Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree.

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan.

Association of asthma exacerbations with paper mulberry (Broussenetia papyrifera) pollen in Islamabad: An observational study.

Background: Although the role of airborne plant pollen in causing allergic rhinitis has been established, the association of concentrations of paper mulberry (Broussenetia papyrifera) pollens in the air and incidence of asthma exacerbations has not, despite an observed increase in the number of asthma patients attending physician clinics and hospital Accident and Emergency (A&E) Departments during the paper mulberry pollen season. We aimed to assess the association between paper mulberry pollen concentrations (typically peaking in March each year) and asthma exacerbations in the city of Islamabad.

Methods: We used three approaches to investigate the correlation of paper mulberry pollen concentration with asthma exacerbations: A retrospective analysis of historical records (2000-2019) of asthma exacerbations of patients from the Allergy and Asthma Institute, Pakistan (n = 284), an analysis of daily nebulisations in patients attending the A&E Department of the Pakistan Institute of Medical Sciences (March 2020 to July 2021), a prospective peak expiratory flow rate (PEFR) diary from participants (n = 40) with or without asthma and with or without paper mulberry sensitisation.

The (Mis)Information Game: A social media simulator.

Given the potential negative impact reliance on misinformation can have, substantial effort has gone into understanding the factors that influence misinformation belief and propagation. However, despite the rise of social media often being cited as a fundamental driver of misinformation exposure and false beliefs, how people process misinformation on social media platforms has been under-investigated. This is partially due to a lack of adaptable and ecologically valid social media testing paradigms, resulting in an over-reliance on survey software and questionnaire-based measures.

Promoting and countering misinformation during Australia's 2019-2020 bushfires: a case study of polarisation.

Unlabelled: During Australia's unprecedented bushfires in 2019-2020, misinformation blaming arson surfaced on Twitter using #ArsonEmergency. The extent to which bots and trolls were responsible for disseminating and amplifying this misinformation has received media scrutiny and academic research. Here, we study Twitter communities spreading this misinformation during the newsworthy event, and investigate the role of online communities using a natural experiment approach-before and after reporting of bots promoting the hashtag was broadcast by the mainstream media.

Exploring the effect of streamed social media data variations on social network analysis.

To study the effects of online social network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affect social network analyses.

Exploiting contextual information to improve call prediction.

With the increase in contact list size of mobile phone users, the management and retrieval of contacts has becomes a tedious job. In this study, we analysed some important dimensions that can effectively contribute in predicting which contact a user is going to call at time t. We improved a state of the art algorithm, that uses frequency and recency by adding temporal information as an additional dimension for predicting future calls.

An energy efficient cooperative hierarchical MIMO clustering scheme for wireless sensor networks.

In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network.