Deploying unmanned aerial vehicle (UAV) swarms in delivery systems are still in its infancy with regard to the technology, safety, and aviation rules and regulations. Optimal use of UAVs in dynamic environments is important in many aspects, e.g., increasing efficacy and reducing the air traffic, resulting in a safer environment, and it requires new techniques and robust approaches based on the capabilities of UAVs and constraints. This paper analyzes several delivery schemes within a platform, such as delivery with and without using air highways and delivery using a hybrid scheme along with several delivery methods (i.e., optimal, premium, and first-in first-out) to explore the use of UAV swarms as part of the logistics operations. In this platform, a dimension reduction technique, ''dynamic multiple assignments in multidimensional space,'' and several other new techniques along with Hungarian and cross-entropy Monte Carlo techniques are forged together to assign tasks and plan 3D routes dynamically. This particular approach is performed in such a way that UAV swarms in several warehouses are deployed optimally given the delivery scheme, method, and constraints. Several scenarios are tested on the simulator using small and big data sets. The results show that the distribution and the characteristics of data sets and constraints affect the decision on choosing the optimal delivery scheme and the method. The findings are expected to guide the aviation authorities in their decisions before dictating rules and regulations regarding effective, efficient, and safe use of UAVs. Furthermore, the companies that produce UAVs are going to take the demonstrated results into account for their functional design of UAVs along with other companies that aim to deliver their products using UAVs. Additionally, private industries, logistics operators, and municipalities are expected to benefit from the potential adoption of the simulator in strategic decisions before embarking on the practical implementation of UAV delivery systems.

In this paper an automatic deception detection system, which analyses participant deception risk scores from non-verbal behaviour captured during an interview conducted by an Avatar, is demonstrated. The system is built on a configuration of artificial neural networks, which are used to detect facial objects and extract non-verbal behaviour in the form of micro gestures over short periods of time. A set of empirical experiments was conducted based a typical airport security scenario of packing a suitcase. Data was collected through 30 participants participating in either a truthful or deceptive scenarios being interviewed by a machine based border guard Avatar. Promising results were achieved using raw unprocessed data on un-optimized classifier neural networks. These indicate that a machine based interviewing technique can elicit non-verbal interviewee behavior, which allows an automatic system to detect deception.

No abstract

No abstract

A literature review of publically available information was undertaken to summarize current understanding and gaps in knowledge about Middle East respiratory syndrome coronavirus (MERS-CoV), including its origin, transmission, effective control measures and management. Major databases were searched and relevant published papers and reports during 2012-2015 were reviewed. Of the 2520 publications initially retrieved, 164 were deemed relevant. The collected results suggest that much remains to be discovered about MERS-CoV. Improved surveillance, epidemiological research and development of new therapies and vaccines are important, and the momentum of recent gains in terms of better understanding of disease patterns should be maintained to enable the global community to answer the remaining questions about this disease.