Recent Advances of GFRP Composite Cross Arms in Energy Transmission Tower: A Short Review on Design Improvements and Mechanical Properties.

Currently, pultruded glass fibre-reinforced polymer (pGFRP) composites have been extensively applied as cross-arm structures in latticed transmission towers. These materials were chosen for their high strength-to-weight ratio and lightweight characteristics. Nevertheless, several researchers have discovered that several existing composite cross arms can decline in performance, which leads to composite failure due to creep, torsional movement, buckling, moisture, significant temperature change, and other environmental factors. This leads to the composite structure experiencing a reduced service life. To resolve this problem, several researchers have proposed to implement composite cross arms with sleeve installation, an addition of bracing systems, and the inclusion of pGFRP composite beams with the core structure in order to have a sustainable composite structure. The aforementioned improvements in these composite structures provide superior performance under mechanical duress by having better stiffness, superiority in flexural behaviour, enhanced energy absorption, and improved load-carrying capacity. Even though there is a deficiency in the previous literature on this matter, several established works on the enhancement of composite cross-arm structures and beams have been applied. Thus, this review articles delivers on a state-of-the-art review on the design improvement and mechanical properties of composite cross-arm structures in experimental and computational simulation approaches.

PM2.5 forecasting for an urban area based on deep learning and decomposition method.

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.

A systematic literature review of deep learning neural network for time series air quality forecasting.

Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.