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导航 1991年第2期27卷 91-97页

作者： wolf,m.H 魏还毅

Chinese nurses’ perceived impact of international educational experiences and cultural beliefs following a 1-year study abroad program: an exploratory study

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Frontiers of Nursing 2020年第1期7卷 23-29页

作者： Debra mwolf Hui-Xin Wub Kathleen Spadaro Diane FHunker Nursing and Healthcare Informatics Chatham UniversityPittsburghPennsylvania 15232USA School of Nursing Davis&Elkins CollegeElkinsWest Virginia 26241USA School of Nursing Chatham UniversityPittsburghPennsylvania 15232USA

Objectives:To examine the perceived impact of international educational experiences and cultural beliefs before and after completing a 1-year masters of Science in Nursing ***:An exploratory study was conducted among Chinese nurses studying abroad in a private medium-sized university in the United *** 27-item electronic pre-survey was administered within 1 week of starting the program and the post-survey was administered 1 year later at the completion of the ***:majority of participants(n=25)were female,23–36 years of *** revealed that the perceived impact of international educational experiences assessed at the start of the program was similar to the perceived impact measured at the end of the 1-year program,suggesting that the students learned and experienced what they *** was a significant impact on cultural beliefs from the start of the program compared with the end of the program suggesting that a 1-year study abroad program does influence cultural ***:Understanding what is important to provide in a study abroad program and providing the educational experiences identified by students as impactful are avenues to help host universities best develop their *** suggested that the 1-year program can influence foreign students’cultural beliefs,yet the professional and personal impact of this change warrants further study.

关键词： international Chinese nurses cultural beliefs learning needs educational impact

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Enhanced wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in mammograms

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Computers, materials & Continua 2024年第2期78卷 2207-2223页

作者： Afnan m.Alhassan College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly *** identifying BAC could provide an expense,and be *** Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased ***,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting *** also is not able to capture the patterns and irregularities presented in the *** solve the overfitting problem,an optimal feature set has been formed by Enhanced wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet *** this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and *** bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher *** major contribution of the work is to design the best features selected by Enhanced wolf Pack Algorithm(EWPA),and modified Support Vector machine(mSVm)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image *** bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller *** proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized *** proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening mammography(DDSm).

关键词： Breast arterial calcification cardiovascular disease semantic segmentation transfer learning enhanced wolf pack algorithm and modified support vector machine

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Delta100/110穿经机的应用经验

国际纺织导报 2002年第3期30卷 34-34,36-39页

作者： R.Furrer m.wolf 郭建生 Stubli公司

随着Delta 10 0和Delta 110自动穿经机的投放市场 ,并得到市场的验证和认可 ,促进了自动穿经技术的应用和发展 ,而迄今为止在这一领域中的应用还不多见。同时也使自动穿经越来越接近成为标准配置的目标。本文对Delta系列穿经机的研制以... 详细信息

随着Delta 10 0和Delta 110自动穿经机的投放市场 ,并得到市场的验证和认可 ,促进了自动穿经技术的应用和发展 ,而迄今为止在这一领域中的应用还不多见。同时也使自动穿经越来越接近成为标准配置的目标。本文对Delta系列穿经机的研制以及在实践中应用的经验进行了介绍。

关键词：穿经机性能应用纺织自动穿经 Delta系列

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桑格尔——完全重复使用的有翼运载器

导弹与航天运载技术 1988年第1期 1-8页

作者： R.E.LO D.m.wolf 王向阳

本文对水平和垂直起飞的有翼运载器作了比较。对于水平起飞的运载器研究了采用不同类型起落架和不同起飞速度的情况。起飞前的加速方式对单级入轨的运载器是起支配作用的因素,但对两级入轨的运载器就不怎么重要。这个结论对于不同的起... 详细信息

本文对水平和垂直起飞的有翼运载器作了比较。对于水平起飞的运载器研究了采用不同类型起落架和不同起飞速度的情况。起飞前的加速方式对单级入轨的运载器是起支配作用的因素,但对两级入轨的运载器就不怎么重要。这个结论对于不同的起飞速度和起落架类型部是适用的。在所有单级入轨的运载器方案中,对垂直和水平起飞这两种方案的运载能力进行了比较,在几乎所有范围内,垂直起飞方案的运载能力要大。但有两种例外情况,这就是起飞速度为0.6马赫数和起飞重量较小的方案以及滑撬助推运载器某一特定范围的方案。对于两级入轨的运载器,水平和垂直起飞的运载能力并没有多大区别。本文最后对运载能力达20吨的不同运载器方案(单级入轨方案和桑格尔方案)进行了比较。结果表明,桑格尔两级入轨方案的性能要好,研制风险较低。

