This paper outlines the results of a project to develop and test an augmented reality smart phone application (‘app’) to aid the safety of navigation of leisure sailors with limited navigational skills. The app works in two modes: in the ‘turned off mode’ the app gives an alarm 30 seconds before the boat is predicted to ground. The navigator then immediately stops the boat, picks up the phone and, uses the app to view the surrounding water where NoGo areas less than 3 metres in depth are outlined in red. By panning the smart phone around, safe escape routes with deep water are visible. The prototype app was tested on a group of boat owners in western Norway with very good results, both from technical and usability perspectives. This report outlines the concept of operation of the app, details some of the difficulties encountered in its development and testing, specifies the issues that remain to be resolved to turn the concept into an effective system, and outlines future development plans.

Budget Planner is a whole-farm or enterprise budgeting software program that is simple to use for farmers, extension agents, and other budgeters who are sometimes inexperienced, but that also provides the detail necessary to be accurate. Program defaults eliminate repetitive questions that change little from budget to budget. Defaults can be temporarily overridden, or they can be permanently changed with a detailed modify program. The program leads a user through a sequence similar to that a producer might utilize. Input forms were created to enlarge the user clientele and eventually increase computer use by farmers and extension agents.

Routing in a multimodal urban public transportation network, according to the user's preferences, can be considered as a multi-objective optimisation problem. Solving this problem is a complicated task due to the different and incompatible objective functions, various modes in the network, and the large size of the network. In this research, two optimisation algorithms are considered for solving this problem. The multi-colony and multi-pheromone Ant Colony Optimisation (ACO) algorithms are two different modes of the Multi-Objective ACO (MOACO) algorithm. Moreover, according to the acquired information, the algorithms implemented in the public transportation network of Tehran consist of four modes. In addition, three objective functions have been simultaneously considered as the problem's objectives. The algorithms are run with different initial parameters and afterwards, the results are compared and evaluated based on the different obtained routes and with the aid of the convergence and repeatability tests, diversity and convergence metrics.