Non-conventional yeasts have attracted increasing interest due to their biochemical characteristics and potential applications. Yarrowia lipolytica is a non-conventional yeast with specific ...characteristics and physiology. The potential physiological and metabolic capabilities of Y. lipolytica, which can assimilate many different carbon sources, including typical hydrophilic and hydrophobic materials, are reviewed in this paper. Concerning the uptake and metabolism substrates, this review focuses particularly on low-cost raw materials, such as glycerol. Moreover, this review presents the results of safety studies of Y. lipolytica. Finally, the wide applications of Y. lipolytica, such as functional enzyme production, metabolite synthesis and environmental bioremediation, are reviewed in this paper. Recently, with the development of system biology and synthetic biology, it was concluded that these technologies will provide new opportunities for potential applications of Y. lipolytica in the future.
•The comprehensive information including the biochemical characteristics and physiology of Yarrowia lipolytica was summarized.•The review presented the results of safety studies of Yarrowia lipolytica.•The metabolic capabilities and wide applications of Yarrowia lipolytica using typical hydrophilic and hydrophobic materials as carbon sources were illustrated.•This review especially highlighted the metabolite production using low-cost raw materials as carbon sources.•This review will serve as a meaningful resource for researchers working on Yarrowia lipolytica.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
► We review the literature on FMEA published between 1992 and 2012. ► We use a classification framework to classify 75 papers by the approaches used. ► The most important shortcomings, the most ...popular approaches and their inadequacies are identified. ► We offer directions for future research to address the FMEA deficiencies.
Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in systems, processes, designs or services and has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticized to have many deficiencies and various risk priority models have been proposed in the literature to enhance the performance of FMEA. However, there has been no literature review on this topic. In this study, we reviewed 75 FMEA papers published between 1992 and 2012 in the international journals and categorized them according to the approaches used to overcome the limitations of the conventional RPN method. The intention of this review is to address the following three questions: (i) Which shortcomings attract the most attention? (ii) Which approaches are the most popular? (iii) Is there any inadequacy of the approaches? The answers to these questions will give an indication of current trends in research and the best direction for future research in order to further address the known deficiencies associated with the traditional FMEA.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes, ...and services. However, the conventional FMEA method has been criticized for inherent drawbacks that limit effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members' weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of the cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison to existing methods.
Due to the increasing awareness of environmental and social issues, many practitioners and researchers have paid much attention to the sustainable supply chain management (SSCM) in recent years. ...Sustainable supplier selection is one of the most critical activities in the SSCM which can affect supply chain performance. However, the previous literature rarely considers the interrelationships between economic, environmental, and social evaluation criteria in the supplier selection. Moreover, the effect of the criteria importance on the criteria interrelationships is scarcely discussed in previous researches. To deal with these problems, a novel integrated methodology is developed in this paper. The proposed method integrates the merit of pairwise comparison method in determining relative importance, the strength of decision making trial and evaluation laboratory (DEMATEL) in manipulating the complex and intertwined problems with fewer data, and the rough number's advantage in flexibly dealing with vague information. A case study in a solar air-conditioner manufacturer is provided to show the feasibility and effectiveness of the proposed methodology.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•n-Alkane concentration varied among different submerged plants.•Algal n-alkane concentration very low.•Aquatic plants contained high relative amount of long chain homologues.•Submerged plant ...n-alkane contribution to lacustrine sediments should be considered.•Algae contributed n-alkanes minimally to the lake sediments.
