External Validation Criteria Research Articles

In silico aquatic toxicity prediction of chemicals toward Daphnia magna and fathead minnow using Monte Carlo approaches

The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. Daphnia magna and Fathead minnow are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure–toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward Daphnia magna and Fathead minnow. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF1, TF2, and TF3 are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF3 demonstrate the best statistical quality with R 2 values ranging from 0.9467 to 0.9607, Q 2 values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.

Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

Abstract Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with R values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.

Опросник академической компетентности студентов

<p>The results of the development and standardization of an academic competence questionnaire are presented. The method is based on the understanding of academic competence as a complex of independently developed personal qualities, abilities and skills that ensure the success of educational activities in conditions of its variability during higher education. The theoretical model includes 6 structural and content components (academic motivation, self-organization of educational activities, emotional self-regulation, skills and abilities to work with information, proficiency in oral and written language, communication skills), presented at 3 levels of generality and freedom of transformation (reproductive, reflective, functional). Based on the results of Study 1 (N=199), the preliminary version of the questionnaire was reduced from 144 to 69 items. Comparing the results of successful and unsuccessful students confirmed predictive validity. External criterion validity using the contrast group method is based on comparison of groups of undergraduate and graduate students, correlations with self-organization and academic motivation questionnaires (AMS). Study 2 (N=355) demonstrated a relatively acceptable fit of the data to the theoretical model based on confirmatory factor analysis, consistency with educational experience questionnaire scales. The internal consistency of the scales was confirmed by Cronbach's alpha indicators based on data from studies 1 and 2. Descriptive statistics for Study 2 are provided. Standardization is proposed based on percentile values, because the distributions of values for most scales differ significantly from normal.</p>

Some psychometric properties of the school refusal assessment scale–revised: Parent and child version in Tanzanian context

Considering the increase in primary and secondary school dropout rates worldwide, it is necessary to identify the underlying causes, particularly in places with high school refusal rates, such as Tanzania. Today, only a limited number of studies can explain, through valid and reliable measurements, why students in Tanzania refuse to attend school. The current study used confirmatory factor analysis techniques to investigate some psychometric properties of the child’s and parent’s versions of the School Refusal Assessment Scale–Revised (SRAS–R) in the Tanzanian population. Since previous studies found significant associations between the parent–child relationship and the risk of school refusal, we included the associations between the SRAS–R scales and measures of psychological control (parent ratings) and parent–child communication (child ratings) as external validity criteria. We collected data from five public primary schools, including 502 grades three to seven children with their parents (N = 465) in Ruvuma, Tanzania. Confirmatory factor analyses revealed that the four–factor model (avoidance of stimuli provoking negative affectivity, escape from aversive social situations, attention seeking, and tangible rewards) showed a good fit for both the parent and child versions of the SRAS–R. In line with expectations, we found a negative association between parent–child communication and school refusal and positive associations between the parent version of the SRAS–R subscales and the psychological control scale. The findings of this study support the use of this measurement in an African context and Tanzania specifically. The discussion stresses the appropriateness of Western cultural instruments in the African context.

Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag

Using sustainable cement-based alternatives, such as secondary cementitious raw materials (SCMs), could be a viable option to decrease CO2 emissions resulting from cement production. Previously conducted studies to determine the optimal mix designs of concrete primarily focused on either experimental approaches or empirical modeling techniques. However, in these experimental approaches, few tests could be performed for optimization due to time restrictions and lack of resources, and empirical modeling methods cannot be relied on without external validation. The machine learning-based approaches are further characterized by certain shortcomings, including a smaller number of data points, a less robust connection among the controlling factors, and a lack of comparative analyses among machine learning models. Furthermore, the literature on predicting the performance of concrete utilizing binary SCMs (silica fume (SF) and ground granulated blast furnace slag (GGBS)) is not available. Therefore, to address these drawbacks, this research aimed to integrate ML-based models with experimental validations for accurate predictions of the compressive strength (CS) and tensile strength (TS) of concrete that includes SF and GGBS as SCMs. Three soft computing techniques, namely the ANN, ANFIS, and GEP methods, were used for prediction purposes. Eight major input parameters, including the W/B ratio, cement, GGBS, SF, coarse aggregates, fine aggregates, superplasticizer, and the age of the specimens, were considered for modeling. The validity of the established models was assessed by using external experimental validation criteria, statistical metrics, and performance measures. In addition, sensitivity and parametric analyses were performed. Based on statistical measures, the ANFIS models outperformed other models with higher correlation and lower statistical error values. However, the GEP models exhibited superior performance compared to ANFIS and ANN with respect to the closeness of the RMSE, MAE, RSE, and R2 values between the training, validation, and testing sets for both the CS and TS models. Experimental validation showed strong evidence for the applicability of the proposed models with an R2 of 0.88 and error percentages of less than 10%. Sensitivity and parametric investigations demonstrated that the input variables exhibited the patterns described in the experimental dataset and the available literature. Hence, the proposed models are accurate, have better prediction performance, and can be used for design purposes.

