A short account of the life, sickness, and death of Elizabeth Merritt : a child eleven years of age : also Some account of the last illness of Ann Waring, late of the city of New-York.

The first work written by John and Sarah Ann Merritt.

Tilskipan um Quarantaine-Rádstafanir í Danmørku og Norvegi. útgefin þann 8. Febrúari 1805.

Bibl.Not.IV.

Some remarks of Ann Waring.

Shaw & Shoemaker

Sharḥ Kitāb al-Durr al-aʻlá : manuscript, 1805

Written in one column, 21 lines per page, in black and red ink.

William Blair Townsend letter and receipt books, 1743-1805, bulk 1744-1777

The collection consists of two volumes, which date from 1743 to 1805, spanning his whole career as a merchant. Volume one is a letter book containing Townsend's business correspondence from November 23, 1743 to December 12, 1774. Most of the letters were written to American (many in North Carolina) and British (predominately in London) merchants. His earliest letters document his efforts to establish himself as a trader. Over time his letters turn to illustrate the common problems faced by many merchants: damaged goods, overpriced goods, embargos, and high freight costs. Particularly enlightening are his comments on the challenges of doing business throughout the French and Indian War and the years leading up to the American Revolution. He most frequently corresponded with London merchants Champion & Hayley, Lane & Booth, Lane Son & Fraser, Harrison & Ansley, and Leeds merchant Samuel Elam. In addition he frequently corresponded with Eliakim Palmer, colonial agent and merchant in London, as well as Dr. Walley Chauncy of North Carolina. He dealt in a wide variety of goods including molasses, rum, tar, medicines, pitch, saddles, tallow, hides, skins, pickled beef and pork, and wine. The letters also document Townsend's involvement in the slave trade through his occasional purchases of slaves.

Descriptiones plantarum minus cognitarum Sibiriae, praesertim orientalis, quas in itinere ann. 1805 et 1806 observavit /

Mem. Soc. Nat. Mosc. iii:231-252. 1834. Plate.

A diary of the religious experience of Mary Waring, daughter of Elijah and Sarah Waring, late of Godalming.

Mode of access: Internet.

Bemerkungen auf einer reise aus Thüringen nach Wien im Winter 1805 bis 1806.

Mode of access: Internet.

Cromwell on foreign affairs, together with four essays on international matters,

Cromwell on foreign affairs.--Neutral trade in arms and ships.--Intervention among states.--The burning of Boer farms and the bombardment of coast towns.--The extent of territorial waters.--Nelson and the admiralty.

A diary of the religious experience of Mary Waring, daughter of Elijah and Sarah Waring; late of Godalming.

Biographical sketch (p. vii-xi) signed: S. Waring.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.