This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling’s framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners’ perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.

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Dust storms are one of the natural hazards whose incidence has increased in the recent years over Sahara desert, Australia and northern China. Thus, it is important to know the causation, movement and radiation effects of dust storms. Satellite remote sensing is the most common method for monitoring Dust Storms but its use over sandy ground is still limited as they have similar characteristics. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data. However, there have been fewer studies for detecting dust storms at night. The key elements of this study are to use a back-propagation artificial neural network with Brightness Temperature of band 31 and four Brightness Temperature Differences calculated using data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop a method for detecting dust storms during both day and night. Results have shown that the method can detect dust storms at both day and night and also over different land surfaces. 

The development of accounting profession and auditing in developed

countries have received considerable attention in the accounting literature.

However, the accounting profession and auditing in developing countries have

been neglected. Therefore, this study critically evaluates the development of the

accounting profession in the developing country context, specifically in Libya.

This paper sheds light on the phases of accounting development in Libya, taking

into account some factors that affect the accounting practice in the country. These

factors include: the Libyan economy; Libyan culture; foreign investment; Big 4

auditors in the country; compliance to the Global Accounting Standards (e.g.

IASs, ISA and IFRS) and Libya’s application to WTO.