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关键词： 上海市包装技术协会   回收利用   垃圾分类   回收再利用   管理条例   生活垃圾   专家学者   企业代表  

摘要： 为切实施行《上海市生活垃圾管理条例》,认真做好垃圾源头减量、包装物减量和回收利用工作,上海市包装技术协会于2019年6月11日下午召开“垃圾分类包装减量回收利用”圆桌会议。会议邀请政府有关部门领导,组织各方面的专家学者、有关行业的企业代表三十余人共同探讨研究包装源头减量、末端回收再利用问题。会上,费钧德会长首先作了致词。

关键词： 氧化性固体   物理形态   包衣处理   O.3   试验方法  

摘要： 为了研究物理形态及包衣处理对氧化性固体氧化性质的影响,选择颗粒状过碳酸钠作为氧化性固体待测物质,在此基础上进行0.5 mm 过筛和研磨处理。根据联合国《关于危险货物运输的建议书试验和标准手册》中O.3:氧化性固体的试验方法测试燃烧速率。通过实验结果可以得出样品的氧化性为:过碳酸钠<过碳酸钠(<0.5 mm)<过碳酸钠(粉末)。通过比较包衣/未包衣的实验结果可以证明包衣处理对氧化性固体氧化性质起到明显的减缓作用。建议对氧化性固体的形态和情况更加全面和精确的描述,确保氧化性固体分类判定的准确性。

关键词： 检验科   临床沟通   住培医师   沟通模式  

摘要： 检验医学在很大程度上推动着临床医学的发展,医院的检验科与临床科室相得益彰,相辅相成密不可分。通过结合该院检验科的沟通实践,介绍检验科与临床科室之间的沟通措施及认识体会、实施效果,旨在探讨检验科与临床科室之间的沟通模式,如何有效地发挥检验科住培医师的作用,对当前较薄弱而需亟待加强的该方面工作提供一定的参考。

关键词： 微生物检验   感染性疾病   预防   诊疗  

摘要： 目的对于感染性疾病而言,解析微生物检验方式应用后的预防和诊疗效果。方法选取2018年9月至2019年5月新疆伊犁州疾病预防控制中心实收治的110例患者为研究对象,依据平均分组方式分成实验组和参照组,各55例。参照组患者实施常规检验,实验组患者实施微生物检验方法,分析两组患者的临床数据。结果实验组中患者的重度与中度感染数据显著比参照组的数据低;而轻度感染数据则是实验组高于参照组数据(P<0.05)。实验组总有效率94.55%,高于参照组的72.73%(P<0.05)。结论对感染性疾病实施微生物检查,其能够做到有效预防,并且提升临床诊断准确率。

关键词： Normative values   Vergence measures   Accommodative measures   Stereoacuity   School children  

摘要： Aim: To determine normative values for stereoacuity, accommodative and vergence measures for high school populations. Methods: Using a multi-stage random cluster sampling, 1211 children (481 males and 730 females) between 13 and 18 years of age, with a median age of 16 years, were selected. Visual acuity, stereoacuity and suppression, refractive errors, near point of convergence, heterophoria and fusional vergences, as well as, amplitude of accommodation, accommodative response, facility and relative accommodation were evaluated. Results: Most data did not have a normal distribution. The range of normality for the vergence measures were: near point of convergence break, 5-10 cm, recovery, 6-13 cm, near lateral phoria, 2.5-6 prism dioptre (pd) (nasal), near vertical, orthophoria to 0.50 pd, negative fusional vergence break, 12-23 pd, recovery, 8-17 pd, positive fusional vergence break, 16-35 and recovery 11-24 pd. For accommodative measures, the range of normality for accommodative measures was: amplitude of accommodation, 12-18 pd, accommodative response, plano to +0.75 D, binocular accommodative facility, 5-12 cycles per minute (cpm), negative relative accommodation, 1.75-2.50 DS, positive relative accommodation, -2.0 to -3.0 DS and 17-69 s arc for stereoacuity. Conclusion: This study provides norms for clinical measures which could be used to classify accommodative and vergence parameters for children aged 13-18 years in this population or beyond. Findings should be applied in the context of the measurement techniques and the associated limitations outlined in this report. (C) 2018 Spanish General Council of Optometry. Published by Elsevier Espana, S.L.U.

