高速电机驱动的空气循环制冷技术研究

空气循环制冷技术是一种常用的制冷技术 ,广泛应用在地面设备空调通风和飞机环境控制系统等诸多领域 ,保障人员的舒适和设备的安全。高速电机驱动的空气循环制冷技术是高速电机驱动技术与空气循环制冷技术结合产生的新型技术构想 ,采用该技术的空气循环系统具有结构简单、节约能源等优点。通过介绍该技术方案的工作原理 ,分析了高速电机驱动技术在空气循环制冷系统中应用的可能性及其实现手段。研究表明 ,高速电机驱动技术将为空气循环制冷技术的发展带来较大的影响     

Are national biodiversity strategies and action plans appropriate for building responsibilities for mainstreaming biodiversity across policy sectors? The case of Finland

The Convention on Biological Diversity's national biodiversity strategies and action plans (NBSAPs) are major mechanisms for mainstreaming biodiversity into national policies. This article examines whether and how the NBSAPs contribute to mainstreaming biodiversity across policy sectors in Finland in order to halt biodiversity loss. We have developed an innovative analytical framework where the concept of responsibility addresses how motivations for mainstreaming can be built, and the concept of social learning outcomes addresses the extent of institutional changes for biodiversity. The Finnish NBSAP processes have been able to build diverse forms of responsibility (liability, accountability, responsiveness and care) in different policy sectors by providing new knowledge, careful process design and developing institutional linkages. Despite pro-biodiversity outcomes in the targeted policy sectors, the responsibilities do not diffuse from the environmental administration to other policy sectors to a sufficient extent. Closing this ‘responsibility gap’ is a key challenge for building effective environmental policies.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

Societal implications of sustainable energy action plans: from energy modelling to stakeholder learning

This article investigates the potential impact of sustainable energy action plans (SEAPs) on local development through a two-step methodology involving participatory planning and quantitative analysis. The first phase relies on a participatory system mapping (PSM) approach and generates a causal structure at the basis of the urban model. In the second phase, we transform the qualitative map into a system dynamic model which evaluates the effect of the SEAP on social, economic and environmental indicators. This methodology was applied to the case of Cascina Municipality (Italy). Through scenario analysis, we show that some indirect feedback can harm the achievement of the 20% emission reduction target. This process allows the local authority and stakeholders to evaluate the impact of emission reduction policies on CO₂ emissions and local development, thereby generating collective learning on the systemic implications of the plan. We show that this method can enhance the ambition of emission mitigation efforts by small towns.     

Developing an interdisciplinary and cross‐sectoral community of practice in the domain of forests and livelihoods

Although significant resources are being spent researching and fostering the relationship between forests and livelihoods to promote mutually beneficial outcomes, critical gaps in understanding persist. A core reason for such gaps is that researchers, practitioners, and policy makers lack the structured space to interact and collaborate, which is essential for effective, interdisciplinary research, practice, and evaluation. Thus, scientific findings, policy recommendations, and measured outcomes have not always been synthesized into deep, systemic understanding; learning from practice and implementation does not easily find its way into scientific analyses, and science often fails to influence policy. Communities of practice (CofPs) are dynamic sociocultural systems that bring people together to share and create knowledge around a common topic of interest. They offer participants a space and structure within which to develop new, systemic approaches to multidimensional problems on a common theme. Uniquely informed by a systems‐thinking perspective and drawing from the scientific and gray literatures and in‐depth interviews with representatives of established CofPs in the natural resource management and development domain, we argue that a well‐designed and adequately funded CofP can facilitate interdisciplinary and cross‐sectoral relationships and knowledge exchange. Well‐designed CofPs integrate a set of core features and processes to enhance individual, collective, and domain outcomes; they set out an initial but evolving purpose, encourage diverse leadership, and promote collective‐identity development. Funding facilitates effective communication strategies (e.g., in person meetings). We urge our colleagues across sectors and disciplines to take advantage of CofPs to advance the domain of forests and livelihoods.     

