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| 1 | Consumer acceptability and sensory profiling of sesame oils obtained from different processes显示文摘This study aimed to investigate the effect of oil processing technologies on the sensory qualities of sesame oils and to identify drivers of liking.Using a check-all-that-apply(CATA)question and a hedonic scale,150 consumers evaluated the acceptability and sensory characteristics of 5 sesame oil samples including an aqueous extracted oil(S1),a cold-pressed oil(S2),two batches of screw-pressed oils(S3 and S5)and one crude sesame oil(S4).Solid-phase microextraction-gas chromatography-olfactometry-mass spectrometry(SPME-GC-O-MS)was used to identify odour-active compounds.The results showed that roasting,extraction techniques and filtration process influenced sensory perception and the acceptability of sesame oils.Consumers liked roasted sesame oils more than the cold-pressed sesame oil and liked the aqueous extracted sesame oil the most.Sensory attributes'sweet smell','mellow','roasted','nutty','persistent','high-intense flavour'and'cooked sesame seed flavour'were drivers of liking,while'green','raw sesame seed','rancid','woody'and'fishy'were drivers of disliking.'Burnt'flavour was liked by some while disliked by others.Pyrazines contributed to roasted flavour;2-acetylpyrrole,acetophenone and furfural contributed to nutty flavour;2-pentyl-furan,5-methyl-2-furancarboxaldehyde,and 2-phenyl-2-butenal contributed to sweet odour in the roasted sesame seeds.Nonanal,hexanal,1-hexanol and ocimene were responsible for the'green'flavour perceived in the cold-pressed oil.This study provides valuable information for sesame manufacturers on how to improve the sensory qualities of sesame oils through process manipulation to meet the needs of diverse consumers. | Wenting Yin Maradza Washington Xueting Ma Xi Yang Aliu Lu Rui Shi Renyong Zhao Xuede Wang | 2020 | Grain & Oil Science and Technology2020，3，2： | 5 |
| 2 | Determination of the draft force for different subsoiler points using discrete element method显示文摘Generally,a subsoiler is comprised of a shank and a point.The point shape has a significant effect on the draft force of a subsoiler.In this study,the draft force of subsoilers with four different points were compared under the speed of 0.8 m/s and the depth of 350 mm in the soil bin.Discrete Element Method(DEM)was applied in simulating the working process of the subsoiler.The stiffness of soil particles used in DEM was calibrated by comparing the simulated draft force of a standard arc-shaped subsoiler with the experiment.The calibrated soil particle stiffness was 1.1×104 N/m.The validated model was then used to compare the draft force of subsoilers with four different points under the same condition in the test.Results showed that different points would cause different draft forces.The subsoiler with short chisel point caused the smallest draft force(2885 N)while the point with short face and wings had the largest force(4474 N).The relative errors of the simulated results were less than 4%,which proved that DEM was an effective way for predicting the draft force of subsilers.The velocity field and contact force filed could show the movement of soil around the subsoiler. | Li Bo Xia Rui Liu Fanyi Chen Jun Han Wenting Han Bing | 2016 | International Journal of Agricultural and Biological Engineering2016，9，3： | 5 |
| 3 | Shading Fault Detection Method for Household Photovoltaic Power Stations Based on Inherent Characteristics of Monthly String Current Data Mapping显示文摘The poor outdoor operating conditions of household photovoltaic(PV)make the power station prone to various faults.However,the dispersion of household PV installations often increases the difficulty and cost of operation and maintenance(O&M).Although the remote monitoring and fault detection of a PV power station can be realized by the use of operation data,the particularity of a household power station also brings many problems to fault detection.In this study,we propose a shading fault detection method of household PV power based on inherent characteristics of monthly string current data mapping.The ideal current peak obtained by a new fitting method is used to normalize string current data.The current probability density function(PDF)at each time point is estimated by kernel density estimation(KDE).Through the normalized current data corresponding to the maximum probability density,the inherent characteristics of the strings are obtained,such that whether the strings have shading can be judged and the shading degree can then be evaluated.Not only are no additional sensors needed to collect environmental data,such as irradiation and temperature,but also simulating the detailed parameters of the power station is not required.The interference caused by meteorological factors can thus be eliminated,which can be easily used in old power stations and newly constructed power stations.The effectiveness and performance of the proposed shading fault detection method is verified by experimental data collected from the actual household PV power station.Index Terms-Data fitting,fault detection,household photovoltaic(PV),kernel density estimation(KDE),shading degree. | Wenting Ma Mingyao Ma Hai Wang Zhixiang Zhan Rui Zhang Jun Wang | 2023 | CSEE Journal of Power and Energy Systems2023，9，4： | 0 |
| 4 | Influence of epitaxial layer structure and cell structure on electrical performance of 6.5 kV SiC MOSFET显示文摘Silicon carbide(SiC)material features a wide bandgap and high critical breakdown field intensity.It also plays an important role in the high efficiency and miniaturization of power electronic equipment.It is an ideal choice for new power electronic devices,especially in smart grids and high-speed trains.In the medium and high voltage fields,SiC devices with a blocking voltage of more than 6.5 kV will have a wide range of applications.In this paper,we study the influence of epitaxial material properties on the static characteristics of 6.5 kV SiC MOSFET.6.5 kV SiC MOSFETs with different channel lengths and JFET region widths are manufactured on three wafers and analyzed.The FN tunneling of gate oxide,HTGB and HTRB tests are performed and provide data support for the industrialization process for medium/high voltage SiC MOSFETs. | Lixin Tian Zechen Du Rui Liu Xiping Niu Wenting Zhang Yunlai An Zhanwei Shen Fei Yang Xiaoguang Wei | 2022 | Journal of Semiconductors2022，43，8： | 0 |
| 5 | Deep Learning‑Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas显示文摘This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular subtyping.Forty-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this study.Histopathology and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome X-linked gene(ATRX)subtypes.Tumor segmentation was performed manually using Insight Toolkit-SNAP program(www.itksnap.org).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices.Fivefold cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:1.The performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRX loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRX loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRX loss. | Wenting Rui Shengjie Zhang Huidong Shi Yaru Sheng Fengping Zhu YiDi Yao Xiang Chen Haixia Cheng Yong Zhang Ababikere Aili Zhenwei Yao Xiao‑Yong Zhang Yan Ren | 2023 | Phenomics2023，3，3： | 0 |
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