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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]

A recurring question is whether rating scales should be considered metrically scaled or merely ordinally scaled. This has direct implications for the permissible statistical procedures for significance testing. Based on the results of a simulation study, it is shown that the use of parametric procedures for rating scales has distinct advantages over the nonparametric alternatives. It is also shown that the parametric procedures are robust to violations of the assumption of normality, which only result in a modest loss of power compared with continuous variables. This loss should be taken into account when calculating the optimal sample size. The results suggest that sample sizes about 25% larger should be chosen for discrete rating scales than for continuous variables. [ABSTRACT FROM AUTHOR]

Statistics can be used to evaluate the prevalence of diseases, differences between patient groups, links between test results, compare the effectiveness of different types of treatment, test the hypothesis and model the interdependencies between variables. In medical universities, preference is given to special medical disciplines. Yet, a skilled medical specialist should have an adequate understanding of statistical data analysis. Purpose: to systematize and demonstrate statistical analysis techniques that could be useful for an ophthalmologist. Results. The major methodological aspects of statistical analysis in ophthalmology are considered. An overview of statistical techniques and an algorithm of choosing such techniques is given. Descriptive statistics and features of in-depth statistical analysis used in ophthalmological research are presented. Examples of scientific work using these methods are listed. Conclusion. The basics of statistical analysis expand the scope of instruments that ophthalmologists have at their disposal. The paper provides links to open sources in which statistical techniques and results achieved therewith are presented. These links increase the ophthalmologist’s awareness of the instruments considered.

Several different arguments support the use of Webster's method when seats in a parliament are to be apportioned proportionally according to populations. This note—instigated by a new property—summarizes the reasons. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Ingenieria, Matematicas y Ciencias de la Informacion is the property of Corporacion Universitaria Republicana 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.)

Copyright of REDER: Revista de Estudios Latinoamericanos sobre Reducción del Riesgo de Desastres is the property of Corporacion Gestion de Riesgos y Desastres (GRID-Chile) 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.)

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]

The marine microalga Nannochloropsis oculata has garnered significant interest as a potential source of lipids, both for biofuel and nutrition, containing significant amounts of C16:0, C16:1, and C20:5, n-3 (EPA) fatty acids (FA). Growth parameters such as temperature, pH, light intensity, and nutrient availability play a crucial role in the fatty acid profile of microalgae, with N. oculata being no exception. This study aims to identify key variables for the FA profile of N. oculata grown autotrophically. To that end, the most relevant literature data were gathered and combined with our previous work as well as with novel experimental data, with 121 observations in total. The examined variables were the percentages of C14:0, C16:0, C16:1, C18:1, C18:2, and C20:5, n-3 in total FAs, their respective ratios to C16:0, and the respective content of biomass in those fatty acids in terms of ash free dry weight. Many potential predictor variables were collected, while dummy variables were introduced to account for bias in the measured variables originating from different authors as well as for other parameters. The method of multiple imputations was chosen to handle missing data, with limits based on the literature and model-based estimation, such as using the software PHREEQC and residual modelling for the estimation of pH. To eliminate unimportant predictor variables, LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis with a novel definition of optimal lambda was employed. LASSO regression identified the most relevant predictors while minimizing the risk of overfitting the model. Subsequently, stepwise linear regression with interaction terms was used to further study the effects of the selected predictors. After two rounds of regression, sparse refined models were acquired, and their coefficients were evaluated based on significance. Our analysis confirms well-known effects, such as that of temperature, and it uncovers novel unreported effects of aeration, calcium, magnesium, and manganese. Of special interest is the negative effect of aeration on polyunsaturated fatty acids (PUFAs), which is possibly related to the enzymatic kinetics of fatty acid desaturation under increased oxygen concentration. These findings contribute to the optimization of the fatty acid profile of N. oculata for different purposes, such as production of, high in PUFAs, food or feed, or production of, high in saturated and monounsaturated FA methyl esters (FAME), biofuels. [ABSTRACT FROM AUTHOR]

Introduction. A two-dimensional configuration of the receiving antenna array (AA) is used to measure the angular coordinates of radar targets – azimuth and elevation. A transformation of one-dimensional uniform AA into a flat two-dimensional AA with a fixed number of antenna elements (AEs) and constant aperture size leads to a nonuniform arrangement of AE in the AA rows. As a result, the AA becomes sparse, which negatively affects the quality of the AA three-dimensional antenna pattern (AP). The section method based on the modified parametric Burg algorithm is a promising and relevant method for constructing directional characteristics. This method can be recommended for spatial processing of reflected signals in a passive coherent radar with a two-dimensional sparse receiving AA.Aim. To analyze the azimuthal and elevation sections of three-dimensional APs obtained using a modified Burg method for spatial processing of reflected signals in a passive coherent radar, the AEs of which are located horizontally and vertically with a step that is a multiple of the half the wavelength λ of the used illumination signal carrier oscillation.Materials and methods. The construction of directional characteristics was implemented via computer simulation in the MATLAB environment with the effect of uncorrelated additive complex normal noise on the receiving channels in each AE as an interference.Results. The possibility and conditions for the application of the modified parametric Burg method in the problems of single signal detecting and angular resolution of equal-power signals in a passive coherent radar, which includes a two-dimensional sparse AA, were determined. The obtained Burg method directional characteristics were compared with the directional characteristics obtained using conventional algorithms based on the discrete Fourier transform. The use of the Burg method allowed the AP side lobe level to be reduced to a practically acceptable level of -12 ... -17 dB at a signal to noise ratio 6 dB. In addition, the Rayleigh resolution of signals in the AA was significantly improved.Conclusion. The presented modified Burg method is suitable for signal processing in two-dimensional sparse AA, subject to restrictions on the AE placing method and the AA aperture size. This allows the Burg method to be recommended for use in passive coherent radars.

