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As the process yield has significantly raised because of the advanced development of manufacturing technology today, engineers would logically attempt to inspect fewer sample items for the quality evaluation of processes or products. Therefore, in this paper, an efficient sampling inspection method based on the process yield index Spk is developed for lot sentencing, wherein the inspection is performed only on a fractional submitted lot rather than examining every following submission. Both the average sample number (ASN) and operating characteristic (OC) functions of the proposed method are derived on the basis of the Markov chain technique. Further, an optimization model that minimizes the ASN and constrains two OC functions restricted to given quality requirements and tolerable risks is constructed. Performance comparisons in terms of economy and discriminatory power are analyzed by contrasting ASN and OC curves with existing Spk-based methods under the same quality conditions to emphasize the superiority of the proposed method. For easy implementation, we prove the applicability of the proposed method by demonstrating a case study taken from an integrated circuit packaging company. [ABSTRACT FROM AUTHOR]

Polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs), and per- and poly-fluoroalkyl substances (PFAS) are persistent organic pollutants (POPs) that remain detrimental to critical subpopulations, namely pregnant women. Required tests for biomonitoring are quite expensive. Moreover, statistical models aiming to discover the relationships between pollutants levels and human characteristics have their limitations. Therefore, the objective of this study is to use machine learning predictive models to further examine the pollutants' predictors, while comparing them. Levels of 33 congeners were measured in the serum of 269 pregnant women, from whom data was collected regarding sociodemographic, dietary, environmental, and anthropometric characteristics. Several machine learning algorithms were compared using "Python" for each pollutant: support vector machine (SVM), random forest, XGBoost, and neural networks. The aforementioned characteristics were included in the model as features. Prediction, accuracy, precision, recall, F1-score, area under the ROC curve (AUC), sensitivity, and specificity were retrieved to compare the models between them and among pollutants. The highest performing model for all pollutants was Random Forest. Results showed a moderate to acceptable performance and discriminative power among all POPs, with OCPs' model performing slightly better than all other models. Top related features for each model were also presented using SHAP analysis, detailing the predictors' negative or positive impact on the model. In conclusion, developing such a tool is of major importance in a context of limited financial and research resources. Nevertheless, machine learning models should always be interpreted with caution by exploring all evaluation metrics. [ABSTRACT FROM AUTHOR]

Purpose – This study aims to determine the quality of multiple choice items on human skeletal material in terms of validity, reliability, difficulty level, and differentiating power. Method – This research uses a quantitative descriptive approach. the subjects in this study were grade V students of State Elementary School 1 Sungai Bungin, totaling 22 students. the sample selection technique used saturated sampling technique. the data collection technique was carried out by the documentation method from the test results. the collected data were analyzed using the SPSS IBM Statistics 27 application to determine the criteria for the items. Findings – This study provides a detailed description of the validity, reliability, differentiating power, and difficulty of the questions. It found 17 valid items (68%), with high reliability (Cronbach's Alpha: 0.798 and 0.792). A total of 4 items had sufficient differentiating power, 19 items were good, and 2 items were excellent. The level of difficulty of the questions varied: 4 difficult questions (16%), 13 medium questions (52%), and 8 easy questions (32%). Research Implications – This research has important implications for improving the quality of education in elementary schools. The results of analyzing the accuracy of multiple choice questions on human skeletal material can be used to improve and develop questions that are more appropriate in measuring student knowledge. Teachers can adjust teaching methods based on the difficulty level of the questions, improve learning effectiveness, and provide more constructive feedback. In addition, the findings can assist in more targeted curriculum development and teacher training in the creation of high-quality questions. Thus, this study contributes to the improvement of student learning outcomes and the implementation of data-driven evaluation in the primary education system.

Purpose: A new high-resolution next-generation sequencing (NGS)-based method was established to type closely related European type II Toxoplasma gondii strains. Methods: T. gondii field isolates were collected from different parts of Europe and assessed by whole genome sequencing (WGS). In comparison to ME49 (a type II reference strain), highly polymorphic regions (HPRs) were identified, showing a considerable number of single nucleotide polymorphisms (SNPs). After confirmation by Sanger sequencing, 18 HPRs were used to design a primer panel for multiplex PCR to establish a multilocus Ion AmpliSeq typing method. Toxoplasma gondii isolates and T. gondii present in clinical samples were typed with the new method. The sensitivity of the method was tested with serially diluted reference DNA samples. Results: Among type II specimens, the method could differentiate the same number of haplotypes as the reference standard, microsatellite (MS) typing. Passages of the same isolates and specimens originating from abortion outbreaks were identified as identical. In addition, seven different genotypes, two atypical and two recombinant specimens were clearly distinguished from each other by the method. Furthermore, almost all SNPs detected by the Ion AmpliSeq method corresponded to those expected based on WGS. By testing serially diluted DNA samples, the method exhibited a similar analytical sensitivity as MS typing. Conclusion: The new method can distinguish different T. gondii genotypes and detect intra-genotype variability among European type II T. gondii strains. Furthermore, with WGS data additional target regions can be added to the method to potentially increase typing resolution. [ABSTRACT FROM AUTHOR]