Preschool Peer Aggression Scale (Teacher Form): Validity Reliability Study

This study aimed to develop and establish the validity and reliability of the Preschool Peer Aggression Scale (Teacher Form) (PPAS-TF). The study employed two distinct sample groups. The scale's factor structure was established through Principal Components Analysis on the initial group, which comprised 1106 children (F=508; M=597). The second sample group, which included 551 children (F:255; M:296) was utilized to conduct Confirmatory Factor Analysis to validate the factor structure. The scale's validity was also assessed through content and external criterion validity. For content validity, a literature review and expert consultation were employed. Convenience validity was used for criterion validity. For reliability internal consistency coefficients, item-total correlation coefficients, and correlations between the factors were examined. As a result, the 28-item PPAS-TF for 4-6-year-old children was categorized into three sub-dimensions: social aggression, physical aggression, and verbal aggression. Teachers can use this form to assess aggression levels, with each sub-dimension's score indicating the extent of that specific aggression type. Higher scores in the sub-dimensions signify a greater propensity for that type of aggression. Additionally, an overall assessment can be made using the total score. The distinctiveness of the PPAS-TF arises from its development with a Turkish sample and its incorporation of three sub-dimensions.

Psychometric Evaluation of the School Climate and School Identification Measure-Student on Chilean Students: A Bifactor Model Approach.

School climate is a relevant construct for understanding social relations at school. The SCASIM-St has been widely defined as a multidimensional construct; however, new factor structures have not been explored through evidence that allows for interpreting school climate scores from an approach that respects the multidimensionality of the scale and, at the same time, allows for identifying the degree of essential unidimensionality in the data. Consequently, the objective was to analyze the psychometric properties of the SCASIM-St from a bifactor model approach, evaluating the influence of a general school climate factor versus five specific factors. The study involved 1860 students of both sexes (42% males and 58% females), with an average age of 16.63 years (SD = 0.664), from 17 secondary schools in Chile. The results obtained by a confirmatory factor analysis provided evidence that the best model was the bifactor model for the 38 items, with one general factor and five specific factors. The Explained Common Variance (ECV) values and reliability levels by hierarchical omega accounted for a strong general school climate factor with high levels of reliability. Evidence of external criterion validity, assessed through the attitude toward authority scale (AIA-A), showed a theoretically expected and significant relationship between the factors of both instruments. This study confirmed the psychometric robustness of the SCASIM-St scale by means of a bifactor model, allowing for a new, essentially unidimensional interpretation of the scale scores and providing an instrument to measure school climate in Chile.

Q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis

ABSTRACT A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure–Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r 2, Q 2 F1 and Q 2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model’s dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

Application of QSAR models for acute toxicity of tetrazole compounds administrated orally and intraperitoneally in rat and mouse

Tetrazoles and their derivatives possess various biological activities, such as antibacterial, anti-fungal, and other activities. However, these compounds may induce specific cumulative and toxic effects in living organisms. Therefore, quantitative structure-activity relationship (QSAR) models were constructed to study the acute oral toxicity of tetrazoles in rats and mice. The toxicity data of 111 tetrazole compounds were collected using the ChemIDplus, ChEMBL and ECHA databases as response variables, while the PaDEL-descriptor generated the 2D descriptors as independent variables. The models were developed and validated following the OECD guidelines by the DTC-QSAR tool. Three QSAR models were successfully established for the oral routes of rat and mouse and the intraperitoneal route of mouse, respectively. The scatter plots showed high consistency between the training and test data sets. All the models successfully met the external and internal validation criteria. Most of the descriptors kept in the final models exhibited positive correlations with toxicity, whereas only 6 descriptors exhibited negative associations. Several chemicals were identified as response or structural outliers, based on the standardized residuals and leverage values. In conclusion, the findings of this investigation demonstrate that the proposed QSAR models hold promise in forecasting the acute toxicity of recently developed or synthesized tetrazole compounds, thereby mitigating potential risks to human health and the environment.

Psychometric properties of the affect integration inventory - short form in a sample of patients with personality disorder.

