With modern society and the ever-increasing consumption of polymeric materials, the way we look at products has changed, and one of the main questions we have is about the negative impacts caused to the environment in the most diverse stages of the life cycle of these materials, whether in the acquisition of raw materials, in manufacturing, distribution, use or even in their final disposal. The main methodology currently used to assess the environmental impacts of products from their origin to their final disposal is known as Life Cycle Assessment (LCA). Thus, the objective of this work is to evaluate how much the biodegradable polymer contributes to the environment in relation to the conventional polymer considering the application of LCA in the production mode. This analysis is configured through the Systematic Literature Review (SLR) method. In this review, 28 studies were selected for evaluation, whose approaches encompass knowledge on LCA, green biopolymer (from a renewable but non-biodegradable source), conventional polymer (from a non-renewable source) and, mainly, the benefits of using biodegradable polymers produced from renewable sources, such as: corn, sugarcane, cellulose, chitin and others. Based on the surveys, a comparative analysis of LCA applications was made, whose studies considered evaluating quantitative results in the application of LCA, in biodegradable and conventional polymers. The results, based on comparisons between extraction and production of biodegradable polymers in relation to conventional polymers, indicate greater environmental benefits related to the use of biodegradable polymers.

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.

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.

Open-source software (OSS) has emerged as a transformative tool whose implementation has the potential to modernise many libraries around the world in the digital age. OSS is a type of software which permits its users to inspect, share, modify, and enhance through its freely accessed source code. The accessibility and openness of the source code permits users to manipulate, change, and improve the way in which a piece of software, program, or application works. OSS solutions therefore provide cost-effective alternatives that enable libraries to enhance their technological infrastructure without being constrained by proprietary systems. Hence, many countries have initiated and formulated policies and legislative frameworks to support the implementation and use of OSS library solutions such as DSpace, Alfresco, and Greenstone. The purpose of the study reported on was to investigate the leveraging of OSS to modernise public libraries in South Africa. Content analysis was adopted as the research methodology for this qualitative study, which was based on a literature review integrating insights from the researchers’ experiences with the use of OSS in libraries The findings of the study reveal that the use of OSS has the potential to modernise public libraries, especially those located outside cities or urban areas. These libraries are often less well equipped with the necessary technology infrastructure to meet the demands of the digital age, such as online books and open access materials. The study culminated in an OSS framework that may be implemented to modernise public libraries. This framework may help public libraries to integrate OSS solutions and further allow users access to digital services.

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.