We develop a framework to simultaneously compute the unobservable parameters underlying the structural-parametric models for bankruptcy prediction. More specifically, we compute the unobservable parameters such as, asset value and asset volatility, through learning by embedding in the structural models a neural network that maps the neural network's input space (e.g. companies' observable financial and market data) to the unobservable parameter space. Within such a 'neuro-structural' framework, the neural network and the structural model work together as a one unit during the learning phase by providing to each other forward and backward information, respectively, until the weights of the neural network are optimized according to a merit function. Empirical results show that structural models, like the Black-Scholes-Merton and the Down-and-Out option models, with parameters computed with our approach, perform better than alternative specifications of the structural models, out of sample, in terms of discriminatory power, information content and economic impact. Importantly, they also perform better than a standard neural network, suggesting that the co-joint dynamics between the neural network and the structural model are useful during the learning phase and can improve the prediction performance (and the training efficiency) of neural networks. Finally, our approach provides methodological (and empirical) enhancements over logit specifications such as, Campbell et al. [In search of distress risk. J Finance, 2008, 63, 2899–2939]. There, financial and market data are the inputs, and the output is the probability of bankruptcy whereas our approach includes an intermediary step to obtain the unobservable parameters and subsequently the probability of bankruptcy. [ABSTRACT FROM AUTHOR]

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

Abstract Background Systematic reviews demonstrated that gait variables are the most reliable predictors of future falls, yet are rarely included in fall screening tools. Thus, most tools have higher specificity than sensitivity, hence may be misleading/detrimental to care. Therefore, this study aimed to determine the validity, and reliability of the velocity field diagram (VFD -a gait analytical tool), and the Timed-up-and-go test (TUG)-commonly used in Nigeria as fall screening tools, compared to a gold standard (known fallers) among community-dwelling older adults. Method This is a cross-sectional observational study of 500 older adults (280 fallers and 220 non-fallers), recruited by convenience sampling technique at community health fora on fall prevention. Participants completed a 7-m distance with the number of steps and time it took determined and used to compute the stride length, stride frequency, and velocity, which regression lines formed the VFD. TUG test was simultaneously conducted to discriminate fallers from non-fallers. The cut-off points for falls were: TUG times ≥ 13.5 s; VFD’s intersection point of the stride frequency, and velocity regression lines (E1) ≥ 3.5velots. The receiver operating characteristic (ROC) area under the curves (AUC) was used to explore the ability of the E1 ≥ 3.5velots to discriminate between fallers and non-fallers. The VFD’s and TUG’s sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined. Alpha was set at p

The purpose of this study is to provide an efficient method for the selection of input–output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis–Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model.

Background: Although larger amounts of scientific attention have been directed toward the concept of positive aspects of caregiving (PAC) in recent years, a globally uniform definition and a suitable, scientifically valid questionnaire for all informal caregivers have yet to be developed. On the basis of the questionnaires that already exist for measuring PAC, the authors aimed to (a) concretize the concept and (b) develop a new scale by focusing only on items that show that family caregivers experience a benefit for themselves and that the benefit they experience is the result of their caregiving activities. Methods: The Benefits of Being a Caregiver Scale (BBCS) was validated on data from 961 informal caregivers. Cronbach's alpha was calculated to assess the internal consistency of the items, and a factor analysis was conducted to determine the structure of the BBCS. The discriminatory power and item difficulties were examined. Construct validity was established by testing four hypotheses. Results: The factor analysis confirmed the single-factor structure of the BBCS. Cronbach's alpha for the total scale was 0.922. One of the 15 items did not show good to very good discriminatory power and was excluded from the final version of the scale. A higher BBCS score was observed if the caregiver experienced more positive aspects of caregiving and tended to have better general coping skills and a positive relationship with the care-receiver. The BBCS score was not associated with the subjective burden of the caregiver. Results confirmed the validity of the BBCS. Conclusion: The BBCS is a valid assessment instrument for measuring the benefits that caregivers experience from their caregiving work and can easily be used in research and practice. The BBCS is available free of charge in English and German (http://www.caregiver-benefits.de). [ABSTRACT FROM AUTHOR]

Background: West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods: We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results: Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions: Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). [ABSTRACT FROM AUTHOR]