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]

This article introduces and studies two clique relaxation models with fractional objectives: the maximum ratio s$$ s $$‐plex problem and the maximum ratio s$$ s $$‐defective clique problem. The decision version of each problem is shown to be strongly NP$$ NP $$‐complete; we also discuss some related computational complexity issues. The base optimization models are single‐ratio fractional 0‐1 problems. We describe two general types of solution methods: the first one is based on mixed‐integer linear programs (MILPs) obtained by linearizing the original nonlinear 0‐1 models; the other one exploits parametric methods (namely, a binary search and Newton's method) that solve MILPs in an iterative manner. For the proposed MILPs, we derive valid inequalities that are shown to substantially improve the performance of an off‐the‐shelf MILP solver. Finally, the considered solution approaches are compared via extensive numerical experiments using a set of artificially generated and real‐life network instances. [ABSTRACT FROM AUTHOR]

Abstract [11C]UCB-J is a PET radioligand that binds to the presynaptic vesicle glycoprotein 2A. Therefore, [11C]UCB-J PET may serve as an in vivo marker of synaptic integrity. The main objective of this study was to evaluate the quantitative accuracy and the 28-day test–retest repeatability (TRT) of various parametric quantitative methods for dynamic [11C]UCB-J studies in Alzheimer’s disease (AD) patients and healthy controls (HC). Eight HCs and seven AD patients underwent two 60-min dynamic [11C]UCB-J PET scans with arterial sampling over a 28-day interval. Several plasma-input based and reference-region based parametric methods were used to generate parametric images using metabolite corrected plasma activity as input function or white matter semi-ovale as reference region. Different parametric outcomes were compared regionally with corresponding non-linear regression (NLR) estimates. Furthermore, the 28-day TRT was assessed for all parametric methods. Spectral analysis (SA) and Logan graphical analysis showed high correlations with NLR estimates. Receptor parametric mapping (RPM) and simplified reference tissue model 2 (SRTM2) BPND, and reference Logan (RLogan) distribution volume ratio (DVR) regional estimates correlated well with plasma-input derived DVR and SRTM BPND. Among the multilinear reference tissue model (MRTM) methods, MRTM1 had the best correspondence with DVR and SRTM BPND. Among the parametric methods evaluated, spectral analysis (SA) and SRTM2 were the best plasma-input and reference tissue methods, respectively, to obtain quantitatively accurate and repeatable parametric images for dynamic [11C]UCB-J PET.

The marine microalga Nannochloropsis oculata has garnered significant interest as a potential source of lipids, both for biofuel and nutrition, containing significant amounts of C16:0, C16:1, and C20:5, n-3 (EPA) fatty acids (FA). Growth parameters such as temperature, pH, light intensity, and nutrient availability play a crucial role in the fatty acid profile of microalgae, with N. oculata being no exception. This study aims to identify key variables for the FA profile of N. oculata grown autotrophically. To that end, the most relevant literature data were gathered and combined with our previous work as well as with novel experimental data, with 121 observations in total. The examined variables were the percentages of C14:0, C16:0, C16:1, C18:1, C18:2, and C20:5, n-3 in total FAs, their respective ratios to C16:0, and the respective content of biomass in those fatty acids in terms of ash free dry weight. Many potential predictor variables were collected, while dummy variables were introduced to account for bias in the measured variables originating from different authors as well as for other parameters. The method of multiple imputations was chosen to handle missing data, with limits based on the literature and model-based estimation, such as using the software PHREEQC and residual modelling for the estimation of pH. To eliminate unimportant predictor variables, LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis with a novel definition of optimal lambda was employed. LASSO regression identified the most relevant predictors while minimizing the risk of overfitting the model. Subsequently, stepwise linear regression with interaction terms was used to further study the effects of the selected predictors. After two rounds of regression, sparse refined models were acquired, and their coefficients were evaluated based on significance. Our analysis confirms well-known effects, such as that of temperature, and it uncovers novel unreported effects of aeration, calcium, magnesium, and manganese. Of special interest is the negative effect of aeration on polyunsaturated fatty acids (PUFAs), which is possibly related to the enzymatic kinetics of fatty acid desaturation under increased oxygen concentration. These findings contribute to the optimization of the fatty acid profile of N. oculata for different purposes, such as production of, high in PUFAs, food or feed, or production of, high in saturated and monounsaturated FA methyl esters (FAME), biofuels.

