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Phyton-international Journal of experimental Botany 2020年第3期89卷 765-777页

作者： Jutamart Monkai Ruvishika s.Jayawardena sajeewa s.N.Maharachchikumbura Kevin D.Hyde Center of excellence in Fungal Research Mae Fah Luang UniversityChiang Rai57100Thailand school of science Mae Fah Luang UniversityChiang Rai57100Thailand school of Life science and Technology University of Electronic Science and Technology of ChinaChengdu611731China

We isolated an interesting fungus from dead leaves of Magnolia liliifera collected from Chiang Mai,*** novel strain is related to Plectosphaer-ellaceae based on the morphology of its asexual morph and the analysis of sequence *** analyses using a combined gene analysis of LsU and iTs sequence data showed that this strain is clustered in the same clade with Furcasterigmium furcatum with high statistical *** new strains produced the asexual morph in culture which is morphologically similar to ***,we identified this strain as the sexual morph of *** is the first record of sexual morph for the monotypic genus Furcasterigmium and the first record of this genus on Magnolia.

关键词： Acremonium phylogeny sexual morph sordariomycetes taxonomy

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综述大型露天矿山测量技术的研究与应用

铜业工程 2001年第2期 16-20页

作者：罗发明江西铜业公司德兴铜矿

本文以概述矿山测量的支撑技术与现代任务入手，以特大型露天矿山──江西铜业公司德兴铜矿为例，对近几年来该矿开展的大型露天矿山控制测量技术、工程测量技术、变形监测技术、计算机技术和“３Ｓ”技术在矿山测量工作中的研究应用成... 详细信息

本文以概述矿山测量的支撑技术与现代任务入手，以特大型露天矿山──江西铜业公司德兴铜矿为例，对近几年来该矿开展的大型露天矿山控制测量技术、工程测量技术、变形监测技术、计算机技术和“３Ｓ”技术在矿山测量工作中的研究应用成果进行了较为全面地综述，并提出了当今矿山测量工作中存在的问题和今后努力的方向。

关键词：矿山测量技术进步露天矿山

Artificial neural network model for predicting drill cuttings settling velocity

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Petroleum 2020年第4期6卷 340-352页

作者： Okorie e.Agwu Julius U.Akpabio Adewale Dosunmu Department of Chemical and Petroleum engineering University of UyoAkwa Ibom StateNigeria Department of Petroleum and Gas engineering University of PortHarcourtRivers StateNigeria

The traditional method of using the coefficient of drag–Reynolds number relationship to predict cuttings settling velocity involves an implicit procedure that requires repeated,time-consuming and tedious iterations using Newtonian or mostly non-Newtonian ***,these correlations are limited to certain fluid flow ***,most of the explicit and direct cuttings settling velocity models that exist are based on the assumption that the cuttings are spherical ***,in the field,the cuttings are a mixture of various shapes and are hardly spherical,hence these models when applied to field conditions come off with huge *** objective of this work was to use a nature-inspired algorithm(artificial neural network-ANN)to develop a model for estimating cuttings settling velocity that would be robust and useful in the field that would take into account the shape of the *** data used for this work was obtained from research experiments in the *** model was then evaluated using four performance metrics namely:mean squared error(Mse),root mean square error(RMse),sum of squares error(sse)and goodness of fit(R2).it was found that the model's predictions obtained in this work agreed with experimental ***,the developed model possesses the capacity to generalize across new input datasets and can be applied to particles of any shape,hence,defining the novelty of this research and bridging the gap between theory and *** compared with state-of-the-art models,the developed models show a high degree of robustness,as the ANN model performed reasonably well with an Mse of 7.5×10−4,an R2 of 0.978,RMse of 0.0274 and sse of *** generalize the results across new input datasets,the developed model was cross-validated with new data that was not part of the training *** was found that the ANN model had an Mse value 0.00807,RMse of 0.0898,mae of 0.065,sse of 2.74 and MAPe of 0.675%.To ensure the replicabi

关键词： Drill cuttings Artificial neural network Mud Cuttings settling velocity

Multiple extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

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intelligent Automation & soft Computing 2023年第6期36卷 2819-2834页

