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Nagoya-shi, Japan

Aichi Toho University] is a private university in Meitō-ku, Nagoya, Aichi Prefecture, Japan. The predecessor of the school was founded in 2001. The present name was adopted in 2007. Wikipedia.


Ito R.,Nihon Fukushi University | Nakano M.,Aichi Toho University | Yamane M.,Aichi Mizuho College | Amano M.,Tokai Gakuen University | Matsumoto T.,Chukyo University
International Journal of Sports Medicine | Year: 2013

Environmental factors tend to influence the performance of individuals who exercise for extended periods. The present study aimed to determine energy metabolism while running in cold, wet conditions using a climatic chamber that can precisely simulate rainy conditions. 7 healthy men (age, 23.3±2.9 (SD) y; height, 168.6±7.5 cm; weight, 65.9±8.1 kg; V. O 2max, 52.0±5.7 mLkg- 1min- 1) ran on a treadmill at 70% ̇VO2max intensity for 30 min in a climatic chamber at an ambient temperature of 5°C in the presence (RAIN) or absence (CON) of 40 mm/h of precipitation. Expired air, esophageal temperature, heart rate, mean skin temperature, rating of perceived exertion and blood samples were measured. Esophageal temperature and mean skin temperature were significantly lower (P<0.05) in RAIN than in CON all. Minute ventilation, oxygen consumption and levels of plasma lactate and norepinephrine were significantly higher (P<0.05) in RAIN than in CON. In conclusion, the higher oxygen consumption and plasma lactate in RAIN indicated that energy demand increases when running in cold conditions. © 2013 Georg Thieme Verlag KG Stuttgart · New York. Source


Sato H.,Daido University | Narita R.,Aichi Toho University
Frontiers in Artificial Intelligence and Applications | Year: 2014

Regular Polygon based Search Algorithm (RPSA) was developed for approximately processing aggregate queries on remote spatial databases. However, Precision regarding maximum queries is required to be more precise under comparison with that regarding sum queries. To this end, RPSA is revised by making it sensitive to farthest-point Voronoi regions with regard to a set of query points. Experimental results on the revised RPSA using synthetic and real datasets show that Precision regarding maximum k-Nearest Neighbor (k-ANN) queries ranges between 0.98 and 1.00. Also, Precision regarding maximum range queries ranges between 0.95 and 1.00. From these results, the revised RPSA successfully improve Precision of maximum queries which becomes nearly equal to Precision of sum queries. © 2014 The authors and IOS Press. All rights reserved. Source


Sato H.,Daido University | Narita R.,Aichi Toho University
Procedia Computer Science | Year: 2013

Supporting aggregate range queries on remote spatial databases suffers from 1) huge and/or large numbers of databases, and 2) limited type of access interfaces. This paper applies the Regular Polygon based Search Algorithm (RPSA) to effectively addressing these problems. This algorithm requests a series of k-NN queries to obtain approximate aggregate range query results. The query point of a subsequent k-NN query is chosen from among the vertices of a regular polygon inscribed in a previously searched circle. Experimental results for maximum range query searches show that Precision is over 0.87 for a uniformly distributed dataset, over 0.92 for a skew-distributed dataset, and over 0.90 for a real dataset. Also, Number of Requests (NOR) ranges between 3.2 and 4.3, between 3.9 and 4.9, and between 3.0 and 4.2, respectively. © 2013 The Authors. Source


Sato H.,Daido University | Narita R.,Aichi Toho University
Smart Innovation, Systems and Technologies | Year: 2012

Processing sum k-Nearest Neighbor (NN) queries on remote spatial databases suffers from a large amount of communication. In this paper, we propose RQP-M search algorithm for efficiently searching sum k-NN query results to overcome the difficulty. It refines query results originally searched by RQP-S algorithm with subsequent k-NN queries, whose query points are chosen among vertices of a regular polygon inscribed in a before-searched circle. Experimental results show that Precision is over 0.99 for uniformly distributed data, over 0.95 for skew-distributed data, and over 0.97 for real data. Also, NOR (Number of Requests) ranges between 3.2 and 4.0, between 3.1 to 3.8, and between 2.9 and 3.5, respectively. Precision of RQP-M increases by 0.04-0.20 for uniformly distributed data, in comparison with that of RQP-S. © Springer-Verlag Berlin Heidelberg 2012. Source


Sato H.,Daido University | Narita R.,Aichi Toho University
Frontiers in Artificial Intelligence and Applications | Year: 2013

Supporting aggregate range queries on remote spatial databases suffers from 1) huge and/or large number of databases, and 2) limited type of access interfaces. This paper proposes Regular Polygon based Search Algorithm (RPSA) to overcome these difficulties. RPSA requests a series of k-NN queries to obtain approximate aggregate range query results. The query point of a subsequent k-NN query is chosen among vertices of a regular polygon inscribed in a before-searched circle. Experimental results on sum range query search show that Precision is over 0.99 for uniformly distributed dataset, over 0.97 for skew-distributed dataset, and over 0.97 for real dataset. Also, Number of Requests (NOR) ranges between 3.1 and 3.9, between 3.4 to 4.3, and between 2.7 and 3.7, respectively. © 2013 The authors and IOS Press. Source

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