羧基改性阴极对微生物电合成系统产乙酸性能的影响机制

微生物电合成系统(microbial electrosynthesis systems,MESs)可利用微生物将二氧化碳转化为有价化合物,有望实现温室气体的资源化利用,然而,其合成效率仍需进一步提高.本研究通过电化学还原重氮盐反应将特定的官能团—COOH接枝到碳布电极表面,探究改性阴极对于MESs性能的影响.结果发现,经—COOH改性的阴极材料亲水性显著提高,而循环伏安扫描电流变弱. MESs在启动阶段性能差异最大,运行48 h,改性组CA-H、CA-M、CA-L的产氢速率是CK的21. 45、28. 60和22. 75倍;运行120 h,CA-H、CA-M和CA-L的乙酸累积浓度是CK的2. 01、2. 43和1. 44倍. MESs运行324 h后,各阴极的电化学活性无明显差异,生物膜蛋白量无明显差异(～0. 47 mg·cm~(-2)).阴极生物膜的群落结构分析发现,属水平上由Acetobacterium、norank_p_Saccharibacteria和Thioclava占据主导,总相对丰度占到59. 6%到82. 1%;各阴极之间产乙酸功能菌Acetobacterium的相对丰度差别不大(31. 3%～40. 1%),而消耗乙酸的norank_p_Saccharibacteria属在CA-H、CA-M、CA-L和CK的相对丰度分别为:16. 1%、24. 6%、31. 1%和37. 5%.羧基改性阴极对MESs的启动阶段影响较大,可为MESs的快速启动提供新的思路.     

笔谈:食物浪费

食物浪费是我们长期高度关注和社会发展迫切需要解决的问题,如何正确认识食物浪费并尽量减少食物浪费是研究的重点。文章认为食物浪费是一面折射社会文明的多棱镜,需要从多角度认识食物浪费产生的原因及后果,谨防减少食物浪费的努力变成更大的浪费,减少食物浪费要多管齐下,既要节约,又要推进健康饮食文化,借鉴国外研究方法和经验,填补中国食物浪费数据空白,构建资源智慧型食物系统。     

A systems approach to extraordinarily major coal mine accidents in China from 1997 to 2011: an application of the HFACS approach

This study aimed to provide a greater understanding of the systemic factors involved in coal mine accidents and to examine the relationships between the contributing factors across all levels of the system. Ninety-four extraordinarily major coal mine accidents that occurred in China from 1997 to 2011 were analyzed using the human factors analysis and classification system (HFACS). The empirical results showed that the frequencies of unsafe behaviors, inadequate regulation and failure to correct hidden dangers were the highest among five levels, 14 categories and 48 indicators, respectively. The odds ratio technique was applied to quantitatively examine the relationships between contributing factors. Various statistically significant associations were discovered and should receive greater attention in future attempts to develop accident measures. In addition, several strategies concerning the main contributing factors and routes to failure are proposed to prevent accidents from reoccurring in an organization.     

Exploring relationships between observed activation rates and functional attributes of lane departure prevention

Objective: Drivers’ use of lane departure warning and prevention systems is lower than use of other crash avoidance technologies and varies significantly by manufacturer. One factor that may affect use is how well a system prevents unintended departures. The current study evaluated the performance of systems that assist in preventing departures by providing steering or braking input in a 2016 Chevrolet Malibu, 2016 Ford Fusion, 2016 Honda Accord, and 2018 Volvo S90. These vehicles were selected because a prior observational study found that the percentage of privately owned vehicles that had lane departure prevention systems turned on varied among these 4 automakers.

Method: In each vehicle, a test driver induced 40 lane drifts on left and right curves by steering the vehicle straight into the curve so that vehicles departed in the opposite direction and 40 lane drifts on straightaways by slight steering input to direct the vehicle to left and right lane markers.

Results: Vehicles from automakers with higher observed lane departure prevention use rates (Volvo, Chevrolet) featured systems that provided steering input earlier and more often avoided crossing lane markers by more than 35?cm compared to vehicles from automakers with lower observed use rates (Ford, Honda).

Conclusion: The study identified functional characteristics (i.e., timing of steering input, prevention of departures more than 35?cm) of lane departure prevention systems that were strongly associated with observed activation of these systems in privately owned vehicles. Although this relationship does not imply causation, the findings support the hypothesis that functional characteristics of lane departure prevention systems affect their use. Designers may be able to use these results to maximize driver acceptance of future implementations of lane departure prevention.     

Regional air quality prediction models using the knowledge-based system approach

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Cleaner production as a means for effective environmental management

Cleaner production is a strategy to prevent emissions at the source and to initiate a continuous preventive improvement of environmental performance of organizations. In terms of Cleaner production the focus of management should be on prevention rather than on cure in avoiding environmental problems. The inclusion of commitment to prevention in the environmental policy is one prerequisite of the standard ISO 14001. But how can a formalized environmental management system help to start a process of continuous improvement and voluntary self regulation? We conclude from our experience that Cleaner production and ISO 14001 go well together and support each other in helping an organization to really decrease its environmental effect. Analysing environmental management systems introduced on the basis of Cleaner production projects demonstrates, that Cleaner production supplies management and employees with systematic tools to decrease the environmental impact and at the same time save costs from inefficient use of materials and energy and motivates the organization as well by creating awareness throughout the enterprise.     

Options to reduce the environmental impact by eucalyptus-based Kraft pulp industry in Thailand: model description

Kraft pulp industry contributes to several environmental problems, including global warming, acidification, eutrophication, smog, toxicity and the production of solid waste. The objective of this study is to identify options to reduce the environmental pressure caused by Kraft pulp industry in Thailand, and to describe a model that quantifies the environmental impact. The model can be used to evaluate the effects of the options on the environmental impact, and the associated costs. The model includes 14 groups of options to reduce emissions and the production of waste.     

金属体积塑性成形过程数值模拟技术与仿真系统

介绍了金属体积塑性成形过程数值模拟方法、关键应用技术及其仿真系统的构成和国内外相关软件系统 ,对二维、三维有限元网格自动生成技术进行了较为详细的论述 ,综述了金属塑性成形过程优化设计方法、有限体积法以及无网格方法的国内外现状。最后给出了目前存在的问题及其将来应努力的方向     

USE AND MISUSE OF COMPLEX MODELS: EXAMPLES FROM WATER DEMAND MANAGEMENT1

ABSTRACT: The power of computers has increased in recent decades, and one might expect improved management to result because decisions can be made with understanding available only via models. However, there is potential for quite the opposite: poor decisions due to unrealistic model output generated by users without access to appropriate training in the use of models. We discuss and, by reference to water demand models (IWR-MAIN, MWD-MAIN), illustrate three areas in which unintended errors of judgment by untrained personnel may cause difficulty:

• * Attributes of management models; if output from any type of model has no measure of confidence, then results may be over- or undervalued
• * Input data; with complex models, problems here typically will be difficult to detect.
• * Calibration and history-matching (verification); if these steps or data are combined, then users should be less trustful of model output than otherwise.

Because all models have weaknesses and because there always is uncertainty about output from any model, we end with suggestions for coping with complex models. Monitoring programs play a central role in such efforts because they can identify discrepancies between model predictions and actual events and because they can ensure time is available to develop solutions for problems unanticipated in the modeling effort.     

Hierarchical Bayesian space-time models

Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.