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.     

Adaptation or conflict? Responses to climate change in water management in Bangladesh

The potential of climate change to impact local conflict and cooperation over natural resources has received relatively little attention. Bangladesh floodplains are highly vulnerable to environmental stresses that are worsening with climate change, and community organisations have to respond to water insecurity − seasonally too little or too much. Two case studies based on action research in contrasting water and climate stressed floodplain environments in Bangladesh investigate local conflicts over water management that worsened when water regimes changed. By overcoming conflicts and improving adaptation for all local actors the cases reveal the importance of local knowledge, innovations in institutions, external facilitation, and incentives provided by disadvantaged groups who contribute towards costs in return for a share in decision making power and better adapted water management. The cases show how community organisations diversified their responsibilities and took up the challenge of water management to address local priorities and overcome conflicts. Without a more flexible and enabling approach, public investments in adaptation are likely to focus on strengthening existing water management infrastructure without understanding local social interactions and complexity. This may strengthen elite dominance and local conflicts if there is no comparable investment in developing robust and fair local institutions.     

INVESTMENT SEQUENCING RECOGNIZING EXTERNALITIES IN WATER DESALTING1

ABSTRACT. The failure to recognize the learning process in new technologies such as desalting may lead to incorrect water resource investment decisions for two reasons. First, to neglect cost reductions stemming from “learning by doing” implies an overestimation of desalting costs. Second, since learning in a particular plant may result in external (learning) benefits to other plants, these may serve as the basis for a subsidy intended to internalize such benefits. Accordingly, the research reported below includes an estimation of learning functions for desalting and the results of a formulation designed to measure external benefits on the basis of these learning functions. These results are then incorporated into a decision framework for water resource investments which recognizes uncertainty in determining optimal timing of desalting construction.     

Associative learning of odor with food- or blood-meal by Culex quinquefasciatus Say (Diptera: Culicidae)

The ability of many insects to learn has been documented. However, a limited number of studies examining associative learning in medically important arthropods has been published. Investigations into the associative learning capabilities of Culex quinquefasciatus Say were conducted by adapting methods commonly used in experiments involving Hymenoptera. Male and female mosquitoes were able to learn a conditioned stimulus that consisted of an odor not normally encountered in nature (synthetic strawberry or vanilla extracts) in association with an unconditioned stimulus consisting of either a sugar (males and females) or blood (females) meal. Such information could lead to a better understanding of the ability of mosquitoes to locate and select host and food resources in nature.     

Song ranging by the dusky antbird,Cercomacra tyrannina: ranging without song learning

In a population of dusky antbirds (Cercomacra tyrannina), less aggressive responses to distance-degraded playbacks than to undegraded playbacks of pair duets show that this tropical suboscine passerine uses sound degradation to range distance from singing conspecifics. This is the first example of song-ranging in a species that does not learn songs, supporting the hypothesis that ranging preceded the song learning that occurs in more recently evolved passerine birds (oscines). Both sexes sing and are able to use song degradation to range distance from singers when their sex-specific song is played back. Received: 27 May 1994/Accepted after revision: 24 March 1996     

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.     

Kinship, familiarity and social status modulate social learning about “micropredators” (biting flies) in deer mice

Animals can learn to recognize and respond to dangerous, threatening factors through either individual or social learning, whereby an individual learns and acquires the defensive behaviors and avoidance responses of another. Here we show that kinship, familiarity, and relative dominance of the interacting individuals affect social learning of defensive responses to micropredators in deer mice, Peromyscus maniculatus. Brief exposure of individual male deer mice to biting flies (stable fly, Stomoxys calcitrans) induced a decrease in pain sensitivity, or analgesia, and active self-burying avoidance responses. The defensive analgesic responses and their fear/anxiety/stress associated correlates facilitate the display of behavioral avoidance responses. Preparatory analgesia and avoidance responses were evident 1–3 days later when the mice were exposed to biting flies that were altered to be incapable of biting but were not displayed to similar-sized non-biting house flies. These anticipatory avoidance and analgesic responses to biting flies were also acquired through social learning—without direct individual aversive experience with biting flies. Fly-naive mice (observers) that witnessed other mice (demonstrators) being attacked by biting flies but themselves were not bitten did not display any avoidance responses. However, when exposed 1–3 days later to altered flies with biting mouth parts removed, the observers displayed socially acquired analgesic and self-burying avoidance responses. Observers whose demonstrators were either kin (siblings) and, or members of a familiar pair (kin or non-kin) displayed enhanced social learning of defensive responses. Also, within the familiar pairs, social status affected learning with subordinate observers displaying better social learning than dominant observers. These findings indicate that kinship, familiarity and social status modulate social learning of defensive responses to, and the recognition of, dangerous and threatening stimuli, likely including that of predators.Communicated by G. Wilkinson     

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.     

基于机器学习的电热防除冰表面温度变化趋势预测

针对飞机部件周期控制律电加热防除冰的应用,提出基于机器学习以预测电加热防除冰表面温度的变化趋势.依靠大型结冰风洞及其电加热防除冰控制系统完成防除冰试验,获得有效的试验数据,以通、断电周期为分割单元,将试验数据划分成验证集和训练集.根据电热防除冰过程的换热情况,构建样本的特征参数,利用机器学习的有监督学习方式,选择KNN近邻回归算法和局部线性加权回归算法预测温度变化率,再转换为温度,得到的温度变化与测量数据的线性相关性达到80％以上的高相似度结果,表明使用机器学习可快速预测电热防除冰部件的表面温度变化趋势,且不同的回归算法针对模型不同测温点位置的预测效果存在差异.     

基于机器学习和卫星遥感的PM2.5/10 空间连续分布反演方法研究

在过去的几十年里,快速的经济发展以及工业化、城镇化进程加速使得中国的资源环境承担的压力不断加大。作为影响空气质量的首要污染物,PM_2.5和PM₁₀（记为PM_2.5/10）直接影响着广大人民群众的身体健康。因此,针对PM_2.5/10浓度进行遥感反演研究,对环境监测和控制改善全国空气环境质量具有重要的意义。近些年来,随着对近地面PM_2.5/10浓度研究的不断深入,基于遥感影像数据进行PM_2.5/10浓度的反演方法也日益增多。本文利用Google Earth engine（GEE）平台获取了海量的Landsat 8 OLI遥感影像数据,并结合气象信息、空间特征等参数,采用机器学习中常用的多层映射反向传播神经网络构建了波段反射率与PM_2.5/10浓度之间的反演模型,以获得PM_2.5/10在研究区域的连续分布。为了提高基础PM_2.5/10反演模型的反演精度,还从影响因素和前溯时间两个维度出发,探寻了模型的最优化输入参数组合,并最终实现了对PM_2.5/10浓度的精准反演。以北京市地区为例,模型的PM_2.5和PM₁₀的反演精度R²分别达到0.814和0.796,均方根误差RMSE分别为19.21 μg?m^?3和28.31 μg?m^?3。鉴于该反演结果具有较高的准确性和可靠性,本文所建立的方法模型为研究PM_2.5/10在空间上的连续分布特征提供了新的思路和方法,具有较为重要的科研意义与广泛的应用价值。