995 resultados para 69-504B


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(PDF contains 2 pages)

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Aegagropila sauteri is a peculiar green algae, the branched thalli of which, according to the amount of growth, forms velvety spheres of a diameter of 3-4, sometimes to 5-6 cm. and bigger. Investigators attribute it to a special genus of green algae. The authors examine Aegagropila sauteri in Lake Markakol (Kazakhstan).

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研究了科尔沁沙地 6 9种植物的繁殖体 (30种为种子 ,39种为果实 )重量。结果表明 :1 )传播体为果实的植物可分为 4个组别 (即果实单粒重 <0 .1mg、0 .1~ 0 .9999mg、1~ 9.9999mg、1 0~ 99.9999mg) ;黄蒿 (Artemisiasco paria)果实 (0 .0 5 1 7mg)和小香蒲 (Typhaminima)果实 (0 .0 6 82mg)最轻 ,苍耳 (Xanthiumsibiricum)单个果实 (77.894 3mg)最重 ;2 )传播体为种子的植物可分为 3个组别 (即种子单粒重 0 .1~ 0 .9999mg、1~ 9.9999mg、1 0~ 99.9999mg) ;马齿苋 (Portulacaoleracea)种子 (0 .1 5 1 4mg)最轻 ,苦参 (Sophoraflavescens)种子 (4 6 .781 6mg)最重 ;3)黄蒿、马齿苋、轮叶沙参 (Adenophoratetraphylla)、碱地肤 (Kochiasieversiana)、狼尾花 (Lysimachiabarystachys)、灰绿藜 (Chenopodiumglaucum)、刺沙蓬 (Salsolaruthenica)、菟丝子 (Cuscutachinensis)、大籽蒿 (Artemisiasieversiana)、狗尾草 (Setariaviridis)、野古草 (Arundinellahirta)等植物所以广泛分布可能是因为它们繁殖体轻 (<1mg)且具有持久土壤种子库 ;4 )流沙上的先锋植物或沙生演替系列前期植物沙蓬 (Agriphyllumsquarrosum)、差巴嘎蒿 (Artemisiahalodendron)、乌丹蒿 (Artemisiawudanica)、狗尾草、雾冰藜 (Bassia

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对 Ni6 9.5Al2 8Y2 .5粉末进行球磨 ,获得 Ni Al金属间化合物 .研究粉末结构与球磨时间的关系 ,探索以中间合金的形式加入稀土元素的机械合金化方法 .结果表明 ,加入稀土元素后 ,用机械合金化的方法可以生成新的金属间化合物.

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Thie examines the continuing role that the landed class enjoyed in Northern Ireland in contrast to the experience of their fellow landlords in the rest of Ireland following Partition. It argues that the senses of tradition and continuity which the unionist population in particular attributed to the old landed elite gave them an important role in bolstering the newly created state of Northern Ireland. In turn this allowed them to continue acting as a social elite long after the economic and political foundations of their ascendancy had been removed.

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A six-year prospective study of 144 newly diagnosed, symptomatic diabetic patients aged 40-69 years showed that 21 (15%) required insulin therapy, commencing 1-61 months after diagnosis. The plasma insulin response to oral glucose was assessed at the time of diagnosis. All 12 patients with very low peak insulin response (less than or equal to 6 mU/l) required insulin therapy. Thirty-six patients had an intermediate insulin response (greater than 6 less than or equal to 18 mU/l); of these, 7 with a mean weight 88% (range 73-96%) of average body weight required insulin, while 29 with a mean weight 117% (range 98-158%) of average body weight, did not. Ninety-six patients had a peak insulin response (greater than 18 mU/l); 2 patients whose weights were 96% and 100% of average body weight, required insulin, while the remainder did not. Consideration of initial body weight and peak insulin response provides a useful prediction of the eventual need for insulin.

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We prove with the help of a counterexample that Lemma 6 and Corollary 7 from Eeckhout [1] are incorrect.

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Obesity and overweight are suggested to increase the risk of occupational injury but longitudinal evidence to confirm this is rare. We sought to evaluate obesity and overweight as risk factors for occupational injuries.

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In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models - Multiple regression, Random Forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply Random Forest or Quantile regression techniques to the machining domain. The performance of these models was compared to each other to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).