Cross-Platform Framing and Cross-Cultural Adaptation: Examining Elephant Conservation in Thailand

This case study investigates cross-platform framing process and framing alignment strategies of the world famous elephant conservation NGO. It examines how the intercultural adaptation facilitates the frame transformation against entrenched domesticated elephant traditions in Thailand. Besides web observation, this study conducted two field trips to interview the NGO staff and conservation experts, and observed human–elephant interaction. Enhancing the understanding of ecotourism, this study expands the framing theory by incorporating cross-platform and cross-cultural adaptation factors and provides insights of an effective Asian ecotourism model which transcends cultural differences and overcomes resistance by disseminating culturally sensitive and integrated framing strategies through various communication channels.     

Estimating dyad association probability under imperfect and heterogeneous detection

In animal behaviour studies, association indices estimate the proportion of time two individuals (i.e. a dyad) spend in association. In terms of dyads, all association indices can be interpreted as estimators of the probability that a dyad is associated. However, traditional indices rely on the assumptions that the probability to detect a particular individual (p) is either approximately one and/or homogeneous between associated and not associated individuals. Based on marked individuals we develop a likelihood based model to estimate the probability a dyad is associated (ψ) accounting for p < 1 and possibly varying between associated and not associated individuals. The proposed likelihood based model allows for both individual and dyadic missing observations. In addition, the model can easily be extended to incorporate covariate information for modeling p and ψ. A simulation study showed that the likelihood based model approach yield reasonably unbiased estimates, even for low and heterogeneous individual detection probabilities, while, in contrast, traditional indices showed moderate to strong biases. The application of the proposed approach is illustrated using a real data set collected from a population of Commerson's dolphin (Cephalorhynchus commersonii) in Patagonia Argentina. Finally, we discuss possible extensions of the proposed model and its applicability in animal behaviour and ecological studies.     

Estimating influence of stocking regimes on livestock grazing distributions

Livestock often concentrate grazing in particular regions of landscapes while partly or wholly avoiding other regions. Dispersing livestock from the heavily grazed regions is a central challenge in grazing land management. Position data gathered from GPS-collared livestock hold potential for increasing knowledge of factors driving livestock aggregation patterns, but advances in gathering the data have outpaced advancements in analyzing and learning from it. We fit a hierarchical seemingly unrelated regression (SUR) model to explore how season of stocking and the location where cattle entered a pasture influenced grazing distributions. Stocking alternated between summer on one side of the pasture one year and fall on another side of the pasture the next year for 18 years. Waypoints were recorded on cattle for 50 d each year. We focused our analysis on the pasture's 10 most heavily grazed 4-ha units, because these units were the most prone to negative grazing impacts. Though grazing of the study units was always disproportionately heavy, it was much heavier with the summer than fall stocking regime: Bayesian confidence intervals indicate summer grazing of study units was approximately double the average fall grazing value. This is our core result, and it illustrates the strong effect stocking season or date or both can have on grazing distributions. We fit three additional models to explore the relative importance of stocking season versus location. According to this analysis, stocking season played a role, but stocking location was the main driver. Ostensibly minor factors (e.g. stocking location) can greatly influence livestock distributions.     

区块链与端边云技术赋能城市应急预警系统*

为解决现有城市应急预警系统（urban emergency early-warning system,UEES）存在的缺乏设备身份认证、数据共享性差、数据传输处理效率低、区域性智能自治能力不足等问题,根据区块链与端边云技术特点和现有系统的业务需求,提出区块链与端边云技术相融合的城市应急预警系统（UEES integrating blockchain and end-to-end cloud technology,UEES-BE）框架;对该框架存在的边缘节点认证与数据共享、区块链与边缘智能、边云协同分类分级分布式存储3个关键问题进行探讨;以城市内涝灾害预警为例,将区块链与端边云技术与城市内涝灾害预警系统业务相结合,提出4点优化建议。结果表明:区块链和端边云技术理论上可实现城市灾害安全、共享、实时、智能的预警,对构建城市应急预警系统具有指导意义。     

基于时序片段的油气管道运行工况识别方法*

为准确识别管道系统运行工况,提高对油气管道突发事故的响应速度,综合提升管网安全管理水平,提出1种基于时序片段的油气管道运行工况识别方法。首先,构建基于概率分布的状态变化识别模型,提取油气管道中不同运行状态点;其次,建立基于时间序列片段的工况识别模型,快速识别不同时间长度内油气管道运行工况;最后,以国内某成品油管道为例进行方法验证。研究结果表明:该方法可有效识别成品油管道阀门开关状态、泵异常停机和阀门内漏3种运行工况。对比传统的识别方法,该方法可降低状态变化点的漏报率,提升管道运行工况识别的准确率。研究结果可为油气管道系统运行工况识别提供新的借鉴方法。     

建筑火灾中值得深入研究的几个问题

提出了一些建筑火灾中值得进一步研究的,流动、传热、热解、燃烧、污染物生成等方面的基础问题。着重讨论了烟气扩散、流动中的混沌现象,提出了建立建筑火灾数据库、专家系统的设想,和建立非线性燃烧模型、开发智能型火灾自动报警系统的技术思路。     

Is bird song a reliable signal of aggressive intent?

In this commentary, we discuss recent experiments on the reliability of bird song as a signal of aggressive intent during territorial conflicts. We outline relevant theoretical views on honest signaling, highlighting the vulnerability handicap hypothesis as a possible explanation for soft song’s reliability in predicting attack. We also sketch possible methods of testing whether soft song agrees with key predictions of the vulnerability handicap hypothesis. Finally, we suggest possible empirical refinements that may be useful in future studies of signals of intent, both in birds and in animals broadly. In particular, we argue that future studies of intent should strive to incorporate the following elements into their experimental design: (1) multi-modal signal components, (2) interaction dynamics, and (3) minimal time intervals. Simulated exchanges using dynamically interactive models may provide a powerful means of incorporating all three of these design features simultaneously.

畜禽养殖污染是不容忽视的新的环境问题

本文论述了畜禽养殖业对环境带来的污染已成为新的环境问题,并提出了改进的措施。     

工业纤维废渣利用的生物技术研究

用平板分离法从树皮中选育出“５０８８号菌种”，使麻纺厂的废弃下脚料转化成可替代饲料中玉米成份的“媲谷菌饲”。并利用造纸厂白泥，粘胶纤维厂废水处理产生的污泥作饲料钙，锌元素添加剂获成功。     

基于神经网络的东北地区秸秆焚烧预测

选取农作物秸秆露天燃烧严重的东北地区,采用人工神经网络的方法,结合卫星火点和气象数据,开展秸秆露天燃烧预测研究.结果表明：人工神经网络预测模型成功验证了松嫩平原地区2015年10月25日~11月15日的秸秆露天燃烧情况,其准确度为67.1%,经过多次试验,在神经网络建模与验证数据配比为80：20时,预测准确度最高,可达69.7%,同时该模型的稳定性较好.而对不同区域,不同时间段的预测研究表明,人工神经网络较适用于长时间序列的预测.就影响因素而言,相对湿度是影响秸秆露天燃烧的最重要因素.本研究结果可为空气质量模式提供火点预测数据,提高其预报预警能力,为区域联防联控政策的制定提供科技支持.