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Clustering is a ubiquitous task in ecological and environmental sciences and multiple methods have been developed for this purpose. Because these clustering methods typically require users to a priori specify the number of groups, the standard approach is to run the algorithm for different numbers of groups and then choose the optimal number using a criterion (e.g., AIC or BIC). The problem with this approach is that it can be computationally expensive to run these clustering algorithms multiple times (i.e., for different numbers of groups) and some of these information criteria can lead to an overestimation of the number of groups. To address these concerns, we advocate for the use of sparsity-inducing priors within a Bayesian clustering framework. In particular, we highlight how the truncated stick-breaking (TSB) prior, a prior commonly adopted in Bayesian nonparametrics, can be used to simultaneously determine the number of groups and estimate model parameters for a wide range of Bayesian clustering models without requiring the fitting of multiple models. We illustrate the ability of this prior to successfully recover the true number of groups for three clustering models (two types of mixture models, applied to GPS movement data and species occurrence data, as well as the species archetype model) using simulated data in the context of movement ecology and community ecology. We then apply these models to armadillo movement data in Brazil, plant occurrence data from Alberta (Canada), and bird occurrence data from North America. We believe that many ecological and environmental sciences applications will benefit from Bayesian clustering methods with sparsity-inducing priors given the ubiquity of clustering and the associated challenge of determining the number of groups. Two R packages, EcoCluster and bayesmove, are provided that enable the straightforward fitting of these models with the TSB prior., (© 2021 The Ecological Society of America.)

Purpose: This paper aim to propose a methodological mapping approach for the evaluation of dissertations and theses of graduate programs in the area of environmental sciences in Brazil in relation to the UN sustainable development goals (SDGs). Design/methodology/approach: The research is characterized as exploratory with qualitative/quantitative approach. The proposed model was developed as a computational algorithm with a pilot being adopted as a professional master in national network for teaching in environmental sciences (ProfCiAmb) comprising associated courses from nine Brazilian public universities. 230 dissertations completed between 2018 and 2020 were analyzed. Findings: A total of 266 correlations were identified between the texts and descriptors of each SDG. Correlation values between 0.100 (minimum value -- Vmin) and 0.464 (maximum value -- Vmax) were observed. SDG 4 -- Quality Education (Vmax = 0.399) and SDG 6 -- Drinking Water and Sanitation (Vmax = 0.464) were those with the highest correlation values, followed by SDG 3 -- Health and Well-Being (Vmax = 0.299) and SDG 17 -- Partnerships and Means of Implementation (Vmax = 0.249). Practical implications: The construction of the computational algorithm provided consistent quantitative analyses with potential to contribute to the improvement of the multidimensional evaluation of graduate studies, as well as to support public policies related to teaching and research and strategic planning of the programs. Originality/value: The relevance of this study lies in the creation of a model that involves the creation of metrics and tools regarding the impact of graduate studies on society.

Paper aims: This article investigates the worldwide trend of growth in the number of recalls, as well as in the number of products involved in each campaign. Originality: To investigate these facts, a study of the automotive recall was developed, comprising Brazil, the European Union, and the United States of America. Research method: Due to the different availabilities between the locations, search tools and software were developed to obtain and group hidden data from 2010 to 2019. Main findings: In this work, the impacts of the recall were analyzed using three categories of algorithms: clustering, classification, and regression. Analyzes were made about the results obtained and discussions were built about the importance of applying the machine learning technique. Implications for theory and practice: The use of search tools and software to obtain and group hidden data in databases and opens the opportunity for new research in various areas. [ABSTRACT FROM AUTHOR]

Background: Brazil has consolidated a relevant position in the world market, being the largest exporter and second producer of beef. Genetics, feeding system, geographic origin and climate influence the multielement profile of beef. The feasibility of combining classification algorithms with major and trace elements was evaluated as a tool for authentication of beef cuts., Methods: Animals of Angus, Nelore and Wagyu crossbreeds, raised in a vertically integrated system, were sampled at the slaughterhouse for chuck steak, rump cap and sirloin steak. Supervised learning algorithms i.e. Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naïve Bayes (NB), Random Forest (RF) and Sequential Minimal Optimization (SMO) were used to build classification models based on the multielement profile of beef determined by neutron activation analysis., Results: Br, Co, Cs, Fe, K, Na, Rb, Se and Zn were determined in the beef samples. The classification accuracy values obtained for the beef cuts were 96% (MLP), 95% (SMO), 91% (RF), 86% (NB) and 70% (CART)., Conclusion: The Multilayer Perceptron algorithm provided the best classification performance towards authentication of beef cuts on basis of major and trace element mass fractions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier GmbH. All rights reserved.)

As the main methods of the coronavirus disease (COVID-19) transmission are air and physical contact, actions to mitigate and suppress its spread must be developed in order to change population dynamics and provide efficient control strategies. Here, these actions are described as a simple heuristic framework to establish public policies. Two control systems were studied: the first organized in the form of an algorithm stratified into three levels and the second as a minimization problem similar to optimal control strategies, applied to both social distancing and vaccination. The possible effects of these actions are modeled and applied to an extension of the Susceptible - Infected - Removed (SIR) compartmental model. The control system is developed, which is organized in the form of an algorithm stratified into three levels. These levels intend to represent social distancing strategies implemented by sanitary authorities around the globe, representing stronger or weaker grades of isolation intensity according to the ability of the healthcare system to cope with symptomatic individuals. The algorithm control is applied in a simulation, and the results give evidence of the effectiveness of the procedures adopted against the coronavirus. The model dynamics are analyzed and validated with simulations considering parameters obtained from epidemiological data from Brazil and Uruguay and in a more detailed way for three Brazilian states: São Paulo, Minas Gerais and Rio de Janeiro. The model was validated using cumulative data on cases and deaths. For cases of death, the results were satisfactory, while for case data, the response was reasonable, considering the possibility of adding delays or variations in parameters in the model. In addition, the effective reproduction number was proposed for the cities studied in Brazil, the result being relevant because it has a qualitative behavior similar to that published by official centers. This paper also discusses the implementation and optimization of social distancing and vaccination control strategies, considering different parameters and their effects on reducing the number of cases and deaths. Model simulations present promising results for developing strategies to attack COVID-19 dissemination. [ABSTRACT FROM AUTHOR]

