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This study examines the potential for enhancing carbon capture and storage (CCS) processes by machine learning to markedly improve performance across diverse capture methods, including as absorption, adsorption, membrane separation, and cryogenic distillation. Through the systematic adjustment of critical operating parameters, including temperature, pressure, flow rates, and sorbent characteristics using machine learning algorithms, we saw significant improvements in CO₂ collection efficiency. The use of optimum operating parameters, namely a temperature range of 40-60°C for absorption and a pressure range of 3-5 bar for adsorption, resulted in a 30% enhancement in capture efficiency. Moreover, machine learning models, namely Random Forest and Support Vector Machines (SVM), achieved a maximum enhancement of 20% in forecasting ideal operating parameters for membrane separation and cryogenic systems. Reduced cycle durations in adsorption processes, facilitated by predictive modeling, resulted in a 15% improvement in CO₂ removal rates. The models’ capacity to forecast sorbent regeneration conditions led to a 10% decrease in energy use. Machine learning algorithms adeptly optimized process-specific parameters, including material composition and flow dynamics, enhancing membrane performance by 18% and cryogenic systems by 12%. These results highlight the significance of using machine learning to customize CCS methods for particular materials and situations, facilitating more sustainable, efficient, and scalable carbon capture systems.

Palm vein identification relies on unique patterns within the palm veins, illuminated by Near Infrared Light (NIR) with wavelengths from 760 nm to 820 nm, penetrating the skin up to 5mm. Absorption of NIR by deoxygenated blood in veins creates distinct dark patterns. However, this high wavelength light may cause skin and tissue infection. Vein networks are captured via infrared-sensitive cameras, with captured images pre-processed to remove noise and features extracted for recognition. Feature extraction primarily involves network segmentation, creating reference maps for subsequent recognition. These feature maps serve as blueprints for neural networks, facilitating streamlined identification processes.

This work emphasises sustainability as a crucial component of the design process and offers a novel approach to antenna design. A miniature circular microstrip antenna array with 1800 hybrid coupler operating at different modes of frequency, specifically aiming for multiband wireless communication is presented. The Circular patch, two element broad side array and end fire array structures are designed and analysed by using High Frequency Software Simulator (HFSS) software. Measured results are also presented for validating the simulated results.

Flooding is the most frequent natural disaster that has an impact on humans. There is a history of widespread damage from floods. The organizations and competent authorities use a variety of sophisticated techniques to track the flood level in areas with a high risk of flooding. The majority of these devices are quite expensive to use and maintain. In the proposed system, data from sensors including those measuring water level, water flow, and ultrasonic sensor can be sent to the cloud using a GSM device. If environmental condition threshold values rise, a warning message will be sent to the relevant authorities, who will then inform the residents of the flood-prone area. With the aid of such a system, both commercial and public organizations can develop their flood mitigation strategies before the situation worsens. The most widely used mobile messaging platform, the Global System for Mobile Communication (GSM), is used to send the SMS to the villages as a warning simultaneously a buzzer will ring which is placed in every village of the flood-prone area. This buzzer will work with the technology of LoRa transceiver, which had a long range of communication.

In an increasingly interconnected world, the ability to overcome language barrier has become a crucial requirement. for effective communication. Language translator headphones have emerged as a promising technological solution to facilitate seamless multilingual conversations. This research paper aims to evaluate their accuracy, usability, and potential impact on communication dynamics. The paper begins by discussing the significance of language translation technology in various domains, such as tourism, business, and diplomacy. It highlights the limitations of traditional translation methods and emphasizes the need for innovative as well as sustainable solutions that can enhance communication efficiency and convenience with the help of raspberry pi as a microcontroller.