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The article focuses on the episode of a southern swell in the Canary Islands from August 9 to 16, 2023. It examines the socioeconomic effects, characterization, and behavior of the waves, analyzing their origins, impacts, and predictive models. It mentions that the study reveals the significant impact of such events on the southern coasts of the Canary Islands, detailing their origins, characteristics, and predictive patterns.

The objective of this study is to carry out a comparative analysis with the application of two models, the Autoregressive Integrated Moving Average (ARIMA) and the Vectorial Autoregression (VAR), in the sponsoring companies of the Entidades Fechadas de Previdência Complementar, listed in Brasil, Bolsa, Balcão (B3). Through a quasi-experimental action, two models were utilized, the Autoregressive Integrated Moving Average (ARIMA) and the Vector Autoregression model (ARV). Based on the predictive results of these models, it was possible to identify that the data run by the ARIMA method did not present a good fit, it was possible to conclude that the model created by ARV was more robust in predicting future situations of the analyzed companies, and that, decisions based on predictive models, pointed to the need to anticipate decisions on the discount rate, the inflation rate, the rate of salary increase and on the guaranteeing assets. [ABSTRACT FROM AUTHOR]

This paper investigates the phenomenon of judicial reorganization in Brazil between the years 2013 to 2018, identifying key factors that explain the process of judicial reorganization. Specifically, this research answers to the following question: for the Brazilian case, what are the relevant variables to forecast judicial reorganization? Thus, it seeks to put down an existing gap, since no work has been found in the literature dealing with the forecast of judicial reorganization in Brazil. This research is justified by the following factors: relevance and growth of judicial reorganization, seen from the perspective of the Brazilian reorganization law; the scarce empirical literature that deals with judicial reorganization in Brazil; the importance of the existence of quantitative models that explain with high precision the judicial reorganization of companies in Brazil. This research advances the frontier on judicial recovery in Brazil and increases the empirical knowledge about the applicability of Brazilian Law 11.101/2005. To answer the question above, two predictive models were developed, comparing them, a Cox model and a logistic model. The results show the use and effectiveness of both methods, although highlights the superiority of the logistic model as the predictive method of judicial reorganization in Brazil, based in indicators of liquidity, profitability, indebtedness and rotativity. [ABSTRACT FROM AUTHOR]

Mango production presents challenges, such as the maturation of the mango branches, which, combined with good nutrition and biochemicals involved in this process, such as carbohydrate and starch favor the development of the plant. Therefore, the use of non-destructive, fast techniques to determine the levels of these components in the plant, such as spectroscopy, can optimize the analysis of these components. Therefore, this work aimed to develop predictive models for determination of starch and carbohydrate contents in "Palmer" mango leaves using vis-nir spectroscopy subjected to different potassium sources. The work was carried out in the region of San Francisco Valley, using the following steps: (1) leaf sampling; (2) spectral analysis; (3) lab determination of carbohydrate and starch contents; and (4) development of predictive regression and classification models. The predictive regression models used were Principal Components Regression (PCR) and Partial Least Squares Regression (PLSR). Supervised discriminant models were also developed to classify mango leaves according to different potassium sources used, using linear discriminant analysis (LDA). Vis-NIR spectroscopy showed low values for the non-destructive evaluation of "Palmer" mango leaves using PCR and PLSR for carbohydrate and starch prediction with R2 of 0.58 lower than the models considered excellent (R² >0.90); The development of classification models did not allow the discrimination of different sources of potassium in "Palmer" mango leaves with an accuracy of 64.2%. [ABSTRACT FROM AUTHOR]

Objective: To map care technologies capable of subsidizing safety in the stages of the organ transplantation process. Methods: Scope review developed in five stages through searches in six databases, between October and November 2020. Results: Among the selected publications, 2020 was the year with the highest number of studies (25.92%), the PubMed database had the largest selection of articles (37.03%) and the country with the largest number of publications was the United States (51.85%). As for the care technologies identified, 77.77% were light-hard and 22.22% were hard. Regarding the type of technology, 51.85% are digital, 22.22% machines / equipment, 14.81% predictive models and others related to protocol and robotic technology (11.11%). Conclusion: In relation to the potential of the technologies, they provide monitoring, data tracking, health education, permanent education, rapid and effective communication of organs, as well as quality in the stages of the transplant and safety processes. The impact of this study is the visualization of the panorama of technological development in health in the context of organ transplants, highlighting the areas that are being studied and the problems in this context that are being solved through health technologies. [ABSTRACT FROM AUTHOR]