Introduction. In the case of a nonuniform (NU) design of the antenna elements (AEs) of the receiving antenna array (AA), the antenna pattern (AP) features sidelobes (SL) with a significantly higher noise level than acceptable values. Under low signal-to-noise ratios (SNR), this noise leads to angular coordinate measuring errors thus worsening the statistical accuracy characteristics (ACs) of the signal. It is of relevance to construct the ACs of angular coordinates when a modified parametric Burg method (BM) is applied to spatial reflected signal processing in a transportable decametre range radar (DRR) with a nonuniform array (NUA) and linear accuracy characteristics. Aim. To analyse the statistical ACs of angular coordinate objects when using a modified BM for spatial reflected signal processing in a DRR with a linear NUA, in which AEs are located with a random step in the range from λ/2 to several λ, where λ is the operating carrier wavelength.Materials and methods. Statistical ACs were constructed by computer modelling in the MatLab software, the reliability of which was confirmed by conventional discrete Fourier transform methods, as well as by comparing the obtained ACs with asymptotic bounds, including Cramer-Rao bounds.Results. The possibility and conditions of using a modified parametric BM for estimating the azimuthal coordinates of reflected radar signals were determined for the case of a nonuniform design of the over-the-horizon DRR receiving AA AEs. Statistical ACs were obtained and compared with the asymptotically optimal ACs of the maximum likelihood estimations corresponding to the uniform AE design.Conclusion. The obtained results confirm the suboptimality of the BM modified for signal processing in the NUA at a random AE spacing step in the range from λ/2 to 2λ, making it applicable for use in transportable DRRs.

The efficiency of known approaches to predicting the creep rupture strength diagrams of heat-resistant materials at elevated temperatures is analyzed. Extrapolation results for long deformation terms with known Larson–Miller, Orr–Sherby–Dorn, Trunin, Manson–Haferd parametric methods, and the modified base diagram method are presented. Creep rupture strength characteristics were calculated on the basis of existing experimental life curves for heat-resistant materials. The reliability of calculations is corroborated by comparing with known experiments for 18Cr-12Ni-Mo and 1.25Cr-0.5Mo-Si steels. The advantages of the modified base diagram method for creep rupture strength prediction on large deformation databases are demonstrated. Mathematical models for extrapolation of creep rupture strength diagrams are examined in view of a limited number of experimental data and the nonfulfilment of the hypothesis of a single curve. [ABSTRACT FROM AUTHOR]

Chaoshan vernacular house is an important branch of Lingnan traditional houses in South China, with a long architectural history and excellent climate responsiveness to the hot-humid climate. The modern rural houses learn the traditional experiences mainly in the form but fail to inherit the climate-responsive design and wisdom of the traditional houses. This study took the widely distributed Xiashanhu houses in Chaoshan as the object, generated 128 Xishanhu parametric models by combinations of various parameters on design factors of building, opening, shading, construction, and street layout, and investigated the climate-responsive design techniques by using the parametric methods. The results show that the hall width, room width, cornice height, orientation, and wall construction are the key climate-responsive design factors oriented towards thermal performance of Xiashanhu houses. The optimized designs are: the smaller hall width (4.05m，4.32m，4.59m), the smaller room width (2.7m，2.97m，3.24m), the larger cornice height (4.92m，5.1m), the orientation ranged from 30° north by west to 30° north by east, and the brick and concrete wall. The rationality and validity were verified by applying the optimized climate-responsive designs to the re-construction of a new rural house. Compared to the original case, the thermal performance is significantly improved by 5.18-9.98%. A detailed discussion was also provided on considering the actual situation and needs of the modern buildings in the process of "using the past for the present." This study is believed to provide valuable references for the research and modern applications of climate-responsive designs of traditional vernacular houses. It also contributes to the preservation of architectural and cultural regional characters and the inheritance of architectural climate responsiveness in contemporary times.

Introduction. The possibility of application of modified parametric methods of spatial signal processing in a sparse antenna array (SEAA) of the receiving position of transportable over-the-horizon decameter range radar (DRR) intended for all-weather remote monitoring of the shelf zone is considered in this paper. With an operational deployment of DRR on unprepared coast, problems of the equidistant location of antenna elements (AEs) often arise. In the case of nonequidistant AEs location and matched spatial processing, antenna pattern has interference sidelobes, which level can significantly exceed the allowable or calculated one for an equidistant AA. A well-known alternative to matched processing are parametric methods of spectral analysis based on the using of models with a finite number of parameters, but their direct application requires an equidistant sampling of the spatial signal.Aim. The aim of the research is to develop and analyze the method of parametric processing of spatial signals of the SEAA which AEs are located on the line with a random step in the range from λ/2 to several λ, where λ is the DRR wavelength.Materials and methods. To construct the detection characteristics (DC) computer modeling in the MatLab environment, the reliability of which was confirmed by the construction of known and theoretically calculated DC, was used.Results. The developed method includes a procedure of restoring (synthesizing) of artificial signal of equidistant AA with subsequent application of Burg parametric algorithm to obtain an estimate of the angular spatial frequency spectrum. To prove the applicability of the parametric method of SEAA signals processing in the case of location signals detecting, DC were obtained and compared with optimal ones.Conclusions. The obtained results have proved the suboptimality of the parametric method of SEAA signal processing at the random AEs spacing step lying in the range from λ/2 to 3λ, what makes it possible to recommend it for using in transportable DRRs.

