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Technical efficiency in banking is a critical aspect of the financial industry and has been widely studied using various measurement techniques. This systematic literature review offers a comprehensive examination of 305 studies on the application of technical efficiency measurement techniques in both Islamic and conventional banking sectors from 1989 to 2019. Our comprehensive analysis not only provides a broad view of the efficiency measurement literature but also outlines a future research agenda. Despite the extensive research in this field, several issues remain unresolved, including input–output selection, a comparison of efficiency between Islamic and conventional banks, limited cross-country studies, and a lack of exploration into the impact of regulation and Shariah principles. To address these gaps, this review highlights the most commonly used methods, variables, and findings and provides three key recommendations for future research. Three key themes emerge from our examination. First, there is a need to better understand and the application of new frontier techniques other than the traditional methods, which currently dominate the existing literature. Second, the intermediation approach is the most frequently used in variable selection, thus more studies with comparative findings with applications of production and value-added approaches are suggested. Third, the most frequently used input variables are 'labor', 'deposits' and 'capital', whilst 'loans' and 'other earning assets' are the most popular output variables. We recommend three vital directions for future research: (i) non-interest expenses to be included amongst the inputs, while non-interest income should be added to the list of outputs, especially when estimating efficiency scores of Islamic banks. (ii) The impact of environmental variables such as, inter alia, Shariah principles, country-specific factors, and management quality is suggested to be considered simultaneously in models measuring and comparing the efficiency of Islamic and conventional banks. (iii) The selection of performance metrics employed should be expanded to include both the standard efficiency scores and the Malmquist Total Factor Productivity Index (TFP). The paper concludes with research needs and suggests directions for future research. [ABSTRACT FROM AUTHOR]

Background & Objective: Investigating the stability and compatibility of a genotype in different environmental conditions in order to introduce it for planting in specific and known environmental conditions in multi-breeding programs is one of the basic needs and to achieve sustainable production in order to achieve sustainable self-sufficiency in It is necessary to produce strategic products, especially wheat. Methods & Materials: In the present study, in order to investigate the stability and identify high-yielding and compatible genotypes, the yield of 32 lines of recombinant inbred from the crossing of two cultivars Roshan and Superhead in the form of a randomized complete block design with three replications in six The region (Tabriz, Ahar, Ardabil, Faghan, Shabestar and Urmia) were investigated for two years. Results: The results of the present study showed that there was a significant difference between the locations in terms of performance. Also, the difference between genotypes, as well as the interaction effect of genotype × year, genotype × location, and the interaction effect of genotype × year × location were significant, which indicated the difference between the performance of genotypes in different environments, which indicated the necessity of stability analysis. Conclusion: The results of analysis of simple and compound variances showed a significant difference between the lines in terms of grain yield. Comparing the average of all the lines, lines No. 3, 12, 38, 42, 47, 95 and Roshan had the highest yield and the lowest average seed yield was related to line 51 with 3.023 tons per hectare. Line 90 showed good general compatibility compared to other lines. Lines No. 3, 38, 42, 95, their regression coefficient was less than one and the deviation from their regression line was higher. One of the practical aspects of this research is the identification of promising lines for carrying out further breeding studies in order to release the variety and introduce it to farmers. According to the relative alignment of the results obtained from different methods, it can be stated that in this research, lines 10, 12, 31 and the Roshan variety were recognized as the most stable genotypes and line 51 as the most unstable line and It was recognized as special for unfavorable areas. [ABSTRACT FROM AUTHOR]

Perceived pain is a multi-factorial subjective variable, commonly measured by numeric rating scales, verbal descriptive scales (VDS), or by a position on an analogue line (VAS). A major question is whether an individual’s VAS and VDS pain assessments, on the same occasion, could be comparable. The aim was to compare continuous and discretized VAS pain data with verbal descriptive pain datasets from the Oswestry Disability Index (ODI) and the European Quality of Life Scale (EQ-5D) in paired pain datasets.

