Cell-centric and user-centric multi-user scheduling in visible light communication aided networks

Visible Light Communication (VLC) combined withadvanced illumination has been expected to become an integralpart of next generation heterogeneous networks at the time ofwriting, by inspiring further research interests. From both theCell-Centric (CC) and the User-Centric (UC) perspectives, variousVLC cell formations, ranging from fixed-shape regular cellswith different Frequency Reuse (FR) patterns and merged cellsemploying advanced transmission scheme to amorphous userspecificcells are investigated. Furthermore, different Multi-UserScheduling (MUS) algorithms achieving Proportional Fairness(PF) are implemented according to different cell formations.By analysing some critical and unique characteristics of VLC,our simulation results demonstrate that, the proposed MUSalgorithms are capable of providing a high aggregate throughputand achieving modest fairness with low complexity in most of thescenarios considered.<br/

Discovering Barriers to Opioid Addiction Treatment from Social Media: A Similarity Network-Based Deep Learning Approach

Opioid use disorder (OUD) refers to the physical and psychological reliance on opioids. OUD costs the US healthcare systems $504 billion annually and poses significant mortality risk for patients. Understanding and mitigating the barriers to OUD treatment is a high-priority area. Current OUD treatment studies rely on surveys with low response rate because of social stigma. In this paper, we explore social media as a new data source to study OUD treatments. We develop the SImilarity Network-based DEep Learning (SINDEL) to discover barriers to OUD treatment from the patient narratives and address the challenge of morphs. SINDEL reaches an F1 score of 76.79%. Thirteen types of OUD treatment barriers were identified and verified by domain experts. This study contributes to IS literature by proposing a novel deep-learning-based analytical approach with impactful implications for health practitioners