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关键词： 技术创新   工业绿色化   作用机理   路径依赖   市场传导机制  

摘要： 技术创新是工业绿色化的重要动力,研究技术创新对工业绿色化的影响,对推进中国工业绿色化具有重要的理论意义和实践价值。本文将技术创新对工业绿色化影响的作用机理归纳为直接作用机理、市场传导机制和路径依赖三大部分,基于2007-2016年中国省际面板数据和系统GMM方法,将技术创新对工业绿色化影响的作用机理进行实证检验。研究结果显示:①技术创新有利于工业绿色化;技术创新还通过技术溢出效应促进邻近地区工业绿色化,但人力资本溢出效应则相反;市场开放形成的国际市场潜力促进工业绿色化,但国内市场潜力与之相反;上一期工业绿色化积累有利于本期工业绿色化。②技术溢出效应与国内外市场潜力联动及人力资本溢出效应与国内外市场潜力联动均有利于工业绿色化,但国内外市场潜力联动对工业绿色化起抑制作用。③技术溢出效应和人力资本溢出效应与国内外市场潜力联动对工业绿色化影响及国内外市场潜力联动对工业绿色化影响都存在明显的区域异质性。④以技术研发创新投入和人力资本水平因子分别替代发明专利因子并作为技术研发创新的测度指标,实证结果仍具有一定稳健性。因此,为推进中国工业绿色化,一是全面提升技术创新能力,加大技术创新政策支持力度,鼓励企业实施清洁型技术创新;二是推进区域经济一体化建设,降低制度对人力资本市场流动束缚,消除创新外溢与市场联动对工业绿色化影响的区域异质性;三是统筹内外市场联动发展,释放创新外溢与市场联动对工业绿色化的促进作用,并抑制国内外市场联动对工业绿色化的不利格局。

关键词： 安全生产检测检验   矿山安全生产   作用  

摘要： 在安全生产理念得到不断实践与检验的过程中,矿山安全生产也已得到了有关部门的重点关注。为了提升矿山内施工作业的安全程度,并为施工监管提供有效的技术支持,安全生产检测检验技术已被广泛应用在了矿山工程之中。为探究安全生产检测检验技术的实际价值,文章将对该项技术在矿山安全生产中的表现与作用进行分析与说明。

关键词： 副会长单位   分类分级   工业数据   京山轻机   美盈森   (中国)   联合体   宣贯  

摘要： 4月28日,中国包装联合会召开工业数据分类分级宣贯线上会议。会议由王跃中主持,MPA中国联合体联席主席兼首席专家镇锡惠主讲,MPA中国联合体联席主席高振刚、国家工业信息安全发展研究中心李红飞出席。高斯(中国)、美盈森、浙江大胜达、东经科技、北京博源包装、华兴玻璃、厦门合兴、奥瑞金、京山轻机、河南银金达、中荣印刷等中国包联副会长单位代表及中国包联相关负责人出席会议。

关键词： 理化检验质量控制   突发公共卫生事件   控制效果  

摘要： 目的分析理化检验质量控制在突发公共卫生事件中的应用价值。方法以2015年10月~2019年10月在皇姑区疾病预防控制中心登记的82例突发公共卫生事件为研究对象,将2017年11月前未实施理化检验质量控制的41例作为参照组,将2017年11月~2019年10月实施理化检验质量控制的41例作为观察组,分析理化检验质量控制的应用价值。结果观察组报告发出时间(4.09±1.02 h)、有效控制时间(22.08±3.95 h)及质量控制满意度均优于参照组(7.26±1.63 h、30.27±4.58 h),差异显著(P<0.05)。结论将理化检验质量控制应用于突发公共卫生事件控制当中,可提升控制效率、民众满意度。

关键词： Machine learning   Multi-temporal change detection   Landsat satellite imagery   Label-noise robust active learning   Multi-date classification   Automatic sample collection   SUPPORT VECTOR MACHINES   IMAGES  