Human activities and the climate change affects the river flow therefore monitoring flow rate of river for an extended period can reveal the detail of involved mechanisms in these changes. The previous studies show impact of human activities and climate change on river temporal variations varies in different locations. Water scarce is one of most problem in this area therefore finding affected parameters in water accessibility is important for water management in Middle East. This study aimed to investigate the trend of annual and monthly flow changes in the Dez River branches in southwestern Iran by several nonparametric methods. A structural equation model was used to assess the effects of land use and climate changes on river discharge. The study results showed that the annual precipitation at all stations has no significant trend, but temperature and evaporation at most stations increased significantly. Additionally, more than 30% of the study area's rangeland and forestlands have been converted into agricultural and residential lands. The results showed that land use and climate can determine 43.2% of discharge changes. Also, land use changes are more effective than climate change on river discharge changes.

In this review, papers published in the chemometrics field were selected in order to gather information and conduct a systematic review regarding food science and technology; more precisely, regarding the domain of bioactive compounds and the functional properties of foods. More than 50 papers covering different food samples, experimental techniques and chemometric techniques were selected and presented, focusing on the chemometric methods used and their outcomes. This study is one way to approach an overview of the current publications related to this subject matter. The application of the multivariate chemometrics approach to the study of bioactive compounds and the functional properties of foods can open up even more in coming years, since it is fast-growing and highly competitive research area. [ABSTRACT FROM AUTHOR]

A quantitative study of groundwater resources and accurate monitoring of changes over time, especially in areas facing limited water resources, is considered essential for proper management and sustainable exploitation of these resources. Golestan province, one of the semi-arid provinces of Iran has faced a drop in the groundwater level and an increase in the salinity of the groundwater due to the excessive withdrawals from the groundwater table and the reduction of atmospheric precipitation in the past few years. Gorgan Plain with an area of about 4727 square kilometers is one of the largest plains in Iran and the most important plain of Golestan province in terms of water supply for agricultural and drinking purposes. In this plain, there is a network of piezometers and observation wells that include continuous monthly measurements for more than 30 years. The objective of this research was to investigate the changes in the groundwater level of shallow (30 years (1989-2018)) and deep (22 years (1997-2018)) wells. The Man-Kendall method was used to reveal the trend and Pettitt, Normal Standard, and Buishand methods were used to identify sudden change points in a time series of groundwater levels in 49 shallow wells and 12 deep wells. The results of this research showed that the groundwater level in most of the studied wells had a significantly decreasing trend at a significant level of 5%. Also, the largest amount of groundwater loss was in the southern and southwestern parts of the plain, which can be attributed to a large amount of water taken from the wells due to their proximity to urban areas and some local conditions such as the proximity of the wells of this area are located in altitudes and at the entrance border of the aquifer. In the same way, as it rises, the fall decreases in the middle of the plain, and the amount of fall decreases in the northern areas and the edge of the Caspian Sea. It can be related to the proximity to the Caspian Sea and the high water table, and as a result, the inappropriate quality of water and land (high salinity and low fertility), which has caused the water withdrawal from this area to be less.

We study the Feasible Generalized Least-Squares (FGLS) estimation of the parameters of a linear regression model in the presence of heteroskedasticity of unknown form in the errors. We suggest a Lasso based procedure to estimate the skedastic function of the residuals. The advantage of using Lasso is that it can handle a large number of potential covariates, yet still yields a parsimonious specification. Using extensive simulation experiments, we show that our suggested procedure always provide some improvements in the precision of the parameter of interest (lower Mean-Squared Errors) when heteroskedasticity is present and is equivalent to OLS when there is none. It also performs better than previously suggested procedures. Since the fitted value of the skedastic function falls short of the true specification, we form confidence intervals using a bias-corrected version of the usual heteroskedasticity-robust covariance matrix estimator. These have the correct size and substantially shorter length than when using OLS. Our method is applicable to both cross-section (with a random sample) and time series models, though here we concentrate on the former. [ABSTRACT FROM AUTHOR]

