Search

Background: Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet. Methodology: Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings' conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas. Principal findings: PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases. Author summary: The strategies to control Ae. aegypti require intensive work and considerable financial resources, are time-consuming, and are commonly affected by operational problems requiring urgent improvement. The PCI is a good tool for identifying higher-risk areas; however, its measure requires a high amount of human and material resources, and the aforementioned issues remain. In this paper, we propose a novel approach capable of predicting the PCI of buildings based on street-level images. This first work combines deep learning-based methods with street-level data to predict facade conditions. Considering the good results obtained with PCINet and the good correlations of facade conditions with PCI components, we could use this methodology to classify building conditions without visiting them physically. With this, we intend to overcome the high cost of identifying high-risk areas. Although we have a long road ahead, our results show that PCINet could help to optimize Ae. aegypti and arbovirus surveillance and control, reducing the number of in-person visits necessary to identify buildings or areas at risk. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging may impose a substantial power demand on existing low voltage (LV) and medium voltage (MV) networks, which are usually not prepared for high power demands in short time intervals. The influx of E-mobility may require an increase in grid reinforcements, but these can be reduced and optimized by a combination of new technologies, tools, and strategies, such as the deployment of solar PV generation integrated with aggregated energy storage systems. One of the challenges in the implementation of charging infrastructures in public stations is coupling the projected sizes of energy demand and power requirements in each location for each charger. This paper describes a method to estimate projected values for energy consumption and power demand in EV fast charging stations (CS). The proposed ideas were applied in a concept facility located in Campinas, Brazil, in a structure equipped with two 50 kW DC Fast Chargers, local 12.5 kW/13.2 kWp PV generation (to reduce energy impacts to the grid), and a 100 kW/200 kWh storage system, using electrochemical batteries (to minimize peak power requirements). [ABSTRACT FROM AUTHOR]

By definition, thefts are considered the act of taking away other people's mobile possessions for personal use or for others, affecting crime rates, economic indicators and enabling recent studies to create risk zones in society, contributing to insurance pricing in actuarial methods. This paper analyzes the number of vehicle thefts of 38 locations near Campinas/S˜ao Paulo, Brazil, using a GLARMA(p,q) model with Poisson and Negative Binomial response. The main feature of GLARMA(p,q) is to consider the peculiarities of counting data as high dispersion. As a result, it was possible to verify the adequacy and usefulness of the model for counting data. With specific techniques for estimating time series related to the public security area, patterns can be better understood, revealing relevant information that can be added to decision-making processes to direct public policies. [ABSTRACT FROM AUTHOR]

The particulate matter PM 10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models' development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models' performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models' superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models' efficiency to assist the hospital admissions management during high air pollution episodes. [ABSTRACT FROM AUTHOR]