Differences in the effectiveness of reproduction among ten breeding strains of the common carp Cyprinus carpio L. after stimulation of ovulation with carp pituitary homogenate or [(D‐Ala6, Pro9NEt) mGnRH‐a + metoclopramide] (Ovopel)

This study focused on the reproduction effectiveness of 10 breeding strains of common carp (Polish strains 2, 3, 6; Hungarian strains 0, W, 7; Lithuanian strain B; French strain F; Israeli strain D and Yugoslavian strain J) after stimulation of ovulation with carp pituitary homogenate or Ovopel. The percentage of females with recorded ovulation became higher after Ovopel treatment in as many as eight breeding strains (2, 3, 6, 0, W, B, F, D). The interaction between the spawning agent and the breeding strain was statistically significant (P ≤ 0.05) for the mass of spawn and non‐significant for traits determining the quality of eggs. Among the 10 breeding strains, in 4 (3, 7, B, J) the least‐squares means (LSM) for the weight of egg (g) were higher for fish stimulated with Ovopel. Among the strains tested, in seven (3, W, F, J, 2, 7, B), the LSM for the percentage of living embryos after 36 h incubation were higher for fish treated with Ovopel. The evaluation of reproduction effects based on the values of such parameters as the percentage of ovulating females, weight of eggs (g) and the percentage of live embryos (36 h) showed that after Ovopel treatment the poorer effects were obtained only for strains 6, 0 and D.     

圆柱镂空型人工鱼礁波流水动力特性数值模拟

研究人工鱼礁在波流作用下的水动力特性,对于人工鱼礁的设计具有重要的意义。基于有限体积法,采用边界造波,利用自由表面捕捉法(VOF)捕捉自由水面,建立了可以分别模拟纯波、均匀流以及波流共同作用下人工鱼礁水动力特性的多功能三维数值波流水槽。基于该数值模型对不同波流工况作用下圆柱型镂空人工鱼礁水动力特性进行数值模拟,并与物理模型试验结果进行比较。结果显示,人工鱼礁数值模拟受力与模型试验结果吻合良好,人工鱼礁所受的波流力最大值随着波高、周期和水流流速的增大而增大;人工鱼礁处于波流场波峰正下方时,背涡流的面积随着水流流速的增大而增大,随着波高、周期增大而减小。对单独均匀流作用、单独波浪作用和波流联合作用下人工鱼礁的水动力特性对比研究表明,人工鱼礁所受的最大波流力比最大波浪力、水流力都大,波流联合作用下的流场效应最显著,在礁体的后部形成了较大规模的漩涡结构。     

Potential application of baited remote underwater video to survey abundance of west coast rock lobster Jasus lalandii

The first assessment of the baited remote underwater video system (BRUVs) for monitoring rock lobster Jasus lalandii (Milne‐Edwards) in South Africa was compared to annual ring net and trap survey data. Count data from 58 BRUVs, 36 ring net and 95 trap samples were fitted with generalised additive models using depth, habitat, time and spatial gradient as independent variables. A power analysis was used to determine the number of samples needed per annum to detect a 5 and 10% increase and decrease in population size. The BRUVs outperformed the nets and traps in terms of sampling effort and were better at detecting changes in abundance of lobster than abundance of three fish species. Considering factors such as cost, analysis time, additional data and environmental impact, traps are the most feasible method in deeper waters but BRUVs could replace nets and traps in depths less than 60 m, low‐density areas and marine reserves.     

奶牛人工授精的优缺点和技术要点

奶牛人工授精技术加速了奶牛品种改良进程,实现了将优秀种公牛精液跨区域、超时限的利用共享,极大地促进了养牛业的发展。以奶牛人工授精应用现状为出发点,介绍了人工授精技术的优点和不足,最后结合自身工作经验,从发情鉴定、适时配种、精液解冻、精液检测、输精、精液保存和妊娠检测等技术要点进行概述,以期更好地促进奶牛人工授精技术的应用。     

一种新型卷积神经网络植物叶片识别方法

为提高植物叶片识别的准确率及减少计算代价,在Pytorch框架下提出一种融合了深度卷积生成式对抗网络(DCGAN)和迁移学习(TL)的新型卷积神经网络叶片识别方法。首先,对植物叶片图像进行预处理,通过DCGAN对样本数据库扩充;其次,利用迁移学习将Inception v3模型应用于图像数据处理上,以提高植物叶片识别的准确率;最后,通过对比实验对该方法的有效性进行验证。结果表明:该方法可以获得96.57%的植物叶片识别精度,同时参数训练的迭代次数由4000次缩短到560次。     