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The research endeavor delves into the intricate agricultural disparities prevalent in Western Odisha, focusing on crucial metrics such as land area, yield rates, and production trends spanning the years 2020 to 2022. The study encompassed an extensive scope, encompassing 50 blocks distributed across six carefully selected districts: Nuapada, Jharsuguda, Boudh, Sundargarh, Sambalpur, and Baragarh. These districts were meticulously chosen through a process of simple random sampling from a pool of ten districts in the Western Odisha region. To distill meaningful insights, the research harnessed the power of composite indices, drawn from a comprehensive set of fifteen indicators, each illuminating distinct facets of agricultural development. Through the application of Principal Component Analysis (PCA), five key indicators were expertly extracted from this data set. Drawing upon secondary data sourced from the esteemed Statistical Abstracts of Western Odisha districts, and the Directorate of Economics and Statistics (DES), Government of Odisha (2019-2020), the study validated its assumptions by subjecting the extracted components to the rigors of the Kolmogorov-Smirnov test for normal distribution. Primary data was diligently collected from a cohort of 300 households via meticulously structured questionnaires, encompassing vital parameters such as land area (measured in acres), yield rates (measured in kilograms), and production figures (measured in quintals). It was discovered that the data exhibited deviations from normality, prompting the application of non-parametric methodologies. The ensuing Kruskal-Wallis tests unearthed significant disparities among the identified groups, emphasizing substantial distinctions between the Meteoric, Progressive, Mediocre, and Laggard classifications. To gauge the extent of these disparities, the Gini Coefficient (GC) was aptly employed. The findings underscored that the Meteoric group exhibited more pronounced disparities in land area compared to the other groups, along with marked differences in yield rates. Additionally, this group displayed slightly elevated disparities in production figures. These revelatory results furnish a nuanced understanding of the diverse variances in land area, yield rates, and production levels among the distinct groups. This research endeavor, by shedding light on the dynamic agricultural landscape of Western Odisha, not only highlights the disparities but also offers valuable insights into the underlying factors influencing these agricultural outcomes. These insights, in turn, pave the way for targeted interventions aimed at augmenting agricultural productivity in the region. Addressing these identified disparities emerges as a critical step towards fostering a more equitable and sustainable agricultural sector in Western Odisha. [ABSTRACT FROM AUTHOR]

Climate change is expected to increase the frequency and intensity of extreme weather events. To properly assess the increased economical risk of these events, actuaries can gain in relying on expert models/opinions from multiple different sources, which requires the use of model combination techniques. From non-parametric to Bayesian approaches, different methods rely on varying assumptions potentially leading to very different results. In this paper, we apply multiple model combination methods to an ensemble of 24 experts in a pooling approach and use the differences in outputs from the different combinations to illustrate how one can gain additional insight from using multiple methods. The densities obtained from pooling in Montreal and Quebec City highlight the significant changes in higher quantiles obtained through different combination approaches. Areal reduction factor and quantile projected changes are used to show that consistency, or lack thereof, across approaches reflects the uncertainty of combination methods. This shows how an actuary using multiple expert models should consider more than one combination method to properly assess the impact of climate change on loss distributions, seeing as a single method can lead to overconfidence in projections. [ABSTRACT FROM AUTHOR]

In the last few decades, in many research fields, different methods were introduced to discover groups with the same trends in longitudinal data. The clustering process is an unsupervised learning method, which classifies longitudinal data based on different criteria by performing algorithms. The current study was performed with the aim of reviewing various methods of longitudinal data clustering, including two general categories of non-parametric methods and model-based methods. PubMed, SCOPUS, ISI, Ovid, and Google Scholar were searched between 2000 and 2021. According to our systematic review, the non-parametric k-means Clustering Method utilizing Euclidean distance emerges as a leading approach for clustering longitudinal data This research, with an overview of the studies done in the field of clustering, can help researchers as a toolbox to choose various methods of longitudinal data clustering in idea generation and choosing the appropriate method in the classification and analysis of longitudinal data.

