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关键词： 足迹检验技术   刑侦破案   应用探究  

摘要： 我国的科技不断发展社会经济整体水平提升,近年来的侦查案件之中,发现犯罪分子的手段呈多样化发展趋势。其中足迹检验技术应用范围拓展,也是公安机关侦查案件常见的 技术应用依据。所以本文 将结合足迹检验技术的应用实际,将其在侦查破案环节的应用途径探析,将足迹技术的科学性体现,全面分析足迹检验技术还存在的一些问题,希望能给广大同仁带来借鉴参考。

关键词： Stroke   Classification   Aetiology  

摘要： BackgroundInter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke *** performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at *** 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from fair' to very good' and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n=502 in each arm, increasing to 605 at 5% contamination and 973 at 20% *** aetiological classification sys

关键词： Small-sample image classification   Few-shot   Ensemble learning   Deep convolutional neural network  

摘要： Small-sample classification is a challenging problem in computer vision. In this work, we show how to efficiently and effectively utilize semantic information of the annotations to improve the performance of small-sample classification. First, we propose an image-text dual neural network to improve the classification performance on small-sample datasets. The proposed model consists of two sub-models, an image classification model and a text classification model. After training the sub-models separately, we design a novel method to fuse the two sub-models rather than simply combine their results. Our image-text dual neural network aims to utilize the text information to overcome the training problem of deep models on small-sample datasets. Then, we propose to incorporate a decision strategy into the image-text dual neural network to further improve the performance of our original model on few-shot datasets. To demonstrate the effectiveness of the proposed models, we conduct experiments on the LabelMe and UIUC-Sports datasets. Experimental results show that our method is superior to other models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Anatomy   Biochemistry   Biology and life sciences   Blood   Blood plasma   Body fluids   Cancers and neoplasms   Cell biology   Cell signaling   Condensed matter physics   Engineering and technology   Equipment   Lipid metabolism   Lipid signaling   Lipids   Lung and intrathoracic tumors   Magnetism   Measurement equipment   Medicine and health sciences   Metabolism   Metabolites   NMR spectroscopy   Nuclear magnetic resonance   Oncology   Physical sciences   Physics   Physiology   Research and analysis methods   Research Article   Signal transduction   Spectrometers   Spectrum analysis techniques  

摘要： Nuclear magnetic resonance (NMR) spectroscopy is a principal analytical technique in metabolomics. Extracting metabolic information from NMR spectra is complex due to the fact that an immense amount of detail on the chemical composition of a biological sample is expressed through a single spectrum. The simplest approach to quantify the signal is through spectral binning which involves subdividing the spectra into regions along the chemical shift axis and integrating the peaks within each region. However, due to overlapping resonance signals, the integration values do not always correspond to the concentrations of specific metabolites. An alternate, more advanced statistical approach is spectral deconvolution. BATMAN (Bayesian AuTomated Metabolite Analyser for NMR data) performs spectral deconvolution using prior information on the spectral signatures of metabolites. In this way, BATMAN estimates relative metabolic concentrations. In this study, both spectral binning and spectral deconvolution using BATMAN were applied to 400 MHz and 900 MHz NMR spectra of blood plasma samples from lung cancer patients and control subjects. The relative concentrations estimated by BATMAN were compared with the binning integration values in terms of their ability to discriminate between lung cancer patients and controls. For the 400 MHz data, the spectral binning approach provided greater discriminatory power. However, for the 900 MHz data, the relative metabolic concentrations obtained by using BATMAN provided greater predictive power. While spectral binning is computationally advantageous and less laborious, complementary models developed using BATMAN-estimated features can add complementary information regarding the biological interpretation of the data and therefore are clinically useful.

关键词： fault detection   sensors   unmanned aerial vehicles   flight control system   one-class support vector machine   local density  

摘要： Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority.

关键词： 公司债券   债券违约   处理方法  

摘要： 截止2017年10月末,中国资本市场上共有将近130只债券违约事件发生,累计违约资金达700多亿元。在这些违约债券中,私募债券违约数量最多,定向工具违约规模最大。在建筑、工程、钢铁、贸易公司、建材等周期性较强的行业违约比重较大。华东、华北、东北地区为债券违约的高发区域。对于债券违约,人们习惯政府来处理,政府的"兜底"政策甚至被视为我国处理债券违约问题的"常规"做法。党的"十三届三中全会"提出的"发展并规范债券市场,提高直接融资比重",充分表明未来债券市场必须打破以往依赖政府来处理的惯例,研究并思考如何应对债券偿付危机办法,减少损失。

关键词： 妊娠   高血压   D-二聚体   凝血指标   临床检验  

摘要： 目的探讨妊娠期高血压患者D-二聚体与凝血指标临床检验的作用。方法选2017年3月至2018年4月在我院进行就诊的妊娠期高血压患者30例作为实验组,选同期体检正常孕妇30例作为对照组,对两组人员均进行D-二聚体与凝血指标检测,对两组检验指标情况进行观察。结果 D-二聚体指标水平会随着孕妇孕周的增加而升高,和对照组检测数据比较,数据差异明显(P <0.05)。实验组孕妇凝血酶时间、部分凝血活酶时间、凝血酶时间、纤维蛋白原指标情况与对照组孕妇比较,数据差异有统计意义(P <0.05)。结论 D-二聚体联合凝血指标检验能够有效提高妊娠期高血压患者病症的检出率,与正常孕妇比较,妊娠期高血压孕妇具备较高的凝血状态,并且有血栓形成的倾向,并且会随着病情的加重而加重。