We present a review of how Type Ia Supernovae (SN Ia) are used in cosmology to estimate cosmological models parameters. In particular, we discuss how they helped with the paradigm shift observed at the turn of the last century to this one: the Universe is in an accelerated expansion rate. For this, we will discuss how to delimit cosmological parameters, especially the deceleration parameter, with the definition of a type of distance in cosmology, which is calculated knowing the luminosity of astrophysical objects. We developed a software to be used with this article by teachers in their classes or orientations, as well as by students in their individual studies. The SimECosmo, acronym for Simulador para o Ensino de Cosmologia, has a user interface and allows the visualization of how cosmological parameters affect model predictions, comparing with SN Ia data from the Union 2.1 and Pantheon Sample compilations. [ABSTRACT FROM AUTHOR]

Objective: To build an evaluation predictive model of time between Human Immunodeficiency Virus diagnosis and first hospitalization. Methods: An epidemiological, descriptive, and retrospective study, with a sample of 200 medical records of people with Acquired Immunodeficiency Syndrome, monitored in a specialized service. Survival analysis was performed using the Kaplan-Meier estimator, the Weibull parametric model, and the Cox semi-parametric model. Results: There were significant differences in time between diagnosis and hospitalization regarding age, sexual orientation, therapy discontinuation records, and the number of medical and multidisciplinary consultations. The Weibull model presented the record of therapy discontinuity as a significant co-variable. In the Cox model, the occupation, alcohol and psychotropic use, T-CD4+ lymphocyte count, viral load, and psychiatric history variables were significant to explain the risk of hospitalization. Conclusion: It was possible to identify the risk and protection factors for the outcome hospitalization. The models also pointed out factors that influence the time between human immunodeficiency virus diagnosis and first hospitalization, and enabled an enriching discussion about the intrinsic aspects of health care for people living with the virus and the disease. [ABSTRACT FROM AUTHOR]

Predictive modeling for customer churn classification is a common practice among companies from different sectors, however even though it is a widely explored subject, choosing a suitable classifier may be a difficult task, given the particularities of each company and the variety of available algorithms. Therefore, the purpose of this article is to choice of an algorithm for customer churn predictive modeling in a Brazilian startup. The decision-making process used the SAPEVO-M method to obtain the weights of criteria and to define an interpretability measure for the evaluated algorithms, and the VIKOR method was used to evaluate the alternatives. After applying the methods, the classifiers trained with SVM (radial kernel) and Logistic Regression algorithms were considered the most suitable for the business model of the company in question. [ABSTRACT FROM AUTHOR]

A study of correlations was conducted to determine the main morphometric measurements of the carcass that influence the determination of the commercial cuts from the forequarter and hindquarter in Nellore heifers. Eighty-one Nellore heifers, randomly chosen form a commercial herd, were slaughtered according to the actual regulation. The animal's body weight was measured before the slaughter. After the slaughtering, the hot and cold carcasses were determined and then divided into half-carcasses and weighed after cooling. The morphometric measurements evaluated were the internal carcass length, width, perimeter and depth of leg, chest depth, ribeye area, and subcutaneous fat thickness. The half-carcasses were divided into forequarter and hindquarter to establish the commercial meat cuts. The data were analysed using Pearson's correlation (p<0,05), followed by the development of multiple linear equations using the stepwise method. The highest positive correlations (> 0.60) were found in the variable weight, as the best correlation between the cuts of the forequarter and hindquarter was with the half carcass weight. The variables related to length, width, depth, and perimeter presented low to moderate correlations (<0.40) as the ribeye area showed a moderate and positive correlation (<0.40), yet the subcutaneous fat thickness did not correlate with the commercial meat cuts. The weight of the half carcass was the first variable that participated in the multiple linear regression model for the forequarter and hindquarte, and the other tested variables did not show a high increase in the prediction model. The observed and predicted values were calculated and compared, and they were broadly similar, thus showing the high prediction of the regression equations, and it can be well applied to estimate the cuts of the forequarter and hindquarter. The most important variable to predict the commercial cuts of Nellore heifers was the half-carcass weight. [ABSTRACT FROM AUTHOR]

