吉林省民营高等学校资源配置效率研究

民营高等学校是我国重要的人才培养渠道,自20世纪80年代以来,民营高等教育进入了飞速发展时期。吉林省民营高校经过多年的改革和探索已有了跨越式的发展,但如何使其从粗放型发展向内涵式发展转变,实现教育资源合理配置是关键。本文从调查研究吉林省民营高等学校资源配置的实际情况出发,采用数据包络分析方法（DEA）,评价民营高等学校资源配置效率问题,可以看出,吉林省民营高等院校在资源配置上依然存在着非效率的情况,本文据此提出相应建议。     

The importance of safety signals in animal handling and training

This review considers the importance of safety for various species of domestic animals and explains how the need for safety may motivate them to offer a variety of unwelcome responses. We argue that the value of safety to animals is often overlooked by trainers and handlers. As a result, animal owners, handlers, trainers, and veterinarians are regularly injured, and training may fail. Reinforced responses that increase the animal's perceived sense of safety but simultaneously endanger the safety of handlers or trainers may lead to the inadvertent training of dangerous responses. This review offers suggestions about how safety can be used effectively and humanely as a resource in operant training. Training calmness in the presence of a specific stimulus that is associated with safety may deserve closer attention. The ethics of creating the need for safety in domestic animals as part of a training regime are discussed. It emerges that the highly prized attribute of so-called trust in animal–trainer dyads may, at least sometimes, be a manifestation of trainers acting as safety signals. Similarly, animals said to have confidence in and regard for their handlers may value the relative safety they afford.     

The slow rise of technology: Computer vision techniques in fish population connectivity

1. Technological advancements in data collection and analysis are producing a new generation of ecological data. Among these, computer vision (CV) has received increased attention for its robust capabilities for rapidly processing large volumes of digital imagery.
2. In marine ecosystems, the study of fish connectivity provides fundamental information for assessing fisheries stocks, designing and implementing protected areas and understanding the impact of habitat loss. While the field of fish connectivity has benefited from technological advancements, the extent to which novel techniques, such as CV, have been utilized has not been assessed. To inform future directions and developments, this study reviewed the current use of CV in fish connectivity research, quantified how the implementation of such technology in fish connectivity research compared with other areas of marine research and described how this field could benefit from CV.
3. The review found that the use of remote camera systems in fish connectivity research is increasing, but the implementation of automated analysis of digital imagery has been slow. Successful implementation and expansion of CV frameworks in aquaculture and coral reef ecology suggest that CV techniques could greatly benefit fish connectivity research.
4. A case study of potential use of CV in fish connectivity research, scaling up optimal foraging models to predict marine population connectivity, highlights how beneficial it could be.
5. The capacity for CV techniques to be adopted alongside traditional approaches, the unparalleled speed, accuracy and reliability of these approaches and the benefits of being able to study ecosystems along multiple spatial–temporal scales, all make CV a valuable tool for assessing connectivity. Ultimately, these technologies can assist data-driven decisions that directly influence the health and productivity of marine ecosystems.

     

Social learning in fishes: a review

Animals acquire skills and knowledge from other animals, and fish are no exception. There is now strong experimental evidence that many species of fish exhibit social learning and traditional behaviours. Here, we review the literature pertaining to social learning in fish, focusing on (i) antipredator behaviour, (ii) migration and orientation, (iii) foraging, (iv) mate choice and (v) eavesdropping.     

The role of learning in fish orientation

Past research on fish orientation and migration has tended to focus on genetically fixed patterns of behaviour. However, in most aquatic environments, the physical landscape as well as biologically important locations will be subject to varying degrees of change, favouring an ability to learn. Here, we review the evidence from field and laboratory‐based experiments that fish can and do use learning and memory to orientate within their natural environments, relying on information from a diverse array of different sources. Comparisons of different species and populations suggest that fish may be predisposed to learn specific associations at specific times or places, appropriate for the particular navigational problems that they are likely to encounter.     