作者： J.Jalaney R.s.Ganesh Department of electronics and Communication engineering Noorul Islam Centre for Higher EducationKumaracoil629180TamilnaduIndia Department of electronics and Communication engineering R.M.K.Engineering CollegeChennai601206Tamil NaduIndia

Due to fast-growing urbanization,the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an intelligent public transportation system where informa-tion regarding all the buses connecting in a city will be gathered,processed and accurate bus arrival time prediction will be presented to the *** linear and time-varying parameters such as distance,waiting time at stops,red signal duration at a traffic signal,traffic density,turning density,rush hours,weather conditions,number of passengers on the bus,type of day,road type,average vehi-cle speed limit,current vehicle speed affecting traffic are used for the *** proposed model exploits the feasibility and applicability of eLM in the travel time forecasting *** eLMs(MeLM)for explicitly training dynamic,road and trajectory information are used in the proposed approach.A large-scale dataset(historical data)obtained from Kerala state Road Transport Corporation is used for *** are carried out by using MATLAB *** experiments revealed that the efficiency of MeLM is independent of the time of day and day of the *** can manage huge volumes of data with less human intervention at greater learning *** is found MeLM yields prediction with accuracy in the range of 96.7%to 99.08%.The mae value is between 0.28 to 1.74 minutes with the proposed *** study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of *** research has proved that MeLM is superior for arrival time pre-dictions in terms of accuracy and error,compared with other approaches.

关键词： Arrival time prediction public transportation extreme learning machine traffic density

Advances in the phylogenesis of Agaricales and its higher ranks and strategies for establishing phylogenetic hypotheses

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Journal of Zhejiang University-science B(Biomedicine & Biotechnology) 2008年第10期9卷 779-786页

作者： Rui-lin ZHAO Dennis e. DesJARDiN Kasem sOYTONG Kevin D. HYDe Faculty of Biology Conservation Southwest Forestry University Kunming 650224 China Department of Biology San Francisco State University San Francisco California 94132 USA Faculty of Agricultural Technology King Mongkut＇s Institute of Technology Ladkrabang （KMITL） Ladkrabang Bangkok 10520 Thailand international Fungal Resource and Development Centre the Research Institute of Resources Insects Chinese Academy of Forestry Kunming 650224 China Fungal Research Group School of Science Mae Fah Luang University Tasud Chiang Rai 57100 Thailand

We present an overview of previous research results on the molecular phylogenetic analyses in Agaricales and its higher ranks (Agaricomycetes/Agaricomycotina/Basidiomycota) along with the most recent treatments of taxonomic systems in these taxa. establishing phylogenetic hypotheses using DNA sequences, from which an understanding of the natural evolutionary relationships amongst clades may be derived, requires a robust dataset. it has been recognized that single-gene phylogenies may not truly represent organismal phylogenies, but the concordant phylogenetic genealogies from multiple-gene datasets can resolve this problem. The genes commonly used in mushroom phylogenetic research are summarized.

关键词： Gene Molecular Mushrooms Phylogeny

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基于深度学习技术的水稻环境因素产量预测

电子技术应用 2024年第4期50卷 81-86页

作者：张春磊李颜娥丁煜罗煦钦浙江农林大学数学与计算机学院浙江杭州311300 浙江省林业智能监测与信息技术实验室浙江杭州311300 林业感知技术与智能装备国家林业局重点实验室浙江杭州311300 杭州市临安区农业农村信息服务中心浙江杭州310000

水稻作为全球重要的粮食作物,准确预测水稻产量在农业发展中起着重要作用。由于水稻在环境因子与其生长机理的作用下往往呈现出非线性的特点,难以对其做出较为准确的预测,因此,提出Ce-CGRU水稻产量预测模型,对非线性环境因子Copula熵(Ce... 详细信息

