ECOLOGICAL NETWORKS IN AGROECOSYSTEMS: APPROACHES AND APPLICATIONS

● Agricultural intensification reduced the complexity and connectance of soil food webs. ● Agricultural intensification impaired the robustness of pollination networks. ● High connectance in co-occurrence networks indicates efficient nutrient utilization. Complex network theory has been increasingly used in various research areas, including agroecosystems. This paper summarizes the basic concepts and approaches commonly used in complex network theory, and then reviews recent studies on the applications in agroecosystems of three types of common ecological networks, i.e., food webs, pollination networks and microbial co-occurrence networks. In general, agricultural intensification is considered to be a key driver of the change of agroecosystems. It causes the simplification of landscape, leads to the loss of biocontrol through cascading effect in food webs, and also reduces the complexity and connectance of soil food webs. For pollination networks, agricultural intensification impaired the robustness by reducing specialization and enhancing generality. The microbial co-occurrence networks with high connectance and low modularity generally corresponded to high efficiency in utilization of nutrients, and high resistance to crop pathogens. This review aims to show the readers the advances of ecological networks in agroecosystems and inspire the researchers to conduct their studies in a new network perspective.     

新升本院校图书馆随书光盘管理模式比较研究

以许昌学院图书馆为例,对新升本院校图书馆在管理和利用随书光盘时常用的统一管理模式、分散管理模式以及网络管理模式进行比较分析,并对现行网络管理模式中存在的不足进行了深入探讨,提出一些具有可行性的改进措施。     

Prediction of milled rice grades using Fourier transform near-infrared spectroscopy and artificial neural networks

This paper describes a method to rapidly and objectively predict the grades of milled rice according to the surface lipid content (SLC), which was determined by using near-infrared (NIR) spectroscopy. Sixty-six rice varieties were milled to different degrees. Then each sample was graded by a three-member panel. After the NIR spectra for each sample were collected over the wavenumber range of 11,000–4000 cm⁻¹, the SLC of each sample was measured according to the official method. The calibration equations relating the Fourier Transform Near-infrared (FT-NIR) spectra to the measured SLC were developed based on the partial least square (PLS) regression. The best model gave the root mean square error of the prediction (RMSEP) of 0.0248% and the determination coefficients of 0.9905. If the relationships between the grades and the SLC predicted by the developed NIR model were described with the linear and the logarithmic regression equations, the correct prediction percents (CCP) were 75.76% and 83.33%, respectively. When the back propagation artificial neural network (BP-ANN) model was developed to estimate the grades according to SLC, the resultant CCP was 95.45%, indicating that the milled rice grades could be predicted by the proposed BP-ANN model with satisfactory accuracy.     

Glass transition behavior of corn distillers dried grains with solubles (DDGS)

Distillers dried grains with solubles (DDGS), a major co-product from the corn ethanol industry, has high feed value for its chemical composition. The ratio of wet distillers grains (WDG) and condensed distillers solubles (CDS) added during the production process determines the chemical composition of DDGS. Effect of changing this ratio and the influence of moisture content on glass transition behavior of DDGS was studied. Five prediction models were evaluated to explain the glass transition of DDGS. The mixed trends in the result indicated the complex behavior of DDGS particles and revealed that both moisture content and chemical composition impact the glass transition behavior of DDGS. Onset glass transition temperature ranged from 20 to 30 °C depending on the chemical composition. Kwei equation predictions were better than the predictions by Gordon-Taylor equation. The glass transition temperature of DDGS can be mathematically explained by combining the moisture content and chemical components protein, fiber, glycerol, and sugar percent. The artificial neural network (ANN) model gave a better prediction of onset and midpoint glass transition temperature of DDGS and this might be due to the accurate mapping of the interaction between chemical components.     

