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This paper examines the national solidarity in Brunei Darussalam during the COVID-19 pandemic and its consequential impact on younger generations. Utilising Emile Durkheim's solidarity theories, I examine how young people's social media use builds on state discourse in the pandemic. I contend that a shift towards an organic society is visible through a social cohesion that is based on differentiated roles. I argue that the citizenry plays a vital role in the forward momentum toward Industrial Revolution (IR) 4.0, which illustrates that solidarity cannot be forged as a top-down directive. By prompting economic and creative divisions of labour, the local use of social media in a public health crisis has shown the government a new way to foster solidarity. Significant implications for youth as future leaders of the nation are discussed.

The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy.

A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs.

The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects.

A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample.

This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

Improvements on the energy efficiency of the induction motors bear on not only these motors but also on the whole industry as a result of preference of these types of motors. In recent projects, energy efficiency of the induction motors is approaching to 90 per cent. The first necessary condition of the efficiency improvements is an accurate estimation of energy efficiency. This study aims to estimate the energy efficiency of induction motors by using three innovative estimation methods.

Data for 307 motors were taken from three different companies and their torque, power, power factor and speed data were used. Three hybrid models were created by estimating the error of three autoregressive (AR)-based efficiency estimation models with the back-propagation artificial neural network (ANN) structure. In these proposed hybrid models, the AR models were supported with artificial neural networks to obtain a minimum estimation error. These three hybrid models were called as AR1-ANN, AR4-ANN and residual-ANN.

Without hybridization of AR models by back-propagation ANNs, the best estimation result was obtained by residual model. On the other hand, for the proposed hybrid models, the best estimation was obtained by AR1-ANN, followed by AR4-ANN and finally the residual-ANN according to ME values.

Proposed AR-ANN hybrid models relieve of longtime experiments for the energy efficiency measurement of induction motors. Furthermore, these AR-ANN models give more accurate results than the available methods in the literature. Engineering value of this research is three different issues in finding energy efficiency. The first one is minimizing of the test cost, the second one is no requirement the test equipment and the third one is not interrupting the motor. Every company that needs motors can use these estimation methods due to the advantages.

Novel three AR-ANN hybrid models for energy efficiency estimation were studied. These novel methods give better response than the other methods which were used for estimation of induction motors in the literature.

This paper aims to investigate whether the amount of local governments’ debt can be predicted by the level of political competition.

The study uses the artificial neural network (ANN) to test whether ANN can “learn” from the observed data and make reliable out-of-sample predictions of the target variable value (i.e. a local government’s debt level) for given values of the predictor variables. An ANN is a non-parametric prediction tool, that is, not susceptible to the common limitations of regression-based parametric forecasting models, e.g. multi-collinearity and latent non-linear relations.

The study finds that “political competition” is a useful predictor of a local government’s debt level. Moreover, a positive relationship between political competition and debt level is indicated, i.e. increases in political competition typically leads to increases in a local government’s level of debt.

The study contributes to public sector reporting literature by investigating whether public debt levels can be predicted on the basis of political competition while discounting factors such as “political ideology” and “fragmentation”. The findings of the study are consistent with the expectations posited by public choice theory and have implications for public sector auditing, policy and reporting standards, particularly in terms of minimising potential political opportunism.

Introduces the theory of artificial neural networks (ANN). Discusses its application to the valuation of residential apartments. Compares the performance of the back propagation neural network (BP) model in estimating sale prices of apartments against the traditional multiple regression analysis (MRA) model. Concludes that the neural network model is an easy‐to‐use, black‐box alternative to the MRA model.

The assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.

An extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.

The models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).

All the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.

The accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.

To date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

Despite the increasing popularity of natural dyeing of textiles, the low substantivity between the fibers and the natural dyes is a problem. Several methods have been used to overcome this problem. In this study, wool fibers were pretreated with oxygen plasma under different conditions and dyed with the extract of grape leaves. The purpose of this study is to investigate the effects of plasma treatment parameters on the color strength of the dyed samples using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) and evaluate the ability of these methods for predicting the color strength.

Woolen yarns were modified under different conditions of oxygen plasma treatment. Oxygen flow rate, power and time were considered as the treatment variable factors. Plasma-treated samples were dyed under constant conditions with the extract of grape leaves as a natural dye. ANN and ANFIS were applied to model and analyze the effect of plasma treatment parameters on the color strength of the dyed samples.

The results showed that increasing all the plasma treatment process variables, including oxygen flow rate, power and time increased the color strength of the dyed samples. The results showed that the developed ANN and ANFIS could accurately predict the experimental data with correlation coefficients of 0.986 and 0.997, respectively. According to the obtained correlation coefficients, ANFIS had a higher accuracy in prediction of the results of this study compared with the ANN and RSM models (correlation coefficient = 0.902, from our previous study).

This study uses ANN and ANFIS for predicting color strength of naturally dyed textiles for the first time. The use of computational intelligence for the optimization and prediction of the effects plasma treatment for the improvement of natural dyeing of wool is another novelty of this study.

The recent financial crises in the world have brought attention to the need for a new international financial architecture which rests on crisis prevention, crisis prediction and crisis management. It is therefore both desirable and vital to explore new predictive techniques for providing early warnings to regulatory agencies. The purpose of this study is to propose a new technique to prevent future crises, with reference to the last banking crises in Turkey.

ANN is utilized as an inductive algorithm in discovering predictive knowledge structures in financial data and used to explain previous bank failures in the Turkish banking sector as a special case of EFMs (emerging financial markets).

The empirical results indicate that ANN is proved to differentiate patterns or trends in financial data. Most of the bank failures could be predicted long before, with the utilization of an ANN classification approach, but more importantly it could be proposed to detect early warning signals of potential failures, as in the case of the Turkish banking sector.

The regulatory agencies could use ANN as an alternative method to predict and prevent future systemic banking crises in order to minimize the cost to the economy.

This paper reveals that the ANN approach can be proposed as a promising method of evaluating financial conditions in terms of predictive accuracy, adaptability and robustness, and as an alternative early warning method that can be used along with the most common alternatives such as CAMEL, financial ratio and peer group analysis, comprehensive bank risk assessment, and econometric models.

This article looks at girls who fight in order to evaluate theories of education for marginalized girls. As oppositional culture and educational resistance theories suggest for boys’ misconduct in school, girl fights are found to be a product of deindustrialization, family expectations, and peer culture. Within peer groups of marginalized students an oppositional culture develops such that girls gain respect from their peers by fighting because they demonstrate a necessary toughness. Girls who fight have a complicated relationship to education. Contrary to oppositional culture theory, these girls value educational achievement. However, the girls’ relationships with teachers are strained. Teachers do not appreciate “tough” girls. Race, class, and gender together construct a student culture that produces girls who fight in school.