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This tutorial discusses a methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and estimation of many novel parameters and mathematically generalizes many commonly used parameters, such as the average treatment effect. Longitudinal modified treatment policies apply to a wide variety of exposures, including binary, multivariate, and continuous, and can accommodate time-varying treatments and confounders, competing risks, loss to follow-up, as well as survival, binary, or continuous outcomes. Longitudinal modified treatment policies can be seen as an extension of static and dynamic interventions to involve the natural value of treatment and, like dynamic interventions, can be used to define alternative estimands with a positivity assumption that is more likely to be satisfied than estimands corresponding to static interventions. This tutorial aims to illustrate several practical uses of the longitudinal modified treatment policy methodology, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions that can be answered using longitudinal modified treatment policies. We go into more depth with one of these examples, specifically, estimating the effect of delaying intubation on critically ill COVID-19 patients' mortality. We demonstrate the use of the open-source R package lmtp to estimate the effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial. [ABSTRACT FROM AUTHOR]

Recent advancements in high-throughput phenotyping have led to the use of drones with RGB sensors for evaluating plant traits. This study explored the relationships between vegetation indices (VIs) with grain yield and morphoagronomic and physiological traits in maize genotypes. Eight maize hybrids, including those from the UENF breeding program and commercial varieties, were evaluated using a randomized block design with four replications. VIs were obtained at various stages using drones and Pix4D Mapper 4.7.5 software. Analysis revealed significant differences in morphoagronomic traits and photosynthetic capacity. At 119 days after planting (DAP), the RGB vegetation index VARI showed a significant correlation (r = 0.99) with grain yield. VARI also correlated with female flowering (r = −0.87), plant height (r = −0.79), 100-grain weight (r = −0.77), and anthocyanin concentration (r = −0.86). PCA showed a clear separation between local and commercial hybrids, explaining 46.7% of variance at 91 DAP, 52.3% at 98 DAP, 64.2% at 112 DAP, and 66.1% at 119 DAP. This study highlights the utility of VIs in maize phenotyping and genotype selection during advanced reproductive stages. [ABSTRACT FROM AUTHOR]

Wire Arc Additive Manufacturing (WAAM) represents a disruptive technology in the field of metal additive manufacturing. Understanding the relationship between input factors and layer geometry is crucial for studying the process comprehensively and developing various industrial applications such as slicing software and feedforward controllers. Statistical tools such as clustering and multivariate polynomial regression provide methods for exploring the influence of input factors on the final product. These tools facilitate application development by helping to establish interpretable models that engineers can use to grasp the underlying physical phenomena without resorting to complex physical models. In this study, an experimental campaign was conducted to print steel components using WAAM technology. Advanced statistical methods were employed for mathematical modeling of the process. The results obtained using linear regression, polynomial regression, and a neural network optimized using the Tree-structured Parzen Estimator (TPE) were compared. To enhance performance while maintaining the interpretability of regression models, clusterwise regression was introduced as an alternative modeling technique along with multivariate polynomial regression. The results showed that the proposed approach achieved results comparable to neural network modeling, with a Mean Absolute Error (MAE) of 0.25 mm for layer height and 0.68 mm for layer width compared to 0.23 mm and 0.69 mm with the neural network. Notably, this approach preserves the interpretability of the models; a further discussion on this topic is presented as well. [ABSTRACT FROM AUTHOR]

With unprecedented and growing interest in data science education, there are limited educator materials that provide meaningful opportunities for learners to practice statistical thinking, as defined by Wild and Pfannkuch, with messy data addressing real-world challenges. As a solution, Nolan and Speed advocated for bringing applications to the forefront in undergraduate statistics curriculum with the use of in-depth case studies to encourage and develop statistical thinking in the classroom. Limitations to this approach include the significant time investment required to develop a case study—namely, to select a motivating question and to create an illustrative data analysis—and the domain expertise needed. As a result, case studies based on realistic challenges, not toy examples, are scarce. To address this, we developed the Open Case Studies (opencasestudies.org) project, which offers a new statistical and data science education case study model. This educational resource provides self-contained, multimodal, peer-reviewed, and open-source guides (or case studies) from real-world examples for active experiences of complete data analyses. We developed an educator’s guide describing how to most effectively use the case studies, how to modify and adapt components of the case studies in the classroom, and how to contribute new case studies (opencasestudies.org/OCS_Guide).

