Chhattisgarh Swami Vivekanand Technical University

Composite materials based on polymers are being identified as helping to promote sustainable infrastructure by providing lightweight, durable, and corrosion-resistant solutions. Their applications span construction, transportation, energy, and marine sectors, enabling longer service life, reduced maintenance, and enhanced performance of modern structural systems. The curing process plays an essential role in determining the performance and reliability of polymer composites, influencing their mechanical, thermal, and chemical properties. This review aims to explore the advances, challenges, and opportunities in curing methods for polymer composites, focusing on both traditional and emerging techniques. Key motivations include the need for faster curing times, improved material properties, and enhanced sustainability in the processing of composite materials. The review covers all forms of curing, ranging from heat, ultraviolet, and microwave curing to more recent technologies like additive manufacturing. It further addresses existing challenges, including process optimization in curing, enhancement of energy efficiency, and ensuring compatibility across materials. Various research highlights that significant progress has already been made, still there is a need to overcome current challenges and design the full potential of polymer composites for the next-generation market. The review also explores sustainable curing approaches that reduce environmental impact by minimizing energy consumption and incorporating eco-friendly additives, thus advancing the development of green polymer composites for building and construction applications.

Ensuring public safety in densely populated urban environments remains a critical challenge, necessitating the deployment of intelligent and automated video surveillance systems. Traditional surveillance approaches rely heavily on manual monitoring, which is inefficient and susceptible to human fatigue, delayed response, and observational errors. To overcome these limitations, this work presents a real-time object detection-based surveillance framework. The proposed system focuses on detecting guns, knives, and region-specific blunt objects commonly involved in violent activities in Indian surveillance scenarios. A key contribution of this work is the use of a custom-created dataset collected using a mobile camera, consisting of 336 labeled images of blunt objects such as iron rods, wooden sticks, and plastic rods. This dataset is combined with a publicly available dataset of 7,623 images of guns and knives, forming a consolidated dataset of 7,959 images across three classes: gun, knife, and blunt object. The combined dataset is used to train a YOLOv8-based object detection model for real-time performance. Experimental evaluation shows that increasing the training duration significantly improves recall and average precision for the blunt object class without signs of overfitting. Overall, the proposed framework achieves an effective balance between accuracy and efficiency, making it suitable for deployment in real-world surveillance environments such as campuses, public spaces, and transportation areas.

Incoloy 825, known for its impressive corrosion resistance, exceptional capability to survive in acidic and chloride environment and mechanical strength, finds extensive application in aerospace, chemical, nuclear, marine and various other industries. Nevertheless, the wide range of application of dry turning in machining Incoloy 825 presents significant challenges, such as toughness, high heat resistance, low thermal conductivity and creep resistance. These issues make the machining of Incoloy 825 a formidable task. In the present investigation, analysing the suitability of advanced cutting tools in dry turning environment by comparison of the result of the uncoated carbide tool, cryo-treated carbide tool and coated carbide tool for various testing parameters such as surface temperature, surface roughness, tool wear and the chip reduction coefficient. A round bar of Incoloy having a diameter of 51 mm and a length of 320 mm was turned at three distinct cutting speeds viz. 51, 76 and 107 m/minute, for cutting intervals of 30, 60, 90 and 120 seconds, respectively. However, the values of the other two machining variables were kept as constant, that is, feed rate = 0.1 mm/rev and depth of cut = 0.5 mm throughout the experiment. The result suggests that the coated and cryo-treated inserts perform better at all predefined cutting speeds as compared to the uncoated counterpart. Coated tool was found to be most favourable as it demonstrated superior results for all the testing parameters except tool wear.

This study develops an integrated Economic Dispatch (ED)-Load Frequency Control (LFC) scheme for twoarea thermal power systems. The framework utilizes EDderived participation factors, with a Genetic Algorithm (GA) allocating generation during load transients. Proportional-Integral-Derivative (PID) controllers are optimized using the Integral of Time Absolute Error (ITAE) and Integral Squared Error (ISE) criteria. Simulink validation demonstrates that the proposed scheme reduces the frequency nadir by 40-52%, accelerates Area Control Error (ACE) inactivation by 35-45%, and lowers tie-line power variance by 28%. The tie-line bias Automatic Generation Control (AGC) implementation maintains scheduled interchange compliance (±0.08 p.u. maximum deviation), regulates frequency in accordance with the North American Electric Reliability Corporation (NERC) Protection and Control (PRC) 024-3 standards, and compensates for governor dead-band effects. Operational costs are minimised through dynamic adjustment of the participation factor, 5-minute ED rescheduling cycles, and ramp-rateconstrained unit commitments, resulting in a 17% reduction in overall generation costs.

Intelligent transportation systems (ITS) depend on precise vehicle identification and speed estimation to provide real-time traffic monitoring, accident avoidance, and effective traffic flow management. This paper, a novel deep neural network (DNN)-based method for real-time vehicle detection and speed estimate is presented. For accurate vehicle detection, we use cutting-edge object detection models like YOLOv5, and we use computer vision algorithms to determine a vehicle's speed from video frames. The technology operates reliably in a range of environmental circumstances and processes real-time traffic video streams. High detection accuracy (above 95%) and accurate speed estimation with an average error margin of less than 5 km/h are demonstrated by the experimental findings. A scalable and economical architecture for traffic surveillance applications is provided by the suggested approach.