Research on the evaluation of green suppliers of high energy-consuming enterprises--based on rough number-grey correlation TOPSIS method

With the Goal-Establishment of “carbon compliance” and “carbon neutrality”, enterprises should upgrade “green development” to a strategic position and implement it through all aspects of business operations. Green supplier selection is the initial phase of supply chain management, therefore a green supplier evaluation system is needed to achieve green development. Based on a literature analysis, We selected 45 metrics as candidates for evaluating suppliers. Then through expert interviews, some indicators were revised and supplemented, and finally a green supplier evaluation index system for high energy-consuming enterprises was constructed. A unique aspect of this paper is the introduction of rough number theory into the supplier evaluation process to improve the indicator assignment and the grey correlation TOPSIS method, which optimizes the processing of uncertain semantic information in the evaluation process. The rough number-grey correlation TOPSIS supplier evaluation model developed in this paper has been verified to be applicable and stable in case studies and successfully implemented

An MILP model for optimization of byproduct gases in the integrated iron and steel plant

In iron and steel industry, byproduct gases are important energy. Therefore it is significant to optimize byproduct gas distribution to achieve total cost reduction. In this paper, a dynamic mixed integer linear programming (MILP) model for multi-period optimization of byproduct gases is used to optimize byproduct gas distribution. Compared with the previous optimization model, the proposed model simultaneously optimizes the distribution of byproduct gases in byproduct gas system, cogeneration system and iron- and steel-making system. Case study shows that the proposed model finds the optimal solution in terms of total cost reduction.Iron and steel industry MILP Byproduct gas distribution Scheduling

Towards a Systematic Description of Fault Tree Analysis Studies Using Informetric Mapping

Fault tree analysis (FTA) is one of the important analysis methods of safety system engineering commonly utilized in various industries to evaluate and improve the reliability and safety of complex systems. To grasp the current situation and development trend of FTA research and to further point out FTA’s future development directions, 1469 FTA-related articles from the literature were retrieved from the SCIE and SSCI databases. Informetric methods, including co-authorship analysis, co-citation analysis and co-occurrence analysis, were adopted for analyzing the cooperation relationship, research knowledge base, research hotspots and frontier in the FTA research field. The results show that China has the highest number of publications, and the Loughborough University of England has the highest number of publications of relevant institutions. Dynamic fault tree analysis, fuzzy fault tree analysis and FTA based on binary decision diagrams are recognized as the knowledge bases in FTA studies. “Reliability Engineering and System Safety”, “Safety Science” and “Fuzzy Sets and Systems” are the core journals in this field. Fuzzy fault tree analysis, dynamic fault tree analysis based on Bayesian networks and FTA combined with management factors may be both the main research hotspots and the frontiers. Then, by deriving the above results, this study can help scholars better master the current research status and frontiers of FTA to improve system reliability and safety

“In-Between Area” Design Method: An Optimization Design Method for Indoor Public Spaces for Elderly Facilities Evaluated by STAI, HRV and EEG

The indoor public spaces of most elderly facilities in China have a monotonous space form, which, thus, causes low comprehensive performance and is less likely to satisfy participants’ various requirements. This study proposes an optimization design method of “In-Between Area” for a space form operation to improve the performance of indoor public spaces. First, two models were established: Model A to reflect current indoor public spaces and Model B to represent the indoor public spaces designed by using the “In-Between Area” method. Second, a walk-through video was created from each model, with a duration of 196 s. Subjective assessment (STAI) data and objective physiological data (HRV and EEG), were collected from 40 participants while they were watching walk-through videos. The comparison analysis showed statistically significant differences between Model A and Model B. The results of STAI, HRV and EEG proved that the “In-Between Area” method, as an optimization design method, created a more pleasant and comfortable environment for the elderly and improved the overall efficiency of the indoor space

Visualizing the Knowledge Base and Research Hotspot of Public Health Emergency Management: A Science Mapping Analysis-Based Study

Public health emergency management has been one of the main challenges of social sustainable development since the beginning of the 21st century. Research on public health emergency management is becoming a common focus of scholars. In recent years, the literature associated with public health emergency management has grown rapidly, but few studies have used a bibliometric analysis and visualization approach to conduct deep mining and explore the characteristics of the public health emergency management research field. To better understand the present status and development of public health emergency management research, and to explore the knowledge base and research hotspots, the bibliometric method and science mapping technology were adopted to visually evaluate the knowledge structure and research trends in the field of public health emergency management studies. From 2000 to 2020, a total of 3723 papers related to public health emergency management research were collected from the Web of Science Core Collection as research data. The five main research directions formed are child prevention, mortality from public health events, public health emergency preparedness, public health emergency management, and coronavirus disease 2019 (COVID-19). The current research hotspots and frontiers are climate change, COVID-19 and related coronaviruses. Further research is needed to focus on the COVID-19 and related coronaviruses. This study intends to contribute inclusive support to related academia and industry in the aspects of public health emergency management and public safety research, as well as research hotspots and future research directions