The Approximate Bayesian Computation (ABC) provides a powerful tool for signature-domain calibration of hydrological models where hydrological signatures are incorporated into calibration objectives. However, the efficiency of ABC relies strongly on the use of a vector of sufficient signatures that can fully represent relevant information in raw data. The application of ABC with randomly chosen signatures can result in inaccurate calibration results. To fill this gap, a hybrid time- and signature-domain Bayesian inference framework for calibration of hydrological models is proposed. In this framework, a set of approximately sufficient signatures is pursued through simultaneous consideration of the information redundancy analysis (IRA) and discriminatory power analysis (DPA) procedures. While the IRA deals with the information redundancy inherent in the pool of available signatures, DPA quantifies the discriminatory power of a given signature as the reliability and sharpness of the associated probabilistic predictions generated by ABC. The verified residual error scheme in time-domain inference is approximated as the probabilistic model in the acceptance test of ABC. The proposed framework is then tested on the Xin'anjiang rainfall-runoff model applied to the Ren River basin (RRB) of China. The use of IRA and DPA provides a probabilistic model prediction statistically equivalent to that of classical time-domain inference in terms of the reliability and sharpness. The comparison to signature-domain inference using the complete set of hydrological signatures further demonstrates the importance of IRA and DPA in improving the quality of Bayesian model calibration in the signature domain and reducing the total predictive uncertainty. The framework makes it practically possible to maintain adequate accuracy of model predictions produced by signature-domain inference, improving the efficiency of ABC in solving the model calibration problems and consequently promotes the use of ABC in signature-domain model calibration. [ABSTRACT FROM AUTHOR]

Acceptance sampling plans are preferably used when sample testing becomes destructive or costly to make reliable lot determination from a reasonable size of samples. Previous decades have seen various sampling strategies developed for specific purposes and the evolution of samplingmethods continue to ensure that lot sentencing becomes more efficient and economic. In this study, we proposed a modified variables repetitive group sampling (VRGS) plan integrated with a critical-value-adjusted switching mechanism. Such a design provides an adaptive assessment of the varied submitted quality level. The operating characteristic function is derived from the sampling distribution of process yield index Spk, which possesses a one-to-one transformation to process yield. By solving the developed nonlinear minimization model, the plan parameters of the proposed method are satisfied with tolerable sampling risks and desirable quality levels simultaneously. Furthermore, by comparing the average sample number (ASN) reduction and discriminatory power with other approaches, the proposed method outperforms conventional methods. Numerical illustrations are provided to show its practicability in real applications. [ABSTRACT FROM AUTHOR]

Endothelial dysfunction (ED) starts early in chronic kidney disease (CKD) and is the hallmark of atherosclerosis in these patients. During recent years, numerous markers have emerged, aiming to predict the onset of ED in CKD patients. Therefore, there is a need to evaluate and assess the discriminatory ability (or diagnostic accuracy) of such a marker (i.e., the ability to correctly classify individuals as having a given disease or not) and identify the optimal cut-off value. A receiver operating characteristic (ROC) curve analysis has been used in the majority of the research papers evaluating the predictive ability of a marker of ED. It is a graphical plot combining pairs of sensitivity (true positive rate) on the y axis and the complement of specificity (1—specificity, false positive rate) in the x axis, corresponding to several of the cut-off values covering the complete range of possible values that this test/marker might take. Herein, using a series of practical examples derived from clinical studies on ED in the special population of CKD, we address the principles, fundamentals, advantages and limitations regarding the interpretation of the ROC analysis. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide an efficient method for the selection of input–output indicators in the data envelopment analysis (DEA) approach, in order to improve the discriminatory power of the DEA method in the evaluation process and performance analysis of homogeneous decision-making units (DMUs) in the presence of negative values and data. For this purpose, the Shannon entropy technique is used as one of the most important methods for determining the weight of indicators. Moreover, due to the presence of negative data in some indicators, the range directional measure (RDM) model is used as the basic model of the research. Finally, to demonstrate the applicability of the proposed approach, the food and beverage industry has been selected from the Tehran stock exchange (TSE) as a case study, and data related to 15 stocks have been extracted from this industry. The numerical and experimental results indicate the efficacy of the hybrid data envelopment analysis–Shannon entropy (DEASE) approach to evaluate stocks under negative data. Furthermore, the discriminatory power of the proposed DEASE approach is greater than that of a classical DEA model. [ABSTRACT FROM AUTHOR]

