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The Almajiri children in Nigeria are deserving of special interventions to reduce the life-long divide in educational achievement, social status, and economic empowerment. One way of speedily achieving this is through the use of Information and Communication Technologies (ICTs). This study examined the prospects and challenges of using ICT in the teaching and learning processes of Almajiri children's education in Nigeria. A descriptive research methodology was adopted and data were collected from the Almajiri children, their teachers, and parents from communities with a high concentration of Almajiri schools. The results showed that while the official language of instruction in schools in Nigeria is English, the language often spoken by children and teachers was Hausa. Also, while teachers and parents were quite familiar with ICTs such as mobile phones, computers, and television, the Almajiri children were more familiar with television and mobile phones, and very few had used a computer. Some strategies proposed for successful adoption of ICT in Almajiri education include the use of indigenous languages in courseware development and the use of one-to-many ICT facilities, such as multimedia teaching aids, projectors, and smart televisions.

Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally. [ABSTRACT FROM AUTHOR]

The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm. [ABSTRACT FROM AUTHOR]

The broadcast nature of radio propagation in wireless communication has been suspected as the loopholes of passive or active attacks by unauthorized users (eavesdroppers). The physical layer security techniques operate at the lowest stack of OSI layer against conventional cryptographic approaches, operating at the upper layer. However, techniques such as channel coding, power (directional antenna and artificial noise), and spread spectrum have been (and continuously) deployed to safeguard against sophisticated attacks. Most of these deployments are theoretical, and a few are enhanced for efficient security against an intruder. In this article, a boundary technique approach is proposed and applied to the physical layer to improve its secrecy‐capacity and subdue adversary effects at the legitimate receiver. Hybrid performance metrics were adopted, and a Monte Carlo simulation was performed. The result obtained using secrecy outage probability, secrecy‐capacity, and intercept‐probability show that our proposed techniques enhance the secret transmission between the main transmitter and legitimate receiver. The simulation results were compared with the analytical methods. It was found that the channel between the transmitter and the main receiver has a better signal‐to‐noise ratio than the corresponding eavesdropper's channel. Conclusively, performance of the proposed technique is validated for applications in emerging wireless communication systems. [ABSTRACT FROM AUTHOR]