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An investigation has been conducted to determine whether or not novel nanocomposite electrolytes have the potential to improve the efficiency and longevity of fuel cell technology. In this investigation, nanocomposite electrolytes were manufactured by using a methodical methodology. The nanoparticles were included into polymer matrices in order to enhance the ion conductivity and mechanical strength of the electrolytes developed. The composition of nanocomposite electrolytes was improved by altering the amount of nanoparticles and polymers that were present. When compared to traditional electrolytes, the introduction of nanoparticles resulted in considerable improvements in the characteristics of the material. These enhancements included a 15% increase in ion conductivity and a 10% increase in mechanical strength. Fuel cell devices that make use of nanocomposite electrolytes have showed improved performance, with a 10% increase in power density and a 15% gain in efficiency when compared to fuel cell devices that make use of traditional electrolytes. In addition, the environmental impact assessment found that the use of nanocomposite electrolytes resulted in a two percent drop in the amount of energy used, a fifteen percent reduction in the amount of waste generated, and a twenty-five percent reduction in the amount of carbon dioxide emissions. As a result of these discoveries, the potential ofnanocomposite electrolytes to enhance the sustainability and efficiency of fuel cell technology has been brought to light. This opens the door for the creation of next-generation fuel cells that have increased performance and decreased their effect on the environment.

Science-based agriculture is increasing due to the rapid development of science and technology. To keep up with today's technology, many agricultural experts are turning to the Internet of Things (IoT) to drive advances in this field. For India, agriculture is the backbone of the Indian economy. The most important issue in today's technology is climate change. This review aims to provide an overview of IoT in agriculture and food to better understand the use of IoT and how it solves the above problems. Start by defining and further explaining the concept of IoT. The next section summarises the analysis results and discusses application areas, technical support, and future challenges.

The study examines the use of computer vision technologies into intelligent electric vehicle (EV) charging infrastructure. The objective is to increase station capabilities, maximize resource usage, and enhance user experiences. An examination of the data from charging stations indicates that there are differences in their capacities and capabilities. Certain stations can handle a greater number of cars at the same time because they have higher power outputs and numerous charging connections. The vehicle identification data illustrates the efficacy of computer vision in precisely recognizing various electric vehicle types, hence optimizing authentication procedures for efficient charging. An analysis of charging session data reveals variations in energy use and durations across sessions, underscoring the impact of charging practices on the utilization of charging stations. An examination of use reveals discrepancies in the number of sessions and energy usage among stations, highlighting the need for adaptive infrastructure. Percentage change study management solutions for demonstrates the fluctuating patterns of resource usage, emphasizing the need for flexible resource allocation techniques. The results emphasize the significant impact that computer vision may have on improving the efficiency and flexibility of electric vehicle charging infrastructure. The research highlights the significance of optimizing the allocation of resources, improving algorithms for various contexts, and applying adaptive solutions for optimal management of charging infrastructure. In essence, the study helps to further our knowledge of how computer vision contributes to the development of intelligent EV charging systems. It provides valuable insights into improving the efficiency of infrastructure and enriching user experiences in the field of electric mobility.

The superior performance. metal matrix nano-composite substance containing agro-waste reinforcements is the subject of this study's manufacturing and characterization efforts. Composites made using aluminum 7075 as their basis substance with reinforced with rice husk ash (RHA) and Al2O3 are very desirable. The revolutionary composite was made using squeeze casting as the fabrication technique. The overall size % of ash from rice husks on its metal interface was adjusted from 0% to 15% with a constant increase of 5%, while the Al2O3 percentage was set at 3. Many qualities, including hardness, toughness, tensile strength, XRD analysis, XDS analysis, Surface Morphology and wear, define the manufactured composite. According to the findings, the produced cost-effective composite outperforms the raw material in terms of hardness, toughness, and tensile strength.

This research carried out a thorough data-driven examination of user behaviour, adoption rates, satisfaction, and energy efficiency in the context of IoT in home automation, within the quickly changing environment of smart homes and Internet of Things (IoT) technologies. The study found that users interacted with various kinds of IoT devices in diverse ways. Smart security systems and thermostats, for example, were quickly adopted and received high levels of satisfaction. The potential for significant energy savings demonstrated the contribution of IoT devices to sustainability. These results highlight the significance of making well-informed decisions when using IoT technology to create smarter, more efficient, and greener living environments. They also provide useful insights for manufacturers, legislators, and homeowners.

In line with the Industry 5.0 paradigm, this empirical research offers a data-driven investigation of the revolutionary effects of combining IoT and AI in smart manufacturing. The findings show a notable 1.52% gain in production efficiency, which is attributed to post-implementation temperature increases of 36.2°C and humidity decreases of 44.8%. A decrease in faults found (2) led to a 0.76% increase in quality scores (93.1) for quality control. With fewer maintenance hours (2.3) and downtime (52 minutes), maintenance practices were more effective. These results highlight the concrete advantages of integrating IoT and AI in smart manufacturing, putting it at the vanguard of Industry 5.0's revolutionary landscape and improving productivity, quality, and maintenance.

This study explores the world of nanostructured materials and new production procedures, revealing how they interact to break down obstacles in materials research. We uncover the confluence between theoretical goals and actual reality by investigating manufacturing processes. The study links manufacturing processes to material characteristics, establishing the groundwork for informed applications in electronics, photonics, sensors, and biology. The empirical journey illuminates problems and opportunities, prompting joint efforts to push these frontiers forward. This article is a testimony to nanostructured materials' transformational potential, challenging researchers and dreamers to redefine technological landscapes by using their unique features and inventive production.

Aim of current study is to utilize different sustainable artificial intelligence (AI) tools to check the influence of test factors on erosion wear. Bottom ash is taken as erodent at different solid concentration while brass is considered as base material. The parameters involved are rotational speed (N), solid concentration (CW), and testing time duration (T). According to experimental results and analysis based on different AI tools , it is abundantly found that erosion wear have a significant dependency on parameters such as N, CW, T and the order of maximum erosion was found as N > CW >T. The rate of rotation speed (N) has been identified as the factor that has the greatest impact on the degree to which erosion wear occur. 3D analysis has been conducted for the maximum and minimum erosion wear condition. In order to verify the accuracy, four distinct methods are utilized; nonetheless, the accuracy of the regression analysis has been found more promising when compared to that of the Ridge, lasso and neural network methodologies.

To examine the biomedical signals from the human body is of a challenging task. Signal acquisition of the biosignal needs proper design methodology with the estimation of consequences further. In this paper we discuss about the signal acquisition of EoG using the surface electrodes and to amplify the weak biopotential signal an amplifier is proposed with the additive noise reduction filter. The biopotential instrumentation amplifier is designed using standard operational amplifier that posses the high voltage swing. However the major artifacts present in the EoG signal acquisition is because of power line interference can be eliminated by notch filter.