Traffic Sign Recognition (TSR) is one of the many utilities made possible by embedded systems with internet connections. Through the usage of vehicular cameras, it’s possible to capture and classify traffic signs in real time with Artificial Intelligence (AI), more specifically, Convolutional Neural Networks (CNNs) based techniques. This article discusses the implementation of such TSR systems, and the building process of datasets for AI training. Such datasets include a brand new class to be used in TSR, vegetation occlusion. The results show that this approach is useful in making traffic sign maintenance faster since this application turns vehicles into moving sensors in that context. Leaning on the proposed technique, identified irregularities in traffic signs can be reported to a responsible body so they will eventually be fixed, contributing to a safer traffic environment. This paper also discusses the usage and performance of different YOLO models according to our case studies.

This article explores the structure of connections between the hospitals that are members of a hospital management innovation and learning network. This study was based on the assumption that there are limitations to encourage the communication and diffusion of knowledge between health service organizations if they are not effectively connected through social networks. Social Network Analysis was used as a strategy for monitoring the dissemination of information between hospitals. Theoretical concepts of diffusion of knowledge allowed emphasizing the role of the phenomena and communication and learning processes as the driving forces for health service innovation. The results showed weak interactions between hospitals and a lack of cohesion within the network. Therefore, there is a need for policies to promote the flow of data and information, which requires network openness to foster the exchange of innovative processes. Interactions between these hospitals in horizontal and disseminated structures have yet to be stimulated, established, incorporated, and developed by individuals, institutions and health service organizations.

This work introduces a model for lead-acid battery health monitoring in automobiles, focusing on detecting degradation before complete failure. With the proliferation of electronic modules and increasing power demands in vehicles, along with enhanced sensor data availability, this study aims to investigate battery lifespan. Dead batteries often lead to customer dissatisfaction and additional expenses due to inadequate diagnosis. This study seeks to enhance predictive diagnostics and provide drivers with timely warnings about battery health. The proposed method employs the Detrended Cross-Correlation Analysis Coefficient for end-of-life detection by analyzing the cross-correlation of voltage signals from batteries in different states of health. The results demonstrate that batteries with a good state of health exhibit a coefficient consistently within the statistically significant cross-correlation zone across all time scales, indicating a strong correlation with reference batteries over extended time scales. In contrast, batteries with a deteriorated state of health compute a coefficient below 0.3, often falling within the non-significant cross-correlation zone, confirming a clear decline in correlation. The method effectively distinguishes batteries nearing the end of their useful life, offering a low-computational-cost alternative for real-time battery monitoring in automotive applications.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by the depletion of dopamine-producing neurons in the substantia nigra, leading to motor dysfunction and alterations in speech patterns. Temporal variations in speech signals have been identified as potential biomarkers of PD, emphasizing the importance of early detection for slowing disease progression and allowing timely therapeutic interventions. This study presents an optimized Convolutional Neural Network (CNN) classifier designed for accurate and efficient PD detection. The model leverages sustained vowel phonations (“ah”) from the Parkinson’s Disease Voice Recording Data Set, which includes samples from 147 patients with PD and 48 healthy controls. Sustained vowels are widely used in clinical protocols and research because they minimize articulatory variability and highlight relevant vocal biomarkers such as jitter, shimmer, and the harmonic-to-noise ratio (HNR). The preprocessing pipeline involved denoising, feature normalization, and Bayesian hyperparameter tuning to optimize performance. The CNN achieved a detection accuracy of 96.85%, along with high precision, sensitivity, and a well-balanced F1 score, demonstrating robust predictive capabilities. These findings validate the model’s reliability in detecting PD-specific voice patterns, even in noisy conditions, underscoring its potential for integration into clinical diagnostic workflows.