Cyclicity in the structure of female baboon social networks

There is an established and very influential view that primate societies have identifiable, persistent social organizations. It assumes that association patterns reflect long-term strategic interests that are not qualitatively perturbed by short-term environmental variability. We used data from two baboon troops in markedly different habitats over three consecutive seasons to test this assumption. Our results demonstrate pronounced cyclicity in the extent to which females maintained differentiated relationships. When food was plentiful, the companionships identified by social network analysis in the food-scarce season disappeared and were replaced by casual acquaintanceships more representative of mere gregariousness. Data from the fourth, food-scarce, season at one site indicated that few companions were re-united. It is likely that this reflected stochastic variation in individual circumstances. These results suggest that attention could profitably be paid to the effects of short-term local contingencies on social dynamics, and has implications for current theories of primate cognitive evolution. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

基于GA-ANN改进的空气质量预测模型

基于人工神经网络的空气质量预测模型优于传统的逐步回归模型,但由于性能差异不明显而较少在空气质量预报中应用. 设计了将遗传算法和神经网络算法相结合的基于GA_-ANN的空气质量预测模型,并利用天津市2003—2007年气象和污染物监测资料对该模型进行验证. 对2007年全年的ρ(SO₂),ρ(NO₂)和ρ(PM₁₀₎进行预测,预测值与实测值的相关系数分别为0.899 6,0.828 3和0.600 0. 与一般的人工神经网络预测模型相比较,GA_-ANN模型将空气质量等级预报的准确率从77.57%提高到79.67%. GA_-ANN模型可结合其他方法进行日常空气质量预报.      

A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment

In this paper, we report an application of neural networks to simulate daily nitrate-nitrogen and suspended sediment fluxes from a small 7.1 km² agricultural catchment (Melarchez), 70 km east of Paris, France. Nitrate-nitrogen and sediment losses are only a few possible consequences of soil erosion and biochemical applications associated to human activities such as intensive agriculture. Stacked multilayer perceptrons models (MLPs) like the ones explored here are based on commonly available inputs and yet are reasonably accurate considering their simplicity and ease of implementation. Note that the simulation does not resort on water quality flux observations at previous time steps as model inputs, which would be appropriate, for example, to predict the water chemistry of a drinking water plant a few time steps ahead. The water quality fluxes are strictly mapped to historical mean flux values and to hydro-climatic variables such as stream flow, rainfall, and soil moisture index (12 model input candidates in total), allowing its usage even when no flux observations are available. Self-organizing feature maps based on the network structure established by Kohonen were employed first to produce the training and the testing data sets, with the intent to produce statistically close subsets so that any difference in model performance between validation and testing has to be associated to the model and not to the data subsets. The stacked MLPs reached different levels of performance simulating the nitrate-nitrogen flux and the suspended sediment flux. In the first instance, 2-input stacked MLP nitrate-nitrogen simulations, based on the same-day stream flow and on the 80-cm soil moisture index, have a performance of almost 90% according to the efficiency index. On the other hand, the performance of 3-input stacked MLPs (same-day stream flow, same-day historical flux, and same-day stream flow increment) reached a little more than 75% according to the same criterion. The results presented here are deemed already promising enough, and should encourage water resources managers to implement simple models whenever appropriate.     

Using accelerometer,high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.     

生物膜水解-好氧循环系统处理活性艳蓝RB-19的微生物群落动态变化

为了研究生物膜水解-好氧循环系统处理蒽醌类染料活性艳蓝RB-19效果及其中微生物群落动态变化,利用基于16S rDNA的PCR-DGGE技术获得了微生物群落的DNA特征指纹图谱。结果表明,该系统能有效地降解活性艳蓝RB-19模拟废水,在RB-19浓度≤400 mg/L时,RB-19去除率维持在82%～96%之间,COD去除率维持在95%左右,但当RB-19浓度提高到500 mg/L时,RB-19去除率降低到58%,COD去除率降低到85%。DGGE分析表明,生物膜上的微生物群落结构随着RB-19浓度递增有显著变化,好氧、水解反应器内的细菌Shannon指数分别从1.32和1.20降低到1.11和1.19。UPGMA聚类分析和NMDS散点分析表明,水解、好氧反应器内的微生物并没有因为同处一个系统内而使得其菌群落结构产生明显的趋同倾向。系统内的多种优势菌群为兼性细菌,克隆测序的结果发现,在水解反应器存在一类具有很强还原能力的古细菌——Methanobacterium sp.MB4。     

改进的"两相优化法"在排水管网优化设计中的应用

介绍了改进的"两相优化法"在排水管网管径和埋深的优化设计中的应用.改进的方法依次从充满度、坡度、流速3方面对管段进行优化设计.通过算例分析可知,改进的方法较原方法优化效果更好,工程造价更低.     