Affect integration comprises the capacity to access and utilize the motivational and signal properties of affects. This capacity is essential for personal adjustment, mental health, and well-being. Affect integration is commonly operationalized through the Affect Integration Inventory. This study examines the psychometric properties of a short-form (AII-SF-42) of the instrument in a sample of patients with personality disorders (n = 87). Analyses of internal-consistency reliability, along with standardized mean differences-, and associations between short- and long-forms are reported. Internal structure was assessed by confirmatory factor analyses and external criterion validity was addressed by tests of associations between the AII-SF-42-scale scores and measures of alexithymia, symptom distress, interpersonal problems and level of personality dysfunction. The study demonstrated satisfactory reliability and validity for scores derived from the AII-SF-42, including acceptable internal consistency and strong correspondence with long-form scores, a consistent factor structure organized according to discrete affects, and systematic patterns of convergent and discriminant associations with external measures. Taken together, the results of the study demonstrate that in clinical settings, including patients with personality disorders the AII-SF-42 is a valid and useful alternative to the full-length version of the instrument.

Computational assessment of the reactivity and pharmaceutical potential of novel triazole derivatives: An approach combining DFT calculations, molecular dynamics simulations, and molecular docking

The worldwide prevalence of cancer and its increasing frequency make it a key research area in drug discovery programs. The current research paper describes the development of QSAR models based on the in vitro against topoisomerase II, which identified the structural origin of anticancer activity for derivatives of triazole moieties linked to mansonone E. The models PLS regression QSARs validated by LOO showed an R2 of 0.92, 0.89 and 0.99 and a Q2 of 0.75, 0.62 and 0.88 for CoMFA, CoMSIA and HQSAR respectively. External validation criteria were used to validate the reliability of the models. These results show the impact of electrostatic and steric fields and of the hydrogen bond donor on the activity of the compounds studied. Based on these results, seven novel inhibitors with high activity were designed, which successfully passed Lipinski's rule of five for oral bioavailability. The evaluations of ADME/Tox parameters and synthetic accessibility for chemical synthesis showed acceptable results. Ligand interactions in binding site protein were assessed using molecular docking. The results show the correct conformational pose of the designed compounds especially the compound T1 where it forms hydrogen and hydrophobic interactions with the main binding site residues. The stability of the complexes was confirmed by the MD study and the calculation of the free binding energy. The T1 synthesis reaction was carried out according to the 1,3 cycloaddition reaction. The study of the local and global reactivity and the energy of activation of this reaction have shown the predicted of the regioselectivity of compound T1. Also we have described the state of transition of two isomers T1,4 and T1.5. Finally, this study would be interesting to help identify and optimize avenues for early discovery of anticancer drugs.

What Motivates Students to Physical Activity: Development and Validation of the Students Physical Activity Motivation Scale

In India, physical inactivity in children and youth is a public health concern, and it is unfortunate to see that most Indian children do not accomplish prescribed levels of physical activity. We strongly believe that the availability of a physical activity motivation instrument specific to the Indian population can significantly promote physical activities in India. This study aimed to develop the student’s physical activity motivation scale (SPAMS). The convenience sampling method was used to collect data from 622 participants from 5 states and 1 union territory of India. A multi-stage approach using exploratory and confirmatory factor analysis was computed to develop and validate its psychometric properties in two different samples (EFA-311 and CFA-311). Factor analysis revealed three distinct factors underlying physical activity motivation: skill development, appearance and positive health. Based on the findings, it can be concluded that SPAMS meets the concept and external validity criteria and that it is suitable for theoretical and practical applications.

Forecasting the strength characteristics of concrete incorporating waste foundry sand using advance machine algorithms including deep learning

The incorporation of waste foundry sand (WFS) into concrete has been recognized as a sustainable approach to improve the strength properties of waste foundry sand concrete (WFSC). However, machine learning (ML) techniques are still necessary to forecast the characteristics of WFSC and evaluate the dominant input features for the suitable mix design. For this purpose, the present work selected five ML-based techniques based on gene expression programming (GEP), deep neural network (DNN), and optimizable Gaussian process regressor (OGPR) to predict the mechanical characteristics of WFSC. To build up the predictive models, a database containing 397 values of compressive strength (CS) and 169 values of flexural strength (FS) is collected from published literature. The models' performance was evaluated via various statistical metrics and additionally, external validation criteria were employed to validate the developed models. Furthermore, the Shapley additive explanation (SHAP) was carried out to interpret the model's prediction. The DNN2 model exhibited superior performance, with R-values of 0.996 (training), 0.999 (testing), and 0.997 (validation) for the compressive strength estimation. In contrast, the GEP2 model showed poor accuracy in estimating the CS compared to other developed models, with R-values of 0.851, 0.901, and 0.844 for the training, testing, and validation sets, respectively. Similarly, for the flexural strength estimation, the DNN2 model provided R-values of 0.999 for training, 0.996 for testing, and 0.999 for validation sets, indicating its robust performance. The SHAP analysis revealed that the age, water-cement ratio, and coarse aggregate-to-cement ratio have the prime influence in determining flexural and compressive strength, respectively. The comparison of the models provided that the DNN2 model accurately estimated the output with high accuracy and lower error values and might be utilized in practical fields to reduce labor and cost by optimizing the mix combinations. Finally, for future studies, it is recommended to utilize ensemble and hybrid algorithms, as well as post-hoc explanatory techniques, to forecast the characteristics of WFSC accurately.