Background: Systematic reviews demonstrated that gait variables are the most reliable predictors of future falls, yet are rarely included in fall screening tools. Thus, most tools have higher specificity than sensitivity, hence may be misleading/detrimental to care. Therefore, this study aimed to determine the validity, and reliability of the velocity field diagram (VFD -a gait analytical tool), and the Timed-up-and-go test (TUG)-commonly used in Nigeria as fall screening tools, compared to a gold standard (known fallers) among community-dwelling older adults.Method: This is a cross-sectional observational study of 500 older adults (280 fallers and 220 non-fallers), recruited by convenience sampling technique at community health fora on fall prevention. Participants completed a 7-m distance with the number of steps and time it took determined and used to compute the stride length, stride frequency, and velocity, which regression lines formed the VFD. TUG test was simultaneously conducted to discriminate fallers from non-fallers. The cut-off points for falls were: TUG times ≥ 13.5 s; VFD's intersection point of the stride frequency, and velocity regression lines (E1) ≥ 3.5velots. The receiver operating characteristic (ROC) area under the curves (AUC) was used to explore the ability of the E1 ≥ 3.5velots to discriminate between fallers and non-fallers. The VFD's and TUG's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined. Alpha was set at p < 0.05.Results: The VFD versus TUG sensitivity, specificity, PPV and NPV were 71%, 27%, 55%, and 42%, versus 39%, 59%, 55%, and 43%, respectively. The ROC's AUC were 0.74(95%CI:0.597,0.882, p = 0.001) for the VFD. The optimal categorizations for discrimination between fallers/non-fallers were ≥ 3.78 versus ≤ 3.78 for VFD (fallers versus non-fallers prevalence is 60.71% versus 95.45%, respectively), with a classification accuracy or prediction rate of 0.76 unlike TUG with AUC = 0.53 (95% CI:0.353,0.700, p = 0.762), and a classification accuracy of 0.68, and optimal characterization of ≥ 12.81 s versus ≤ 12.81 (fallers and non-fallers prevalence = 92.86% versus 36.36%, respectively).Conclusion: The VFD demonstrated a fair discriminatory power and greater reliability in identifying fallers than the TUG, and therefore, could replace the TUG as a primary tool in screening those at risk of falls. [ABSTRACT FROM AUTHOR]

For banks using the Advanced Internal Ratings-Based Approach in accordance with Basel III requirements, the amount of required regulatory capital relies on the banks' estimates of the probability of default, the loss given default and the conversion factor for their credit risk portfolio. Therefore, for both model development and validation, assessing the models' predictive and discriminatory abilities is of key importance in order to ensure an adequate quantification of risk. This paper compares different measures of discriminatory power suitable for multi-class target variables such as in loss given default (LGD) models, which are currently used among banks and supervisory authorities. This analysis highlights the disadvantages of using measures that solely rely on pairwise comparisons when applied in a multi-class setting. Thus, for multi-class classification problems, we suggest using a generalisation of the well-known area under the receiver operating characteristic (ROC) curve known as the volume under the ROC surface (VUS). Furthermore, we present the R-package VUROCS, which allows for a time-efficient computation of the VUS as well as associated (co)variance estimates and illustrate its usage based on real-world loss data and validation principles. [ABSTRACT FROM AUTHOR]

In vitro surrogate tests are broadly used for evaluation of the critical release characteristics of semi-solid dosage forms. In general, these tests are limited to assess the effect of crucial manufacturing process related steps on the physicochemical characteristics and overall drug product performance. In vitro release tests (IVRT) do not directly anticipate in vivo performance, but may detect in vitro changes that may correlate with in vivo performance. The objective of this work was to develop a suitable in vitro release test for evaluation of the similarity between two marketed Clotrimazole 1% Cream formulations. A systematic approach was used to address some essential qualification parameters and validation concepts described in EMA's draft guideline on quality and equivalence of topical products. The procedure included suitable evaluation of the receptor medium, membrane qualification, followed by evaluation of method precision and method robustness. For evaluation of data, the comparison of Clotrimazole release profile met the relevant acceptance criteria for the 90% Confidence Interval for the ratio of means of the pairwise comparisons falling inside the limits of 90--111%. The linearity of the IVRT method as function of the drug concentration in the formulation was evaluated with 50% and 200% API formulations. In addition, the discriminatory power of the method was confirmed with formulation with altered viscosity as critical quality attribute. After validation of the method, the Clotrimazole 1% cream formulations were compared and their similarity was assessed. The approach was found to be useful and comprehensive in performing validation activities. [ABSTRACT FROM AUTHOR]