水稻作为全球重要的粮食作物,准确预测水稻产量在农业发展中起着重要作用。由于水稻在环境因子与其生长机理的作用下往往呈现出非线性的特点,难以对其做出较为准确的预测,因此,提出Ce-CGRU水稻产量预测模型,对非线性环境因子Copula熵(Ce)方法进行提取特征并与CNN和GRU技术结合在一起。其目的是在水稻品种确定的条件下,识别产量预测的重要特征。根据使用浙江省临安区真实数据分析和比较所提出的模型的性能,构建了其他5个产量预测模型进行对比,分别是MLR、RF、LsTM、GRU和CNN-LsTM。结果显示,Ce-CGRU模型的mae、Mse和MAPe分别为0.677、0.87和5.029%,表明Ce-CGRU模型具有更好的能力来捕捉水稻产量与环境因素之间的复杂非线性关系。此外,还对不同的特征选择方法以及不同时间步长进行了比较和分析。

关键词：水稻产量预测 Copula熵深度学习 Ce-CGRU

investigating species boundaries in Colletotrichum

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Fungal Diversity 2021年第2期107卷 107-127页

作者： Chitrabhanu s.Bhunjun Chayanard Phukhamsakda Ruvishika s.Jayawardena Rajesh Jeewon itthayakorn Promputtha Kevin D.Hyde Center of excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand school of science Mae Fah Luang UniversityChiang Rai 57100Thailand institute of Plant Protection College of AgricultureJilin Agricultural UniversityChangchun 130118JilinPeople’s Republic of China engineering Research Center of Chinese Ministry of education for edible and Medicinal Fungi Jilin Agricultural UniversityChangchun 130118JilinPeople’s Republic of China Department of Health sciences Faculty of Medicine and Health SciencesUniversity of MauritiusReduitMauritius Department of Biology Faculty of ScienceChiang Mai UniversityChiang Mai 50200Thailand

Colletotrichum is one of the most important plant pathogenic genera that is responsible for numerous diseases which can have a profound impact on the agricultural *** delineation is difficult due to a lack of distinctive phenotypic ***,in this study three different genomic approaches based on phylogenetic,evolutionary and coalescent-based methods are applied to establish robust species *** reliability of five different DNA barcodes was also assessed to provide further insights into species *** iTs region can resolve the placement of taxa up to the species complex *** GAPDH and TUB2 markers are determined to be the most informative for most ***,no single marker could discriminate between species in all complexes,therefore different molecular approaches based on multi-locus datasets are *** is the first study to provide an estimated divergence time for all species complexes in *** estimated divergence time for species complexes ranged between 4.8 to 32.2 *** on the high level of congruent results obtained from the different molecular approaches,a new species complex,the Colletotrichum agaves complex is *** complex consists of five taxa which are characterised by the presence of straight or slightly curved conidia with obtuse *** study shows that coalescent approaches and multi-locus phylogeny are crucial to establish species boundaries in *** taxonomic placement of three singleton taxa Colletotrichum axonopodi,*** and *** is *** accept 248 species and provide recommendations regarding species boundaries in the graminicola-caudatum complex.

关键词： BeAsT General mixed yule coalescent method Glomerellaceae Multi-rate poisson tree process sordariomycetes Taxonomy

evaluation and prediction of blast induced ground vibration using support vector machine

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Mining science and Technology 2010年第1期20卷 64-70页

作者： KHANDeLWAL M KANKAR P K HARsHA s P Department of Mining engineering College of Technology & Engineering Maharana Pratap University of Agriculture & Technology Udaipur 313001 India Department of Mechanical & industrial engineering Indian Institute of Technology Roorkee 247667 India

We present the application of support Vector Machine (sVM) for the prediction of blast induced ground vibration by taking into consideration of maximum charge per delay and distance between blast face to monitoring point. To investigate the suitability of this approach, the predictions by sVM have been compared with conventional predictor equations. Blast vibration study was carried out at Magnesite mine of Pithoragarh, india. Total 170 blast vibrations data sets were recorded at different strate-gic and vulnerable locations in and around to mine. Out of 170 data sets, 150 were used for the training of the sVM network as well as to determine site constants of different conventional predictor equations, whereas, 20 new randomly selected data sets were used to compare the prediction capability of sVM network with conventional predictor equations. Results were compared based on Co-efficient of Determination (CoD) and Mean Absolute error (mae) between monitored and predicted values of Peak Particle Veloc-ity (PPV). it was found that sVM gives closer values of predicted PPV as compared to conventional predictor equations. The coef-ficient of determination between measured and predicted PPV by sVM was 0.955, whereas it was 0.262, 0.163, 0.337 and 0.232 by UsBM, Langefors-Kihlstrom, Ambraseys-Hendron and Bureau of indian standard equations, respectively. The mae for PPV was 11.13 by sVM, whereas it was 0.973, 1.088, 0.939 and 1.292 by UsBM, Langefors-Kihlstrom, Ambraseys-Hendron and Bureau of indian standard equations respectively.