A quantitative study of groundwater resources and accurate monitoring of changes over time, especially in areas facing limited water resources, is considered essential for proper management and sustainable exploitation of these resources. Golestan province, one of the semi-arid provinces of Iran has faced a drop in the groundwater level and an increase in the salinity of the groundwater due to the excessive withdrawals from the groundwater table and the reduction of atmospheric precipitation in the past few years. Gorgan Plain with an area of about 4727 square kilometers is one of the largest plains in Iran and the most important plain of Golestan province in terms of water supply for agricultural and drinking purposes. In this plain, there is a network of piezometers and observation wells that include continuous monthly measurements for more than 30 years. The objective of this research was to investigate the changes in the groundwater level of shallow (30 years (1989-2018)) and deep (22 years (1997-2018)) wells. The Man-Kendall method was used to reveal the trend and Pettitt, Normal Standard, and Buishand methods were used to identify sudden change points in a time series of groundwater levels in 49 shallow wells and 12 deep wells. The results of this research showed that the groundwater level in most of the studied wells had a significantly decreasing trend at a significant level of 5%. Also, the largest amount of groundwater loss was in the southern and southwestern parts of the plain, which can be attributed to a large amount of water taken from the wells due to their proximity to urban areas and some local conditions such as the proximity of the wells of this area are located in altitudes and at the entrance border of the aquifer. In the same way, as it rises, the fall decreases in the middle of the plain, and the amount of fall decreases in the northern areas and the edge of the Caspian Sea. It can be related to the proximity to the Caspian Sea and the high water table, and as a result, the inappropriate quality of water and land (high salinity and low fertility), which has caused the water withdrawal from this area to be less. [ABSTRACT FROM AUTHOR]

Introduction: In the last few decades, in many research fields, different methods were introduced to discover groups with the same trends in longitudinal data. The clustering process is an unsupervised learning method, which classifies longitudinal data based on different criteria by performing algorithms. The current study was performed with the aim of reviewing various methods of longitudinal data clustering, including two general categories of non-parametric methods and model-based methods. Methods: In this research, to obtain related scientific articles, PubMed, Science Direct Scopus, ISI, and Google Scholar were searched between 2000 and 2021. Results: According to our systematic review, the non-parametric k-means Clustering Method utilizing Euclidean distance emerges as a leading approach for clustering longitudinal data. Conclusion: This research, with an overview of the studies done in the field of clustering, can help researchers as a toolbox to choose various methods of longitudinal data clustering in idea generation and choosing the appropriate method in the classification and analysis of longitudinal data. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive overview of the importance of selecting the right statistical method for data analysis in biomedical research. It explains the differences between parametric and non-parametric methods and highlights the need to consider the purpose of the study, the type of data, and the measurements when choosing a statistical test. The article offers a range of parametric and non-parametric approaches for comparing means, proportions, and other statistical techniques. It also discusses the benefits and drawbacks of non-parametric methods, emphasizing their usefulness when data does not meet the assumptions of parametric tests. The article stresses the significance of sample size and the p-value in determining statistical significance and concludes by emphasizing the importance of researchers having a basic understanding of statistics to select the appropriate statistical methods for their research. [Extracted from the article]

Insurance to deal with prolonged drought periods in rural Africa requires a practical method to estimate accurate premium values that minimize economic losses. We use non-parametric methods to determine the risk non-neutral insurer’s premium for drought insurance on rain-fed crops. Premium values are estimated on the basis of percentage of the expected yield losses over the potential yields. Expected yield losses are estimated based on data on the levels of rainfall, potential evapotranspiration and water-holding capacity of the soil, and water requirement of the crop. Maize crop in West Kenya, and rice crop in the Central High Plains of Madagascar are taken as case studies. To check if farmer’s choice of starting seasons affects the expected yields and the values of premium, we employ forecasted yields for two different sowing dates (October vs. November) for maize, and two different transplantation dates (November vs. December) for rice. The mean-variance (E-V), the First-Degree Stochastic Dominance (FSD), and the Second-Degree Stochastic Dominance (SSD) efficiency criteria are used to rank each pair of distributions. Results show that an insurer for maize production in Western Kenya would require a premium value between 43 and 55% of the potential yields to fully cover the loss caused by lack of rainfall. Under E-V and FSD, the two yield distributions cannot be ranked, but under SSD the yield distribution of the October-sown maize dominates that of November. For lowland rice in the Central High Plains of Madagascar, all three efficiency criteria indicate that the yield distribution of the December-transplanted rice dominates that of November and the premium values are less than 4 % of the potential yields.