Since the onset of the coronavirus disease, COVID-19 pandemic, concern arose for those who might be at higher risk of a worse COVID-19 prognosis, such as those with cystic fibrosis (CF). In this context, we evaluated the features of hospitalized patients with CF due to severe acute respiratory infection (SARI) in Brazil and we also performed a systematic review including all the studies published from the beginning of the first case of COVID-19 (17 November 2019) to the date of this search (23 May 2022) which included, concomitantly, patients with CF and COVID-19 in the worldwide population. In our Brazilian data, we evaluated the period from December 2019 to March 2022, and we included 33 demographical and clinical patients' features. We classified the patients into groups: (G1) SARI due to another viral infection than severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (23; 5.4%), (G2) SARI due to an unknown etiological agent (286; 67.1%), and (G3) SARI due to SARS-CoV-2 infection (117; 27.5%). The individuals in G3 tended to be older, especially over 50 years old, and presented a higher prevalence of dyspnea, peripheral capillary oxygen saturation (SpO2) <95%, and cardiopathy. The highest prevalence for intensive care unit (ICU) treatment (52; 44.4%) and invasive mechanical ventilation (29; 24.8%) was for patients in G3. Almost half of the patients in G3 died (51; 43.6%); in contrast, none in G1 died. However, we observed 43 (15.0%) deaths in G2. In addition, 12 (4.2%) and one (0.9%) death not associated with SARI occurred, respectively, in the G2 and G3. The patients who died due to SARS-CoV-2 infection had a higher frequency of SpO2 <95% (46; 90.2%), ICU treatment (34; 66.7%), and invasive mechanical ventilation (27; 52.9%) when compared to those who recovered. The systematic review comprised a total of 31 papers published as observational studies. These studies comprised 661,386 patients in total, including children, adults, and elderly age groups. However, only 19,150 (2.9%) patients were diagnosed with CF and, from these patients, 2523 (0.4%) were diagnosed with both CF and COVID-19. It was observed that the most common outcome was the need for hospitalization (n = 322 patients with CF), and the need for oxygen support (n = 139 patients with CF). One hundred patients with CF needed intensive care units, fifty patients needed non-invasive mechanical ventilation support, and only three patients were described as receiving invasive mechanical ventilation support. Deaths were described in 38 patients with CF. Importantly, lung-transplanted patients with CF represented an increased risk of death in one publication; in accordance, another study described that lung transplantation and moderate to severe lung disease were independent risk factors for severe outcomes after SARS-CoV-2 infection. In contrast with the literature, in conclusion, Brazilian patients in G3 presented a severe phenotype, even though most of the other studies did not observe worse outcomes in patients with CF and COVID-19. [ABSTRACT FROM AUTHOR]

Disease surveillance is used to monitor ongoing control activities, detect early outbreaks, and inform intervention priorities and policies. However, data from disease surveillance that could be used to support real-time decisionmaking remain largely underutilised. Using the Brazilian Amazon malaria surveillance dataset as a case study, in this paper we explore the potential for unsupervised anomaly detection machine learning techniques to discover signals of epidemiological interest. We found that our models were able to provide an early indication of outbreak onset, outbreak peaks, and change points in the proportion of positive malaria cases. Specifically, the sustained rise in malaria in the Brazilian Amazon in 2016 was flagged by several models. We found that no single model detected all anomalies across all health regions. Because of this, we provide the minimum number of machine learning models top-k models) to maximise the number of anomalies detected across different health regions. We discovered that the top three models that maximise the coverage of the number and types of anomalies detected across the thirteen health regions are principal component analysis, stochastic outlier selection, and the minimum covariance determinant. Anomaly detection is a potentially valuable approach to discovering patterns of epidemiological importance when confronted with a large volume of data across space and time. Our exploratory approach can be replicated for other diseases and locations to inform monitoring, timely interventions, and actions towards the goal of controlling endemic disease. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members. Design/methodology/approach: As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation. Findings: The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association. Research limitations/implications: This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus. Practical implications: The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA. Social implications: Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs. Originality/value: This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way. [ABSTRACT FROM AUTHOR]

Chest radiographs allow for the meticulous examination of a patient's chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing., (© 2022. The Author(s).)

Statistics show that an estimated 64 million people worldwide suffer from glaucoma. To aid in the detection of this disease, this paper presents a new public dataset containing eye fundus images that was developed for glaucoma pattern-recognition studies using deep learning (DL). The dataset, denoted Brazil Glaucoma, comprises 2000 images obtained from 1000 volunteers categorized into two groups: those with glaucoma (50%) and those without glaucoma (50%). All images were captured with a smartphone attached to a Welch Allyn panoptic direct ophthalmoscope. Further, a DL approach for the automatic detection of glaucoma was developed using the new dataset as input to a convolutional neural network ensemble model. The accuracy between positive and negative glaucoma detection, sensitivity, and specificity were calculated using five-fold cross-validation to train and refine the classification model. The results showed that the proposed method can identify glaucoma from eye fundus images with an accuracy of 90.0%. Thus, the combination of fundus images obtained using a smartphone attached to a portable panoptic ophthalmoscope and artificial intelligence algorithms yielded satisfactory results in the overall accuracy of glaucoma detection tests. Consequently, the proposed approach can contribute to the development of technologies aimed at massive population screening of the disease. [ABSTRACT FROM AUTHOR]

