Social network properties within a fish assemblage invaded by non-native sunbleak Leucaspius delineatus

Network theory is commonly used to reveal social interactions and the organisation of interconnected nodes—but has not yet been applied to animal invasions. Non-native species invasions are now considered one of the foremost threats to natural ecosystems and biodiversity. This is the first attempt to assess social network properties within a freshwater fish assemblage invaded by a non-native fish species. We show that invasive sunbleak Leucaspius delineatus is socially more strongly interconnected with native species than the native species with each other. The social networks also reveal characteristics of a ‘small world’ such as low clustering coefficients C and short path lengths L. The findings may indicate potential traits of successful invaders and the implication for the spread of pathogens between individuals within a group of animals that contain a non-native invasive species. The success of establishment and subsequent invasion may be highlighted not only by the capacity of the new species to adapt to the new environment, but also in its capacity to penetrate the social circle of the native community.     

Understanding the effects of rainfall on elephant-vegetation interactions around waterholes

The distribution of surface water affects herbivore-vegetation interactions in arid and semi-arid regions. Limited access to surface water typically results in the emergence of vegetation gradients around natural and artificial water sources. In particular, African elephants can create large-scale gradients of woody vegetation. Understanding the dynamics of these gradients is of particular importance for the conservation of other, less mobile herbivores that depend on woody vegetation in areas close to water. While rainfall is known to be a key determinant of herbivore-vegetation interactions in dry areas, we only have limited understanding on how it impacts woody vegetation gradients around waterholes. To address this problem, we developed a deterministic simulation model that describes the interplay of rainfall, elephants and woody vegetation in the vicinity of waterholes. The model is based on elephant telemetry data and the ecological conditions in Etosha National Park (ENP), Namibia. We found that decreasing amounts of rainfall led to an increased degradation of woody vegetation, which was particularly severe in areas close to water. Based on this result we conclude that low rainfall was an important driver of recently observed patterns of vegetation degradation in ENP. More generally, rainfall appears to be a key factor that determines elephant-vegetation interactions and thus dynamics of woody vegetation gradients around waterholes. Using long-term rainfall data from ENP, we also demonstrate that an increase in the number of water sources during periods of low rainfall can mitigate the destructive impact of elephants in areas close to water. However, more research is required to assess the sustainability and effectiveness of rainfall-adapted strategies of artificial water provisioning in more detail. In particular it is important to investigate potential effects on elephant population dynamics.     

基于RBF神经网络的鄱阳湖表层水体总悬浮颗粒物浓度遥感反演

为进一步提高湖泊总悬浮颗粒物浓度遥感反演的准确性,引进适应复杂非线性映射的RBF神经网络模型,以鄱阳湖通江湖体为例进行了实证分析,根据实测水体悬浮颗粒物浓度和MODIS遥感数据,对遥感数据进行预处理,建立了RBF神经网络悬浮颗粒物浓度反演模型,神经元个数为8个,误差性能目标值为0.001,对悬浮颗粒物浓度进行反演。研究结果表明,验证样本相关系数R2=0.956 8,均方根误差RMSE=0.54。利用神经网络模型反演水悬浮颗粒物浓度是有效的,其反演结果优于非线性回归模型的结果。     

Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

● Reducting the sampling frequency can enhance the modelling process. ● The pyrolysis of HDPE was investigated at three different heating rates. ● The average E_a and k₀ were calculated by Friedman, KAS, FWO, and CR methods. ● ANN was employed to predict the HDPE weight loss with the optimal MSE and R². Pyrolysis is considered an attractive option and a promising way to dispose waste plastics. The thermogravimetric experiments of high-density polyethylene (HDPE) were conducted from 105 °C to 900 °C at different heating rates (10 °C/min, 20 °C/min, and 30 °C/min) to investigate their thermal pyrolysis behavior. We investigated four methods including three model-free methods and one model-fitting method to estimate dynamic parameters. Additionally, an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss (wt.%) of HDPE, and optimized via assessing mean squared error and determination coefficient on the test set. The optimal MSE (2.6297 × 10⁻²) and R² value (R² > 0.999) were obtained. Activation energy and pre-exponential factor obtained from four different models achieves the acceptable value between experimental and predicted results. The relative error of the model increased from 2.4 % to 6.8 % when the sampling frequency changed from 50 s to 60 s, but showed no significant difference when the sampling frequency was below 50 s. This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space. This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters. It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics.     

《昆明-蒙特利尔全球生物多样性框架》下的生物多样性监测

生物多样性监测是执行和评估《昆明-蒙特利尔全球生物多样性保护框架》（简称《昆-蒙框架》）长期目标和行动指标所需要的关键数据的主要来源。简要介绍了《昆-蒙框架》下的全球生物多样性观测网络（The global biodiversity observation network,GEO BON）概况、中国已有的生物多样性监测/观测网络,构建全球生物多样性观测系统（A global biodiversity observation system,GBiOS）的目的和核心监测指标,分析了生物多样性监测网络建设的一般步骤和GEO BON建议的4种生物多样性监测新方法。提出,地理空间单元与生物类群的全覆盖监测是建设生物多样性监测网络的主要目标,传统监测方法和新监测技术的互补是当前生物多样性监测的重点工作内容。     

海南省省控土壤环境监测基础点位布设思路与方法

为满足新时期土壤污染防治管理要求,掌握海南省土壤环境质量状况及变化趋势,海南省亟需加强省控土壤环境监测基础点位布设,完善土壤环境监测网。通过梳理海南省土壤环境监测工作基础和监测网络构建情况,形成了省控基础点位布设思路、原则和方法,并应用GIS技术完成点位布设。海南省省控土壤环境监测基础点位基本覆盖了海南省农用地主要土壤类型和成土母岩,监测结果可以反映海南省农用地土壤环境质量整体状况。     

提高人工神经网络洪水峰值预报精度的研究

以动量项与学习率自适应调整相结合的改进BP算法作为人工神经网络计算方法，阐述了洪水预报网络模型的构造和数据处理，以及转移函数对洪水预报精度的影响，提出了合理的洪水预报网络模型的设计方案。     

污水处理过程自动控制研究与应用

就国内外近几年的污水处理过程自动控制的研究进展进行了综述,介绍了各种先进控制理论和先进控制技术在污水处理主要工段和典型参数控制中的应用,并提出污水处理过程中先进控制的前景。     

建筑内人员疏散的一种网络模型算法的讨论

针对疏散模型问题进行了一些讨论,在分析疏散模型的基础上,认为网络模型较为适合工程应用,确定发展网络疏散模型。在讨论网络疏散模型路径时,首先将整个建筑物模型划分为四种不同的结构,并假设了模型成立的条件,再按照典型的流量限制原理和该单元内疏散的特点,分别讨论了这四种不同类型的结构的算法。     

Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.