ICDAS Adaptation for Early Childhood Caries: An Epidemiological Study in 11 Latin American Countries
https://bit.ly/48vnCXk Background: The assessment of epidemiological information on the oral health of children younger than 6 y in the region of the Americas is challenging due to methodological differences. An International Association for Dental Research Regional Development Project supported researchers from 11 Latin American countries in Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Panama, Paraguay, Peru, Uruguay, and Venezuela to train and conduct standardized epidemiological studies of dental caries in children <6 y of age. Objectives: To assess the feasibility of an International Caries Detection and Assessment System (ICDAS)–adapted protocol in early childhood caries (ECC) epidemiological studies and provide comparable data. Methods: Eleven researchers from the selected countries were standardized using the following simplified criteria and protocol aimed to reduce the time of evaluation: (1) starting the examination with the detection of caries lesions, (2) eliminating ICDAS category 1 (as no air for drying), (3) applying one code for tooth surfaces with the same clinical finding (e.g., missing), and (4) using a customized data entry form. The 11 researchers trained and standardized 10 to 15 local examiners in each country and conducted cross-sectional studies in convenience samples of preschool children aged 12 to 71 mo living in disadvantaged communities according to each country’s criteria. Results: A total of 4,535 children were included in the present analysis. There were notable differences by country and age. For example, the d2-6prevalence in the 12- to 23-mo group varied between 13% in Venezuela and 48% in Argentina, while the d5-6prevalence varied between 0% in Venezuela and 18% in Argentina. In general, the occurrence of more severe clinical presentations increased with age. Conclusions: There was a considerable variation in the prevalence of dental caries in the 11 countries. The wide variation with age indicates the need to report ECC estimates by detection threshold and individual age groups. The ICDAS-adapted protocol is suitable for ECC epidemiological studies. Knowledge Transfer Statement: The results of this study show the high occurrence and variation of early childhood caries (ECC) in Latin American children, which shows the need for more detailed and comprehensive surveillance efforts in this age group. The ICDAS-adapted protocol facilitates data entry and the recording of clinical observations for epidemiological studies in young children by reducing examination time. We have contributed to the standardization of ECC data collection in Latin America by using common ICDAS-adapted criteria. https://doi.org/10.1177/23800844251368372
Entropy-Based Assessment of AI Adoption Patterns in Micro and Small Enterprises: Insights into Strategic Decision-Making and Ecosystem Development in Emerging Economies
http://bit.ly/4nKrjwZ This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation. https://doi.org/10.3390/info16090770
Multivariate System Identification of Differential Drive Robot: Comparison Between State-Space and LSTM-Based Models
http://bit.ly/4nKrjwZ Modeling mobile robots is crucial to odometry estimation, control design, and navigation. Classical state-space models (SSMs) have traditionally been used for system identification, while recent advances in deep learning, such as Long Short-Term Memory (LSTM) networks, capture complex nonlinear dependencies. However, few direct comparisons exist between these paradigms. This paper compares two multivariate modeling approaches for a differential drive robot: a classical SSM and an LSTM-based recurrent neural network. Both models predict the robot’s linear (v) and angular ((Formula presented.)) velocities using experimental data from a five-minute navigation sequence. Performance is evaluated in terms of prediction accuracy, odometry estimation, and computational efficiency, with ground-truth odometry obtained via a SLAM-based method in ROS2. Each model was tuned for fair comparison: order selection for the SSM and hyperparameter search for the LSTM. Results show that the best SSM is a second-order model, while the LSTM used seven layers, 30 neurons, and 20-sample sliding windows. The LSTM achieved a FIT of 93.10% for v and 90.95% for (Formula presented.), with an odometry RMSE of 1.09 m and 0.23 rad, whereas the SSM outperformed it with FIT values of 94.70% and 91.71% and lower RMSE (0.85 m, 0.17 rad). The SSM was also more resource-efficient (0.00257 ms and 1.03 bytes per step) compared to the LSTM (0.0342 ms and 20.49 bytes). The results suggest that SSMs remain a strong option for accurate odometry with low computational demand while encouraging the exploration of hybrid models to improve robustness in complex environments. At the same time, LSTM models demonstrated flexibility through hyperparameter tuning, highlighting their potential for further accuracy improvements with refined configurations. https://doi.org/10.3390/s25185821
Beyond Quality: Predicting Citation Impact in Business Research Using Data Science
https://bit.ly/4794agG The volume of scientific publications has increased exponentially over the past decades across virtually all academic disciplines. In this landscape of information overload, objective criteria are needed to identify high-impact research. Citation counts have traditionally served as a primary indicator of scientific relevance; however, questions remain as to whether they truly reflect the intrinsic quality of a publication. This study investigates the relationship between citation frequency and a wide range of editorial, authorship, and contextual variables. A dataset of 339,609 articles indexed in Scopus was analyzed, retrieved using the search query TITLE-ABS-KEY (management) AND LIMIT-TO (subarea, “Busi”). The research employed a descriptive analysis followed by two predictive modeling approaches: a Random Forest algorithm to assess variable importance, and a binary logistic regression to estimate the probability of a paper being cited. Results indicate that factors such as journal quartile, country of affiliation, number of authors, open access availability, and keyword usage significantly influence citation outcomes. The Random Forest model explained 94.9% of the variance, while the logistic model achieved an AUC of 0.669, allowing the formulation of a predictive citation equation. Findings suggest that multiple determinants beyond content quality drive citation behavior, and that citation probability can be predicted with reasonable accuracy, though inherent model limitations must be acknowledged. https://doi.org/10.3390/publications13030042
Metaheuristic-Based PID Controller Design with MOOD Decision Support Applied to Benchmark Industrial Systems
https://bit.ly/48Zpvvp This paper presents a comprehensive methodology for the multiobjective tuning of MIMO proportional integral derivative (PID) controllers using advanced metaheuristic strategies. The proposed approach formulates a cost function based on two conflicting performance criteria—the integral of absolute error (IAE) and the integral of absolute derivative of control (IADU)—to explore the trade-off between tracking performance and control effort systematically. Three metaheuristic techniques are employed: stochastic hill climbing, a Voronoi-based heuristic, and the Nondominated Sorting Genetic Algorithm (NSGA-II). A novel Multiobjective Optimization Design (MOOD)-based classification framework is incorporated to facilitate decision making across the Pareto front. The methodology is validated on three benchmark MIMO plants, demonstrating its robustness and generalizability. The results highlight that the NSGA-II controller achieves the lowest IADU value of 0.3694 in the mass damper system while maintaining acceptable performance metrics. The inclusion of a PID-split strategy further enhances system flexibility. This study emphasizes the value of metaheuristics in navigating complex design spaces and delivering tailored control solutions for multiobjective scenarios. https://doi.org/10.3390/electronics14183630