Assortment of latent heat storage materials using multi criterion decision making techniques in Scheffler solar reflector

Rana, Virendra Singh; ul haq, Md Zia; Mathur, Nishant; Khera, Gurbir Singh; Dixit, Saurav; Singh, Subhav; Prakash, Anshika; Viktorovna, Glazkova Valeriya; Soloveva, O. V.; Solovev, S. A.

doi:10.1007/s12008-023-01456-9

Cited by 35 publications

(14 citation statements)

References 54 publications

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“…Employee Satisfaction: In line with Industry 5.0's human-centric philosophy, employee input and satisfaction were significant research components. Employee satisfaction showed a consistent upward trend throughout the course of the longitudinal data, with an average rating Employee Satisfaction (1)(2)(3)(4)(5) , 01058 (2024) BIO Web of Conferences https://doi.org/10.1051/bioconf/20248601058 86 RTBS-2023 rise from 4.2 in 2020 to 4.7 in 2024. This rise demonstrates the beneficial effects of AI-driven sustainability initiatives on worker satisfaction and well-being.…”

Section: Fig 3 Employee Satisfaction Survey Resultsmentioning

confidence: 91%

Evaluating the Impact of AI-Based Sustainability Measures in Industry 5.0: A Longitudinal Study

Valeriya,

Kirola,

Gupta

et al. 2024

BIO Web Conf.

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In the context of Industry 5.0, this long-term study assesses the significant influence of AI-based sustainability metrics. It also illuminates a novel paradigm in which artificial intelligence (AI) and human expertise work together to jointly drive sustainability, financial performance, employee satisfaction, and overall ecological responsibility. AI-driven sustainability efforts produced a surprising 12% reduction in trash creation, an amazing 7% reduction in energy usage, and an 8% drop in CO2 emissions over a five-year period. Financially speaking, these actions showed up as a steady 4% annual revenue growth, $2 million in cost reductions on average each year, and a cumulative 3.4% gain in return on investment. The human factor is even more notable, with employee satisfaction ratings rising from 4.2 to 4.7 and work-life balance scores significantly rising from 4.1 to 4.6. By 2024, 70% of workers will have adopted AI, demonstrating how essential AI has become to the working. An all-encompassing sustainability score that included these dynamic components increased from 60 to 75 in 2024, indicating a general improvement in sustainability. This study emphasizes the mutually beneficial relationship between artificial intelligence (AI) and sustainability in Industry 5.0. It shows how AI fosters a sustainable and balanced industrial future by improving environmental responsibility and workforce satisfaction while also producing significant financial benefits.

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Section: Fig 3 Employee Satisfaction Survey Resultsmentioning

confidence: 91%

Evaluating the Impact of AI-Based Sustainability Measures in Industry 5.0: A Longitudinal Study

Valeriya,

Kirola,

Gupta

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

show abstract

“…These networks should be able to meet the changing needs for electric vehicle charging in urban settings. [31]- [35] The literature review provides a concise overview of the development of wireless charging technology, the difficulties involved in improving charging networks, the effectiveness of genetic algorithms in tackling these difficulties, and the emerging patterns that will shape the future of optimizing wireless charging networks. These findings provide the basis for the current work, highlighting the importance of genetic algorithms in tackling intricate optimization issues and enhancing the efficiency and accessibility of wireless charging infrastructures for electric cars.…”

Section: Literature Reviewmentioning

confidence: 99%

Genetic algorithms for optimizing the layout of wireless charging networks

Mittal,

Shamila

2024

MATEC Web Conf.

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This study explores the improvement of wireless charging network configurations for electric cars (EVs) using genetic algorithms, with the goal of increasing charging efficiency and network performance. The network optimization process takes into account the starting characteristics of include their geographical coordinates, power capacity, and beginning energy levels. Examination of the distance matrix exposes diverse distances between nodes, which impact energy consumption and charging efficiency. The energy consumption estimates between pairs of nodes illustrate the charging needs across the network, revealing that nodes that are farther away have greater energy consumption. The use of genetic algorithms yields a wide range of layouts that are assessed based on their fitness ratings, indicating the excellence of configurations in terms of coverage and connection. Percentage change study demonstrates the modifications in power capacity and node energy levels after optimization, showing prospective improvements in charging capabilities and efficiency. The correlation between node location and energy use is apparent, as nodes in closer proximity demonstrate decreased energy utilization. The convergence of fitness scores demonstrates the algorithm's effectiveness in achieving solutions that are very close to ideal, resulting in significant improvements in charging coverage and energy efficiency. The study highlights the effectiveness of genetic algorithms in improving wireless charging networks, providing valuable information on spatial optimization tactics, energy use patterns, and the resulting improvements in network performance. These results have implications for creating wireless charging infrastructures that are more efficient and long-lasting, in order to satisfy the changing needs of electric car charging networks.

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“…Numerous studies have shown the effective use of PA in Internet of Things-driven farming. One such instance is the use of drones to monitor fields from the air [33]- [39]. These unmanned aerial vehicles can collect thermal data, multispectral data, and high-resolution photos since they are fitted with a variety of sensors.…”

Section: Test Cases For Precision Agriculturementioning

confidence: 99%

Precision Agriculture and Sustainable Yields: Insights from IoT-Driven Farming and the Precision Agriculture Test

Ivanovich Vatin,

Kumar Joshi,

Acharya

et al. 2024

BIO Web Conf.

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This study clarifies how precision agriculture powered by the Internet of Things may optimize agricultural productivity and sustainability. Important connections, like the positive association between agricultural output and soil moisture, are revealed by analyzing data from Internet of Things sensors. Test findings for Precision Agriculture show impressive production increases: 20% better yields for wheat, 15% higher yields for maize, and 5% higher yields for soybeans. Interestingly, these improvements come with significant resource savings, with a 10% to 20% reduction in the use of pesticides and fertilizers. The evaluation of sustainable yield highlights efficiency levels between 92% and 95%. These results demonstrate how precision agriculture has the potential to completely transform contemporary agricultural methods by maximizing crop output, promoting sustainability, and reducing environmental impact.

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Assortment of latent heat storage materials using multi criterion decision making techniques in Scheffler solar reflector

Cited by 35 publications

References 54 publications

Evaluating the Impact of AI-Based Sustainability Measures in Industry 5.0: A Longitudinal Study

Evaluating the Impact of AI-Based Sustainability Measures in Industry 5.0: A Longitudinal Study

Genetic algorithms for optimizing the layout of wireless charging networks

Precision Agriculture and Sustainable Yields: Insights from IoT-Driven Farming and the Precision Agriculture Test

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