The Hezar-Masjed Mountain range is one of the main highlands of the northeasten Iran. The vegetation of its upper elevations has been poorly studied. This study aimed to compare the plant biodiversity of different aspects of the Hezar-Masjed Summit. A total number of 139 random 1m2 plots were recorded from different aspects of the study area. In each plot, the abundance and canopy cover percent of vascular plant species were recorded. Hill’s numbers (i.e., N1, N2) along with the Camargo evenness index and total species richness with bootstrapping approach were calculated. Rarefaction method was used to compare species richness among the aspects. Also, Hill’s diversity profile for each aspect was drawn. For each aspect, rank-abundance method was used to evaluate the dominant species. Biodiversity calculations and statistical analyses were performed using R software. The east- and south- facingslopes were found to have the highest and lowest indices in richness, evenness, and diversity, respectively. Despite adequate water availability in north-facing slopes, they show lower diversity than that of the east-facing slope, probably due to the of dominance of a poisonous species (Seseli transcaucasicum). Our results shown that in the Hezar-Masjed Summit, aspects have different biodiversity which is a result of their various environmental conditions.

[11C]UCB-J is a PET radioligand that binds to the presynaptic vesicle glycoprotein 2A. Therefore, [11C]UCB-J PET may serve as an in vivo marker of synaptic integrity. The main objective of this study was to evaluate the quantitative accuracy and the 28-day test–retest repeatability (TRT) of various parametric quantitative methods for dynamic [11C]UCB-J studies in Alzheimer's disease (AD) patients and healthy controls (HC). Eight HCs and seven AD patients underwent two 60-min dynamic [11C]UCB-J PET scans with arterial sampling over a 28-day interval. Several plasma-input based and reference-region based parametric methods were used to generate parametric images using metabolite corrected plasma activity as input function or white matter semi-ovale as reference region. Different parametric outcomes were compared regionally with corresponding non-linear regression (NLR) estimates. Furthermore, the 28-day TRT was assessed for all parametric methods. Spectral analysis (SA) and Logan graphical analysis showed high correlations with NLR estimates. Receptor parametric mapping (RPM) and simplified reference tissue model 2 (SRTM2) BPND, and reference Logan (RLogan) distribution volume ratio (DVR) regional estimates correlated well with plasma-input derived DVR and SRTM BPND. Among the multilinear reference tissue model (MRTM) methods, MRTM1 had the best correspondence with DVR and SRTM BPND. Among the parametric methods evaluated, spectral analysis (SA) and SRTM2 were the best plasma-input and reference tissue methods, respectively, to obtain quantitatively accurate and repeatable parametric images for dynamic [11C]UCB-J PET. [ABSTRACT FROM AUTHOR]

The provision of high-quality food is a primary factor in ensuring adequate nourishment and preventing malnourishment-related diseases in Pakistan. This study, therefore, aimed to quantify the impact of income on nutrient consumption in Pakistan, with the hypothesis that income has a primary role in reducing malnourishment in the developing world. To do this, we estimated nutrient–income elasticity—defined as the proportion of change in nutrient consumption in response to a change in income—for total calories, macronutrients, and micronutrients, using the nationally representative Household Integrated Economic Survey data (2010–2011) for Pakistan. Nutrient–income elasticity values were derived using several parametric regression approaches. We also assessed the non-linearity and endogeneity of the relationship. Calorie–income elasticity was found to be significantly different from zero, irrespective of the estimation technique used. Income elasticity for macronutrients and micronutrients was also found to be significantly different from zero, ranging from 0.29 to 0.65. This study, therefore, supports the hypothesis that increased household income likely improves nutrient consumption.

Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation. [ABSTRACT FROM AUTHOR]

One of the most important issues in planning and managing water resources is the accurate estimation of monthly input discharge of the reservoirs in the future years, which is always associated with uncertainty. To cover these uncertainties, synthetic stream flow data generation models have been used by various researchers to generate stochastic time series. The computational basis of different stochastic models for generating monthly data has been different and this can have a significant effect on their performance. Therefore, selection of the best model of stochastic data generation for accurate planning and management of a water resource system is one of the major concerns of water resources specialists. In this research, the performance of parametric models of synthetic stream flow generation including Thomas-Fiering, Fragment and ARMA (1,1) and ARMA (1,2) combined with Valencia-Schaake and Mejia and Rousselle models were compared and evaluated. For this purpose, 30 years data of monthly discharge of Marun river in Khuzestan province were used and 900 synthetic monthly flow time series were generated using each of the models mentioned above. Based on the obtained results, the ARMA (1,2) model combined with the Valencia-Schaake model was recognized as the best model, considering the very desired performance in preserving the statistical parameters of historical data and generating maximum and minimum discharges related to wet and dry periods in different probabilities. This model can be used with greater confidence to analyze river systems and reservoirs, manage drought and apply water rationing rules in future drought conditions.