Brazil has an extensive coastline and Exclusive Economic Zone (EEZ) area, which are of high economic and strategic importance. A Maritime Surveillance System becomes necessary to provide information and data to proper authorities, and target tracking is crucial. This paper focuses on a multitarget tracking application to a large-scale maritime surveillance system. The system is composed of a sensor network distributed over an area of interest. Due to the limited computational capabilities of nodes, the sensors send their tracking data to a central station, which is responsible for gathering and processing information obtained by the distributed components. The local Multitarget Tracking (MTT) algorithm employs a random finite set approach, which adopts a Gaussian mixture Probability Hypothesis Density (PHD) filter. The proposed data fusion scheme, utilized in the central station, consists of an additional step of prune & merge to the original GM PHD filter algorithm, which is the main contribution of this work. Through simulations, this study illustrates the performance of the proposed algorithm with a network composed of two stationary sensors providing the data. This solution yields a better tracking performance when compared to individual trackers, which is attested by the Optimal Subpattern Assignment (OSPA) metric and its location and cardinality components. The presented results illustrate the overall performance improvement attained by the proposed solution. Moreover, they also stress the need to resort to a distributed sensor network to tackle real problems related to extended targets.

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In sucker-rod pumping wells, due to the lack of an early diagnosis of operating condition or sensor faults, several problems can go unnoticed. These problems can increase downtime and production loss. In these wells, the diagnosis of operation conditions is carried out through downhole dynamometer cards, via pre-established patterns, with human visual effort in the operation centers. Starting with machine learning algorithms, several papers have been published on the subject, but it is still common to have doubts concerning the difficulty level of the dynamometer card classification task and best practices for solving the problem. In the search for answers to these questions, this work carried out sixty tests with more than 50,000 dynamometer cards from 38 wells in the Mossoró, RN, Brazil. In addition, it presented test results for three algorithms (decision tree, random forest and XGBoost), three descriptors (Fourier, wavelet and card load values), as well as pipelines provided by automated machine learning. Tests with and without the tuning of hypermeters, different levels of dataset balancing and various evaluation metrics were evaluated. The research shows that it is possible to detect sensor failures from dynamometer cards. Of the results that will be presented, 75% of the tests had an accuracy above 92% and the maximum accuracy was 99.84%.

This paper focuses on the numerical study and precise forecast of Zika epidemic. An early warning index algorithm of Zika epidemic is given, and the applications of this algorithm are investigated. We pay attention to the mathematical approaches to estimate the tendency of Zika epidemic in detail. A numerical experiment is provided to illustrate the effectiveness of the proposed method by the numerical calculation of early warning index of Brazil. [ABSTRACT FROM AUTHOR]

Fire influences global change and tropical ecosystems through its connection to land-cover dynamics, atmospheric composition, and the global carbon cycle. As such, the climate change community, the Brazilian government, and the Large-Scale Biosphere–Atmosphere (LBA) Experiment in Amazonia are interested in the use of satellites to monitor and quantify fire occurrence throughout Brazil. Because multiple satellites and algorithms are being utilized, it is important to quantify the accuracy of the derived products. In this paper the characteristics of two fire detection algorithms are evaluated, both of which are applied to Terra’s Moderate Resolution Imagine Spectroradiometer (MODIS) data and with both operationally producing publicly available fire locations. The two algorithms are NASA’s operational Earth Observing System (EOS) MODIS fire detection product and Brazil’s Instituto Nacional de Pesquisas Espaciais (INPE) algorithm. Both algorithms are compared to fire maps that are derived independently from 30-m spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A quantitative comparison is accomplished through logistic regression and error matrices. Results show that the likelihood of MODIS fire detection, for either algorithm, is a function of both the number of ASTER fire pixels within the MODIS pixel as well as the contiguity of those pixels. Both algorithms have similar omission errors and each has a fairly high likelihood of detecting relatively small fires, as observed in the ASTER data. However, INPE’s commission error is roughly 3 times more than that of the EOS algorithm. [ABSTRACT FROM AUTHOR]

This paper propounds a new metaheuristic optimization algorithm to obtain the optimum site and size of distributed generation (DG) in radial distribution system (RDS). This problem is concomitant with reducing the active power loss, the total voltage deviation and the voltage stability index of the RDS considering different types of load models (such as constant power (CP), industrial (IL), residential (RES) as well as commercial (COM)). To solve this weighting factor-based multi-objective DG allocation problem, a novel metaheuristic optimization algorithm, student psychology-based optimization (SPBO), is suggested in this article. A multi-criteria approach (such as the analytic hierarchy process) is employed to optimize the weighting factors involved. To the best of the authors' knowledge, this is the first time that this novel SPBO algorithm is being used for optimal siting and sizing of DGs in the RDS for different types of load models (like CP, IL, RES and COM) for IEEE 33-bus, IEEE 69-bus and practical Brazil 136-bus RDS. The simulation results attained by the proposed SPBO algorithm are compared with the results provided by recently surfaced Harris hawks optimization (HHO) algorithm and other state-of-the-art algorithms. The outcomes prove that the suggested SPBO algorithm is more efficient to solve the optimal multiple DG allocation problem with minimum real power loss, less computational time and prominent convergence rate. [ABSTRACT FROM AUTHOR]

In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate. [ABSTRACT FROM AUTHOR]

Background The emergence of coronavirus disease 2019 (COVID-19) in 2020 highlighted the relevance of surveillance systems in detecting early signs of potential outbreaks, thus enabling public health authorities to act before the pathogen becomes widespread. Syndromic digital surveillance through web applications has played a crucial role in monitoring the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. However, this approach requires expensive infrastructure, which is not available in developing countries. Pre-existing sources of information, such as encounters in primary health care (PHC), can provide valuable data for a syndromic surveillance system. Here we evaluated the utility of PHC data to identify early warning signals of the first COVID-19 outbreak in Bahia-Brazil in 2020. Methods We compared the weekly counts of PHC encounters due to respiratory complaints and the number of COVID-19 cases in 2020 in Bahia State - Brazil. We used the data from December 2016 to December 2019 to predict the expected number of encounters in 2020. We analysed data aggregated by geographic regions (n = 34) and included those where historical PHC data was available for at least 70% of the population. Results Twenty-one out of 34 regions met the inclusion criteria. We observed that notification of COVID-19 cases was preceded by at least two weeks with an excess of encounters of respiratory complaints in 18/21 (86%) of the regions analysed and four weeks or more in 10/21 (48%) regions. Conclusions Coronavirus disease 2019 (COVID-19) syndromic surveillance systems based on already established PHC databases may add time to preparedness and response to emerging epidemics. [ABSTRACT FROM AUTHOR]

