Nowadays, the combustion of fossil fuels for transportation has a major negative impact on the environment. All nations are concerned with environmental safety and the regulation of pollution, motivating researchers across the world to find an alternate transportation fuel. The transition of the transportation sector towards sustainability for environmental safety can be achieved by the manifestation and commercialization of clean hydrogen fuel. Hydrogen fuel for sustainable mobility has its own effectiveness in terms of its generation and refueling processes. As the fuel requirement of vehicles cannot be anticipated because it depends on its utilization, choosing hydrogen refueling and onboard generation can be a point of major concern. This review article describes the present status of hydrogen fuel utilization with a particular focus on the transportation industry. The advantages of onboard hydrogen generation and refueling hydrogen for internal combustion are discussed. In terms of performance, affordability, and lifetime, onboard hydrogen-generating subsystems must compete with what automobile manufacturers and consumers have seen in modern vehicles to date. In internal combustion engines, hydrogen has various benefits in terms of combustive properties, but it needs a careful engine design to avoid anomalous combustion, which is a major difficulty with hydrogen engines. Automobile makers and buyers will not invest in fuel cell technology until the technologies that make up the various components of a fuel cell automobile have advanced to acceptable levels of cost, performance, reliability, durability, and safety. Above all, a substantial advancement in the fuel cell stack is required.

Optical Coherence Tomography (OCT) plays a crucial role in diagnosing ocular diseases, yet conventional CNN-based models face limitations such as high computational overhead, noise sensitivity, and data imbalance. This paper introduces HDL-ACO, a novel Hybrid Deep Learning (HDL) framework that integrates Convolutional Neural Networks with Ant Colony Optimization (ACO) to enhance classification accuracy and computational efficiency. The proposed methodology involves pre-processing the OCT dataset using discrete wavelet transform and ACO-optimized augmentation, followed by multiscale patch embedding to generate image patches of varying sizes. The hybrid deep learning model leverages ACO-based hyperparameter optimization to enhance feature selection and training efficiency. Furthermore, a Transformer-based feature extraction module integrates content-aware embeddings, multi-head self-attention, and feedforward neural networks to improve classification performance. Experimental results demonstrate that HDL-ACO outperforms state-of-the-art models, including ResNet-50, VGG-16, and XGBoost, achieving 95% training accuracy and 93% validation accuracy. The proposed framework offers a scalable, resource-efficient solution for real-time clinical OCT image classification.

In recent years, harnessing renewable energy from different sources of energies readily available to us in different forms such as solar, wind, wave, hydro, tidal, biomass and hydrogen has gained popularity among researchers in different fields to counter the emissions released into the atmosphere by utilization of fossil fuels. Amongst these, wave energy is driving researchers to develop efficient systems due to its remarkably high potential providing a reliable and sustainable energy source both onshore and offshore. This paper provides a comprehensive review of the developments, various design innovations, and performance optimization techniques in the recent years of the two most common wave energy converters installed across the globe i.e., point absorber wave energy converter and oscillating water column wave energy converter. The research papers on numerical modeling, experimental analysis on laboratory scaled models, and hydrodynamic performance simulations using various softwares on both the types of wave energy converters are tabulated in a year wise format. Moreover, a list of all the installed point absorber wave energy converters and oscillating water column wave energy converters are tabulated in a year wise format along with their installed capacities, location of deployment and the country of deployment. Lastly the key advantages of both the wave energy converters are outlined below.

Various factors affect the growth of the plants, which will later result in low productivity and quality, the foremost necessary factors that affects the growth of plants are temperature, humidity, water, and light. The aim of this study is analytics of temperature and humidity, as these are necessary for better crop yield. Therefore, in this work, low cost smart autonomous remote monitored Agri system for polyhouse and greenhouse is developed. In smart autonomous remote monitored system, collected temperature and humidity data is stored in cloud for further analytics. DHT22 temperature and humidity sensor are the main component of setup which is connected to nodemcu and nodemcu wirelessly communicates with raspberry pi. Also, developed an android application as a dashboard for monitoring. Experiments have been conducted to validate the performance of proposed monitoring system in polyhouse and greenhouse of G H Raisoni University Saikheda.

Currently, space robots such as planetary robots and flexible-link manipulators (FLMs) are finding specific applications to reduce the cost of launching. However, the structural flexible nature of their arms and joints leads to errors in tip positioning owing to tip deflection. The internal model uncertainties and disturbance are the key challenges in the development of control strategies for tip-tracking of FLMs. To deal with these challenges, we design a tip-tracking controller for a two-link flexible manipulator (TLFM) by developing a sampled-data extended state observer (SD-ESO). It is designed to reconstruct uncertain parameters for accurate tip-tracking control of a TLFM. Finally, a backstepping (BS) controller is designed to attenuate the estimation error and other bounded disturbances. Convergence and stability of the proposed control system are investigated by using Lyapunov theory. The benefits (control performance and robustness) of the proposed SD-ESO-based BS controller are compared with other similar approaches by pursuing both simulation and experimental studies. It is observed from the results obtained that SD-ESO-based BS Controller effectively compensates the deviation in tip-tracking performance of TLFM due to non-minimum phase behavior and model uncertainties with an improved transient response.