关键词：重复使用载人航天器有翼运载火箭

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环境中镉及其他微量元素

农业资源与环境学报 1987年第2期 35-37页

作者： Ann m.wolf Dale E.Baker 刘远嵘

本文的目的是综述镉与微量元素在食物链中迁移及其潜在毒性有关的主要原理,主要讨论在污泥中镉及其他微量元素含量增高对植物生长和对动物与人体健康所产生的潜在性损害作用。一、微量元素在土壤-植物-动物系统中的迁移施用于土壤中的... 详细信息

本文的目的是综述镉与微量元素在食物链中迁移及其潜在毒性有关的主要原理,主要讨论在污泥中镉及其他微量元素含量增高对植物生长和对动物与人体健康所产生的潜在性损害作用。一、微量元素在土壤-植物-动物系统中的迁移施用于土壤中的微量元素,可以(1)以植物可利用形态保存,(2)通过土壤剖面沥滤至地下水中,或(3)与土壤成分反应而固定。如果植物摄入元素过量,则会引起毒性症状,也可能作用轻

关键词：微量元素食物链吸持痕量元素土壤溶液土壤成分粘土矿物粘粒矿物硅酸盐矿物阳离子交换毒性作用

高血压患者的成纤维细胞生长因子23、心力衰竭风险和肾素血管紧张素醛固酮系统阻断:多民族动脉粥样硬化研究

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中华高血压杂志(中英文) 2019年第11期 1098-1098页

作者： Akhabue E Vu TT Vaidya A michos ED de Boer IH Kestenbaum B Allison m Szklo m Ouyang P Yancy CW wolf m Isakova T Carnethon mR

高浓度的成纤维细胞生长因子-23(fibroblast growth factor-23,FGF23)被发现与心力衰竭有关。实验数据显示,FGF23可直接刺激心肌肥大,可能还会增强肾素血管紧张素醛固酮系统的活性。在高血压人群中,FGF23是否与心力衰竭风险增加有关,以... 详细信息

高浓度的成纤维细胞生长因子-23(fibroblast growth factor-23,FGF23)被发现与心力衰竭有关。实验数据显示,FGF23可直接刺激心肌肥大,可能还会增强肾素血管紧张素醛固酮系统的活性。在高血压人群中,FGF23是否与心力衰竭风险增加有关,以及这种相关性是否在血管紧张素转换酶抑制剂(angiotensin converting enzyme inhibitor,ACEI)或血管紧

关键词：肾素血管紧张素醛固酮系统 FGF 心力衰竭成纤维细胞生长因子高血压患者动脉粥样硬化

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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Computers, materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed m.El-kenawy marwa m.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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以事件控制的过程链──信息管理的基础

工业工程与管理 1997年第3期2卷 31-35页

作者： W.Ernst B.Viergutz m.wolf 王继平德国KaiserslauternW.Ernst信息股份有限公司德国汉堡W.Ernst信息股份有限公司

文章概述了当前信息管理的途径：以事件控制的过程链使信息管理发生了革命性的变化。描述了一个系统供应商相应的开发情况。

关键词：过程链信息管理 PPS 事件控制

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Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

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Petroleum 2024年第1期10卷 150-164页

作者： Leonardo Goliatt C.m.Saporetti L.C.Oliveira E.Pereira Department of Computational and Applied mechanics Federal University of Juiz de Fora Department of Computational modeling Polytechnic Institute Rio de Janeiro State University Petrobras Department of Stratigraphy and Paleontology Rio de Janeiro State University

Rock samples' TOC content is the best indicator of the organic matter in source rocks. The origin rock samples' analysis is used to calculate it manually by specialists. This method requires time and resources because it relies on samples from many well intervals in source rocks. Therefore, research has been done to aid this effort. machine learning algorithms can estimate total organic carbon instead of well logs and stratigraphic studies. In light of these efforts, the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and precision. Genetic algorithms, differential evolution, particle swarm optimization, grey wolf optimization, artificial bee colony, and evolution strategies were used to improve machine learning models to predict TOC. The six metaheuristics were integrated into four machine learning methods: extreme learning machine, elastic net linear model, linear support vector regression, and multivariate adaptive regression splines. Core samples from the YuDong-Nan shale gas field, located in the Sichuan basin, were used to evaluate the hybrid strategy. The findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict TOC. The results show that, independent of the metaheuristic used to guide the model selection, optimized extreme learning machines attained the best performance scores according to six metrics. Such hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.

关键词： Total organic carbon Hybrid models Automated machine learning Evolutionary algorithms Geology