TUSS is a Brazilian health procedure standard used by the supplementary health providers. Currently, there is no available mapping between TUSS and other standards. In this paper, we analyze performance of two term weighting algorithms when classifying TUSS procedure description. The TF-IDF classified 99% of chapters, 89% of groups, and 33% of subgroups; Doc2Vec classified 65%, 43%, and 33%, respectively, showing that those algorithms can support creation of an accurate mapping between those procedure standards.

Avin et al (2005) showed that, in the presence of exposure-induced mediator-outcome confounding, decomposing the total causal effect (TCE) using standard conditional exchangeability assumptions is not possible even under a nonparametric structural equation model with all confounders observed. Subsequent research has investigated the assumptions required for such a decomposition to be identifiable and estimable from observed data. One approach was proposed by VanderWeele et al (2014). They decomposed the TCE under three different scenarios: (1) treating the mediator and the exposure-induced confounder as joint mediators; (2) generating path-specific effects albeit without distinguishing between multiple distinct paths through the exposure-induced confounder; and (3) using so-called randomised interventional analogues where sampling values from the distribution of the mediator within the levels of the exposure effectively marginalises over the exposure-induced confounder. In this paper, we extend their approach to the case where there are multiple mediators that do not influence each other directly but which are all influenced by an exposure-induced mediator-outcome confounder. We provide a motivating example and results from a simulation study based on from our work in dental epidemiology featuring the 1982 Pelotas Birth Cohort in Brazil. [ABSTRACT FROM AUTHOR]

Information on several papers discussed at the sixth workshop on Logic, Language, Information and Computation (WoLLIC) held held at Hotel Simon, National Park of Itatiaia, State of Rio de Janeiro, Brazil is presented. Topics include game semantics, stability and finite model theory and markov decision processes and bayesian networks.

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios. [ABSTRACT FROM AUTHOR]

Background: Mortality rate estimation in small areas can be difficult due the low number of events/exposure (i.e. stochastic error). If the death records are not completed, it adds a systematic uncertainty on the mortality estimates. Previous studies in Brazil have combined demographic and statistical methods to partially overcome these issues. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010 and forecasted probabilistic mortality rates and life expectancy between 2010 and 2030. Methods: We used a combination of the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS), Bayesian Model, Spatial Smoothing Model and an ad-hoc procedure to estimate age- and sex-specific mortality rates for all Brazilian municipalities for 2010. Then we adapted the Lee-Carter model to forecast mortality rates by age and sex in all municipalities between 2010 and 2030. Results: The adjusted sex- and age-specific mortality rates for all Brazilian municipalities in 2010 reveal a distinct regional pattern, showcasing a decrease in life expectancy in less socioeconomically developed municipalities when compared to estimates without adjustments. The forecasted mortality rates indicate varying regional improvements, leading to a convergence in life expectancy at birth among small areas in Brazil. Consequently, a reduction in the variability of age at death across Brazil's municipalities was observed, with a persistent sex differential. Conclusion: Mortality rates at a small-area level were successfully estimated and forecasted, with associated uncertainty estimates also generated for future life tables. Our approach could be applied across countries with data quality issues to improve public policy planning. [ABSTRACT FROM AUTHOR]

Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil's National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) identification has been paramount in the last years. However, it is not straightforward to obtain labelled datasets to perform a supervised NTL recognition task. In this paper, the optimum-path forest (OPF) clustering algorithm has been employed to identify irregular and regular profiles of commercial and industrial consumers obtained from a Brazilian electrical power company. Additionally, a model for the problem of NTL recognition as an anomaly detection task has been proposed when there are little or no information about irregular consumers. For such purpose, two new approaches based on the OPF framework have been introduced and compared against the well-known k -means, Gaussian mixture model, Birch, affinity propagation and one-class support vector machines. The experimental results have shown the robustness of OPF for both unsupervised NTL recognition and anomaly detection problems. In short, the main contributions of this paper are fourfold: (i) to employ unsupervised OPF for non-technical losses detection, (ii) to model the problem of NTL as being an anomaly detection task, (iii) to employ unsupervised OPF to estimate the parameters of the Gaussian distributions, and (iv) to present an anomaly detection approach based on unsupervised optimum-path forest. [ABSTRACT FROM AUTHOR]

This paper initially proposes a heuristic algorithm for the p-median problem designed for large weighted graphs. The problem is approached through the construction of p trees whose shapes are progressively modified according to successive tests over the stability of their roots and vertices. The algorithm seems promising because: (i) on a regular PC it can handle problems of the order of 500 vertices, while the mainframe version goes indefinitely further, (ii) contrary to what normally would be expected, execution times seem to be inversely proportional to p, and even for large problems, they may be reasonable, especially if p is large relative to the number of vertices, and (iii) it produces solutions of good quality and in most of the cases studied, it outperforms the traditional heuristic of Teitz and Bart. A real application of the algorithm embedded in a methodology to evaluate the location of 85 public schools, among 389 possible vertices, in the metropolitan area of Rio de Janeiro is reported. Results confirmed the conjecture of poor location and the procedure was able to identify several micro-regions simply void of schools. The methodology is being well received by the education authorities and its extension to the whole metropolitan area is being considered. [ABSTRACT FROM AUTHOR]

