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关键词： 食品安全   粮油检验监督   作用  

摘要： 作为国有控股粮食购销企业的食品安全监督人员,必须切实发挥自身在粮油检验监督中的作用,以达到确保食品安全的目的。为此,结合国有控股粮食购销企业食品安全监督工作的实践,对粮油检验监督在食品安全中的作用进行梳理,并提出加强粮油检验监督的对策。

关键词： 医学检验   高等教育   产学结合  

摘要： 医学检验高等教育在我国发展迅速,但随着市场经济的发展和社会的进步,医学检验高等教育呈现出一些"不适应"。产学结合为医学检验人才培养提供了新思路。文章在产学结合的背景下,结合医学检验专业教育现状和存在的问题对医学检验专业产学结合的重大意义、存在的问题及发展策略进行了深入的探讨,旨在促进医学检验教育产学结合的可持续发展。

关键词： 头颈部恶性肿瘤   检验诊断   生物治疗   心理疾病   康复治疗   心理干预   相关检验   副作用  

摘要： 调查显示,头颈部恶性肿瘤发病人数呈逐年上升趋势,已经成为世界上第六大常见癌症,同时也是全球第八大致死性癌症。我国头颈部恶性肿瘤的发病率>5%,其主要的治疗方式为手术和化疗。有超过70%的患者需要在治疗过程中同步进行放疗、生物治疗等联合康复治疗。在整个治疗过程中,患者一般会有较为严重的副作用,再加上自身外观的变化,极易导致患者产生相关心理疾病。

关键词： EMSLIBS contest   Chemometrics   Laser-induced breakdown spectroscopy (LIBS)   Machine learning   Classification benchmark  

摘要： We present results of the classification contest organized for the EMSLIBS 2019 conference. For this publication, we chose only the five best approaches and discussed their algorithm in detail. The main focus of the contest reflected both recent and long-term challenges of Laser-Induced Breakdown Spectroscopy (LIBS) data processing. The contest was designed with a purpose to raise a challenge in handling and processing a very large dataset, containing high-dimensional elemental spectra. For the contest, 138 samples were measured using a lab-based LIBS system. In total, the data set consisted of 70,000 spectra, separated into 12 classes according to their elemental composition. Due to its extensivity and complexity, the data set is unique within the LIBS community. The central idea was to simulate the so-called "out-of-sample" classification (i.e. according to similar elemental composition), implying various real-world applications. Even more, it reflects the current level of expertise in the LIBS community and the capability of the LIBS method itself.

关键词： few labeled samples   point clouds   classification   indoor scenario  

摘要： The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89-19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios.

关键词： Data processing   Optical techniques   Optical spectroscopy  

摘要： Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental. A tutorial for multivariate classification analysis of vibrational spectroscopy data (Fourier-transform infrared, Raman and near-IR) is presented. Guidelines are provided for data preprocessing, data selection, feature extraction, classification and model validation.

关键词： Affinity propagation   Sample entropy   EEMD   Intrinsic mode functions   Bearing fault signal classification  

摘要： For bearing fault signal classification, the performance of the affinity propagation (AP) method is limited by its way of measuring similarity. In this paper, we have proposed an improved version for the AP method, which is termed as IMFSE-AP. The key point of the proposed IMFSE-AP method is the improvement on the way of measuring signal similarity. Instead of the Euclidean distance, a novel method based on the sample entropy of IMFs (IMFSE) is proposed, which can measure signal similarities better with respect to data complexity. In the proposed similarity measurement method, signals are first decomposed into IMFs by the EEMD method. Then the distances of sample entropy vectors of IMFs are computed to evaluate similarities between signals. Numerical experiments conducted on synthetic signals and real bearing fault signals illustrate the good performance of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.

摘要： Organizational scholars increasingly view institutionalization as a process through which actors adapt or translate seemingly successful practices in a field to create variations that are specific to their own organization. Yet little is known about how outsiders who seek to diffuse 'best practice' affect translation. We examined interactions between management consultants and their clients in two different consulting projects, which focused on embedding the practice of 'lean' in one and the practice of a quality improvement framework in the other. Our findings provide insights into the processes of translation through which promoters and adopters iterate at different stages to reach a compromise, illuminating how the evolution of an imported practice signals the outcome of a negotiation. Furthermore, we demonstrate that management practices are not translated in isolation but enveloped by peripheral practices that are adopted by association. We highlight how the peripheral practice of benchmarking, in both cases, was rarely contested or negotiated and thus proved more resistant to translation. Our analysis allows us to unpack the agency inherent withintranslation-as-negotiationand integrate research on diffusion and translation.

关键词： Autism spectrum disorder   Autism-related traits   Military   Suicide risk   Unit cohesion  

摘要： Background: Suicide rates are elevated among United States (U.S.) military service members. Research has found that service members with autism spectrum disorder-related (ASD-related) traits are at increased risk for suicide. Complementary lines of inquiry have suggested that unit cohesion is a protective factor against developing suicidal ideation in military service members. However, given the social difficulties inherent in ASD, it is unclear whether unit cohesion might protect against suicide risk in this population. Method: Our sample consisted of 285 active duty U.S. military service members recruited online. We examined the interaction between ASD-related traits (as measured by the Autism Spectrum Quotient [AO] ), unit cohesion, and suicide risk (as measured by the Suicidal Behaviors Questionnaire-Revised [SBQ-R]). We also conducted exploratory analyses to examine whether unit cohesion attenuates the association between ASD-related traits and suicidal intent. Results: Elevated ASD-related traits were independently associated with higher levels of global suicide risk;however, unit cohesion was not independently associated with suicide risk. Unit cohesion did not significantly interact with ASD-related traits to predict suicide risk. Finally, we found that elevated ASD-related traits and unit cohesion have an independent effect on current suicidal intent. Discussion: Our findings suggest that unit cohesion might not buffer the effect of ASD-related traits on suicide risk. However, our results do show that greater unit cohesion may be independently associated with decreased suicidal intent. This study is limited by a cross-sectional design and use of self-report measures.