基于灰色-BP神经网络模型的多情景交通用地需求预测——以长江中游城市群为例

为探索科学预测区域交通用地需求的合理途径,指导国土空间规划编制中交通用地规模的划定,拟通过灰色-BP神经网络模型识别主要社会经济影响因素,以长江中游城市群为例,在预测其远景交通用地需求规模的同时,基于城市群发展的阶段特征选取典型城市群样本设置3类情景,对不同情景下的交通用地需求分别进行预测。结果表明:1)城镇化水平、产业结构高度化程度和劳动力资源禀赋是当前影响长江中游城市群交通用地需求的主要社会经济因素。2)通过系统仿真试验对比不同方法的交通用地需求预测结果,可以发现基于灰色-BP神经网络模型的预测方法精度较高,误差较小,该预测方法对于区域交通用地规模的预测具有一定的适用性。预测得到的长江中游城市群2020和2030年交通用地需求分别为31.22万和49.07万hm2。3)不同情景下长江中游城市群交通用地需求预测结果存在明显差异,底线情景可作为划定交通用地规模的底限,一般情景可作为基准,极限情景可作为红线,长江中游城市群交通用地合理规模应以基准为参考,介于底线和红线之间。     

Genotyping,pedigree reconstruction and endocrinological characterization of Acipenser naccarii (Bonaparte, 1836) using microsatellite markers and plasma steroid levels

This study aimed to set up a method for the long‐term management of Adriatic sturgeon, through the analysis of sex steroids and the genetic profiling of individuals in order to maximize the already reduced genetic variability of this species. Ten A. naccarii adults (nine of captive origin, one captured in the Ticino river and then moved into captivity) and eight subadults located in a semi‐natural land‐locked pond in Abbiategrasso (Milan, Italy) were analysed. Plasma testosterone differed significantly in the eight analysed subadults allowing their differentiation into two groups: the first group with an average testosterone concentration of 5.42 ± 1.31 ng/ml (probably female) and the second group with an average of 423.14 ± 75.97 ng/ml (probably male), as subsequently confirmed by artificial stripping. The plasma testosterone level was also significantly different between adult males and females (371.37 ± 43.58 vs. 95.34 ± 51.10 ng/ml), while the E2 levels showed no significant differences. Animals were genotyped on the basis of 10 microsatellite loci and their parental relationships were defined: four adults, two females and two males, generated the eight subadults. On the basis of pedigree analyses and genetic distances, 15 unrelated couples were identified for the future breeding seasons. Finally, the adult female captured in the Ticino River showed an interesting genetic profile, widely different from all of the other 17 specimens analysed, and represents a valuable source of genetic diversity.     

基于YOLO v3与图结构模型的群养猪只头尾辨别方法

在利用视频监控技术对群养猪只进行自动行为监测时,对猪只准确定位并辨别其头尾位置对提高监测水平至关重要,基于此提出一种基于YOLO v3(You only look once v3)模型与图结构模型(Pictorial structure models)的猪只头尾辨别方法。首先,利用基于深度卷积神经网络的YOLO v3目标检测模型,训练猪只整体及其头部和尾部3类目标的检测器,从而在输入图像中获得猪只整体及头尾部所有的检测结果;然后,引入图结构模型,描述猪只的头尾结构特征,对每个猪只整体检测矩形框内的头尾部位组合计算匹配得分,选择最优的部位组合方式;对部分部位漏检的情况,采取阈值分割与前景椭圆拟合的方法,根据椭圆长轴推理出缺失部位。在实际猪场环境下,通过俯拍获得猪舍监控视频,建立了图像数据集,并进行了检测实验。实验结果表明,与直接利用YOLO v3模型相比,本文方法对头尾定位的精确率和召回率均有一定提高。本文方法对猪只头尾辨别精确率达到96.22%,与其他方法相比具有明显优势。     

基于深度学习的牛肉大理石花纹等级手机评价系统

大理石花纹是影响牛肉品质等级的重要指标,目前中国牛肉加工企业对大理石花纹的评价是由专业分级人员参照标准图谱完成,具有主观性强、耗费人工的缺点。针对上述问题,该研究提出了基于深度学习的智能分级方法,设计一种具有4层卷积的神经网络结构,实现了大理石花纹特征的自动提取,并基于智能手机开发了牛肉大理石花纹检测软件。该研究共采集样本图像1 800张,按3:1:1分为校正集、验证集和测试集。为进一步验证模型,将该方法与传统机器视觉方法进行了比较,提取了牛肉大理石花纹的大、中、小脂肪颗粒个数,脂肪总面积和脂肪分布均匀度5个参数,并建立了多元线性回归模型。试验结果表明,该研究所用方法大理石花纹检测准确率更高,验证集检测正确率为97.67%。最后编写了手机软件,将模型移植入Android手机,在手机平台上调用模型进行大理石花纹检测。试验表明,该软件对测试集样本的检测准确率为95.56%,单张检测时间低于0.5 s。该研究结合卷积神经网络分类能力强和智能手机运行速度快等优点,开发了牛肉大理石花纹的手机评价系统,具有较好的实用性和便携性,可提高牛肉大理石花纹检测效率,有助于提高农畜产品检测的智能化水平。