Abstract Background The low cost and rapidity of microsatellite analysis have led to the development of several markers for many species. Because in non-invasive genetics it is recommended to genotype individuals using few loci, generally a subset of markers is selected. The choice of different marker panels by different research groups studying the same population can cause problems and bias in data analysis. A priority issue in conservation genetics is the comparability of data produced by different labs with different methods. Here, we compared data from previous and ongoing studies to identify a panel of microsatellite loci efficient for the long-term monitoring of Apennine brown bears (Ursus arctos marsicanus), aiming at reducing genotyping uncertainty and allowing reliable individual identifications overtimes. Results We examined all microsatellite markers used up to now and identified 19 candidate loci. We evaluated the efficacy of 13 of the most commonly used loci analyzing 194 DNA samples belonging to 113 distinct bears selected from the Italian national biobank. We compared data from 4 different marker subsets on the basis of genotyping errors, allelic patterns, observed and expected heterozygosity, discriminatory powers, number of mismatching pairs, and probability of identity. The optimal marker set was selected evaluating the low molecular weight, the high discriminatory power, and the low occurrence of genotyping errors of each primer. We calibrated allele calls and verified matches among genotypes obtained in previous studies using the complete set of 13 STRs (Short Tandem Repeats), analyzing six invasive DNA samples from distinct individuals. Differences in allele-sizing between labs were consistent, showing a substantial overlap of the individual genotyping. Conclusions The proposed marker set comprises 11 Ursus specific markers with the addition of cxx20, the canid-locus less prone to genotyping errors, in order to prevent underestimation (maximizing the discriminatory power) and overestimation (minimizing the genotyping errors) of the number of Apennine brown bears. The selected markers allow saving time and costs with the amplification in multiplex of all loci thanks to the same annealing temperature. Our work optimizes the available resources by identifying a shared panel and a uniform methodology capable of improving comparisons between past and future studies.

Data envelopment analysis (DEA) is a popular and universal method for examining the efficiency with which decision-making units (DMUs) transform multiple inputs into multiple outputs. However, DEA has its limitations, one of them being its decreasing discriminatory power when the number of analyzed DMUs is insufficient or when there are too many variables (inputs/outputs) describing them. When resigning from any of the variables is impossible or undesired, or when the number of units cannot be increased, CI-DEA, a method proposed in this article, proves to be helpful. It consists of replacing the inputs and/or outputs of the studied DMUs with a smaller number of composite indicators. The aggregation of variables is not based on subjective decisions of the analyst, but depends solely on correlations that exist among variables. The construction of the CI-DEA model makes the interpretation of the results unambiguous and easy. The reliability of the results obtained with CI-DEA have been confirmed by extensive simulation studies performed under conditions of predetermined real-efficiency of DMUs. The usefulness of CI-DEA on real data has been demonstrated on the example of the efficiency assessment of the digitalization in the life of the Generation 50+ in 32 European countries. [ABSTRACT FROM AUTHOR]

A flexible sampling policy integrated with the consideration of past records is discussed in this paper for the quality evaluation of submission based on process yield. Lot sentencing plays the main role in buyer-seller business contracts for deliveries. A continuous partnership between the vendor and buyer can help in maintaining historically traceable results whereby the recording information is considered valuable. Hence, we propose a new sampling strategy with an integrated operating mechanism that considers the preceding inspection results to lessen the required average sample number based on process yield. Compared with the conventional methods, the proposed modified sampling strategy has the advantage of having a small number of samples for inspection while providing desirable protection under the same quality requirements and tolerable sampling risks. An application taken from the contact lens industry is presented to demonstrate the practicability of this research. [ABSTRACT FROM AUTHOR]

In this paper, we estimate coefficients of bankruptcy forecasting models, such as logistic and neural network models, by maximizing their discriminatory power as measured by the Area Under Receiver Operating Characteristics (AUROC) curve. A method is introduced and compared with traditional logistic and neural network models, using out-of-sample analysis, in terms of discriminatory power, information content and economic impact while we forecast bankruptcy one year ahead, two years ahead but also financial distress, which is a situation that precedes firm bankruptcy. Using US public firms over the period 1990–2015, in all, we find that training models to maximize AUROC, provides more accurate out-of-sample forecasts relative to training them with traditional methods, such as maximizing the log-likelihood function, highlighting the benefits arising by using models with maximized AUROC. Among all models, however, a neural network trained with our method is the best performing one, even when we compare it with other methods proposed in the literature to maximize AUROC. Finally, our results are more pronounced when we increase the forecasting difficulty, such as forecasting financial distress. The implementation of our method to train bankruptcy models is robust in various settings and therefore well-justified. [ABSTRACT FROM AUTHOR]

Endothelial dysfunction (ED) starts early in chronic kidney disease (CKD) and is the hallmark of atherosclerosis in these patients. During recent years, numerous markers have emerged, aiming to predict the onset of ED in CKD patients. Therefore, there is a need to evaluate and assess the discriminatory ability (or diagnostic accuracy) of such a marker (i.e., the ability to correctly classify individuals as having a given disease or not) and identify the optimal cut-off value. A receiver operating characteristic (ROC) curve analysis has been used in the majority of the research papers evaluating the predictive ability of a marker of ED. It is a graphical plot combining pairs of sensitivity (true positive rate) on the y axis and the complement of specificity (1—specificity, false positive rate) in the x axis, corresponding to several of the cut-off values covering the complete range of possible values that this test/marker might take. Herein, using a series of practical examples derived from clinical studies on ED in the special population of CKD, we address the principles, fundamentals, advantages and limitations regarding the interpretation of the ROC analysis.

