which comprises the mission of identifying the...     

Design and experimental validation of looped-tube thermoacoustic engine

The aim of this paper is to present the design and experimental validation process for a thermoacoustic looped-tube engine.The design procedure consists of numerical modelling of the system using DELTA EC tool,Design Environment for Low-amplitude ThermoAcoustic Energy Conversion,in particular the effects of mean pressure and regenerator configuration on the pressure amplitude and acoustic power generated.This is followed by the construction of a practical engine system equipped with a ceramic regenerator-a ...     

Vesicle‐Mediated Growth of Tubular Branches and Centimeter‐Long Microtubes from a Single Molecule

The mechanism by which small molecules assemble into microscale tubular structures in aqueous solution remains poorly understood, particularly when the initial building blocks are non‐amphiphilic molecules and no surfactant is used. It is here shown how a subnanometric molecule, namely p‐aminothiophenol (p‐ATP), prepared in normal water with a small amount of ethanol, spontaneously assembles into a new class of nanovesicle. Due to Brownian motion, these nanostructures rapidly grow into micrometric vesicles and start budding to yield macroscale tubular branches with a remarkable growth rate of ～20 μm s^?1. A real‐time visualization by optical microscopy reveals that tubular growth proceeds by vesicle walk and fusion on the apex (growth cone) and sides of the branches and ultimately leads to the generation of centimeter‐long microtubes. This unprecedented growth mechanism is triggered by a pH‐activated proton switch and maintained by hydrogen bonding. The vesicle fusion‐mediated synthesis suggests that functional microtubes with biological properties can be efficiently prepared with a mixture of appropriate diaminophenyl blocks and the desired macromolecule. The reversibility, timescale, and very high yield (90%) of this synthetic approach make it a valuable model for the investigation of hierarchical and structural transition between organized assemblies with different size scales and morphologies.     

Measurement of Wet Antenna Losses on 26?GHz Terrestrial Microwave Link in Malaysia

This letter discusses the effect of antenna losses due to rain on a 26 GHz microwave link and the technique of extracting the losses from the measured rain attenuation. A 2-feet parabolic dish antenna with horizontal polarization has been used in the study. The measurement results have been compared with those obtained from other locations in Malaysia and some other Published Research works. The study will provide useful information in the microwave link planning and design in tropical regions; and it can also be adapted to satellite communication operating at ka-band.     

Sequential Template Decomposition to Adjust the Performance of Imperfect Zeolite BEA Membranes

Zeolite membranes offer superior thermal, chemical, and mechanical stability compared to polymeric membranes. However, it is still a challenge to prepare completely defect‐free membranes without any intercrystalline voids, which is necessary for gas separation processes. In this study zeolite beta (*BEA) membranes on stainless‐steel supports were prepared by applying the multiple in situ crystallization technique. The membranes were used as a model system to systematically study the decomposition of the organic structure directing agent tetraethylammoniumhydroxide (TEA‐OH). It was evaluated if the organic decomposition products of TEA‐OH can be used for enhancing the membranes selectivity. Post‐treatment experiments have been carried out to adjust surface properties and pore size dimensions in the zeolitic membrane layer. The results show that membranes calcined at lower temperatures exhibit a higher gas selectivity.     

Towards Understanding the Decomposition/Isomerism Channels of Stratospheric Bromine Species: Ab Initio and Quantum Topology Study

The present study aims at a fundamental understanding of bonding characteristics of the C–Br and O–Br bonds. The target molecular systems are the isomeric CH₃OBr/BrCH₂OH system and their decomposition products. Calculations of geometries and frequencies at different density functional theory (DFT) and Hartree–Fock/Møller–Plesset (HF/MP2) levels have been performed. Results have been assessed and evaluated against those obtained at the coupled cluster single-double (Triplet) (CCSD(T)) level of theory. The characteristics of the C–Br and O–Br bonds have been identified via analysis of the electrostatic potential, natural bond orbital (NBO), and quantum theory of atoms in molecules (QTAIM). Analysis of the electrostatic potential (ESP) maps enabled the quantitative characterization of the Br σ-holes. Its magnitude seems very sensitive to the environment and the charge accumulated in the adjacent centers. Some quantum topological parameters, namely ∇²ρ, ellipticity at bond critical points and the Laplacian bond order, were computed and discussed. The potential energy function for internal rotation has been computed and Fourier transformed to characterize the conformational preferences and origin of the barriers. NBO energetic components for rotation about the C–Br and O–Br bonds as a function of torsion angle have been computed and displayed.     

Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image. Furthermore, Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network (SNN)-based classification are employed to detect and classify the apple plant leaf images. A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset. The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.     

Implications of Onshore Development on Global Software Engineering

Recently software industry has paid significant attention to customizing software products across distributed boundaries. Communicating the requirements of multiple clients across distributed borders is a crucial challenge for the software customization process. Local decision-making and local development at the client site are considered methods for reducing difficulties in communicating the requirements of multiple clients across distributed boundaries. This paper introduces a new model called the onshore development model (ODM) for accomplishing the customization requests in the distributed development process of software. This model presents a scenario for enhancing the onsite development of specific requirements to reduce delays and misunderstandings between the clients and the team involved. This model depends on moving the development process to the client’s location. Three empirical studies were conducted to evaluate the proposed model to measure its productivity, time performance, and cost reduction. The proposed model has been compared with two other models: the basic model (BM), which allocates the decision-making process and the development process for teams at the vendor’s location, and the local decision-making model (LDec), which assigns the decision-making process for team at the client’s location. The results of the empirical studies showed significant outperforming of the proposed model over the basic model and local decision-making model in productivity, time performance, and cost reduction. The productivity of the proposed model improved by 39% and 10% more than the basic model and the local decision-making model, respectively. In addition, the time performance of the proposed model became faster by 49% and 20.8% than the basic model and the local decision-making model, respectively. Also, it reduced the total cost of the development process by 31% in terms of the salaries of all persons involved in requirements collecting, decision-making, and development.     

Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter weights of the Bidirectional Recurrent Neural Networks (BRNN). Furthermore, a time series dataset is tested in the experiments of e-commerce demand forecasting. Finally, the results were compared to many versions of the state-of-the-art optimization techniques such as the Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). A statistical analysis has proven that the proposed algorithm can work significantly better by statistical analysis test at the P-value less than 0.05 with a one-way analysis of variance (ANOVA) test applied to confirm the performance of the proposed ensemble model. The proposed Algorithm achieved a root mean square error of RMSE (0.0000359), Mean (0.00003593) and Standard Deviation (0.000002162).