炭化条件对竹原纤维炭孔隙结构的影响分析

为研究炭化竹原纤维的特性,进一步提升竹原纤维的使用性能,拓宽竹原纤维的应用领域,采用可控电炉制备了不同炭化温度和不同保温时间条件下的炭化竹原纤维,利用全自动比表面积及孔隙度分析仪测试了炭化竹原纤维的比表面积、比孔容及平均孔径,探讨了炭化条件对其性能的影响。结果表明：随炭化温度的升高和保温时间的延长,炭化竹原纤维的比表面积、比孔容和孔径分布先增大后减小,在较优的工艺条件下,炭化竹原纤维的比表面积和比孔容最大值分别可达819.35m2/g和0.7358cm3/g,平均孔径最小可达2.0836nm。     

Assessing the deposition and canopy penetration of nozzles with different spray qualities in an oat (Avena sativa L.) canopy

Consistent spray coverage that is evenly distributed throughout the canopy is necessary to control pest populations that can negatively affect yield. As applicators are switching to Coarser spray quality nozzles to reduce risk and liability of pesticide spray drift, concerns about efficacy loss are growing. Previous research has indicated that small droplets are the most effective at penetrating through crop canopies, but newer nozzle technologies have improved the effectiveness of larger droplet or Coarser sprays. Research was conducted to assess the canopy penetration of nozzles that produce Coarse, Very-Coarse and Extremely-Coarse spray qualities compared to nozzles that produce Fine and Medium spray qualities. Kromekote collectors were positioned in four configurations in an oat (Avena sativa L.) var. ‘Yarran’ (AusWest Seeds, Forbes, NSW, Australia) crop to quantify the coverage and droplet number densities (droplets cm⁻²) across three application carrier volume rates: 50, 75 and 100 L ha⁻¹. Applications were made in the field in 30 cm tall, tillering oats, with collectors arranged in a randomised complete block design with three replications. The entire study was repeated on the following day. Results showed that droplet number densities were inversely related to the droplet size produced by the nozzles, yet coverage was increased more by application volume rate than droplet size. Thus, both spray drift reduction and improved canopy penetration can be achieved with proper nozzle selection and operation parameters for the control of agronomic pests.     

Effects of a high milk intake during the pre‐weaning period on nutrient metabolism and growth rate in Japanese Black cattle

This study aimed to determine the effects of feeding an increased volume of high‐fat milk during the early post‐natal life on metabolite concentrations in the blood, the expression of key genes regulating intermediary metabolism in the skeletal muscles, and the rate of growth of Japanese Black cattle. All calves were fed a high‐fat milk replacer (crude protein, 26%; crude fat, 25.5%; total dissolved nitrogen, 116%). Control calves (n = 4) were nursed with 500 g milk replacer until 3 months of age, whereas calves in the experimental group (n = 4) were nursed with 1800 g milk replacer until 3 months, and then the volume was gradually reduced until 5 months. Body weight was significantly higher in the experimental group than in the control group at 7 months. Plasma glucose concentrations were significantly lower in the experimental group. Expression of glucose‐transporter‐4 messenger RNA (mRNA) was lower, whereas that of glucose transporter 1, cluster of differentiation 36, and carnitine palmitoyltransferase‐1b mRNA was significantly higher in the Longissimus thoracis of the experimental group. Nutritional status during early post‐natal life appears to strongly influence the growth rate and glucose and lipid metabolism in Japanese Black cattle.     

Respond of Stock Trading Volume to Earning Report

Earning report is a main accounting report that transmits earning information to users. It is the core of financial reports system. Earning information is significant to users' decision making. This paper studies the respond of stock trading volume to earning report and draws the curve of trading volume before and after earning report. This paper further does a regression analysis of accidental earning and abnormal return rate, and draws the relation curve of them on the earning report date to prove stock trading volume does have remarkable respond to earning report. This research is significant to either lessen small investors blindness investment and help them make reasonable forecast to markets and regulate the disclosure of listed companies.     

