The Aedes aegypti mosquito is the main vector of dengue and is a synanthropic insect and due to its anthropophilic nature, it has specific reproductive needs. In addition to that, it also needs tropical regions that provide climate-prone conditions that favor vector development. In this article, we propose the cross-correlation analysis between the climatic variables air temperature, relative humidity, weekly average precipitation and dengue cases in the period from 2017 to early 2021 in the municipality of Alagoinhas, Bahia, Brazil. To do so, we apply the trend-free cross-correlation, $$\rho DCCA$$ρ D C C A , being a generalization of the fluctuation analysis without trend, where we calculate the cross correlation between time series to establish the influence of these variables on the occurrence of dengue disease. The results obtained here were a moderate correlation between relative humidity and the incidence of dengue cases, and a low correlation for relative air temperature and precipitation. However, the predominant factor in the incidence of dengue cases in the city of Alagoinhas is relative humidity and not air temperature and precipitation.

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.

Analysis of a Sorter Cascade Applied to Control a Wheelchair | InTechOpen, Published on: 2016-10-19. Authors: Marcos Figueredo, Alexandre Nascimento, Roberto L.S. Monteiro, et

This article consists of a literature review that explores the intersection between neuroscience and education, highlighting how the impact of neuroscientific knowledge can influence the improvement of the educational process. Based on an analysis of the main findings of neuroscience regarding learning, attention, memory, emotion, and brain plasticity, there has been a constant and ongoing interest in recent years. This is due to the ability of these studies to provide crucial information about brain functioning and how it can affect human development and learning. This work will explore the interfaces between neuroscientific knowledge and the educational process, highlighting the importance of this information to improve the quality of education and maximize the potential of students, as well as foster a culture of lifelong learning that allows individuals to develop essential skills and competencies for the contemporary world.

This paper presents a comprehensive system of mathematical models to describe, simulate, and optimize the operational and energetic aspects of rotary furnaces within the cement industry. To achieve this, we delve into the interaction between the furnace and other aggregates within the plant. We define the key variables governing the furnace’s operation through a meticulous analysis. Leveraging the power of genetic algorithms, we successfully validate the model’s performance under static and dynamic operational conditions. A pivotal aspect of our approach involves considering the behavior of combustion gases as analogous to a piston flow system. This consideration enhances our understanding of the complex processes occurring within the furnace. Furthermore, we establish shutdown criteria based on predetermined values obtained from the Case study facility: TR (Total Runtime) of 47 minutes, GT (Gas Temperature) at 97 percent, and ℵ (Agitation Speed) at 35 percent. These predefined values align with the desired outcomes of our objective function (Z). Through integrating and implementing our findings, a promising avenue emerges for improving the final product’s fuel consumption rate and quality. By concurrently addressing the plants and furnaces efficiency indicators, we set the stage for a more sustainable and productive operation in the cement industry.

This study investigated Dunnian runoff in the Sauípe River basin, Bahia, Brazil, analyzing the relationship between soil moisture, terrain slope, and land use. It utilized Landsat satellite images, annual water balance data, and rainfall data from the last 10 days. The Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) were calculated, along with image classification using the Random Forest machine learning algorithm. (1) Saturated zones with potential for Dunnian runoff were identified, especially on steeper slopes, with a notable negative influence of eucalyptus on soil moisture, except in areas with steeper slopes. (2) Dunnian runoff was predominantly observed from the middle course to the mouth, following the east-west direction of the watershed. (3) Higher areas exhibited Dunnian runoff with high soil moisture values, while areas with less steep slopes showed low moisture levels. (4) The results indicate a positive correlation between steeper slopes and Dunnian runoff and a negative correlation between eucalyptus plantations and soil moisture. (5) Forest fragments exhibited high NDVI and NDWI values, suggesting dense forests with high moisture, especially in areas with steep slopes. This suggests that forest fragments are in good moisture conditions, acting to delay Dunnian runoff. (6) In areas with savannization or without vegetation, significant moisture content was not observed, indicating the absence of intense rainfall in the last ten days of image acquisition. This confirms the importance of this runoff for forest remnants.