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease.

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Validity and reliability test of the brief symptom rating scale 5 (BSRS-5) questionnaire in Indonesian version as an assessment tool for psychological disorders in Indonesia.

Psychological disorders in workers is associated with physical health problems and decreased work productivity costing the impact of workplace accidents. We can minimize these problems by introducing screening programs with a simple screening tool for psychological disorders. One of the questionnaires used in several countries to assess psychological disorders is the Brief Symptom Rating Scale - 5 (BSRS-5). Thus, this study aimed to assess the validity and reliability of the Brief Symptom Rating Scale - 5 (BSRS-5) in the Indonesian version. The BSRS-5 was translated into a local language (Bahasa), and experts judgment in the forward and backward translation process. What collected BSRS-5 data in a primary health care setting on 64 respondents. Internal reliability was tested using Cronbach's alpha. Factorial validity was done using exploratory factor analysis to investigate whether BSRS-5 items properly measure the underlying dimensions of psychological disorders. External (criterion) validity was analysed by looking at the relationship between BSRS-5 and Depression, Anxiety and Stress Scale -21 (DASS-21), using the coefficient correlation. The BSRS-5 questionnaire was produced from transcultural validation using the ISPOR method. The results of the construct validity test for all questions between (0.634-0.781) with a significance level <0.05. The factor analysis values for all statements >0.3 were all items having Eigenvalues >1 yielded into one factor. The instrument performed well in detecting common psychological disorders. The BSRS-5 had good internal reliability (α=0.770). The external validity test results with DASS-21 showed that BSRS-5 correlated with the DASS-21 dimensions of depression and stress with correlation values of 0.397 and 0.399. In opposition to correlated BSRS-5 with the dimension of anxiety in DASS-21 showed no correlation with a correlation value of 0.237. Therefore, another gold standard questionnaire is required to evaluate psychological distress based on each item in BSRS-5. The BSRS-5 is a satisfactory screening tool for identifying common psychological disorders such as Insomnia, Anxiety, Depression, Hostility, and Inferiority in the community. The absence of correlation with anxiety in this assessment tool needs another gold standard questionnaire, or professional assistance is required for further evaluation to follow up on psychological disorders.

Adaptation of the Early Childhood Reading Motivation Scale to Turkish: A Validity and Reliability Study

The aim of this study is to adapt the Early Childhood Reading Motivation Scale to Turkish. A general survey model was used as the research method. The study group of the research consists of 571 primary school students aged 6-7 years. The Early Childhood Reading Motivation Scale to be adapted into Turkish was used as the data collection tool, while the Reading Skills Assessment Scale was used in the external criterion validity analysis. The SPSS 22.0 and LISREL 8.5 statistical software programs were used for data analysis. Following the validity and reliability analyses, it was concluded that the Early Childhood Reading Motivation Scale is a valid and reliable scale for measuring reading motivation in primary school students aged 6-7. When the literature was examined, it was seen that no scale aimed at measuring early childhood reading motivation had been developed or adapted to Turkish. In this sense, it is thought that this study will have an important place in the literature.

Revealing representative day-types in transport networks using traffic data clustering

Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.

Cross-Cultural Adaptation and Validation of the Methotrexate Intolerance Severity Score Questionnaire in Portuguese (Brazil) for Children and Adolescents with Juvenile Idiopathic Arthritis.

