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This paper aims to compare two diagnostic performance measures, i.e. signal‐to‐noise ratio (S/N ratio) and partial area under receiver operating characteristic curves (pAUC). It proposes the use of S/N ratio rather than pAUC for establishing optimal cut‐off point for diagnostic biomarkers.

This paper discusses the properties, uses, advantages and shortcomings of the two performance measures, namely the partial area under receiver operating characteristic curve (pAUC) and Taguchi's signal‐to‐noise (S/N) ratio. The benefits of S/N ratio have been illustrated in a sample of four biomarkers, each having five cut‐off points. The S/N ratio is compared to the pAUC index. The SAS software is employed to calculate pAUC and AUC.

This paper shows that S/N ratio can be used as a measure of diagnostic accuracy. The cut‐off point with the highest S/N ratio is the optimal cut‐off point for the biomarker. The proposed method has the advantages of being easier, more practical and less costly than that of pAUC.

This paper includes implications for the development of a more practical, equally powerful and less costly means of measuring clinical accuracy thereby reducing the costs and risks resulting from wrong selection of cut‐off point can be decreased.

This paper supports suggestions in the recent literature to replace pAUC with a new, more meaningful index.

This article is concerned with the role of theory in explaining the inter‐industry variation of vertical integration (VI). Why, for example, is the world aluminium industry highly integrated (Stukey, 1983) whereas the tin industry is not (Hennart, 1982)? The article is not concerned with explaining differences in the average level of VI across countries, although these are undoubtedly significant (Chandler and Daeins, 1980).

The purpose of this paper is to use the kernel method to produce a smoothed receiver operating characteristic (ROC) curve and show how baby gender can influence Clinical Risk Index for Babies (CRIB) scale according to survival risks.

To obtain the ROC curve, conditioned by covariates, two methods may be followed: first, indirect adjustment, in which the covariate is first modeled within groups and then by generating a modified distribution curve; second, direct smoothing in which covariate effects is modeled within the ROC curve itself. To verify if new-born gender and weight affects the classification according to the CRIB scale, the authors use the direct method. The authors sampled 160 Portuguese babies.

The smoothing applied to the ROC curves indicates that the curve's original shape does not change when a bandwidth h=0.1 is used. Furthermore, gender seems to be a significant covariate in predicting baby deaths. A higher value was obtained for the area under curve (AUC) when conditional on female babies.

The challenge is to determine whether gender discriminates between dead and surviving babies.

The authors constructed empirical ROC curves for CRIB data and empirical ROC curves conditioned on gender. The authors calculate the corresponding AUC and tested the difference between them. The authors also constructed smooth ROC curves for two approaches.

Classification techniques have been applied to many applications in various fields of sciences. There are several ways of evaluating classification algorithms. The analysis of such metrics and its significance must be interpreted correctly for evaluating different learning algorithms. Most of these measures are scalar metrics and some of them are graphical methods. This paper introduces a detailed overview of the classification assessment measures with the aim of providing the basics of these measures and to show how it works to serve as a comprehensive source for researchers who are interested in this field. This overview starts by highlighting the definition of the confusion matrix in binary and multi-class classification problems. Many classification measures are also explained in details, and the influence of balanced and imbalanced data on each metric is presented. An illustrative example is introduced to show (1) how to calculate these measures in binary and multi-class classification problems, and (2) the robustness of some measures against balanced and imbalanced data. Moreover, some graphical measures such as Receiver operating characteristics (ROC), Precision-Recall, and Detection error trade-off (DET) curves are presented with details. Additionally, in a step-by-step approach, different numerical examples are demonstrated to explain the preprocessing steps of plotting ROC, PR, and DET curves.

This paper aims to study the residual test results under uni-axial compression of tie confined pre-damaged normal strength concrete short columns subjected to elevated temperatures.

The test variables included temperature of exposure, spacing of transverse confining reinforcement and pre-damage level. An experimental program was designed and carried out involving testing of hoop confined concrete cylindrical specimens exposed to elevated temperatures ranging from room temperature to 900 °C.

The test results indicate that the residual strength, strain corresponding to the peak stress and the post-peak strains of confined concrete are not affected significantly up to an exposure temperature of 300 °C. However, the peak confined stress falls and the corresponding strain increase considerably in the temperature range of 600 to 900 °C. It is shown that an increase in the degree of confinement reinforcement results in an increased residual strength and deformability of pre-damaged confined concrete.

It is applicable in finding the residual strength and strain of the pre-damaged confined concrete in uni-axial compression after exposure to elevated temperature.

The practical implications is that the test result is applicable in finding the residual strengths of pre-damaged confined concrete under uni-axial compression after exposure to elevated temperature.

The main aim of the present investigation is to provide experimental data on the residual behaviour of pre-damaged confined concrete subjected to high temperatures.

The results of this study may be useful for developing the guidelines for designing the confinement reinforcement of reinforced concrete columns against the combined actions of earthquake and fire, as well as for designing the retrofitting schemes after these sequential disasters.

The wetting balance is used for the measurement of solderability of electronic components. The wetting force is measured dynamically and the technique gives information about both the degree and the speed of wetting. For practical quality assessment of electronic components, a simple‐to‐use index is required that incorporates the data on both degree and speed of wetting. The index must also have a uniform discrimination between different wetting properties, across the full range encountered in practical soldering. This paper reviews critically the numerous indices suggested in the literature, and supports with quantitative data the choices previously made subjectively in some soldering standards.

The purpose of this paper is to test the discriminative validity of the Substance Use Beliefs Questionnaire (SUBQ) with alcohol dependent users, by assessing if the new tool can successfully differentiate between two extreme groups.

The criterion used to select the two extreme groups was participation or not in treatment for alcohol dependence. Score of the Severity of Alcohol Dependence Questionnaire (SADQ) was used as a secondary confirmation criterion of extreme difference.

In all, 98 staff and 94 people in treatment for alcohol dependence were recruited. The treatment group scored 30.83 higher than the control on SADQ, 10.76 on positive and 28.98 on negative expectancies. Negative expectancies score had correctly classified 88.5 per cent and positive expectancies score only 66 per cent of the original grouped cases. The area under the Receiver Operating Characteristics (ROC) curve for negative expectancies was 0.94 (very good) with a cut-off point of 43.5 with 89 per cent sensitivity and 92 per cent specificity. The area under the ROC curve for positive expectancies was 0.73 (fair). Due to the shape it was difficult to identify a cut-off point.

The results support the discriminative validity of the negative expectancies sub scale of the SUBQ between two extreme groups. With only the use of negative expectancies score participants could be classified correctly to those of the control and those of the treatment group.

SUBQ is the first tool to measure outcome expectancies across substances, facilitating relevant research with poly substance users. Future research needs to explore the discriminative validity of the tool with the other three substance groups (smokers, stimulant and opioids users), involved in the development and validation of the SUBQ.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.

Diagnostic tests are widely used in many areas of modern technological society, but they are of particular importance in medicine, where early and accurate diagnosis can decrease morbidity and mortality rates of disease. How the quality of diagnostic information and decisions should be measured in a meaningful way has become increasingly important in recent years as an abundance of new diagnostic tests have been introduced. A number of seemingly independent indices are studied for evaluating diagnostic performance such as the receiver operating characteristic curves and signal‐to‐noise ratios. Designing robustness into diagnostic tests can only be achieved by minimizing the variation in the total number of false diagnosis. This article has undertaken a comparison of signal‐to‐noise ratios developed by Taguchi in quality engineering and system performance in manufacturing industry. A hybrid is also computed and its relevance to physicians as an efficient assessment method is proposed and strongly encouraged.