Tribological and mechanical characteristics of AA5083 alloy reinforced by hybridising heavy ceramic particles Ta2C & VC with light GNP and Al2O3 nanoparticles

In this study, AA5083 sheets were reinforced with four different hybrid nanoparticles by friction stir processing (FSP) for the development of surface nanocomposites used in advanced engineering applications. The present research focused on improving the properties and tribological behaviour of AA5083 alloy surfaces, including novel hybrid nanoparticles and the intermetallic phase formed during FSP. A tribometer tester with a constant normal load was used to examine the tribological performance of the hybrid composites. After the wear test, a surface profiler inspector was used to analyse the morphology and surface roughness of the examined materials. The Vickers micro-hardness of the base metal and the manufactured composites were measured. During FSP, a new intermetallic phase of AlV₃ was successfully formed at 300–400 °C in the hybrid nanocomposites containing VC particles. The reinforcements resulted in additional grain refining than FSP. The AA5083/Ta2C–Al₂O₃ exhibited the greatest grain refinement, a sixty-fold reduction in grain size compared to that of the base alloy. The results revealed that the hybrid nanocomposites containing VC particles demonstrated the most significant microhardness values inside the stirred zone as a result of the presence of the AlV₃ phase, which was increased by 25–30%. Moreover, the mechanical properties were significantly improved for all manufactured nanocomposites. The tensile strength was increased by 28% through the hybridisation of AA5083 using a hybrid of VC-GNPs. The dispersion of Ta₂C-GNPs and VC-GNPs in the matrix led to excellent interfacial adhesion, resulting in an enhancement in the mechanical properties. The AA5083/VC-GNPs surface composite outperformed other manufactured composites regarding wear resistance. In addition, due to GNPs soft nature, it reduced the coefficient of friction (COF) of the manufactured composites by 20–25% compared to other reinforcements.     

Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

In this article, an adaptive denoising method is suggested to accurate investigate the optical and structural features of polymeric fibers from noisy phase shifting microinterferograms. The mixed class of noise that may produce in the phase-shifting interferometric techniques is established. To our knowledge, this is an early study considered the mixing noises that may occur in microinterferograms. The suggested method utilized the convolution neural networks to detect the noise class and then denoising, it according to its class. Four convolution neural networks (Googlenet, VGG-19, Alexnet, and Alexnet–SVM) are refined to perform the automatic classification process for the noise class in the established data set. The network with the highest validation and testing accuracy of these networks is considered to apply the suggested method on realistic noisy microinterferograms for polymeric fibers, polypropylene and antimicrobial polyethylene terephthalate)/titanium dioxide, recoded using interference microscope. Also, the suggested method is applied on noisy microinterferograms include crazing and nanocomposite material. The demodulated phase maps and the three-dimensional birefringence profiles are calculated for tested fibers according to the suggested method. The obtained results are compared with the published data for these fibers and found to be in good agreements.     

风险矩阵法在A水泥工厂风险分级的应用初探

本文介绍“风险矩阵法”进行风险分级工作的基本思路，结合水泥厂的生产特点通过危险有害因素辨识，获得危险源分布情况，采用风险矩阵法对风险进行评估，按风险值将风险等级划分为重大风险、较大风险、一般风险和低风险，为水泥生产企业的安全风险分级工作提供参考。     

Unraveling specific role of carbon matrix over Pd/quasi-Ce-MOF facilitating toluene enhanced degradation

Metal organic frameworks (MOFs) derivatives represented by quasi-MOFs have excellent physical and chemical properties and can be applied for the catalytic combustion of volatile organic compounds (VOCs). In this work, Pd/quasi-Ce-BTC synthesized by simple one-step N₂ pyrolysis was applied to the oxidation of toluene, showing excellent toluene catalytic activity (T₉₀ = 175 °C, 30000 mL/(g·h)). Microscopic analyses indicate the formation and interaction of a carbon matrix composite quasi-MOF structure interface. The results show that the amorphous carbon matrix formed during the partial pyrolysis of Ce-BTC significantly improves the adsorption and activation capacity of toluene in the reaction, and constructs a reductive system to maintain high concentrations of Ce³⁺ and Pd⁰, which can facilitate the activation and utilization of oxygen in reaction. Quasi in-situ XPS proves that carbon matrix is indirectly involved in the activation and storage of oxygen, and Pd⁰ is the crucial active site for the activation of oxygen. Stability and water resistance tests display good stability of Pd/quasi-Ce-BTC. This work provides a potential method for designing quasi-MOF catalysts towards VOCs effective abatement.     