Spatial structure refers to the horizontal and vertical arrangement of individual trees, and the most accurate way to describe it within a community is to characterize tree strata in terms of their dimensions. The aim of this study was to determine the horizontal and vertical structure of pure stands of Pinus ayacahuite Ehrenb. ex Schltdl., in forests of southern Mexico. Forest measurement data from 24 sample plots were used. For analysis of the horizontal structure, diameters within a range of 0.20 cm to 77 cm were used, while for the vertical structure, heights were from 0.09 m to 40.9 m. Non-parametric histograms and Kernel density methods were used in the analysis, and Fisher and Marron multimodality tests were performed. The homogeneity of the forest stands was determined by the coefficient of homogeneity, and the vertical and horizontal structures were described using the stratification proposed by Pretzsch. The results indicate that the horizontal structure corresponds to a diameter distribution with a reversed "J" shape in 79.2% of the sample plots, while 91.8% of the sites were classified as irregular with coefficients of homogeneity of 1.0 to 3.0. In the vertical structure, it was observed that the lower stratum predominated in 75% of the plots, while 25% had a higher concentration of individuals in the middle stratum. The upper stratum had accumulation percentages ranging from 1.3% to 33.3% but did not predominate in any of the plots. According to the multimodality tests, 50% of the plots present multimodality in the horizontal structure, while in the vertical structure this condition is present in 38% of the plots. Knowledge of the spatial structure of Pinus ayacahuite forest stands is essential to define silvicultural strategies that ensure the sustainable functioning of the ecosystem in terms of yield continuity and conservation.

Research on the long-term effects of health in early life has predominantly relied on parametric methods to assess differences between groups of children. However, this approach leaves a wealth of distributional information untapped. The objective of this study was to assess distributional differences in earnings and mental health in young adulthood between individuals who suffered a chronic illness in childhood compared to those who did not using the non-parametric relative distributions framework. Using data from the Panel Study of Income Dynamics, we find that young adults who suffered a chronic illness in childhood fare worse in terms of earnings and mental health scores in adulthood, particularly for individuals reporting a childhood mental health/developmental disorder. Covariate decompositions suggest that chronic conditions in childhood may indirectly affect later outcomes through educational attainment: had the two groups had similar levels of educational attainment, the proportion of individuals with a report of a chronic condition in childhood in the lower decile of the relative earnings distribution would have been reduced by about 20 percentage points. Findings may inform policy aimed at mitigating longer run effects of health conditions in childhood and may generate hypotheses to be explored in parametric analyses.

This research aims to study the growth of productivity in the service sector in the former Central and Eastern European Countries (CEECs) and their determinants. For this purpose, non-parametric frontier techniques were used to measure the variations in productivity and determine the explanatory factors of these changes in total factor productivity; the methodology of the Malmquist index with output orientation and its decomposition in technical change, pure technical efficiency and scale efficiency was used for the period 2000–2019. The results obtained indicate that the productivity of services in the most recently incorporated countries grew by 1.3 per 100 on average per year compared to 1.6 per 100 in manufacturing. The most important driver of such growth was found to be improvement in technical change (frontier shift) rather than improvement in efficiency. [ABSTRACT FROM AUTHOR]

The accuracy of crop mapping based on remotely sensed hyperspectral imagery has been significantly improved through the use of deep learning. However, traditional deep learning can be computationally intensive, requiring millions of parameters, which can make it 'expensive' to deploy and optimize. Inspired by studies in natural language processing, we consider the spectral signature corresponding to each pixel as text. Specifically, we first feed the hyperspectral image (HSI) data into the Channel2Vec module to generate channel embeddings. Based on the channel embeddings, we use a lossless compressor and Normalized Compression Distance (NCD) to create a spectral tokenizer. It can segment the spectral signature corresponding to each pixel into multiple windows along the channel dimension, and then extract local sequence information from each window. By combining the local sequence information with the original HSI data, we construct spectral embeddings. Finally, we again use the lossless compressor to compute the NCD between the spectral embeddings, and then classify using only the k -nearest-neighbor classifier (k NN). The proposed framework is ready-to-use and lightweight. Without any training, it achieves results competitive with deep learning models on three benchmark datasets. It outperforms the average of 11 advanced deep learning methods trained at scale. Moreover, it outperforms more than half of these models in the few-shot scenario, where there are not enough labels to effectively train a neural network. • Introducing compressor-based classification from NLP into HSI classification. • Channel2Vec and Spectral Tokenizer are proposed for spectral representation. • The framework is ready-to-use and lightweight without any training. • The framework outperforms 11 deep learning models on average across three datasets. [ABSTRACT FROM AUTHOR]

