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Essential oils are widely used in the food industry as natural food preservatives to extend product shelf life and as flavoring agents. However, not much has been done on their use in soymilk. The aim of the study is to determine the compounds of five essential oils by GC-MS and their effect on fungal growth inhibition and sensory acceptability of soymilk. The components of the essential oils of five spices, namely citronella, basil, cinnamon, eucalyptus and mint were analysed by gas chromatography-mass spectrometry (GC-MS). The minimum inhibitory concentration (MIC) of the essential oils was tested on the fungus Aspergillus flavus 3.4408 on PDA (agar dilution method). Sensory evaluation of soymilk flavored with the essential oils of citronella, basil and mint at different concentrations was done by ten member panelists using a 9-point hedonic scale. The main compound for basil was eugenol 83.26%. Cinnamon contained cinnamaldehyde (97.3%). The main compounds in citronella (Cymbopogon nardus) were limonene (38.51%), citronellal (30.29%). Eucalyptus (Eucalyptus globulus) essential oil mainly contained eucalyptol/cineole (76.70%), and Mint (Mentha arvensis): Menthol 42.72%, Menthone 25.72%. The MICs of citronella, basil, cinnamon, eucalyptus and mint were 5-10 µl/ml, 0.5-1 µl/ml, ≤0.1 µl/ml, >>10 µl/ml and 10-20 µl/ml, respectively. Thus, cinnamon was the most effective in inhibiting fungal growth, while eucalyptus was the least effective. These essential oils improved the soymilk flavor. Mint was the most preferred flavor, followed by citronella and basil. Thus, essential oils especially mint and citronella can be used for improving acceptability of soymilk at low concentration.

In the context of Beijing’s accelerated economic growth, a high urbanization rate and associated urban problems pose challenges. We collected panel data for the period 2005–2014 to examine the relationship between Beijing’s urbanization efficiency and economic growth rate as well as its spatial patterns of dynamic and static urbanization efficiency. Specifically, we developed a comprehensive index system for assessing Beijing’s economic growth rate and urbanization efficiency at the district (county) level. Economic level was selected as an indicator of the economic growth rate. Economic urbanization and consumption levels were selected as indicators of urbanization efficiency. We applied a sequential Malmquist total factor productivity index to estimate the dynamic urbanization efficiency and economic growth rate at the district/country level from 2005 to 2014. We measured Beijing’s static urbanization efficiency in 2014 using a data envelopment analysis model and assessed its spatiotemporal dynamics and urbanization efficiency pattern using a Getis–Ord General Gi index. The results indicated an overall average increase of 1.07% in the total factor urbanization efficiency (TFUE), with an average value of 0.91, while the total factor economic growth rate (TFEE) remained stable at an average value of 0.979. The low TFUE level evidently continues to significantly constrain TFEE. Both TFUE and TFEE levels in the Capital Function Core (CFC) area were significant, exhibiting high inputs and outputs, while these levels in the Urban Function Development (UFD), City Development Zone (CDZ), and Ecological Conservation Development (ECD) areas were below 1 for most periods, strongly indicating inefficient factor allocation. In view of this spatial pattern, TFUE’s regional spatial distribution appears remarkable, showing a decreasing trend from north to south in Beijing, excluding CFC areas. During the period 2005–2014, the CFC area and northeastern Beijing gradually developed into high urbanization efficiency cluster regions. The dominant factors accounting for the difference in total factor productivity indices between TFUE and TFEE were technical change (TC) and scale efficiency change (SEC), and the main factors driving the regional spatial distribution pattern for urbanization efficiency were TC and technical efficiency change (TEC). Accordingly, local governments should promote TC, SEC, and TEC to improve urbanization levels, with optimal strategies entailing strengthening policy support and encouraging investments in technology in UFD, CDZ, and ECD areas. Within Beijing, Dongcheng, Xicheng, Shijingshan, Mentougou, and Yanqing demonstrated effectively balanced static urbanization efficiency levels in 2014, whereas these levels in the city’s remaining 11 districts were not optimal, with extensive development. County governments should therefore promote efforts to reduce input redundancy and improve pure technical efficiency to maintain sustainable and steady development.

Urban vegetation is considered a crucial factor for realizing environmental quality goals and foreseeing sustainable local development in the framework of Local Agenda21 guiding environmental politics. High-resolution image offer abundance function in improving method of urban environment and guideline of evaluation. The method of Object-oriented classification, considered both spectral characters and structural information, can provide high-resolution images' classification precision and reduce large data redundancy compared with the traditional pixel-based classification methods. In this paper, we use object-oriented method to extract different green space based on various urban environmental land use, such as commonality green, street green and so on.. The QuickBird image is segmented to a set of different scale image, we have make many experiment in order to obtain the optimal segmentation of every level, so the image is segmented by scale 6, and then each object is classified into non-vegetation and 3 kinds of vegetation classes based on the nearest neighbour classifier (NN), Due to the effect of mixed pixels, the second segmentation level scale 5 is carried out into the non-vegetation class and several omitted vegetation objects are derived. Finally, we use a GIS mapping data combined with the results of vegetation classification to separate urban vegetation area into different usages (i.e. commonality green, street green...). Through analyse to the result of classification, the area of the vegetation is 597.63 hm2, the urban garden fraction is 25.38%. This method offers a new application approach for classifying the high-resolution remote sensing image and the result of accuracy can reach 98%.