Propagation of olfactory information within the honeybee hive

Transfer of information about food source characteristics within insect societies is essential to colony-foraging success. The food odor communicated within honeybee hives has been shown to be important for food source exploitation. When successful foragers return to the nest and transfer the collected nectar to hive mates through mouth-to-mouth contacts (trophallaxis), potential recruits receiving these samples learn the food odor by associative learning. The food then becomes rapidly distributed among colony members, which is mainly a consequence of the numerous trophallaxes between hive-mates of all ages during food processing. We tested whether the distribution of food among hive mates causes a propagation of olfactory information within the hive. Using the proboscis extension response paradigm, we show that large proportions of bees of the age groups representing the main worker castes, 4 to 9-day-old bees (nurse-aged bees), 12 to 16-day-old bees (food processor-aged bees), and actual foragers (about 17+ day old bees) associatively learn the food odor in the course of processing food that has been collected by only a few foragers. Results further suggest that the information is shared more or less equally between bees of the three age groups. This shows that olfactory information about the flower species exploited by foragers is distributed within the entire colony and is acquired by bees of all age groups, which may influence many behaviors inside and outside the hive.     

RS-PSO-ELM下腐蚀管道失效压力预测

为提高腐蚀管道失效压力的预测精度并简化其计算过程,提出基于粗糙集(RS)和粒子群算法(PSO)融合极限学习机(ELM)的腐蚀管道失效压力预测模型。通过属性约简提取影响失效压力的关键因素,选用PSO优化ELM的输入权值和隐含层偏差,将归一化的核心指标数据代入计算。结果表明:该模型预测结果与实际值基本一致,与单一ELM模型相比,预测结果的均方差(MSE)降至0.255;与其他蚀管道失效压力评价模型相比,该模型预测结果的绝对误差平均值降至0.32。     

北京市黑臭水体治理的动态遥感监测及影响因素分析

为及时、准确掌握黑臭水体治理进展,基于“北京二号”影像数据和同期的野外综合水体实测数据,采用深度学习算法对黑臭水体进行识别,并引入地理探测器对黑臭水体影响因素进行定量分析。结果表明：基于Faster R-CNN算法的黑臭水体遥感识别,总准确率达到90%左右,短时间内(5~33 h)即可完成北京市建成区黑臭水体的筛查工作;在空间维度上,黑臭水体主要分布在中心城区以外,并在通州区、朝阳区和大兴区较为集中;在时间维度上,专项治理期间(2015—2018年)内,黑臭水体的数量和长度总体趋势都是递减的,但偶尔也有反黑现象;2018年底,在全市建成区范围内,已全面消除黑臭现象;在一年内,第1季度水体环境最好,第2季度次之,第3季度最差,从第4季度开始好转;在北京市大兴区,土壤全氮量(贡献率为32.07%)和周边养殖场排污(贡献率为27.04%)是黑臭水体形成的主要影响因素,高程(贡献率为8%)、土壤类型(贡献率为7.6%)和土地利用类型(贡献率为6.1%)的贡献率较弱。由此可以看出,基于Faster R-CNN算法识别影像中的黑臭水体识别准确率高,可及时、准确地监测城市黑臭水体治理情况,使用地理探测器可定量分析并确定各影响因素的贡献率。本研究成果可为城市黑臭水体的动态监测和治理提供有力的技术支撑。     

The vulnerable do‐gooders: security strategies of German aid agencies

Humanitarian and development agencies have confronted growing insecurity in some of the regions in which they work over the course of the past decade. Consequently, aid agencies are changing their approach to security issues. Compared to aid agencies from other countries, especially the United Kingdom and the United States, German aid agencies have been relatively slow to adopt security measures. In addition, the security measures they have selected differ from one agency to another, even in comparable security contexts. The literature on organisational learning helps to explain these differences. The findings show that external and internal organisational factors influence an aid agency's choice of a particular security measure. The different responses of German aid agencies to the changing security environment demonstrate that security measures are not only influenced by an organisation's identity but also can be a way for them to demonstrate their identity and thereby distinguish themselves from other actors that deliver aid.     