This paper analyzes the sea surface height dataset from the TOPEX, Jason-1, and Jason-2 satellites of a 19-yr time series in order to extract the tide harmonic constituents for the region limited by latitude 5°N-35°S and longitude 55°-20°W. The harmonic analysis results implemented here were compared with the tidal constituents estimated by three classical tidal models [i.e., TOPEX/Poseidon Global Inverse Solution 7.2 (TPXO7.2), Global Ocean Tide 4.7 (GOT4.7), and Finite Element Solution 2102 (FES2102)] and also with those extracted from in situ measurements. The Courtier criterion was used to define the tide regimes and regionally they are classified as semidiurnal between the latitude range from approximately 5°N to 22°S, semidiurnal with diurnal inequality from 22° to about 29°S, and mixed southward of latitude 22°S. The comparison results among all tide approaches were done by analyzing the root-sum-square misfit (RSSmisfit) value. Generally, the RSSmisfit difference values are not higher than 12 cm among them in deep-water regions. On the other hand, in shallow water, all models have presented quite similar performance, and the RSSmisfit values have presented higher variance than the previous region, as expected. The major discrepancy results were particularly noted for two tide gauges located in the latitude range from 5°N to 2°S. The latter was investigated and conclusions have mainly pointed to the influence of the mouth of the Amazon River and the considerable distance between tide measurements and the satellite reference point, which make it quite hard to compare those results. In summary, the results have showed that all models presently generate quite reliable results for deep water; however, further study should done in order to improve them in shallow-water regions too. [ABSTRACT FROM AUTHOR]

This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computa-tional phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (Î,) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India. [ABSTRACT FROM AUTHOR]

There are many corneal diseases that can be detected using an eye-care device called videokeratograph. The videokeratograph is based on the principle of an apparatus called Placido disc and is used to precisely measure the anterior surface of the cornea. This disc contains rings alternately white and black, which are reflected on the patient's cornea during the examination. The device can find anomalies by analyzing the reflected image, using image-processing algorithms. Although the efficiency of most commercial videokeratographs is acceptable, manufacturers do not disseminate to the scientific community the technique used in the image analysis algorithms. This makes it difficult for the specialized researcher in order to find better algorithms for the image-processing and, consequently, increase the instrument's precision. In this work we have segmented the Placido disc in polar coordinates by implementing a diagonal section of the image, in the radial direction. The objective is to find the inflection points in the signal obtained. In this paper the signal is studied by using the Mumford-Shah segmentation method. The results are compared to those obtained with other classic methods in the literature, e.g. Marr-Hildreth filters, numerical derivative, Fourier derivative, morphological Laplacian and Canny derivative. The best result was achieved by using the Mumford-Shah functional. Using this technique it was possible to find the inflection positions with higher accuracy. The method did not detect any false inflection. Mumford-Shah's method demonstrated also a high precision in the task of eliminating noises from the original signal.

This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that is using this innovative system, the algorithm automatically forecasts the number of SOs that will need to be executed in each location in several time steps (hourly, monthly and yearly basis). The forecasted emergency SOs demand, which is related to energy outages, are stochastically distributed, projecting the impacted consumers and its individual interruption indexes. This spatio-temporal forecasting is the main input for a web-based platform for optimal bases allocation, field team sizing and scheduling implemented in the eleven distribution utilities of Energisa group in Brazil. [ABSTRACT FROM AUTHOR]

Spatial patterns in biodiversity variation at a regional scale are rarely taken into account when a natural reserve is to be established, despite many available methods for determining them. In this paper, we used dimensions of occurrence of 105 species of Anura (Amphibia) in the cerrado region of central Brazil to create a regional system of potential areas that preserves all regional diversity, using three different algorithms to establish reserve networks: "greedy", rarity, and simulated annealing algorithms. These generated networks based on complementarity with 10, 12, and 8 regions, respectively, widely distributed in the biome, and encompassing various Brazilian states. Although the purpose of these algorithms is to find a small number of regions for which all species are represented at least once, the results showed that 67.6%, 76.2%, and 69.5% of the species were represented in two or more regions in the three networks. Simulated annealing produced the smallest network, but it left out three species (one endemic). On the other hand, while the greedy algorithm produce a smaller solution, the rarity-based algorithm ensured that more species were represented more than once, which can be advantageous because it takes into consideration the high levels of habitat loss in the cerrado. Although usually coarse, these macro-scale approaches can provide overall guidelines for conservation and are useful in determining the focus for more local and effective conservation efforts, which is especially important when dealing with a taxonomic group such as anurans, for which quick and drastic population declines have been reported throughout the world.

This paper presents a data mining study and cluster analysis of social data obtained on small producers and family farmers from six country cities in Ceará state, northeast Brazil. The analyzed data involve demographic, economic, agriculture and food insecurity information. The goal of the study is to establish profiles for the small producer families that reside in the region and to identify relevant features which differentiate these profiles. Moreover, we provide an efficient data mining methodology for analysis of social data sets which is capable of handling its natural challenges, such as mixed variables and abundance of null values. We use the Silhouette method for the estimation of the best number of natural groups within the data, along with the Partitioning Around Medoids clustering algorithm in order to compute the profiles. The Correlation-Based Feature Selection method is used to identify which social criteria are the most important to differentiate the families from each profile. Classification models based on support vector machines, multilayer perceptron and decision trees were developed aiming to predict in which of the identified clusters an arbitrary family would be best fit. We obtained a good separation of the families into two clusters, and a multilayer perceptron model with approximately 93.5% prediction accuracy. [ABSTRACT FROM AUTHOR]

