Carbon based sensors for air quality monitoring networks; middle east perspective.

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.

Rebound effects undermine carbon footprint reduction potential of autonomous electric vehicles.

Autonomous vehicles offer greater passenger convenience and improved fuel efficiency. However, they are likely to increase road transport activity and life cycle greenhouse emissions, due to several rebound effects. In this study, we investigate tradeoffs between improved fuel economy and rebound effects from a life-cycle perspective. Our results show that autonomy introduces an average 21.2% decrease in operation phase emissions due to improved fuel economy while manufacturing phase emissions can surge up to 40%. Recycling efforts can offset this increase, cutting emissions by 6.65 tons of Carbon dioxide equivalent per vehicle. However, when examining the entire life cycle, autonomous electric vehicles might emit 8% more greenhouse gas emissions on average compared to nonautonomous electric vehicles. To address this, we suggest; (1) cleaner and more efficient manufacturing technologies, (2) ongoing fuel efficiency improvements in autonomous driving; (3) renewable energy adoption for charging, and (4) circular economy initiatives targeting the complete life cycle.

Student perspectives on the integration of artificial intelligence into healthcare services.

Background: Healthcare workers are often overworked, underfunded, and face many challenges. Integration of artificial intelligence into healthcare service provision can tackle these challenges by relieving burdens on healthcare workers. Since healthcare students are our future healthcare workers, we assessed the knowledge, attitudes, and perspectives of current healthcare students at Qatar University on the implementation of artificial intelligence into healthcare services. Methods: This was a cross-sectional study of QU-Health Cluster students via an online survey over a three-week period in November 2021. Chi-squared tests and gamma coefficients were used to compare differences between categorical variables. Results: One hundred and ninety-three QU-Health students responded. Most participants had a positive attitude towards artificial intelligence, finding it useful and reliable. The most popular perceived advantage of artificial intelligence was its ability to speed up work processes. Around 40% expressed concern about a threat to job security from artificial intelligence, and a majority believed that artificial intelligence cannot provide sympathetic care (57.9%). Participants who felt that artificial intelligence can better make diagnoses than humans also agreed that artificial intelligence could replace their job (p = 0.005). Male students had more knowledge (p = 0.005) and received more training (p = 0.005) about healthcare artificial intelligence. Participants cited a lack of expert mentorship as a barrier to obtaining knowledge about artificial intelligence, followed by lack of dedicated courses and funding. Conclusions: More resources are required for students to develop a good understanding about artificial intelligence. Education needs to be supported by expert mentorship. Further work is needed on how best to integrate artificial intelligence teaching into university curricula.

Distinct Clinical and Prognostic Features of Myelodysplastic Syndrome in Patients from the Middle East, North Africa, and Beyond: A Systemic Review.

Myelodysplastic syndrome (MDS) describes a group of bone marrow malignancies with variable morphologies and heterogeneous clinical features. The aim of this study was to systematically appraise the published clinical, laboratory, and pathologic characteristics and identify distinct clinical features of MDS in the Middle East and North Africa (MENA) region. We conducted a comprehensive search of the PubMed, Web of Science, EMBASE, and Cochrane Library databases from 2000 to 2021 to identify population-based studies of MDS epidemiology in MENA countries. Of 1935 studies, 13 independent studies published between 2000 and 2021 representing 1306 patients with MDS in the MENA region were included. There was a median of 85 (range 20 to 243) patients per study. Seven studies were performed in Asian MENA countries (732 patients, 56%) and six in North African MENA countries (574 patients, 44%). The pooled mean age was 58.4 years (SD 13.14; 12 studies), and the male-to-female ratio was 1.4. The distribution of WHO MDS subtypes was significantly different between MENA, Western, and Far East populations (n = 978 patients, p < 0.001). More patients from MENA countries were at high/very high IPSS risk than in Western and Far East populations (730 patients, p < 0.001). There were 562 patients (62.2%) with normal karyotypes and 341 (37.8%) with abnormal karyotypes. Our findings establish that MDS is prevalent within the MENA region and is more severe than in Western populations. MDS appears to be more severe with an unfavorable prognosis in the Asian MENA population than the North African MENA population.

Noise pollution in the hospital environment of a developing country: A case study of Lahore (Pakistan).

The present study investigates the noise pollution levels in public- and private-sector hospitals of Lahore. The noise pollution parameters were investigated from 20 public and 10 private hospitals. We observed that the equivalent continuous sound level (Leq) values varied significantly in different departments of the hospitals as well as at different times of the day. The public-sector hospitals had significantly higher noise pollution compared to the private-sector hospitals. The Wilcoxon Mann-Whitney two-sample rank-sum test revealed significant difference between noise levels in intensive care unit (ICU) during morning and in emergency, waiting area, intensive care unit (ICU), and reception during daytimes. However, no significant differences were found for any department during the evening. The Leq values were found to be higher than the international norms (WHO standards) for all hospitals, higher than USEPA for 29 hospitals and higher than local standards for 27 hospitals. Overall, significantly lower sound levels were always observed in private hospitals.