台湾甘蔗种子发芽力影响因素的研究概况

台湾糖业研究所对甘蔗种子的发芽力开展了一系列研究，结果表明，甘蔗双亲花穗的花粉数量多，产生的种子发芽力就高；高压法养茎产生的种子发芽力高于亚硫酸法；随着冷藏时间的延长，甘蔗种子的发芽力逐渐降低；甘蔗花穗中的种子越重，萌发的实生苗就越多。     

Self-organizing map network for automatically recognizing color texture fabric nature

The method of recognizing color texture brought forth in the present study is to employ unsupervised learning network to automatically recognize the fabric type and the main texture types. Firstly, the color scanner is adopted to extract fabric image which is afterwards saved as the digital image. Secondly,CIE-Lab color model is taken to obtain the feature value and wavelet transform is utilized to display the texture of the fabric image. Thirdly, co-occurrence matrix is employed to figure out the feature values of the texture structure such as angular second moment, entropy, homogeneity, contrast. Finally, self-organizing map (SOM) network is used as the classifier. The experiment result shows that the study can automatically and accurately classify the fabric types (including shuttle-woven fabric, jersey fabric and non-woven fabric) and main texture type of the fabric (such as plain weave, twill weave, satin weave, single jersey, double jersey and non-woven fabric).     

基于粗神经网络的旅游需求预测研究

旅游需求的合理预测是旅游业持续健康的重要前提之一,根据粗糙集理论提出一种旅游需求预测的粗神经网络模型,该模型通过消除噪声提高神经网络预测的精度,有效地克服神经网络算法中知识获取以及推理过程的黑箱性弱点。通过对云南近年入境旅游发展趋势的预测实例分析,验证模型的有效性。     

计算机网络安全课程虚拟实验室建设

以Packet Tracer 5.3.2仿真软件为基础,构建了计算机网络安全虚拟实验室,介绍了Packet Tracer 5.3.2仿真软件提供的网络安全技术;以广域网安全实验为例,从终端安全接入、无线局域网加密技术、路由器身份鉴别、访问控制列表和基于上下文的访问控制等方面论述了广域网安全实验的内容,通过实验拓扑结构的设计和IP地址规划、安全协议的配置和实验结果测试等步骤完成仿真实验;旨在锻炼学生设计、配置和测试网络安全的能力,加深对计算机网络安全理论和概念的掌握和理解。     

新型太阳能光伏照明系统的研究

研究了新型太阳能光伏照明系统,依据蓄电池的自维持时间和最短恢复时间,对太阳能电池与蓄电池的容量进行研究.以ATMEGA8单片机为控制芯片,研究了包括蓄电池过充与过放保护、多挡时控等功能的新型数控太阳能路灯控制系统的软件、硬件实现方法,并对现有LED驱动电路进行了改进,提高了变换效率.     

变压器隔离型DSTATCOM在不平衡系统中的补偿性能

【目的】针对配电网三相不平衡负载补偿问题,研究变压器隔离型配电网静止同步补偿器(DSTAT-COM)的补偿性能。【方法】以变压器隔离型逆变器为DSTATCOM的主电路,建立其数学模型,采用基于旋转坐标变换的方法计算DSTATCOM的补偿参考电流,提出瞬时无功功率分相控制与载波移相SPWM(Cps-SPWM)调制技术相结合的控制策略。在PSCAD/EMTDC环境中,对变压器隔离型DSTATCOM的补偿性能进行仿真研究。【结果】基于旋转坐标变换的补偿电流计算方法,能快速计算各相补偿电流,瞬时无功功率分相控制与Cps-SPWM调制技术相结合的控制策略,能有效地降低系统电流的不对称度,提高系统的功率因数,优化负荷电流的谐波性能。在各种负荷不平衡情况下,变压器隔离型DSTATCOM均具有良好的补偿性能。【结论】变压器隔离型DSTATCOM主电路结构简单,易于分相控制;瞬时无功功率分相控制与Cps-SPWM调制技术相结合的控制策略,能对不平衡负荷实现快速补偿,满足配电网对不平衡负荷的补偿要求。