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- 2019
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Journal article (peer-reviewed)
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- Klionsky, Daniel J., et al.
(author)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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In: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Research review (peer-reviewed)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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- Kang, Kai, et al.
(author)
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Auction-based cloud service allocation and sharing for logistics product service system
- 2021
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In: Journal of Cleaner Production. - : Elsevier Ltd. - 0959-6526 .- 1879-1786. ; 278
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Journal article (peer-reviewed)abstract
- Sustainability is increasingly viewed as a desired goal of social development. The logistics industry without exception is aware of the importance of sustainable development. To achieve this goal, the logistics industry is leveraging cutting-edge technologies, such as product service systems (PSS) and cloud manufacturing (CMfg), to design logistics product service systems (LPSS). In LPSS, public logistics resources are allocated to customers and private logistics resources are shared between customers. However, logistics resource allocation and sharing services have been impeded by a lack of efficient methods. In this context, this paper proposes an auction-based cloud service allocation and sharing method for LPSS. Firstly, LPSS is defined and elaborated based on the adoption of PSS in the logistics industry. Secondly, multi-unit Vickery (MV) auctions and one-sided Vickrey-Clarke-Groves (O-VCG) combinatorial auctions are proposed to address logistics resource allocation and sharing problems respectively in LPSS. Two auctions are introduced specifically, and their relevant properties are investigated, including incentive compatibility, allocative efficiency, budget balance, and individual rationality. Thirdly, computational studies are conducted to examine the performance of two auctions. The results reveal that MV auctions can efficiently allocate public logistics resources through ensuring the utility of logistics service providers under dynamic supply and demand. Additionally, O-VCG auctions can effectively integrate and share idle private logistics resources, which promotes sustainability in the logistics industry. Through integrating MV auctions with O-VCG auctions, the utility of logistics service providers can be increased.
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- Li, Lianhui, et al.
(author)
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Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin
- 2020
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In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 8, s. 174988-175008
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Journal article (peer-reviewed)abstract
- As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.
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- Li, Zhi, et al.
(author)
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Blockchain-enabled product lifecycle management
- 2021
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In: Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology. - : Elsevier BV. ; , s. 349-379
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Book chapter (other academic/artistic)abstract
- The rapid advances of information technology have pushed society into the fourth industrial revolution (I4.0). It requires an integration of value chain organization and management across the product lifecycle driven by the Internet industrialization, industrial digitalization and intellectualization, and industrial integration. PLM is the business activity of managing a company's products from the idea of product development to the end of product life. It aims to seamlessly manage all products, information, and knowledge generated throughout the product's lifecycle in order to achieve business competitiveness. Blockchain is an emerging technology that brings transparency to opaque systems, verifiability, and immutability to transactions and processes, which gives it the potential to drive I4.0 revolution. This chapter will demonstrate the real-world applicability of blockchain potential using existing industrial case studies. Besides, an industrial blockchain-based PLM framework is introduced to facilitate data exchange and service sharing in the product lifecycle.
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- Lu, Shaoping, et al.
(author)
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A passive RFID tag-based locating and navigating approach for automated guided vehicle
- 2018
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In: Computers & industrial engineering. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0360-8352 .- 1879-0550. ; 125, s. 628-636
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Journal article (peer-reviewed)abstract
- This research is motivated by the industrial applications of Cyber-physical System (CPS) such as smart warehouse and intelligent manufacturing which require the support of AGV (Automated Guided Vehicle). One of the key research is locating and navigating approach that should be with much flexibility for dealing with complex industrial applications. This paper takes a passive radio frequency identification (RFID) tag-based locating and navigating approach for AGV to examine the influences of tags, antennas, and environmental aspects. The approach was validated and a prototype system was built up. Several key observations are significant from the implementation and simulation study. Firstly, it is observed that adding angle reflector could eliminate the back lobe and restrain backward reflection. Secondly, RFID antenna center and the tag center should be designed at the same height could reduce the impact area of reflection. Lessons and insights from this study will be significant for industrial practitioners to implement AGVs in smart warehouse and manufacturing management.
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- Zhang, Yingfeng, et al.
(author)
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Analytical target cascading for optimal configuration of cloud manufacturing services
- 2017
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In: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 151, s. 330-343
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Journal article (peer-reviewed)abstract
- Combining with advanced technologies (e.g., cloud computing, Internet of Things, and service-oriented technology), cloud manufacturing was proposed and gained wide attention. By managing a huge amount of distributed and idle manufacturing resources to meet various manufacturing requirements, cloud manufacturing provides sustainable means for promoting cleaner production. Manufacturing service configuration plays an important role in implementing cloud manufacturing. Most research adopted central optimization methods to get optimal service configuration results. However, these all-in-one methods with an individual decision model can hardly maintain the autonomous decision rights of different service providers. Consequently, service providers may lose their flexibility to achieve private decision objectives, which is unfavorable for keeping the sustainable competitive advantages of enterprises. In this paper, a decentralized decision mechanism named analytical target cascading is introduced to solve the manufacturing service configuration problem. An analytical target cascading model for the manufacturing service configuration problem is proposed based on the hierarchical structure of cloud manufacturing system. Elements in the proposed model are formulated and solved in a loose coupling and distributed manner. The situation when alternative service providers owned autonomous decision rights to configure their respective upstream manufacturing stages is also considered. A case study is employed to verify the effectiveness of analytical target cascading in solving the manufacturing service configuration problem. It shows that analytical target cascading can not only obtain the same manufacturing service configuration results as central optimization method but also maintain the autonomous decision rights of different service providers. (C) 2017 Elsevier Ltd. All rights reserved.
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| 10. |
- Zhang, Yongping, et al.
(author)
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Data-driven smart production line and its common factors
- 2019
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In: The International Journal of Advanced Manufacturing Technology. - : Springer. - 0268-3768 .- 1433-3015. ; 103:1-4, s. 1211-1223
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Journal article (peer-reviewed)abstract
- Due to the wide usage of digital devices and easy access to the edge items in manufacturing industry, massive industrial data is generated and collected. A data-driven smart production line (SPL), which is a basic cell in a smart factory, is derived primarily. This paper studies the data-driven SPL and its common factors. Firstly, common factors such as integration, data-driven, service collaboration, and proactive service of SPL are investigated. Then, a data-driven method including data self-perception, data understanding, decision-making, and precise control for implementing SPL is proposed. As a reference, the research of the common factors and the data-driven method could offer a systematic standard for both academia and industry. Moreover, in order to validate this method, this paper presents an industrial case by taking an energy consumption forecast and fault diagnosis based on energy consumption data in a prototype of LED epoxy molding compound (EMC) production lines for example.
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