Sequential simulation is an extensively used algorithm for geostatistical simulations, especially the sequential Gaussian algorithm. This algorithm assumes a multivariate Gaussian model. This assumption is very convenient as it allows the local uncertainty model or conditional cumulative distribution function (ccdf) to be inferred through a few parameters given by simple kriging. The set of simulated values are drawn through Monte Carlo methods, randomly sampling the ccdf L times. In theory, this method characterises the space of uncertainty as the number of realisations increases. In practice, the number of realisations necessary varies according to the characteristics of the conditioning data. It is important that the L simulations appropriately describe the space of the uncertainty according to the objective addressed. However, in some situations, the number of simulations needs to be large, making the procedure computationally intense and time-consuming. This paper presents a more efficient strategy to generate the local ccdf based on the Latin Hypercube Sampling (LHS) technique. The objective is to replace the Monte Carlo simulation method by the LHS in order to improve the efficiency of the sequential Gaussian simulation algorithm. The use of the modified algorithm has shown that the space of uncertainty related to the random variable being modelled was obtained faster than by traditional Monte Carlo simulation for a given degree of precision. This approach also ensures that the ccdf is better represented in its entirety. This is illustrated using a case study from a Brazilian iron ore deposit. [ABSTRACT FROM AUTHOR]

The Brazilian government annually assesses the extent of deforestation in the Legal Amazon for a variety of scientific and policy applications. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less data-intensive assessment of annual deforestation using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution is evaluated. Landsat-derived deforestation estimates are compared to MODIS-derived estimates for six Landsat scenes with five change-detection algorithms and a variety of input data—Surface Reflectance (MOD09), Vegetation Indices (MOD13), fraction images derived from a linear mixing model, Vegetation Cover Conversion (MOD44A), and percent tree cover from the Vegetation Continuous Fields (MOD44B) product. Several algorithms generated consistently low commission errors (positive predictive value near 90%) and identified more than 80% of deforestation polygons larger than 3 ha. All methods accurately identified polygons larger than 20 ha. However, no method consistently detected a high percent of Landsat-derived deforestation area across all six scenes. Field validation in central Mato Grosso confirmed that all MODIS-derived deforestation clusters larger than three 250-m pixels were true deforestation. Application of this field-validated method to the state of Mato Grosso for 2001–04 highlighted a change in deforestation dynamics; the number of large clusters (>10 MODIS pixels) that were detected doubled, from 750 between August 2001 and August 2002 to over 1500 between August 2003 and August 2004. These analyses demonstrate that MODIS data are appropriate for rapid identification of the location of deforestation areas and trends in deforestation dynamics with greatly reduced storage and processing requirements compared to Landsat-derived assessments. However, the MODIS-based analyses evaluated in this study are not a replacement for high-resolution analyses that estimate the total area of deforestation and identify small clearings. [ABSTRACT FROM AUTHOR]

Background: Timely detection of cleaning failure is critical for quality assurance within Sterilising Service Units (SSUs). Rapid Adenosine Triphosphate (ATP) testing provides a real time and quantitative indication of cellular contaminants, when used to measure surface or device cleanliness. The aim of this study was to investigate the use of an ATP algorithm and to whether it could be used as a routine quality assurance step, to monitor surgical instruments cleanliness in SSUs prior to sterilisation. Methods: Cleanliness monitoring using rapid ATP testing was undertaken in the SSUs of four hospitals located in the western (Amazonia) region of Brazil. ATP testing was conducted (Clean Trace, 3M) on 163 surgical instruments, following manual cleaning. A sampling algorithm using a duplicate swab approach was applied to indicate surgical instruments as (i) very clean, (ii) clean, (iii) equivocal or (iv) fail, based around a 'clean' cut-off of 250 Relative Light Units (RLU) and a 'very clean' <100 RLU. Results: The four cleanliness categories were significantly differentiated (P≤0.001). The worst performing locations (hospitals A & C) had failure rates of 39.2% and 32.4%, respectively, and were distinctly different from hospitals B & D (P≤0.001). The best performing hospitals (B & D) had failure rates of 7.7% and 2.8%, respectively. Conclusion: The ATP testing algorithm provides a simple to use method within SSUs. The measurements are in real time, quantitative and useful for risk-based quality assurance monitoring, and the tool can be used for staff training. The four-tiered approach to the grading of surgical instrument cleanliness provides a nuanced approach for continuous quality improvement within SSU than does a simple pass/fail methodology. [ABSTRACT FROM AUTHOR]

Spatial analysis of epidemiological data can be a useful tool for identifying patterns of disease occurrence and can provide substantial support for prevention and control strategies. To obtain the greatest spatial resolution, it is important to use the smallest available areal units with homogeneous population. However, small areas usually have a small population, introducing spurious variability in the chosen indicators of disease occurrence. This paper describes an approach for combining small geographical units to stabilize mortality rates by pooling information across areas according to specified risk profiles. The procedure is based on a principal component analysis, followed by a cluster analysis of social-economic indicators to classify the risk profile of each small area. The classification is used in an algorithm to join neighbouring areas with similar profiles until an estimated population size is achieved. We applied this method to two Administrative Regions of the city of Rio de Janeiro, Brazil, using the census tracts as the basic areal unit. Census tracts were classified according to four socioeconomic categories distributed spatially as a mosaic, where tracts of differing categories neighbour each other. The aggregation algorithm produced a new partition of the region studied, with the created areal units preserving the internal socioeconomic homogeneity.

Over the years, electricity consumption behavior in Brazil has been analyzed due to financial and social problems. In this context, it is important to simulate energy prices of the energy efficiency auctions in the Brazilian electricity market. The Markov Chain Monte Carlo (MCMC) method generated simulations; thus, several samples were generated with different sizes. It is possible to say that the larger the sample, the better the approximation to the original data. Then, the Kernel method and the Gaussian mixture model used to estimate the density distribution of energy price, and the MCMC method were crucial in providing approximations of the original data and clearly analyzing its impact. Next, the behavior of the data in each histogram was observed with 500, 1000, 5000 and 10,000 samples, considering only one scenario. The sample which best approximates the original data in accordance with the generated histograms is the 10,000th sample, which consistently follows the behavior of the data. Therefore, this paper presents an approach to generate samples of auction energy prices in the energy efficiency market, using the MCMC method through the Metropolis–Hastings algorithm. The results show that this approach can be used to generate energy price samples. [ABSTRACT FROM AUTHOR]