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

A critical issue for society today is the emergence and decline of democracy worldwide. It is unclear, however, how democratic features, such as elections and civil liberties, influence this change. Democracy indices, which are the standard tool to study this question, are based on the a priori assumption that improvement in any individual feature strengthens democracy overall. We show that this assumption does not always hold. We use the V-Dem dataset for a quantitative study of electoral regimes worldwide during the twentieth century. We find a so-far overlooked trade-off between election capability and civil liberties. In particular, we identify a threshold in the democratization process at which the correlation between election capability and civil liberties flips from negative to positive. Below this threshold, we can thus clearly separate two kinds of non-democratic regimes: autocracies that govern through tightly controlled elections and regimes in which citizens are free but under less certainty—a distinction that existing democracy indices cannot make.

Applied statistics is a branch of statistical science that focuses on the application of statistics in various fields. Applied statistics has an important role in helping solve various problems in various fields. The aim of this research is to determine the role of applied statistics in the fields of business, health and the environment. This study used qualitative research methods. The data collection technique in this research is literature study. The data analysis technique in this research goes through three stages, including data reduction, data presentation and drawing conclusions. The research results show that in various fields of business, medicine and the environment, statistics plays an important role. In the business sector, statistics are used to analyze company finances, plan marketing strategies, and forecast market trends. Meanwhile, in the medical world, statistics is key in designing clinical trials and developing new therapies or drugs. In the environmental realm, experts use statistics to monitor air, water and land pollution, and study climate change. The application of statistics contributes to more accurate decision making, increases the efficiency of programs, and deepens understanding of complex phenomena and problems. [ABSTRACT FROM AUTHOR]

Wire Arc Additive Manufacturing (WAAM) represents a disruptive technology in the field of metal additive manufacturing. Understanding the relationship between input factors and layer geometry is crucial for studying the process comprehensively and developing various industrial applications such as slicing software and feedforward controllers. Statistical tools such as clustering and multivariate polynomial regression provide methods for exploring the influence of input factors on the final product. These tools facilitate application development by helping to establish interpretable models that engineers can use to grasp the underlying physical phenomena without resorting to complex physical models. In this study, an experimental campaign was conducted to print steel components using WAAM technology. Advanced statistical methods were employed for mathematical modeling of the process. The results obtained using linear regression, polynomial regression, and a neural network optimized using the Tree-structured Parzen Estimator (TPE) were compared. To enhance performance while maintaining the interpretability of regression models, clusterwise regression was introduced as an alternative modeling technique along with multivariate polynomial regression. The results showed that the proposed approach achieved results comparable to neural network modeling, with a Mean Absolute Error (MAE) of 0.25 mm for layer height and 0.68 mm for layer width compared to 0.23 mm and 0.69 mm with the neural network. Notably, this approach preserves the interpretability of the models; a further discussion on this topic is presented as well.

Recent advancements in high-throughput phenotyping have led to the use of drones with RGB sensors for evaluating plant traits. This study explored the relationships between vegetation indices (VIs) with grain yield and morphoagronomic and physiological traits in maize genotypes. Eight maize hybrids, including those from the UENF breeding program and commercial varieties, were evaluated using a randomized block design with four replications. VIs were obtained at various stages using drones and Pix4D Mapper 4.7.5 software. Analysis revealed significant differences in morphoagronomic traits and photosynthetic capacity. At 119 days after planting (DAP), the RGB vegetation index VARI showed a significant correlation (r = 0.99) with grain yield. VARI also correlated with female flowering (r = −0.87), plant height (r = −0.79), 100-grain weight (r = −0.77), and anthocyanin concentration (r = −0.86). PCA showed a clear separation between local and commercial hybrids, explaining 46.7% of variance at 91 DAP, 52.3% at 98 DAP, 64.2% at 112 DAP, and 66.1% at 119 DAP. This study highlights the utility of VIs in maize phenotyping and genotype selection during advanced reproductive stages.

