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Infrared thermography to assess fatigue, injury risk factors and recovery in soccer: a systematic review of original studies
Background: Recovery after a training session or match is a key factor in injury prevention and sports performance. The purpose of this systematic review was to analyze and consolidate the available scientific evidence from the main databases on the use of infrared thermography in the assessment of fatigue, injury risk factors, and recovery in soccer players.Methods: The literature search was conducted following the PRISMA guidelines and the PICOS model until June 30, 2025, in the main scientific databases (ScienceDirect, EMBASE, Web of Science (WOS), Cochrane Library, SciELO, MEDLINE/PubMed, SPORTDiscus, and Scopus). The risk of bias and methodological quality were assessed using the Cochrane Handbook guidelines and the PEDro scale.”Results: The initial literature search yielded a total of 510 records. After applying the inclusion and exclusion criteria, the final sample consisted of 20 studies, which were of high methodological quality. The results showed the effects of infrared thermography in assessing fatigue, identifying injury risk factors, and monitoring recovery processes in soccer players. The studies also systematically reported the characterization of the population, the assessment methods used, the variables analyzed, the methodological design, the main results, and the effects of the intervention.Conclusions: Infrared thermography shows promise as a valid, reliable, and non-invasive tool for assessing skin temperature, reflecting temperature changes in response to physiological processes. It allows for the analysis of structural or metabolic fatigue and thermal asymmetries. Therefore, thermography could be used to design individualized recovery protocols.
Environmental burden of fish in healthy and sustainable diets
Fish is widely promoted as part of healthy dietary patterns. The aim of this review was to summarise current literature on the environmental footprint of fish and its role within sustainable diets. Fish generally represents a minor share of total dietary environmental impacts, contributing to a smaller proportion of greenhouse-gas emissions (GHGe), land and water use than meat and other animal products. Several modelling studies showed that substituting meat with fish or increasing fish intake within optimised dietary patterns can reduce environmental impacts, although the magnitude varies by country, diet type, and fish species. However, some analyses reported increased GHGe associated with higher fish intake, especially in models ensuring nutritional quality. Overall, fish consumption is compatible with achieving nutritionally adequate and lower environmental impacts, although optimal match between environmental boundaries and nutritional needs is not always possible. These findings suggest that fish can play a constructive role in sustainable diets when integrated thoughtfully within broader dietary shifts.
Enhanced weather classification using xception with SENet and attention mechanisms
Introduction: Weather classification plays a crucial role in applications such as environmental monitoring, disaster management, and smart city infrastructure. Accurate and efficient classification of weather conditions from images remains a challenging task due to variations in illumination, texture, and atmospheric conditions.Methods: This study proposes an efficient deep learning framework for multi-class weather classification by integrating the Xception architecture with Squeeze-and-Excitation (SE) blocks and a spatial attention mechanism. Transfer learning with pre-trained ImageNet weights was employed, and a comparative analysis was conducted using EfficientNet-B3, ResNet152V2, and Xception architectures. The proposed enhanced Xception model incorporates channel-wise recalibration and spatial feature refinement to improve representational capability. The model was trained and evaluated on the Multi-Class Weather Dataset (MWD), which consists of 1,125 images categorized into four classes: sunshine, cloudy, rain, and sunrise. To ensure robustness and generalization, 5-fold cross-validation, statistical significance testing, calibration analysis, and robustness evaluation under image perturbations were performed.Results: The proposed model achieved a classification accuracy of 99.06% on the test set. Additionally, it attained a macro precision of 98.3%, macro recall of 97.7%, and macro F1-score of 98.0%. The model demonstrated strong generalization capability and robustness under varying perturbation conditions, with only moderate computational overhead.Discussion: The integration of SE blocks and spatial attention significantly enhances feature representation by emphasizing informative channels and spatial regions. Compared to baseline architectures, the proposed framework shows superior performance in terms of accuracy and robustness. These results indicate that the model is well-suited for real-world weather classification applications, particularly in intelligent environmental monitoring systems.
Correction: Enhancing fault detection in new energy vehicles via novel ensemble approach
In the original version of this Article, Umair Shahid was incorrectly listed as a corresponding author. The correct corresponding authors for this Article are Imran Ashraf and Kashif Munir. Correspondence and request for materials should be addressed to ashrafimran@live.com and kashif.munir@kfueit.edu.pk.
An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants
The Polyphagous Shot Hole Borer (PSHB) is a highly invasive beetle that has been spreading like an epidemic across agricultural and forestry landscapes in recent years. Its rapid and destructive spread has turned it into a major global threat, causing widespread damage that continues to grow with time. Countries like South Africa, the United States, and Australia have implemented extensive measures to control the spread of PSHB, including the establishment of specialized agricultural support centers for early detection. However, there is still a strong need to make PSHB detection more accessible, allowing even non-experts to easily identify infections at an early stage. Artificial Intelligence (AI) has shown great promise in plant disease detection, but a major challenge in the case of PSHB was the lack of a suitable dataset for training AI models. In the proposed work, we first created a dedicated dataset by collecting images of trees infected with PSHB. We applied a range of preprocessing techniques to refine the dataset and prepare it for AI applications. Building on this, we developed a novel AI-based method, where we trained a deep learning model using a multi-convolutional layer network combined with a Fourier transformation layer. Additionally, an attention mechanism and advanced feature extraction techniques were incorporated to further boost model performance. As a result, the proposed approach achieved an impressive top accuracy of 92.3% in detecting PSHB infections, showing the potential of AI to offer a simple, efficient, and highly accurate solution for early disease detection.