Scientific activity has become increasingly complex in recent years. The need for international research collaboration has thus become a common pattern in science. In this current landscape, countries face the problem of maintaining their competitiveness while cooperating with other countries to achieve relevant research outputs. In this international context, publications from international collaborations tend to achieve greater scientific impact than those from domestic ones. To design policies that improve the competitiveness of countries and organizations, it thus becomes necessary to understand the factors and mechanisms that influence the benefits and impact of international research. In this regard, the aim of this study is to confirm whether the differences in impact between international and domestic collaborations are affected by their topics and structure. To perform this study, we examined the Library and Information Science category of the Web of Science database between 2015 and 2019. A science mapping analysis approach was used to extract the themes and their structure according to collaboration type and in the whole category (2015–2019). We also looked for differences in these thematic aspects in top countries and in communities of collaborating countries. The results showed that the thematic factor influences the impact of international research, as the themes in this type of collaboration lie at the forefront of the Library and Information Science category (e.g., technologies such as artificial intelligence and social media are found in the category), while domestic collaborations have focused on more well-consolidated themes (e.g., academic libraries and bibliometrics). Organizations, countries, and communities of countries must therefore consider this thematic factor when designing strategies to improve their competitiveness and collaborate.

One of the most crucial problems in the field of business is financial forecasting. Many companies are interested in forecasting their incoming financial status in order to adapt to the current financial and business environment to avoid bankruptcy. In this work, due to the effectiveness of Deep Learning methods with respect to classification tasks, we compare the performance of three well-known Deep Learning methods (Long-Short Term Memory, Deep Belief Network and Multilayer Perceptron model of 6 layers) with three bagging ensemble classifiers (Random Forest, Support Vector Machine and K-Nearest Neighbor) and two boosting ensemble classifiers (Adaptive Boosting and Extreme Gradient Boosting) in companies' financial failure prediction. Because of the inherent nature of the problem addressed, three extremely imbalanced datasets of Spanish, Taiwanese and Polish companies' data have been considered in this study. Thus, five oversampling balancing techniques, two hybrid balancing techniques (oversampling-undersampling) and one clustering-based balancing technique have been applied to avoid data inconsistency problem. Considering the real financial data complexity level and type, the results show that the Multilayer Perceptron model of 6 layers, in conjunction with SMOTE-ENN balancing method, yielded the best performance according to the accuracy, recall and type II error metrics. In addition, Long-Short Term Memory and ensemble methods obtained also very good results, outperforming several classifiers used in previous studies with the same datasets.

The growing global demand for computational resources, particularly in Artificial Intelligence (AI) applications, raises increasing concerns about energy consumption and its environmental impact. This study introduces a newly developed energy consumption index that evaluates the energy efficiency of Deep Learning (DL) models, providing a standardized and adaptable approach for various models. Convolutional neural networks, including both classical and modern architectures, serve as the primary case study to demonstrate the applicability of the index. Furthermore, the inclusion of the Swin Transformer, a state-of-the-art and modern non-convolutional model, highlights the adaptability of the framework to diverse architectural paradigms. This study analyzes the energy consumption during both training and inference of representative DL architectures, including AlexNet, ResNet18, VGG16, EfficientNet-B3, ConvNeXt-T, and Swin Transformer, trained on the Imagenette dataset using TITAN XP and GTX 1080 GPUs. Energy measurements are obtained using sensor-based tools, including OpenZmeter (v2) with integrated electrical sensors. Additionally, software-based tools such as CarbonTracker (v1.2.5) and CodeCarbon (v2.4.1) retrieve energy consumption data from computational component sensors. The results reveal significant differences in energy efficiency across architectures and GPUs, providing insights into the trade-offs between model performance and energy use. By offering a flexible framework for comparing energy efficiency across DL models, this study advances sustainability in AI systems, supporting accurate and standardized energy evaluations applicable to various computational settings.