The empirical literature considers firm-specific aspects affecting corporate sustainability decisions but generally omits the influence of the competition. We advocate that sustainability actions of a company impact its marketplace and vice versa. Therefore, the sustainability return of the single firm is a function of the other firms' sustainability decisions. We approach sustainability decisions as strategic decisions and evaluate the effect of competition and spillovers in a static market entry game. We estimate the parameters of the discrete choice model using the social performance ratings from MSCI KLD 400 Social Index as proxy for sustainability decisions and financial information from Wharton Research Data Services' COMPUSTAT dataset. When strategic interaction is not accounted for, we find that an increase in the number of competitors increases the likelihood of sustainability investments, seemingly shows the spillover effect dominates the competition. When we apply the multi-stage approach, which incorporates competitive interaction, we provide empirical evidence that the effect of competition on the likelihood of entry into the sustainability market dominates the effect of spillover. We find that strategic motives, typically ignored in the empirical literature, appear to be an important factor in understanding sustainability-related decisions. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal trading algorithm based on a novel application of conventional control engineering (CE). We consider a fundamental CE concept, namely, the feedback control, and apply it to algorithmic trading (AT). The concrete feedback control strategy is designed in a form of the celebrated proportional–integral–derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realization of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above enables the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in financial engineering, namely, the "stock market energy" concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python-based prototype software. We finally apply the corresponding prototype software to a data set from the Binance BTC/USDT (Bitcoin/Tether) stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT. [ABSTRACT FROM AUTHOR]

ABSTRACT Pedosphere-hydrosphere interface accounts for the association between soil hydrology and landscape, represented by topographic and Remote Sensing data support and integration. This study aimed to analyze different statistical radiometric and spectral data selection methods and dimensionality reduce environment-related data to support the classification of soil physical-hydric properties, such as soil basic infiltration rate (bir) and saturated hydraulic conductivity (Ksat); as well as to act in data mining processes applied to hydropedological properties digital mapping. Accordingly, research integrated information from Visible to Infrared (VIS-IR) spectral indices and Sentinel’s 2A mission Multispectral Instrument (MSI) sensor bands, terrain numerical modeling and aerogeophysics set to model soil-water content in two soil layers (0.00-0.20 m and 0.20-0.40 m). Pre-processed data were subjected to statistical analysis (multivariate and hypothesis tests); subsequently, the methods were applied (variation inflation factor - VIF, Stepwise Akaike information criterion – Stepwise AIC, and recursive feature elimination - RFE) to mine covariates used for Random Forest modeling. Based on the results, there were distinctions and singularities in spectral and radiometric data selection for each adopted method; the importance degree, and contribution of each one to soil physical-hydric properties have varied. According to the applied statistical metrics and decision-making criteria (highest R2 and lowest RMSE / MAE), the chosen methods were RFE (0.00-0.20 m layers) and Stepwise AIC (0.20-0.40 m layers) - both concerned with the assessed variables (bir and Ksat). This approach captured the importance of environmental variables and highlighted their potential use in hydropedological digital mapping at Guapi-Macacu watershed.

This work presents a framework to assess the robustness of manufacturing systems. Robustness, which is an indicator of the system's ability to maintain its desired performance in face of disturbances, is quantified considering the variance of manufacturing system performance indicators. According to the framework, key objectives are first explicitly defined to guide a thorough exploration of the manufacturing system structural and dynamic characteristics. Several simulation experiments, orchestrated methodically through experimental design, are run and statistically analysed through analysis of variance (ANOVA) tests, including also financial implications. The framework has been tested and validated against a case study where the robustness of the manufacturing system with regard to six aerospace product types is evaluated. The mentioned case study proved that the framework has the potential to improve the robustness of manufacturing systems, identifying the most and least disruptive dispatching policies. [ABSTRACT FROM AUTHOR]

Statistical agencies are often asked to produce small area estimates (SAEs) for positively skewed variables. When domain sample sizes are too small to support direct estimators, effects of skewness of the response variable can be large. As such, it is important to appropriately account for the distribution of the response variable given available auxiliary information. Motivated by this issue and in order to stabilize the skewness and achieve normality in the response variable, we propose an area-level log-measurement error model on the response variable. Then, under our proposed modeling framework, we derive an empirical Bayes (EB) predictor of positive small area quantities subject to the covariates containing measurement error. We propose a corresponding mean squared prediction error (MSPE) of EB predictor using both a jackknife and a bootstrap method. We show that the order of the bias is O (m − 1) , where m is the number of small areas. Finally, we investigate the performance of our methodology using both design-based and model-based simulation studies. [ABSTRACT FROM AUTHOR]