Desalinated versus recycled water: public perceptions and profiles of the accepters

Many countries' water resources are limited in both quantity and quality. While engineering solutions can now safely produce recycled and desalinated water from non-potable sources at a relatively low cost, the general public is sceptical about adopting these alternative water sources. Social scientists, policy makers and technical experts need to better understand what is causing this lack of acceptance by the general population and how acceptance levels for recycled and desalinated water can be increased. This study is the first to conduct a comparative analysis of knowledge, perceptions, and acceptability, and determine segments of residents who are more open-minded than the general population toward the use of recycled and desalinated water. The Australian population once perceived desalinated water as environmentally unfriendly, and recycled water as a public health hazard. The general level of knowledge about these two concepts as potential water sources has historically been low. After nearly five years of serious drought, accompanied by severe water restrictions across most of the country, and subsequent media attention on solutions to water scarcity, Australians now show more acceptance of desalinated water for close-to-body uses, and less resistance to recycled water for garden watering and cleaning uses. The types of people likely to be strong accepters of the two alternative water sources are distinctly different groups, and can be reached through different media mixes. This finding has significant implications for policy makers and water practitioners.     

高校人工湿地系统的规划设计——以福建工程学院新校区为例

人工湿地处理技术具有投资低、出水水质好、耗能低、抗冲击力强、操作简单和运行费用低等优点。本文阐述了人工湿地系统应用于高校生活污水回收利用的意义,以福建工程学院新校区的北校区为例,利用前期在福州大学模拟人工湿地系统处理生活污水的实验数据,规划设计人工湿地系统,计算确定相关设施的面积和尺寸,并对人工湿地系统进行平面布置,绘制出系统流程的剖面图。     

Predicting Debris Yield From Burned Watersheds: Comparison of Statistical and Artificial Neural Network Models1

Abstract: Alluvial fans in southern California are continuously being developed for residential, industrial, commercial, and agricultural purposes. Development and alteration of alluvial fans often require consideration of mud and debris flows from burned mountain watersheds. Accurate prediction of sediment (hyper‐concentrated sediment or debris) yield is essential for the design, operation, and maintenance of debris basins to safeguard properly the general population. This paper presents results based on a statistical model and Artificial Neural Network (ANN) models. The models predict sediment yield caused by storms following wildfire events in burned mountainous watersheds. Both sediment yield prediction models have been developed for use in relatively small watersheds (50‐800 ha) in the greater Los Angeles area. The statistical model was developed using multiple regression analysis on sediment yield data collected from 1938 to 1983. Following the multiple regression analysis, a method for multi‐sequence sediment yield prediction under burned watershed conditions was developed. The statistical model was then calibrated based on 17 years of sediment yield, fire, and precipitation data collected between 1984 and 2000. The present study also evaluated ANN models created to predict the sediment yields. The training of the ANN models utilized single storm event data generated for the 17‐year period between 1984 and 2000 as the training input data. Training patterns and neural network architectures were varied to further study the ANN performance. Results from these models were compared with the available field data obtained from several debris basins within Los Angeles County. Both predictive models were then applied for hind‐casting the sediment prediction of several post 2000 events. Both the statistical and ANN models yield remarkably consistent results when compared with the measured field data. The results show that these models are very useful tools for predicting sediment yield sequences. The results can be used for scheduling cleanout operation of debris basins. It can be of great help in the planning of emergency response for burned areas to minimize the damage to properties and lives.     

三效催化转化器智能故障诊断系统知识库设计

在分析三效催化转化器故障诊断知识特点的基础上,采用一些措施构建知识库,如采用神经网络方法对转化器工作状态参数进行处理,采用多层前馈神经网络模型和自组织神经网络模型获取知识,采用产生式规则形式和二元化产生式规则形式表达知识,设计量化模块和知识库模块转化知识,并采用多个神经网络来记忆知识。应用效果表明,三效催化转化器神经网络故障诊断专家系统的预报准确率较高,具有较大的实用性。