The article deals with the subject of the impact which road surfaces have onto cargo and the securing of cargo against shocks during road transport. The applicability of the key EN 12191‑1:2010 standard is discussed, not only for common conditions, but also for specific transport conditions on low‑quality road surfaces. Part of the article involves carrying out a transport experiment on a road paved with granite blocks and on a highway, from which data on the impact of shocks on cargo (acceleration coefficients values) were obtained. The data were statistically evaluated and compared to the normatively determined values of these acceleration coefficients. A parametric statistical analysis (two‑sample t‑test) was used to compare the values of the acceleration coefficients from the two types of surfaces. The analysis conducted shows statistically significant differences between the data measured on the road paved with granite blocks and on the highway. Using correlation analysis, the dependence of the acceleration coefficient values and inertial forces affecting cargo during transport were verified. For the relevant axes (x – longitudinal and y – transverse), a very strong correlation was found.

This review article presents an assessment of trends in statistical methods and an evaluation of their appropriateness in articles published in the Archives of Plastic Surgery (APS) from 2012 to 2017. We reviewed 388 original articles published in APS between 2012 and 2017. We categorized the articles that used statistical methods according to the type of statistical method, the number of statistical methods, and the type of statistical software used. We checked whether there were errors in the description of statistical methods and results. A total of 230 articles (59.3%) published in APS between 2012 and 2017 used one or more statistical method. Within these articles, there were 261 applications of statistical methods with continuous or ordinal outcomes, and 139 applications of statistical methods with categorical outcome. The Pearson chi-square test (17.4%) and the Mann-Whitney U test (14.4%) were the most frequently used methods. Errors in describing statistical methods and results were found in 133 of the 230 articles (57.8%). Inadequate description of P-values was the most common error (39.1%). Among the 230 articles that used statistical methods, 71.7% provided details about the statistical software programs used for the analyses. SPSS was predominantly used in the articles that presented statistical analyses. We found that the use of statistical methods in APS has increased over the last 6 years. It seems that researchers have been paying more attention to the proper use of statistics in recent years. It is expected that these positive trends will continue in APS.

Abstract Background Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called “short fat data” problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Results Through our simulation study we propose a collective feature selection approach to select features that are in the “union” of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger’s MyCode Community Health Initiative (on behalf of DiscovEHR collaboration). Conclusions In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

The aim of this study was to investigate the test–retest (TRT) repeatability of various parametric quantification methods for [18F]Flortaucipir positron emission tomography (PET). We included eight subjects with dementia or mild cognitive impairment due to Alzheimer's disease and six cognitively normal subjects. All underwent two 130-min dynamic [18F]Flortaucipir PET scans within 3 ± 1 weeks. Data were analyzed using reference region models receptor parametric mapping (RPM), simplified reference tissue method 2 (SRTM2) and reference logan (RLogan), as well as standardized uptake value ratios (SUVr, time intervals 40–60, 80–100 and 110–130 min post-injection) with cerebellar gray matter as reference region. We obtained distribution volume ratio or SUVr, first for all brain regions and then in three tau-specific regions-of-interest (ROIs). TRT repeatability (%) was defined as |retest–test|/(average (test + retest)) × 100. For all methods and across ROIs, TRT repeatability ranged from (median (IQR)) 0.84% (0.68–2.15) to 6.84% (2.99–11.50). TRT repeatability was good for all reference methods used, although semi-quantitative models (i.e. SUVr) performed marginally worse than quantitative models, for instance TRT repeatability of RPM: 1.98% (0.78–3.58) vs. SUVr80–100: 3.05% (1.28–5.52), p < 0.001. Furthermore, for SUVr80–100 and SUVr110–130, with higher average SUVr, more variation was observed. In conclusion, while TRT repeatability was good for all models used, quantitative methods performed slightly better than semi-quantitative methods. [ABSTRACT FROM AUTHOR]

The non-parametric methods have been proposed in the research literature as an alternative to parametric methods for time series forecasting. However, scarce evidence is available about the relative performance and computational ability of both parametric and non-parametric methods. This paper reviews the comparative studies conducted for evaluating the accuracy of parametric and non-parametric methods, especially ma-chine learning methods. For this, we briefly review widely applicable the parametric and non-parametric methods. Moreover, an empirical study has been carried out on real time series datasets to evaluate the comparative performance of parametric methods over machine learning methods. Additionally, the limitations of the machine learning methods are highlighted which leads to the selection of parametric methods over non-parametric methods by the researchers in recent years. Further, some recommendations for future research are presented. [ABSTRACT FROM AUTHOR]

Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation.