The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the "big picture"--the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime, Disasters, Finance, Physical Environment, Politics, and Health and Population. We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions. [ABSTRACT FROM AUTHOR]

The pandemic caused by the COVID-19 virus has given rise to numerous analyses and studies due to the implications and serious consequences it has had on all areas of human development worldwide. The data unquestionably reflect the degree of impact it has had, not only on the mortality rate, but also on the economic indices of nations. In analysing all these indicators, the question arises as to whether some key elements, such as the number of incidences, the variables of the effective reproductive factor of the disease could better reflect the predictability of the cases and, in turn, evaluate the mitigating measures to placate the incidence of new cases. This analysis is especially significant considering that the pandemic is not over, and that more and better resolutions are still needed to address this ongoing crisis. In this context, the present study aims to analyse, from the theoretical mathematical models, what has been the contribution of this area of science to find and predict possible solutions to quell the effects of this global pandemic. For this purpose, statistical analyses based on three models will be used : non-linear phenomenological models, data modeling and the generalised logistic model, which are expected to contribute to a better evaluation and understanding of the measures taken to face this health crisis and, in the future, the importance of understanding the use of data and the technological tools available to mankind today in the face of any new virus. [ABSTRACT FROM AUTHOR]

In neuroscience research, longitudinal data are often analysed using analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) for repeated measures (rmANOVA/rmMANOVA). However, these analyses have special requirements: The variances of the differences between all possible pairs of within‐subject conditions (i.e., levels of the independent variable) must be equal. They are also limited to fixed repeated time intervals and are sensitive to missing data. In contrast, other models, such as the generalized estimating equations (GEE) and the generalized linear mixed models (GLMM), suggest another way to think about the data and the studied phenomenon. Instead of forcing the data into the ANOVAs assumptions, it is possible to design a flexible/personalized model according to the nature of the dependent variable. We discuss some advantages of GEE and GLMM as alternatives to rmANOVA and rmMANOVA in neuroscience research, including the possibility of using different distributions for the parameters of the dependent variable, a better approach for different time length points, and better adjustment to missing data. We illustrate these advantages by showing a comparison between rmANOVA and GEE in a real example and providing the data and a tutorial code to reproduce these analyses in R. We conclude that GEE and GLMM may provide more reliable results when compared to rmANOVA and rmMANOVA in neuroscience research, especially in small sample sizes with unbalanced longitudinal designs with or without missing data. [ABSTRACT FROM AUTHOR]

In this study, Ti 6Al-4 V (grade 5) ELI alloy was machined with minimum energy and optimum surface quality and minimum tool wear. The appropriate cutting tool and suitable cutting parameters have been selected. As a result of the turning process, average surface roughness (Ra), tool wear and energy consumption were measured. The results have been analyzed by normality test, linear regression model, Taguchi analysis, ANOVA, Pareto graphics and multiple optimization method. It has been observed that high tool wear value increases Ra and energy consumption. In multiple optimization, it was concluded that it made predictions with 89,1% accuracy for Ra, 58,33% for tool wear, 96,75% for energy consumption. While the feed rate was the effective parameter for Ra and energy consumption, the effective parameter in tool wear was the cutting speed. Our study has revealed that by controlling energy consumption, surface quality can be maintained and tool wear can be controlled. [ABSTRACT FROM AUTHOR]

Predictive mathematical models are a cornerstone of science and engineering. Yet selecting, calibrating, and validating said science‐based models often remains an art in practice. Model‐based design of experiments (MBDoE) provides a systematic framework to maximize information gain from experiments while minimizing time and resource costs. But MBDoE remains limited to niche application areas, in part because practitioners must integrate expertise in statistics, computational optimization, and modeling. To help reduce this barrier, we introduce Pyomo.DOE, an open‐source package for MBDoE. Pyomo.DOE uses a nonlinear sensitivity analysis code k_aug to quickly approximate the Fisher information matrix and leverages a new stochastic programming abstraction. We demonstrate Pyomo.DOE with the first application of MBDoE to fixed‐bed breakthrough experiments, which highlights the power of Pyomo.DOE to quantify the value of experimental modifications a priori for large‐scale partial differential‐algebraic equation (PDAE) models. We also provide a mathematical primer on MBDoE targeted at general chemical engineers. [ABSTRACT FROM AUTHOR]