Background: The low cost and rapidity of microsatellite analysis have led to the development of several markers for many species. Because in non-invasive genetics it is recommended to genotype individuals using few loci, generally a subset of markers is selected. The choice of different marker panels by different research groups studying the same population can cause problems and bias in data analysis. A priority issue in conservation genetics is the comparability of data produced by different labs with different methods. Here, we compared data from previous and ongoing studies to identify a panel of microsatellite loci efficient for the long-term monitoring of Apennine brown bears (Ursus arctos marsicanus), aiming at reducing genotyping uncertainty and allowing reliable individual identifications overtimes. Results: We examined all microsatellite markers used up to now and identified 19 candidate loci. We evaluated the efficacy of 13 of the most commonly used loci analyzing 194 DNA samples belonging to 113 distinct bears selected from the Italian national biobank. We compared data from 4 different marker subsets on the basis of genotyping errors, allelic patterns, observed and expected heterozygosity, discriminatory powers, number of mismatching pairs, and probability of identity. The optimal marker set was selected evaluating the low molecular weight, the high discriminatory power, and the low occurrence of genotyping errors of each primer. We calibrated allele calls and verified matches among genotypes obtained in previous studies using the complete set of 13 STRs (Short Tandem Repeats), analyzing six invasive DNA samples from distinct individuals. Differences in allele-sizing between labs were consistent, showing a substantial overlap of the individual genotyping. Conclusions: The proposed marker set comprises 11 Ursus specific markers with the addition of cxx20, the canid-locus less prone to genotyping errors, in order to prevent underestimation (maximizing the discriminatory power) and overestimation (minimizing the genotyping errors) of the number of Apennine brown bears. The selected markers allow saving time and costs with the amplification in multiplex of all loci thanks to the same annealing temperature. Our work optimizes the available resources by identifying a shared panel and a uniform methodology capable of improving comparisons between past and future studies. [ABSTRACT FROM AUTHOR]

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Background. The morphological characterizations and varietal descriptions of Jatropha curcas do not consider all morphological variability of this species. The phenotypes of J. curcas that have been described represent only a part of the variability of this species, making it difficult to distinguish between accessions based on morphological traits. Objective. Propose a method to assign trait states to discriminate between accessions based on their phenotype, and to evaluate the efficiency of these phenotypes as morphological traits. Methodology. Thirteen morphological traits were selected, and trait states were assigned according to their variability. Implications. The results show that some traits, such as the number of male flowers and the ratio of male to female flowers provide redundant information, while other traits do not provide information with enough discriminatory power. By other hand, trait associated with the frequency of one, two, three or four seeds per fruit set the pattern for the identification of phenotypes. Conclusion. The proposed method allowed to use the variability of traits as a descriptor without limiting the use of morphological traits not included in this work.

Soil moisture is an important variable in hydrological studies, but has been little used for model evaluation due to its high sensitivity to local conditions. We explore the possibility to derive hydrological signatures from soil moisture data that could overcome this limitation and be helpful for model evaluation. A set of eight hydrological signatures was built, encompassing long‐term to short‐term time scales. These signatures were tested according to robustness, representativeness and discriminatory power, using in situ data sets from New Zealand, including national network and experimental watershed data. Field capacity, type of soil moisture distribution, and starting dates of seasonal transitions typically meet the criteria, subject to uniform sensor depths and homogeneous land uses. Durations of seasonal transitions and event‐based signatures showed higher variability and lower discriminatory power. In general, long‐term signatures are more robust, more representative of large areas, and have a high discriminatory power, thus showing a good potential for use in diagnostic evaluation of regional models. [ABSTRACT FROM AUTHOR]

Background/aims: This study aimed to estimate the discriminatory power of 26 items on the Stroke Specific Quality of Life Scale questionnaire to evaluate the participation component of the International Classification of Functioning, Disability and Health. Methods: A prospective study was conducted using accuracy procedures based on the Standards for Reporting Diagnostic Accuracy Studies to evaluate individuals with hemiparesis stemming from a stroke. Discriminatory power was estimated based on the area under the receiver operating characteristic curve with a 95% confidence interval. Two groups were defined for the analysis: community-dwelling and institutionalised individuals. A 5% level of significance (α=0.05) was considered for all analyses. Results: The area under the receiver operating characteristic curve was 0.888 (95% confidence interval: 0.794–0.949; P=0.001). Analysis indicated a cut-off point of ≥80 with 73.9% sensitivity and 100% specificity. Conclusions: The 26 items of the Stroke Specific Quality of Life questionnaire that evaluate the participation component of the International Classification of Functioning, Disability and Health demonstrate adequate discriminatory power. A cut-off point of ≥80 seems to best discriminate the perception of participation between community-dwelling and institutionalised stroke survivors. [ABSTRACT FROM AUTHOR]

