The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting.

The Canary Islands, internationally recognized as a biodiversity hot spot, possess unique ecological characteristics including endemic reptile species that face substantial threats from invasive alien species. Particularly, concerning is the California kingsnake ( Lampropeltis californiae ), which exhibits remarkable adaptability and inflicts severe ecological damage on endangered endemic fauna. This proposal aims to address biodiversity conservation challenges in the Canary Islands ecosystem by advancing reptile species classification through the implementation of Swin Transformer architectures. The integration of these advanced neural network architectures with conservation biology provides an automated, precise tool for species identification across different taxonomic levels that can enhance monitoring and control strategies for biodiversity preservation worldwide. This technical approach addresses urgent conservation requirements in the Canary Islands while simultaneously establishing a methodological framework applicable to other ecological contexts facing comparable biodiversity threats. The methodology employed pre-trained Swin Transformer models, initially developing fine-grained reptile species discrimination capabilities through hyperparameter optimization on a limited-scale database (160 images, 40 per species) using nested cross-validation to ensure statistical rigor and independence. The optimized configuration was subsequently transferred to a substantially larger database (4,400 images) for binary classification distinguishing snake from non-snake reptiles, where training on only 400 samples achieved 99.38% accuracy on 4,000 independent test samples, directly addressing the invasive alien species monitoring challenge. The Swin-B Transformer model demonstrated robust performance, achieving up to 100% accuracy in multiclass species classification and 99.38% accuracy in the challenging, taxonomically broader binary snake versus non-snake classification. These results establish the transferability and scalability of the hyperparameter optimization methodology across datasets of substantially different magnitudes.

Intensive Care Unit (ICU) readmissions following Intracerebral Hemorrhage (ICH) are associated with increased mortality and resource burden. Current prediction models predominantly rely on static admission features, failing to capture the temporal evolution of physiological instability. This study proposes a novel deep learning framework to predict ICU readmission by leveraging high-resolution time-series data from the MIMIC-III and MIMIC-IV databases. We developed a Stacked Gated Recurrent Unit (GRU) Architecture Ensemble, integrated with Time-series Generative Adversarial Networks (TimeGAN) to address the inherent class imbalance of readmission events. Our model achieved a state-of-the-art Area Under the Receiver Operating Characteristic Curve (AUC) of 0.912, significantly outperforming traditional machine learning baselines and static feature models. The sensitivity of 88.1% highlights the model’s efficacy in minimizing unsafe premature discharges. Furthermore, interpretability analysis using SHAP values identified Length of Stay, MELD Score, and Monocytes as critical predictors, revealing that readmission risk is driven by a complex interplay between systemic organ dysfunction and inflammatory response. These findings demonstrate that incorporating temporal dynamics and generative data augmentation significantly enhances risk stratification, offering a robust clinical decision support tool to optimize discharge timing in neurocritical care.