Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.     

黄瓜花叶病毒浙江白术分离物全基因组序列测定与分析

正白术(Atractylodes macrocephala Koidz.)为菊科(Compositae)苍术属(Atractylodes)多年生草本植物,其根茎入药具补脾健胃、燥湿利水、止汗安胎等功效,在我国广泛栽培,并以于术(浙江临安于潜)品质最佳[1]。近年来白术生产由于品种单一,长期连作等因素导致病毒病害日趋严重。据报道,黄瓜花叶病毒(Cucumber mosaic virus,CMV)~([2])、蚕豆萎蔫病毒2号(Broad bean wilt virus 2,     

基于机器视觉的奶牛发情行为自动识别方法

及时检测奶牛发情、适时人工授精、减少空怀奶牛，是奶牛养殖场增加产奶量的关键手段。针对基于运动量和体温等体征的接触式奶牛发情识别方法会造成奶牛应激反应且识别准确率不高的问题，提出了一种非接触式奶牛发情行为自动识别方法。该方法首先使用改进的高斯混合模型实现运动奶牛目标检测，然后基于颜色和纹理信息去除干扰背景，再利用AlexNet深度学习网络训练奶牛行为分类网络模型，识别奶牛爬跨行为，最终实现对奶牛发情行为的自动识别。在供试数据集上的试验结果表明，本文方法对奶牛发情的识别准确率为100%，召回率为88.24%。本文方法可应用于奶牛养殖场的日常发情监测中，为生产管理提供辅助决策。     

基于深度学习的小浆果一体化智能采摘与分选研究进展

小浆果因具有较高的营养价值和保健作用深受人们喜爱，被国际市场誉为第3代水果。随着市场需求量的增加，林业小浆果的智能采摘与分选成为一种新的趋势。目前，由于现有技术自动化和智能化程度不高，导致大量人力、物力的投入。深度学习方法的飞速发展促进了检测技术的精确作业和检测精度的提高。文中概述了深度学习的发展进程，通过总结国内外关于深度学习方法在小浆果采摘与分选中的应用，提出深度学习方法在实际应用过程中存在的问题及解决措施，并展望深度学习方法在小浆果智能采摘与分选检测方面的发展趋势。     

Evaluation of Dynamic Structural Disorders in the Upper Airways and Applied Rein Tension in Healthy Dressage Horses During Riding in Different Gaits and Head–Neck Positions

Flexion of the horse’s head and neck during dressage riding reduces the pharyngeal lumen with the risk of increased upper airway resistance and upper airway obstructions. According to the Fédération Equestre Internationale, hyperflexion is achieved through force, whereas the position low–deep–round is nonforced. The objectives of this study were to evaluate (1) applied rein tension and (2) dynamic structural disorders in the upper airways in dressage horses in different gaits and different head–neck positions (HNPs). Overground endoscopy (OGE) and rein tension were evaluated in 13 clinically healthy and high-performance Warmblood dressage horses while being ridden in a standardized program comprised of four different gaits (halt, walk, trot, and canter) and in four HNPs (unrestrained, competition frame, hyperflexion, and low–deep–round). All included horses were able to achieve the desired HNPs. The HNP low–deep–round showed significantly lower rein tension than competition frame (P < .001) and hyperflexion (P < .001). An association was found between dynamic structural disorders in the upper airway tract evaluated by OGE and head–neck flexion, but this association was not linked to the degree of flexion. The HNP hyperflexion was neither associated with greater rein tension nor severe dynamic structural disorders than the HNP competition frame. This study confirms that low–deep–round is a nonforced position, in contrast to hyperflexion. Further studies are needed to evaluate whether dynamic structural disorders are a result of flexion or if the degree of flexion has an impact.     

基于供应网络能力研究框架分析我国乡村旅游供应链发展现状

游客需求的多样化、旅游目的地功能强化、信息技术影响加剧都在表明传统的旅游模式已经 无法再满足的客户的需求与市场的变化，旅游供应链的构建与优化是发展旅游业的大势所趋。本文 将借助供应网络能力的研究框架，从网络定位、网络构造、网络管理三个方面分析我国旅游供应链的 发展现状。     

基于SegNet模型的高原鼠兔的图像分割

针对高原鼠兔图像目标尺寸小、背景复杂、特征不显著、基于活动轮廓的图像分割模型无法有效分割的问题，采用基于卷积神经网络的SegNet语义模型对高原鼠兔图像进行分割：首先将采集的高原鼠兔图像进行预处理，尺度归一化后制作成与Pascal VOC数据集格式一致的数据集；然后将数据集分为训练集与测试集，采用训练集对SegNet模型训练，测试集对模型进行分割测试。对高原鼠兔图像分割的试验结果表明：与基于活动轮廓的Chan_Vese模型相比，基于卷积神经网络的SegNet模型对高原鼠兔图像分割时的交并比、平均像素精度、Dice相似性指数和Jaccard指数分别提高了68.33%、9.35%、30.61%和47.98%，过分割率和欠分割率分别降低了87.20%、16.52%。     

基于PLSR-BP复合模型的红壤有机质含量反演研究

对红壤地区土壤有机质进行快速预测,以满足智慧农业与精准施肥的需要。以江西省奉新县北部为研究区域,采用1 km×1 km标准格网划分研究区进行采样,共得到红壤样本248个。对土壤光谱进行了包含分数阶导数在内的3种数学变换方法,将经过P=0.01显著性检验的波段用于模型的构建,选用偏最小二乘回归(PLSR)和BP神经网络建立土壤有机质含量预测模型。结果表明:当对红壤光谱数据进行1.5阶导数变换后再使用PLSR-BP复合模型对土壤有机质含量进行预测时的结果为最优,训练集R~2=0.89,RMSE=4.68g·kg~(-1),验证集R~2=0.87,RMSE=5.55g·kg~(-1),RPD=2.75。1.5阶导数对红壤光谱数据的变换能够更好地突出与有机质相关的特征信息,有助于其含量预测。PLSR-BP复合模型预测精度优于单一模型,能够较好地预测红壤有机质含量,为精准农业快速监测红壤有机质含量提供了新的途径。     

林木采伐及木材精深加工机器人研究进展

林木采伐及木材精深加工与木材生产的效益息息相关，机器人在木材生产中的应用促进了木材生产质量和效率，为木材工业发展提供了新的增长动力。针对机器人在林木采伐及木材精深加工领域的应用，文中从林木采伐、人造板生产、家具制造3方面详细论述了采伐机器人、旋切定心机器人、喷涂机器人、码垛机器人、弯曲木机器人、雕刻机器人等机器人研究现状，提出未来林木采伐及木材精深加工机器人的发展方向应集中于仿生移动机构、多功能集成机器人、智能算法及柔性制造系统等方面。     

Asterosaponins: Structures,Taxonomic Distribution,Biogenesis and Biological Activities

Asterosaponins are a class of steroid oligoglycosides isolated from starfish with characteristic structures and diverse biological activities. In this review, we have attempted to combine the most important data concerning asterosaponins and give a list of these secondary metabolites with their structural peculiarities. The purpose of this review is to provide a brief but as complete as possible principal information about their chemical structures, taxonomic distribution in the marine environment, distribution in different geographical areas and depths, some properties, biological activities, and functions. Some other rare steroid metabolites from starfish, closely related in structures and probably biogenesis to asterosaponins, are also discussed.