This paper details the case-study of incorporating project-based learning through a virtual materials characterization project. This project exposes students to characterization technology. manufacturing processes, data analysis and the application statistical tools. Moreover, by utilizing the project-based learning methodology, students develop skills in teamwork, oral and written communication, and problem solving that do not come from the back-of-the-book. In the present case study, material properties of a pair of failed multipurpose crafting shears labeled as 'stainless' are characterized. The student teams are challenged with identifying the manufacturing process that was used based on the data acquired from metal spectrometer testing, Rockwell C hardness testing, and scanning electron microscope (SEM) imaging of the failure surface of the sample. Based upon the analysis ofthis data, students are tasked with identifying the type of metal alloy for the crafting shears. the manufacturing process used, and possible root cause of failure. This case study provides a pedagogical framework to bridge concepts of materials science with manufacturing methodology and statistical analysis tools as well as creative thinking and problem-solving skills. By investigating and determining the root cause of failure the students have gained a better understanding of the relationship between manufacturing process, material properties, and product quality. The outcomes could be used directly in an existing course, since all the data have been provided or it could also be adapted for different contexts by replacing the existing data with a new data set. [ABSTRACT FROM AUTHOR]

A critical issue for society today is the emergence and decline of democracy worldwide. It is unclear, however, how democratic features, such as elections and civil liberties, influence this change. Democracy indices, which are the standard tool to study this question, are based on the a priori assumption that improvement in any individual feature strengthens democracy overall. We show that this assumption does not always hold. We use the V-Dem dataset for a quantitative study of electoral regimes worldwide during the twentieth century. We find a so-far overlooked trade-off between election capability and civil liberties. In particular, we identify a threshold in the democratization process at which the correlation between election capability and civil liberties flips from negative to positive. Below this threshold, we can thus clearly separate two kinds of non-democratic regimes: autocracies that govern through tightly controlled elections and regimes in which citizens are free but under less certainty-a distinction that existing democracy indices cannot make., Competing Interests: We declare we have no competing interests., (© 2024 The Author(s).)

The benefits that mathematical sciences from statistics can generate for companies are still unknown. However, thanks to the technological advances that have taken place in recent decades, we can observe a rapprochement of business environments to this type of instrument. To analyze the most outstanding aspects that statistics offers for the management oof information at the enterprise level, the data collected after consulting research in Spanish and English are presented below. The results presented are a guide for entrepreneurs, students, and those who are considering the option of starting a business idea, to choose one of the above options as a support to achieve better results. [ABSTRACT FROM AUTHOR]

We consider a single-period single-item inventory system. The demand is a compound Poisson process with price-dependent intensity and continuous batch size distribution. The intensity of the customers’ arrivals is sufficiently high to use a diffusion approximation of the demand process. Equations for retail price maximising of an expected profit with the optimal order quantity are obtained and an approximate solution is proposed. Numerical results illustrating the percentage of the increase in profit for linear price-intensity dependence are given. An approximate distribution of the selling time of a large order is obtained. Demand parameters estimation procedures based on two censored samples – the observed selling durations and demands – are discussed. [ABSTRACT FROM AUTHOR]

In most highly competitive manufacturing industries, the sample sizes are usually very small in pilot runs, in order to quickly launch new products. However, it is always difficult for engineers to improve the quality in mass production runs based on the limited data obtained in this way. Past research has demonstrated that adding artificial samples can be an effective approach when learning with small data-sets. However, a prior analysis of the data is needed to deduce the appropriate sample distributions within which the artificial samples are generated. Johnson transformation is one of the well-known models that can be applied to bring data close to a normal distribution with the satisfaction of certain statistical assumptions. The sample size required for such data transformation methods is usually large, and this thus motivates the efforts of the current study to develop a new method which is suitable for small data-sets. Accordingly, this research proposes the small Johnson Data Transformation method to transform small raw data to normal distributions to generate virtual samples. When compared with four other methods, the results obtained with a real small data-set drawn from the Film Transistor Liquid Crystal Display industry in Taiwan demonstrate that the proposed method is able to effectively improve the forecasting ability with small sample sizes. [ABSTRACT FROM AUTHOR]

