A novel NIRS modelling method with OPLS-SPA and MIX-PLS for timber evaluation

The identification of timber properties is important for safe application.Near Infrared Spectroscopy(NIRS)technology is widely-used because of its simplicity,efficiency,and positive environmental attributes.However,in its application,weak signals are extracted from complex,overlapping and changing information.This study focused on the stability of NIR modeling.The Orthogonal Partial Least Squares(OPLS)and Successive Projections Algorithm(SPA)eliminates noise and extracts effective spectra,and an ensemble learning method MIX-PLS,is applied to establish the model.The elastic modulus of timber is taken as an example,and 201 wood samples of three species,Xylosmacongesta(Lour.)Merr.,Acer pictum subsp.mono,and Betula pendula,samples were divided into three groups to investigate modelling performance.The results show that OPLS can preprocess the near-infrared spectroscopy information according to the target object in the face of the system error and reduce errors to minimum.SPA finally selects 13 spectral bands,simplifies the NIR spectral data and improves model accuracy.The Pearson's correlation coefficient of Calibration(Rc)and the Pearson's correlation coefficient of Prediction(Rp)of Mix Partial Least Squares(MIX-PLS)were 0.95 and 0.90,and Root Mean Square Error of Calibration(RMSEC)and Root Mean Square Error of Prediction(RMSEP)are 2.075 and 6.001,respectively,which shows the model has good generalization abilities.

Design and simulation of a novel 4H-SiC LGAD timing device

Purpose Silicon-based fast timing detectors have been widely used in high-energy physics,nuclear physics,space exploration and other fields in recent years.However,silicon detectors often require complex low-temperature systems when operating in irradiation environment,and their detection performance decreases with the increase in the irradiation dose.Compared with silicon,silicon carbide(SiC)has a wider band gap,higher atomic displacement energy,saturated electron drift velocity and thermal conductivity.Simultaneously,the low-gain avalanche detector avoids cross talk and high noise from high multiplication due to its moderate gain,and thus can maintain a high detector signal without increasing noise.Aim Thus,the 4H-SiC particle detector,especially the low-gain avalanche detector,has the potential to detect the minimal ionizing particles under extreme irradiation and high-temperature environments.Method In this work,the emphasis was placed on the design of a 4H-SiC low-gain avalanche detector(LGAD),especially the epitaxial structure and technical process which play main roles.In addition,a simulation tool—RASER(RAdiation SEmiconductoR)—was developed to simulate the performances including the electrical properties and time resolution of the 4H-SiC LGAD we proposed.Conclusion The working voltage and gain effectiveness of the LGAD were verified by the simulation of electrical performances.The time resolution of the LGAD is(35.0±0.2)ps under the electrical field of−800 V,which is better than that of the 4H-SiC PIN detector.

局部-全局关系耦合的低照度图像增强

卷积神经网络目前在人工智能多个领域均取得了不同程度的进展.卷积计算是基于参数共享的滑窗机制,这导致卷积神经网络更多地关注特征信息的局部关系,对全局关系的建模能力有限.局部关系和全局关系对特征的表达均具有重要的作用.为此,本文聚焦于如何对特征信息的局部-全局关系进行构建并有效耦合,从而挖掘更加丰富的特征信息,提高特征的判别性.本文提出了局部-全局关系耦合模块,该模块是由特征提取、基于深度卷积(depth-wise convolution,DWConv)的局部关系构建分支、基于多头自注意力机制(mutli-head self-attention,MHSA)的全局关系构建分支和基于点向卷积(point-wise convolution,PWConv)的关系耦合4部分组成.基于此模块,本文构建了编解码结构的局部-全局关系耦合神经网络,该网络可以对特征信息的局部-全局关系进行建模,增强特征信息的表征能力,进而提升模型的性能.为验证所提算法的有效性,本文在低照度图像增强任务上,使用基准数据集与其他算法进行了实验对比.实验结果表明,本文所提出的方法取得了较好的图像增强结果,优于当前先进的图像增强方法.最后,本文通过消融实验和扩展实验从多个角度进一步验证了有效耦合局部-全局关系的重要性和可扩展性.

Norlichexanthone purified from plant endophyte prevents postmenopausal osteoporosis by targeting ERa to inhibit RANKL signaling

Although different types of drugs are available for postmenopausal osteoporosis,the limitations of the current therapies including drug resistances and adverse effects require identification of novel anti-osteoporosis agents.Here,we defined that norlichexanthone(NOR),a natural product,is a ligand of estrogen receptor-alpha(ERα)and revealed its therapeutic potential for postmenopausal osteoporosis.We used mammalian-one hybrid assay to screen for ERαmodulators from crude extracts of several plant endophytes.As a result,NOR purified from the extract of endophyte ARL-13 was identified as a selective ERαmodulator.NOR directly bound to ERαwith an affinity in nanomolar range,revealing that it is a natural ligand of ERα.NOR induced osteoblast formation in MC3T3-E1 precursor cells.Conversely,NOR inhibited receptor activator of nuclear factor-kappa B ligand(RANKL)-induced osteoclast formation in both RAW264.7 macrophages and mouse primary monocytes.Mechanistically,NOR inhibited RANKL-induced association of ERαand TRAF6 to prevent ERα-mediated TRAF6 activation via Lys63-linked ubiquitination.Importantly,NOR exhibited potent anti-osteoporosis efficacy in an ovariectomized mouse model.Comparing to estrogen,NOR was of much less capability in stimulating endometrial hyperplasia and promoting mammalian cancer cell proliferation.Taken together,our study identified NOR as a natural and high affinity ligand of ERαwith substantial anti-osteoporosis but less estrogenic activity.

Groundwater net discharge rates estimated from lake level change in Badain Jaran Desert, Northwest China

Over a hundred sizable lakes,some even larger than^1 km2,are distributed across the lowlands between megadunes in the southeastern Badain Jaran Desert(BJD),northwest China.With rather limited precipitation and hardly any surface runoff,these lakes are fed mainly by groundwater.However,the source of the groundwater and the groundwater discharge rates to these lakes are poorly understood.Water level and temperature of four representative lakes in the southeastern BJD were monitored continuously between 2013 and 2017.Water surface evaporation and rainfall in this area were also measured.Combining these acquired data,groundwater discharge rates were estimated using water balance models.The results show that the four lakes have similar recharge and discharge patterns,but at different average net groundwater discharge rates ranging from 1.79 to 3.09 mm d?1.The lake level variation mainly depends on groundwater discharge and lake surface evaporation.We found that diurnal lake level variation may be controlled by earth tide and atmospheric pressure change,and is five times greater than the evaporation.The desert precipitation and deep confined groundwater with high temperature were found also recharging the desert lakes.An Empirical Mode Decomposition(EMD)method was used to identify the lake level trend.All lake levels increased over the last four years,except one decreased in 2015 and 2016.The lake levels’increasing trend is synchronously similar with the precipitation in north China.This study analyzes annual and seasonal lake level variations,and also finds the diurnal water cycle between the groundwater and lake water for the first time.