Knowledge of techniques to obtain information on the future production trend is essential for rural managers. Therefore, the purpose of this work was to make predictions. For that, time series models implemented in free software R of Brazilian corn production for the 2017/2018, 2018/2019 and 2019/2020 harvests were used. ARIMA (Autoregressive Integrated Moving Average) and ETS (Error, Trend, Seasonal - Error, Trend, Seasonal) methodologies were applied. Both models proved to be suitable. The model that presented the best forecast results was ARIMA, whose mean absolute percentage error of the predictions was less than that presented in the ETS model, when compared with the data reserved for verifying the predictive efficiency of the adjusted models. The results demonstrate the applicability of prediction models and computational tools that are easy to use. Such techniques aim to contribute to the decision-making and planning process by the rural manager, who has seen the corn crop in recent years, present production records and be one of the main cultivars that contributes to the Brazilian economy. [ABSTRACT FROM AUTHOR]

This work is based on a bibliometric review aiming to provide an overview of the use of geotechnology applied to plan and management in medium-sized cities, as outlining the main trends and gaps, Scopus was selected as the key database for this study, considering its global use in the academic setting. The researched key terms appear only in 2007, evidencing the lack of papers about medium cities in highperformance journals. It was highlighted predictive models of urban sprawl and assessment of the ecological safety level of cities. Major innovations have adopted multiple satellite data sources, programming validation and optimization routines of image classifying algorithms, as well as the increment of semi-automated open and open-source processing chains. Given the various applications derived from geotechnologies, the adoption of these tools by municipal secretariats is essential to replace the archaic or inefficient territorial planning and management. [ABSTRACT FROM AUTHOR]

A comparison between the rolling loads calculated through the theoretical models of Sims, Orowan-Pascoe and Ekelund is performed in order to verify the performance of the models compared to the real values recorded during the processing of plain carbon-manganese steels and niobium steels during the finish rolling in the hot strip mill. To determine the hot deformation resistance, the equations of Shida, Poliak & Siciliano, Siciliano, Marini & Bruna, Siciliano, Leduc & Hensger, Misaka and Siciliano & Jonas are used. In the direct comparison between the calculated values and the actual values the best level of precision is obtained when the load prediction models are combined with the Shida equation, respectively Sims, Orowan-Pascoe and Ekelund. In order to better adjust calculated loads to the reality of the industrial process, adjustment factors are proposed for each of the seven stands of the finishing mill. With the application of the adjustment factors, the load prediction equations presented global mean errors in the finishing mill, varying between 4.3 and 5.6% for C-Mn steels and 4.6 and 6.3% for niobium steels. [ABSTRACT FROM AUTHOR]

Objective: to trace epidemiological profile of HIV / AIDS in Montes Claros, Minas Gerais and analyze the trend line of growth of the disease. Method: it is an epidemiological, cross-sectional, descriptive-analytical study with a quantitative approach. The data were collected through DATASUS, SINAN and SIM, referring to the period between 1986 and 2016. The analysis of the predictive model was by Simple Linear Regression, for which the validation assumptions were met. Results: since the first diagnosis notified in 1986, there was a great spread of the disease, totaling 523 confirmed cases. The distribution was 59.8% (313) for men; 31.9% (167) in the 30-39-year age group; 33.7% (176) in brown-skinned; and 48.4% (256) of heterosexual sexual contact exposure. Mortality corresponds to 71.9% (23) of males; 43.8% (14) in the age range of 30-39 years. The trend curve of the epidemic denotes a progressive increase in incidence according to the function of -2227.779 + 1.122* (year). Conclusion: the data show that the HIV / AIDS epidemic is still expanding. However, since its onset, the sociodemographic profile of the disease assumes a changing character, which requires constant vigilance for adequacy of control policies. [ABSTRACT FROM AUTHOR]

The aim of the present study was to verify the relation between migraine and stress, and to identify the best predictor model for migraine, considering personal and environment variables. Migraine was identified applying the Test of the Brazilian Headache Society to 75 women without psychiatric history. Stress was identified by the Stress Symptom for Adults Inventory. Simultaneously, life events, socioeconomic level, and sample characteristics were assessed. According to the results, 55% of women presented migraine and 59% showed stress. The prediction model revealed that the presence of stress was the only predictor of the presence of migraine in women. These findings suggest an association between migraine and stress, which should be considered in the health assistance of women. [ABSTRACT FROM AUTHOR]

