Methods to prevent side marks from rusting on heavy steel plates

When a steel plate produced by a steel plant leaves the factory,its upper surface should be marked with the specification,variety,batch number,and other identifying information,and its side should be marked with a side marking.The side marking contains the plate number,specification,batch number,and other information,which are convenient for customers to accurately identify relevant information after stacking.However,because of the influence of weather,environment,and other factors,the side marking becomes unidentifiable when the steel plate is corroded,affecting the customer’s loading,unloading,and other operations.The development and application of side anticorrosion devices and cleaning of finished heavy steel plates with antirust agents will solve the problem of unrecognizable side marking due to corrosion.

Rust语言在Web开发的应用研究

随着互联网技术的飞速发展,Web应用已成为人们日常生活和工作中不可或缺的一部分。但随着Web应用的规模和复杂性的不断增长,传统的开发语言在性能、安全性和并发性方面逐渐面临挑战。Rust作为一种新兴的系统级编程语言,以其高效、安全和并发性强的特点,逐渐在Web开发中得到广泛应用。

Rust语言在核电安全级仪控研发应用探讨

在核安全级(1E)仪控应用中,基于处理器的应用绝大部分使用C语言来实现。针对C语言存在弱内存管理、弱数据类型、易于出错的指针控制等天然语言缺陷,提出了更安全的Rust编程语言在核电仪控中应用可能性的探讨。首先分析了C语言固有特性的安全缺陷和Rust语言的安全优势,其次结合核电行业安全软件关键需求探讨了Rust语言的切实应用场景,并阐述了Rust语言应用于核电仪控面临着极大的挑战。最后,为推动核电仪控领域应用更安全的编程语言,提出了积极的展望。

Rust语言安全研究综述

Rust是为了解决系统编程领域的安全性问题,而设计的一门面向系统编程的兼具类型安全、内存安全和并发安全的新型程序设计语言,强调安全性和高性能,已经在操作系统内核、Web浏览器、网络协议栈、数据库和区块链等底层软件系统的构建中得到了越来越广泛的应用。现有研究表明,尽管Rust的设计目标是保证安全性,但其自身仍然存在许多安全问题。作为一门系统编程语言,Rust的安全性直接影响到基于Rust开发的软件系统的安全性。随着Rust的广泛应用,对Rust语言安全的研究显得尤为重要。Rust语言安全研究正在成为研究热点,并且在近几年已经取得了较大研究进展。本综述基于该研究领域已经公开发表的46篇研究论文,对该领域的相关研究进行了系统整理、分析和总结:首先,研究分析了Rust的核心安全特性,包括函数式编程范式、强多态类型系统、基于所有权模型的自动内存管理、对非安全代码的显式标记和隔离;其次,提出了Rust语言安全研究领域的分类学,将已有研究分为安全实证研究、漏洞检测研究、安全增强研究和形式化验证研究四个热点方向,并分别对这四个方向上的相关研究进行了综述、深入分析和总结,同时分析了四个研究方向的内在联系;最后,指出了该研究领域的待解决的科学问题,并对未来可能的研究方向进行了展望,提出了四个潜在的研究方向,以期为相关领域的研究者提供有价值的参考。

Green rusts-derived iron oxide nanostructures catalyze NO reduction by CO

Green rusts with brucite-like layers of hydroxide intercalated with anions constitute a family of diverse precursors for the synthesis of iron oxides via dehydration,but precise structural control of the resulting oxides with respect to the size and shape at the nanometer level remains challenging due to the easy oxidation of the ferrous species.Herein,we report a new synthetic strategy for the facile preparation of fibrous-like green rusts by using appropriate balancing anions(CO_(3)^(2-)and SO_(4)^(2-))in ethylene glycol to regulate the morphology.Depending on the type of the intercalating anion,the green rusts were converted into hematite with fibrous-or plate-like shapes upon thermal activation.When evaluated in the reaction of NO reduction by CO,these iron oxides showed a prominent shape-dependent catalytic behavior.The fibrous-like Fe_(2)O_(3)was much more catalytically active and structurally robust than the plate-like analogue.Combined spectroscopic and microscopic characterizations on the nanostructured iron oxides revealed that the superior performance of the fibrous-like Fe_(2)O_(3)stemmed from a facile Fe_(2)O_(3)/Fe_(3)O_(4)redox cycle and a higher density of active sites for NO activation.

