Manuscript in preparation for <Emphasis Type="Italic">Behavioral Ecology and Sociobiology</Emphasis> Bumble bee pollen foraging regulation: role of pollen quality,storage levels,and odor

The regulation of protein collection through pollen foraging plays an important role in pollination and in the life of bee colonies that adjust their foraging to natural variation in pollen protein quality and temporal availability. Bumble bees occupy a wide range of habitats from the Nearctic to the Tropics in which they play an important role as pollinators. However, little is known about how a bumble bee colony regulates pollen collection. We manipulated protein quality and colony pollen stores in lab-reared colonies of the native North American bumble bee, Bombus impatiens. We debut evidence that bumble bee colony foraging levels and pollen storage behavior are tuned to the protein quality (range tested: 17–30% protein by dry mass) of pollen collected by foragers and to the amount of stored pollen inside the colony. Pollen foraging levels (number of bees exiting the nest) significantly increased by 55%, and the frequency with which foragers stored pollen in pots significantly increased by 233% for pollen with higher compared to lower protein quality. The number of foragers exiting the nest significantly decreased (by 28%) when we added one pollen load equivalent each 5 min to already high intranidal pollen stores. In addition, pollen odor pumped into the nest is sufficient to increase the number of exiting foragers by 27%. Foragers directly inspected pollen pots at a constant rate over 24 h, presumably to assess pollen levels. Thus, pollen stores can act as an information center regulating colony-level foraging according to pollen protein quality and colony need. An erratum to this article can be found at     

基于GIS的城市生活垃圾规划管理智能决策方法

对城市生活垃圾治理系统进行规划管理是克服治理过程中混乱不协调所带来的问题、提高治理决策有效性的根本方法.提出了基于GIS的城市生活垃圾规划管理智能决策方法.将"城市生活垃圾治理系统"的决策划分为3个层次,在此基础上构建总体规划方案,并将人工智能技术和GIS与总体规划中的模型相集成,给出了该方法的一般性决策模型,对该方法的决策过程和主要创新也进行了详细描述,为构建基于GIS的城市生活垃圾规划管理智能决策系统提供了依据.     

Modelling collective foraging by means of individual behaviour rules in honey-bees

An individual-oriented model is constructed which simulates the collective foraging behaviour of a colony of honey-bees, Apis mellifera. Each bee follows the same set of behavioural rules. Each rule consists of a set of conditions followed by the behavioural act to be performed if the conditions are fulfilled. The set of conditions comprises the state of external information available to the bee (e.g. the dancing of other bees) and internal information variables (like memorised location of a food source and homing motivation). The rules are partly observational (i.e. they capture the observable regularities between the present external information and the individual bee's behaviour), and partly involve hypothesised internal-state variables (e.g. abandoning tendency and homing motivation), because no observable (physiological) aspect has as yet been detected in the bee which correlates with changes in the internal motivation. Our aim is to obtain a set of rules that is necessary and sufficient for the generation of the collective foraging behaviour observed in real bees. We simulated an experiment performed by Seeley et al. in which a colony of honey-bees chooses between two nectar sources of different profitabilities which are switched at intervals. A good fit between observed and simulated collective forager patterns was obtained when the model included rules in which the bees (1) relied on the information acquired from previous flights to a source (e.g. profitability and time of day when the source was found), (2) used positional information obtained by attending recruitment dances and (3) did not abandon a (temporarily) deteriorated source too fast or too slowly. The significance of the following issues is discussed: the role of internal and external information, source profitability, the spatial precision of the dance communication, the ability to search for a source after the source position has been transmitted, the tendency to abandon a deteriorated source, and the concepts of scout, recruit, (un)employed forager, and foraging history. Received: 26 January 1998 / Accepted after revision: 16 May 1998     

Factors driving deforestation in common-pool resources in northern Mexico

The theory of collective action has been extensively used to explain the relationship between common-based property regimes and the conservation of natural resources. However, there are two key components of the theory that literature reports as puzzles in which no consensus exists about their effect on the performance of common-pool resources. These are group size and heterogeneity. This study analyzes the effects of these two key components on the effectiveness of community-based forestry, called ejidos, to protect their forest resources in northern Mexico. We used a multinomial logit model to determine the contribution of 16 explanatory variables to the dependent variable, a measure of success of ejidos defined by the presence of deforested, degraded, or forested conditions. The results show that corn yield, marginality, percent of forest area, total population, a forest value index, distance to markets, roads and towns, were all statistically significant in driving deforested conditions. Deforestation becomes more attractive for poor communities and as corn yield and distance to towns, roads, and markets decrease. In general, group size and heterogeneity had no significant effects on the presence of deforested conditions. Deforestation is driven by resource-specific characteristics, such as location and soil productivity, not by ejidos' attributes, such as total area or number of members. We argue that current institutional policies focusing on the structure of property right arrangements should be shifted (1) to provide better technology for land cultivation; (2) to reduce the marginality problem in poor communities; and (3) to strengthen local institutions.     

Experimental evidence for a dual pathway model analysis of coping with the climate crisis

Two experiments examined the psychological power of fear and group efficacy beliefs to increase environmental action intentions against the climate crisis. Extending a dual pathway model of coping with collective disadvantage, results showed evidence for emotion-focused approach coping: Environmental action intentions were increased by individuals' manipulated fear of the negative future consequences of the climate crisis (Experiments 1–2). Additionally, results showed evidence for problem-focused approach coping: Individuals' measured (Experiment 1) and manipulated group efficacy beliefs (Experiment 2) increased environmental action intentions. The results thus suggest that the dual pathway model can be successfully applied to a psychological analysis of emotion- and problem-focused approach coping with the climate crisis. We discuss the theoretical and practical implications of these results.     