The data about high students' failure rates in introductory programming courses have been alarming many educators, raising a number of important questions regarding prediction aspects. In this paper, we present a comparative study on the effectiveness of educational data mining techniques to early predict students likely to fail in introductory programming courses. Although several works have analyzed these techniques to identify students' academic failures, our study differs from existing ones as follows: (i) we investigate the effectiveness of such techniques to identify students likely to fail at early enough stage for action to be taken to reduce the failure rate; (ii) we analyse the impact of data preprocessing and algorithms fine-tuning tasks, on the effectiveness of the mentioned techniques. In our study we evaluated the effectiveness of four prediction techniques on two different and independent data sources on introductory programming courses available from a Brazilian Public University: one comes from distance education and the other from on-campus. The results showed that the techniques analyzed in our study are able to early identify students likely to fail, the effectiveness of some of these techniques is improved after applying the data preprocessing and/or algorithms fine-tuning, and the support vector machine technique outperforms the other ones in a statistically significant way. [ABSTRACT FROM AUTHOR]

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Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007–2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities. [ABSTRACT FROM AUTHOR]

Background: Maternal mortality remains high, despite all the advances and efforts that have occurred in recent years and is directly related to the quality of care provided during pregnancy, childbirth, and in the puerperium. Purpose: Identify the possible nursing diagnoses of mothers of newborns admitted to neonatal intensive care in the prepartum period, childbirth, and the puerperium. Methods: Observational, cross‐sectional study carried out by analyzing the medical records of mothers of newborns who required hospitalization in neonatal intensive care and determining the diagnoses through a process of diagnostic inference, based on NANDA‐I Taxonomy, during the period from 2007 to 2016. Findings: After reviewing the medical records of 272 mothers, a total of 3,843 observations were identified, distributed in 42 diagnoses, with Excessive fluid volume presenting the greatest predictive power over the outcome variable. The second group of more predictive variables comprised the Risk of vascular trauma, Risk of infection, Impaired parenthood, and Risk of body temperature imbalance. The third group, in turn, consisted of the following diagnoses: Insomnia, Pain during labor, Risk of impaired bonding, Acute pain, and Ineffective Breastfeeding. Conclusions: The present study enabled the identification of the most frequent NANDA‐I nursing diagnoses occurring during the prepartum period, childbirth, and the immediate puerperium of mothers of newborns admitted to neonatal intensive care, as well as those of greatest importance. Based on the encountered diagnoses, it can be concluded that future studies should be carried out in order for validation. Implications: The knowledge of these diagnoses can contribute to the instrumentalization of nursing professionals, with consequent improvements in the nursing process and the provided assistance, aiming to assist in the reduction of maternal morbidity and mortality. [ABSTRACT FROM AUTHOR]

This work presents the evolution of the main interarea modes of the Brazilian Interconnected Power System (BIPS) in the 2020–2024 horizon with high levels of wind and solar generation in the Northeast region of Brazil. The software PacDyn is chosen for the simulations using the Dominant Pole Spectrum Eigensolver algorithm to help identify the modes of interest. Besides that, the impacts of the expansion of BIPS's interconnections on the interarea modes are studied. The results showed that the main interarea modes of the BIPS are sufficiently damped. [ABSTRACT FROM AUTHOR]

In the framework of forensic anthropology osteometric techniques are generally preferred over visual examinations due to a higher level of reproducibility and repeatability; qualities that are crucial within a legal context. The use of osteometric methods has been further reinforced by incorporating statistically-based algorithms and large reference samples in a variety of user-friendly software applications. However, the continued increase in admixture of human populations have made the use of osteometric methods for estimation of ancestry much more complex, which confounds one of major requirements of ancestry assessment - intra-population homogeneity. The present paper tests the accuracy of ancestry and sex assessment using four identification software tools, specifically FORDISC 2.0, FORDISC 3.1.293, COLIPR 1.5.2 and 3D-ID 1.0. Software accuracy was tested in a sample of 174 documented human crania of Brazilian origin composed of different ancestral groups (i.e., European Brazilians, Afro-Brazilians, and Japanese Brazilians and of admixed ancestry). The results show that regardless of the software algorithm employed and composition of the reference database, all methods were able to allocate approximately 50% of Brazilian specimens to an appropriate major reference group. Of the three ancestral groups, Afro-Brazilians were especially prone to misclassification. Japanese Brazilians, by contrast, were shown to be relatively easily recognizable as being of Asian descent but at the same time showed a strong affinity towards Hispanic crania, in particularly when the classification based on FDB was carried out in FORDISC. For crania of admixed origin all of the algorithms showed a considerable higher rate of inconsistency with a tendency for misclassification into Asian and American Hispanic groups. Sex assessments revealed an overall modest to poor reliability (60-71% of correctly classified specimens) using the tested software programs with unbalanced individual rates for males and females. The highest and atypically balanced rate of classification for sex assessment was provided by COLIPR software, which reached 78% of correctly assessed crania. [ABSTRACT FROM AUTHOR]

This work uses high-quality 3D seismic data to assess the importance of mass-transport deposits (MTDs) as markers of fault propagation. We mapped three distinct MTDs and several fault families on the continental slope of Espírito Santo, SE Brazil. Fault mapping was based on seismic attributes such as seismic coherence and structural smoothing, and was further completed using ant tracking algorithms. Genetically related fault families were analysed in terms of their throw-depth (t-z) and throw-distance (t-x) gradient curves. A key result in this paper is that vertical fault propagation can be hindered by MTDs, as demonstrated for Eocene to Early Miocene faults in parts of the study area. Throw-depth variations in faults affected by MTDs are associated with: a) lithologic controls resulting from the presence of MTDs, b) local fault segmentation and c) reactivation by dip linkage. Based on their orientation and degree of interaction with MTDs, interpreted faults can be classified as decoupled and non-decoupled. Importantly, faults decoupled by MTDs have quasi-elliptical t-x profiles and show smaller cumulative throw values and fault propagation rates when compared to their non-decoupled counterparts. Recurrent MTDs can therefore be used as markers to estimate structural decoupling between distinct fault families. [ABSTRACT FROM AUTHOR]