基于机器视觉的鱼类行为特征提取与分析

近年来,水污染问题备受关注。生物式水质监测成为目前国家环境保护工作的重要任务之一。为准确监测水质污染情况,本文以青鳉鱼(Oryzias latipes)为研究对象,采用非接触式的机器视觉监测技术,提取青鳉鱼的生理特征(呼吸频率)和运动特征(胸鳍和尾鳍的摆动频率),并分析这些特征与水质之间的关系。本文采用支持向量机(Support Vector Machine,SVM)准确提取鱼鳃,并根据鱼鳃呼吸面积大小变化计算出鱼的呼吸频率。基于形态学细化算法提取青鳉鱼骨架,求出胸鳍和尾鳍的摆动频率。结果显示:不同浓度铜离子暴露实验测得的青鳉鱼生理特征和运动特征与实际情况一致;通过对不同铜离子浓度下的毒性实验数据对比,发现了青鳉鱼的生理特征和运动特征会随不同的铜离子浓度发生相应变化,可以作为水质监测的评价标准。     

基于GM-SVR的小样本条件下化工设备可靠性预测

为了准确预测化工设备可靠性趋势，针对化工设备失效寿命数据为小样本的情形，基于灰色估计法与支持向量回归机在小样本数据处理中的优势，建立了失效寿命时间服从三参数威布尔分布的化工设备可靠性模型；结合GM（1，1）和SVR对模型进行参数估计，在压缩机可靠性分析中进行了实例应用，对比分析了最小二乘法、灰色估计法和GM-SVR的估计效果。研究结果表明:GM-SVR对威布尔分布参数的估计精度明显优于最小二乘法和灰色估计法，可以有效地应用于化工设备失效数据为小样本时的可靠性预测。     

绿色全要素生产率省际空间学习效应实证

当前中国资源环境约束日益凸显,依靠全要素生产率成为解决这些问题的共识。然而仅仅依靠全要素生产率不能转变生产方式,关键在于区分绿色与非绿色偏向。研究使用中国省际面板数据,运用面板与动态空间面板模型方法,对比分析绿色全要素生产率、绿色"软技术"、绿色"硬技术"的影响机制。研究发现:1依靠全要素生产率驱动发展需要区别绿色与非绿色偏向;2必须注重绿色全要素生产率对绿色发展的作用及其运行机制;3不同偏向的绿色全要素生产率的省际空间学习效应不同;4绿色"软技术"对绿色发展贡献不充分。这就意味着,在当前中国资源环境约束日趋收紧的背景下,要区别绿色与非绿色全要素生产率的不同作用,否则将不能促进生产方式转变,实现绿色可持续发展。推动绿色可持续发展,一方面需要重视提升绿色全要素生产率的作用,另一方面需要重视绿色管理技术等绿色"软技术"对绿色发展的贡献。提升绿色全要素生产率,既要重视研发投入、人力资本投入,又要考虑技术市场、劳动智力结构、劳动者年龄结构的影响。提升绿色"软技术"的贡献率,改善绿色技术效率变动的吸纳能力。通过绿色全要素生产率的省际空间学习效应,实现中国省际区域间绿色可持续协同发展。此外,在引进吸收国外先进绿色技术时,既要重视对绿色"硬技术"的吸收,又要重视对绿色"软技术"的再学习。总之,"打铁还需自身硬",中国要继续鼓励支持绿色"硬技术"自主研发,巩固提升绿色"硬技术"对绿色发展的贡献。     

提高GPS定位精度的群调度滤波算法*

为解决现行安全生产科技领域中全球定位系统（GPS）/惯性导航系统（INS）融合系统容易产生较大时变误差的关键问题，满足GPS定位信号的精度和信号数据处理时间的要求，提出1种群调度滤波算法，通过实验仿真，分别与模糊算法、机器学习算法及卡尔曼滤波网络算法进行比较研究。结果表明:群调度滤波算法兼顾了定位精度与数据处理时间，具有较大优势，可在航空安全、船舶安全、应急监测、灾后抢险等安全科技领域广泛应用。     

Development of machine learning based prediction models for hazardous properties of chemical mixtures

Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.