the selection of Eucalyptus genotypes appropriate for charcoal production depends on both assessing wood features and correlations among them. However, the large number of variables of wood and charcoal makes interpretation complex; thus, it is necessary using statistical techniques to analyze the cluster of variables that influence each sampling element, simultaneously. In addition, the high cost and long time required to set wood and charcoal properties makes it unfeasible to feature a large number of genotypes within a short period-of-time. Accordingly, the aims of the present study are to develop a pre-selection methodology and to recommend Eucalyptus genotypes for charcoal production, based on using cluster multivariate statistics. In total, 64 Eucalyptus genotypes from a clonal database were used in the study; their wood basic density, volume and dry mass per hectare were determined, as well as their heartwood/sapwood ratio. These parameters were used in the pre-selection procedure, which combined classification based on median, variables' individual rank, and Mulamba and Mock selection index. The determination of properties presented by wood and charcoal belonging to the pre-selected genotypes was carried out, and they were used as input variables for clustering analysis based on the Tocher method and on Unweighted Pair Group Method with Arithmetic Mean (UPGMA). The pre-classification methodology was efficient, because it allowed selecting 16 genotypes showing the desired values for the four features used in the pre-selection process. Overall, the properties of wood and charcoal belonging to the pre-selected genotypes were considered satisfactory to be used in steel production. The clustering methods were effective in solving genotype classification issues. Tocher optimization method application was much more practical, because it led to the optimum number of clusters without the need of researcher's intervention, besides the fact that clusters' compliance was similar to that assessed through the UPGMA method. It was possible concluding that genotype numbers 1954, 1453, 1923 and 4296 are the most recommended ones for charcoal production, according to the herein used analysis techniques, based on the study of mean values recorded the features of wood and charcoal in each cluster. • Charcoal production from planted-forest wood is essential for sustainable steel production. • The large amount of genetic Eucalyptus materials available makes the task of selecting the best ones complex. • The large number of quality variables set to each material also impairs the selection of the best ones. • It is essential using statistical techniques to analyze the set of variables that simultaneously influence each sampling element. • The pre-selection and cluster methods could rank the best genetic materials for charcoal production. [ABSTRACT FROM AUTHOR]

The characterization of spatial and temporal patterns of wind is essential to several sectors, including energy, urban climate, and applied meteorology. However, few studies describe the regional characteristics of the wind regime over the Brazilian Southeast (SEB), the most developed and populated part of the country. The objectives of the current work were (a) to assess the spatial patterns of the wind regime using cluster analysis (CA) and (b) to apply principal components analysis (PCA) to investigate which meteorological systems influence the spatial and temporal patterns of the wind regime. The dataset consisted of wind speed and direction from 70 automatic weather stations with records from 2008 to 2019. According to the CA method, four groups of homogeneous wind speed (G1–G4) were identified; G4 presented the highest magnitudes of wind speed (wind speed >5 m·s−1, with maxima of 7.2 m·s−1). Seasonal well‐defined minima (March–June) and maxima (July–October) were observed only for G2 and G3. The systems South Atlantic Subtropical Anticyclone (SASA), Frontal Systems (FS), and the South Atlantic Convergence Zone (SACZ) influenced these groups. In addition, the mesoscale meteorological systems likely influence other groups such as breezes from land/sea (G1, G3, and G4), lakes (G2 and G3), and valley/mountain (G2 and G3). The topography had a strong influence on G3, mainly due to the mountain ranges of Mantiqueira and Serra do Mar and Paraíba River valley. Dominant wind directions were E, N/NE, and NW, associated with SASA, in addition to S and SE/SW, influenced by FS and SACZ. The wind speed range of 1.4–3.9 m·s−1 was dominant in all groups, except for G4 (range of 2.4–5.5 m·s−1). According to PCA analysis, two PCs are enough to explain the wind pattern in the SEB with 69.9% of the total variance explained. The surface roughness accounted for 29% of the total variance explained in PC1. The latitude (37.6%) and distance to the coast (30.8%) are the most important variables for PC2. Region's physiography and meteorological systems strongly influenced the wind regime over the Brazilian Southeast. The assessment of wind regime variability presented in this work is expected to support public policies on renewable energy applications, air pollution, and climate change mitigation. [ABSTRACT FROM AUTHOR]

