Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication,data,energy,etc.,to detect and evaluate vehicle nodes.However,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy sufficiency.In order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this paper.First,the four tuple method is adopted,to qualitatively describing various types of nodes of IoV;Second,analyzing the behavioral features and correlation of various nodes based on route forwarding rate,data forwarding rate and physical location;third,designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes;fourth,establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer.Accordingly,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV.The results show that comparing with methods which only considers energy or communication parameters,the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV;especially,with the double detection feature parameters and node correlative detection model combined,detection accuracy is effectively improved,and the calculation time of node detection is largely reduced.

Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning

High toughness is highly desired for low-alloy steel in engineering structure applications,wherein Charpy impact toughness(CIT)is a critical factor determining the toughness performance.In the current work,CIT data of low-alloy steel were collected,and then CIT prediction models based on machine learning(ML)algorithms were established.Three feature construction strategies were proposed.One is solely based on alloy composition,another is based on alloy composition and heat treatment parameters,and the last one is based on alloy composition,heat treatment parameters,and physical features.A series of ML methods were used to effectively select models and material descriptors from a large number of al-ternatives.Compared with the strategy solely based on the alloy composition,the strategy based on alloy composition,heat treatment parameters together with physical features perform much better.Finally,a genetic programming(GP)based symbolic regression(SR)approach was developed to establish a physical meaningful formula between the selected features and targeted CIT data.

Microstructure and strength of a tantalum-tungsten alloy after cold rolling from small to large strains

Microstructural evolution of a refractory tantalum-tungsten alloy(Ta-4%W)after cold rolling from small to large von-Mises strains(0.12-2.7)was quantitatively studied using transmission electron microscopy.Grain subdivision was observed to take place at two levels.Geometrically necessary boundaries nearly paralleling to slip planes enclosed volumes further divided by diffuse cells and by remnants of Taylor lattices.With increasing strain,the diffuse cells evolved into clear incidental dislocation boundaries enclosing cells,while the Taylor lattices disappeared.Grain subdivision was thus intermediate between those observed in cell forming and in non-cell forming alloys.Meanwhile,the average misorientation angle across all boundaries increased while the average boundary spacing decreased.Distributions of the microstructural parameters at each strain level were found to exhibit universal scaling laws.The microstructural evolution was found closely linking to the observed high strength and strain hardening of this alloy.Based on measured microstructural parameters,the flow stress was calculated utilizing linearly addition of the strengthening by solutes,incidental dislocation boundaries(Taylor strengthening)and geometrically necessary boundaries(Hall-Petch equation).The relative contribution of each strength mechanism evolved with increasing strain and with microstructural evolution:solutes and friction stress dominated at small strains while boundaries dominated at larger strains.Calculated strengths were in close agreement with experimental tension tests and demonstrated an unexpectedly high and continuous parabolic hardening without transition across this large strain range.

帕博利珠单抗联合化疗一线治疗晚期三阴性乳腺癌的成本-效果分析

本文旨在从中国卫生体系角度评价帕博利珠单抗联合化疗治疗晚期TNBC的成本-效果。建立具有三种健康状态的Markov模型,该模型的模拟时限为10年,循环周期为21天。通过单因素敏感性分析和概率敏感性分析来验证模型的稳健性,进行情景分析来探讨中国患者援助计划(PAP)下帕博利珠单联合化疗治疗晚期TNBC的经济性。基础分析结果表明,帕博利珠单抗联合化疗可多获得0.67 QALYs,增量成本为$77096.78,ICER为115896.30$/QALY。单因素敏感性分析结果显示,帕博利珠单抗成本对结果的影响最大。概率敏感性分析结果显示,在中国的意愿支付阈值下(33782.23$/QALY),帕博利珠单抗联合化疗具有经济性的概率为0。从中国卫生体系角度来看,与安慰剂联合化疗相比,帕博利珠单抗联合化疗一线治疗晚期TNBC不具有成本-效果优势。如果考虑帕博利珠单抗纳入PAP,虽仍然不具有经济性,但是ICER更加接近于中国3倍人均GDP。

财产课税税种设置的国际比较：以G20为样本

本文以除中国、欧盟以外的其他G20成员为样本考察财产课税的税种类型与税种组合方式。在税种类型方面,将财产课税划分为综合财产税与分类财产税,进而将分类财产税划分为财产占用税与财产转让税,并将财产占用税划分为不动产占用税、动产占用税与资产税,将财产转让税划分为财产有偿转让税与财产无偿转让税。在税种组合方式方面,将财产课税划分为分类财产税制度、综合财产税制度与分类综合财产税制度三种模式,并将分类财产税制度划分为财产占用领域全域财产税制度、非全域财产税制度和财产占用与转移领域全域财产税制度、非全域财产税制度四种具体模式。借鉴G20成员财产课税的税种类型,我国应单独设置燃料税、不动产转让税、证券交易税、遗产税与赠与税。借鉴G20成员财产课税的税种组合方式,我国应借鉴实行分类财产税制度。

锂离子电池正极界面修饰用电解液添加剂

提高电压是提高锂离子电池比能量的重要途径之一。例如,Li Ni0.5Mn1. 5O4(4.7V)、Li Ni PO4(5.1V)和富锂锰基等电极材料在较高的充电截止电压下表现出较高的能量密度和较低的成本,具有很好的应用前景。另外,提高Li CoO2和三元电池体系的充电截止电压是提升电池能量密度的简单有效措施。但是,当电池充电截止电压提高时,不仅会造成电解液在正极/电解液界面的氧化分解,还会加速正极中金属阳离子在电解液中的溶解,造成电池循环性能和安全性下降。采用不同的正极界面修饰用电解液添加剂,既可以有效钝化正极/电解液界面,抑制电解液的分解,还可以有效抑制正极结构的破坏。本文从添加剂的分子结构出发,介绍了磺酸酯、硼酸酯、磷酸酯、氟代碳酸酯、腈类、酸酐和锂盐等添加剂在正极界面的相关研究成果,并对不同添加剂的作用机理进行了详细的解释和归纳;另外,介绍了添加剂的联用技术在不同电池体系中的最新研究成果;最后,对新型正极界面修饰用电解液添加剂的开发进行了展望。

Gefitinib facilitates PINK1/Parkin-mediated mitophagy by enhancing mitochondrial recruitment of OPTN

Gefitinib,a well-known epidermal growth factor receptor(EGFR)tyrosine kinase inhibitor for the targeted therapy of lung cancer,induces autophagy in association with drug resistance.However,it remains unclear whether gefitinib treatment can affect the selective form of autophagy(i.e.,mitophagy)and be beneficial for the treatment of human diseases with decreased autophagy,such as neurodegenerative diseases.Here,we show that gefitinib treatment promotes PINK1/Parkin-mediated mitophagy in both nonneuronal and neuronal cells,and this effect is independent of EGFR.Moreover,we found that gefitinib treatment increases the recruitment of the autophagy receptor optineurin(OPTN)to damaged mitochondria,which is a downstream signaling event in PINK1/Parkin-mediated mitophagy.In addition,gefitinib treatment significantly alleviated neuronal damage in TBK1-deficient neurons,resulting in impeded mitophagy.In conclusion,our study suggests that gefitinib promotes PINK1/Parkin-mediated mitophagy via OPTN and may be beneficial for the treatment of neurodegenerative diseases that are associated with defective mitophagy.