Understanding the innovative developments with two-stage technology S-curve of nuclear energy projects

Meng, Yue; Dınçer, Hasan; Yüksel, Serhat

doi:10.1016/j.pnucene.2021.103924

Cited by 25 publications

(7 citation statements)

References 69 publications

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“…This contributes to minimizing the risks faced by energy importing countries. In summary, nuclear energy projects have a very important role in the economic development of countries (Meng et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Digital Transformation in Logistics: Lights-Out Logistics

Yıldız¹,

Çiğdem²

2022

Financial and Economic Issues in Emerging Markets

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The fast growth of digital technologies is causing a lot of trouble in the logistics industry, which is causing the industry to change in a big way. The idea of "lights-out logistics," which refers to the automation of logistics operations through the use of digital technologies such as robotics, autonomous vehicles, and artificial intelligence, is one of the most significant shifts that has taken place. In this chapter, a summary of the lights-out logistics concept is presented, along with a discussion of its advantages, challenges, and potential effects on the logistics industry. Aside from that, the chapter focuses on the critical technologies and applications that enable lights-out logistics, as well as the strategies and best practices that businesses can use to successfully implement this concept. This chapter uses different case studies and examples to show how lights-out logistics can help businesses improve their operational efficiency, lower their costs, and improve the customer experience. A discussion of the future prospects of lights-out logistics and its potential to transform the logistics industry in the digital age serves as the chapter's conclusion.

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Section: Discussionmentioning

confidence: 99%

Digital Transformation in Logistics: Lights-Out Logistics

Yıldız¹,

Çiğdem²

2022

Financial and Economic Issues in Emerging Markets

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“…Significant progress has been made in addressing the challenge of data oversimplification, yet the development of more sophisticated algorithms remains a priority. Meng et al [111] observed this while using principal component analysis (PCA) for image data simplification. Hence, the quest for superior algorithms and additional data acquisition methods is crucial for advancing additive manufacturing research [115].…”

Section: Challenges and Future Directionsmentioning

confidence: 94%

“…Meng et al. 111 observed this while using principal component analysis (PCA) for image data simplification. Hence, the quest for superior algorithms and additional data acquisition methods is crucial for advancing additive manufacturing research 115.…”

Section: Challenges and Future Directionsmentioning

confidence: 94%

“…It paves the way for novel applications across various industries and poses significant challenges. For instance, the amalgamation of ML and additive manufacturing is instrumental in optimizing parameters and detecting anomalies in the additive manufacturing process [36,111]. However, while the fusion of these disciplines creates opportunities, it comes with the complexities inherent in applying ML to additive manufacturing [112][113][114].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Bin Abu Sofian,

Lim,

Chew

et al. 2024

ChemBioEng Reviews

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The integration of machine learning (ML) with algae‐derived biopolymers in 3D printing is a burgeoning area with the potential to revolutionize various industries. This review article delves into the challenges and advancements in this field, starting with the critical problem it addresses the need for sustainable and efficient additive manufacturing processes. Algae‐based biopolymers, such as alginate and carrageenan, are explored for their viability in 3D printing, highlighting their environmental benefits and technical challenges. The role of ML in enhancing material selection, predictive modeling, and quality control is examined, showcasing how this synergy leads to significant improvements in 3D printing processes. Key findings include the enhanced mechanical properties of algae‐based biopolymers and the optimization of printing parameters through ML algorithms. Examples like the use of Spirulina in creating a range of materials and the application of carrageenan in bone tissue engineering are discussed. The conclusion underscores the transformative impact of combining ML with algae‐based biopolymers in 3D printing, paving the way for innovative, sustainable solutions in additive manufacturing. Despite existing challenges, this integration holds promise for a future of advanced, eco‐friendly manufacturing techniques.

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“…For example, in the energy field, Meng et al. [ 63 ], use two S-curves to find the most important parts of incremental innovation in nuclear energy projects. Madvar et al [ 64 ] conducted research to determine whether biofuels technologies are ready to meet global energy demand by analysing seven technologies and their technology life cycles.…”

Section: Literature Reviewmentioning

confidence: 99%