Seasonal variation and principle of cyanobacterial biomass and forms in the water source area of Chaohu City,China

We investigated seasonal variations in cyanobacterial biomass and the forms of its dominant population(M. aeruginosa) and their correlation with environmental factors in the water source area of Chaohu City,China from December 2011 to October 2012. The results show that species belonging to the phylum Cyanophyta occupied the maximum proportion of phytoplankton biomass,and that the dominant population in the water source area of Chaohu City was M. aeruginosa. The variation in cyanobacterial biomass from March to August 2012 was well fitted to the logistic growth model. The growth rate of cyanobacteria was the highest in June,and the biomass of cyanobacteria reached a maximum in August. From February to March 2012,the main form of M.aeruginosa was the single-cell form; M.aeruginosa colonies began to appear from April,and blooms appeared on the water surface in May. The maximum diameter of the colonies was recorded in July,and then gradually decreased from August. The diameter range of M. aeruginosa colonies was 18.37–237.77 μm,and most of the colonies were distributed in the range 20–200 μm,comprising 95.5% of the total number of samples. Temperature and photosynthetically active radiation may be the most important factors that influenced the annual variation in M. aeruginosa biomass and forms. The suitable temperature for cyanobacterial growth was in the range of 15–30°C. In natural water bodies,photosynthetically active radiation had a significant positive influence on the colonial diameter of M.aeruginosa(P0.01).     

A nonlinear multiparameter prestack seismic inversion method based on hybrid optimization approach

Multiparameter prestack seismic inversion is one of the most powerful techniques in quantitatively estimating subsurface petrophysical properties. However, it remains a challenging problem due to the nonlinearity and ill-posedness of the inversion process. Traditional regularization approach can stabilize the solution but at the cost of smoothing valuable geological boundaries. In addition, compared with linearized optimization methods, global optimization techniques can obtain better results regardless of initial models, especially for multiparameter prestack inversion. However, when solving multiparameter prestack inversion problems, the application of standard global optimization algorithms maybe limited due to the issue of high computational cost (e.g., simulating annealing) or premature convergence (e.g., particle swarm optimization). In this paper, we propose a hybrid optimization-based multiparameter prestack inversion method. In this method, we introduce a prior constraint term featured by multiple regularization functions, intended to preserve layered boundaries of geological formations; in particular, to address the problem of premature convergence existing in standard particle swarm optimization algorithm, we propose a hybrid optimization strategy by hybridizing particle swarm optimization and very fast simulating annealing to solve the nonlinear optimization problem. We demonstrate the effectiveness of the proposed inversion method by conducting synthetic test and field data application, both of which show encouraging results.     

Experimental study of the mechanical properties of expansive soil with added nanomaterials

The main purpose of this paper is to describe ways to improve the microstructure of expansive soil by adding nanomaterials. Mechanical tests were done to explore the changes in shear strength and compression index of expansive soil that was modified by adding different amounts of two kinds of nanomaterials (nano-alumina and nano-silica). The test results show that adding 1.2% nano-alumina and about 2% nano-silica to expansive soil provides the optimal compression index. The test results show that adding 1.2% nano-alumina and about 1.5% nano-silica to expansive soil provides the optimal unconfined compression stress. Scanning electron microscopy of the microstructure of expansive soil modified by nanomaterials provided a deeper understanding of the effects of nanomaterials on improving expansive soil.     

Do sedentary behavior and physical activity spatially cluster? Analysis of a population-based sample of Boston adolescents

Sedentary behavior and lack of physical activity are key modifiable behavioral risk factors for chronic health problems, such as obesity and diabetes. Little is known about how sedentary behavior and physical activity among adolescents spatially cluster. The objective was to detect spatial clustering of sedentary behavior and physical activity among Boston adolescents. Data were used from the 2008 Boston Youth Survey Geospatial Dataset, a sample of public high school students who responded to a sedentary behavior and physical activity questionnaire. Four binary variables were created: (1) TV watching (>2 h/day), (2) video games (>2 h/day), (3) total screen time (>2 h/day); and (4) 20 min/day of physical activity (≥5 days/week). A spatial scan statistic was utilized to detect clustering of sedentary behavior and physical activity. One statistically significant cluster of TV watching emerged among Boston adolescents in the unadjusted model. Students inside the cluster were more than twice as likely to report >2 h/day of TV watching compared to respondents outside the cluster. No significant clusters of sedentary behavior and physical activity emerged. Findings suggest that TV watching is spatially clustered among Boston adolescents. Such findings may serve to inform public health policy-makers by identifying specific locations in Boston that could provide opportunities for policy intervention. Future research should examine what is linked to the clusters, such as neighborhood environments and network effects.     

基于BP神经网络的溶洞规模预测及应用

复杂岩溶地区的溶洞发育规模受地质构造、地区岩性、地下水动力系统等多种因素的影响,具有高度复杂性和非线性的特征。通过对岩溶区溶洞的赋存规律研究,确定影响溶洞发育规模的控制因素进行定量处理,收集已探明溶洞的样本数据。为克服已有研究对溶洞发育规模定性描述的模糊性,文章利用BP（Back Propagation）神经网络对自组织、自适应特性对数据样本的非线性关系揭示的能力,实现对溶洞发育规模的预测,并基于MATLAB实现BP神经网络结构的设计、训练、预测,其结果表明:BP神经网络模型对溶洞规模预测的精度高、收敛性能好。      

Identifying the determinants of access to agricultural credit in Southern Punjab of Pakistan

Based on the survey data of 250 farmers from the Multan district of Southern region of Punjab, Pakistan this study aims to empirically examine the determinants of access to agricultural credit. This study used the probit model to analyze the data. The results revealed that formal education, farm size, level of farm mechanization, farm revenue and landholding size positively and significantly influenced access to agricultural credit while the age of the farmer’s, distance, and off- farm income negatively and insignificantly influenced farmer’s accessibility to agricultural credit. The findings of the current study offer a policy guideline to streamline national policy on agricultural finance. This study also recommends that ZaraiTaraqiati Bank (ZTBL) and other Commercial Banks should improve their agricultural credit schemes to fulfil the diversified needs of small farm holders.

     

黔中隆起北缘五峰-龙马溪组页岩元素地球化学特征及其地质意义

为探讨黔中隆起北缘五峰—龙马溪组富有机质页岩的沉积环境和物源区背景,对区内两口井的样品进行了有机碳含量、微量元素和稀土元素地球化学测试和分析.结果表明,研究区五峰—龙马溪组页岩在沉积时以贫氧—厌氧环境为主,富氧环境也偶尔存在,且龙马溪组沉积水体还原程度比五峰组高.La_N/Yb_N值的变化反映沉积速率为:龙马溪组下部 < 五峰组 < 观音桥段,表明五峰—龙马溪组页岩沉积时存在沉积速率先变快后变缓的过程.Mo/TOC值显示五峰组页岩沉积时盆地滞留程度较强,进入志留纪相对减弱,盆地滞留程度对有机质富集的正向影响可能不大,海平面升降造成的氧化还原条件以及生产力条件的变化或许控制了有机质的富集.微量和稀土元素组合及比值特征,反映研究区五峰—龙马溪组沉积时的原始物质来自上地壳,五峰组沉积时存在混合物源,而龙马溪组沉积时物源相对单一,总体上源岩以长英质（花岗岩）为主.源区构造背景以主动大陆边缘为主,受到一定热液活动的影响,同时也表现出一定的大陆岛弧构造背景的特征.