基于战略联盟的企业协同创新模型研究

随着经济的发展,企业边界变得越来越模糊,战略联盟已逐渐成为企业间合作的重要方式。战略联盟的组织结构形式可以分为横向联盟和纵向联盟。通过建立企业协同创新模型对战略联盟行为主体间的协同创新关系进行研究。研究表明:市场机会的识别是确立战略联盟的目标前提,风险分担与利益共享是其价值基础。战略联盟内成员企业的创新动力及创新需求随时间变化呈现下降趋势,伴随任务的完成、目标的实现战略联盟将会最终解散,协同创新终止。     

土壤肥力综合评价研究进展

土壤肥力是土壤诸多基本特性的综合反映，在评价土壤肥力时不能只着眼于个别的肥力因素，需从整体的观点出发，需要评价土壤肥力的综合指标，这个指标既是定性的又是定量的，即土壤肥力的数值化综合评价。详细介绍土壤肥力综合评价的主要研究内容及进展，同时指出其中存在的问题。参27。     

Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting

Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.     

高技术产业演化的生态组织形式及策略选择

根据高技术产业生态位的基本特征指标,分析高技术产业群生态位结构动态演替过程,以生态位宽度为基础深入研究高技术产业与环境资源要素之间互动所形成的生态组织形式。依据生态位的三个描述截面：幅度、纹路、变化性研究高技术产业需求生命周期各阶段所具有的环境变异特性。研究认为：高技术产业生命周期的不同阶段由于市场规模的差异．导致环境所能够提供的资源规模不同,环境所能容纳的产业群密度以及所拥有的生存空间不同;资源丰富程度的差异造成适合环境特性的生命繁衍方式也有所不同。高技术产业在发展过程中应当立足自身实力和优势,分析环境的特点以及产业与环境的协调关系．利用产业的能动性．依据行业发展态势和经济发展态势,构建适合自身的生态位,选择适当的生态策略。     

Hybrid Model Based on Wavelet Decomposition for Electricity Consumption Prediction

The effective supply of electricity is the basis of ensuring economic development and people's normal life. It is difficult to store electricity, as leading to the production and consumption must be completed simultaneously. Therefore, it is of great significance to accurately predict the demand for electricity consumption for the production planning of electricity and the normal operation of the society. In this paper, a hybrid model is constructed to predict the electricity consumption in China. The structural breaks test of monthly electricity consumption in China from January 2010 to December 2016 is carried out by using the structural breaks unit root test. Based on the existence of structura breaks, the electricity consumption data are decomposed into low-frequency and high-frequency components by wavelet model, and the separated low frequency signal and high frequency signal are predicted by autoregressive integrated moving average(ARIMA) and nonlinear autoregressive neural network(NAR), respectively. Therefore the wavelet-ARIMA-NAR hybrid model is constructed. In order to compare the effect of the hybrid model, the structural time series(STS) model is applied to predicting the electricity consumption. The results of prediction error test show that the hybrid model is more accurate for electricity consumption prediction.     

基于系统动力学的黑龙江省农业生态系统能值分析

农业生态系统为人类提供生存和发展的基本产品,构建可以独立核算的农业生态系统模型对科学评价区域农业状况有重大意义.该研究借助系统动力学方法,构建基于能值投入、产出及综合评价性指标的农业生态系统SD模型,对黑龙江省农业生态系统发展状况及时间变化特征进行了分析与预测,同时对系统进行灵敏性测试.结果表明:(1)黑龙江省农业生态系统能值投入及产出均呈持续增加趋势,但系统可持续性能逐年降低;(2)黑龙江省过度依赖种植业及畜牧业,导致区域生产力稳定性的波动降低;(3)区域农业生态系统对自然因素的响应较弱,对人为因素的响应较为明显,可知人为因素对黑龙江省农业生态系统影响较大.该研究为黑龙江省今后农业投入产出结构的调整提供了依据.     

人工构建微生物组对制革废水的氨氮去除及微生物组的对比变化分析

为探究复合人工微生物组对制革废水处理体系中微生物群落的影响,在传统的厌氧/好氧(A/O)污水处理工艺处理制革废水的基础上,投加微生物复合菌形成人工微生物组。利用复合人工微生物组强化废水处理,应用高通量测序技术测定各样品中的细菌16S rRNA V3-V4变异区序列,并对测序数据进行生物信息学分析、Alpha多样性分析以及物种组成分析。结果表明,投加微生物组后,化学需氧量(COD)和氨氮的处理效果得到提升,COD的去除率约为82.60%,氨氮的去除率约为99.47%。高通量测序结果表明,人工投加微生物组使得活性污泥中微生物群落丰度以及多样性提高,污染物降解功能菌占比有所提升,陶厄氏菌属(Thauera)成为其最主要的优势菌属。复合人工微生物组的投加对强化制革废水处理系统有一定潜力。     

R448A和R455A在复叠制冷系统中替代R404A的性能分析

现阶段广泛使用的R404A制冷剂的GWP值（全球变暖潜能值）较高、对环境不友好,为了探究GWP值较低的新型替代制冷剂对制冷系统性能的影响,本文建立了复叠制冷系统的热力学模型,研究了R455A和R448A制冷剂在不同工况下的性能表现,并从低温级蒸发温度、高温级冷凝温度、中间温度,以及高、低温级过冷度等方面与R404A系统进行对比分析。结果表明,在不同工况下,R455A/R23系统的COP（制冷系数）始终最高,R404A/R23系统的COP最小。在相同蒸发温度下,R455A/R23和R448A/R23系统的最佳中间温度低于R404A/R23系统,最佳COP分别比R404A/R23系统高6.77%,5.25%。另外,高温级过冷度增加会使系统COP增大,其中,R404A/R23系统COP的增加幅度最大,R448A/R23系统COP的增加幅度最小,而低温级过冷度的增加则会使系统性能下降。