✦ LIBER ✦

Review of environmental software in Asia

✍ Scribed by Meenakshi, P. ;Khan, Sheeba ;Kumar, Ashok

Publisher: American Institute of Chemical Engineers
Year: 2007
Tongue: English
Weight: 102 KB
Volume: 26
Category: Article
ISSN: 0278-4491
DOI: 10.1002/ep.10195

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Sources of environmental software

✍ Kumar, Ashok ;Srinivas, Kode 📂 Article 📅 1992 🏛 American Institute of Chemical Engineers 🌐 English ⚖ 415 KB

Synanthropic primates in Asia: Potential

Synanthropic primates in Asia: Potential sentinels for environmental toxins

✍ Gregory Engel; Todd M. O'Hara; Tamara Cardona-Marek; John Heidrich; Mukesh K. Ch 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 240 KB

## Abstract Macaques are similar to humans both physiologically and behaviorally. In South and Southeast Asia they are also synanthropic, ecologically associated with humans. Synanthropy with humans raises the possibility that macaques come into contact with anthropogenic toxicants, such as lead an

Software tool for environmental plant mo

Software tool for environmental plant modeling

✍ Caputo, Antonio C. ;Pelagagge, Pacifico M. ;Scacchia, Federica 📂 Article 📅 2001 🏛 American Institute of Chemical Engineers 🌐 English ⚖ 255 KB

## Abstract Waste disposal and the control of polluting emissions represent a critical operational issue for industrial enterprises. In this paper a software tool for computer‐aided design and economical analysis of environmental plants is presented. The tool has been developed in order to assist p

Lightning hypertext of pathology: Softwa

Lightning hypertext of pathology: Software review

✍ Sue Zaleski; Michael B. Cohen 📂 Article 📅 1994 🏛 John Wiley and Sons 🌐 English ⚖ 172 KB 👁 1 views

A review of four software directories

✍ Kumar, Ashok ;Sahore, Sandeep 📂 Article 📅 1993 🏛 American Institute of Chemical Engineers 🌐 English ⚖ 469 KB 👁 1 views

Review of ‘Robustbase’ software for R

✍ Robert Finger 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 123 KB

Robust statistical methods are powerful tools to increase the reliability and accuracy of statistical modeling and data analysis because these methods still work well when some observations deviate from (or violate) the assumed model. For many statistical models (e.g. location, scale and regression)