Designing policies for water systems management requires the capability to assess the economic impacts of water availability and to effectively couple water withdrawals by human activities with natural hydrologic dynamics. At the core of any scientific approach to these issues there is the estimation of water withdrawals by industrial sectors in the form of water coefficients, which are measurements of the quantity of water withdrawn per dollar of GDP or output. In this work we focus on the contiguous United States and on the estimation of water coefficients for regional scale analyses. We first compare an established methodology for the estimation of national water coefficients with a parametric one we propose. Second, we introduce a method to estimate water coefficients at the level of ecological regions and we discuss how they reduce possible biases in regional analyses of water systems. We conclude discussing advantages and limits of regional water coefficients.

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The article deals with the subject of the impact which road surfaces have onto cargo and the securing of cargo against shocks during road transport. The applicability of the key EN 12191-1:2010 standard is discussed, not only for common conditions, but also for specific transport conditions on low-quality road surfaces. Part of the article involves carrying out a transport experiment on a road paved with granite blocks and on a highway, from which data on the impact of shocks on cargo (acceleration coefficients values) were obtained. The data were statistically evaluated and compared to the normatively determined values of these acceleration coefficients. A parametric statistical analysis (two-sample t-test) was used to compare the values of the acceleration coefficients from the two types of surfaces. The analysis conducted shows statistically significant differences between the data measured on the road paved with granite blocks and on the highway. Using correlation analysis, the dependence of the acceleration coefficient values and inertial forces affecting cargo during transport were verified. For the relevant axes (x - longitudinal andy - transverse), a very strong correlation was found. [ABSTRACT FROM AUTHOR]

Statistical analysis is an integral part of every experiment, since it helps researchers to make conclusions out of the work done. Textbooks with detailed description of the algorithms for statistical analysis exist, as well as diverse software packages. Statistical methods are, however, often applied incorrectly, which leads to erroneous and inadequate conclusions. The present article is an attempt to determine the main problems emerging in the course of statistical analysis of experimental results in the field of environmental microbiology. For instance, the classical parametric tests, which are most often used in experimental articles (t-test, analysis of variance, Pearson correlation coefficient, etc.), are applicable only when a random variable of n independent observations has normal distribution. Importantly, the normality of the distribution must be proved, and it is possible to do only for minimal sample size of 20‒30 independent observations per group. The latter is crucial for the incubation, isotope, and molecular biological experiments. The family of normal distributions is not the only family of parameter-dependent (parametric) distributions of random variables. Moreover, real-life distributions usually differ from the normal ones, and normal distribution may be considered only as a certain approximation. High diversity of parametric families complicates the choice of criteria and statistical tests for analysis of a set of data collected from n independent observations. Nonparametric statistics may be of help, since it is free of sample size and distribution requirements, although, as any method of analysis, it also has limitations. An increasing number of experimental data, including those in the field of environmental microbiology, are nowadays analyzed by using nonparametric statistics, which indicates a certain tendency for substitution of parametric methods by nonparametric ones. [ABSTRACT FROM AUTHOR]

The article discusses the method of algorithmic construction of Islamic ornaments, which can be used as a decorative element in architectural design. Two necessary stages are highlighted in the generation of an ornamental motif by means of algorithmic design – the first includes operations on a base mesh consisting of a combination of regular or semiregular cells, for example, drawing rays from the centers of the edges of the original lattice or rotating figures relative to its nodes; the second stage involves the creation of a basic pattern of the ornamental design that fits into a regular or semi-regular polygon, and the decorative motif in the figure is made up of a primitive that is symmetrically reflected relative to any lines, usually rays, emanating from opposite corners of the polygon. The paper analyses the traditional patterns of Islamic ornaments, on the basis of which new designs were built, using visual programming tools (Rhino and Grasshopper).

Statistics is the main science by which researchers validate the results of scientific work, and the choice of an inadequate statistical method may lead to conclusions that are considered questionable by reviewers. This study had the objective of describing the characteristics of the statistical methods used in the papers published in Acta Scientiarum. Agronomy from 1998 to 2016 as part of a a critical analysis of the journal to pinpoint possible failures in the application of these methods. All scientific articles (n = 1,237) published in the journal were surveyed, of which 54.1% addressed areas of crop production. The mean comparison methods were the most commonly used (75.5%) and, consequently, they represented the highest proportion of errors (60.8%) of the authors in the journal.