关键词： blast vibration support vector machine generalised predictor equations

高眼压治疗研究中青光眼视野终点后的正常视野结果

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美国医学会眼科杂志（中文版） 2006年第3期18卷 189-190页

作者： John L Keltner Chris A. Johnson Richard A. Levine Juanjuan Fan Kimbedy e. CeUo Michael A. Kass mae O. Gordon 杨帆（译）雷嘉启（校） for the Ocular Hypertension Treatment study Group 不详

目的：为了比较高眼压治疗研究中在2次和3次出现可靠连续视野异常后的正常视野结果。

关键词：高眼压治疗青光眼视野异常

What is a species in fungal plant pathogens?

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Fungal Diversity 2021年第4期109卷 239-266页

作者： Ruvishika s.Jayawardena Kevin D.Hyde Antonio Roberto Gomes de Farias Chitrabhanu s.Bhunjun Himashi s.Ferdinandez Dimuthu s.Manamgoda Dhanushka Udayanga indunil s.Herath Kasun M.Thambugala ishara s.Manawasinghe Achala J.Gajanayake Binu C.samarakoon Digvijayini Bundhun Deecksha Gomdola Naruemon Huanraluek Ya-ru sun Xia Tang itthayakorn Promputtha Marco Thines innovative institute of Plant Health Zhongkai University of Agriculture and EngineeringHaizhu DistrictGuangzhou 510225People’s Republic of China Center of excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand Departamento de Genética Universidade Federal de PernambucoAv.Prof.Moraes Rego1235Cidade UniversitáriaRecifePE 50670-901Brazil Department of Botany Faculty of Applied SciencesUniversity of Sri JayewardenepuraNugegodaSri Lanka Department of Biosystems Technology Faculty of TechnologyUniversity of Sri JayewardenepuraPitipanaHomagama 10200Sri Lanka Genetics and Molecular Biology Unit Faculty of Applied SciencesUniversity of Sri JayewardenepuraGangodawilaNugegodaSri Lanka school of science Mae Fah Luang UniversityChiang Rai 57100Thailand Department of Biology Faculty of ScienceChiang Mai UniversityChiang Mai 50200Thailand Department of Biological sciences Institute of EcologyEvolution and DiversityGoethe UniversityMax-von-Laue-Str.1360486 Frankfurt am MainGermany senckenberg Biodiversity and Climate Research Centre Senckenberganlage 2560325 Frankfurt am MainGermany

scientific names are crucial for communicating knowledge concerning fungi and fungus-like *** plant pathology,they link information regarding biology,host range,distribution and potential risk to agriculture and food *** the past,delimitation among pathogenic taxa was primarily based on morphological *** to distinct species sharing overlapping characteristics,the morphological identification of species is often neither straightforward nor ***,the phylogenetic species concept based on molecular phylogenetic reconstructions gained *** present opinion discusses what a fungal species is and how identification of species in plant pathology has changed over the past *** this context,host-specialization and species complexes are ***,species concepts in plant pathology are examined using case studies from Bipolaris,Colletotrichum,Curvularia,Diaporthe,Diplodia,Meliola,Plasmopara,rust fungi and *** entry contains a brief introduction to the genus,concepts used in species identification so far and the problems in describing a species followed by *** importance of correctly naming and identifying a species is addressed in the context of recent introductions,and we also discuss whether the introduction of new species in pathogenic genera has been *** also provide guidelines to be considered when introducing a new species in a plant pathogenic genus.

关键词： Dual nomenclature Host-specificity Polyphasic approach Pathology Phylogeny species delimitation systematics