Insurance to deal with prolonged drought periods in rural Africa requires a practical method to estimate accurate premium values that minimize economic losses. We use non-parametric methods to determine the risk non-neutral insurer's premium for drought insurance on rain-fed crops. Premium values are estimated on the basis of percentage of the expected yield losses over the potential yields. Expected yield losses are estimated based on data on the levels of rainfall, potential evapotranspiration and water-holding capacity of the soil, and water requirement of the crop. Maize crop in West Kenya, and rice crop in the Central High Plains of Madagascar are taken as case studies. To check if farmer's choice of starting seasons affects the expected yields and the values of premium, we employ forecasted yields for two different sowing dates (October vs. November) for maize, and two different transplantation dates (November vs. December) for rice. The mean-variance (E-V), the First-Degree Stochastic Dominance (FSD), and the Second-Degree Stochastic Dominance (SSD) efficiency criteria are used to rank each pair of distributions. Results show that an insurer for maize production in Western Kenya would require a premium value between 43 and 55% of the potential yields to fully cover the loss caused by lack of rainfall. Under E-V and FSD, the two yield distributions cannot be ranked, but under SSD the yield distribution of the October-sown maize dominates that of November. For lowland rice in the Central High Plains of Madagascar, all three efficiency criteria indicate that the yield distribution of the December-transplanted rice dominates that of November and the premium values are less than 4 % of the potential yields. [ABSTRACT FROM AUTHOR]

Uncertainty assessment of distribution systems performance is an obligation because of the intermittent nature of solar and wind distributed energy resources, as well as uncertainties in power demand and charging stations of electric vehicles. Consequently, efficient tools are required for load flow analysis. Many of the existing papers assume a set of given probability density functions (PDFs) to model uncertainties and develop parametric probabilistic load flow tools. However, the uncertainties might not fall in any standard class of PDFs. As a result, non-parametric tools are required. This study compares parametric and non-parametric approaches for determining the PDFs of load flow outputs, as well as Monte Carlo simulation. To compare the methods, the unscented transform and two-point estimation approaches have been considered as parametric methods, while for non-parametric methods, saddle point approximation and kernel density estimation methods have been considered. To examine the performance of the proposed parametric and non-parametric methods, IEEE 28-bus, 33-bus, 37-bus, 69-bus and 210-bus test systems are taken into consideration and results are compared with generalized Polynomial Chaos algorithm, Latin Hypercube Sampling with Cholesky Decomposition, Cornish–Fisher expansion and clustering analysis. In terms of both accuracy and execution time, the results produced by non-parametric approaches are compared to those obtained by parametric methods. They show that the non-parametric estimators produce reliable results in estimating the density function of output random variables, while can reduce the run time for the power-flow problem in an acceptable level. [ABSTRACT FROM AUTHOR]

The problem of quickest detection of a change in the mean of a sequence of independent observations is studied. The pre-change observations are assumed to be stationary, while the post-change observations are allowed to be non-stationary. The case where the pre-change distribution is known is studied first, and then the extension where only the mean and variance of the pre-change distribution are known. No knowledge of the post-change distributions is assumed other than that the means of the observations are above some pre-specified threshold larger than the pre-change mean. For the case where the pre-change distribution is known, a test is derived that asymptotically minimizes the worst-case detection delay over all possible post-change distributions, as the false alarm rate goes to zero. Towards deriving this asymptotically optimal test, some new results are provided for the general problem of asymptotic minimax robust quickest change detection in non-stationary settings. Then, the limiting form of the optimal test is studied as the gap between the pre- and post-change means goes to zero, called the Mean-Change Test (MCT). It is shown that the MCT can be designed with only knowledge of the mean and variance of the pre-change distribution. The performance of the MCT is also characterized when the mean gap is moderate, under the additional assumption that the distributions of the observations have bounded support. The analysis is validated through numerical results for detecting a change in the mean of a beta distribution. The use of the MCT in monitoring pandemics is also demonstrated. [ABSTRACT FROM AUTHOR]

