Estimation of Correlation Distance of Soil Properties by Geostatistics

Random field theory has been used to model the spatial average soil properties, whereas the most widely used, geostatistics, on which also based a common basis (covariance function) has been successfully used to model and estimate natural resource since 1960s. Therefore, geostistics should in principle be an efficient way to model soil spatial variability Based on this, the paper presents an alternative approach to estimate the scale of fluctuation or correlation distance of a soil stratum by geostatistics. The procedure includes four steps calculating experimental variogram from measured data, selecting a suited theoretical variogram model, fitting the theoretical one to the experimental variogram, taking the parameters within the theoretical model obtained from optimization into a simple and finite correlation distance 6 relationship to the range a. The paper also gives eight typical expressions between a and b. Finally, a practical example was presented for showing the methodology.

Essex Adult Community Learning: PTLLS for distance and blended learning

Funded by the LSIS Excellence and Improvement Fund, Essex Adult Community Learning has produced a distance/blended learning resource on CD-Rom for tutors in the post-16 sector to achieve the City & Guilds PTLLS (7303 Preparing to Teach in the Lifelong Learning Sector) at Level 4. The aim of the resource is to improve teaching and learning by providing a flexible way to access initial teacher training where candidates may otherwise find it difficult or impossible to attend a taught course. It is also intended to increase tutors' own e-learning skills.

ITS Training: 'VLE - no distance too far - enhancing the learner journey and business processes'

ITS Training has undergone a revolutionary transformation in the past two years. The company has saved money, generated more business, improved recruitment, retention and achievement and expanded throughout the world because of its virtual learning environment (VLE): the ‘Student Campus'. Under its previous paper-based distance learning programme it was a hard enough task to deliver lectures to students in Liverpool. Now, the company has a classroom of learners in Tokyo, Japan.

Developing and Validating Distance Learning Awards Guidance Document (MMU)

The principal purpose of this document is to assist programme teams throughout the development process when they are considering the development or review of a route through the award where it will be delivered wholly, or primarily, via online distance learning. Please note that this document is current as of Sept 2015 but it is considered to be an evolving document and is updated/tweaked from time to time.

Outputs from the Manchester Metropolitan University distance learning think tank event

An overview of, and the emergent themes from, the Manchester Metropolitan University distance learning think tank event which took place on 10th July 2014 and aimed to provide a forum to explore and discuss some of the key issues for colleagues across the university involved in planning, setting-up and delivering such programmes at MMU.

Poster: Outputs from the Manchester Metropolitan University online distance learning think tank event

Poster from the overview of, and the emergent themes from, the Manchester Metropolitan University distance learning think tank event which took place on 10th July 2014 and aimed to provide a forum to explore and discuss some of the key issues for colleagues across the university involved in planning, setting-up and delivering such programmes at MMU.

Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance

[EN]The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper we deal with the problems of sampling and learning (estimating) such distributions when the metric on permutations is the Cayley distance. We propose new methods for both operations, whose performance is shown through several experiments. We also introduce novel procedures to count and randomly generate permutations at a given Cayley distance both with and without certain structural restrictions. An application in the field of biology is given to motivate the interest of this model.

Sampling and learning the Mallows and Weighted Mallows models under the Hamming distance

[EN]In this paper we deal with distributions over permutation spaces. The Mallows model is the mode l in use. The associated distance for permutations is the Hamming distance.

Sampling and learning the Mallows model under the Ulam distance

[EN]In this paper we deal with probability distributions over permutation spaces. The Probability model in use is the Mallows model. The distance for permutations that the model uses in the Ulam distance.

Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals.