STUDY ON THE HALF-SIB FAMILY SELECTION FOR THE SUPERIOR TREE OF SCOTS PINE

Investigation and rescarches are carricd out in MaocrshanForcst Farm attached to Northeast Forcstry University in 1988. Comparedwith the contrast, 29 superior familics and 21 inferior families areselected from 112 families of half sib and open pollination of seven-year old scots pine stands by using multicharacters correlations analysis and variance analysis (ANOVA) as well as principal component index (PCI) selection. This index selection which collects multicharacters information has more advantage than monocharacter selection. It is very important to select fine famiIies for buildingthe first generation seed orchard and changing from trial stand toseeding orchard.

Trends in per capita ecological footprint and biocapacity in China 1949-2013

This paper proposes two concepts: the ecological footprint component index(EFCI) and the biocapacity component index(BCCI), based on the ecological footprint(EF) and Shannon entropy approaches. Per capita EFCI and BCCI in China 1949-2013 are analyzed using empirical mode decomposition(EMD). Nonlinear models of per capita EFCI and BCCI in China 1949-2013 are presented and their cycles and predictions from 2014 to 2023 are analyzed. The results over the last 65 years show:(1) EFCI in China has increased constantly with fluctuations, while BCCI has slowly decreased. Their annual change rates are 2.81% and-1.26%, respectively. The increasing EFCI indicates a gradual improvement in China's sustainable development potential; the decreasing BCCI indicates severe environmental and population challenges.(2) The cycles of per capita EFCI have periods of 5.4 and 16.3 years, while cycles of per capita BCCI have periods of 3.6, 13,and 21.7 years. The predictive models indicate that EFCI will first decrease, reaching 0.02725 in2014, and will subsequently increase to 0.03261 in 2021. BCCI will increase, reaching 0.01365 in2014 and 0.01541 in 2022. EFCI and BCCI will reach 0.03037 and 0.01537, respectively, in 2023.Policymakers should ensure that the EFCI and BCCI increase in 2023.

IMPROVED COVARIANCE DRIVEN BLIND SUBSPACE IDENTIFICATION METHOD

An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.