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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]

Copyright of Movimento (0104754X) is the property of Movimento, da Escola de Educacao, Fisica, Fisioterapia e Danca and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Anaerobic digestion (AD) is an attractive process for bioenergy production and is considered to be an alternative way to reduce landfills. AD improves municipal solid waste (MSW) management, representing a profitable application of the circular economy and could reduce environmental impact. The methane (CH4) potential of four different organic fractions of MSW—paper (PFW), garden (GFW), food (FFW), and a mixture of these three (OFMSW)—via AD was used to investigate the energy potential and the economic and environmental impact of Campinas. Theoretical and experimental biochemical methane potential (BMP) and substrate biodegradability were determined using the Buswell and Müller equation and the VDI 4630 method. The Gompertz model was used to predict the kinetics of the biochemical processes. The highest experimental BMP (410.7 NmLCH4 gVS−1) and biodegradability (86.6%) were reached with OFMSW. OFMSW can avail an energetic potential of approximately 119 GWh year−1, with a biomethane production equivalent to diesel at 49.9 × 103 m3 year−1, hence, potentially curtailing the CO2 emissions of heavy-duty vehicles by almost 133 kt year−1. The electricity demand for approximately 11% of the households in Campinas could be met by the biogas produced by OFMSW, thus increasing local energy security. The replacement of fossil diesel with biomethane to fuel garbage trucks in Campinas could reduce 25% of the diesel demand. [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]

Air pollution levels exceeding the recommended limit can be the main cause of illnesses that affect human health, mainly diseases of the respiratory system. Consequently, this high exposure can impact public health management, given the increase in hospital admissions. One of the most influential air pollution parameters related to respiratory diseases is particulate matter (PM) concentrations. Thus, this paper proposes to estimate hospital admissions due to respiratory diseases caused by PM concentration with an aerodynamic diameter less than 10 μ m (PM 10 ), using artificial neural networks. Three hybrid neural network models are developed by combining two architectures denoted unorganized machines: extreme learning machines and echo state networks. These models also comprise extension strategies that seek to improve the generalization capability and the variation in the nonlinear outputs. Case studies explore three cities' datasets from São Paulo state, Brazil: Cubatão, Campinas, and São Paulo, to assess the quality of the hospital admissions estimations obtained by applying the proposed models. Results demonstrate that the hybrid models outperform the previously developed standard approaches in several scenarios. An overall analysis shows that the hybrid models can be a suitable strategy considering the instance particularities, especially in large datasets. [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]

Copyright of Revista Ciência & Saúde Coletiva is the property of Associacao Brasileira de Pos-Graduacao em Saude Coletiva and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Patients with liver pathology benefit from image-guided interventions. Training for interventional procedures is recommended to be performed on liver phantoms until a basic proficiency is reached. In the last 40 years, several attempts have been made to develop materials to mimic the imaging characteristics of the human liver in order to create liver phantoms. There is still a lack of accessible, reproducible and cost-effective soft liver phantoms for image-guided procedure training. Methods: Starting from a CT-scan DICOM file, we created a 3D-printed liver mold using InVesalius (Centro de Tecnologia da informação Renato Archer CTI, InVesalius 3 open-source software, Campinas, Brazil) for segmentation, Autodesk Fusion 360 with Netfabb (Autodesk software company, Fusion 360 2.0.19426 with Autodesk Netfabb Premium 2023.0 64-Bit Edition, San Francisco, CA, USA) for 3D modeling and Stratasys Fortus 380 mc 3D printer (Stratasys 3D printing company, Fortus 380 mc 3D printer, Minneapolis, MN, USA). Using the 3D-printed mold, we created 14 gelatin-based liver phantoms with 14 different recipes, using water, cast sugar and dehydrated gelatin, 32% fat bovine milk cream with intravenous lipid solution and technical alcohol in different amounts. We tested all these phantoms as well as ex vivo pig liver and human normal, fatty and cirrhotic liver by measuring the elasticity, shear wave speed, ultrasound attenuation, CT-scan density, MRI signal intensity and fracture force. We assessed the results of the testing performed, as well as the optical appearance on ultrasound, CT and MRI, in order to find the best recipe for gelatin-based phantoms for image-guided procedure training. Results: After the assessment of all phantom recipes, we selected as the best recipe for transparent phantoms one with 14 g of gelatin/100 mL water and for opaque phantom, the recipes with 25% cream. Conclusions: These liver gelatin-based phantom recipes are an inexpensive, reproducible and accessible alternative for training in image-guided and diagnostic procedures and will meet most requirements for valuable training. [ABSTRACT FROM AUTHOR]

