Fabrication of crescent-shaped ceramic microparticles based on single emulsion microfluidics

Ceramic microparticles have great potentials in various fields such as materials engineering, biotechnology, microelectromechanical systems, etc. Morphology of the microparticle performs an important role on their application. To date, it remains difficult to find an effective and controllable way for fabricating nonspherical ceramic microparticles with 3D features. This work demonstrates a method that combines UV light lithography and single emulsion opaque-droplet-templated microfluidic molding to prepare the crescent-shaped ceramic microparticles. By tailoring the intensity of UV light and flow rate of fluid, the shapes of microparticles are accordingly tuned. Therefore, varieties of crescent-shaped microparticles and their variations have been fabricated. After sintering, the crescent-shaped alumina ceramic microparticles were obtained. Benefitting from the light absorption and scattering behavior of most ceramic nanoparticles, this system can serve as a general platform to produce crescent-shaped microparticles made from different materials, and hold great potentials for applications in microrobotics, structural materials in MEMS, and biotechnology.     

4D Printing of Shape Memory Materials for Textiles: Mechanism,Mathematical Modeling,and Challenges

Shape memory materials (SMMs) in 3D printing (3DP) technology garnered much attention due to their ability to respond to external stimuli, which direct this technology toward an emerging area of research, “4D printing (4DP) technology.” In contrast to classical 3D printed objects, the fourth dimension, time, allows printed objects to undergo significant changes in shape, size, or color when subjected to external stimuli. Highly precise and calibrated 4D materials, which can perform together to achieve robust 4D objects, are in great demand in various fields such as military applications, space suits, robotic systems, apparel, healthcare, sports, etc. This review, for the first time, to the best of the authors’ knowledge, focuses on recent advances in SMMs (e.g., polymers, metals, etc.) based wearable smart textiles and fashion goods. This review integrates the basic overview of 3DP technology, fabrication methods, the transition of 3DP to 4DP, the chemistry behind the fundamental working principles of 4D printed objects, materials selection for smart textiles and fashion goods. The central part summarizes the effect of major external stimuli on 4D textile materials followed by the major applications. Lastly, prospects and challenges are discussed, so that future researchers can continue the progress of this technology.     

一种新型地雷战斗部的数值模拟研究

针对地雷的毁伤目标和特性,提出一种新颖的多棱柱状药型罩结构,其可看作由八个楔形罩对称排列相邻连接而成。应用非线性有限元软件完成了爆炸载荷下多棱柱状药型罩形成射流过程的数值模拟,结果表明新型药型罩结构能够实现预期设想,形成一股汇聚射流。研究结果为地雷战斗部研究提供了一种新的选择。     

气相色谱法测定焦油中微量苯稠杂环化合物

利用蒸馏分离—气相色谱法技术,建立了同时测定煤焦油中苊、氧芴和芴的分析方法。通过对色谱条件的优化,以甲苯为溶剂,正十二烷为内标物,将煤焦油馏分采用DB-5毛细管柱,对煤焦油中苊、氧芴和芴定量分析。分析结果表明:3种主要成分线性关系良好,相关系数均大于0.9995,加标回收率为95.4%~102.4%,相对标准偏差为2.89%~7.14%。该方法分离效果好,检测结果准确、可靠。     

Categorical observers for metamerism

A method is proposed to generate categorical colour observer functions (individual colour matching functions) for any field size based on the CIE 2006 system of physiological observer functions. The method combines proposed categorical observer techniques of Sarkar et al with a physiologically-based individual observer model of Asano et al and a clustering technique to produce the optimal set of categorical observers. The number of required categorical observers varies depending on an application with as many as 50 required to predict individual observers' matches when a laser projector is viewed. However, 10 categorical observers are sufficient to represent colour-normal populations for personalized colour imaging. The proposed and recommended categorical observers represent a robust and inclusive technique to examine and quantify observer metamerism in any application of colorimetry.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

On one boundary problem for nonlinear stationary controlled system

The object of study is nonlinear stationary controlled system of ordinary differential equations with constant disturbance in the right part. The problem of constructing the synthesising control function providing the transfer of this system from the initial state to the origin is considered. The sufficiently simple for numerical implementation algorithm of solution of the above-mentioned problem is obtained. It is shown that for local null controllability of the considered system, it is sufficient that the conditions of the Kalman's type were satisfied. In addition, the estimates restricting the choice of initial conditions and external disturbances under which the transfer is guaranteed are obtained. The main idea of the method of construction of the desired control function consists in reducing the original problem to stabilisation of a special kind linear non-stationary system and solving the Cauchy problem for an auxiliary system of ordinary differential equations closed by stabilising control. The simplicity of the realisation of this algorithm is determined by the construction of the auxiliary system and its stabilisation that could be obtained by analytical methods. The effectiveness of the method is illustrated by solving the problem of crane control and its numerical simulation.     

Risk factors associated with online game addiction: A hierarchical model

Online gaming addiction has been increasingly recognized as a mental disorder. However, the predictive factors that lead to online gaming addiction are not well established. The aim of this study was to identify factors that may influence the development of online gaming addiction. A total of 263 patients with problematic online gaming addiction (255 males (97%) and 8 females (3%), age: mean = 20.4 ± 5.8 years) and153 healthy comparison subjects (118 males (77%) and 35 females (23%), age: 21.2 ± 5.5 years, range) were recruited for participation in the current study. Hierarchical logistic regression analyses among each set of variables were conducted. Individual factors (sex and age), cognitive factors (IQ and perseverative errors), psychopathological conditions (ADHD, depression, anxiety, and impulsivity), and social interaction factors (family environment, social anxiety, and self-esteem) were evaluated in a stepwise fashion. All four factors were associated with online gaming addiction, with psychopathological conditions being the strongest risk factors for the addiction. Individual factors, psychological factors, and social interactions were associated with the development of pure online gaming addiction. As before, psychological factors (attention, mood, anxiety and impulsivity) were the strongest risk factors for online gaming addiction in patients with pure online gaming addiction. Psychopathologies, including ADHD and depression, were the strongest factors associated with the development of online gaming addiction in individuals.