针对细微特征进行K-means聚类的电台分选识别技术

现代战场中的无线通信设备日益增多，精准获取个体信息已成为研究热点，但也是难点。针对通信电台，提出了一种分选识别技术。该技术从电台物理层特性出发，对其辐射信号的细微特征进行K-means聚类以实现分选，分选的同时提取各个个体的特征属性值，未知信号通过与特征属性值相关运算实现个体识别。该技术无需先验知识，无需训练运算，通过实验验证，其可行、高效，易于工程实现。     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

The (Poly)Pharmacology of Cannabidiol in Neurological and Neuropsychiatric Disorders: Molecular Mechanisms and Targets

Cannabidiol (CBD), the major nonpsychoactive Cannabis constituent, has been proposed for the treatment of a wide panel of neurological and neuropsychiatric disorders, including anxiety, schizophrenia, epilepsy and drug addiction due to the ability of its versatile scaffold to interact with diverse molecular targets that are not restricted to the endocannabinoid system. Albeit the molecular mechanisms responsible for the therapeutic effects of CBD have yet to be fully elucidated, many efforts have been devoted in the last decades to shed light on its complex pharmacological profile. In particular, an ever-increasing number of molecular targets linked to those disorders have been identified for this phytocannabinoid, along with the modulatory effects of CBD on their cascade signaling. In this view, here we will try to provide a comprehensive and up-to-date overview of the molecular basis underlying the therapeutic effects of CBD involved in the treatment of neurological and neuropsychiatric disorders.     

Osteosarcoma Pathogenesis Leads the Way to New Target Treatments

Osteosarcoma (OS) is a rare condition with very poor prognosis in a metastatic setting. Basic research has enabled a better understanding of OS pathogenesis and the discovery of new potential therapeutic targets. Phase I and II clinical trials are already ongoing, with some promising results for these patients. This article reviews OS pathogenesis and new potential therapeutic targets.     

Xanthohumol for Human Malignancies: Chemistry,Pharmacokinetics and Molecular Targets

Xanthohumol (XH) is an important prenylated flavonoid that is found within the inflorescence of Humulus lupulus L. (Hop plant). XH is an important ingredient in beer and is considered a significant bioactive agent due to its diverse medicinal applications, which include anti-inflammatory, antimicrobial, antioxidant, immunomodulatory, antiviral, antifungal, antigenotoxic, antiangiogenic, and antimalarial effects as well as strong anticancer activity towards various types of cancer cells. XH acts as a wide ranging chemopreventive and anticancer agent, and its isomer, 8-prenylnaringenin, is a phytoestrogen with strong estrogenic activity. The present review focuses on the bioactivity of XH on various types of cancers and its pharmacokinetics. In this paper, we first highlight, in brief, the history and use of hops and then the chemistry and structure–activity relationship of XH. Lastly, we focus on its prominent effects and mechanisms of action on various cancers and its possible use in cancer prevention and treatment. Considering the limited number of available reviews on this subject, our goal is to provide a complete and detailed understanding of the anticancer effects of XH against different cancers.     

飞机撞击下高温气冷堆乏燃料厂房损伤研究

本文针对典型高温气冷堆乏燃料厂房在双发商用飞机撞击载荷下的响应及结构完整性开展研究，并探讨结构特性对撞击损伤的影响。对乏燃料厂房及飞机分别建立有限元模型，通过弹体-目标相互作用分析模拟了飞机撞击过程，综合IAEA与NRC的评价准则对乏燃料厂房在飞机撞击下的损伤程度进行评估。数值结果表明：厂房上对应于机身及发动机的撞击位置发生可接受的局部损伤；乏燃料贮存井墙体对于提高构筑物抗飞机撞击能力有重要作用。此外，构筑物外形对损伤有很大影响，圆柱形壳体的抗飞机撞击能力显著强于方形厂房，是核电厂厂房设计的优化方向之一。     

A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

基于GFU和分层LSTM的组群行为识别研究方法

提出一种以"关键人物"为核心，使用门控融合单元（GFU，Gated Fusion Unit）进行特征融合的组群行为识别框架，旨在解决两个问题：①组群行为信息冗余，重点关注关键人物行为特征，忽略无关人员对组群行为的影响；②组群内部交互行为复杂，使用GFU有效融合以关键人物为核心的交互特征，再通过LSTM时序建模成为表征能力更强的组群特征.最终，通过softmax分类器进行组群行为类别分类.该算法在排球数据集上取得了86.7%的平均识别率.     

Chemical-Shift Perturbations Reflect Bile Acid Binding to Norovirus Coat Protein: Recognition Comes in Different Flavors

Bile acids have been reported as important cofactors promoting human and murine norovirus (NoV) infections in cell culture. The underlying mechanisms are not resolved. Through the use of chemical shift perturbation (CSP) NMR experiments, we identified a low-affinity bile acid binding site of a human GII.4 NoV strain. Long-timescale MD simulations reveal the formation of a ligand-accessible binding pocket of flexible shape, allowing the formation of stable viral coat protein–bile acid complexes in agreement with experimental CSP data. CSP NMR experiments also show that this mode of bile acid binding has a minor influence on the binding of histo-blood group antigens and vice versa. STD NMR experiments probing the binding of bile acids to virus-like particles of seven different strains suggest that low-affinity bile acid binding is a common feature of human NoV and should therefore be important for understanding the role of bile acids as cofactors in NoV infection.