翡翠评估价的量化探索

由于翡翠评估本身具有现实性、市场性、预测性等特性,传统的评估方法过于依赖评估人员的经验,主观随意性大,难以保证评估结果的客观、科学、公正.针对翡翠价格与其质量因素之间的复杂、不确定关系的特性,我们提出利用特尔斐法来判定各权重之间关系,从而利用数学模型计算出各质量因素之间的权重,以减少由于评估人员主观因素造成评估结果的偏差,使评估人员运用此方法能够迅速、准确、公正的对翡翠估价前期的数据进行量化处理.     

小尺度范围内植被类型对土壤呼吸的影响

2005~2007年用红外气体分析仪在太原天龙山自然保护区对油松、草地和灌木地的土壤呼吸进行了测定.结果表明,3个样地的土壤呼吸具有明显的季节变化特点,夏秋季高、冬春季低.土壤呼吸的年(3~12月)平均值分别为(3.58±2.50)、(3.82±2.75)和(4.42±3.38)μmol.(m2.s)-1(p0.05).3~12月土壤CO2释放量在854.9~1 297.2 g.(m2.a)-1之间,年间和样地之间的土壤CO2释放量均无显著差异.3个样地土壤呼吸与土壤温度的指数方程均为极显著(p0.01),R2值在0.61~0.81之间;Q10值在2.60~4.50之间,R10值在1.70~3.02μmol.(m2.s)-1之间.3种植被条件下土壤呼吸与土壤水分的关系均不显著(p0.05),最大的R2值仅0.12;但是,用土壤温度10℃时的数据进行分析时,土壤呼吸与土壤水分的关系显著(p0.05).4个双变量复合关系方程的R2值大部分在0.7以上,最高达0.91.     

Debt for brands: tracking down a bias in financing photovoltaic projects in Germany

What kinds of PV project configurations do lenders prefer to finance? Recent developments in the field of renewable energy project finance have reinforced the need for investigation, as fundraising has become more challenging and project evaluation by banks more demanding. To contribute to the limited research in this field, we focus on photovoltaic projects and report from an Adaptive Choice-Based Conjoint experiment with German experts in project finance. We find a bias which we call “debt for brands”. Simulations reveal that debt investors prefer projects with premium brand technology (modules, inverters) to low-cost technology. Although we assumed that lenders prefer projects with the highest Debt Service Cover Ratio (DSCR), they favor projects with lower DSCR, as long as those projects include premium brand technology. We find that, if premium brands were engaged, lenders would also choose projects with higher risk. Our findings have implications for renewable energy project finance in practice and research.     

国内安全生产监管体系的数学模式分析

要实现安全生产监管的全面化、科学化、规范化,必须建立安全生产监管体系。笔者借鉴安全评价的事故树分析方法和安全检查表法,根据安全生产监管体系的众多因素,提出安全监管"体系树",计算出资金保障、法律法规、机构建设、监管人员理论水平和业务能力、自身修养等在体系中的重要程度排序。用"重要度比例计算"方法进行验证,获得相似的结果。安全监管"体系树"为各地区的安全监管体系建设提供参考。     

(火用)分析法在企业清洁生产实践中的应用

阐述清洁生产、节能降耗与可持续发展的关系,介绍一种比传统热能分析方法更加全面的、将能量的"质"与"量"相结合的能量评价法--(火用)分析法,阐明了(火用)的概念及基本分析原理、分析方法,并举例采用"黑箱-灰箱"模型进行能量(火用)分析,提出将(火用)分析法应用于企业清洁生产的实践中,帮助企业找出真正的耗能点,以采取有效措施达到节能降耗的目的.     

小风条件下大气扩散模式发展回顾研究

由于小风对污染事件的潜在影响,以及与中等风速和大风条件相比边界层结构、湍流和扩散的特殊性,常规的稳态高斯烟流模式在这种条件下具有局限性,建立适用于小风条件的大气扩散模式具有重要的理论和实际意义。本文回顾了近年来小风条件下大气扩散模式的建立和发展,从较简单的解析模式到复杂的数值模式,并对不同模式的参数化方案、与示踪物实验的比较结果、适用性和不足之处进行了总结。综合考虑不同模式的优劣,在应用方面有利于根据实际情况选择合适的模式,在模式开发方面,有助于建立更加完备的大气扩散模式。     

Evaluating the Impacts of Environmental Flow Alternatives on Reservoir and Recreational Operations Using System Dynamics Modeling

Providing environmental flows is increasingly a management obligation in many water resource systems. Evaluating the impacts of environmental flow alternatives on other water uses in a basin can be a challenge, especially when collaborating with stakeholders. We demonstrate the use of system dynamics (SD) modeling to assess the impacts of four environmental flow alternatives in the Rio Chama, New Mexico. The model was developed to examine impacts of each alternative on reservoir storage and releases, hydropower production and revenue, and whitewater boating access. We simulated each alternative within a stochastic framework in order to explicitly incorporate hydrologic uncertainty into the analyses. The environmental flow alternatives were developed at a collaborative workshop of geomorphology, hydrology, and ecology experts. Results from the model indicate that the proposed flow recommendations on the Rio Chama will generally decrease annual reservoir storage, increase median flows, and have minimal impacts on hydropower production and whitewater rafting on the system. The Rio Chama case study is a promising example of how SD modeling can be used in the early stages of environmental flow studies and why it is compatible with collaborative modeling.     

People,protected areas and ecosystem services: a qualitative and quantitative analysis of local people's perception and preferences in Côte d'Ivoire

The long‐term integrity of protected areas (PAs), and hence the maintenance of related ecosystem services (ES), are dependent on the support of local people. In the present study, local people's perceptions of ecosystem services from PAs and factors that govern local preferences for PAs are assessed. Fourteen study villages were randomly selected from three different protected forest areas and one control site along the southern coast of Côte d'Ivoire. Data was collected through a mixed‐method approach, including qualitative semi‐structured interviews and a household survey based on hypothetical choice scenarios. Local people's perceptions of ecosystem service provision was decrypted through qualitative content analysis, while the relation between people's preferences and potential factors that affect preferences were analyzed through multinomial models. This study shows that rural villagers do perceive a number of different ecosystem services as benefits from PAs in Côte d'Ivoire. The results based on quantitative data also suggest that local preferences for PAs and related ecosystem services are driven by PAs' management rules, age, and people's dependence on natural resources.     

Influence of the Haizhou Open Pit Coal Mine on the atmospheric flow over Fuxin, China

The influence of the Haizhou Open Pit Mine on the atmospheric flow in nearby Fuxin City in China was analyzed with the aid of the steady-state Navier-Stokes equations. The finite element method was used to obtain numerical solutions to these equations. The results showed that the Haizhou Open Pit Coal Mine contributes to the turbulent flow in the Fuxin City and its surroundings. However,when compared with the climatic effects, the open pit mine has a relatively small impact on the atmospheric flow over Fuxin.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.