Plant blindness and the implications for plant conservation

Plant conservation initiatives lag behind and receive considerably less funding than animal conservation projects. We explored a potential reason for this bias: a tendency among humans to neither notice nor value plants in the environment. Experimental research and surveys have demonstrated higher preference for, superior recall of, and better visual detection of animals compared with plants. This bias has been attributed to perceptual factors such as lack of motion by plants and the tendency of plants to visually blend together but also to cultural factors such as a greater focus on animals in formal biological education. In contrast, ethnographic research reveals that many social groups have strong bonds with plants, including nonhierarchical kinship relationships. We argue that plant blindness is common, but not inevitable. If immersed in a plant‐affiliated culture, the individual will experience language and practices that enhance capacity to detect, recall, and value plants, something less likely to occur in zoocentric societies. Therefore, conservation programs can contribute to reducing this bias. We considered strategies that might reduce this bias and encourage plant conservation behavior. Psychological research demonstrates that people are more likely to support conservation of species that have human‐like characteristics and that support for conservation can be increased by encouraging people to practice empathy and anthropomorphism of nonhuman species. We argue that support for plant conservation may be garnered through strategies that promote identification and empathy with plants.     

The role of digital data entry in participatory environmental monitoring

Many argue that monitoring conducted exclusively by scientists is insufficient to address ongoing environmental challenges. One solution entails the use of mobile digital devices in participatory monitoring (PM) programs. But how digital data entry affects programs with varying levels of stakeholder participation, from nonscientists collecting field data to nonscientists administering every step of a monitoring program, remains unclear. We reviewed the successes, in terms of management interventions and sustainability, of 107 monitoring programs described in the literature (hereafter programs) and compared these with case studies from our PM experiences in Australia, Canada, Ethiopia, Ghana, Greenland, and Vietnam (hereafter cases). Our literature review showed that participatory programs were less likely to use digital devices, and 2 of our 3 more participatory cases were also slow to adopt digital data entry. Programs that were participatory and used digital devices were more likely to report management actions, which was consistent with cases in Ethiopia, Greenland, and Australia. Programs engaging volunteers were more frequently reported as ongoing, but those involving digital data entry were less often sustained when data collectors were volunteers. For the Vietnamese and Canadian cases, sustainability was undermined by a mismatch in stakeholder objectives. In the Ghanaian case, complex field protocols diminished monitoring sustainability. Innovative technologies attract interest, but the foundation of effective participatory adaptive monitoring depends more on collaboratively defined questions, objectives, conceptual models, and monitoring approaches. When this foundation is built through effective partnerships, digital data entry can enable the collection of more data of higher quality. Without this foundation, or when implemented ineffectively or unnecessarily, digital data entry can be an additional expense that distracts from core monitoring objectives and undermines project sustainability. The appropriate role of digital data entry in PM likely depends more on the context in which it is used and less on the technology itself.     

ABC算法优化SVR的磨损故障预测模型

目的为了提高故障预测的精度,针对支持向量回归SVR(Support vector machine for regression,SVR)参数选择困难的问题,提出一种采用人工蜂群(artificial bee colony,ABC)算法优化支持向量回归(SVR)的故障预测模型(ABC-SVR)。方法该模型先对样本数据进行重构,然后将故障预测误差(适应度)作为优化目标,通过ABC算法寻优找到最优的SVR参数,建立故障预测模型。最后通过实例仿真验证模型的优越性。结果采用ABC算法优化的SVR故障预测模型进行时间序列预测,能够较好地跟踪发动机滑油金属元素浓度的变化过程,并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题,能够更加准确地表现故障变化规律,提高了故障预测精度。     

兰州市公共交通的生态足迹测算研究

以兰州市公共交通(分为公交车和出租车两种)为研究对象,运用生态足迹法,测算2010年-2014年兰州市客运交通生态足迹,并计算兰州市公共交通的生态效率和生态足迹强度.结果表明:公共交通生态足迹年均占比为7.41%;公共交通生态足迹和客运量呈同步增长态势,公共交通生态足迹年均增长1.71%;在影响公共交通生态足迹的因素中,公交车和出租车的化石能源足迹年均占比分别为76.06%和62.32%;就生态效率而言,公交车生态效率是出租车生态效率的6.24倍.     

基于微信公众平台的空气质量显示系统的设计与实现研究

空气质量的好坏直接影响着人类的出行和健康,创建便于查询的空气质量显示系统,可以使人们简单直观的了解当日的空气质量指数(AQI).随着互联网的迅猛发展,"微信"逐渐成了人们日常通信的重要工具.本系统利用物联网技术与微信公众平台相结合,提出了空气质量显示系统的设计与实现方案,来建设空气质量显示系统.该系统可从各个区内监测点得到空气质量指数数据,进而将每日各个时段的空气质量指数(AQI)以动态图的方式直观清晰的展示给用户,为人们的出行带来了方便.     

环境影响评价公众意见调查的真实性评价探讨

公众参与在环境影响评价中扮演着重要的角色,认真做好公众意见调查工作,可以促进环境影响评价工作公正、公开、公平地发展。虽然在建设项目环境影响评价工作中,公众参与起到了一定的积极作用,但是在公众参与的实际过程中,还是遇到了许多具体问题,本文对环境影响评价工作中公众意见调查的结果应如何评估,提出意见和建议。     

环境保护与公众参与

从公众言语的起源和内涵出发,分析了我国公众参与在环境保护中存在的主要问题,提出了健全和完善环保法律、法规等相对应的对策建议。     

规划环境影响评价中的公众参与实例研究

公众参与作为规划环境影响评价的重要组成部分,具有重要的作用和意义。以大同市医药工业园区规划环境影响评价中的公众参与为例,介绍了公众参与的工作程序、方式、分析结果及意见的落实,证实了公众参与的必要性。     

中国污染治理设施的公共物品属性分析及市场化融资的建议

污染治理设施是城镇基础设施的重要组成,与社会公共利益直接相关。对城镇污染治理设施的投资建设,可以为大范围的公众提供清洁的生态环境与和谐的社会环境。本文分析中国污染治理设施的公共物品属性,结合中国实际提出通过发行市政债券筹集资金进行污染治理设施建设,并在保留产权公有的前提下对污染设施实施租赁管理经营的新型融资机制。