“双创”背景下饲料企业经营主体多维经营研究

随着时代的变迁和世界局势的发展,创新创业逐渐成为了当前的发展新势态和经济发展新引擎。在此背景下,饲料行业需要与时俱进,深入实施创新,驱动发展,促进企业结构调整,优化产能和经营,以实现可持续发展的目的。本文紧密结合“双创”的深刻涵义,就饲料企业经营主体的经营现状及面临的问题展开分析,对企业的多维经营进行探索与研究,提出了一系列具体的对策与思路。     

互联网背景下的食用菌产业现状和机遇及发展对策

介绍了我国互联网发展背景下食用菌产业发展现状,给出了食用菌产业在转变经济发展方式中所面临的问题和困难,并对整个产业发展的源动力和发展路径进行了分析。在此基础上,采用数学模型对食用菌的物流成本进行了核算;在互联网飞速发展的条件下,食用菌产业可以尝试订单生产模式等一些新型的商业来积极寻求转型升级,整合互联网资源,共同应对机遇和挑战,从而达到合作共赢、共同发展的目标。     

Impact of the COVID-19 pandemic on agricultural exports

The COVID-19 outbreak has shocked the world's economies in the year of 2020. As this pandemic spreads around the globe, many experts feared that the global food supplies might start running short, especially if supply chains were disrupted. COVID-19 also added to the uncertainty of the business operation. Therefore, it is critical to understand how COVID-19 pandemic affected global food supply and market. In this study, we investigate the impact of COVID-19 on agricultural export companies in China using a unique firm-level survey data. We found that although on average agricultural businesses experienced declines in the exports, exports of some agricultural products especially grain and oil held strong and even increased, implying the essential demand for staple food during the pandemic. Not surprisingly, exports of medicinal herb also increased significantly during the pandemic. However, exports of goods such as edible fungus and horticultural products sharply decreased. Our results also showed that in general, impact of COVID-19 on smaller firms was more severe than that on larger firms. The results of this study can provide useful guidance and implications for agricultural businesses and policy makers on their COVID-19 mitigation efforts to navigate this global pandemic.     

绿色经济体系下饲料企业经济模式转变研究

近年来,绿色经济成为席卷全球的潮流。绿色经济带给经济社会的改变将是全面而深刻的,饲料行业也不会例外。绿色经济既会给饲料行业带来落后产能淘汰、产业结构升级的机遇,也会带来利润下降、承包上升的短期阵痛。饲料企业想要抓住机遇、迎接挑战,就必须尽快转变当前的经济模式,建立绿色企业文化,加大研发投入,调整生产模式,加强上下游产业链的选择和配合,如果遇到资金困难,可以利用绿色金融进行融资。     

基于BP神经网络的人机联合率定SWMM研究

为解决雨洪管理模型(SWMM)在率定过程中参数复杂、过程繁琐等问题。以西宁市某地块为例建立SWMM模型,利用Morris筛选法进行参数灵敏度分析,并根据灵敏度分析的结果进行人工率定;另外利用BP神经网络对模型进行率定,并结合参数灵敏度对其进行优化。对3种率定方案进行分析,结果表明:水文水力模块参数的相对灵敏度基本一致,其中灵敏度较大的参数为子汇水区面积(Area)、不透水率(Imperv)和不透水区洼地蓄积量(Destore-Imperv),并且不同降雨条件下模型参数的灵敏度存在差异。经过优化后的BP神经网络参数率定方法的模型模拟效果最好,纳什系数最大。结合灵敏度优化BP神经网络的人机联合率定方法一方面能提高BP神经网络率定的准确性,另一方面又能提高传统人工率定的效率。     

农村电商发展背景下高职商务英语人才教育与培养路径

随着国家对农村市场的重视和乡村振兴战略的提出，农村电子商务也如雨后春笋般不断诞 生。农村电子商务的不断发展为高职商务英语专业人才提供了新的机遇和挑战。本文对传统高职商 务英语人才的培养现状进行分析，并对农村电商新环境下的高职商务英语专业人才教育与培养的路 径进行了探索和建议。     

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。     

Prediction of baking results from farinograph measurements by using stepwise linear regression and artificial neuronal networks

In an effort to extract additional data from farinograph experiments a model was developed to simulate the measurements and correlate the parameters of the model with results from baking tests. This additional information can be used in bakeries to predict the baking properties of the flours and adjust the recipes to maintain a constant product quality. For this eight different flours were characterized with a farinograph and 13 different results from baking experiments. An approach with five nonlinear differential equations was able to model the farinograph measurements very well (average R² = 0.995 ± 0.005). While a stepwise multilinear regression only showed weak correlations in cross validation between a single parameter of the model and the baking volume (R² = 0.745) and the volume yield (R² = 0.796) respectively, the artificial neuronal network was more successful. For the baking weight (R² = 0.926), the dough yield gross (R² = 0.909) and net (R² = 0.913) strong correlations were found. A good correlation for the baking volume (R² = 0.853) was also determined, while the volume yield showed comparable results to the linear regression (R² = 0.792).     

Artificial neural network modeling of microbial community structures in the Atlantic Forest of Brazil

Microbial communities vary across the landscape in forest soils, but prediction of their biomass and composition is a difficult challenge due to the large numbers of variables that influence their community structures. Here we examine the use of artificial neural network (ANN) models for extraction of patterns among soil chemical variables and microbial community structures in forest soils from three regions of the Atlantic Forest of Brazil. At each location, variations in soil chemical properties and FAME profiles of microbial community structures were mapped at 20 × 20 m intervals within 10 ha parcels. Geostatistical analyses showed that spatial variability in soil physical and chemical variables could be mapped at scale distances of 20 m, but that FAME profiles representing the microbial communities were highly variable and had no spatial dependence at the same scale in most cases. RDA analysis showed that FAME signatures representing different microbial groups were positively associated with soil pH, OM, P and base cations concentrations, whereas microbial biomass was negatively associated with the same environmental factors. In contrast, ANN models revealed clear relationships between microbial community structures at each parcel location, and generated verifiable predictions of variations in FAME profiles in relation to soil pH, texture, and the relative abundances of base cations. The results suggest that ANN modeling provides a useful approach for describing the relationships between microbial community structures and soil properties in tropical forest soils that were not able to be captured using geostatistical and RDA analyses.     

基于WT-CNN-LSTM的溶解氧含量预测模型

溶解氧（Dissolved oxygen, DO）含量是影响水产养殖产量的重要因素之一，具有时序性、不稳定性和非线性等特点，且其影响因子过多、存在复杂的耦合关系，难以实现精准预测。针对传统长短时记忆神经网络（Long short term memory, LSTM）预测模型易引入冗余数据，且在训练过长序列时会出现梯度消失现象，从而不能捕捉因子间长期的依赖性问题，提出了基于小波变换（Wavelet transform, WT）、卷积神经网络（Convolutional neural network, CNN）和LSTM的溶解氧含量预测模型。首先，使用WT降低数据噪声；然后，使用CNN深度挖掘各变量之间的潜在关系；最后，利用LSTM的时序性预测2h后的水产养殖溶解氧含量。结果表明，本文提出的WT-CNN-LSTM模型预测效果良好，其平均绝对误差、均方根误差和决定系数分别为0.138、0.229和0.954，比传统LSTM模型分别优化了28.87%、21.03%和4.61%。