Mental health challenges among PhD scholars are a growing global concern, with a survey in the UK revealing that at least 86% of students face depression and anxiety. Social media platforms offer valuable insights into the depression levels of PhD students. Sentiment analysis for social media content can help identify indicators of anxiety, such as negative language, stress expressions, or mental health struggles. This paper uses social media and surveys to develop a dataset for Pakistani graduate students. The dataset collects 5096 social media posts from 1170 users, categorising them into anxiety (46.7%), depression (12.6%), and motivation (40.7%) based on mental health levels. The survey responses are combined with the social media dataset. Machine learning models, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF), are used to detect the mental health status of PhD scholars. The study finds that 59.3% of graduate students in Pakistan face anxiety and mental health issues, indicating a need for policy reformulation in graduate programmes. The research data is available online for further research (https://github.com/dr-m-wasim/PhD-Scholars-Mental-Health).

Topic modeling enables the discovery of concealed themes and patterns in extensive text collections. It facilitates a thorough examination of the messages present in religious texts. Topic modeling for Quranic verses is a trending study area, with various translations already explored including Bahasa, English, and Arabic. Yet, there is a need for further research, particularly in Urdu translations of the Quran. In this study, we propose applying the BERTopic framework to Urdu translations of the Holy Quran. By leveraging the BERTopic approach, which incorporates a fine-tuned BERT model, we aim to capture the contextual nuances and linguistic complexities unique to the Quran. In this study, we utilized existing Urdu translations of the Quran from eight different translators sourced from Tanzil, a renowned resource for Quranic text and translations. We assessed the performance of our proposed BERTopic model compared to traditional techniques like LDA and NMF, using coherence and diversity metrics. The results indicate that our BERT-based approach outperforms these conventional methods, achieving an average coherence improvement of 0.03 and a diversity score of 0.83. These findings highlight the effectiveness of BERTopic in extracting meaningful topics from Urdu translations of the Holy Quran and contribute to the computational analysis of religious texts, supporting scholarly endeavors in comparative studies of Quranic translations in Urdu.

The popularity of video streaming on smartphones has led to rising demands for high-quality mobile video streaming. Consequently, we are observing growing support for higher resolution videos (e.g., HD, FHD, QHD) and higher video frame rates (e.g., 48 FPS, 60 FPS). However, supporting high-quality video streaming on smartphones introduces new challenges---besides the available network capacity, the smartphone itself can become a bottleneck due to resource constraints, such as low available memory. In this paper, we conduct an in-depth investigation of memory usage on smartphones and its impacts on mobile video streaming. Our investigation - driven by a combination of a user study, user survey, and experiments on real smartphones - reveals that (i) most smartphones observe memory pressure (i.e., low available memory scenarios), (ii) memory pressure can have a significant impact on mobile video QoE when streaming high-quality videos, e.g., resulting in the mean frame drop rate of 9--100% across smartphones and significantly lower user ratings, and (iii) the drop in mobile video QoE happens primarily due to the way in which video processes interact with kernel-level memory management mechanism, with opportunities for improving mobile video QoE through better adaptation by video clients.