As Crocodile strolls along wistfully, admiring beautiful creatures, she has one question: "Why, oh why, am I a crocodile?" Just as she's at her lowest, she encounters a blind monkey who introduces her to some newfound friends. Will Monkey be able to change Crocodile's view on the world and will she learn to love herself? A fun, rhyming children's book exploring emotional issues, such as low self-esteem and negative self-image. This lovely bedtime story, for toddlers, young children (and grown-ups!), is the perfect tool for discussing these all-important, ever-increasing feelings in a child-friendly way. The vibrant illustrations, with quirky extra details, will keep kids totally engaged and they will love joining in with the repetitive element of the book. Why, oh why, am I a crocodile? is the perfect addition to any child's bookshelf.

Dreaming of being debt free? Wondering what a tumultuous property market will mean for your financial future? Wanting to buy, sell or invest in property without being ripped off or paying a mortgage for the term of your natural life?

If you answered ‘yes' to any of these questions, then "Mortgage Stressbusters "is a must-read. This plain-English, Q&A guide to mortgages and residential property will help you to end mortgage stress once and for all. Not only will you learn how to save money -- and time -- on your mortgage, but you'll also learn the tricks, tips and tactics that will get you ahead in the property game.

Inside you'll discover: how to get the best mortgage deal how to buy property successfully, no matter what the market is doing how to pay off your mortgage -- fast what to do if you can't pay your mortgage.

Whether you're stuck in the mortgage mire or just trying to stay ahead, "Mortgage Stressbusters "is the book for you

This book is concerned with planning and acting under uncertainty in partially-observable continuous domains. It focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that the most likely state is correct, rather than considering the entire distribution. This book formulates an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. The algorithms have been implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. Results show that this approach produces significantly more robust behaviour when compared with heuristic planners which consider only the most likely states and outcomes.