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- 저자 : ( Jiafeng Chen ) (검색결과 5 건)
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Chemical kinetics of bean straw biofuel pyrolysis using maximum volatile release method
Wei Chen,Kalyan Annamalai,Jiafeng Sun,Yuming Chen 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.8
Maximum volatile release (MVR) methods, including single point (MVR-S) and multiple (MVR-M) points, are proposed to extract the kinetics used to predict the fuel bean straw pyrolysis process. The simulation results were compared to those of the distributed activation energy model (DAEM). For the TGA (thermogravimetric analysis) experiments, fuels were heated from ambient temperature to 1,173 K at the heating rates of 10, 20, 40, 60, 80 K min−1, and the corresponding maximum volatile release points were obtained. For the MVR-S method, both the activation energy (E) and pre-exponential factor (B) increased with increasing heating rates. For the pyrolysis weight loss process, the DAEM showed best agreement with the experimental data, followed by the MVR-M, and then the MVR-S method. However, for weight loss rate prediction, MVR-S method had the best match with the experimental data, whereas the DAEM and MVR-M method generated more errors.
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Jiafeng Wang,Hui Wen,Ming Li,Tao Guo,Chun Chen 한국식물학회 2020 Journal of Plant Biology Vol.63 No.3
N-Glycosylation, one of the most prominent and abundant protein post-translational modifcations in eukaryotic cells, is involved in diverse biological processes. To date, large-scale profling of the N-glycoproteome has been only reported in rice germinating embryos, but that in rice leaves has not been profled. Here, we report the frst rice N-glycoproteome in leaves, determined by combining Concanavalin A (ConA) lectin afnity chromatography enrichment and high-resolution LC–MS/MS. In total, 282 N-glycopeptides, corresponding to 556 proteins and 643 sites, were identifed from the leaves of H4 (indica) and LTH (japonica). Two conserved canonical N-glycosylation motifs N-X-T and N-X-S and two more noncanonical motifs N-X-S-X-N and A-X-X-N-X-S were revealed in rice. More than 50% of the identifed proteins are localized to the chloroplast, extracellular part, and plasma membrane. Bioinformatics analysis revealed that N-glycosylation occurs on proteins involved in a wide variety of biological processes, especially photosynthesis and carbon metabolism. Protein– protein interaction networks of these proteins provided further evidence that N-glycosylation contributes to a wide range of regulatory functions. In summary, these fndings revealed the complexity of the rice N-glycoproteome and provided useful information to further explore the regulatory roles of N-glycosylation in the growth, development, and stress responses of rice.
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Optimistic Fault Diagnosis in Discrete Event Systems by Labeled Petri Nets and Basis Markings
Guanghui Zhu,Jiafeng Zhang,Zhong Zheng,Shan Luan,Te Chen,Qiang Ma,Zhiwu Li 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.6
This paper deals with the fault diagnosis problem of discrete event systems modeled with labeled Petri nets. Its main contributions are threefold. First, depending on whether a diagnosis function examines the fault transitions that possibly occur after the last observed event, we formally divide the diagnosis functions into two types: optimistic and pessimistic, which aims to facilitate the exploration of different diagnosis approaches. Second, a framework is proposed, which extends a given diagnosis approach for Petri nets to the case of labeled Petri nets. The main idea of the framework is to compute and combine the diagnosis results of observable transition sequences corresponding to an observed word. Third, we convert a basis-marking-based approach that is originally pessimistic to the optimistic case and prove the correctness of this conversion.
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Efficient Compression Algorithm with Limited Resource for Continuous Surveillance
( Ling Yin ),( Chuanren Liu ),( Xinjiang Lu ),( Jiafeng Chen ),( Caixing Liu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.11
Energy efficiency of resource-constrained wireless sensor networks is critical in applications such as real-time monitoring/surveillance. To improve the energy efficiency and reduce the energy consumption, the time series data can be compressed before transmission. However, most of the compression algorithms for time series data were developed only for single variate scenarios, while in practice there are often multiple sensor nodes in one application and the collected data is actually multivariate time series. In this paper, we propose to compress the time series data by the Lasso (least absolute shrinkage and selection operator) approximation. We show that, our approach can be naturally extended for compressing the multivariate time series data. Our extension is novel since it constructs an optimal projection of the original multivariates where the best energy efficiency can be realized. The two algorithms are named by ULasso (Univariate Lasso) and MLasso (Multivariate Lasso), for which we also provide practical guidance for parameter selection. Finally, empirically evaluation is implemented with several publicly available real-world data sets from different application domains. We quantify the algorithm performance by measuring the approximation error, compression ratio, and computation complexity. The results show that ULasso and MLasso are superior to or at least equivalent to compression performance of LTC and PLAMlis. Particularly, MLasso can significantly reduce the smooth multivariate time series data, without breaking the major trends and important changes of the sensor network system.
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Strategy for Fabricating Wafer-Scale Platinum Disulfide
Xu, Hongjun,Huang, Hsin-Pan,Fei, HaiFeng,Feng, Jiafeng,Fuh, Huei-Ru,Cho, Jiung,Choi, Miri,Chen, Yanhui,Zhang, Lei,Chen, Dengyun,Zhang, Duan,Coileá,in, Cormac Ó,Han, Xiufeng,Chang, Ching-Ra American Chemical Society 2019 ACS APPLIED MATERIALS & INTERFACES Vol.11 No.8
<P>PtS<SUB>2</SUB> is a newly developed group 10 2D layered material with high carrier mobility, wide band gap tunability, strongly bound excitons, symmetrical metallic and magnetic edge states, and ambient stability, making it attractive in nanoelectronic, optoelectronic, and spintronic fields. To the aim of application, a large-scale synthesis is necessary. For transition-metal dichalcogenide (TMD) compounds, a thermally assisted conversion method has been widely used to fabricate wafer-scale thin films. However, PtS<SUB>2</SUB> cannot be easily synthesized using the method, as the tetragonal PtS phase is more stable. Here, we use a specified quartz part to locally increase the vapor pressure of sulfur in a chemical vapor deposition furnace and successfully extend this method for the synthesis of PtS<SUB>2</SUB> thin films in a scalable and controllable manner. Moreover, the PtS and PtS<SUB>2</SUB> phases can be interchangeably converted through a proposed strategy. Field-effect transistor characterization and photocurrent measurements suggest that PtS<SUB>2</SUB> is an ambipolar semiconductor with a narrow band gap. Moreover, PtS<SUB>2</SUB> also shows excellent gas-sensing performance with a detection limit of ∼0.4 ppb for NO<SUB>2</SUB>. Our work presents a relatively simple way of synthesizing PtS<SUB>2</SUB> thin films and demonstrates their promise for high-performance ultrasensitive gas sensing, broadband optoelectronics, and nanoelectronics in a scalable manner. Furthermore, the proposed strategy is applicable for making other PtX<SUB>2</SUB> compounds and TMDs which are compatible with modern silicon technologies.</P> [FIG OMISSION]</BR>
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