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油酸甲酯基琥珀酸、单甲酯、双甲酯的合成及防锈性能

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石油学报（石油加工） 2024年第1期40卷 146-154页

作者：张羿蔺建民夏鑫李妍中石化石油化工科学研究院有限公司北京100083

以油酸甲酯与马来酸酐(MA)为原料制备了油酸甲酯基琥珀酸酐(ASA)、油酸甲酯基琥珀酸(MSA)、油酸甲酯基琥珀酸单甲酯(MMS)和油酸甲酯基琥珀酸双甲酯(MDS)作为防锈剂,采用气相色谱-质谱联用、电喷雾电离质谱、傅里叶红外光谱、核磁共振氢... 详细信息

以油酸甲酯与马来酸酐(MA)为原料制备了油酸甲酯基琥珀酸酐(ASA)、油酸甲酯基琥珀酸(MSA)、油酸甲酯基琥珀酸单甲酯(MMS)和油酸甲酯基琥珀酸双甲酯(MDS)作为防锈剂,采用气相色谱-质谱联用、电喷雾电离质谱、傅里叶红外光谱、核磁共振氢谱等手段分析确定了4种防锈剂的结构,并考察4种防锈剂在加氢柴油和150SN润滑油基础油中的防锈蚀性能。结果表明:在加氢柴油中,MSA添加质量分数25μg/g时防锈效果能达到无锈级别,MMS添加质量分数50μg/g时的防锈效果能达到无锈级别;在润滑油中,MSA添加质量分数50μg/g时防锈效果能达到无锈级别,MMS添加质量分数50μg/g时防锈效果能达到无锈级别。并且MSA酸值比工业防锈剂T746低100 mgKOH/g, MMS酸值比工业防锈剂T747低50 mgKOH/g,适用于调制酸值要求低的汽轮机油和液压油。

关键词：油酸甲酯琥珀酸酐阿德尔-烯反应防锈剂加氢柴油润滑油基础油酸值

碱激发下粉煤灰-市政污泥固废基混凝土抗冻融性能

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工程科学与技术 2024年第3期56卷 277-286页

作者：贺盛夏鑫覃志笛于鹏广西大学土木建筑工程学院广西南宁530004 广西博世科环保科技股份有限公司广西南宁530007 广西大学广西防灾减灾与工程安全重点实验室广西南宁530004 广西大学工程防灾与结构安全教育部重点实验室广西南宁530004

为改善市政污泥对混凝土抗冻融性能的劣化效果,在氢氧化钠及水玻璃的复合激发下,用经生石灰改性后的市政污泥取代10%细骨料,粉煤灰分别取代10%、20%与30%水泥,制备了混凝土;分析不同冻融循环次数下,混凝土的表观现象、相对质量、相对动... 详细信息

为改善市政污泥对混凝土抗冻融性能的劣化效果,在氢氧化钠及水玻璃的复合激发下,用经生石灰改性后的市政污泥取代10%细骨料,粉煤灰分别取代10%、20%与30%水泥,制备了混凝土;分析不同冻融循环次数下,混凝土的表观现象、相对质量、相对动弹性模量、相对抗压强度及微观结构随粉煤灰掺量与碱当量的变化规律;分别以相对动弹性模量与相对抗压强度为损伤变量建立不同的冻融损伤模型。研究结果表明:随冻融循环次数增加,混凝土表观损伤愈发严重,内部孔隙增多,相对动弹性模量及相对抗压强度逐渐降低;与对照组相比,粉煤灰及碱激发剂的加入可以改善市政污泥对混凝土冻融耐久性的劣化效果,降低混凝土在冻融循环作用下的质量损失、动弹性模量损失及抗压强度损失;当粉煤灰掺量10%、碱当量4%时,粉煤灰会在碱激发剂及改性污泥中残余碱的联合作用下被较充分地激发,消纳改性污泥中的残余碱,减弱市政污泥对混凝土抗冻融性能的劣化效果,此时,混凝土水化程度最高,在冻融循环作用下的质量损失、动弹性模量损失及抗压强度损失最小,且服役寿命最长,约为16 a以上;相对动弹性模量与相对抗压强度相关性良好,分别以两者为损伤变量的冻融损伤模型与试验结果均吻合良好。

关键词：市政污泥固废资源化利用冻融循环微观结构冻融损伤模型

A manual inspection of Defects4J bugs and its implications for automatic program repair

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Science China(Information Sciences) 2019年第10期62卷 31-46页

作者： Jiajun JIANG Yingfei XIONG xin xia Key Laboratory of High Confidence Software Technologies Ministry of EducationInstitute of Software Department of Computer Science and Technology Peking University The Faculty of Information Technology Monash University

Automatic program repair techniques, which target to generate correct patches for real-world defects automatically, have gained a lot of attention in the last decade. Many different techniques and tools have been proposed and developed. However, even the most sophisticated automatic program repair techniques can only repair a small portion of defects while producing a large number of incorrect patches.A possible reason for the low performance is the test suites of real-world programs are usually too weak to guarantee the behavior of a program. To understand to what extent defects can be fixed with exiting test suites, we manually analyzed 50 real-world defects from Defects4J, where a large portion(i.e., 82%) of them were correctly fixed. This result suggests that there is much room for the current automatic program repair techniques to improve. Furthermore, we summarized seven fault localization and seven patch generation strategies that are useful in localizing and fixing these defects, and compared those strategies with current techniques. The results indicate potential directions to improve automatic program repair in the future.

