This paper concentrates on the development of a closed-loop tracking of the sun that precisely follows the sun’s trajectory, allowing photovoltaic panels to capture the maximum amount of solar energy. Azimuthal and elevation-tracking mechanisms are included in the proposed system, and a feedback controller based on sensors monitors the brightness of the sun continuously as a reference signal. The controller generates a signal to operate the tracking motor with two axes, orienting the PV panel towards the sun, when the intensity exceeds a set threshold. In both east–west (E-W) and north–south (N-S) directions, the solar tracking system (STS) tracks the sun’s position independently. A dual-axis solar tracking system (DAST) was made of three 335-watt panels (each generating 1 kilowatt of power) in a PV system. Three 335-watt panels were used to successfully execute the dual-axis solar tracking system, with each panel contributing to the PV system’s overall power generation of 1 kilowatt. Overall, the PV system integration of a dual-axis solar tracking system with three 335-watt panels shows the potential for higher power output and energy efficiency. This configuration offers a viable means of maximizing the advantages of renewable energy sources and efficiently harnessing solar energy.

Solar energy is the finest answer among renewable energy sources for overcoming global energy challenges. However, one issue with solar electricity is that it i

The merging of artificial intelligence (AI) together with haptic systems within healthcare is creating a new dimension of sophisticated applications, including remote surgeries, smart prosthetics, and rehabilitation techniques. Haptic catheters powered by artificial intelligence provide real-time, high accuracy tactile feedback, accelerating patient care and training. However, this convergence comes with a plethora of technical challenges such as hardware-software compatibility, constraints in real-time data processing, latency issues, and cyber security threats. In this chapter we explore the main technical hurdles in AI and haptic systems integration in healthcare and also some of the innovative solutions to tackle these barriers. We discuss emerging components of tactile response such as the role of edge computing, deep learning haptic feedback, real-time signal processing, and adaptive machine learning models. The chapter also investigates the role of robotic surgery with artificial intelligence, smart prosthetics, virtual rehabilitation, and immersive medical training.

Inclusion of renewable energy in power grid at micro and macro level has imposed numerous challenges in the recent years. Occurrence and managing congestion in the power transmission line due to unpredictable and stochastic nature of Renewable Energy Source (RES) integration has become a challenging task to the grid operators. Transmission lines operate at bottlenecks during a congestion episode adding to the extra congestion cost and risk in grid stability which becomes burden to the generation as well as end users. Different methodologies are applied to detect and manage the congestion to eliminate the congestion cost factor and maintain grid stability. The presented analysis compares conventional methodology Linear Sensitivity Factors (LSF) and Heuristic methodology Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) methods for congestion management in power transmission lines with integrated RES (wind and solar) system. For comparative analysis, a modified standard IEEE 30 Bus system is chosen which is integrated with real time RES generation. Optimal locations of RES generation with minimized congestion cost and percentage of RES curtailment are chosen as objective function for comparison of the methodologies.

Adaptive haptics, an amalgamation of different haptic systems and machine learning algorithms, has the potential for intelligent feedback in healthcare. Such systems are capable of learning to suit individual user needs in real-time, thereby improving patient care, medical education, and treatment. Targeted techniques of machine learning approaches are reinforcement learning, neural networks, support vector machine, deep learning, etc. These include primary use cases like telemedicine, rehabilitation, and robotic surgery. Yet, barriers exist, including lack of data, limitations in processing in real-time and on various medical problems at scale. Federated learning and edge computing represent possible future research directions, and the case for AI-haptic-based delivery should motivate their inclusion in personalized solutions for healthcare. Federated learning and edge computing could lay the groundwork for the increased coupling of haptic data with ML systems.