The published data indicate that the bacterium Clavibacter michiganensis subsp. sepedonicus (Cms) shows a notably low degree of intraspecific variation, and most of the molecular diagnostic methods could differentiate among subspecies groups but lacked the resolution to detect genetic diversity within subspecies. The present study describes effective differentiation of 50 isolates of C. michiganensis subsp. sepedonicus by the PCR MP method using five restriction enzymes (ApaI, PstI, BamHI, XmaI, HindIII). By analysing the electrophoretic patterns obtained by the PCR MP method performed with the applied restriction enzymes, each of the 50 tested isolates could be identified and distinguished from the remaining isolates. The PCR MP method of bacterial strain identification can be used in epidemiological studies, particularly for excluding the isolates of C. michiganensis subsp. sepedonicus as a source of primary infection. [ABSTRACT FROM AUTHOR]

The study presented in this article relies on the principles of the cultural-historical theory, which defines cultural impact as the main driving force behind psychological development. Based on the assumption that culture is a set of normative situations, the study identifies rules that are typical for primary school students in big Russian cities. These rules are grouped into what we refer to as factors of cultural compliance, which ultimately can be seen as indicators of pupils’ cultural congruence. In specifying the cultural congruence of primary school students, we take into account not only the rules of school life but also the whole range of stable rules for children 7- to 10-years-old. Researchers at the Psychology Institute of the Higher University of the Chinese Academy of Science (Wang, Zhu, & Shi, 2011) call such rules usual or contextually usual. We include rules that govern the behavior of children who have cultural differences, so in this article we are talking about the rules that are typical for children of this age in Russia. The goal of the study was to develop a test to diagnose the level of cultural congruence. The test was exposed to psychometric evaluation for validity, reliability, and discriminatory power. Factor analysis by means of varimax rotation provided for calibration of the rules by consolidating them into factors. These factors underpin the test and include the categories social interaction, academic competence, regulation, obedience, self-service, and self-control. In accordance with the principles employed in psychology, the factors confirm the construct validity of the test in relation to children’s development when they are between 7 and 10 years old. The study confirms that learning is the main activity at this age by introducing a factor that brings together rules inherent in normative situations in the education process. The social setting for psychological development, viewed as a specific relationship between a child at the given age and the environment, is determined by the child’s interaction with an adult. The factor of obedience is the key parameter for children of the age under consideration. New at this age are arbitrariness, self-regulation, self-analysis, and an internal action plan. Self-control is also conceptually linked to these factors. The study offers a new look at the cultural determination of psychological development in ontogenesis. Validated in the course of the investigations, the test can be used to diagnose cultural congruence — that is, the compliance of a primary school student with rules inherent in normative situations.

In psychological research, many questionnaires use verbal response scales with vague linguistic terms (e.g., frequency expressions). The words’ meanings can be formalized and evaluated using fuzzy membership functions (MFs), which allow constructing distinct and equidistant response scales. The discriminatory power value of MFs indicates how distinct the functions and, hence, the verbal expressions are. The present manuscript interrogates the threshold of discriminatory power necessary to indicate a sufficient difference in meaning. Using an empirical validation procedure, participants (N = 133) estimated (1) three correspondence values for verbal expressions to determine MFs, and (2) similarities of words by pairwise comparison ratings. Results show a non-linear relationship between discriminatory power and similarity, and fuzzy MFs, as well as the searched-for threshold value for discriminatory power. Implications for the selection of verbal expressions and the construction of verbal categories in questionnaire response scales are discussed.

• Rapid assessment methods provide an intermediate scale of assessment that is cost-effective, efficient, and ecologically meaningful. • We apply a rapid ecological integrity assessment method across multiple watersheds, and regions in the United States. • Our multi-metric approach evaluates multiple ecological factors, including landscape context (landscape, buffer) and on-site condition (vegetation, hydrology, soil). • Twelve metrics were minimally redundant and have good discriminatory power in response to a gradient of stressors. • Landscape and buffer metrics provide important insights into the on-site condition of wetlands. Many ecological monitoring and assessment programs include rapid assessment methods that employ indicators or metrics to track the degree of divergence of ecosystem condition from reference conditions. Although these rapid assessment methods use a combination of metrics to rate overall ecological condition, they rarely include tests of either the merits of the component metrics being assessed or the method of aggregating the metrics into an overall rating. We used a conceptual model of ecological integrity for wetlands and field data to select and test 15 rapid assessment indicators (using specific metrics) across a spectrum of major ecological factors or MEFs (landscape, buffer, vegetation, hydrology, soil). We applied these metrics to 220 wetland sites across six states (Colorado, Indiana, Michigan, New Hampshire, New Jersey, and Washington), using two assessment area (AA) approaches: 106 sites used 0.5 ha point-based AAs; 114 sites used variable-sized polygon-based AAs. We statistically tested metric ratings and factor scores for their discriminatory power (DP) in relation to a stressor index using the Kruskal-Wallis test, and for redundancy using Spearman rank correlation and scattergrams. Of the 15 metrics, 12 had good or strong DP and were not redundant. Across all metrics, only two pairs (vegetation pair and buffer pair) were strongly correlated. The soil metric had the lowest DP, but it was among the least redundant of any metric. The DP of buffer metrics was lower for point-based approaches than for polygon-based approaches because the buffer for point-based AAs often included additional wetland area. Aggregating individual metrics into MEF scores (e.g., vegetation, hydrology, soil), primary factors scores (Landscape Context, on-site Condition) and overall ecological integrity ratings, either maintained or improved the interpretability of the ratings. Our analyses support the use of 12 rapid field-based metrics, spanning Landscape Context and on-site Condition, to assess the ecological integrity of wetlands. Although tested here for wetlands, the models and metrics are also being applied to upland terrestrial ecosystems. Our findings confirm the merits of our rapid assessment method in providing an intermediate level of assessment that is efficient and ecologically meaningful, within states and across watersheds and regions. [ABSTRACT FROM AUTHOR]