Fine mapping and transcriptome sequencing reveal candidate genes conferring all-stage resistance to stripe rust on chromosome arm 1AL in Chinese wheat landrace AS1676

Stripe rust, caused by Puccinia striiformis f. sp. tritici(Pst), threatens wheat production worldwide, and resistant varieties tend to become susceptible after a period of cultivation owing to the variation of pathogen races. In this study, a new resistance gene against Pst race CYR34 was identified and predicted using the descendants of a cross between AS1676, a highly resistant Chinese landrace, and Avocet S, a susceptible cultivar. From a heterozygous plant from a F7recombinant inbred line(RIL) population lacking the Yr18 gene, a near-isogenic line(NIL) population was developed to map the resistance gene. An allstage resistance gene, YrAS1676, was identified on chromosome arm 1AL via bulked-segregant exomecapture sequencing. By analyzing a large NIL population consisting of 6537 plants, the gene was further mapped to the marker interval between KA1A_485.36 and KA1A_490.13, spanning 485.36–490.13 Mb on1AL. A total of 66 annotated genes have been reported in this region. To characterize and predict the candidate gene(s), an RNA-seq was performed using NIL-R and NIL-S seedlings 3 days after CYR34 inoculation. Compared to NIL-S plants, NIL-R plants showed stronger immune reaction and higher expression levels of genes encoding pathogenesis-associated proteins. These differences may help to explain why NIL-R plants were more resistant to Pst race CYR34 than NIL-S plants. By combining fine-mapping and transcriptome sequencing, a calcium-dependent protein kinase gene was finally predicted as the potential candidate gene of YrAS1676. This gene contained a single-nucleotide polymorphism. The candidate gene was more highly expressed in NIL-R than in NIL-S plants. In field experiments with Pst challenge,the YrAS1676 genotype showed mitigation of disease damage and yield loss without adverse effects on tested agronomic traits. These results suggest that YrAS1676 has potential use in wheat stripe rust resistance breeding.

High-resolution genetic mapping and identification of candidate genes for the wheat stem rust resistance gene Sr8155B1

Stem rust,caused by Puccinia graminis f.sp.tritici(Pgt),threatens global wheat production.Development of cultivars with increased resistance to stem rust by identification,mapping,and deployment of resistance genes is the best strategy for controlling the disease.In this study,we performed fine mapping and characterization of the all-stage stem rust resistance(Sr)gene Sr8155B1 from the durum wheat line 8155-B1.In seedling tests of biparental populations,Sr8155B1 was effective against six Chinese Pgt races tested.In a segregating population of 5060 gametes,Sr8155B1 was mapped to a 0.06-cM region flanked by markers Pku2772 and Pku43365,corresponding to 1.5-and 2.7-Mb regions in the Svevo and Chinese Spring reference genomes.Both regions include several typical nucleotide-binding leucine-rich repeat(NLR)and protein kinase genes that represent candidate genes.Among them,three NLR genes and three receptor-like protein kinases were highly polymorphic between the parental lines and their transcripts were upregulated in the homozygous resistant line TdR2 relative to its susceptible sister line TdS4.Four markers(Pku2772,Pku43365,Pku2950,and Pku3721)developed in this study,together with seedling resistance responses,correctly predicted Sr8155B1 absence or presence in 78 tetraploid wheat genotypes tested.The presence of Sr8155B1 in tetraploid wheat accessions CItr 14916,PI 197492,and PI 197493 was confirmed by mapping in three F_(2)populations.The genetic map and linked markers developed in this study may accelerate the deployment of Sr8155B1-mediated resistance in wheat breeding programs.

Prevention of Q235 Steel Corrosion using Waterborne Rust Inhibitor

Sorbitol,triethanolamine,sodium benzoate,boric acid,and sodium carbonate were mixed to prepare a waterborne rust inhibitor.A temperature and humidity accelerated corrosion test was applied to investigate the corrosion behaviour of waterborne rust inhibitor coated Q235 steel and original Q235 steel,which was carried out in a temperature and humidity test chamber(WSHW-1000)at a temperature of 80℃and humidity of 95%.Compared with the original Q235,waterborne rust inhibitor coated Q235 has better resistance to corrosion in hot and humid ambient conditions.Electrochemical impedance spectroscopy and potentiodynamic polarization were measured with a three-electrode cell in 3.5%NaCl aqueous solution on a CHI760E potentiostat/galvanostat.Molecular dynamics was simulated to verify the synergistic corrosion inhibitory mechanism of sodium carbonate and triethanolamine.The test shows that the prepared waterborne rust inhibitor can reduce the tendency of Q235 to corrosion and can also effectively reduce the corrosion rate.

