The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI’s predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

Rural sub-Saharan Africa faces limited medical access, healthcare worker shortages, and inadequate health information systems. Mobile health (mHealth) technologies offer potential solutions but remain underdeveloped in these settings. This review aims to explore the sociocultural context of mHealth adoption in rural sub-Saharan Africa to support sustainable implementation. A comprehensive Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) search was conducted in databases like PubMed, MEDLINE, and African Journals Online, covering peer-reviewed literature from 2010 to 2024. Qualitative studies of mHealth interventions were included, with quality assessed via the Critical Appraisal Skills Program (CASP) checklist and data synthesized using a meta-ethnographic approach. Out of 892 studies, 38 met the inclusion criteria. Key findings include sociocultural factors like community trust influencing technology acceptance, local implementation strategies, user empowerment in health decisions, and innovative solutions for infrastructure issues. Challenges include privacy concerns, increased healthcare worker workload, and intervention sustainability. While mHealth can reduce healthcare barriers, success depends on sociocultural alignment and adaptability. Future interventions should prioritize community co-design, privacy protection, and sustainable, infrastructure-aware models.

Given the increasing demand for sustainable energy sources and the challenges associated with the limited efficiency of solar cells, this review focuses on the application of gold quantum dots (AuQDs) in enhancing solar cell performance. Gold quantum dots, with their unique properties such as the ability to absorb ultraviolet light and convert it into visible light expand the utilization of the solar spectrum in solar cells. Additionally, these quantum dots, through plasmonic effects and the enhancement of localized electric fields, improve light absorption, charge carrier generation (electrons and holes), and their transfer. This study investigates the integration of quantum dots with gold plasmonic nanoparticles into the structure of solar cells. Experimental results demonstrate that using green quantum dots and gold plasmonic nanoparticles as intermediate layers leads to an increase in power conversion efficiency. This improvement highlights the significant impact of this technology on solar cell performance. Furthermore, the reduction in charge transfer resistance and the increase in short-circuit current are additional advantages of utilizing this technology. The findings of this research emphasize the high potential of gold quantum dots in advancing next-generation solar cell technology.