The same intervention can produce different effects in different sites. Existing transport mediation estimators can estimate the extent to which such differences can be explained by differences in compositional factors and the mechanisms by which mediating or intermediate variables are produced; however, they are limited to consider a single, binary mediator. We propose novel nonparametric estimators of transported interventional (in)direct effects that consider multiple, high-dimensional mediators and a single, binary intermediate variable. They are multiply robust, efficient, asymptotically normal, and can incorporate data-adaptive estimation of nuisance parameters. They can be applied to understand differences in treatment effects across sites and/or to predict treatment effects in a target site based on outcome data in source sites. [ABSTRACT FROM AUTHOR]

This study aimed to introduce non-parametric tests and guard bands to assess the compliance of some river water properties with Brazilian environmental regulations. Due to the heterogeneity of the measurands pH, Biochemical Oxygen Demand (BOD), manganese molar concentration, and Escherichia coli, which could be wrongly treated as outliers, as well as the non-Gaussian data, robust methods were used to calculate the measurement uncertainty. Next, based on guard bands, the compliance assessment was evaluated using this previous uncertainty information. For these four measurands, partial overlaps between their uncertainties and the specification limit could generate doubts about compliance. The non-parametric approach for calculating the uncertainty connected to the guard bands concept classified pH and BOD as "conform", with a risk to the consumer of up to 4.0% and 4.9%, respectively; in contrast, manganese molar concentration and Escherichia coli were "not conform", with a risk to the consumer of up to 25% and 7.4%, respectively. The methodology proposed was satisfactory because it considered the natural heterogeneity of data with non-Gaussian behavior instead of wrongly excluding outliers. In an unprecedented way, two connected statistical approaches shed light on the measurement uncertainty in compliance assessment of water analysis. [ABSTRACT FROM AUTHOR]

This study aimed to perform a genome-wide association analysis (GWAS) using the Random Forest (RF) approach for scanning candidate genes for age at first calving (AFC) in Nellore cattle. Additionally, potential epistatic effects were investigated using linear mixed models with pairwise interactions between all markers with high importance scores within the tree ensemble non-linear structure. Data from Nellore cattle were used, including records of animals born between 1984 and 2015 and raised in commercial herds located in different regions of Brazil. The estimated breeding values (EBV) were computed and used as the response variable in the genomic analyses. After quality control, the remaining number of animals and SNPs considered were 3,174 and 360,130, respectively. Five independent RF analyses were carried out, considering different initialization seeds. The importance score of each SNP was averaged across the independent RF analyses to rank the markers according to their predictive relevance. A total of 117 SNPs associated with AFC were identified, which spanned 10 autosomes (2, 3, 5, 10, 11, 17, 18, 21, 24, and 25). In total, 23 non-overlapping genomic regions embedded 262 candidate genes for AFC. Enrichment analysis and previous evidence in the literature revealed that many candidate genes annotated close to the lead SNPs have key roles in fertility, including embryo pre-implantation and development, embryonic viability, male germinal cell maturation, and pheromone recognition. Furthermore, some genomic regions previously associated with fertility and growth traits in Nellore cattle were also detected in the present study, reinforcing the effectiveness of RF for pre-screening candidate regions associated with complex traits. Complementary analyses revealed that many SNPs top-ranked in the RF-based GWAS did not present a strong marginal linear effect but are potentially involved in epistatic hotspots between genomic regions in different autosomes, remarkably in the BTAs 3, 5, 11, and 21. The reported results are expected to enhance the understanding of genetic mechanisms involved in the biological regulation of AFC in this cattle breed. [ABSTRACT FROM AUTHOR]