Statistics is the main science by which researchers validate the results of scientific work, and the choice of an inadequate statistical method may lead to conclusions that are considered questionable by reviewers. This study had the objective of describing the characteristics of the statistical methods used in the papers published in Acta Scientiarum. Agronomy from 1998 to 2016 as part of a a critical analysis of the journal to pinpoint possible failures in the application of these methods. All scientific articles (n = 1,237) published in the journal were surveyed, of which 54.1% addressed areas of crop production. The mean comparison methods were the most commonly used (75.5%) and, consequently, they represented the highest proportion of errors (60.8%) of the authors in the journal. [ABSTRACT FROM AUTHOR]

With the continuous increase of the running speed, the head shape of a high-speed train turns out to be the critical factor to boost the speed further. In order to reduce the time required to design the head of a high-speed train and to improve the modelling efficiency, various parametric modelling methods have been widely applied in the optimization design of the head of a high-speed train to obtain an optimal head shape so that the aerodynamic effect acting on the head of a high-speed train can be reduced and more energy can be saved. This paper reviews these parametric modelling methods and classifies them into four categories: two-dimensional, three-dimensional, CATIA-based, and mesh deformation-based parametric modelling methods. Each of the methods is introduced, and the advantages and disadvantages of these methods are identified. The simulation results are presented to demonstrate that the aerodynamic performance of the optimal models constructed by these parametric modelling methods has been improved when compared with the numerical calculation results of the original models or the prototype models of running trains. Since different parametric modelling methods used different original models and optimization methods, few publications could be found which compare the simulation results of the aerodynamic performance among different parametric modelling methods. In spite of this, these parametric modelling methods indicate that more local shape details will lead to more accurate simulation results, and fewer design variables will result in higher computational efficiency. Therefore, the ability of describing more local shape details with fewer design variables could serve as a main specification to assess the performance of various parametric modelling methods. The future research directions may concentrate on how to improve such ability. [ABSTRACT FROM AUTHOR]

Epidemiologic studies are frequently susceptible to missing information. Omitting observations with missing variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values are not missing completely at random. Even when missingness is completely random, complete-case analysis can reduce the efficiency of estimated parameters, because large amounts of available data are simply tossed out with the incomplete observations. Alternative methods for mitigating the influence of missing information, such as multiple imputation, are becoming an increasing popular strategy in order to retain all available information, reduce potential bias, and improve efficiency in parameter estimation. In this paper, we describe the theoretical underpinnings of multiple imputation, and we illustrate application of this method as part of a collaborative challenge to assess the performance of various techniques for dealing with missing data (Am J Epidemiol. 2018;187(3):568-575 ). We detail the steps necessary to perform multiple imputation on a subset of data from the Collaborative Perinatal Project (1959-1974), where the goal is to estimate the odds of spontaneous abortion associated with smoking during pregnancy. [ABSTRACT FROM AUTHOR]

The ability to control production costs, taking into account the qualitative performance of products, is a central objective of an enterprise located in a strong competitive environment. In order to optimize production costs, parametric methods specifically adapted wood processing industry with mass production are applied. Starting from a theoretical approach of parametric methods, it then proceeds to determine the actual costs and results achieved. For a wider perspective, the costs of a product already in production versus costs of a new product the same range are analyzed. The results obtained following the application of parametric methods emphasize the possibility of developing technological manufacturing process, leading indicators of optimizing of production costs.

Here, we propose and investigate a hybrid model that combines parametric option pricing models such as Black-Scholes (BS) option pricing model, Monte Carlo option pricing model, and finite difference method with nonparametric machine learning techniques such as support vector regression (SVR) and extreme learning machine-based regression models. The purpose of this model is to support better investment decisions by forecasting the option price with high predictive accuracy. To further reduce the forecasting error, we incorporate a homogeneity hint (i.e., training the model by categorizing the options data based on moneyness and time-to-maturity of the option contract) into the model. We examine the feasibility and effectiveness of this model using a case study to predict the one-day-ahead price of index options traded in the National Stock Exchange of India Limited. Our experimental results show that the proposed new hybrid model is viable and effective and provides better predictive performance as compared with our benchmark models (standard BS Model, standard Monte Carlo, standard finite difference model, and standard SVR Model). For example, the proposed hybrid model using SVR improved, respectively, the root-mean-square error and mean absolute error by 83.66 and 85.46 % (D1 dataset), 78.02 and 76.0 % (D2 dataset), 91.86 and 90.62 % (D3 dataset), and 87.7 and 90.29 % (D4 dataset), when compared with the benchmarked BS model. We observe similar improvements over the other benchmarked models. Therefore, the proposed new hybrid model is a suitable alternative model for option pricing when higher predictive accuracy is desired. [ABSTRACT FROM AUTHOR]

The time-dependent-asymmetric-linear parsimony is an ancestral state reconstruction method which extends the standard linear parsimony (a.k.a. Wagner parsimony) approach by taking into account both branch lengths and asymmetric evolutionary costs for reconstructing quantitative characters (asymmetric costs amount to assuming an evolutionary trend toward the direction with the lowest cost). A formal study of the influence of the asymmetry parameter shows that the time-dependent-asymmetric-linear parsimony infers states which are all taken among the known states, except for some degenerate cases corresponding to special values of the asymmetry parameter. This remarkable property holds in particular for the Wagner parsimony. This study leads to a polynomial algorithm which determines, and provides a compact representation of, the parametric reconstruction of a phylogenetic tree, that is for all the unknown nodes, the set of all the possible reconstructed states associated with the asymmetry parameters leading to them. The time-dependent-asymmetric-linear parsimony is finally illustrated with the parametric reconstruction of the body size of cetaceans. [ABSTRACT FROM AUTHOR]