我国小型潜水电泵行业发展与思考

论述了我国小型潜水电泵行业概况及主要生产基地浙江省温岭市小型潜水电泵现状，并对小型潜水电泵今后发展提出要扶强治劣、加强技术和信息交流、积极开拓国际市场、提高技术水平、推行名牌战略等建设性建议。     

基于ALS数据和哑变量技术森林蓄积量估测

基于机载LiDAR数据,分析哑变量对林分蓄积量估测精度的影响。以广西高峰林场为研究对象,借助机载激光雷达点云数据和96个样地数据,将样地数据按7∶3的比例随机划分为建模样本和测试样本,采用随机森林模型(RFR)和支持向量机模型(SVR)对建模样本与对应的点云特征回归建模,将树种组(针叶林和阔叶林)和龄组分别作为哑变量引入到回归模型。利用测试样本的估测精度评价模型的估测精度,引入树种组哑变量,随机森林模型决定系数R2从0.59提高到0.64,支持向量机模型决定系数R2从0.49提高到0.50。引入龄组哑变量,随机森林模型决定系数R2从0.59提高到0.65,支持向量机模型决定系数R2从0.45提高到0.55。根据模型的建模精度和验证精度结果得出,引入哑变量对蓄积量估测模型的精度提升是相对有效的。龄组哑变量对模型精度提升效果优于树种组哑变量。     

基于激光雷达数据的东北林区航空林分材积表编制

利用东北林区云冷杉林、落叶松林、樟子松林、红松林、栎树林、桦树林、杨树林、榆树林、椴树林和水胡黄林10种森林类型的1947个样地的激光雷达数据和地面实测蓄积量数据,首先通过多元线性回归和非线性回归方法,分别建立基于机载激光雷达数据的森林蓄积量回归估计模型,并通过对比分析,确定统一形式的基础回归模型;然后利用哑变量建模方法,建立基于不同森林类型参数和相同激光雷达变量的蓄积量模型。结果表明,研究建立的10种森林类型的线性蓄积量回归模型的解释变量个数在2~7之间,确定系数在0.460~0.858之间;非线性蓄积量回归模型的解释变量个数在2~4之间,确定系数在0.461~0.846之间。基于点云平均高度和平均强度建立的10种森林类型的二元蓄积量模型(研究称之为标准模型),其确定系数在0.440~0.815之间,平均预估误差在2.88%~4.42%之间,平均百分标准误差在16.76%~25.52%之间,预估精度基本达到森林资源规划设计调查技术规定要求。依据研究建立的10种森林类型的蓄积量模型,可以编制基于激光雷达数据的航空林分材积表,在森林资源调查实践中推广应用。     

人工降雨条件下不同坡度红壤坡面迳流泥沙变化特征研究

在人工模拟降雨强度为1.44 mm/min的条件下,研究了不同坡度(β)红壤近似裸地坡面迳流、泥沙的变化特征,研究结果表明:①地表迳流量随坡度的增大而迅速增加。②当β≤23°时,土壤侵蚀量随坡度的增大而迅速增加;当25°≤β≤40°时,土壤侵蚀量随坡度变化较小。③坡面土壤侵蚀的临界坡度则介于23°~25°之间。④坡度与侵蚀量关系可用函数表示为:S=199.1×L1.65×tan1.4β(0°≤β≤23°)。     

补灌量和覆盖量变化对旱地春小麦叶面积指数的影响

免耕覆盖中补灌量和秸秆覆盖量变化对叶片生长有较大影响,以甘肃省定西市安定区1979—2019年历史气象数据为基础,运用APSIM模型对补灌量与覆盖量耦合变化时旱地春小麦的叶面积指数进行模拟,并采用方差分析、二次多项式回归、单因素分析等方法,研究补灌量和覆盖量对旱地春小麦叶面积的影响机制。结果表明：在试验设计范围内,旱地春小麦叶面积指数随着补灌量变化在分蘖—拔节期呈开口向下的二次抛物线先增后减变化,补灌量在252.09 mm时春小麦叶面积指数出现最大值为1.83,其他各个时期均呈开口向上的二次抛物线递增变化。随着秸秆覆盖量变化,叶面积指数在出苗—分蘖期,呈开口向下的二次抛物线先增后减变化,试验设计范围内秸秆覆盖量为2397.09 kg/hm²时春小麦叶面积指数出现最大值为0.59,分蘖—拔节期呈开口向下的二次抛物线递增变化;其他各个时期均呈开口向上的二次抛物线递增变化。相同阶段下,补灌量每增加50 mm,春小麦叶面积指数最大增幅13.95%;秸秆覆盖量每增加1000 kg/hm²,春小麦叶面积指数最大增幅3.7%。补灌量对春小麦叶面积指数的影响...     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.