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.     

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.     

Accident in a French dynamite factory: An example of an organisational investigation

The 27th of March 2003, an explosion caused the death of four employees in a Nitrochimie pyrotechnic plant, at Billy Berclau, in the north of France. Following the accident, the ministry of Ecology and Sustainable Development appointed INERIS to perform an investigation. According to the terms of reference, the investigation would cover technical (origins of the explosion, extent of damages) as well as organisational issues, as defined by SEVESO II safety management system requirements. This paper has a threefold purpose. It intends first to illustrate with an empirical case the current trend in safety auditing and accident investigation, targeting organisational factors, alongside human factors. There are not so many published cases of accidents analysed with an organisational perspective. Secondly, it shows that it is possible to investigate organisational dimensions (through articulation of safety engineering, safety management and human and social sciences) within reasonable time frames and a reasonable amount of resources. By focusing on key actors and asking appropriate questions related to key dimensions, investigating organisational accidents might not necessarily imply spending much more resources than other steps such as damage assessment, chronological construction or identification of technical scenarios, although there are also some prerequisite conditions needed to achieve this. Finally this paper should be seen as a technical communication beyond the pyrotechnic industry.     

Effects of seismic intensity and socioeconomic status on injury and displacement after the 2007 Peru earthquake

Earthquakes are a major cause of displacement, particularly in developing countries. Models of injury and displacement can be applied to assist governments and aid organisations in effectively targeting preparedness and relief efforts. A stratified cluster survey was conducted in January 2008 to evaluate risk factors for injury and displacement following the 15 August 2007 earthquake in southern Peru. In statistical modelling, seismic intensity, distance to rupture, living conditions, and educational attainment collectively explained 54.9 per cent of the variability in displacement rates across clusters. Living conditions was a particularly significant predictor of injury and displacement, indicating a strong relationship between risk and socioeconomic status. Contrary to expectations, urban, periurban, and rural clusters did not exhibit significantly different injury and displacement rates. Proxies of socioeconomic status, particularly the living conditions index score, proved relevant in explaining displacement, likely due to unmeasured aspects of housing construction practices and building materials.     

芳纶网橡胶复合粘弹阻尼器性能研究∗

在橡胶中加入芳纶网制成新型的复合粘弹阻尼器,对其进行疲劳试验、变形相关性试验和频率相关性试验,并研究新型阻尼器的最大剪应力、存储剪切模量和等效阻尼比等指标的性能变化规律。研究结果表明:该新型复合阻尼器力学性能稳定、阻尼器承受的最大剪应力增大、抗疲劳性能良好、该阻尼器的储存剪切模量和等效阻尼比受变形幅值的影响较大,受加载频率影响较小。根据试验屈服点和硬化点,采用Matlab建立双线性和多线性模型,模拟结果与试验结果拟合良好。     

Assessment of the benefits of climate model weights for ensemble analysis in three urban precipitation frequency studies

In hydrology, projected climate change impact assessment studies typically rely on ensembles of downscaled climate model outputs. Due to large modeling uncertainties, the ensembles are often averaged to provide a basis for studying the effects of climate change. A key issue when analyzing averages of a climate model ensemble is whether to weight all models in the ensemble equally, often referred to as the equal-weights or unweighted approach, or to use a weighted approach, where, in general, each model would have a different weight. Many studies have advocated for the latter, based on the assumption that models that are better at simulating the past, that is, the models with higher hindcast accuracy, will give more accurate forecasts for the future and thus should receive higher weights. To examine this issue, observed and modeled daily precipitation frequency (PF) estimates for three urban areas in the United States, namely Boston, Massachusetts; Houston, Texas; and Chicago, Illinois, were analyzed. The comparison used the raw output of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The PFs from these models were compared with the observed PFs for a specific historical training period to determine model weights for each area. The unweighted and weighted averaged model PFs from a more recent testing period were then compared with their corresponding observed PFs to determine if weights improved the estimates. These comparisons indeed showed that the weighted averages were closer to the observed values than the unweighted averages in nearly all cases. The study also demonstrated how weights can help reduce model spread in future climate projections by comparing the unweighted and weighted ensemble standard deviations in these projections. In all studied scenarios, the weights actually reduced the standard deviations compared to the equal-weights approach. Finally, an analysis of the results' sensitivity to the areal reduction factor used to allow comparisons between point station measurements and grid-box averages is provided.