Long chain n-alkanes (C27–C33) in lake sediment records are commonly considered to be terrestrial plant biomarkers when reconstructing paleoclimatic and paleolimnological history. However, the extent to which their accumulation is influenced by n-alkanes originating from algae and submerged plants is largely unclear. Furthermore, to our knowledge, few studies have systematically analyzed the variation in n-alkane concentration or distributions between different submerged plant and algal species. We systematically investigated the n-alkane distributions of 13 algae (including 10 Cladophora and 3 Spirogyra), 68 submerged plants (including 37 Potamogeton, 7 Myriophyllum, 4 Ruppiaceae and 20 Chara) and 13 terrestrial plants (including 7 grasses and 6 shrubs) from 16 Qinghai-Tibetan Plateau lakes. The results indicate that the total n-alkane (C21–C33) concentration varied between different submerged plants. Potamogeton, Myriophyllum and Ruppiaceae exhibited high concentration, with average values 235.8, 295.9 and 275.9μg/g, respectively. These values were slightly higher than the concentrations found in terrestrial plant leaves (avg. 206.4μg/g), whereas the average concentration in Chara was only 2.0μg/g, significantly lower than that of other submerged plants. Similarly, the concentration in algae was also very low, with average values of 2.0μg/g and 4.0μg/g for Cladophora and Spirogyra, respectively. Submerged plant and algal long chain (C27–C33) alkanes accounted for a large proportion of the total C21–C33n-alkanes, with average ratios (long chain vs. total n-alkanes) of 20, 3, 22 and 27% for Potamogeton, Myriophyllum, Ruppiaceae and Chara, respectively. Cladophora and Spirogyra exhibited average ratios of 34% and 65%, respectively. Therefore, submerged plant long chain n-alkane contributions to lacustrine sediments, especially those of Potamogeton and Ruppiaceae, should not be considered negligible due to their high n-alkane concentration. Conversely, some algae, such as Cladophora and Spirogyra, minimally contributed n-alkanes to the lake sediments.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This work develops a continuous sign language (SL) recognition framework with deep neural networks, which directly transcribes videos of SL sentences to sequences of ordered gloss labels. Previous ...methods dealing with continuous SL recognition usually employ hidden Markov models with limited capacity to capture the temporal information. In contrast, our proposed architecture adopts deep convolutional neural networks with stacked temporal fusion layers as the feature extraction module, and bidirectional recurrent neural networks as the sequence learning module. We propose an iterative optimization process for our architecture to fully exploit the representation capability of deep neural networks with limited data. We first train the end-to-end recognition model for alignment proposal, and then use the alignment proposal as strong supervisory information to directly tune the feature extraction module. This training process can run iteratively to achieve improvements on the recognition performance. We further contribute by exploring the multimodal fusion of RGB images and optical flow in sign language. Our method is evaluated on two challenging SL recognition benchmarks, and outperforms the state of the art by a relative improvement of more than 15% on both databases.
Sustainable supply chain management (SSCM) is gradually becoming a strategic imperative for companies. Different sources of risk factors may appear in SSCM due to its complex nature. Most of the ...previous studies consider less about the effect of strength of each risk factor on the interdependencies. To solve the problem, a rough weighted decision-making and trial evaluation laboratory (DEMATEL) is proposed. Both internal strength and external influence of risk factors are simultaneously considered to fully reflect the priority of risk factors. The novel approach also has merit in flexibly manipulating the vagueness and ambiguity involved in risk analysis. The applicability and effectiveness of the proposed method are validated by applying it to a company providing telecommunications products. The results show that failure to select the right suppliers is the most prominent risk factor for SSCM, because supplier selection plays an important role in achieving the social, environmental, and economic benefits of SSCM. The proposed method can be used as an effective tool to identify critical SSCM risk issues and interrelationships between different risk factors.
•A listing of risk factors for sustainable supply chain management (SSCM) is described.•A new method to identify interdependencies of SSCM risk factors is developed.•The method simultaneously considers the internal strength and external influence of risk factor.•The proposed method is applied to a telecommunications provider in China.•The application results show critical risk factors and key relations between them.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Electric vehicles (EVs) are recognized as one of the most promising technologies worldwide to address the fossil fuel energy resource crisis and environmental pollution. As the initial work of EV ...charging station (EVCS) construction, site selection plays a vital role in its whole life cycle, which, however, is a complicated multiple criteria decision making (MCDM) problem involving many conflicting criteria. Therefore, this work aims to propose a novel integrated MCDM approach by a grey decision making trial and evaluation laboratory (DEMATEL) and uncertain linguistic multi-objective optimization by ratio analysis plus full multiplicative form (UL-MULTIMOORA) for determining the most suitable EVCS site in terms of multiple interrelated criteria. Specifically, the grey DEMATEL method is used to determine criteria weights and the UL-MULTIMOORA model is employed to evaluate and select the optimal site. Finally, an empirical example in Shanghai, China, is presented to demonstrate the applicability and effectiveness of the proposed approach. The results show that the proposed approach is a useful, practical, and effective way to find the optimal location of EVCSs.
Failure mode and effects analysis (FMEA) is a widely used risk management technique for identifying the potential failures from a system, design, or process and determining the most serious ones for ...risk reduction. Nonetheless, the traditional FMEA method has been criticized for having many deficiencies. Further, in the real world, FMEA team members are usually bounded rationality, and thus, their psychological behaviors should be considered. In response, this study presents a novel risk priority model for FMEA by using interval two‐tuple linguistic variables and an integrated multicriteria decision‐making (MCDM) method. The interval two‐tuple linguistic variables are used to capture FMEA team members' diverse assessments on the risk of failure modes and the weights of risk factors. An integrated MCDM method based on regret theory and TODIM (an acronym in Portuguese for interactive MCDM) is developed to prioritize failure modes taking experts' psychological behaviors into account. Finally, an illustrative example regarding medical product development is included to verify the feasibility and effectiveness of the proposed FMEA. By comparing with other existing methods, the proposed linguistic FMEA approach is shown to be more advantageous in ranking failure modes under the uncertain and complex environment.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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