This paper presents a comprehensive analysis of power quality in a distributed generation (DG) system utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Unified Power Quality Conditioner (UPQC). The integration of distributed generation resources, such as solar and wind power, into the electrical grid has posed significant challenges related to power quality, including voltage sags, swells, harmonics, and reactive power issues. To address these challenges, the proposed system employs ANFIS for adaptive and precise control, enhancing the performance and stability of the DG system. The UPQC is integrated to mitigate power quality disturbances by simultaneously compensating for voltage and current harmonics and providing voltage regulation. Detailed simulations are conducted to evaluate the effectiveness of the ANFIS-based control strategy and the performance of the UPQC in various operating conditions. The results demonstrate significant improvements in power quality metrics, highlighting the potential of this approach for efficient and reliable integration of distributed generation into modern power systems. The simulation findings are thoroughly examined across multiple operational scenarios and compared to Fuzzy logic control. Furthermore, the proposed system's efficacy is validated in accordance with the IEEE 1547 and IEEE 519 standards, demonstrating its performance and compliance with industrial needs.

To improve surveillance, the proposed patrolling security system employs autonomous mobile robots outfitted with low-cost night vision cameras. Regular patrols, which are essential for discouraging criminal behavior, are typically conducted by security or law enforcement officers with the use of pricey CCTV equipment. The goal of using autonomous robots is to save expenses while enhancing the quality of patrols in particular regions. Using a night vision camera, the late-night guarding robot detects human movement within its assigned zone while following a random path. Its obstacle-detecting sensors help to prevent crashes and guarantee secure navigation. The robot records incidences, takes pictures with its mounted camera, and carefully scans regions for probable incursions. It then sends the data to the user as quickly as it can. This project's primary goal is to draw attention to suspicious activity in hidden areas. 

When you are driving a car and you are being responsible for your co-passenger and other innocent being on the road, you should be extra responsible. Many fatal and minor accidents happen on the road due to the drowsiness of drivers only. Hence, there is a need to detect drowsiness while driving a car. It has become an important requirement for everyone’s safety. The main objective of this study is to create a highly accurate drowsiness detection system using methods that are both affordable and easy for any car manufacturer to include in their cars. The ultimate objective is to increase road user’s protection by raising the level of safety for both drivers and their cars. This study's main contribution is the implementation of a bimodule method for drowsiness detection. The first module effectively detects signs of drowsiness by analyzing a constant stream of images of the driver in real time using a reinforcement learning model. Simultaneously, the car's second module, which is built into the steering wheel grip, keeps track of the driver's hand pressure when performing turns and emergency scenarios. The findings of the study highlight how well the proposed system works to reduce the risks associated with drowsy driving. It further highlights the value of cutting-edge technology in protecting other drivers and improving driving safety, which has the potential to save lives and avoid accidents.

Problem: The precision, efficiency, and safety of surgical procedures need significant improvements. Traditional methods are limited by human capabilities, and existing robotic systems lack the advanced adaptability required for complex surgical tasks. The integration of reinforcement learning (RL) into robotic surgery represents a potential revolution in the medical field.
Methods: This comparative review synthesizes recent progress in RL applications for robotic surgery. We highlight innovative methodologies and successful applications of RL, focusing on advanced simulations to train RL agents and the importance of human demonstrations in the learning process.
Results: Emerging trends such as the effective use of simulations and human demonstrations to enhance RL in robotic surgery are identified. The review also discusses challenges associated with RL applications, emphasizing the need for clinical validation and ensuring patient safety.
Conclusion: The transformative potential of RL in robotic surgery is evident, though challenges remain. Future work should prioritize clinical validation, patient safety, and interdisciplinary collaboration.

The paper aims to explore the application of CyclePad in modelling air standard thermodynamic cycles. CyclePad is a powerful software tool designed for the simulation and analysis of various thermodynamic cycles. This paper provides an in-depth investigation into its capabilities and effectiveness in modelling air standard cycles, including the analysis of performance parameters such as efficiency, work output, and heat transfer. To explore the potential of CyclePad, Carnot, Otto, Stirling, Ericsson, Diesel, and Dual cycles were explored first thermodynamically and then modelled using the software. These cycles were tested against practical numerical problems, and it has been observed that the results obtained from the CyclePad are in agreement with the existing literature. Moreover, to understand the impact of input parameters on the performance of cycle output and efficiency sensitivity analysis was performed and reported. The results obtained are very encouraging and stem from the fact the CyclePad can be used effectively to understand and analysis any thermodynamic cycle (both open and close) having any level of complexity.