Mathematics anxiety is prevalent in Nigeria where students have a fear of the subject. Much research has been conducted on the level of mathematics anxiety, the differences between the genders, and its relationship with performance. However, little has been done on the factors for mathematics anxiety, and even less on peers' contribution towards mathematics anxiety. The photovoice approach was applied to capture peers' speech and behaviour, which can intensify or minimize mathematics anxiety. The findings revealed that peers' behaviour, such as poor students causing trouble and intelligent students dominating the lesson, and speech, such as using frightening and discouraging statements in the mathematics classroom, has a direct effect on intensifying mathematics anxiety. However, the findings also showed that peer behaviour, such as achievement of peers and group discussions, and speech, such as positive advice, also minimize mathematics anxiety among students. It can be concluded that peers in their day-today interaction can have an effect on students' mathematics anxiety.
Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors to integrate and analyze the data. One possible remedy to overcome these limitations is the interconnection of medical devices through the internet using an intelligent and distributed platform such as the Internet of Things (IoT) or the Internet of Health Things (IoHT) and Artificial Intelligence/Machine Learning (AI/ML). These concepts permit the integration of data from different sources to enhance the diagnosis/prognosis of the patient’s health state. Over the last several decades, the growth of information technology (IT), such as the IoT/IoHT and AI, has grown quickly as a new study topic in many academic and business disciplines, notably in healthcare. Recent advancements in healthcare delivery have allowed more people to have access to high-quality care and improve their overall health. This research reports recent advances in AI and IoT in monitoring vital health signs. It investigates current research on AI and the IoT, as well as key enabling technologies, notably AI and sensors-enabled applications and successful deployments. This study also examines the essential issues that are frequently faced in AI and IoT-assisted vital health signs monitoring, as well as the special concerns that must be addressed to enhance these systems in healthcare, and it proposes potential future research directions.
Smart Grid (SG) systems involves large Information Communication Technology (ICT) infrastructure integrated with the power grid system to allow bidirectional flows of information, for effectiveness and to provide economic and sustainable processes that facilitates constant electricity supplies. As a result of open IoT used in smart grid infrastructure, electricity generation and distribution information are getting more vulnerable to online threats. The need to find effective and mitigating measures to the vulnerability and threats in the systems. As broadband network moves from 4G to 5G networks with its attendant advantages, smart grid interoperability will require a well-structured security strategy to avert cyber-attacks and ensure grid security. Cyber-attack issues are critical factors that need to be understood and mitigating techniques developed to ensure the smooth running of smart grid systems. Research has shown that severe vulnerabilities in the implementation of the smart meter pose a threat to grid infrastructure, as such, it is only when these issues are resolved that we can have a sustainable and realistic smart grid technology. This study addressed these concerns by developing a conceptual model for the mitigation of security vulnerabilities in IoT-based smart grid electric energy distribution systems.
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