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Producción científica reciente
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.
Advanced Wafer Hotspot Detection through Image Segmentation and Stacked Model
The wafer map is a data visualization of a thin semiconductor fabric made of crystalline silicon, such as defects or test results. The wafer map is a base for creating electronic coordinate circuits and photovoltaic cells. During the wafer map production, any fault results in a product failure. The wafer map faults are undetectable to the naked eye, which is a big challenge. Hotspot detection in wafer maps is significantly important to evaluate the manufacturing process and. improve product yield. The hotspot detection in the wafer maps is the primary aim of this research. A novel wafer map hotspot detector (WHD) is proposed based on three stack fully connected conventional neural network layers and a dense layer. Data augmentation uses the segmented images of the wafers to build the proposed model. The proposed model is evaluated through several evalua-tion parameters and state-of-the-art studies comparative analysis. The proposed model achieved a 94% training and 90% testing performance accuracy for hotspot detection and shows better results than existing approaches. This study helps semiconductor engineers improve wafer manufacturing designs and efficiency in the semiconductor industry.
Quantifying Domain-Specific Risk Signals in Lung Cancer Severity Prediction: A Multi-Domain Ablation Study Using XGBoost and SHAP
Predictive modeling for lung cancer severity often struggles with the high dimensionality and multi-domain nature of risk factors. While individual contributors like smoking are well-documented, the relative predictive weight of lifestyle, environmental, and genetic domains remains insufficiently quantified in integrated frameworks. This study proposes an explainable machine learning approach using an XGBoost classifier to evaluate these three distinct risk domains. Utilizing the UCI Machine Learning Repository Lung Cancer Dataset, we implemented a domain-wise ablation study to isolate the predictive signal of each factor group. To ensure scientific rigor and address the “black box” nature of ensemble models, we employed 5-fold stratified cross-validation and SHAP (Shapley Additive Explanations) for feature-level transparency. Our results demonstrate that the integrated model achieves a classification accuracy of 95.7% (AUC-ROC = 0.98) on this dataset. Notably, ablation analysis revealed that the Lifestyle domain retained the highest standalone predictive performance (92.9%), followed by the Genetic/Clinical domain (94.6%), while the Environmental domain showed a more pronounced performance drop (73.3%), suggesting differential information density across risk categories. SHAP analysis identified cumulative smoking exposure as the primary feature influencing model predictions within this dataset. This study presents a proof-of-concept interpretable framework for lung cancer risk stratification, demonstrating that domain-wise ablation combined with explainable AI can provide transparent, feature-level insight to support rather than replace clinical judgment in settings where comprehensive diagnostic testing may be limited.
A Hybrid Temporal-spectral Load Forecasting Model with Static Context Fusion for Smart Cities
Accurate short-term electricity load forecasting is essential for reliable and efficient smart city energy management, particularly in environments characterized by high-dimensional, heterogeneous, and noisy multivariate signals. However, existing forecasting models often struggle to simultaneously capture nonlinear temporal dependencies, multi-scale periodicity, and static contextual influences within a unified framework. To address this challenge, this study proposes a hybrid deep learning architecture that integrates Bidirectional Long Short-Term Memory (BiLSTM) for temporal modeling, an additive attention mechanism for adaptive time-step weighting, Fast Fourier Transform (FFT)-based frequency residual learning for periodicity extraction, and embedding-based static feature fusion for contextual representation. The model is evaluated on the ISO-NE Smart City Energy Dataset for next-hour electricity load forecasting using a two-week input window (336 hours). Experimental results demonstrate that the proposed hybrid framework significantly improves predictive accuracy, achieving an RMSE of 25.51 kW and an R of 0.9905, outperforming recurrent, convolutional, and transformer-based baselines under identical evaluation settings. Ablation analysis confirms that temporal attention and frequency-domain residual modeling contribute substantially to performance gains. These findings indicate that joint temporal–spectral modeling combined with static contextual fusion provides a robust and effective solution for complex smart-city electricity forecasting tasks.
Predicting Academic Award Recognition Across Disciplines Using Publication-Based Bibliometric Indices and SHAP-Driven Explainability
Researcher evaluation underpins critical academic decisions, yet traditional bibliometric indicators lack predictive capability and cross-domain generalizability, while most predictive approaches offer limited interpretability and narrow domain validation. This study proposes a SHAP interpretable, multi-domain supervised learning framework for predicting academic award recognition using thirty two publication count-based bibliometric indices. A balanced dataset was constructed across four disciplines, namely Computer Science, Neuroscience, Mathematics, and Civil Engineering, comprising verified awardees from recognized professional societies and matched non-awardee researchers. Eight classifiers were evaluated under stratified five fold cross validation, assessed via accuracy, precision, recall, F1-score, and ROC AUC. The framework achieved domain-specific F1-scores of 0.70 in Computer Science, 0.73 in Neuroscience, 0.72 in Civil Engineering, and 0.78 in Mathematics, with SVM and XGBoost demonstrating the strongest cross-domain robustness across disciplines. SHAP analysis consistently identified normalized h index, h2 family, q2 index, and g index as dominant cross-domain predictors, while domain-specific indicators, including Rm and w indices in Neuroscience and P index in Civil Engineering, reflected disciplinary recognition patterns. By unifying publication-based feature engineering, multi-domain classification, and SHAP explainability within a single reproducible pipeline, this framework offers a scalable, transparent, and evidence-based tool for institutional researcher evaluation.