Cost estimation of new products has always been difficult as only few design, manufacturing and operational features will be known. In these situations, parametric or non-parametric methods are commonly used to estimate the cost of a product given the corresponding cost drivers. The parametric models use priori determined cost function where the parameters of the function are evaluated from historical data. Non-parametric methods, on the other hand, attempt to fit curves to the historic data without predetermined function. In both methods, it is assumed that the historic data used in the analysis is a true representation of the relation between the cost drivers and the corresponding costs. However, because of efficiency variations of the manufacturers and suppliers, changes in supplier selections, market fluctuations, and several other reasons, certain costs in the historic data may be too high whereas other costs may represent better deals for their corresponding cost drivers. Thus, it may be important to rank the historic data and identify benchmarks and estimate the target costs of the product based on these benchmarks. In this paper, a novel adaptation of cost drivers and cost data is introduced in order to use data envelopment analysis for the purpose of ranking cost data and identify benchmarks, and then estimate the target costs of a new product based on these benchmarks. An illustrative case study has been presented for the cost estimation of landing gears of an aircraft manufactured by an aerospace company located in Montreal, CANADA.

Non-independent evolution of amino acid sites has become a noticeable limitation of most methods aimed at identifying selective constraints at functionally important amino acid sites or protein regions. The need for a generalised framework to account for non-independence of amino acid sites has fuelled the design and development of new mathematical models and computational tools centred on resolving this problem. Molecular coevolution is one of the most active areas of research, with an increasing rate of new models and methods being developed everyday. Both parametric and nonparametric methods have been developed to account for correlated variability of amino acid sites. These methods have been utilised for detecting phylogenetic, functional and structural coevolution as well as to identify surfaces of amino acid sites involved in protein-protein interactions. Here we discuss and briefl y describe these methods, and identify their advantages and limitations.

In this paper three different methods have been described and applied to evaluate the nitrate contamination from agricultural practices in a study area located in the Nitrate Vulnerable Zone (NVZ) of the Arborea plain (Sardinia - Italy). Potential risk of contamination and concentration of nitrate pollution in groundwater has been estimated by using respectively Parametric and both Numerical and Artificial Neural Networks methods. Parametric methods consider the combination of intrinsic aquifer vulnerability to contamination index (SINTACS) and agricultural nitrates hazard index (IPNOA). The transport numerical model is based on flow model, obtained with Three Dimensional Finite Difference Groundwater Flow Model (MODFLOW), and it is made applying 3D Multi Species Transport Model (MT3D). Artificial Neural Networks (ANNs) are used for the estimation of the nitrate concentration in monitoring well. [ABSTRACT FROM AUTHOR]

A method for manganese determination in iron concentrates by reactor activation analysis has been developed, on the basis of discounting the contribution of the Fe(n,p)Mn to the total Mn activity through the Mn/Mn activity ratio, employing the data on the nuclear constants involved. The possibility of using Fe as internal monitor, for the calculation of manganese concentration, is evaluated for different values of both the $$\phi_{\text{epi}} /\phi_{\text{th}}$$ ratios and the α parameter. [ABSTRACT FROM AUTHOR]

The article deals with the scalar-valued score functionSFdefined for a regular unimodal and continuous probability distributionFwith arbitrary interval support, recently introduced by the author. The concept of the central characteristic that describes a relative influence ofis described in a much more general way in order to improve its usefulness in parametric estimation by means of the general moment method. In particular, the whole approach is elucidated by describing it in a more suitable framework than before. Further, we show that the inference function to be used in the moment estimating equations is either the score function of distribution (bounded for heavy-tailed parametric distribution models) or its modification based on the Huber’s approach. [ABSTRACT FROM AUTHOR]

This paper examines moving target detection in distributed multi-input multi-output radar with sensors placed on moving platforms. Unlike previous works which were focused on stationary platforms, we consider explicitly the effects of platform motion, which exacerbate the location-induced clutter non-homogeneity inherent in such systems and thus make the problem significantly more challenging. Two new detectors are proposed. The first is a sparsity based detector which, by exploiting a sparse representation of the clutter in the Doppler domain, adaptively estimates from the test signal the clutter subspace, which is in general distinct for different transmit/receive pairs and, moreover, may spread over the entire Doppler bandwidth. The second is a fully adaptive parametric detector which employs a parametric autoregressive clutter model and offers joint model order selection, clutter estimation/mitigation, and target detection in an integrated and fully adaptive process. Both detectors are developed within the generalized likelihood ratio test (GLRT) framework, obviating the need for training signals that are indispensable for conventional detectors but are difficult to obtain in practice due to clutter non-homogeneity. Numerical results indicate that the proposed training-free detectors offer improved detection performance over covariance matrix based detectors when the latter have a moderate amount of training signals. [ABSTRACT FROM PUBLISHER]