Numerical analysis of the crack growth path in the cement mantle of the reconstructed acetabulum

In this study, we use the finite element method to analyze the propagation's path of the crack in the orthopedic cement of the total hip replacement. In fact, a small python statement was incorporated with the Abaqus software to do in loop the following operations: extracting the crack propagation direction from the previous study using the maximal circumferential stresses criterion, drawing the new path, meshing and calculating again (stresses and fracture parameters). The loop is broken when the user's desired crack length is reached (number of propagations) or the value of the mode I stress intensity factor is negative. Results show that the crack propagation's path can be influenced by human body posture. The existing of a cavity in the vicinity of the crack can change its propagation path or can absolutely attract it enough to meet it. Crack can propagate in the outward direction (toward the acetabulum bone) and cannot propagate in the opposite direction, the mode I stress intensity factor increases with the crack length and that of mode II vanishes.     

Analysis of vapor–gas bubbles in a single artery heat pipe

The noncondensable gas supported bubbles in an arterial heat pipe are studied. The governing conservation equations are solved to study the growth/collapse of spherical bubbles under different conditions by using the finite element method. The criterion used in the design of the venting pores to prime the artery is explained. The diffusion-limited bubble collapse in the condenser and bubble growth due to the phase change in the evaporator are both studied. A theoretical explanation for the capability of venting bubbles under different scenarios is provided. The experimental results, including rapid startup and condenser cooldown, are also presented to prove the ability of the heat pipes to vent vapor–gas bubbles.     

数据与模型协同驱动的智能光网络架构与关键技术

网络的规模升级和超大连接、超高带宽、超低时延应用的不断深化，对光传输网络资源利用和网络差异化服务提出了更高要求，使得传统模型驱动下的网络形态和配置方式面临挑战。基于数据与模型协同驱动思想，提出“3层3循环”架构及其“3可功能”特征的智能光网络技术方案，并对智能化实现技术展开研究，通过开发设计的智能传输网络平台对所提算法的性能进行测试，经验证，数据与模型协同驱动的智能光网络传输性能得到有效提升，为实现网络智能化提供了理论技术支撑。     

A novel solution of deep learning for sleep apnea detection: enhancement of SC and elimination of GVICS

Multimedia Tools and Applications - Convolutional neural network (CNN) classification has not achieved a medically-satisfied level of accuracy in sleep apnea detection due to the negative effect of...     

Power-domain non orthogonal multiple access (PD-NOMA) in cooperative networks: an overview

Wireless Networks - Non-orthogonal multiple access (NOMA) scheme is emerging as a favourable multiple access scheme for future 5G networks. Compared to orthogonal multiple access techniques, NOMA...     

Highly-Directional,Highly-Efficient Solution-Processed Light-Emitting Diodes of All-Face-Down Oriented Colloidal Quantum Well Self-Assembly

Semiconductor colloidal quantum wells (CQWs) provide anisotropic emission behavior originating from their anisotropic optical transition dipole moments (TDMs). Here, solution-processed colloidal quantum well light-emitting diodes (CQW-LEDs) of a single all-face-down oriented self-assembled monolayer (SAM) film of CQWs that collectively enable a supreme level of IP TDMs at 92% in the ensemble emission are shown. This significantly enhances the outcoupling efficiency from 22% (of standard randomly-oriented emitters) to 34% (of face-down oriented emitters) in the LED. As a result, the external quantum efficiency reaches a record high level of 18.1% for the solution-processed type of CQW-LEDs, putting their efficiency performance on par with the hybrid organic-inorganic evaporation-based CQW-LEDs and all other best solution-processed LEDs. This SAM-CQW-LED architecture allows for a high maximum brightness of 19,800 cd m⁻² with a long operational lifetime of 247 h at 100 cd m⁻² as well as a stable saturated deep-red emission (651 nm) with a low turn-on voltage of 1.7 eV at a current density of 1 mA cm⁻² and a high J₉₀ of 99.58 mA cm⁻². These findings indicate the effectiveness of oriented self-assembly of CQWs as an electrically-driven emissive layer in improving outcoupling and external quantum efficiencies in the CQW-LEDs.     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

Synthesis and electrochemical capacitance of binderless nanocomposite electrodes formed by dispersion of carbon nanotubes and carbon aerogels

Novel nanocomposite carbon aerogel (CAG)-multi-walled carbon nanotubes (MWNT) materials have been synthesized and studied in 5 M KOH for electrochemical capacitor applications. The amount of MWNT in the nanocomposite was varied from 3 to 10 wt%. High specific surface areas ranging between 670 and 710 m² g⁻¹ were obtained as measured by nitrogen gas adsorption method, whereas the average pore diameter ranged between 1 and 4 nm.     

Classification of Honey According to Geographical and Botanical Origins and Detection of Its Adulteration Using Voltammetric Electronic Tongue

Main possible honey fraud is the addition of various sugar syrups. But, there are also other types of fraud, such as deception on the geographical and/or botanical origin product. Providing a product of the hive with full authenticity is therefore crucial for the preservation of beekeeping. In this pursuit, voltammetric electronic tongue (VE-tongue) was employed to classify honey samples from different geographical and botanical origins. Furthermore, VE-tongue was used to detect adulterants such as glucose syrup (GS) and saccharose syrup (SS) in honey. The data obtained were analyzed by three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs), and hierarchical cluster analysis (HCA). These methods enabled the classification of 18 honeys of different geographical origins and 7 honeys of different botanical origins. Excellent results were obtained also in the detection of adulterated honey. Therefore, this simple method based on VE-tongue could be useful in the honey packaging and commercialization industry.