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Paper mulberry pollens are known to trigger severe allergies that can also cause asthma exacerbations. In Islamabad, the paper mulberry pollen concentrations are thought to peak between the 10th and 31st of March each year, depending on the weather in the preceding months. However, more accurate prediction of high pollen days would allow patients to take more timely precautionary measures. We developed and validated two prediction algorithms that took historical pollen data and historical weather data in Islamabad as its input. One is based on linear regression and the other is based on phenological modelling. These algorithms gave encouraging results where the mean absolute error for start day was between 2.33 and 3.67 days. On the other hand, for peak day, the mean absolute error was recorded between 3.33 and 4 days. These results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season and its adverse effects.

Visualization has been extensively used in almost all the fields, but unfortunately, there is very little work on the visualization of flight search. The literature discusses only two representations: textual representation and linear representation. In this study, the authors introduce a new form of visualization called circular representation and compare it with the existing visualization techniques. The authors found that each representation has its own merits and demerits. More specifically, they found that users were able to find required flight circular efficiently when using circular representation, while most errors were committed by the user when using textual representation, and users ranked circular representation low in terms of qualitative satisfaction.

The effective utilization of a communication channel like calling a person involves two steps. The first step is storing the contact information of another user, and the second step is finding contact information to initiate a voice or text communication. However, the current smartphone interfaces for contact management are mainly textual; which leaves many emergent users at a severe disadvantage in using this most basic functionality to the fullest. Previous studies indicated that less-educated users adopt various coping strategies to store and identify contacts. However, all of these studies investigated the contact management issues of these users from a qualitative angle. Although qualitative or subjective investigations are very useful, they generally need to be augmented by a quantitative investigation for a comprehensive problem understanding. This work presents an exploratory study to identify the usability issues and coping strategies in contact management by emergent users; by using a mixture of qualitative and quantitative approaches. We identified coping strategies of the Pakistani population and the effectiveness of these strategies through a semi-structured qualitative study of 15 participants and a usability study of 9 participants, respectively. We then obtained logged data of 30 emergent and 30 traditional users, including contact-books and dual-channel (call and text messages) logs to infer a more detailed understanding; and to analyse the differences in the composition of contact-books of both user groups. The analysis of the log data confirmed problems that affect the emergent users’ communication behaviour due to the various difficulties they face in storing and searching contacts. Our findings revealed serious usability issues in current communication interfaces over smartphones. The emergent users were found to have smaller contact-books and preferred voice communication due to reading/writing difficulties. They also reported taking help from others for contact saving and text reading. The alternative contact management strategies adopted by our participants include: memorizing whole number or last few digits to recall important contacts; adding special character sequence with contact numbers for better recall; writing a contact from scratch rather than searching it in the phone-book; voice search; and use of recent call logs to redial a contact. The identified coping strategies of emergent users could aid the developers and designers to come up with solutions according to emergent users’ mental models and needs.

With the increasing popularity of smartphones and its audience including children as young as 2 year old, smartphones can be a hazard for young children in terms of health concerns, time wastage, viewing of inappropriate material and conversely children who are too young can be a threat to the smartphone as well e.g, causing battery drainage, making unwanted calls/text messages, doing physical damage etc. In order to protect the smartphone and children from each other, we require user identification on our devices so the device could perform certain functions for instance restricting adult content once a user is identified as a child. This paper is a user study that aims at detecting the touch patterns of adults and children. To this end we collected data from 60 people, 30 adults and 30 children while they were asked to perform the 6 basic tasks that are performed on touch devices to find the differences in the touch gestures of children from adults. We first perform an exploratory data analysis. We then model the problem as a supervised binary classification problem and use the data as input for different machine learning algorithms to find whether we can classify a user previously unknown to the machine as an adult or a child. Our work shows there are differences in touch gestures among children and adults which are sufficient for user group identification.