製造卸売業の垂直統合からＳＰＡへの転換 : 安踏（アンター）スポーツ用品会社を素材として

departmental bulletin pape

A Study on Evolutionary Optimization Method

名古屋大学Nagoya University博士(工学)名古屋大学博士学位論文 学位の種類:博士(工学) (課程) 学位授与年月日:平成9年3月25日doctoral thesi

ムジナモ自生地緊急調査後４年間の宝蔵寺沼水生動物相の変遷<数学・自然科学>

Hozoji pond is a natural habitat of the almost extinct aquatic carnivorous plant, Aldrovanda vesiculosa L., which the Government has designated as a special natural monument since 1966. Multifaceted research to save the endangered plant was urgently started from 2009. One of them, investigations of aquatic fauna of Hozoji pond was conducted for three years. The investigations revealed that an invasive alien species, Rana catesbeiana was dominant in the pond and it preyed upon A. vesiculosa. Here we showed four years of change in the aquatic fauna in the pond that was continuously investigated after the emergency research. Our data indicated that Palaemon paucidens, R. catesbeiana and Procambarus clarkii were still dominant in the pond. Small fishes, Pseudorasbora parva and Rhinogobius sp. were reduced in number. The Simpson index as a measurement of diversity was fluctuated between 0.313 and 0.802. The pH level of the pond was acidic. On the one hand the acidity is inhabitable for A. vesiculosa, but on the other hand it is not so appropriate for the small fishes. The continuous activity of both exterminating R. catesbeiana and monitoring aquatic fauna in the pond could be necessary in the future.textapplication/pdfdepartmental bulletin pape

Observation of B+→pp̅π+, B0→pp̅K0, and B+→pp̅K*+

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SOME RESULTS ON FIXED POINTS IN UNIFORM SPACES

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研究速報 : Numerical Simulation of Fresh Concrete (2) : Verification of Three Dimensional Discrete Element Model for Fresh Concrete

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Performances (precision, recall, f-measure) for different numbers of features

<p><b>Copyright information:</b></p><p>Taken from "Systematic feature evaluation for gene name recognition"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S9-S9.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869023.</p><p></p> Starting from the full feature set, recursive feature elimination removes the features with the lowest weight vector and we measure the performance after each round

The overall system architecture, including the recursive feature elimination process

<p><b>Copyright information:</b></p><p>Taken from "Systematic feature evaluation for gene name recognition"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S9-S9.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869023.</p><p></p

Impact of removing 10% of the features with the lowest weight vector in each round

<p><b>Copyright information:</b></p><p>Taken from "Systematic feature evaluation for gene name recognition"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S9-S9.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869023.</p><p></p> After 30 iterations, with only 4.28% of all features remaining, the f-measure has dropped only by 2%. The underlying evaluation method only considers the recognition of single tokens rather than whole phrases. The bottom line (65 iterations) shows the impact of the remaining 0.11% of all features. All values are evaluated without the post-expansion step (see text)

Comparison of recall and precision before and after the post-expansion step

<p><b>Copyright information:</b></p><p>Taken from "Systematic feature evaluation for gene name recognition"</p><p></p><p>BMC Bioinformatics 2005;6(Suppl 1):S9-S9.</p><p>Published online 24 May 2005</p><p>PMCID:PMC1869023.</p><p></p> We use the full feature set (marked "100%" in Figure 2) for this evaluation. We obtain the different spots by parallelly shifting the hyperplane