Damage detection in active-sensing, guided-waves-based structural health monitoring (SHM) has evolved through multiple eras of development during the past decades. Nevertheless, there still exist a number of challenges facing the current state-of-the-art approaches, both in the industry as well as in research and development, including low damage sensitivity, lack of robustness to uncertainties, need for user-defined thresholds, and non-uniform response across a sensor network. In this work, a novel statistical framework is proposed for active-sensing SHM based on the use of ultrasonic guided waves. This framework is based on stochastic non-parametric time series models and their corresponding statistical properties in order to readily provide healthy confidence bounds and enable accurate and robust damage detection via the use of appropriate statistical decision-making tests. Three such methods and corresponding statistical quantities (test statistics) along with decision-making schemes are formulated and experimentally assessed via the use of three coupons with different levels of complexity: an Al plate with a growing notch, a carbon fiber-reinforced plastic (CFRP) plate with added weights to simulate local damage, and the CFRP panel used in the Open Guided Waves project, all fitted with piezoelectric transducers under a pitch-catch configuration. The performance of the proposed methods is compared to that of state-of-the-art time-domain damage indices (DIs). The results demonstrate the increased detection sensitivity and robustness of the proposed methods, with better tracking capability of damage evolution compared to conventional approaches, even for damage-non-intersecting actuator–sensor paths. In particular, the Z statistic emerges as the best damage detection metric compared to conventional DIs, as well as the other proposed statistics. Overall, the proposed statistics in this study promise greater damage sensitivity across different components, with enhanced robustness to uncertainties, as well as user-friendly application. [ABSTRACT FROM AUTHOR]

This study aimed to perform a genome-wide association analysis (GWAS) using the Random Forest (RF) approach for scanning candidate genes for age at first calving (AFC) in Nellore cattle. Additionally, potential epistatic effects were investigated using linear mixed models with pairwise interactions between all markers with high importance scores within the tree ensemble non-linear structure. Data from Nellore cattle were used, including records of animals born between 1984 and 2015 and raised in commercial herds located in different regions of Brazil. The estimated breeding values (EBV) were computed and used as the response variable in the genomic analyses. After quality control, the remaining number of animals and SNPs considered were 3,174 and 360,130, respectively. Five independent RF analyses were carried out, considering different initialization seeds. The importance score of each SNP was averaged across the independent RF analyses to rank the markers according to their predictive relevance. A total of 117 SNPs associated with AFC were identified, which spanned 10 autosomes (2, 3, 5, 10, 11, 17, 18, 21, 24, and 25). In total, 23 non-overlapping genomic regions embedded 262 candidate genes for AFC. Enrichment analysis and previous evidence in the literature revealed that many candidate genes annotated close to the lead SNPs have key roles in fertility, including embryo pre-implantation and development, embryonic viability, male germinal cell maturation, and pheromone recognition. Furthermore, some genomic regions previously associated with fertility and growth traits in Nellore cattle were also detected in the present study, reinforcing the effectiveness of RF for pre-screening candidate regions associated with complex traits. Complementary analyses revealed that many SNPs top-ranked in the RF-based GWAS did not present a strong marginal linear effect but are potentially involved in epistatic hotspots between genomic regions in different autosomes, remarkably in the BTAs 3, 5, 11, and 21. The reported results are expected to enhance the understanding of genetic mechanisms involved in the biological regulation of AFC in this cattle breed.

This research aims to study the growth of productivity in the service sector in the former Central and Eastern European Countries (CEECs) and their determinants. For this purpose, non-parametric frontier techniques were used to measure the variations in productivity and determine the explanatory factors of these changes in total factor productivity; the methodology of the Malmquist index with output orientation and its decomposition in technical change, pure technical efficiency and scale efficiency was used for the period 2000–2019. The results obtained indicate that the productivity of services in the most recently incorporated countries grew by 1.3 per 100 on average per year compared to 1.6 per 100 in manufacturing. The most important driver of such growth was found to be improvement in technical change (frontier shift) rather than improvement in efficiency.

This study reports movement patterns and home range estimates of an Andean fox (Lycalopex culpaeus) in Cotopaxi National Park in Ecuador, representing the first GPS-tagging of the species. The GPS functioned well during the 197-day tracking period. Home range sizes ranged between 4.9 and 8.1 km2, depending on the estimation method. Movement speeds averaged 0.17 km/h at day versus 0.87 km/h at night, and distance traveled averaged 0.23 km at day versus 0.89 km at night. These preliminary results highlight the importance of collecting unbiased, high-quality data which enables an enhanced understanding on mammal behavior and human/animal interaction. [ABSTRACT FROM AUTHOR]