A hybrid homogeneously weighted moving average control chart for process monitoring: Discussion

The sensitivity of a monitoring scheme depends on many factors including the variance of the charting statistic which is very important in the computation of the control limits. This paper discusses the computation of the variance of the recently proposed hybrid homogeneously weighted moving average (HHWMA) $\overline{X}$ scheme which was based on an incorrect assumption. The correct variance is used to evaluate the run-length characteristics of the HHWMA $\overline{X}$ scheme. It is observed that the incorrect variance has a significant impact on the sensitivity (or performance) of the HHWMA $\overline{X}$ scheme.     

基于学科竞赛的中医药类院校程序设计课程教学模式的探索

高素质人才培养是我国可持续发展战略、科教兴国战略以及人才强国战略等多项发展战略的一项基础性工程。本文针对中医药类院校程序设计类课程实践教学模式单一等教学实际，以“学科竞赛”为平台，“赛教结合”为理念，以《C语言程序设计》为例，从现存问题、赛教结合模式的引入、建立实施及其评价机制4个方面展开阐述，旨在提高学生学习积极性和能力。     

亲子乐园空调送风方式的数值模拟研究

大型综合室内亲子乐园属于高大空间,设有游乐设施和游戏的特殊性使得对空间的舒适性要求一致,但是送风气流遇阻严重,室内存在较多气流死角,影响室内空气质量和儿童健康。因此其空调设计不仅需要考虑温度、风速的空间均匀度,还要考虑各点的空气龄和PMV-PPD指标。以天津某亲子乐园为研究对象,利用scSTREAM软件对适用于该房间的辐射供冷加新风、置换通风、混合通风三种空调方式的送风效果进行数值模拟分析,从流场的均匀性、人员的热舒适性等方面对模拟结果进行探讨,研究结果表明辐射供冷加新风方式的空间均匀性和PMV指标最佳,混合通风方式的空气龄最小。     

Mg0.5NixZn0.5-xFe2O4 spinel as a sustainable magnetic nano-photocatalyst with dopant driven band shifting and reduced recombination for visible and solar degradation of Reactive Blue-19

Focussing on visible light active ferrites for high performance removal of noxious pollutants, we report the synthesis of Mg_0.5Ni_xZn_0.5-xFe₂O₄ (x = 0.1, 0.2, 0.3, 0.4, & 0.5) ferrite nanoparticle for degradation of reactive blue-19 (RB-19). Lattice parameters calculated using intense X-ray diffraction (XRD) peaks and Nelson-Riley plots (N-R plot) are in well agreement with each other. The sample Mg_0.5Ni_0.4Zn_0.1Fe₂O₄ (M5N4) exhibits best performance with 99.5% RB-19 degradation in 90 min under visible light. Photoluminescence (PL) results confirm that recombination of charge carriers is highly reduced in the photocatalyst. Scavenging experiments suggest that O₂⁻ radicals were the dominant species responsible for photocatalytic performance. The photocatalytic mechanism was explained in terms of dopant driven shifting of conduction bands and valence bands (calculated by Mott-Schottky plots). The thermodynamic probability of radical generation along with role of redox cycles of metal ions has been discussed in the mechanism. The dye degradation was ascertained by detection of intermediates via mass spectrometry analysis and a possible degradation route was also predicted. The findings in this work provide intriguing opportunities to modify the electronic band structure of spinel ferrites for visible and solar light photocatalytic activity for environmental detoxification.     

心源性猝死一级预防植入心律转复除颤器前非持续室性心动过速与植入后恰当治疗的关系

目的探讨植入型心律转复除颤器(implantable cardioverter defibrillator,ICD)用于心源性猝死一级预防时,植入前发生非持续性室性心动过速(non-sustained ventricular tachycardia,NSVT)与植入后ICD恰当治疗的关系。方法本研究为单中心回顾性队列研究,连续纳入2006年1月至2017年12月在北京协和医院住院植入ICD用于一级预防的心脏病患者,患者在住院期间ICD植入前均完善Holter检查。根据Holter检查是否出现NSVT,将患者分为NSVT组及无NSVT组。门诊或电话随访至2018年8月,随访事件包括全因死亡、植入后ICD放电治疗及抗心动过速起搏(antitachycardia pacing,ATP)治疗情况。结果纳入60例符合入选和排除标准的患者,随访时间37 (14～61)个月,KaplanMeier生存曲线及Log-Rank检验提示,NSVT组与无NSVT组相比,全因死亡率无统计学差异(P=0. 108),NSVT组恰当治疗的风险高于无NSVT组(P=0. 033),NSVT和左心室射血分数与ICD恰当治疗独立相关(NSVT:HR=5. 099,95%CI:1. 399～18. 588,P=0. 014;左心室射血分数:HR=1. 077,95%CI:1. 013～1. 145,P=0. 018)。结论 ICD一级预防患者植入前出现NSVT提示接受ICD恰当治疗的风险增加。     

The World robot summit disaster robotics category – achievements of the 2018 preliminary competition

The World Robot Summit is a robot Olympics and aims to be held in a different country every four years from 2020. The concept of the Plant Disaster Prevention challenge is daily inspections, checks, and emergency response in industrial plants, and in this competition, robots must carry out these types of missions in a mock-up plant. The concept of the Tunnel Disaster Response and Recovery challenge is emergency response to tunnel disasters, and is a simulation competition whereby teams compete to show their ability to deal with disasters, by collecting information and removing debris. The Standard Disaster Robotics challenge assesses, in the form of a contest, the standard performance levels of a robot that are necessary for disaster prevention and emergency response. The World Robot Summit Preliminary Competition was held at Tokyo Big Sight in October 2018, and 36 teams participated in the Disaster Robotics Category. UGVs and UAVs contended the merits of new technology for solving complex problems, using core technologies such as mobility, sensing, recognition, performing operations, human interface, autonomous intelligence etc., as well as system integration and implementation of strategies for completing missions, gaining high-level results.     

Mixed coordination mechanisms for scheduling games on hierarchical machines

In this paper, we study scheduling games under mixed coordination mechanisms on hierarchical machines. The two scheduling policies involved are ‐ and ‐, where ‐ (resp., ‐) policy sequences jobs in nondecreasing order of their hierarchies, and jobs of the same hierarchy in nonincreasing (resp., nondecreasing) order of their processing times. We first show the existence of a Nash equilibrium. Then we present the price of anarchy and the price of stability for the games with social costs of minimizing the makespan and maximizing the minimum machine load. All the bounds given in this paper are tight.     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.