We report a method for effectively and homogeneously incorporating carbon nanotubes (CNTs) in the form of double-wall (DWCNTs) and multi-wall (MWCNTs) structures into commercial paints without the use of additives, surfactants, or chemical processes. The process involves the physical mixing of the nanotubes and polymers using the cavitation energy of an ultrasonic bath. It is a simple, fast method that allows for uniform distribution of carbon nanotube bundles within the polymer for direct application. Due to the hydrophobic properties of the carbon nanotubes as grown, we used paint samples containing 0.3% by mass of both types of CNTs and observed an improvement in waterproofing through wettability and water absorption through immersion tests on the samples. Different solvents such as water, formaldehyde, and glycerin were used, and the results showed an increase in paint impermeability of 30% and 25% with the introduction of DWCNTs and MWCNTs, respectively. This indicates a promising, economically viable, and revolutionary method for applying nanotechnology in the polymer industry.

This review summarizes some of the recent advances related to shallow penetration conformance sealants (SPCS) based on cross-linked polymer nanocomposite gels. The cross-linked polymer nanocomposite gels formed a three-dimensional (3D) gel structure upon contact with either water or oil when placed at the downhole. Therefore, the cross-linked polymer nanocomposite gels offer a total or partial water shutoff. Numerous polymeric gels and their nanocomposites prepared using various techniques have been explored to address the conformance problems. Nevertheless, their instability at high temperature, high pressure, and high salinity down-hole conditions (HT-HP-HS) often makes the treatments unsuccessful. Incorporating inert particles into the cross-linked polymer nanocomposite gel matrices improves stability under harsh down-hole conditions. This review discusses potential polymeric nanocomposite gels and their successful application in conformance control.

An alternative to CMOS VLSI called Quantum Cellular Automata (QCA) is presently being researched. Although a few basic logical circuits and devices have been examined, very little, if any, research has been done on the architecture of QCA device systems. In the context of nano communication networks, data transmission that is both dependable and efficient is still critical. The technology known as Quantum Dot Cellular Automata (QCA) has shown great promise in the development of nano-scale circuits because of its extremely low power consumption and rapid functioning. This study introduces a unique nano-communication parity-based arithmetic circuit that is reversible, error-detecting, and error-correcting. The minimal outputs are needed for the proposed structure. Based on QCA technology, the proposed nano-communication network makes use of reversible logic gates. The performance increase of the suggested parity generator and checker circuit is significant in terms of clock delay, size, and number of cells.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

In the context of contemporary global challenges such as the COVID-19 pandemic, geopolitical conflicts, and climate change, food security assumes particular significance, being an integral part of national security. This study aims to investigate the interplay between food security and national security systems, with a focus on identifying gaps in the literature and determining directions for further research. The study conducted a systematic literature review on food security and national security systems employing a rigorous and transparent process. The qualitative analysis is grounded in the quantitative one, encompassing studies from Scopus. The examination of the selected peer-reviewed articles revealed several methodological and thematic limitations in existing research: i Geographic imbalance: There is a predominant focus on developed countries, while food security issues in developing countries remain insufficiently studied; ii Insufficient explication: There is a lack of research dedicated to managerial and economic aspects of food security in the context of national security; iii Methodological constraints: There is a predominance of quantitative methods and retrospective/cross-sectional studies. Recommendations include developing comprehensive strategies at both global and national levels to enhance food stability and accessibility.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.