The application of next-generation sequencing in research and particularly in clinical routine requires highly accurate variant calling. Here we describe UVC, a method for calling small variants of germline or somatic origin. By unifying opposite assumptions with sublation, we discovered the following two empirical laws to improve variant calling: allele fraction at high sequencing depth is inversely proportional to the cubic root of variant-calling error rate, and odds ratios adjusted with Bayes factors can model various sequencing biases. UVC outperformed other variant callers on the GIAB germline truth sets, 192 scenarios of in silico mixtures simulating 192 combinations of tumor/normal sequencing depths and tumor/normal purities, the GIAB somatic truth sets derived from physical mixture, and the SEQC2 somatic reference sets derived from the breast-cancer cell-line HCC1395. UVC achieved 100% concordance with the manual review conducted by multiple independent researchers on a Qiagen 71-gene-panel dataset derived from 16 patients with colon adenoma. UVC outperformed other unique molecular identifier (UMI)-aware variant callers on the datasets used for publishing these variant callers. Performance was measured with sensitivity-specificity trade off for called variants. The improved variant calls generated by UVC from previously published UMI-based sequencing data provided additional insight about DNA damage repair. UVC is open-sourced under the BSD 3-Clause license at https://github.com/genetronhealth/uvc and quay.io/genetronhealth/gcc-6-3-0-uvc-0-6-0-441a694 [ABSTRACT FROM AUTHOR]

Bayesian inference is a powerful tool that is increasingly being used by ecologists. This is largely due to the flexibility in model specification and improvements in software that makes this tool easier to use. However, with increasing ease of use comes a risk of misuse or abuse. We review four major issues we have identified in the use of Bayesian methods and offer reminders and suggestions that will improve the application and reporting of Bayesian inference while at the same time, hopefully, avoiding the pitfalls that have plagued null hypothesis statistical testing (NHST). These issues include; 1) understanding software and model specification; 2) use of prior probability distributions; 3) maximizing utility of posterior probability distributions; and 4) avoiding dichotomous thinking (i.e., the NHST pitfall). We suggest ecologists should strive for openness in their use of statistical software by understanding their model and providing the full computer code used, develop reasonable and informative priors, and make full use of posterior information that Bayesian methods provide. At the same time, ecologists should avoid dichotomizing results into significant/non-significant boxes, eliminate null hypothesis tests (including probability intervals for hypothesis testing), and use clear language when describing results. Finally, quantitative training should be expanded in undergraduate curricula 1 JD and ZF contributed equally to this manuscript to provide students with a larger suite of foundational core concepts that extend beyond NHST. [ABSTRACT FROM AUTHOR]

The Shenzhen-Hong Kong Stock Connect (SHSC) mechanism has created the largest two-way opening of the China capital market, but it has also increased risk transmission. To analyze the impact of SHSC on the volatility of a single market in Shenzhen or Hong Kong, this paper establishes the volatility models of stock markets in Shenzhen and Hong Kong based on the Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) type models with different perturbation terms. The preapplicable test is made and the result shows that the return rate series of Shenzhen and Hong Kong stock markets are stable and heteroscedastic, and they meet the conditions of establishing the GARCH-type models. Then, the GARCH model and EGARCH model are established to analyze the volatility of stock markets in Shenzhen and Hong Kong, respectively. The results show that the opening of SHSC has increased the short-term volatility of the stock markets in Shenzhen and Hong Kong and improved the efficiency of information transmission between these two stock markets. Furthermore, the leverage effect of the Shenzhen stock market is expanding under the effect of SHSC, but the leverage effect of the Hong Kong stock market is decreasing. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, we apply and extend Valcour's model regarding the work-life balance of statisticians in Greece. This is a pilot study which examines the overall level of satisfaction regarding the meeting of work and family or personal role demand. Design/Methodology/Approach: We use survey data, collected from a questionnaire addressed to graduates from the Statistics Department of the Athens University of Economics and Business. Hierarchical Regression model and the Generalized Multiple Linear Regression model (GLM) are used to estimate an extended version of Valcour's model(2007). Findings: Results show a negative relationship between satisfaction (from balancing work and personal or family life) and total working hours, commute time, neuroticism and number of children. At the same time, the use of the GLM model strengthens Valcour's model by receiving additional information about the magnitude of the impact of changes in demographic and social factors on satisfaction. Practical Implications: The results regarding the work -life balance has managerial implcations. An organization which takes into account these findings provides a good work environment and facilities increasing the motivation of the employees. [ABSTRACT FROM AUTHOR]

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