CFETR水冷陶瓷增殖剂包层模块产氚率实验验证

水冷陶瓷增殖剂（WCCB）包层作为中国聚变工程试验堆（CFETR）候选包层之一，承担着氚增殖、核热提取、屏蔽等重要涉核功能，其中子学设计的可靠性直接影响CFETR氚自持目标的实现。为验证中子学设计工具，即MCNP和FNEDL3.0数据库，在WCCB包层中子学设计中的可靠性，基于研制出的WCCB包层模块，在DT中子环境下开展中子学实验，对以产氚率（TPR）为代表的中子学参数进行了模拟值（C）和实验值（E）对比分析。结果表明，模块中轴线位置处TPR的C/E为0.97?1.08，而模块边缘位置处TPR的C/E为0.65?0.82；模块钛酸锂层边缘区197Au（n，γ）198Au反应率的C/E为0.72?0.90，表明模块边缘区存在非期望的散射中子，导致该区TPR模拟值和实验值偏离较大。     

An IoT-Aware System for Managing Patients’ Waiting Time Using Bluetooth Low-Energy Technology

It is a common observation that whenever patients arrives at the front desk of a hospital, outpatient clinic, or other health-associated centers, they have to first queue up in a line and wait to fill in their registration form to get admitted. The long waiting time without any status updates is the most common complaint, concerning health officials. In this paper, UrNext, a location-aware mobile-based solution using Bluetooth low-energy (BLE) technology is presented to solve the problem. Recently, a technology-oriented method, the Internet of Things (IoT), has been gaining popularity in helping to solve some of the healthcare sector’s problems. The implementation of this solution could be illustrated through a simple example of when a patient arrives at a clinic for a consultation. Instead of having to wait in long lines, that patient will be greeted automatically, receive a push notification of an admittance along with an estimated waiting time for a consultation session. This will not only provide the patients with a sense of freedom but would also reduce the uncertainty levels that are generally observed, thus saving both time and money. This work aims to improve the clinics’ quality of services, organize queues and minimize waiting times, leading to patients’ comfort while reducing the burden on nurses and receptionists. The results demonstrate that the presented system is successful in its performance and helps achieves a pleasant and conducive clinic visitation process with higher productivity.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

基于音频特征的工程车辆工况识别研究

高效率地使用工程车辆是工程项目管理中节约成本的有效方法，无人监管环境下工程车辆的工况识别，是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别，提出一种基于GRU循环神经网络的工程车辆工况识别方法，通过对工程车辆在不同工况下产生的音频信号进行分析，从中提取Mel倒谱系数作为主要特征，构建GRU循环神经网络模型进行训练和识别。实验结果表明，该方法可以实现对工程车辆工况的有效识别。     

Shared human–robot path following control of an unmanned ground vehicle

This paper considers the shared path following control of an unmanned ground vehicle by a single person. A passive measure of human intent is used to blend the human and machine inputs in a mixed initiative approach. The blending law is combined with saturated super-twisting sliding mode speed and heading controllers, so that exogenous disturbances can be counteracted via equivalent control. It is proven that when the proposed blending law is used, the combined control signals from both the human and automatic controller respect the actuator magnitude constraints of the machine. To demonstrate the approach, shared control experiments are performed using an unmanned ground vehicle, which follows a lawn mower pattern shaped path.     

Sn-containing Si3N4-based composites for adaptive excellent friction and wear in a wide temperature range

Ceramic design based on reducing friction and wear-related failures in moving mechanical systems has gained tremendous attention due to increased demands for durability, reliability and energy conservation. However, only few materials can meet these requirements at high temperatures. Here, we designed and prepared a Sn-containing Si₃N₄-based composite, which displayed excellent tribological properties at high temperatures. The results showed that the friction coefficient and wear rate of the composites were reduced to 0.27 and 4.88 × 10^?6 mm³ N^?1 m^?1 in air at 800 °C. The wear mechanism of the sliding pairs at different temperatures was revealed via detailed analyses of the worn surfaces. In addition, the tribo-driven graphitization was detected on the wear surfaces and in the wear debris, and the carbon phase was identified by SEM, TEM, and Raman spectrum.