碳中和趋势下数学模拟在污水处理系统中的发展与综合应用

数学模拟技术在污水处理方面被广泛应用,为了系统总结相关技术,本文回顾了污水处理系统中数学模拟技术的发展历程;综述了活性污泥模型(ASM)与机器学习(ML)在水质预测及参数工况优化领域中的应用;重点探究了污水处理系统中温室气体排放模型,以及多目标优化模型在污水处理系统中温室气体排放(GHG)、出水质量(EQI)和运行成本(OCI)的权衡问题;归纳了数学模拟技术在实现污水厂能量自给与资源回收的应用发展.研究结果表明数学模拟技术能准确预测出水水质、快速优化工艺参数、权衡温室气体排放、出水水质与运行成本之间的关系、以及提高资源回收效率等.因此,数值模拟技术可有效指导污水处理工艺的运行优化以及管理,为污水处理行业减污降碳协同增效提供技术支撑.     

基于机器学习的大气PM2.5中金属浓度预测模型的研究

基于2013—2018年哈尔滨市气象数据、大气污染物数据和细颗粒物（PM_2.5）中金属成分数据,采用机器学习方法探索大气PM_2.5中金属浓度预测模型,并选择最优模型进行污染物浓度预测。结果表明,多元线性回归（MLR）、人工神经网络（BP-ANN）、支持向量机（SVM）和随机森林（RF）4种模型中,RF对大气PM_2.5中5种金属［锑（Sb）、砷（As）、铅（Pb）、镉（Cd）、铊（Tl）］的浓度预测效果最佳,在训练集和测试集中表现均较稳定,其中相关系数（r）均＞0.7, 平均绝对误差（MAE）和均方根误差（RMSE）数值较小。RF在大气PM_2.5中金属浓度预测上具有较好的表现,可在缺乏监测和实验数据的情况下,实现对大气颗粒物中金属浓度的快速预测,为全面了解颗粒物中金属污染特征提供数据基础。     

Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of ^131mXe, ¹³³Xe, ^133mXe, and ¹³⁵Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naïve Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.     

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。     

Education for sustainable development – an inter‐disciplinary pilot module for undergraduate engineers and scientists

The coming together of a number of initiatives provided a springboard for an innovative pilot programme in education for sustainable development. For some time problem‐based learning has gathered momentum as an approach to educating professionals. More recently, there has been a burgeoning interest in inter‐disciplinary approaches to the complex societal and environmental issues that face the world as a whole. At the same time, professional engineering institutions have been reflecting on appropriate approaches to the education and development of future engineers. Building on work already undertaken by some of the authors, the Royal Academy of Engineering sponsored an inter‐disciplinary pilot programme in sustainable development for undergraduate engineers and scientists in the University of Manchester. The pilot was innovative not only in its inter‐disciplinary approach to sustainable development but also in its approach to the development of the curriculum. Inter‐disciplinary exercises were designed that enabled a contextual, active, collaborative and cumulative approach to learning. The assessment was also designed to align to the learning approach. Evaluation of the pilot programme suggests that it was well received by the students, and the post‐doctoral researchers who acted as facilitators, and also that there were gains in both understanding of the issues and also in approaches to learning.     

Proposal of Indicators to Assess Urban Sustainability in Brazil

The research related here focuses on municipal situations where problems of sustainability may be defined and tackled on the basis of bottom-up management procedures with the participation of organized society. The aim is to build management models that may be implemented with reasonable administrative effort and cost. Implementation pursues the target of approaching a sustainable situation in the municipality. Environmental parameters for an ideal municipality with undefined geographical location are presented. For each parameter, a set of indicators is developed that can measure the prospect of sustainability. The indicators are defined in terms of numbers or literal concepts according to the possibility of measurement. Examples of management models are presented, which are able to approach the situation defined as sustainable by the indicators. The basic management tool is the learning curve of targeted communities, which is experimentally developed and applied. The indicators are grouped according to their range of applicability.