Digitalization in the construction sector is a need of the modern world. Not only the infrastructure, but also the quality of life, is improved by the digital transformation in the construction sector. Digital technologies are being widely used in construction. The impacts of implementing digital technology on transformation in the construction industry, however, have not yet been thoroughly understood. Considering this aspect of the construction industry, this study reviews the articles in the field of digitalization of various segments in the construction industry. In this manner, the Scopus database was considered to gather the relevant articles based on the keywords (((“Digitalization” OR “Digitalisation”) OR “Reforms”) AND “Construction” AND “Energy”). These keyword combinations provided a list of 126 articles and, following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the number was reduced to 35. The review showed that the major targeted areas in the construction sector based on digital transformation are smart construction, optimization of energy, sustainable environment, wireless technology, and economic and architectural growth in which the role of Building Information Modelling (BIM) and prefabricated construction is noteworthy. The involvement of digitalization in numerous fields has improved quality of life. It increases production and has the potential to automate the industry more effectively. This study shows how the adoption of digital technology has improved comprehension and laid the foundation for a digital transformation in the construction industry.

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.

Resting-state functional magnetic resonance imaging (rs-fMRI) is an efficient tool to measure brain connectivity and it can reveal patterns that distinguish autism spectrum disorder (ASD) from normal controls (NC). It is established that the fractal nature of neuroimaging signals will affect the estimation of brain’s functional connectivity. Therefore, the ordinary correlation of rs-fMRI may not provide the original neuronal activity of the brain. In this work, the non-oscillatory brain connectivity method is proposed to distinguish subtypes of ASD from NC. The three subtypes of ASD namely autistic disorder (ATD), Asperger’s disorder (APD), and Pervasive developmental disorder-not other specified (PDD) are classified from NC by extracting the non-oscillatory connectivity from the BOLD rs-fMRI signal. A number of significant connections are extracted by utilizing the $p$ -value analysis and these significant connections are fed to machine learning (ML) classifiers for classification of ASD subtypes against normal control. The performance for binary classification is recorded at accuracy of 98.6%, 97.2%, 97.2%, respectively, for ATD vs. NC, APD vs. NC and PDD vs. NC. Whereas, for multiclass (ATD, APD, PDD and NC), the best accuracy is 88.9%. Both binary and multiclass classification outperformed the conventional Pearson correlation-based connectivity and benchmark approaches in terms of accuracy, sensitivity, specificity. This work demonstrates the great potential of non-oscillatory connectivity approaches, not only for autism diagnosis but also for other neurological disorders.

The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components. The fractal nature originates from several sources, such as cardiac fluctuations, respiration and system noise, and carries no information on the brain’s neuronal activities. As a result, the conventional correlation of rs-fMRI signals may not accurately reflect the functional dynamic of spontaneous neuronal activities. This problem can be solved by using a better representation of neuronal activities provided by the connectivity of nonfractal components. In this work, the nonfractal connectivity of rs-fMRI is used to distinguish Alzheimer’s patients from healthy controls. The automated anatomical labeling (AAL) atlas is used to extract the blood-oxygenation-level-dependent time series signals from 116 brain regions, yielding a 116 × 116 nonfractal connectivity matrix. From this matrix, significant connections evaluated using the p-value are selected as an input to a classifier for the classification of Alzheimer’s vs. normal controls. The nonfractal-based approach provides a good representation of the brain’s neuronal activity. It outperformed the fractal and Pearson-based connectivity approaches by 16.4% and 17.2%, respectively. The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3% and 83.3% for the XHSLF dataset and ADNI dataset, respectively. For further validation of our proposed work, we combined the two datasets (XHSLF+ADNI) and still received an accuracy of 90.2%. The proposed work outperformed the recently published work by a margin of 8.18% and 11.2%, respectively.