摘要： Timely and reliable land-use/land-cover (LULC) change dynamic monitoring is the basis of urban understanding and planning. However, either the training sample shortage or the error accumulation in the multi-temporal processing inevitably restricts the monitoring performance. In this paper, to overcome these problems, we present a label-noise robust active learning method, which automatically collects reliable and informative samples from the images and builds a unified classification system with these augmented samples. In more detail, a Bayesian sample collection process that fuses the unsupervised transition information and the multitemporal land-cover information is designed to provide candidate samples with "from-to" labels. A reliability-based multi-classifier active learning method is then proposed to adaptively allocate the more reliable samples to the classes that are difficulty to classify. Finally, a fusion of the multiple multi-date classifications trained by the selected samples is implemented to identify the change type of interest. The dynamic monitoring results for Shanghai, Shenzhen, and Shiyan in China, two megacities with rapid and obvious urbanization and a small city with relatively slow urbanization, indicate that the proposed method achieves a significantly higher accuracy than the current state-of-the art methods. The sample accuracy verified by the high spatial resolution reference maps endorses the applicability of the sample collection, while the reliability-based active learning further ensures the robustness of the proposed method in the label-noise situation. The presented method was tested in two difficult situations (a small training sample case and a training sample set without joint labeling), so that the robustness and accuracy of the approach can be expected to be of a similar or better quality in cases with more training samples. Given its effectiveness and robustness, the proposed method could be widely applied in LULC change dyn

关键词： Ensemble learning   Active learning   Cancer classification   Gene expression data   Fuzzy set   Rough set   GENE-EXPRESSION DATA   TUMOR CLASSIFICATION   CLUSTER-ANALYSIS   PREDICTION   ALGORITHM   SELECTION   SVM  

摘要： Background and Objective: Classification of cancer from gene expression data is one of the major research areas in the field of machine learning and medical science. Generally, conventional supervised methods are not able to produce desired classification accuracy due to inadequate training samples present in gene expression data to train the system. Ensemble-based active learning technique in this situation can be effective as it determines few informative samples by all the base classifiers and ensemble the decisions of all the base classifiers to get the most informative samples. Most informative samples are labeled by the subject experts and those are added to the training set, which can improve the classification accuracy. Method: We propose a novel ensemble-based active learning using fuzzy-rough approach for cancer sample classification from microarray gene expression data. The proposed method is able to deal with the uncertainty, overlap and indiscernibility usually present in the subtype classes of the gene expression data and can improve the accuracy of the individual base classifier in presence of limited training samples. Results: The proposed method is validated using eight microarray gene expression datasets. The performance of the proposed method in terms of classification accuracy, precision, recall, F-1-measures and kappa is compared with six other methods. The improvements in accuracy achieved by the proposed method compared to its nearest competitive methods are 2.96%, 9.34%, 0.93%, 3.69%, 7.2% and 4.53% respectively for Colon cancer, Prostate cancer, SRBCT, Ovarian cancer, DLBCL and Central nervous system datasets. Results of the paired t-test justify the statistical relevance of the results in favor of the proposed method for most of the datasets. Conclusion: The proposed method is an effective general purpose ensemble-based active learning adopting the fuzzy-rough concept and therefore can be applied for other classification problem in future.

关键词： Metagenomics   Skin microbiome   Taxonomic profiling   16S sequencing   WGS sequencing   RIBOSOMAL-RNA   MICROBIAL-POPULATIONS   IDENTIFICATION   BACTERIA   DATABASE   GENES   MICROORGANISMS   METAGENOMICS   COMMUNITIES   INSIGHTS  

摘要： The assessment of microbiome biodiversity is the most common application of metagenomics. While 16S sequencing remains standard procedure for taxonomic profiling of metagenomic data, a growing number of studies have clearly demonstrated biases associated with this method. By using Whole Genome Shotgun sequencing (WGS) metagenomics, most of the known restrictions associated with 16S data are alleviated. However, due to the computationally intensive data analyses and higher sequencing costs, WGS based metagenomics remains a less popular option. Selecting the experiment type that provides a comprehensive, yet manageable amount of information is a challenge encountered in many metagenomics studies. In this work, we created a series of artificial bacterial mixes, each with a different distribution of skin-associated microbial species. These mixes were used to estimate the resolution of two different metagenomic experiments - 16S and WGS - and to evaluate several different bioinformatics approaches for taxonomic read classification. In all test cases, WGS approaches provide much more accurate results, in terms of taxa prediction and abundance estimation, in comparison to those of 16S. Furthermore, we demonstrate that a 16S dataset, analysed using different state of the art techniques and reference databases, can produce widely different results. In light of the fact that most forensic metagenomic analysis are still performed using 16S data, our results are especially important.