The Methotrexate (MTX) Intolerance Severity Score (MISS) questionnaire has been developed to identify MTX adverse events in juvenile idiopathic arthritis (JIA). The objective of this study was to translate and validate MISS into Brazilian Portuguese for children and adolescents. The MISS was translated into Portuguese following the standardized guidelines. We analyzed the following psychometric properties: acceptability, internal consistency, test-retest reproducibility, relative-child reliability, and external criterion and discriminant validity. We included 138 JIA patients (age: 8-18 years) and 108 relatives who took less than 5 min to answer MISS. Reproducibility tested after 15 days was good, with a kappa > 0.76. We observed good internal consistency (Cronbach's coefficient 0.75-0.87 (patients) and 0.75-0.79 (relatives)). Reliability between patients and relatives was good except for stomachache and restlessness. Cut-off points of 5 and 6 had good sensitivity (84 and 71, respectively) and specificity (80 and 87, respectively). Using a cut-off value of 6, we observed 86 (62.3%) MTX-intolerant patients. In conclusion, MISS is a viable and practical tool for routine clinical care to identify MTX intolerance in JIA. Parents do not easily identify stomachache and restlessness as adverse MTX events.

Time-dependent flow of water-based ternary hybrid nanoparticles over a radially contracting/expanding and rotating permeable stretching sphere

• The stream of ternary hybrid nanoliquid on a flexible shell of a solid sphere, water-based TiO2, GO and Ag nanoparticles are all employed to study the flow variation. • Study inspects the flow produced by spinning, suction-driven, radially shrinking or expanding stretching sphere. • A detailed explanation of a solution procedure for accommodating non-similar momentum boundary layers is discussed. • The external validation criteria are employed to address the modeling overfitting. This work concentrates on the flow of ternary nanofluid flow over a rotating sphere in view of cooling of rotating spherical products in various industries, machinery fields, and other applications. Here, considered nanoparticles have various practical applications for cooling in various industrial processes. This study is considered innovative because it examines the effect of three different nanoparticles on the flow over a rotating sphere with expansion/contraction reported for the first time. Ternary-hybrid nanofluid is a comprehensive combination of a fluid material with three different types of nano-sized particles. Recent findings concerning the potential and applications of nanofluids have revealed several facts about the nature of nanoparticles and their ability to influence the rate of heat transmission across the base fluid. Considering the aforementioned cause, the objective of this work is to explore the flow produced by spinning, suction-driven, radially shrinking or expanding stretching sphere. Initially, the problem is bounded in the requisite posited form of PDEs which are non-linear and coupled. A details description of a solution procedure for accommodating non-similar momentum boundary layers is also discussed. The terms (non-similar) that make up the obtained converted equations are maintained without approximations. Moreover, these non-similarities set of equations is tackled numerically via the help of bvp4c. Several graphs and tables are equipped for the influence of sundry controlling parameters. It is discovered that in circumstances of contracting, expanding and non-growing wall deformation, the region (equatorial) is efficiently smoothed out by the wall mass transpiration. When the sphere expands, the radial inward flow at the equator is no longer relevant and intense suction results in virtually constant radial suction velocities. Finally, the grid sensitivity analysis test is also performed for the ternary hybrid nanoparticles to show that the outcomes are asymptotically converged.

EASE-NL: Cross-Cultural Adaptation and Validation of the Dutch Version of the Evaluation of Ability to Sing Easily

The Evaluation of the Ability to Sing Easily (EASE) is a self-rating tool that is used to assess the singer's perceptions of the current singing voice status. The purpose of this study was to develop and validate a Dutch translation of the EASE. The original version of the EASE was translated and adapted to Dutch according to the recommendations of the Quality of Life Special Interest Group - Translation and Cultural Adaptation group. Subsequently, the questionnaire was individually completed by 70 singers with a mean age of 35.2 years before and after a singing activity, together with a demographic questionnaire and the Dutch Singing Voice Handicap Index 10 (SVHI-10-NL). Two groups of singers were included between September and June 2020: a group of healthy singers (n=54) and a group of dysphonic singers (n=16). Internal and external consistency, construct and criterion validity, test-retest and split-half reliability were calculated using Cronbach's alpha coefficients, Student's t-test, the paired Wilcoxon tests and Pearson correlation coefficients. Furthermore, the impact of sex and age and the diagnostic accuracy of the EASE-NL was measured using the Mann Whitney U-test, the One Way ANOVA and the Brown Forsythe ANOVA-test. The internal consistency of the EASE was considered good. For the external consistency, the Pearson correlation coefficient showed a positive correlation between the total score of the EASE-NL and the SVHI-10-NL. Dysphonic singers scored significantly higher compared to singers without voice problems and no differences were found between the pre and post singing condition in both groups. Pearson correlations coefficients showed a strong positive correlation between the test and retest condition and between the subscales. A ROC-curve analysis showed a cut-off score of 12.5, with a sensitivity level of 75.0% and a specificity level of 74.1%. No differences for sex and age were found. The original English version of the EASE was translated and validated in Dutch. The EASE-NL is found to be a valid and reliable self-reported tool to assess singer's perceptions of the current status of their singing voice.