This study investigated the effects of incorporating carbon nanotubes (CNTs) into rice husk ash (RHA) sustainable concrete on its mechanical properties, permeability and microstructure characterisation. Mechanical test results suggested that the addition of 0.10 % multiwalled CNTs (MWCNTs) yielded optimal results, with increases in the compressive strength, splitting tensile strength, flexural strength, and elastic modulus of the RHA concrete at 28 days of 7 %, 23.81 %, 17.5 %, and 1.0 %, respectively. However, with further addition of MWCNTs, the mechanical properties ultimately deteriorated. Further, the incorporation of CNTs enhanced the long-term performance of RHA sustainable concrete. The addition of 0.1 % MWCNTs and 15 % RHA yielded a 20 %, 14 %, and 66 % decrease in water absorption, porosity, and chloride diffusion coefficient compared to the mixture solely containing 15 % RHA. Scanning electron microscopy of this mixture revealed the filling and bridging effects of MWCNTs between the hydration products have enhanced the performance of RHA sustainable concrete.
Cities are growing geographically in response to the enormous increase in urban population; consequently, comprehending growth and environmental changes is critical for long-term planning. Urbanization transforms naturally permeable surfaces into impermeable surfaces, causing an increase in urban land surface temperature, leading to the phenomenon known as urban heat islands. The urban heat islands are noticeable across Malaysia's rural communities and villages, particularly in Kuala Lumpur. These effects must be addressed to slow, if not halt, climate change and meet the Paris Agreement's 2030 goal. The study posits an application of thermal remote sensing utilizing a space-borne satellite-based technique to demonstrate urban evolution for urban heat island analysis and its relationship to land surface temperature. The urban heat island (UHI) was analyzed by converting infrared radiation into visible thermal images utilizing thermal imaging from remote sensing techniques. The heat island is validated by reference to the characteristics of the normalized difference vegetation index (NDVI), which define the land surface temperature (LST) of distinct locations. Based on the digital information from the satellite, the highest temperature difference between urban and rural regions for a few chosen cities in 2013 varied from 10.8 to 25.5 °C, while in 2021, it ranged from 16.1 to 26.73 °C, highlighting crucial temperature changes. The results from ANOVA test has substantially strengthened the credibility of the significant temperature changes. Some notable reveals are as follows: The Sungai Batu area, due to its rapid development and industry growth, was more vulnerable to elevated urban heat due to reduced vegetation cover; therefore, higher relative vulnerability. Contrary, the Bukit Ketumbar area, which region lies in the woodland region, experienced the lowest, with urban heat islands reading from 2013 at -0.3044 and 0.0154 in 2021. It shows that despite having urban heat islands increase two-fold from 2013 to 2021, increasing the amount of vegetation coverage is a simple and effective way of reducing the urban heat island effect, as evidenced by the low urban heat islands in the Bukit Ketumbar woodland region. The study findings are critical for advising municipal officials and urban planners to decrease urban heat islands by investing in open green spaces.
In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. The following were the developed best models for their respective study areas: In Peninsular Malaysia, the interactions linear regression model was the best at Pulau Langkawi (RMSE = 19.066), the Matern 5/2 gaussian process regression model at Geting (RMSE = 49.891), and the trilayered artificial neural network at Pulau Pinang (RMSE = 20.026), while the linear regression model was the best at Sandakan in Sabah, East Malaysia (RMSE = 14.054). Other metrics, such as MAE and R-square, were also at their best values, each providing its best values, further substantiating the RMSE respectively, at each of the study areas. These empirical statistics (or metrics) also revealed that despite employing sea level as the sole parameter, results obtained were exceptional better when utilizing a 7-day lag, regardless of the model used. Notably, lag variables with less than a 7-day lag could degrade the model's accuracy in representing ground reality. The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML.
Multinational studies have reported monogenic etiologies in 25%-30% of children with steroid-resistant nephrotic syndrome. Such large studies are lacking in Asia. We established Deciphering Diversities: Renal Asian Genetics Network (DRAGoN) and aimed to describe the genetic and clinical spectrums in Asians. We prospectively studied a cohort of 183 probands with suspected genetic glomerulopathies from South and Southeast Asia, of whom 17% had positive family history. Using multi-gene panel sequencing, we detected pathogenic variants in 26 (14%) probands, of whom one-third had COL4A4 or COL4A5 variants (n = 9, 5%). Of those with COL4A5 defects, only 25% had features suggestive of Alport syndrome. Besides traditional predictors for genetic disease (positive family history and extrarenal malformations), we identified novel predictors, namely older age (6.2 vs. 2.4 years; p = 0.001), hematuria (OR 5.6; 95% CI 2.1-14.8; p 60% when a second risk factor (positive family history or extrarenal manifestation) co-existed. The genetic spectrum of glomerulopathies appears different in Asia. Collagen IV genes may be included in sequencing panels even when suggestive clinical features are absent.