Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

The objective of the present study is to observe the surface morphology, structure and elemental composition of the ash particles produced from some thermal power stations of India using scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDXA). This information is useful to better understand the ash particles before deciding its utility in varied areas.

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

The temporomandibular joint (TMJ) is considered a bicondylar diarthrosis type joint. Imaging evaluation is a fundamental part of its assessment, which should include both bony and soft tissue characteristics and the relationship between them. Magnetic resonance imaging (MRI) represents the gold standard for the study of soft tissues; however, up to now, its main application continues to be the visualization of the articular disc. For this reason, the present article aimed to point out the information available in the literature regarding the visualization of the joint capsule in MRI and to evaluate it as an independent structure.

Cocoa is important for the economy and rural development of Ghana. However, small-scale cocoa production is the leading agricultural product driver of deforestation in Ghana. Uncertain tree tenure disincentivizes farmers to retain and nurture trees on their farms. There is therefore the call for structures that promote tree retention and management within cocoa farming. We examined tenure barriers and governance for tree resources on cocoa farms. Data was collected from 200 cocoa farmers from two regions using multistage sampling technique. Information was gathered on tree ownership and fate of tree resources on cocoa farms, tree felling permit acquisition and associated challenges and illegal logging and compensation payments on cocoa farms. Results suggest 62.2% of farmers own trees on their farms. However, these farmers may or may not have ownership rights over the trees depending on the ownership of their farmlands. More than half of the farmers indicated they require felling permits to harvest trees on their farms, indicative of the awareness of established tree harvesting procedures. Seventy percent of the farmers have never experienced illegal logging on their farms. There is however the need to educate the remaining 30% on their rights and build their compensation negotiation powers for destructions to their cocoa crops. This study has highlighted ownership and governance issues with cocoa farming and it is important for the sustainability of on-farm tree resources and Ghana’s forest at large.