In the 21st century, brand communication has been significantly transformed through the interaction of users and artificial intelligence (AI), who co-create and recreate texts in digital environments. This evolution challenges traditional disciplines and roles, opening new perspectives for textual production on multiple platforms. The study examines the current state and application of the textual component in brand communication, exploring its disciplinary foundations, rhetorical traces, and research methodologies. To this end, a content analysis of 97 relevant publications from 2000 to 2024 was conducted, selected for their impact on the field of brand communication and following the guidelines established in the PRISMA statement. The results identified three sources of textual creation: Organization, users and algorithms. In addition, persuasion and sentiment take precedence at the rhetorical level, while data mining stands out in message analysis. In conclusion, the advertising text, which previously prevailed in brand communication with corporate authorship, formal prefiguration and a closed entity, now expands in a media and networked context. This text originates from a multiplicity of human and automated sources, overlapping rhetorical phases and fluid textualities. The shift implies a transition from unidirectional communication, characterized by repeated impacts, to multidirectional communication with spiraling trajectories and iterative adjustments. This challenges the boundaries of genres and formats, merging the persuasiveness of rhetoric and the imagination of storytelling. This situation demands commercial policies that integrate new professionals and roles, in partnership with the educational sector, and that address copyright with AI and users.

The sustainable development of Madeira Island necessitates the implementation of more precise and targeted planning strategies to address its regional challenges. Given the urgency of this issue within the context of sustainability, planning approaches must be grounded in and reinforced by a comprehensive array of thematic studies to fully grasp the complexities involved. This research leverages Geographic Information Systems (GIS) to analyze land use and occupancy patterns and their evolution within the municipality of Machico on Madeira Island. The study provides a nuanced perspective on the urban structure’s stagnation in the region, while concurrently highlighting the dynamic shifts in agricultural practices. Furthermore, it elucidates the transformation of predominant native vegetation within the municipality from 1990 to 2018. Notably, the research underscores the alarming decline in native vegetation due to anthropogenic activities, emphasizing the need for more rigorous monitoring by regional authorities to safeguard and preserve these valuable landscapes, habitats, and ecosystems.

Climate change has adverse effects on ecosystems and several socio-economic sectors including health. Indeed, infrastructure, continuity of medical services, and the hospital environment are all directly affected by the effects of climate-related risks. This study aims to describe the observations of the effects of climate change risks on health systems in the Greater Lomé health region of Togo. We used an interview guide and a questionnaire to collect information. The observations allowed us to assess the effects caused by climate risks. According to the results, 84.62% of respondents attest that health centers experience flooding during rainy periods and damage caused by strong winds is noticeable among 76.92% of respondents. More than 25.40% and 61.86% respectively of respondents mention that droughts and floods have effects on health systems. The results of this study will allow health system managers to become aware of how to plan useful actions to facilitate the management of climate-related risks in health facilities in the Greater Lomé health region. In view of all these results, it is necessary that measures be taken to strengthen the resilience of health systems through awareness campaigns and training of actors throughout the health pyramid.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.