Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

Visually impaired people encounter many impediments and challenges in their lives such as related to their mobility, education, communication, use of technology, and others. This paper reports the results of an online survey to understand the requirements and challenges blind and visually impaired people face in their daily lives regarding the availability and use of digital devices. The survey was conducted among the blind and visually impaired in Saudi Arabia using digital forms. A total of 164 people responded to the survey most of them using the VoiceOver function. People were asked about the use of smart devices, special devices, operating systems, object recognition apps, indoor and outdoor navigation apps, virtual digital assistive apps, the purpose (navigation, education, etc.) of and difficulty in using these apps, the type of assistance needed, the reliance on others in using the assistive technologies, and the level of satisfaction from the existing assistive technologies. The majority of the participants were 18 – 65 years old with 13% under 18 and 3% above 65. Sixty-five percent of the participants were graduates or postgraduates and the rest only had secondary education. White Cane, mobile phones, Apple iOS, Envision, Seeing AI, VoiceOver, and Google Maps were the most used devices, technologies, and apps used by the participants. Navigation at 39.6% was the most reported purpose of the special devices followed by education (34.1%) and office jobs (12.8%). The information from this survey along with a detailed literature review of academic and commercial technologies for the visually impaired was used to establish the research gap, design requirements, and a comprehensive understanding of the relevant landscape, which in turn was used to design smart glasses called LidSonic for visually impaired.