Productivity Growth in Indian Capital Goods Industry
In: Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics, Band 37, Heft 2, S. 172
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In: Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics, Band 37, Heft 2, S. 172
In: Institutionalised children explorations and beyond, Band 3, Heft 1, S. 91
ISSN: 2349-3011
Securing Next-Generation Connected Healthcare Systems: Artificial Intelligence Technologies focuses on the crucial aspects of IoT security in a connected environment, which will not only benefit from cutting-edge methodological approaches but also assist in the rapid scalability and improvement of these systems. This book shows how to utilize technologies like blockchain and its integration with IoT for communication, data security, and trust management. It introduces the security aspect of next generation technologies for healthcare, covering a wide range of security and computing methodologies.Researchers, data scientists, students, and professionals interested in the application of artificial intelligence in healthcare management, data security of connected healthcare systems and related fields, specifically on data intensive secured systems and computing environments, will finds this to be a welcomed resource
In: Health and Technology, Band 13, Heft 4, S. 679-692
ISSN: 2190-7196
In: Environmental science and pollution research: ESPR, Band 29, Heft 34, S. 50909-50927
ISSN: 1614-7499
SSRN
Working paper
In: Environmental science and pollution research: ESPR
ISSN: 1614-7499
AbstractGlobal energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop floors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid flow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers' flexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversification, and quality metrics, providing more versatile and better solutions for production and energy efficiency.
In: Environmental science and pollution research: ESPR, Band 29, Heft 57, S. 86320-86336
ISSN: 1614-7499
SSRN
Working paper
SSRN
In: SSHO-D-20-00421
SSRN
Working paper
In: Materials & Design, Band 50, S. 728-736
In: Journal of the Society for Gynecologic Investigation: official publication of the Society for Gynecologic Investigation, Band 7, Heft 1, S. 37-44
ISSN: 1556-7117
In: Ageing international, Band 47, Heft 3, S. 373-391
ISSN: 1936-606X
In: Computers and Electronics in Agriculture, Band 175, S. 105456