Investigation of Aegilops umbellulata for stripe rust resistance,heading date,and the contents of iron,zinc,and gluten protein

Aegilops umbellulata(UU)is a wheat wild relative that has potential use in the genetic improvement of wheat.In this study,46 Ae.umbellulata accessions were investigated for stripe rust resistance,heading date(HD),and the contents of iron(Fe),zinc(Zn),and seed gluten proteins.Forty-two of the accessions were classified as resistant to stripe rust,while the other four accessions were classified as susceptible to stripe rust in four environments.The average HD of Ae.umbellulata was significantly longer than that of three common wheat cultivars(180.9 d vs.137.0 d),with the exception of PI226500(138.9 d).The Ae.umbellulata accessions also showed high variability in Fe(69.74-348.09 mg kg^(-1))and Zn(49.83-101.65 mg kg^(-1))contents.Three accessions(viz.,PI542362,PI542363,and PI554399)showed relatively higher Fe(230.96-348.09 mg kg^(-1))and Zn(92.46-101.65 mg kg^(-1))contents than the others.The Fe content of Ae.umbellulata was similar to those of Ae.comosa and Ae.markgrafii but higher than those of Ae.tauschii and common wheat.Aegilops umbellulata showed a higher Zn content than Ae.tauschii,Ae.comosa,and common wheat,but a lower content than Ae.markgrafii.Furthermore,Ae.umbellulata had the highest proportion of γ-gliadin among all the species investigated(Ae.umbellulata vs.other species=mean 72.11%vs.49.37%;range:55.33-86.99%vs.29.60-67.91%).These results demonstrated that Ae.umbellulata exhibits great diversity in the investigated traits,so it can provide a potential gene pool for the genetic improvement of these traits in wheat.

Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

Spray Prediction Model for Aonla Rust Disease Using Machine Learning

Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop.

甘肃陇南小麦品种抗条锈病鉴定与评价

为了解甘肃陇南小麦育成品种和后备品种对当前条锈菌流行小种的抗性水平,利用条锈菌流行小种CYR32、CYR33和CYR34对60份供试小麦品种分别进行苗期、成株期抗病性鉴定。结果表明,在所有供试品种中,对CYR32、CYR33和CYR34表现苗期抗病的品种分别有9、22和10份,表现成株期抗病的品种分别有35、38和30份。结合苗期和成株期鉴定结果,对CYR32、CYR33和CYR34表现苗期感病而成株期抗病的品种分别有28、22和24份,占46.7%、36.7%和40.0%;中梁14号和兰天37号对CYR32、CYR33和CYR34均表现全生育期抗性,占3.3%。甘肃陇南麦区小麦品种对当前条锈菌流行小种多表现为成株期抗性,筛选出部分具有全生育期或成株期抗性的品种,可作小麦育种的亲本资源加以利用。

引进美国小麦种质资源抗病性及品质性状鉴定

为了挖掘新的种质资源,本研究对引自美国的442份小麦材料对白粉病和条锈病的抗病性、蛋白含量以及高分子量麦谷蛋白(HMW-GS)亚基组成进行了鉴定分析。抗病性鉴定结果显示,153份材料抗白粉病,27份抗条锈病,6份兼抗两种病害。蛋白质含量达到GB/T 17892-1999《优质小麦强筋小麦》一等(粗蛋白质含量≥15.0%)的样品有143份。高分子量麦谷蛋白检测发现,供试材料共含18种HMW-GS亚基和100种不同亚基组合类型,其中N,7+9,5+10出现次数最多,优质亚基1、2*、7^(oe)、17+18、5+10的材料分别占总材料数的25.6%、5.2%、27.8%、7.7%和25.6%,含7^(oe),5+10亚基的有36份,含2*,5+10亚基的有1份,含2*,7^(oe)亚基的有7份。综合抗病性及品质分析结果显示,PI 17738等11份材料至少抗1种病害,且蛋白含量≥15.0%,湿面筋含量≥35.0%,同时含有稀有优质亚基。以上11份材料可作为我国优质、抗病小麦培育亲本,为我国小麦抗病和品质遗传改良提供优异基因源。

