Some remarks of Ann Waring.

Shaw & Shoemaker

A institucionalização do ensino da náutica em Portugal (1779-1807)

Tese de doutoramento, História (História dos Descobrimentos e da Expansão), Universidade de Lisboa, Faculdade de Letras, 2014

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.

Letters from Timothy Pickering to his brother and father, 1797-1798

These two handwritten letters by Timothy Pickering were written on February 14, 1797 and June 14, 1798 to his brother John Pickering and his father Timothy Pickering, respectively. The letter to his brother, John, discusses mutual friends, classmate Thomas Lee, and John’s recent attendance at a sermon by Dr. Joseph Priestley. The letter from Timothy to his father includes a discussion of Timothy’s expenses and the amount of money needed to pay his debts, a request for new shoes for commencement, the news of Timothy’s invitation to join honor society Phi Beta Kappa, and a few comments on his forensics course at Harvard.

A poem, delivered in Taunton, September 16th, A. D. 1807, at the anniversary election of the Philandrian society.

Mode of access: Internet.

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.

An acute growth factor treatment that preserves function after spinal cord contusion injury

Inflammation of the spinal cord after traumatic spinal cord injury leads to destruction of healthy tissue. This “secondary degeneration” is more damaging than the initial physical damage and is the major contributor to permanent loss of functions. In our previous study we showed that combined delivery of two growth factors, vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF), significantly reduced secondary degeneration after hemi-section injury of the spinal cord in the rat. Growth factor treatment reduced the size of the lesion cavity at 30d compared to control animals and further reduced the cavity at 90d in treated animals while in control animals the lesion cavity continued to increase in size. Growth factor treatment also reduced astrogliosis and reduced macroglia/macrophage activation around the injury site. Treatment with individual growth factors alone had similar effects to control treatments. The present study investigated whether growth factor treatment would improve locomotor behaviour after spinal contusion injury, a more relevant preclinical model of spinal cord injury. The growth factors were delivered for the first 7d to the injury site via osmotic minipump. Locomotor behaviour was monitored at 1-28d after injury using the BBB score and at 30d using automated gait analysis. Treated animals had BBB scores of 18; Control animals scored 10. Treated animals had significantly reduced lesion cavities and reduced macroglia/macrophage activation around the injury site. We conclude that growth factor treatment preserved spinal cord tissues after contusion injury, thereby allowing functional recovery. This treatment has the potential to significantly reduce the severity of human spinal cord injuries.

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.

Ann and Grete Unger seated outdoors with Dick and Gitta Meier with their infant daughter Karen.

Digital Image