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Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists between the construction industry and economic growth. The consequences of these variations include cost overruns and schedule delays, among others. An accurate forecast of the tender price index is good for controlling the uncertainty associated with its variation. In the present study, the efficacy of using an adaptive neuro-fuzzy inference system (ANFIS) for tender price forecasting is investigated. In addition, the Box–Jenkins model, which is considered a benchmark technique, was used to evaluate the performance of the ANFIS model. The results demonstrate that the ANFIS model is superior to the Box–Jenkins model in terms of the accuracy and reliability of the forecast. The ANFIS could provide an accurate and reliable forecast of the tender price index in the medium term (i.e. over a three-year period). This chapter provides evidence of the advantages of applying nonlinear modelling techniques (such as the ANFIS) to tender price index forecasting. Although the proposed ANFIS model is applied to the tender price index in this study, it can also be applied to a wider range of problems in the field of construction engineering and management.

Social and economic disparities between racial/ethnic groups are a feature of the American context into which immigrants are incorporated and a key determinant of population health. We ask whether racial/ethnic disparities in diabetes vary by nativity and whether native-immigrant disparities in diabetes vary by race and over time in the United States. Using the 2000–2015 National Health Interview Survey, we estimate logistic regressions to examine the interaction of race/ethnicity, nativity, and duration in the US in shaping diabetes patterns. Relative to their native-born co-ethnics, foreign-born Asian adults experience a significant diabetes disadvantage, while foreign-born Hispanic, Black, and White adults experience a significant advantage. Adjusting for obesity, education, and other covariates eliminates the foreign-born advantage for Black and White adults, but it persists for Hispanic adults. The same adjustment accentuates the disadvantage for foreign-born Asian adults. For Black and Hispanic adults, the protective foreign-born effect erodes as duration in the US increases. For foreign-born Asian adults, the immigrant disadvantage appears to grow with duration in the US. Relative to native-born White adults, all non-white groups regardless of nativity see a diabetes disadvantage because the racial/ethnic disadvantage either countervails a foreign-born advantage or amplifies a foreign-born disadvantage. Racial/ethnic differentials in diabetes are considerable and are influenced by each group’s nativity composition. Obesity and (for the foreign-born) time in the US influence these disparities, but do not explain them. These findings underscore the importance of unmeasured, systemic determinants of health in America’s race-conscious society.

This paper aims to examine the effect of audit quality of consolidated financial statements on the accuracy of analysts’ earnings forecasts from the viewpoint of users of financial statements.

This paper investigates the effect of dependence on the work of other auditors on error in analysts’ earnings forecasts based on samples from 2011 to 2012 (the period since implementation of the International Financial Reporting Standards in Korea). In addition, this paper examines the effects of use of Big 4 auditors, use of auditors with industry expertise and the proportion of overseas subsidiaries in relation to all subsidiaries on the association between dependence on the work of other auditors and error in analysts’ earnings forecasts.

This paper finds a positive relation between dependence on the work of other auditors and error in analysts’ earnings forecasts, suggesting that more dependence on the work of other auditors decreases the quality of the audit of consolidated financial statements; thus, to the extent that low-quality audits decrease reporting reliability, analysts’ forecasts are less likely to be accurate. This paper also finds that the positive relationship between dependence on the work of other auditors and error in analysts’ earnings forecasts is weakened when the principal auditor is a Big 4 auditor or one with industry expertise, because such auditors provide higher-quality audit services. However, the positive relationship between dependence on the work of other auditors and error in analysts’ earnings forecasts is further strengthened in cases where the proportion of overseas subsidiaries to all subsidiaries is higher. These results suggest that the complexity of the consolidation process increases as the proportion of overseas subsidiaries increases.

The findings are useful in analyzing the effects of adoption of the New ISA, implemented in 2014, which does not allow the division of audit responsibilities between principal auditors and other auditors. This paper also provides insights for regulators and practitioners to improve the auditor appointment system in the future.

Pyrus Pyrifolia (Sand Pear) is one of the most underused pear variety despite its nutraceutical potential. Therefore, this paper aims to explore the Pyrus Pyrifolia in term of origin, distribution and classification, nutritional and bioactive potential, therapeutic potential and valorization along with future prospectus.

A wide variety of publications (88) were identified through electronic databases (Science direct, PubMed, SciELO, Google scholar, Link springer and Research gate) under the umbrella of different keywords such as bioactive compounds, health benefits, nutrition, sand pear, Pyrus and Pyrus pyrifolia.

Pyrus Pyrifolia (Sand Pear) is abundant in nutritional and bioactive compounds such as phenolic acids, flavonoids, terpenoids, vitamins and minerals. It exhibits therapeutic potential as being an antioxidant, anti-obesity, anti-diabetic, anti-inflammatory and anti-cancer agent. However, P. pyrifolia is not much explored by food researchers and industrialists, hence remaining underused. A few attempts have been made toward the use of P. pyrifolia for jam, jelly, candy and wine preparation. However, more research is required for the commercial processing of P. pyrifolia and to enhance its availability outside its growing area.

In this paper, nutritional and bioactive compounds of P. pyrifolia are discussed that provide knowledge to the researchers for its use as a functional ingredient.

The study seeks to identify the impact of variables in a deep learning-based bankruptcy prediction model, which has achieved superior performance to other prediction models but cannot easily interpret hidden processes.

This study developed three LSTM-RNN–based models that predicted the probability of bankruptcy before 1, 2 and 3 years using financial, the construction market and macroeconomic variables as input variables. Then, the impacts of the input variables that affected prediction accuracy in each model were identified by using Shapley value and compared among the three models. This study also investigated the prediction accuracy using variants of input variables grouped sequentially by high-impact ranking.

The results showed that the prediction accuracies were largely impacted by “housing starts” in all models. As the prediction period increased, the effects of macroeconomic variables on prediction accuracy increased, whereas the impact of “return on assets” on prediction accuracy decreased. It also found that the “current ratio” and “debt ratio” significantly influenced the prediction accuracies in all models. Also, the results revealed that similar prediction accuracies could be achieved using only 8, 10, and 10 variables out of a total of 18 variables for the 1-, 2-, and 3-year prediction models, respectively.

This study provides a Shapley value-based approach to identify how each input variable in a deep-learning bankruptcy prediction model. The findings of this study can not only assist in obtaining better insights into the underlying concept of bankruptcy but also use to select variables by removing those identified as less significant.