小麦面条和面过程不同阶段面絮的划分与自动识别

和面是小麦面条加工过程中重要的一道工序,主要依据人工经验判断和面终点,缺少基于客观分析的自动化评价方法。为了实现和面过程的自动化,该研究依据小麦面条和面过程中面絮颗粒宏观状态,通过面絮状态图像的数据分析,并且从面条质构、水分状态及分布、蛋白质分子特性及微观结构揭示和面阶段划分的依据。结果表明,和面过程可以划分为初始面粉（阶段1）、润湿黏连（阶段2）、聚集成形（阶段3）、破裂分散（阶段4）和稳定平衡（阶段5）5个阶段,阶段5占和面总时间的50%左右;从阶段2到稳定平衡阶段初期（阶段5-1）,面片的硬度、弹性和咀嚼性逐渐增加并达到最大,在稳定平衡阶段中后期（阶段5-2、5-3）,面片硬度等指标数值下降;低场核磁共振与核磁共振成像分析结果表明,阶段2的水分自由度高,和面过程加速了水分在固、液、气三相之间的交换,并在阶段5达到稳定;所有和面阶段中氢键、离子键的强度明显低于疏水相互作用的强度,游离巯基含量在阶段5-2、5-3逐渐降低至稳定,二硫键含量在此阶段逐渐增多并保持稳定,蛋白质实现充分交联,决定了面条微观结构和宏观状态。构建基于深度学习的Transfer-ResNet50网络模型,实现和面阶段面絮图像的自动识别,模型识别准确率达98.5%,具有良好的可靠性,可以实现和面终点的自动判断。综上所述,面絮宏观颗粒状态可以作为和面过程划分的可靠依据,深度学习也为小麦面条和面过程自动控制提供新的思路。     

基于两点机器学习方法的土壤有机质空间分布预测

准确预测土壤有机质（Soil Organic Matter,SOM）空间分布对精细农业、耕地质量建设、生态环境保护以及固碳减排等均具有重要的意义。该研究探讨了基于两点机器学习方法（Two-point Machine Learning,TPML）提高SOM空间分布预测的可行性。以黑龙江省海伦市为研究区,以气候、地形地貌、社会经济和空间位置信息等因素作为辅助变量,充分利用空间位置信息和属性相似关系,有效处理SOM空间分布异质性及其与辅助变量间关系异质性,以提高TPML方法进行SOM空间分布预测的精度。采用随机森林、基于随机森林的回归克里格、反距离权重法和普通克里格（Ordinary Kriging,OK）方法作为对比,以平均绝对误差（Mean Absolute Error,MAE）、均方根误差（Root Mean Square Error,RMSE）、预测值与真实值相关系数（r）和决定系数（R2）作为评价指标,进行不同样本量下的多组对比试验,评价不同方法的预测精度。结果表明：1）研究区SOM含量在1.775～7.188 g/kg之间,平均值为3.179 g/kg,空间分布不均匀,呈东高西低的分布趋势。2）在不同样本量条件下,与其他模型相比,TPML的预测精度均最高,其MAE（0.088～0.097 g/kg）和RMSE（0.116～0.139 g/kg）均为最小,r（0.992～0.996）和R2（0.971～0.985）均为最高。3）预测值的误差标准差（理论误差）与实际误差具有相似的空间模式,说明TPML可以为预测结果提供合理的不确定性估计。综上,TPML模型可以通过同时利用空间自相关性和属性相似性来提高预测精度,该模型适用于预测具有一定空间自相关性且具有可用辅助数据的资源环境变量。     

基于特征迁移学习的变工况下轴向柱塞泵故障诊断

不同工况下的轴向柱塞泵故障数据存在分布差异,现有的基于特征迁移学习的变工况故障诊断方法大多只通过单个传感器信号进行分析,具有一定的局限性和片面性。为了利用多传感器信号提高变工况下轴向柱塞泵故障诊断的性能,该研究提出一种耦合分类器子空间嵌入分布自适应（Subspace Embedded Distribution Adaptation with Coupled Classifiers, SEDACC）方法。该方法利用多传感器信号的频谱数据构造主要数据集和辅助数据集,通过子空间对齐（Subspace Alignment, SA）方法将源域和目标域的主要数据投影到公共子空间中,并采用加权条件最大均值差异（Weighted Conditional Maximum Mean Discrepancy, WCMMD）作为度量进行特征分布的适配。同时,基于结构风险最小化（Structural Risk Minimization, SRM）准则在源域标签数据上学习主分类器,根据主分类器对于目标域的预测结果在目标域辅助数据上学习辅助分类器。通过交替和迭代策略不断优化分类器参数,最后对二者进行加权融合得到最终的诊断模型。通过轴向柱塞泵变工况故障诊断试验进行验证,结果表明,当以垂直于端盖的z方向振动信号为主要数据并使用声音信号（或以平行于端盖的x方向的振动信号）作为辅助数据时,SEDACC方法在6种迁移任务中的平均准确率为99.88%（99.46%）,高于其他方法。此外,所提方法在目标工况样本稀少的情况下仍具有较高的诊断精度,当目标域和源域样本数比值为0.2时,6种迁移任务的平均准确率达到92.66%。研究结果可为更完备与准确的机械故障诊断提供参考。     