关键词： Emotional solidarity   social distance   place attachment   contact   festival   World Heritage Site   DESTINATION IMAGE   MENTAL-ILLNESS   RESIDENTS ATTITUDES   TOURISM RESEARCH   MODEL   INVOLVEMENT   SATISFACTION   EXPERIENCE   PEOPLE   DETERMINANTS  

摘要： Building on common identity theory and intergroup contact theory, this study sought to further understanding of people-place relationships by developing a holistic theoretical model to scrutinize place attachment as an antecedent of social distance, mediated by emotional solidarity and moderated by frequency of contact between tourists and residents. Visitor data analyzed with SEM revealed that place dependence is a significant predictor of social distance given it affects affinity positively and avoidance negatively, both of which are mediated by the three dimensions of emotional solidarity. Furthermore, the mediated relationships (via emotional solidarity) between place attachment and social distance vary by level of visitors' frequency of interaction with residents. This study expands current theorization by examining the merits of emotional solidarity as an affective link in a tourist cognitive-behavioral model. From a practical standpoint, DMOs need to understand these construct linkages and include residents in their marketing strategies to increase repeat visitation.

关键词： Hyperspectral imaging   Deoxynivalenol   Near infrared   Cereal sorting   Contamination prediction   FUSARIUM-DAMAGED KERNELS   HEAD BLIGHT   DEOXYNIVALENOL   FOOD   SPECTROSCOPY   DURUM  

摘要： Near infrared hyperspectral imaging (HSI-NIR) is considered a promising technique able to replace time-consuming, costly and destructive classic methods to predict and classify deoxynivalenol (DON) contaminated wheat kernels or samples by its concentration and level of contamination, respectively. The main objective of the present study was to standardise the HSI-NIR image acquisition method in naturally contaminated whole wheat kernels to obtain a high accuracy method to quantify and classify samples according to DON levels. To confirm the results, wheat samples were analysed by high performance liquid chromatography as the reference method to determine their DON levels. Hyperspectral images for single kernels and whole samples were obtained and spectral data were processed by multivariate analysis software. The initial work revealed that HSI-NIR was able to overcome kernel orientation, position and pixel selection. The subsequent developed Partial Least Squares (PIS) prediction achieved a RMSEP (Root Mean Square Error of Prediction) of 405 mu g/kg and 1174 mu g/kg for a cross-validated model and an independent set validated model, respectively. Moreover, the classification accuracy obtained by Linear Discriminant Analysis (LDA) was 62.7% for two categories depending on the EU maximum level (1250 mu g/kg). Despite of the results are not accurate enough for DON quantification and sample classification, they can be considered a starting point for further improved protocols for DON management.

关键词： Validation   Minisequencing   SNaPshot   Biogeographic origin   Highly degraded samples   Forensic laboratories   PLACENTAL ABRUPTION   MTDNA   IDENTIFICATION   MIXTURES   ORIGIN   ASSAYS  

摘要： Mitochondrial DNA (mtDNA) is a useful tool in forensic investigation as it provides information about the matrilineal ancestry of individuals. In addition, mtDNA can be analyzed when the analysis of other nuclear markers is underperforming. Recently, we developed a minisequencing panel for the simultaneous analysis of 52 mtDNA SNPs to classify maternal lineages into the main haplogroups and their phylogeographic origin. In order to make this panel suitable for forensic genetics laboratories, a validation study has been performed in accordance with the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines, including species specificity, reproducibility, sensitivity, and stability tests. The results demonstrate that the panel of 52 mtDNA SNPs is highly sensitive, since it enables to obtain complete genetic profiles of samples containing minimal amounts of DNA (1 pg). Furthermore, it provides sufficient genetic information to detect the matrilineal biogeographical origin of highly degraded samples, i.e., ancient dating skeletal remains, and samples with the presence of inhibitors, such as hematin and humic acid. In addition, this panel can detect mixtures in samples whose mtDNA haplogroups of contributors are different. Overall, the results of this study demonstrate the suitability of this minisequencing panel of 52 mtDNA SNPs to be used in forensic cases, with samples of low amount or degraded DNA.