改进U_Net网络的钢结构表面锈蚀图像分割方法

为实现锈蚀图像分割网络模型轻量化,同时消除非单一特征背景和锈液等类似特征背景干扰,本文将U_Net网络模型的编码部分替换为MobilenetV3_Large网络,导入基于ImageNet数据集的MobilenetV3_Large网络预训练权重,将U_Net网络模型解码部分的普通卷积替换为深度可分离残差卷积,并在上采样的过程中添加注意力导向AG模块和Dropout机制。经实验验证表明,本文设计的改进U_Net网络模型在非单一特征背景和锈液等类似特征背景干扰下,具有明显的锈蚀图像分割优势,相比于原U_Net网络模型,模型大小减少了81.18%,浮点计算量减少了98.34%,检测效率提升了3.27倍,即从原来不足6 fps,提升至19 fps。网络模型实现轻量化的同时,网络模型的准确率达95.54%,相比于原U_Net网络模型提升了5.04%。

材料腐蚀与防护实验教学案例设计

针对材料类学生教学需求,以科研项目为牵引,设计了稀土合金化增强耐候钢表面锈层保护性能的实验教学案例。该实验案例以海洋大气环境下耐候钢耐蚀性能亟待提高这一实际工程问题为切入点,通过现场大气曝露腐蚀实验、实验室模拟加速腐蚀实验来评价耐候钢的耐腐蚀能力。通过扫描电镜、X射线衍射分析等物理方法和极化曲线等电化学方法,研究分析了稀土耐候钢在近海大气环境下表面锈层的形貌、物相组成、合金元素分布及其电化学腐蚀行为。利用综合实验结果分析了特定腐蚀环境下稀土耐候钢的耐蚀性能和耐蚀机理。该实验教学案例以“问题提出-材料制备-腐蚀行为分析-耐蚀性能评价”为主线,综合性强,涉及材料合金成分设计、电化学腐蚀机理、表征技术、电化学分析等众多知识点,在一定程度上实现了专业知识的系统化和一体化,对学生创新意识和研究能力的培养具有很好的推动作用。

小麦条锈病跨区域全周期绿色防控技术体系的构建与应用

小麦条锈病是我国小麦生产上一种跨区域流行的重大病害。1949年以来,我国小麦条锈病研究,治理能力明显提升,发病面积、流行程度、流行频率明显降低,但由于条锈菌Puccinia striiformis f.sp.tritici新毒性小种不断产生和气候异常变化等因素的影响,近年来其流行规律和成灾机制出现了新的变化,发生危害加重,严重威胁小麦生产和国家粮食安全。基于“十二五”以来我国在小麦条锈病菌毒性新小种产生、有性生殖、侵染循环和大区流行规律等方面的最新研究成果,作者在学习和实践以往病害治理经验的基础上,坚持长短结合、标本兼治、分区治理、综合防治的指导思想,制定了“以绿色防控为基础,以全周期管理为重点,以跨区域联防联控为保障”的防控策略,提出了精准监测预报、毒性小种变异监测、早期菌源控制、抗病品种合理布局和应急防控等关键技术,集成构建了全国小麦条锈病跨区域全周期绿色防控技术体系,制定了越夏易变区、冬季繁殖区(越冬区)和春季流行区分区防控技术体系。经在小麦条锈病主要流行区大面积试验示范,取得了防控效果85%以上,化学农药用量减少10%以上,小麦单产提高5%以上的成效。2022年-2023年在全国小麦主产区大范围组织推广应用,有效控制了小麦条锈病严重流行势头,全国病害发生面积出现了新低,有效减轻了灾害损失,保障了国家粮食安全。