This study presents as objective to establish the causal relationships between culture and organizational strategies adopted by the Higher Education Institutions (HEIs) of the Rio Grande do Norte state. It was a study of the explanatory type, predominantly quantitative with the thirty-one institutions. The data were treated by means of a predictive model of canonical correlation. The results indicated the existence of an entrepreneurial cultural style and a prospective strategic outlook as the predominant type. The canonical correlation results indicated a strong relationship between the variables, so that it was possible to identify that the 77.88% linear variation of the compound (organizational strategy) of the first function was influenced by the variation of another compound (organizational culture) that make it up. Finally, it can be concluded that this study confirms the relationship between the styles of organizational culture types and organizational strategies of the HEIs. [ABSTRACT FROM AUTHOR]

Bankruptcy prediction may have great utility to financial and nonfinancial institutions with regard to take in advance the best possible decisions regarding loans or investments. In specific literature, many bankruptcy prediction models have made use of data mining. The preprocessing step is important to select good quality data for use in mining operations. Still, although the selection of attributes can be very beneficial to pre-select representative data to improve the forecast performance end, it is not known which method is the best selection. This work has as main objective to compare two approaches for evaluating subsets of attributes: Filter and Wrapper. Despite being based on data mining techniques and widely used in the step of feature selection in bankruptcy prediction models, these techniques are rarely used to treat data from financial statements of Brazilian companies. Therefore the empirical basis of this study consists of a sample of Brazilian industrial and commercial enterprises, collecting data for the period 2004 to 2011. The results indicated that, in this sample, the filter approach was more efficient, providing better classification results both for logistic regression (91,80%) and for neural networks (93,98%). It was shown also the importance of making explicit the evaluation stage of the selection of attributes for achieving better results in applications of data mining techniques to predict insolvency. A specific conclusion about the advantages of the filter approach shows that it may be preferred to assess the attributes that will make predictive models. [ABSTRACT FROM AUTHOR]

The withdrawal of artificial respiration in the hospital environment is not always easy, so this decision should be based on the study of the patient in relation to clinical criteria, which will elect him to the spontaneous breathing test. Having success in this step, therefore, absence of signs that lead to interruption of the test, the team of intensivists should then begin the weaning attempt. Despite the existence of criteria in conducting the weaning process, the clinical aspects prevail for decision making, which shows doubt in usage. The aims of this study were: (1) examine the criteria used to choose patients to spontaneous breathing trial, (2) determine the criteria that lead to interruption of the spontaneous breathing test, (3) analyze the parameters observed before extubation and (4) relate items previously described with the outcome of each patient. Data from eight patient records were collected. Eligibility criteria for spontaneous breathing were noted, the parameters used for the disruption of the spontaneous breathing trial and extubation criteria as before. Moreover, the post-extubation regarding successful weaning outcome, the High Intensive Care Unit (ICU) and hospital deaths were recorded. The majority (87.5% to 100%) were positive for election to the spontaneous breathing test criteria. A minority (12.5% to 0%) showed the presence of criteria that lead to interruption of spontaneous breathing test. From the extubated patients (50%), all presented parameters predictive of extubation (seen before extubation) that was associated with positive outcome post-extubation. Non extubated patients (50%) died during hospitalization. It was concluded that could not observe an association between the criteria used to choose patients to spontaneous breathing and those employed to interrupt the spontaneous breathing with the number of individual extubated. [ABSTRACT FROM AUTHOR]

This study examined the adequacy of the functional theory of human values in the Brazilian state of Paraiba, focusing on the content and structure hypotheses derived from the theory. The first predicts the saturation of three value-items in each of the six theorized value subfunctions. The second predicts a duplex structure for the spatial representation of values based on two specific dimensions, type of orientation (personal, central and social) and type of motivator (materialist and idealist). Participants were 12,706 individuals from the general population with a mean age of 20 years, and in their majority female (58.5%), single (38.3%) and with a high school qualification (41.8%). Confirmatory factor analysis tested the content hypothesis, assuming that six value subfunctions would account for the variance among the 18 value-items (predicted model), compared to alternative factor structures with one, two, three or five factors. As expected, the proposed model had good fit to the data (AGFI = 0.94, CFI = 0.88, and RMSEA = 0.05) and was better fitting than alternative models. The structure hypothesis was tested by means of confirmatory multidimensional scaling (Proxscal), using the Tucker Phi (φ) as an indicator of model fit. This fit indicator was above the recommended cut-off (φ = 0.94), indicating that values can be represented in a 3 (type of orientation: personal, central, or social) x 2 (type of motivator: materialist or idealist) bi-dimensional space. Even with limitations, such as the use of a convenience sample instead of a probability sample, it can be concluded that the results support the adequacy of this theory in the studied Brazilian context. [ABSTRACT FROM AUTHOR]