Rural market imperfections and the role of institutions in collective action to improve markets for the poor

Many countries in sub‐Saharan Africa have liberalized markets to improve efficiency and enhance market linkages for smallholder farmers. The expected positive response by the private sector in areas with limited market infrastructure has however been very limited. The functioning of markets is constrained by high transaction costs and coordination problems along the production‐to‐consumption value chain. New kinds of institutional arrangements are needed to reduce these costs and fill the vacuum left when governments withdrew from markets in the era of structural adjustments. One of these institutional innovations has been the strengthening of producer organizations and formation of collective marketing groups as instruments to remedy pervasive market failures in rural economies. The analysis presented here with a case study from eastern Kenya has shown that marketing groups pay 20–25% higher prices than other buyers to farmers while participation was also positively correlated with adoption of improved dryland legume varieties, crops not targeted by the formal extension system. However the effectiveness of marketing groups is undermined by external shocks and structural constraints that limit the volume of trade and access to capital and information, and require investments in complementary institutions and coordination mechanisms to exploit scale economies. Successful groups have shown high levels of collective action in the form of increased participatory decision making, member contributions and initial start‐up capital. Failure to pay on delivery, resulting from lack of capital credit, is a major constraint that stifles competitiveness of marketing groups relative to other buyers. These findings call for interventions that improve governance and participation; mechanisms for improving access to operating capital; and effective strategies for risk management and enhancing the business skills of farmer marketing groups.     

Collective guilt mediates the effect of beliefs about global warming on willingness to engage in mitigation behavior

The present research examines whether collective guilt for an ingroup's collective greenhouse gas emissions mediates the effects of beliefs about the causes and effects of global warming on willingness to engage in mitigation behavior. In Study 1, we manipulate the causes and effects of global warming and then measure collective guilt. Results demonstrate that collective guilt for Americans' greenhouse gas emissions is stronger when participants believe that global warming is caused by humans and will have minor effects. Study 2 employs the same manipulations and then measures collective guilt and collective anxiety, as well as willingness to conserve energy and pay green taxes. This study replicates the effect from Study 1 and rules out collective anxiety as a plausible alternative mediator. Collective guilt for Americans' greenhouse emissions was the only reliable mediator of the effect of beliefs about global warming on willingness to engage in mitigation behaviors. The importance of collective guilt as a tool for promoting global warming mitigation is discussed.     

人工智能助力臭氧催化剂SrFexZr1-xO3的开发

将人工智能应用于催化臭氧氧化催化剂SrFe_xZr_1-xO₃的开发过程,采用共沉淀法制备了50种不同配方的催化剂,考察聚乙二醇（PEG）投加量、煅烧时间、老化时间、氨水投加量和铁掺杂量对SrFe_xZr_1-xO₃催化剂催化臭氧降解间甲酚反应活性的影响.同时,利用人工神经网络（ANN）和响应面（RSM）对催化剂合成条件与TOC去除率和间甲酚转化率的关系进行拟合,训练集中ANN的R²值分别为0.91和0.97,高于RSM的R²值0.35和0.41;在4组测试集上ANN的均方误差（MSE）分别为9.87和17.67,远小于RSM的23.89和28.87.结果表明,ANN模型对催化剂制备过程的复杂体系具有更好的拟合和泛化能力.在ANN训练好的模型中通过枚举法寻找最优合成条件为：PEG投加量为19.00%,煅烧时间为1.25 h,老化时间为26.50 h,氨水投加量为6.21 mL,铁掺杂量为3.37%,所得催化剂为SrFe_0.13Zr_0.87O₃-B.最佳反应条件下,间甲酚转化率和TOC去除率分别达到98.52%和17.21%,优于空白组的73.46%和1.86%.     

流域水污染监测与溯源技术研究进展

水污染特征识别和溯源是实施水污染精准治污的关键一步,也是流域水污染防治工作的前提。该文概述了不同分析监测技术、传统溯源方法与人工智能在水污染监测与溯源中的应用进展。光谱分析由于灵敏度高、准确度好,在实现水污染快速监测中应用最为广泛。传统溯源方法在复杂多样的水污染事故中不能准确快速地确定其污染源类型,而人工智能技术由于可以解决动态环境问题中的不确定性和复杂性,能够准确、智能地识别水质特征和追踪污染源。将人工智能与传统技术相结合是流域水污染溯源的发展趋势。展望了人工智能在水污染溯源方面的应用前景,为实现流域水污染全面监管提供了新的思路。     

面向安全管理的安全情报服务能力影响因素研究*

为研究安全情报服务能力影响因素,根据安全情报与情报服务理论,提出安全情报服务定义及内涵,以4个基本原则为指导,面向安全管理全过程,构建并解析面向安全管理的安全情报服务能力影响因素模型,并提出安全情报服务能力建设的工作建议。研究结果表明:面向安全管理的安全情报服务能力影响因素包括安全情报服务顶层设计、安全情报服务机制和安全情报服务保障3个一级影响因素,且一级影响因素可进一步细分为10个二级影响因素。研究结果可为提升面向安全管理的安全情报服务能力提供重要支撑。