Background: Caesarean delivery on maternal request (CDMR) is an increasing delivery option among women. As such, we aimed to understand the reasons that led pregnant women to request a caesarean delivery. Methods: A phenomenological study was conducted with semi-structured interviews, in a convenience sample, for women who had undergone a CDMR between March and June 2023, in a public reference university hospital in Campinas, Brazil. The interviews were recorded, transcribed and subjected to thematic analysis, supported by Nvivo®, and Reshape®. Results: We interviewed eighteen women between 21 and 43 years of age. The reasons for C-section as their choice were: 1) fear of labour pain, 2) fear for safety due to maternal or fetal risks, 3) traumatic previous birth experiences of the patient, family or friends 4) sense of control, and 5) lack of knowledge about the risks and benefits of C-section. Conclusions: The perception of C-section as the painless and safest way to give birth, the movement of giving voice and respecting the autonomy of pregnant women, as well as the national regulation, contribute to the increased rates of surgical abdominal delivery under request. Cultural change concerning childbirth and better counseling could support a more adequate informed decision-making about delivery mode. [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]

Copyright of Revista Chapingo Serie Horticultura is the property of Universidad Autonoma Chapingo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The detection of Giardia duodenalis and Cryptosporidium spp. was performed, along with the identification of the ciliated protozoa biodiversity, to evaluate the correlation between these protozoa in freshwater quality monitoring. Water and sediment samples from two sites in the Atibaia River (Campinas, São Paulo, Brazil) were collected monthly for 2 years (n = 96). Pathogenic protozoa in water and sediment were detected by using immunomagnetic separation, followed by visualization by immunofluorescence assay (IFA). All positive aliquots in IFA were subjected to DNA extraction and subsequently nested PCR. Qualitative (in vivo observation and silver impregnation) and quantitative (in vivo enumeration) analyses were performed for the ciliated protozoa. Giardia cysts were detected in 62.5% of the surface water samples and Cryptosporidium spp. in 25.0%. In the sediment, cysts were detected in 35.4% samples and oocysts in 16.6%. A total of 57 samples positive for Giardia cysts were subjected to sequencing, 40 of which were harboring G. duodenalis (24 were characterized as sub-assemblage AII). For ciliated protozoa, 73 taxa belonging to 53 genera were identified over the period of the study. These results revealed a high degree of contamination by waterborne protozoa in the main water source which supplies drinking water for more than one million people in Campinas (São Paulo), highlighting the need for continuous monitoring of this catchment site. In addition, the present study provides important data regarding the sources of the water body degradation, i.e., fecal contamination of human origin, in addition to the survey of the ciliated protozoa. [ABSTRACT FROM AUTHOR]

Purpose: The decrease in smell in the elderly population is frequent and considered a natural process. However, sometimes it can be associated with the decline of cognitive functions, and it is considered a warning for the early stage of neurodegenerative diseases and social impairment. Objective: To assess the prevalence of olfactory dysfunction in previous healthy elderly that attended a tertiary hospital in Brazil as escorts and the clinical alterations associated in this population. Methods: Subjects 60 years or over attending the University Hospital of Campinas were evaluated. Each participant answered a questionnaire, followed by an otorhinolaryngological exam with flexible nasal endoscopy and the Connecticut smell test produced by the Connecticut Chemosensory Clinical Research Center (CCCRC). Elderly people with nasosinusal diseases or with a history of nasal surgery were excluded. Results: Of the total of 103 participants, 16 (15.5%) reported olfactory complaints and 68 (66%) presented impairment in the olfactory test. It was observed that older individuals showed more changes in olfactory function (p = 0.001). Gender, education, lifestyle, comorbidities, medications in use and exposure to pollutants did not influence the impairment olfactory function of this population. Conclusions: There is a significant prevalence of olfactory dysfunction in the elderly population evaluated. Most of these elderlies also present an inability to identify odours, not having awareness of this olfactory impairment. [ABSTRACT FROM AUTHOR]

Land surface models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Unlike statistical models and AI models, the JULES model simulates the physical water flux under given meteorological conditions, allowing us to understand and investigate the cause and effect of environmental changes. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. The watershed is suffering increasing hydrological risks, which could be attributed to environmental changes, such as urbanization and agricultural activity. The increasing risks highlight the importance of evaluating the land surface processes of the watershed systematically. We explore the use of local precipitation collection in conjunction with data from a global meteorological reanalysis to simulate the basin hydrology. Our results show that key hydrological fluxes in the basin can be represented by the JULES model simulations. [ABSTRACT FROM AUTHOR]

This work aims to study the factors that increase the risk of death of hospitalized patients diagnosed with COVID-19 through the odd log-logistic regression model for censored data with two systematic components, as well as provide new mathematical properties of this distribution. To achieve this, a dataset of individuals residing in the city of Campinas (Brazil) was used and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The provided properties, such as stochastic representation, identifiability, and moments, among others, can help future research since they provide important information about the distribution structure. The simulation results revealed the consistency of the estimates for different censoring percentages and show that the empirical distribution of the modified deviance residuals converge to the standard normal distribution. The proposed model proved to be efficient in identifying the determinant variables for the survival of the individuals in this study, which can help to find more opportune treatments and medical interventions. Therefore, the new model can be considered an interesting alternative for future works that evaluate censored lifetimes. [ABSTRACT FROM AUTHOR]

