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Energy plays a vital role in promoting sustainable economic development in complex societies. This study has analyzed the impact of electricity consumption on three European Union member countries' economic growth, i.e., Portugal, France, and Finland, caring structural breaks in cointegration analyses. The empirical results indicate a positive impact of electric power consumption on economic growth in the long and short run in Finland and Portugal and in the long run in France. The findings also highlight the positive and significant role of the labor force in boosting economic growth in the long and short run in France and Finland. However, it shrinks economic growth in the long run in Portugal. The study discloses the positive role of capital in the long run in the case of Portugal. Similar results are found in all three countries in the short run. Moreover, the study diagnoses a bidirectional causal relationship between economic growth and electric power consumption in Finland in the long and short run and in France in the long run. A growth-promoting or electricity-led growth hypothesis is found in Portugal. By simulating the mean values of electric power consumption, economic growth follows an increasing trend in all the countries. Hence, electric power consumption has appeared an essential factor in elevating economic growth in all three selected countries. Based on these results, this study suggests that the provision of electricity supply ventures may be expanded in the selected EU member countries in order to enhance economic growth. The study also suggests that emphasis should be shifted from non-renewable energy sources to renewable energy sources to ensure the provision of clean energy to all the public under the umbrella of sustainable development goals of 2030. Hence, the present study contributes to achieving sustainable economic growth in the selected EU member countries., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background and Objective: Lyme disease which is one of the most common infectious vector-borne diseases manifests itself in most cases with erythema migrans (EM) skin lesions. Recent studies show that convolutional neural networks (CNNs) perform well to identify skin lesions from images. Lightweight CNN based pre-scanner applications for resource-constrained mobile devices can help users with early diagnosis of Lyme disease and prevent the transition to a severe late form thanks to appropriate antibiotic therapy. Also, resource-intensive CNN based robust computer applications can assist non-expert practitioners with an accurate diagnosis. The main objective of this study is to extensively analyze the effectiveness of CNNs for diagnosing Lyme disease from images and to find out the best CNN architectures considering resource constraints., Methods: First, we created an EM dataset with the help of expert dermatologists from Clermont-Ferrand University Hospital Center of France. Second, we benchmarked this dataset for twenty-three CNN architectures customized from VGG, ResNet, DenseNet, MobileNet, Xception, NASNet, and EfficientNet architectures in terms of predictive performance, computational complexity, and statistical significance. Third, to improve the performance of the CNNs, we used custom transfer learning from ImageNet pre-trained models as well as pre-trained the CNNs with the skin lesion dataset HAM10000. Fourth, for model explainability, we utilized Gradient-weighted Class Activation Mapping to visualize the regions of input that are significant to the CNNs for making predictions. Fifth, we provided guidelines for model selection based on predictive performance and computational complexity., Results: Customized ResNet50 architecture gave the best classification accuracy of 84.42% ±1.36, AUC of 0.9189±0.0115, precision of 83.1%±2.49, sensitivity of 87.93%±1.47, and specificity of 80.65%±3.59. A lightweight model customized from EfficientNetB0 also performed well with an accuracy of 83.13%±1.2, AUC of 0.9094±0.0129, precision of 82.83%±1.75, sensitivity of 85.21% ±3.91, and specificity of 80.89%±2.95. All the trained models are publicly available at https://dappem.limos.fr/download.html, which can be used by others for transfer learning and building pre-scanners for Lyme disease., Conclusion: Our study confirmed the effectiveness of even some lightweight CNNs for building Lyme disease pre-scanner mobile applications to assist people with an initial self-assessment and referring them to expert dermatologist for further diagnosis., Competing Interests: Declaration of Competing Interest The authors have declared no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)