采用改进SSD网络的海参目标检测算法

随着海参养殖业快速发展,利用水下机器人代替人工作业的海参智能捕捞已成为发展趋势。浅海环境复杂,海参体色与环境区分性差、海参呈现半遮蔽状态等原因,导致目标识别准确率低下。此外由于景深运动,远端海参作为小目标常常未被识别成功。为解决上述问题,该研究提出一种基于改进SSD网络的海参目标检测算法。首先通过RFB（Receptive Field Block）模块扩大浅层特征感受野,利用膨胀卷积对特征图进行下采样,增加海参细节、位置等信息,并结合注意力机制,对不同深度特征进行强化,将计算得出的权重与原特征信息相乘以此获得特征图,使结果包含最具代表性的特征,也抑制无关特征。最后实现特征图融合,进一步提升水下海参的识别精度。以实际拍摄的视频进行测试验证,在网络结构层面上,对传统算法进行改进。试验结果表明,基于改进的SSD网络的海参目标检测算法的平均精度均值为95.63%,检测帧速为10.70帧/s,相较于传统的SSD算法,在平均精度均值提高3.85个百分点的同时检测帧速仅减少2.8帧/s。与Faster R-CNN算法和YOLOv4算法进行对比试验,该研究算法在平均精度均值指标上,分别比YOLOv4、Faster R-CNN算法提高4.19个百分点、1.74个百分点。在检测速度方面,该研究算法较YOLOv4、Faster R-CNN算法分别低4.6帧/s、高3.95帧/s,试验结果表明,综合考虑准确率与运行速度,改进后的SSD算法较适合进行海参智能捕捞任务。研究结果为海参智能捕捞提供参考。     

基于深度学习与激光点云的橡胶林枝干重建及参数反演

树木的几何建模在林木性状评价、森林动态经营管理与可视化研究中具有重要意义。现今,从激光雷达（Light Detection And Ranging,LiDAR）数据中重建树体三维模型并精准获取林木空间枝干结构参数是数字林业发展的必然趋势。该研究提出了一种深度学习与计算机图形学相融合的树木骨架重建与参数反演方法。该方法以PR107、CATAS 7-20-59、CATAS 8-79三个品种的橡胶树为实验对象,首先,采用背包移动激光雷达获取三个橡胶树品种的样地数据,并通过体素剖分和数据增广策略来构建橡胶树训练样本集。其次,构造由四层特征编码层和特征解码层所组成的点云分类深度学习网络,并包含优化的PointConv模块与不同尺度的特征插值模块,以实现在多尺度条件下,全面考虑点云的全局和局部优化特征,引导网络实现枝叶点云的精确分类。最后,面向分类后的枝干点云,运用计算机图形学的空间连通性算法与圆柱拟合策略,重建树木骨架模型,并自动解决叶子点云与对应的一级枝干归属问题,进而在叶团簇尺度下开展对单株树的精细描述与参数反演。通过对三块橡胶树测试样地的验证和与实测值的比对表明,该研究提出的深度学习网络枝叶分类总体准确率在90.32%以上。骨架重建与叶团簇分析结果显示,PR107品种橡胶树具有较为发散的树冠、最大的分枝夹角和叶团簇体积;CATAS 7-20-59品种橡胶树冠呈花瓶型,分枝夹角和叶团簇体积较小;而CATAS 8-79品种橡胶树尽管胸径最粗,但不耐寒害处于落叶期导致冠积最小。同时,反演得到的橡胶树一级枝干直径与实测值比对为：决定系数R2不低于0.94,均方根误差（Root Mean Square Error,RMSE）小于3.01 cm;主枝干与一级枝干的分枝角为：决定系数R2不低于0.91,均方根误差RMSE不高于4.94°。同时发现橡胶树一级枝干的直径与对应的叶团簇体积呈正相关分布。该研究将人工智能的理论模型应用于林木的激光点云数据处理中,为林木激光点云的智能化分析与处理提供了新颖的解决思路。