Stress wave propagation in adhesively bonded functionally graded cylinders: an improved model

This study presents an improved mathematical model to analyse the stress wave propagation in adhesively bonded functionally graded (FG) circular cylinders (butt joint) under an axial impulsive load. The volume fractions of the material constituents in the upper and lower cylinders were functionally tailored through the thickness of each cylinder using a power-law. The effective material properties of both cylinders, which are made of aluminum (Al) and silicon carbide (SiC), at any point were predicted by using the Mori–Tanaka homogenization scheme. In this improved model, the governing equations of the wave propagation include the spatial derivatives of local mechanical properties and were discretized by means of the finite difference method. The influence of these spatial derivatives and the compositional gradient exponent on the displacement and stress distributions of the joint was investigated. The material composition variations of both cylinders affected the displacement and stress fields whereas the compositional gradient exponent had a minor effect. The stress concentrations were alleviated in time, the displacement and stress distributions/variations around/along the upper and lower cylinder-adhesive interfaces were significantly affected by the adhesive layer. The spatial derivatives also affected the temporal histories of the displacement and stress components evaluated at the selected critical points of the upper cylinder, adhesive layer and lower cylinder. The consideration of the spatial local material derivatives provided a more accurate mathematical model of wave propagations through the graded layered structures.     

Increasing Volumetric CO2 Uptake of Hypercrosslinked Polymers through Composite Formation

To overcome the drawbacks of solid microporous materials for CO₂ capture, this proof‐of‐concept study demonstrates a low‐cost and rapid method for producing composites consisting of hypercrosslinked polymers (HCP) with a polyethyleneimine (PEI) binder. The resulting materials capture CO₂ through physical and chemical absorption simultaneously. Compared with HCP, the composites exhibit higher CO₂ uptake, higher volumetric density, and improved tolerance to water which is attributed to the PEI binder.     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Interfacial failure analysis of functionally graded adhesively bonded double supported tee joint of laminated FRP composite plates

This paper deals with three-dimensional non-linear finite element analyses to assess the structural behavior of adhesively-bonded double supported tee joint of laminated FRP composites having embedded interfacial failures. The onset of interfacial failures is predicted by using Tsai–Wu coupled stress failure criterion with pre-determined stress values. The concept of fracture mechanics principle is utilized to study the sustainability of the tee joint having interfacial failures pre-existed at the critical locations. Individual modes of the strain energy release rates (SERR) G_I, G_II and G_III, are considered as the damage growth parameters and, are evaluated using the Modified crack closure integral (MCCI) technique based on the concept of linear elastic fracture mechanics (LEFM). Based on the stress analyses, it has been observed that the interfacial failures in tee joint structure trigger at the interface of base plate and adhesive layer from both ends of base plate. Depending on the SERR magnitudes, it has been noticed that the interfacial failure propagates under mixed mode condition. Therefore total SERR (G_T) is considered as the governing parameter for damage propagation. Furthermore, efforts have been made to retard damage propagation rate by employing functionally graded adhesive (FGA) instead of monolithic adhesive material. Series of numerical simulations have been performed for varied interfacial failure length in functionally graded adhesively bonded double supported tee joint structure in order to achieve the significant effect of FGA with various modulus ratios on SERR. Material gradation of adhesive indicates significant SERR reduction at the incipient stage of failure which necessitates the use of functionally graded adhesive for the tee joint and prolong the service life of the structure.     

基于深度学习的人体姿态估计方法综述

基于深度学习的人体姿态估计方法旨在通过构建合适的神经网络，直接从二维的图像特征中回归出人体姿态信息。主要按照2D人体姿态估计到3D人体姿态估计的顺序，并从单人检测与多人检测、稀疏的关节点检测与密集的模型构建等方面，对近年来基于深度学习的人体姿态估计方法进行系统介绍，从而初步了解如何通过深度学习的方法得到人体姿态的各个要素，包括肢体部件的相对朝向和比例尺度、骨骼关节点的位置坐标和连接关系，甚至更为复杂的人体蒙皮模型信息。最后,对当前研究面临的挑战以及未来的热点动向进行概述，清晰地呈现出该领域的发展脉络。     

LTE系统多小区上行联合功控算法

针对传统的小区内开环功率控制算法通常以提升本小区的吞吐量性能为目标，忽略了当前小区用户对邻小区用户同频干扰的问题，为提升边缘用户性能的同时兼顾系统整体性能，提出了一种LTE系统小区间上行联合功率控制（UJPC）算法。该算法采用单基站三扇区为系统模型，以最优化系统吞吐量比例公平函数为目标，首先根据最小信干噪比（SINR）约束值和用户最大发射功率这两个约束条件得到相应的数学优化模型，然后采用连续凸近似的方法求解优化问题得出各个基站所管辖的小区内所有用户的最优发射功率。仿真结果表明，与基准的开环功控方案相比，联合功控方案在保证系统平均频谱利用率的情况下能够较大幅度地提高小区边缘频谱利用率，其最佳性能增益能达到50%。     

沉管隧道水下最终接头施工技术初探

简述了沉管隧道水下最终接头施工技术的应用现状，结合具体隧道工程实例，从外模工程、混凝土结构两方面介绍了最终接头施工技术，表明了该技术具有运用价值。     

IP网络信源信道联合编码策略

面向IP网络,讨论了一种信源编码的失真估计模型,并用典型的视频编码器作了验证.提出了一种基于包的信道编码方法,并将该方法引入信道编码的失真估计中,对一种信道编码的失真估计模型作了改进.以前述内容为基础,提出了两种信源信道联合编码策略,实验结果表明,这两种策略得到的最优结果在很大程度上提高了视频重建质量.     

Volumetric Change and Surface Properties of Temperature-Sensitive Polyvinylalcohol (PVA) Hydrogel

Polyvinylalcohol (PVA) polymer gel is a temperature sensitive polymeric gel, with a critical transition temperature (with H₂O) of 310 K. At higher than 310 K, this temperature sensitive polymer gel shrinks because of discharging water, whereas at lower temperatures, the gel swelled because of absorbing water. The reversibility of the gel's volume change was confirmed by temperature swing. The adsorption behavior of an organic compound onto the PVA polymer gel in water was tested at various temperatures. The amount of adsorbed organic compound increased remarkably at temperatures higher than about 310 K. Then, it was confirmed that the organic compound in PVA polymer gel could be reversibly adsorbed and desorbed by a temperature change between 293 and 323 K. The mechanism of adsorption of the organic compound onto the PVA polymer gel could be explained by hydration and dehydration of polymer gel.