关键词： automatic defect repair fault localization manual repair software maintenance case study

Natural interface-mediated self-assembly of graphene-isolatednanocrystals for plasmonic arrays construction and personalized information acquisition

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Nano Research 2022年第10期15卷 9327-9333页

作者： Shen Wang Tianhuan Peng Shengkai Li Linlin Wang Liang Zhang Zhiwei Yin xin xia xinqi Cai xiaoxu Cao Long Chen Zhuo Chen Weihong Tan Molecular Science and Biomedicine Laboratory State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of BiologyCollege of Chemistry and Chemical EngineeringAptamer Engineering Center of Hunan ProvinceHunan UniversityChangsha 410082China Faculty of Science and Technology University of MacaoAvenida da UniversidadeTaipa 999078MacaoChina The Cancer Hospital of the University of Chinese Academy of Sciences(Zhejiang Cancer Hospital) Hangzhou Institute of Medicine(HIM)Chinese Academy of SciencesHangzhou 310022China Institute of Molecular Medicine(IMM) Renji HospitalShanghai Jiao Tong University School of Medicineand College of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

As Interface mediated self-assembly of nanocrystals provide excellent strategy for sensing,catalysis or photonics,the construction of innovative interfaces and development of versatile strategies for nanocrystal synthesis are urgently needed.Herein,latent fingerprints(LFPs),the most common markers for human identity,are used as naturally accessible interface for organization of graphene isolated nanocrystals(GINs).Excitingly,the selective adsorption of GINs on lipidic ridge provides a universal approach for the in-situ construction of the plasmonic arrays.Such system with intrinsic chrominance and Raman signal enables the high resolution colorimetric and surfaced-enhanced Raman spectroscopy(SERS)dual-mode imaging,which can detail the structures of the LFPs from 1st to 3rd level even the LFPs are shielded.Furthermore,the interface can be constructed on diverse materials by a simple finger-pressing process and the densely packed arrays can serve as superior SERS substrate for label-free,non-invasive acquisition of molecule information especially residues in LFPs.The combination of chemical composition with detailed structures efficiently recognizes the human identity and could help link it to a crime scene.Overall,the LFPs can act as natural platform for interface mediated localized assembly and personalized information acquisition for forensic science or precise medicine.

关键词： interface self-assembly latent fingerprint personalized information acquisition

新型冠状病毒奥密克戎变异株感染住院患者疫苗接种情况与危重型的相关性分析

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中国循证医学杂志 2024年第3期24卷 258-264页

作者：胡粤霞夏鑫田欣王艳艳四川大学华西医院科技部/四川大学华西护理学院成都610041 四川大学华西医院老年医学中心/四川大学华西医院国家老年疾病临床医学研究中心成都610041

目的分析奥密克戎变异株感染住院患者的疫苗接种情况与奥密克戎危重型发生风险的相关性。方法回顾性分析2022年12月1日—2023年1月31日成都市某三甲医院收治的奥密克戎感染患者的临床资料,根据病情及《新型冠状病毒诊疗方案(试行第十版... 详细信息

目的分析奥密克戎变异株感染住院患者的疫苗接种情况与奥密克戎危重型发生风险的相关性。方法回顾性分析2022年12月1日—2023年1月31日成都市某三甲医院收治的奥密克戎感染患者的临床资料,根据病情及《新型冠状病毒诊疗方案(试行第十版)》把患者分为危重组和非危重组;根据疫苗接种情况,把患者分为未完成接种组、完成接种组、加强接种组。采用多因素Logistic回归分析疫苗接种、入院时的症状体征与危重型发生风险之间的关联。结果最终纳入3603例奥密克戎感染住院患者,危重组730例(20.3%),非危重组2873例(79.7%)。未完成接种组2399人(66.6%)、完成接种组433人(12.0%)、加强接种组771(21.4%)。与未完成接种组相比,完成接种组和加强接种组出现危重型的比例降低,随着年龄增加危重占比增高(P<0.05)。校正年龄、性别、基础疾病后,多因素Logistic分析结果显示完全接种[OR=0.67,95%CI(0.50,0.89)]与增强接种[OR=0.76,95%CI(0.61,0.94)]均与危重型发生风险降低有关。结论按次数接种疫苗、接种加强针均可有效降低奥密克戎感染后危重型发生风险。