The frequency of frontal systems reaching the south of South America is compared with reanalysis NCEP/NCAR and results of GFDL and Hadley models for the future climate. Three regions were analyzed to identify the frontal systems, in areas of Brazil, Uruguay, Paraguay and Argentina. The following criteria were used: increase of sea surface pressure, reduction of the temperature and change of the meridional wind at 850hPa, in two consecutive days. Selected cases with temperature drop above five degrees, considered the most intense, were also analyzed. For the present climate it was observed that the models simulated well the synoptic characteristics of frontal systems. However, when comparing the frequency of frontal occurrence between reanalysis data and models simulations, the GFDL and Hadley models overestimated their numbers. The Hadley model overestimates further more than the NCEP and GFDL in present climate. The positive trend observed in area 3 (65°W-60°W, 33°S-38°S) from NCEP reanalysis is not simulated by the models. When the most intense systems are selected, the GFDL model shows higher overestimation in number of frontal systems. Both models indicate an increase of frontal systems frequency in future in the three areas, although less in area 3. [ABSTRACT FROM AUTHOR]

This study aimed at offering explanations for the differences observed in the level of disclosure of aspects related to the strategy identified in the annual reports of a sample of companies of the United States, the United kingdom, France, and Brazil. Three core theoretical elements were connected to explain the variations in the level of disclosure within the sample: cultural dimensions, legal system, and corporate governance. we built a prediction matrix based on the theoretical assumptions and the strategy disclosure was approached in each report through the technique of thematic content analysis. The sample consisted of 73 annual reports of companies of four countries in 2006. The hypotheses were tested through linear regression analysis and non-parametric tests of mean equalities. The results partially supported the hypothesis presented, which states that there is a significant influence from the cultural variables, the legal system, and the corporate governance of each country on the levels of strategy disclosure observed in the annual reports. The most significant differences are related to each country's legal system. The companies of the sample trading in the United States and the United kingdom, countries that follow a consuetudinary law (Common Law), presented higher levels of strategy disclosure than those trading in France and Brazil, countries that follow the Roman law (Civil Law). This study contributes to the literature on strategy disclosure providing empirical evidence that the legal system does influence on the quantity of strategic information revealed by the open capital companies. However, the process of manual content analysis adopted here is a constraint regarding the sample size. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the precision of the 1-RM prediction equations proposed by Adams (1994), Baechle and Groves (2000), Brzycki (1993), Epley (1985), Lander (1985) and O’Conner et al. (1989) for strength assessment in fitness programs. Thirty one healthy regular strength training male practitioners (mean ± SD: 21.8 ± 4.0 years of age; 75.9 ± 8.4 kg of weight; and 178.1 ± 6.4 cm of height) performed two tests on the bench press exercise: (a) maximum test - determination of the 1-RM load; and (b) submaximum test - determination of the load matching 4 to 10 maximum repetitions. Analysis of variance (ANOVA) found no significant difference (p > .05) between maximum load determination through prediction equations or through the 1-RM test. The coefficient of determination (r2) varied from .94 to .96. The prediction equations had small standard error of estimate (2.7 to 3.2 kg). Results indicate that the 1-RM prediction equations could be used to determine the maximum load at the bench press exercise in subjects with low strength training experience. [ABSTRACT FROM AUTHOR]

The aim of this paper is to apply a susceptibility model to high magnitude wildfires - with the latter defined as the small fraction of the total number of occurrences that causes most of the annual damage. This type of frequency/ magnitude relation is characteristic of wildfire regimes in southern European countries. Drawing on the analysis of burnt-area maps for the period 1990-2007 in the Castelo Branco district, a wildfire classification method is put forth and a model is tested using two alternative data integration techniques: one based on Logistic Regression, the other on Bayesian probability. The results indicate that the Bayesian technique has slightly greater predictive capability and confirm that the proposed model is adjusted to the behaviour of wildfires considered to be of high magnitude. Although the suggested model may usefully undergo future improvements in order to increase its predictive capability, it can already be used to complement other forms of susceptibility/ hazard analysis, by highlighting the areas that are most likely to be affected by the most destructive wildfire events. [ABSTRACT FROM AUTHOR]