Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

The Effect of Seat Location in Lecture Theatres on Academic Performance

The purpose of this research study was to investigate and identify possible patterns relating to academic performance on the effects of university students self-selecting where to sit in a lecture theatre.
The key research questions are:
1. Does seating position affect student performance?
2. Do the most academically able and engaged students regularly sit at the front of lecture theatres?
Academic achievement
Preliminary results suggest significant assessment score differences between those that sit at the front and those that sit further the back. Of those that received a grade of 75%+ (Grade A) 6.67% regularly sat at the back. With the same group 46.67% regularly sat at the front. Of the group that scored less than 50% (Grade D) 0% of students regularly sat at the front. 12.50% regularly sat in the middle zones with 37.50% sitting at the back. It was also observed that the remaining numbers did not consistently sit in the same zone.

Temporal movement
There is little evidence of movement between seating zones of the Grade A group throughout the 24 week period. However there was considerable movement with the Grade D group. Although still under analysis there appears be a pattern of students in this group graduating towards the back seating positions over the course of the programme.

Engagement
The frequency of completed entries on PinPoint was also used as an indicator of engagement. With the Grade A group 75% of them regularly completed an entry whereas in the Grade D group this drops to less than 50%.
Further analysis on the attitudinal factors in relational to seating position and performance are ongoing, but preliminary results suggest that those students that scored highly in attitude tended to sit at the front and middle sections.
It would indeed appear that the more highly engaged and academically capable students voluntarily sit at the front for most lectures. Interestingly as the course progresses those who had lesser engagement and below average midterm results tend to began to sit progressively toward the back. If this is a repeatable pattern then a linear regression analysis of the seating positions and midterm results could help predict students in danger of failing.

Implementation of a cogeneration plant for a food processing facility. A case study

The present work presents an investigation regarding the feasibility analysis of a cogeneration plant for a food processing facility with the aim to decrease the cost of energy supply. The monthly electricity and heat consumption profiles are analyzed, in order to understand the consumption profiles, as well as the costs of the current furniture of electricity and gas. Then, a detailed thermodynamic model of the cogeneration cycle is implemented and the investment costs are linked to the thermodynamic variables by means of cost functions. The optimal electricity power of the co-generator is determined with reference to various investment indexes. The analysis highlights that the optimal dimension varies according to the chosen indicator, therefore it is not possible to establish it univocally, but it depends on the financial/economic strategy of the company through the considered investment index.