关键词：新型冠状病毒感染奥密克戎变异株危重症患者疫苗

基于自适应矩阵的核联合稀疏表示高光谱图像分类

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遥感信息 2024年第2期39卷 19-27页

作者：陈善学夏馨重庆邮电大学通信与信息工程学院重庆400065

针对高光谱图像丰富的空间信息和光谱信息未充分利用的问题,提出了基于自适应矩阵的核联合稀疏表示高光谱图像分类的方法。在特征表示阶段,定义了自适应矩阵特征,通过结合自适应邻域块策略与非线性相关熵度量构成的特征来描述原始光谱像... 详细信息

针对高光谱图像丰富的空间信息和光谱信息未充分利用的问题,提出了基于自适应矩阵的核联合稀疏表示高光谱图像分类的方法。在特征表示阶段,定义了自适应矩阵特征,通过结合自适应邻域块策略与非线性相关熵度量构成的特征来描述原始光谱像素,充分融合了形状可变的空间信息与非线性光谱信息。在分类阶段,考虑自适应矩阵和高光谱图像非线性,采用对数欧式核函数,构建了核联合稀疏表示模型,以获得重构误差。同时利用字典空间信息构建了矩阵相关性,引入平衡参数实现了稀疏重构误差与矩阵相关性的联合分类。在两个数据集上的实验结果表明,该算法充分利用了高光谱图像的空间信息、光谱信息,能够有效提高分类精度。

关键词：高光谱图像分类核联合稀疏表示自适应邻域块自适应矩阵矩阵相关性

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数字普惠金融会影响企业慈善捐赠水平吗

金融与经济 2024年第3期 37-48页

作者：夏鑫宋欣蓉山东工商学院会计学院厦门大学

以2011—2021年A股上市公司为样本,探讨数字普惠金融对企业慈善捐赠水平的影响。研究发现,数字普惠金融对企业的慈善捐赠水平具有正向作用;数字普惠金融通过缓解融资约束、提高内部控制质量来提升企业慈善捐赠水平。进一步研究发现,企... 详细信息

以2011—2021年A股上市公司为样本,探讨数字普惠金融对企业慈善捐赠水平的影响。研究发现,数字普惠金融对企业的慈善捐赠水平具有正向作用;数字普惠金融通过缓解融资约束、提高内部控制质量来提升企业慈善捐赠水平。进一步研究发现,企业规模不同,数字普惠金融对企业慈善捐赠水平的影响呈现异质性;地区经济发展水平高、高管金融背景占比小的企业,数字普惠金融对慈善捐赠水平的正向促进效果更强。

关键词：数字普惠金融慈善捐赠融资约束第三次分配共同富裕

Combined classifier for cross-project defect prediction： an extended empirical study

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Frontiers of Computer Science 2018年第2期12卷 280-296页

作者： Yun ZHANG David LO xin xia Jianling SUN College of Computer Science and Technology Zhejiang University Hangzhou 310027 China School of Information Systems Singapore Management University Singapore 641674 Singapore

To facilitate developers in effective allocation of their testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on the past history of classes, methods, or certain other code elements. These techniques are effective provided that a sufficient amount of data is available to train a prediction model. However, sufficient training data are rarely available for new software projects. To resolve this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, was proposed and is regarded as a new challenge in the area of defect prediction. Thus far, only a few cross-project defect prediction techniques have been proposed. To advance the state of the art, in this study, we investigated seven composite algorithms that integrate multiple machine learning classifiers to improve cross-project defect prediction. To evaluate the performance of the composite algorithms, we performed experiments on 10 open-source software systems from the PROMISE repository, which contain a total of 5,305 instances labeled as defective or clean. We compared the composite algorithms with the combined defect predictor where logistic regression is used as the meta classification algorithm （CODEPLogistic）, which is the most recent cross-project defect prediction algorithm in terms of two standard evaluation metrics： cost effectiveness and F-measure. Our experimental results show that several algorithms outperform CODEPLogistic：Maximum voting shows the best performance in terms of F-measure and its average F-measure is superior to that of CODEPLogistic by 36.88%. Bootstrap aggregation （Bagging J48） shows the best performance in terms of cost effectiveness and its average cost effectiveness is superior to that of CODEPLogistic by 15.34%.

关键词： defect prediction cross-project classifier combination

High-Impact Bug Report Identification with Imbalanced Learning Strategies

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Journal of Computer Science & Technology 2017年第1期32卷 181-198页

作者： xin-Li Yang David Lo xin xia Qiao Huang Jian-Ling Sun College of Computer Science and Technology Zhejiang University Hangzhou 310027 China School of Information Systems Singapore Management University Singapore Singapore

In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects （i.e., surprise bugs）, or break pre-existing functionalities and destroy the user experience （i.e., breakage bugs）. Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE （synthetic minority over-sampling technique）＋ KNN （K-nearest neighbours） for surprise bug identification and RUS （random under-sampling）＋ NB （naive Bayes） for breakage bug identification outperform the Fl-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.

关键词： high-impact bug imbalanced learning bug report identification