Transcriptome Analysis of Medicinal Plant <i>Salvia miltiorrhiza</i> and Identification of Genes Related to Tanshinone Biosynthesis

<div><p><i>Salvia miltiorrhiza</i> Bunge, a perennial plant of Lamiaceae, accumulates abietane-type diterpenoids of tanshinones in root, which have been used as traditional Chinese medicine to treat neuroasthenic insomnia and cardiovascular diseases. However, to date the biosynthetic pathway of tanshinones is only partially elucidated and the mechanism for their root-specific accumulation remains unknown. To identify enzymes and transcriptional regulators involved in the biosynthesis of tanshinones, we conducted transcriptome profiling of <i>S. miltiorrhiza</i> root and leaf tissues using the 454 GS-FLX pyrosequencing platform, which generated 550,546 and 525,292 reads, respectively. RNA sequencing reads were assembled and clustered into 64,139 unigenes (29,883 isotigs and 34,256 singletons). NCBI non-redundant protein databases (NR) and Swiss-Prot database searches anchored 32,096 unigenes (50%) with functional annotations based on sequence similarities. Further assignments with Gene Ontology (GO) terms and KEGG biochemical pathways identified 168 unigenes referring to the terpenoid backbone biosynthesis (including 144 MEP and MVA pathway genes and 24 terpene synthases). Comparative analysis of the transcriptomes identified 2,863 unigenes that were highly expressed in roots, including those encoding enzymes of early steps of tanshinone biosynthetic pathway, such as copalyl diphosphate synthase (SmCPS), kaurene synthase-like (SmKSL) and CYP76AH1. Other differentially expressed unigenes predicted to be related to tanshinone biosynthesis fall into cytochrome P450 monooxygenases, dehydrogenases and reductases, as well as regulatory factors. In addition, 21 <i>P450</i> genes were selectively confirmed by real-time PCR. Thus we have generated a large unigene dataset which provides a valuable resource for further investigation of the radix development and biosynthesis of tanshinones.</p> </div

Phylogenetic analysis of CYP450s from <i>S. miltiorrhiza</i> and other plants.

<p>Amino acid sequences were aligned using CLUSTALW program, and evolutionary distances were calculated using MEGA4 software with the Poisson correction method. Nt, <i>Nicotiana tabacum</i>; Pg, <i>Panax ginseng</i>; Mt, <i>Medicago truncatula</i>; Vv, <i>Vitis vinifera</i>; Sd, <i>Scoparia dulcis</i>; Or, <i>Orobanche ramose</i>; St, <i>Solanum tuberosum</i>; Sm, <i>Salvia miltiorrhiza</i>. The GenBank/EMBL/DDBJ accession numbers of the sequences are ABC69417.1 (NtCYP72A54), AEY75218.1 (PgCYP72A129), ABC69414.1 (NtCYP72A57), ABC69422.1 (NtCYP72A58), ABC69419.1 (NtCYP72A56), ABC59075.1 (MtCYP72A67), ABC59086.1 (MtCYP98A37), XP_002283338 (VvCYP98A2), ABC69384.1 (NtCYP98A33V1), NP_189259.1 (AtCYP71B4), NP_195459.1 (AtCYP81F1), NP_182079.1 (AtCYP76C4), NP_182081.1 (AtCYP76C2), ADA70805.1 (SdCYP71D176), XP_003617706.1 (MtCYP71D10), NP_200536.3 (AtCYP71B10), NP_680107.1 (AtCYP71A25), XP_002266024.1 (VvCYP716B2), NP_850337.1 (AtCYP98A3), NP_850439.1 (AtCYP76C1), NP_172767.1 (AtCYP71B2), XP_002276576.1 (VvCYP76C4), NP_173149.1 (AtCYP72C1), XP_003592376.1 (Mt704G9), AEY75214.1 (PgCYP749A20), NP_196188.1 (AtCYP90A1), AFO63032.1 (PgCYP716A52V2), ABC59076.1 (MtCYP716A12), NP_190635.1 (AtCYP90B1), NP_182082.2 (AtCYP76C3), AFP74115.1 (OrCYP707A2), ABA55732.1 (StCYP707A1), AFP74114.1 (OrCYP707A1), ADA70806.1 (SdCYP71D177), ABC69395.1 (NtCYP71D47V2), ABC69397.1 (NtCYP71D48V1), NP_189261.1 (AtCYP71B34), NP_189264.3 (AtCYP71B37).</p

Sequence length distribution of unigenes in the S. <i>miltiorrhiza</i> transcriptomes of root and leaf.

<p>Sequence length distribution of unigenes in the S. <i>miltiorrhiza</i> transcriptomes of root and leaf.</p