Plants develop a series of adaptive mechanisms capable of tolerating the action of herbicides; however, little is known about the physiological mechanisms developed by cassava. The purpose of this research was to evaluate the influence of post-emergence herbicides on the physiological and anatomical characteristics of two cassava cultivars subjected to six herbicide treatments. The evaluations occurred at 0, 24, 48, 72, and 168 h after herbicide application. Herbicide application induced changes in the physiological and anatomical leaf profile. These changes were observed through the thickening of the leaf blade midrib caused by the herbicides fomesafen and fenoxaprop-p-ethyl in the Campinas cultivar. On the other hand, the leaves of the Sergipana cultivar showed a reduction in the thickness of the midrib tissues. Minor effects on cassava plants were observed with the herbicide fluazifop-p-butyl. [ABSTRACT FROM AUTHOR]

Carjacking calls for a specialized analysis because it occurs at lightning speed within very brief windows of opportunity. This article describes the geographic distribution of carjacking in Campinas, Brazil, and then compares this pattern with Detroit, Michigan. We learn that raw numbers of carjackings are widely dispersed in both cities. Although these cities differ greatly in daily transportation systems, in both cities the carjacking risk estimate is greatest where motorists stop for other reasons—at gas stations or corners (Detroit); on short street blocks (Campinas). We make very limited inferences about carjacking around the world due to the limited number of cities studied thus far. However, we do suggest that carjacking reduction is most amenable to product-based forms of situational crime prevention. [ABSTRACT FROM AUTHOR]

Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in digital images to identify and classify areas based on the socioeconomic index, in order to assist public health programs in the control of diseases linked to the Aedes aegypti mosquito. This study covers four regions of Campinas, São Paulo, characterized by different socioeconomic contexts. With mosaics of images obtained by a 12.1 MP Canon PowerShot S100 (5.2 mm focal length) carried by unmanned aerial vehicles, we developed deep learning algorithms in the scope of computer vision for the detection of water tanks and swimming pools. An object detection model, which was initially created for areas of Belo Horizonte, Minas Gerais, was enhanced using the transfer learning technique, and allowed us to detect objects in Campinas with fewer samples and more efficiency. With the detection of objects in digital images, the proportions of objects per square kilometer for each region studied were estimated by adopting a Chi-square distribution model. Thus, we found that regions with low socioeconomic status had more exposed water tanks, while regions with high socioeconomic levels had more exposed pools. Using deep learning approaches, we created a useful tool for Ae. aegypti control programs to utilize and direct disease prevention efforts. Therefore, we concluded that it is possible to detect objects directly related to the socioeconomic level of a given region from digital images, which encourages the practicality of this approach for studies aimed towards public health. [ABSTRACT FROM AUTHOR]

The rise in the productivity of sweet orange in Brazil has been related to the use of superior rootstocks and higher tree density, among other factors. In order to investigate whether the cropping system and the land use efficiency would benefit from more intensive cultivation, the performance of Valencia sweet orange was evaluated over nine years on four rootstocks, which induced contrasting vigor, at 513, 696 and 1000 trees·ha−1. Agronomic Institute of Campinas (IAC) 1697 and IAC 1710 citrandarins, and diploid and allotetraploid (4×) Swingle citrumelos were classified as semi-dwarfing, super-standard, standard, and dwarfing rootstocks, respectively. The fruit yield per tree was decreased at higher tree densities, notably for more vigorous rootstocks. Conversely, the cumulative productivity was increased over the evaluation period by 27% at 1000 trees·ha−1, irrespective of the rootstock, and the most vigorous rootstock resulted in 2.5 times higher production than the dwarfing one on average. Most fruit quality parameters were seldom influenced by the tree density, while the rootstock was a decisive factor in improving the quality and the soluble solids content. Dwarfing rootstocks allowed for harvesting 17% more fruit per minute by manual pickers. Because the tree row volume per area is lower with such rootstocks, even at higher tree density, spray volume can be reduced, although appropriate equipment should be developed for better spray coverage on smaller trees. Nine years after planting under strict vector control, the cumulative incidence of huanglongbing-symptomatic trees on IAC 1710 was double that on Swingle 4×. Taken together, the results suggested that the land use efficiency in the citrus industry can be further improved by planting vigorous rootstocks at moderate to high tree densities. Nevertheless, obtaining highly productive semi-dwarfing and dwarfing rootstocks is the sine qua non for making high-density pedestrian sweet orange orchards more profitable. [ABSTRACT FROM AUTHOR]