关键词： Small sample   Elman neural network   Partial least squares   Genetic algorithm   PLS-GA-Elman algorithm  

摘要： Aiming at the small sample with high-feature dimension and few numbers will cause a serious problem if simply using the traditional Elman neural network to deal with the small sample;these problems include poor learning ability, the redundancy structure, and incomplete training;these defects will result in lower operating efficiency and poor recognition precision. In this paper, combining the theory of partial least squares (PLS) and genetic algorithm (GA), as well as the nature of Elman neural network, and an optimized Elman neural network classification algorithm based on PLS and GA (PLS-GA-Elman) is established. The new algorithm reduces the feature dimension of small samples by PLS, the relatively ideal low-dimensional data is obtained, and the purpose reduces the neural network's inputs and simplifies its structure. Using GA to optimize the connection weights, threshold values, and the number of hidden neurons and adopting the optimized way of encoding respectively and evolving simultaneously can improve the neural network incomplete training condition, leading to fewer number of samples and improvement in training speed and generalization ability;this ensures the optimal Elman neural network algorithm. A new algorithm based on twice consecutive optimization was the basis for a precise classification model. The results of experimental analysis illustrate that operating efficiency and classification precision of the new algorithm have been improved.

关键词： Classification   Paediatric   Reference interval   Transference  

摘要： The aim was to elude differences in published paediatric reference intervals (RIs) and the implementations hereof in terms of classification of samples. Predicaments associated with transferring RIs published elsewhere are addressed. A local paediatric (aged 0days to <18years) population of platelet count, haemoglobin level and white blood cell count, based on first draw samples from general practitioners was established. PubMed was used to identify studies with transferable RIs. The classification of local samples by the individual RIs was evaluated. Transference was done in accordance with the Clinical and Laboratory Standards Institute EP28-A3C guideline. Validation of transference was done using a quality demand based on biological variance. Twelve studies with a combined 28 RIs were transferred onto the local population, which was derived from 20,597 children. Studies varied considerably in methodology and results. In terms of classification, up to 63% of the samples would change classification from normal to diseased, depending on which RI was applied. When validating the transferred RIs, one RI was implementable in the local population. Conclusion: Published paediatric RIs are heterogeneous, making assessment of transferability problematic and resulting in marked differences in classification of paediatric samples, thereby potentially affecting diagnosis and treatment of children.

关键词： Large sample   Coal's bursting liability   Classification   Index relevance   Distance discriminant analysis   Comprehensive evaluation  

摘要： In order to evaluate the intensity of the coal's bursting liability scientifically and accurately, the index relevance problems existing in the current classification methods were focused on, and the Mahalanobis distance discriminant analysis (DDA) method was introduced on the basis of establishing a measured database with the large sample of the coal's bursting liability. Besides, the DDA model of coal's bursting liability classification was also established. Then, with the duration of dynamic fracture, elastic strain energy index, bursting energy index, and uniaxial compressive strength selected as evaluation indicators, three grade-discrimination functions of the bursting liability were established through training the data of 95 groups of bursting liability of different coal seams that were collected extensively. After training, the accuracy rate of the DDA model reached 96%. The results of the application of DDA model to the classification of coal samples from 10 coal mines exhibited remarkable agreement with the actual situation, which also solved the difficult problem that the fuzzy comprehensive evaluation method could not distinguish 8 kinds of samples. Its application to the engineering project shows that the classification result of coal's bursting liability based on the DDA method is both accurate and easy to calculate, and the DDA model has good engineering application prospect.

关键词： GAN   Active learning   Classification   Chest xray   Informative samples  

摘要： Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.

关键词： Classifier parameter tuning   HSRS image classification   limited training data   synthetic data   variational EM  

摘要： In hyperspectral remote sensing (HSRS), feature data can potentially become very high dimensional. At the same time, manual labeling of that data is an expensive task. As a consequence of these two factors, one of the core challenges is to perform multi-class classification using only relatively few training data points. In this work, we investigate the classification performance with limited training data. First, we revisit the optimization of the internal parameters of a classifier in the context of limited training data. Second, we report an interesting alternative to parameter optimization: classification performance can also be considerably increased by adding synthetic GMM data to the feature space while using a classifier with unoptimized parameters. Third, we show that using variational expectation maximization, we can achieve a much faster convergence in fitting the GMM on the data. In our experiments, we show that the addition of synthetic samples leads to comparable, and, in some cases, even higher classification performance than that for a properly tuned classifier on limited training data. One advantage of the proposed framework is that the reported performance improvements are achieved by a quite simple model. Another advantage is that this approach is computationally much more efficient than classifier parameter optimization and conventional expectation maximization.