Teaching virtually
There is growing evidence that the way in which students respond to their teachers and teaching materials has changed for the worse recently. The idea that we have moved from an age of information to an age of experience has led to students becoming passive and disengaged it seems and that many in disparate courses often cannot see the relevance of what they are learning to their own lives. Unfortunately, it is also difficult if not impossible to teach empathy, systems thinking, creativity, computational literacy, and abstract reasoning, all of which play an increasing role in modern employment and the gig economy. Researchers in the USA have now suggested that virtual reality might bring students back into the fold. VR is an immersive, hands-on tool for learning and could have a unique role in addressing the educational challenges outlined. They reckon that VR could lead to new opportunities that support learners.
Hu-Au, E. and Lee, J.J. (2017) ‘Virtual reality in education: a tool for learning in the experience age’, Int. J. Innovation in Education, Vol. 4, No. 4, pp.215–226.
Problem solving with self-tuning fireflies and ants
Nature abounds with complex systems, networks, and non-linear phenomena. Researchers can tap into such systems and use them as models for other processes in engineering, biomedicine, materials science, sociology, and information technology. Often a system, such as the flashing behaviour of fireflies, the trail-following interactions of ants in a colony, the foraging of a swarm of bees, and other systems can be simulated by an algorithm. Such algorithms have been applied to solving standard problems such as the travelling salesman problem, job shop scheduling problems, and quadratic assignment problems. A team in Sri Lanka has now applied two such models of biological systems – the firefly algorithm and an ant colony system to solve a particular problem in travelling salesman problems. Statistically, their approach in which the self-tuning firefly algorithm tunes the ant colony algorithm works more effectively than many other approaches.
Ariyaratne, M.K.A., Fernando, T.G.I. and Weerakoon, S. (2018) ‘A self-tuning firefly algorithm to tune the parameters of ant colony system’, Int. J. Swarm Intelligence, Vol. 3, No. 4, pp.309–331.
The third degree
In burn victims, it is critical that the area of skin that has been burned can be estimated as a percentage of total body surface area. Treatment and prognosis are fundamentally tied to the degree of burn. However, one of the mainstays of estimating burn degree may be flawed, researchers in the USA have demonstrated. The team has shown that the received wisdom associated with a person’s surface area says that the palm of their hand represents one percent of their total surface and that this can vary wildly and is generally an incorrect estimate.
Liu, T.C., Bhatt, R., Farrell, K.D., Baek, S., Liu, Y.M., Abdel-Malek, K. and Arora, J. (2018) ‘A quantitative assessment of variations in the palm surface area as a percentage of total body surface area within the general population’, Int. J. Human Factors Modelling and Simulation, Vol. 6, No. 1, pp.81–96.
Medical imaging
One of the most challenging aspects of medical imaging is the accurate of detection of neurodegenerative diseases from a tissue scan. Researchers in India point out that manual evaluation, manual reorientation and other time-consuming limitations commonly lead to reduced resolution. They have, therefore, developed an algorithm to assist in the evaluation process. Their improved Adaptive Moving Self-organising Mapping (AMSOM) algorithm improves time iteration rate, reduces mean square error, boosts sensitivity, and raises overall accuracy. The team has now demonstrated efficacy on real magnetic resonance imaging (MRI) data taken from a cross-sectional collection of some 416 subjects aged 18 to 96 years. “The analysis includes different comparison of mapping approaches that reveal features associated with Alzheimer’s disease,” the team reports. The same approach might also be applied to other conditions and perhaps other scanning techniques.
Suwalka, I. and Agrawal, N. (2018) ‘An improved unsupervised mapping technique using AMSOM for neurodegenerative disease detection’, Int. J. Computational Systems Engineering, Vol. 4, Nos. 2/3, pp.185–194.