✦ LIBER ✦

A serial experimental and clinical studies of new measures for treatment of severe burns

✍ Scribed by H. Yue-Sheng; W. Fengjun; L. Haitao; Z. Yaping; P. Xi; W. Jun

Book ID: 116329621
Publisher: Elsevier Science
Year: 2007
Tongue: English
Weight: 87 KB
Volume: 33
Category: Article
ISSN: 0305-4179
DOI: 10.1016/j.burns.2006.10.031

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Serial experimental and clinical studies

Serial experimental and clinical studies on the pathogenesis of multiple organ dysfunction syndrome (MODS) in severe burns

✍ Y.-S. Huang; Z.-C. Yang; X.-S. Liu; F.-M. Chen; B.-B. He; A. Li; R.S. Crowther 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 950 KB

Experimental study of a new polymer cove

Experimental study of a new polymer covering for burns treatment

✍ V. K. Sologub; L. E. Kogan; A. A. Alekseev; R. I. Kaem; V. S. Yakubovich; S. I. 📂 Article 📅 1990 🏛 Springer US 🌐 English ⚖ 243 KB

Prospective clinical and experimental st

Prospective clinical and experimental studies on the cardioprotective effect of ulinastatin following severe burns

✍ Yuesheng Huang; Kang Xie; Jiaping Zhang; Yongming Dang; Zhang Qiong 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 946 KB

Tenonplasty: a new surgical approach for

Tenonplasty: a new surgical approach for the treatment of severe eye burns.

✍ Redbrake, C; Reim, M; Kuckelkorn, R 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 318 KB

European comparative clinical study of I

European comparative clinical study of Inerpan: A new wound dressing in treatment of partial skin thickness burns

✍ Guilbaud, J. 📂 Article 📅 1992 🏛 Elsevier Science 🌐 English ⚖ 532 KB

A new measure of clustering effectivenes

A new measure of clustering effectiveness: Algorithms and experimental studies

✍ E.K.F. Dang; R.W.P. Luk; K.S. Ho; S.C.F. Chan; D.L. Lee 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 863 KB

## Abstract We propose a new optimal clustering effectiveness measure, called CS1, based on a combination of clusters rather than selecting a single optimal cluster as in the traditional MK1 measure. For hierarchical clustering, we present an algorithm to compute CS1, defined by seeking the optimal