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. • An efficient Multi-objective Evolutionary Swarm Hybrid algorithm is proposed. • Development of a nonlinear model to operational control of Hydro-power plants. • Hydro-power plant data regression obtains the maximum efficiency of the power units. • Efficiency energy goals achieved an increasing the profit in the energy production. [ABSTRACT FROM AUTHOR]

Abstract: This paper shows that memory-based learning (MBL) is a very promising approach to deal with complex soil visible and near infrared (vis–NIR) datasets. The main goal of this work was to develop a suitable MBL approach for soil spectroscopy. Here we introduce the spectrum-based learner (SBL) which basically is equipped with an optimized principal components distance (oPC-M) and a Gaussian process regression. Furthermore, this approach combines local distance matrices and the spectral features as predictor variables. Our SBL was tested in two soil spectral libraries: a regional soil vis–NIR library of State of São Paulo (Brazil) and a global soil vis–NIR library. We calibrated models of clay content (CC), organic carbon (OC) and exchangeable Ca (Ca++). In order to compare the predictive performance of our SBL with other approaches, the following algorithms were used: partial least squares (PLS) regression, support vector regression machines (SVM), locally weighted PLS regression (LWR) and LOCAL. In all cases our SBL algorithm outperformed the accuracy of the remaining algorithms. Here we show that the SBL presents great potential for predicting soil attributes in large and diverse vis–NIR datasets. In addition we also show that soil vis–NIR distance matrices can be used to further improve the prediction performance of spectral models. [Copyright &y& Elsevier]

Abstract: This work presents new results of the Brazilian transportation vehicle MagLev-Cobra. This technology proposes a magnetically levitated vehicle composed of small units of 1.5 m length, allowing curves with minimal radius of 50 m, ramps of 10% and velocities up to 70km/h. When these short units are connected, the vehicle resembles a snake or rsquo;cobra’ in Portuguese. Since there is no contact between the vehicle and the magnetic rail, the noise level is low. Also, the load is distributed along the vehicle and not concentrated on single points of contact as in a conventional wheel and rail transportation system, which results in lower mechanical moment and lighter civil engineering constructions. These factors make MagLev-Cobra ideal to run on elevated structures inside cities. The estimated construction costs are 1/3 of that necessary for subways. The levitation technology is based on the flux pinning property of Y-Ba-Cu-O blocks and the magnetic field of Nd-Fe-B magnets. The present paper gives some construction details and describes new measurements of a full scale prototype. The rail of permanent magnets was reached based on optimization algorithms and several quasi-static measurements are reported here. [Copyright &y& Elsevier]

Abstract: This paper aims to present a heuristic algorithm with factor analysis and a local search optimization system for pattern identification problems as applied to large and multivariate aero-geophysical data. The algorithm was developed in MATLAB code using both multivariate and univariate methodologies. Two main analysis steps are detailed in the MATLAB code: the first deals with multivariate factor analysis to reduce the problem of dimension, and to orient the variables in an independent and orthogonal structure; and the second with the application of a novel local research optimization system based on univariate structure. The process of local search is simple and consistent because it solves a multivariate problem by summing up univariate and independent problems. Thus, it can reduce computational time and render the efficiency of estimates independent of the data bank. The aero-geophysical data include the results of the magnetometric and gammaspectrometric (TC, K, Th, and U channels) surveys for the Santa Maria region (RS, Brazil). After the classification, when the observations are superimposed on the regional map, one can see that data belonging to the same subspace appear closer to each other revealing some physical law governing area pattern distribution. The analysis of variance for the original variables as functions of the subspaces obtained results in different mean behaviors for all the variables. This result shows that the use of factor transformation captures the discriminative capacity of the original variables. The proposed algorithm for multivariate factor analysis and the local search system open up new challenges in aero-geophysical data handling and processing techniques. [Copyright &y& Elsevier]

Abstract: Underground distribution systems are normally exposed to permanent faults, due to specific construction characteristics. In these systems, visual inspection cannot be performed. In order to enhance service restoration, accurate fault location techniques must be applied. This paper describes an extended impedance-based fault location algorithm for underground distribution systems. The formulation is developed on phase frame and calculates the apparent impedance using only local voltage and current data. The technique also provides an iterative algorithm to compensate the typical capacitive component current of underground cables. Test results are obtained from numerical simulations using a real underground distribution feeder data from the Electrical Energy Distribution State Company of Rio Grande do Sul (CEEE-D), southern Brazil. Comparative results show the techniques accuracy and robustness in respect to fault type, distance and resistance. [Copyright &y& Elsevier]

In this paper we evaluate the suitability of the spectral unmixing algorithm to map the turbidity in the Curuai floodplain lake and enhance its applicability using autocorrelation modelling. The Spectral Unmixing Model (SMM) was applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance (MOD09) image, taking in-situ measurements close to the acquisition date. Fraction images of inorganic matter-laden water, dissolved organic matter-laden water, and phytoplankton-laden water were generated by SMM, using 4 MODIS spectral bands (blue, green, red, and near infrared). These endmembers were selected based on the dominance of these components, which affect water turbidity. These fraction images allowed assessing the turbidity distribution in the study area but showing only places with high or low turbidity. The kernel estimation algorithm was then used to verify the spatial correlation among the in-situ measurement data. The occurrence of clusters suggests that there are different spatial water regimes. One spatial regression model was then compiled for each water regime, each of which presented a better turbidity estimation as opposed to the one derived from the Ordinary Least Square (OLS). The methodology applied was hence useful to analyze the spatial distribution of turbidity in the Curuai floodplain lake. [Copyright &y& Elsevier]