沉积物粒度分析在厦门市第四纪环境研究和地层划分对比中的应用

采用MS2000型激光粒度分析仪进行测试，利用计算机粒度分析软件对数据进行整理和计算，绘制出样品的频率曲线、概率累积曲线以及粒度众数位值曲线等图件并进行沉积环境分析。厦门地区第四纪环境演化经历了中更新世同安组(含泥中粗砂、含泥细中砂、砂为主)海积一冲积一更新统上部龙海组(含泥粗中砂、含泥细中砂、含泥粗砂、粗砂、细中砂、含细砾中粗砂、中细砂等)冲积一洪冲积一更新世上部东山组(砂、砂砾、含泥中粗砂、粗砂、泥质中细砂、细砂、中粗砂等)冲洪积及部分海积(粉砂、细粉砂、含碳质泥、粘土、淤泥质粘土、粉砂质粘土等)一全新世长乐组(粉砂，淤泥质粘土、粉砂质粘土、碳质粘土、含砂淤泥质粘土，粉砂质亚粘土、粉细砂亚粘土等)海湾沉积。在解释环境变化的同时，说明粒度变化曲线在一定程度上可以作为地层划分的依据之一，并以此对研究区地层进行了详细划分。     

菏泽市地质灾害现状与防治

菏泽市地质灾害类型主要为地震、地面塌陷、地面沉降、地裂缝、砂土液化、土地盐渍化及地氟病等，其发育与分布受地质构造、水文地质条件的控制以及气象、人类活动等因素的影响。就其分布、危害、致灾原因等做了分析，并提出了防治建议和对策。     

恒星内部电荷屏蔽对β衰变率的影响

针对低能反应和共振跃迁,分析了在恒星内部电荷屏蔽对β衰变率的影响．对共振跃迁的分析是基于核的壳层模型．作为例子,详细计算了电荷屏蔽对56Cr的β衰变的影响．结果表明:在较低温度和高密度下,电荷屏蔽使β衰变率有明显的增加。这对恒星晚期演化和超新星爆发研究可能会有重要的影响。     

赣南淘锡坑钨多金属矿床花岗岩和云英岩岩石特征及云英岩中白云母40Ar/39Ar定年

赣南崇义县淘锡坑钨矿位于南岭东西向构造带东段,属于以黑钨矿石英脉型为主的钨多金属矿床。矿床形成与燕山期中酸性岩浆作用有密切关系,石英矿脉受一组北西向断裂控制,穿切新元古界。本文在淘锡坑钨矿区3个揭露到花岗岩体顶部的中段（206、106、56中段）采集到岩体顶部云英岩样品,利用40Ar/39Ar同位素定年测得云英岩中的白云母的坪年龄分别为152.7 ± 1.5 Ma（206中段）,153.4 ± 1.3 Ma（106中段）,155.0 ± 1.4 Ma（56中段）。与前人用其他方法测定的花岗岩成岩和成矿年龄几乎一致。花岗岩和云英岩全岩稀土元素均具有M型四分组效应和强烈的Eu亏损特征,反映出花岗质岩浆经历了高度分异演化及其岩体结晶晚期流体/熔体相互作用。综合前人在南岭地区同类矿床的研究资料,可知南岭地区160～150Ma成岩成矿作用主要分布于南岭中-东段,可能为大陆边缘弧后岩石圈伸展的构造动力学背景的产物。     

Distribution of the C37 tetra-unsaturated alkenone in Lake Qinghai, China: A potential lake salinity indicator

The alkenone unsaturation index U^K′₃₇ has been applied to reconstruct past temperature changes in both marine and lacustrine systems. However, few studies have addressed whether the relative abundance of the C_37:4 alkenone to the total C₃₇ production (%C_37:4) can reflect surface salinity changes in lacustrine systems. Here we present long-chain C₃₇ alkenone distribution patterns in surface sediments from Lake Qinghai, China. Surface sediments were sampled over a large range of surface salinity changes (1.7-25 g/l) within Lake Qinghai and its surrounding lakes, while temperature differences at these sampling locations should be relatively small. We have found that %C_37:4 varies from 15% to 49% as surface salinity decreases. We tentatively describe this %C_37:4-salinity link with a general linear regression: %C_37:4 = 53.4 (±7.8) − 1.73 (±0.45) × S (n = 28, r² = 0.62), although step-wise %C_37:4 changes in response to salinity variation may exist. U^K′₃₇ values vary between 0.10 and 0.16 at these sites and the inferred range of lake water temperature changes is ∼2-3 °C, suggesting that U^K′₃₇ largely reflects temperature signal across a large salinity range, consistent with previous findings that U^K′₃₇ can indicate temperature changes over a large diversity of environmental settings. We have also found that U^K′₃₇ values are correlated with salinity changes (r² = 0.4), and thus cannot exclude potential temperature effect on %C_37:4 and salinity effect on U^K′₃₇ in this study. However, even extreme estimates of temperature differences within the lake are still unable to explain the observed %C_37:4 changes. We therefore suggest that %C_37:4 could be used to infer past lake salinity changes at a regional scale.     

Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system

We describe empirical results from a multi-disciplinary project that support modeling complex processes of land-use and land-cover change in exurban parts of Southeastern Michigan. Based on two different conceptual models, one describing the evolution of urban form as a consequence of residential preferences and the other describing land-cover changes in an exurban township as a consequence of residential preferences, local policies, and a diversity of development types, we describe a variety of empirical data collected to support the mechanisms that we encoded in computational agent-based models. We used multiple methods, including social surveys, remote sensing, and statistical analysis of spatial data, to collect data that could be used to validate the structure of our models, calibrate their specific parameters, and evaluate their output. The data were used to investigate this system in the context of several themes from complexity science, including have (a) macro-level patterns; (b) autonomous decision making entities (i.e., agents); (c) heterogeneity among those entities; (d) social and spatial interactions that operate across multiple scales and (e) nonlinear feedback mechanisms. The results point to the importance of collecting data on agents and their interactions when producing agent-based models, the general validity of our conceptual models, and some changes that we needed to make to these models following data analysis. The calibrated models have been and are being used to evaluate landscape dynamics and the effects of various policy interventions on urban land-cover patterns.