In psychological research, many questionnaires use verbal response scales with vague linguistic terms (e.g., frequency expressions). The words' meanings can be formalized and evaluated using fuzzy membership functions (MFs), which allow constructing distinct and equidistant response scales. The discriminatory power value of MFs indicates how distinct the functions and, hence, the verbal expressions are. The present manuscript interrogates the threshold of discriminatory power necessary to indicate a sufficient difference in meaning. Using an empirical validation procedure, participants (N = 133) estimated (1) three correspondence values for verbal expressions to determine MFs, and (2) similarities of words by pairwise comparison ratings. Results show a non-linear relationship between discriminatory power and similarity, and fuzzy MFs, as well as the searched-for threshold value for discriminatory power. Implications for the selection of verbal expressions and the construction of verbal categories in questionnaire response scales are discussed. [ABSTRACT FROM AUTHOR]

Bloodstream infection (BSI) is a severe infectious complication in critically ill patients. This study was aimed to investigate the diagnostic accuracy of procalcitonin (PCT) to differentiate Gram-negative bacteria (Gram-neg) from Gram-positive bacteria (Gram-pos) and fungal BSI. PCT and other inflammation markers of monomicrobial BSI patients were retrospectively collected and compared between patients with Gram-neg, Gram-pos, or fungal BSI. The differential diagnosis performance of PCT was evaluated by receiver operating characteristic curves (ROC). The area under curve (AUC) of PCT for differentiating Gram-neg BSI from Gram-pos BSI was 0.95 with an optimal cut-off value of 4.15 ng/mL, a sensitivity of 82.05%, and a specificity of 96.15%. AUC of PCT for differentiating Gram-neg BSI from fungal BSI was 0.92 with an optimal cut-off value of 3.13 ng/mL, a sensitivity of 84.62%, and a specificity of 88.89%. Serum PCT concentration can be used to differentiate Gram-neg from Gram-pos and fungal BSI. [ABSTRACT FROM AUTHOR]

Abstract We consider a two-player Tullock rent-seeking contest with uncertain discriminatory power in the contest success function. We examine the cases where both players are either informed or uninformed about the size of the discriminatory power, as well as the case where only one player has private information about it. We show that in all three cases the contest has a unique (Bayesian) Nash equilibrium. In each case we characterize key properties of the equilibrium. Highlights • We consider a two-player Tullock contest with uncertain discriminatory power. • We examine the case where both players are informed or uninformed about it. • We also examine the case with asymmetric information. • In each case we prove the existence of a unique (Bayesian) Nash equilibrium. • In each case we characterize key properties of the equilibrium. [ABSTRACT FROM AUTHOR]

Background/Objective. Disease activity in spondyloarthritis (SpA) is evaluated through conventional scores such as BASDAI or ASDAS. The aim of this study was to determine disease activity in SpA patients under biological therapy with further assessment on the quality of each activity indicator and their clinical value together with their impact on NSAID intake. Materials and methods. Over nine months (January–August 2015) we enrolled 100 patients with SpA on anti-TNF therapy (33% IFX; 35% ADL; 32% ETA). We collected demographic, clinical (BASDAI, ASDAS, PtGA) and laboratory data (ESR, CRP). Frequency of NSAID ingestion expressed as days per week was included in a questionnaire. Considering their biologic treatment, patients were divided into disease activity groups and sample characteristics were correlated and compared. Statistical analysis was performed with SPSS 20.0, using specific tests such as Pearson, Spearman or ANOVA. Results. Patients with active disease, stated by both activity scores had an increased NSAID intake. Out of 86% of patients with a BASDAI under 4, only 82.6% had an ASDAS-CRP lower than 1.3. BASDAI correlated to both ASDAS scores (r = 0.65 and 0.71, P