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Sustainable infrastructural development is vital for both developed and developing countries. The primary concern in hot mix asphalt (HMA) pavements is related to damages caused by moisture. This research makes efforts to evaluate the moisture susceptibility of a dense-graded surface HMA mixture using simple performance tests (SPTs) and Superpave indirect tensile tests (IDTs). Coating of fines (dust) on the aggregate can prevent the asphalt binder from bonding directly to the surface of the aggregate. Therefore, SPT and IDT have been performed by varying the proportion of fines (3%, 6%, and 9%) in the mix. For each proportion of fines, the optimum asphalt content was determined and used for further preparation of test specimens. The addition of hydrated lime [Ca(OH)2] to asphalt mixtures improved the adhesive bond between aggregate and bitumen, as well as reduced the occurrence of stripping. In addition, when aggregates coated with clays, hydrated lime with a pozzolanic ally to remove those deleterious materials. The amount of antistripping agent incorporated was 1.5% by weight of dry aggregate, a proportion commonly used in the industry for such applications. Standard Marshall Mix design procedure was employed to design the asphalt mixtures. The laboratory investigation and subsequent statistical analysis were conducted using Minitab-15 software, which yielded significant insights into the effectiveness of hydrated lime as an antistripping additive in asphalt concrete mixtures. The findings indicate that incorporating hydrated lime substantially facilitates mitigating moisture-induced stripping in asphalt mixtures.

Mixed‐matrix membranes (MMMs) using UiO‐66 nanofillers as dispersing inorganic phase and polyetherimide (PEI) as continuous polymeric matrix were synthesized. Different UiO‐66 loadings of 10, 20, and 30 wt % were applied. The morphology and UiO‐66 dispersion over the prepared MMMs were examined. Gas separation measurements were performed for H2/CO2 gas mixture to evaluate the influence of UiO‐66 on the separation yield of resulting membranes. When compared with pristine PEI membranes, MMMs containing metal‐organic framework (MOF) nanoparticles revealed superior H2 permeability and H2/CO2 separation factor, e.g., maximum permeability values of 14.6 and 5.5 Barrer were observed for H2 and CO2 at a filler loading of 10 wt % and an applied pressure of 4.0 bar. [ABSTRACT FROM AUTHOR]

Smart city is emerging application in which many Internet of Things (IoT) devices are embedded to perform overall monitoring and perform processing automatically. In smart city the authenticity is key problem and many users in the in smart city has faced challenges during COVID-19. The COVID-19 epidemic, a deadly virus, first appeared in the globe in 2019. The World Health Organization (WHO) states that it is almost certainly feasible to contain this virus in its early phases if some precautions are taken. To contain the infection, most nations declared emergencies both inside and outside their borders and prohibited travel. Artificial intelligence and blockchain are being used in smart city applications to monitor the general condition in the nation and reduce the mortality rate. Blockchain has also made it possible to safeguard patient medical histories and provide epidemic tracking. AI also offers the ideal, wanted answer for correctly identifying the signs. The primary goal of this study is to fully investigate blockchain technology and artificial intelligence (AI) in relation to COVID-19. A case study that was recently developed to identify and networked pathogens acquired important knowledge and data. Additionally, AI that can handle massive quantities of medical data and perform difficult jobs will be able to reduce the likelihood of intricacy in data analysis. Lastly, we highlight the present difficulties and suggest potential paths for addressing the 19 diseases in future circumstances. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects billions of smart gadgets so that they may communicate with one another without the need for human intervention. With an expected 50 billion devices by the end of 2020, it is one of the fastest-growing industries in computer history. On the one hand, IoT technologies are critical in increasing a variety of real-world smart applications that can help people live better lives. The cross-cutting nature of IoT systems, on the other hand, has presented new security concerns due to the diverse components involved in their deployment. For IoT devices and their inherent weaknesses, security techniques such as encryption, authentication, permissions, network monitoring, \& application security are ineffective. To properly protect the IoT ecosystem, existing security solutions need to be strengthened. Machine learning and deep learning (ML/DL) have come a long way in recent years, and machine intelligence has gone from being a laboratory curiosity to being used in a variety of significant applications. The ability to intelligently monitor IoT devices is an important defense against new or negligible assaults. ML/DL are effective data exploration techniques for learning about 'normal' and 'bad' behavior in IoT devices and systems. Following a comprehensive literature analysis on Machine Learning methods as well as the importance of IoT security within the framework of different sorts of potential attacks, multiple DL algorithms have been evaluated in terms of detecting attacks as well as anomaly detection in this work. We propose a taxonomy of authorization and authentication systems in the Internet of Things based on the review, with a focus on DL-based schemes. The authentication security threats and problems for IoT are thoroughly examined using the taxonomy supplied. This article provides an overview of projects that involve the use of deep learning to efficiently and automatically provide IoT applications. [ABSTRACT FROM AUTHOR]