基于时间序列植被指数的小麦条锈病抗性等级鉴定方法

条锈病严重影响小麦产量,培育抗条锈病的小麦品种至关重要。针对传统育种中抗性鉴定手段单一、效率低的问题,该研究提出了一种通过小麦冠层植被指数的时间序列实现对条锈病不同抗性等级的高效鉴定方法。该方法利用无人机采集自然发病的育种群体小麦(共600个样本,516个基因型)冠层多时相的光谱图像,使用随机蛙跳算法和ReliefF算法筛选出6个条锈病病害严重度的敏感特征:归一化色素叶绿素指数(normalized pigment chlorophyll index,NPCI)、沃尔贝克指数(woebbecke index,WI)、叶绿素红边指数(chlorophyll index rededge,CIrededge)、绿大气抵抗植被指数(green atmospherically resistant index,GARI)、归一化差分植被指数(normalized difference vi,NDVI)、叶绿素绿指数(chlorophyll index green,CIgreen),这些敏感特征在试验群体中的时间序列符合条锈病的发病规律,验证了其作为条锈病发病严重度敏感特征的有效性;基于支持向量机(support vector machine,SVM)算法使用上述敏感特征建立条锈病病害严重度等级分类模型,在测试集的表现中,与使用未经过筛选的原始特征所建立的模型相比在精度、平均准确率、平均召回率和F1分数上分别仅下降6.2%、3.3%、2.7%、4.0%,证明了所筛选敏感特征的有效性;针对一般机器学习算法难以捕捉不同抗性等级样本之间较小的特征变化差异的问题,提出了一种从植被指数时间序列转化生成的二维图像中提取特征实现条锈病抗性等级分类的方法。将敏感特征中能够较好区分不同抗病等级的4个时间序列植被指数(NPCI、GARI、NDVI、WI),通过格拉姆角场方法生成格拉姆角和场图像,并制作成数据集,使用DenseNet121网络进行训练,以实现不同条锈病抗病等级的分类。建立的条锈病抗性等级分类模型中,由NPCI时间序列图像建立的分类模型测试效果最佳,其准确率为0.837,召回率为0.834,F1分数可达0.833,能够较好地实现对群体小麦不同品种(系)的条锈病抗性等级差异的区分,表明基于光谱植被指数时间序列的小麦条锈病抗性等级识别方法可以用于小麦抗病育种中抗性等级的鉴定,并可为其他作物的病害抗性等级鉴定提供一定的参考。

小麦条锈病抗性基因定位及分子标记技术研究进展

条锈病流行对小麦生产造成巨大损失,选育和种植持久抗性品种是防治小麦条锈病最经济有效的策略。为达到多基因聚合培育持久抗病品种的目标,必须不断发掘抗病种质、解析其抗病遗传机制并开发分子标记。基于文献,对条锈病抗性基因发掘涉及的抗病性、分子标记、基因定位方法和定位进展及其在育种中的应用进行了综述,明确了小麦条锈病基因定位涉及技术的现状、局限性及优势,从而为后续的条锈病抗性基因发掘、多基因聚合和持久抗性小麦品种的选育与生产布局提供技术指导,以降低西北麦区和小麦主产区条锈病流行的频率,进一步促进国家粮食安全。

内河枢纽港水工钢构件锈蚀特征时空分布规律

内河枢纽港框架直立式码头下部结构多采用钢护筒、钢靠船构件、纵横撑等钢构件,钢构件具有强度高、塑性好等优点的同时也存在易锈蚀、耐腐蚀性差等问题,水工钢构件锈蚀对内河枢纽港长期运营安全存在严重威胁。为了探究水沙冲蚀条件下内河枢纽港水工钢构件锈蚀规律,依托某代表性内河枢纽港工程,开展针对其水工钢构件锈蚀程度的现场检测试验。通过对不同排架、高程、受水沙冲蚀方向等钢构件试验数据结果的详细对比分析,揭示不同钢构件锈蚀程度的时空分布规律,并提出所依托工程的水工钢构件锈蚀经验模型。结果表明:钢构件的锈蚀厚度随服役时间的增加而增大,但随着高程的增加而逐渐减小;同一高程位置处,构件迎水面的锈蚀程度相较于侧水面、背水面更严重。

铁锈锈斑污染对丝织品老化影响的研究

铁锈锈斑污染是纺织品文物的主要病害之一,严重影响文物的寿命及观赏价值。本文研究了不同老化条件下锈斑污染样和未污染样间老化程度的差异,旨在明晰锈斑污染及其在环境因素协同作用下丝织品的老化状态。结果表明:锈斑污染破坏了蚕丝纤维的外观形貌,拉伸性能和热稳定性的显著下降,加剧了蚕丝蛋白结构由β-折叠构象向α-螺旋构象和无序结构转变,使得未形成氢键—NH的数量下降,红外光谱中表现为酰胺Ⅰ带特征峰扁平化和I 3280/1620 cm-1峰高比下降;环境因素与锈斑污染起到了明显的协同作用,蚕丝的老化程度远大于环境条件、锈斑污染之和,其中土壤填埋老化的协同作用最强,湿热与紫外光老化次之。