安庆天柱山机场鸟类群落季节性变化与鸟击防范

在2008年2月至2009年2月间,对安庆天柱山机场及其周边地区的鸟类种类、种群数量进行了为期一年的调查.调查共记录到鸟类102种,隶属于12个目29个科,其中留鸟38种,占总数37.25%;夏候鸟28种,占总数27.45%;冬候鸟25种,占总数24.51%;旅鸟11种,占总数14.39%.在地理类型上,机场地区鸟类呈现古北界与东洋界过渡地带的特点,其中广布种37种,占总数36.27%;东洋界种类有34种,占总数33.33%;古北界种类31种,占总数30.39%.在机场及周边地区,春季观察到鸟类物种数要明显多于秋季及冬季,春季鸟类多样性也要高于秋季与冬季.在机场地区,每年5~6月份与10~11月份鸟类活动比较频繁.机场鸟击防范工作,应该根据鸟类活动特点,通过对重点区域栖息地的改造,降低生物多样性,从而减少鸟类在机场地区的活动,避免鸟击事件的发生.     

金佛山4种植物群落下表土炭屑特征初步分析

通过对比分析金佛山4种植被下表土炭屑形态和大小,揭示出森林群落与草本群落表土炭屑存在着较明显的差异.从炭屑形态上来区分:亚高山草甸表土中炭屑较薄,大部分为长方形或长条形,且其纵向边缘平直,横向断口一端相对平直,另一端较参差;森林群落下表土炭屑都较厚,多为近圆形或矩形,边缘参差不齐.炭屑大小上主要表现为:森林群落表土炭屑较小,平均L/W值为1.56±0.05,且L/W值大于3的炭屑不超过10%;亚高山草甸群落表土炭屑L/W值为1.77±0.10,且仍有超过10%的炭屑L/W值大于3.     

喜鹊在山地次生林中鸟类群落组织结构形成的作用

通过山地次生林鸟类群落组织结构的研究,表明喜鹊在鸟类群落组织形成中是关键类群之一.关键类群是通过巢地资源的被利用来实现的.5年中,利用喜鹊巢繁殖的鸟类达5种,利用率为25%~40.7%.     

基于专利引用和文本信息的自动驾驶技术演化

通过分析自动驾驶技术领域专利的引用信息和文本信息,结合社区发现、LDA（latent Dirichlet allocation）主题建模、主路径分析方法,针对自动驾驶专利数据集的特点建立分析框架进行自动驾驶技术演化分析。应用上述方法可以有效识别自动驾驶技术领域的主题分布,弥补传统单一方法不能反映技术全貌的缺陷。我国需重点突破多源传感器融合感知、复杂环境智能决策控制、车路协同、人机交互等关键技术;在测试和自动泊车技术上处于技术领先;在定位、高精度地图、执行机构、运动控制、硬件计算平台技术上处于技术跟随。     

Postdiagenetic Changes in Kerogen Properties and Type by Bacterial Oxidation and Dehydrogenation

A significant part of organic carbon found on the earth is deposited as fossil organic matter in the lithosphere. The most important reservoir of carbon is shale rocks enriched with organic matter in the form of kerogen created during diagenesis. The purpose of this study was to analyze whether the bacterial communities currently inhabiting the shale rocks have had any impact on the properties and type of kerogen. We used the shale rock located on the Fore-Sudetic Monocline, which is characterized by oil-prone kerogen type II. We were able to show that shale rock inhabited by bacterial communities are characterized by oxidized and dehydrated kerogen type III (gas-prone) and type IV (nonproductive, residual, and hydrogen-free). Bacterial communities inhabiting shale rock were dominated by heterotrophs of the Proteobacteria, Firmicutes, and Actinobacteria phyla. Additionally, we detected a number of protein sequences in the metaproteomes of bacterial communities matched with enzymes involved in the oxidative metabolism of aliphatic and aromatic hydrocarbons, which may potentially contribute to the postdiagenetic oxidation and dehydrogenation of kerogen. The kerogen transformation contributes to the mobilization of fossil carbon in the form of extractable bitumen dominated by oxidized organic compounds.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.     

An interpretation of COVID-19 in Tokyo using a combination of SIR models

A year and a half has passed since the outbreak of the COVID-19 pandemic. Mathematical models to predict infection are expected and many studies have been conducted. In this study, a new interpretation was created that could reproduce the daily positive cases in Tokyo using only a simple SIR model. In addition, the data on the ratio of transfer to delta variants could also be simulated. It is anticipated that this interpretation will be a basis for the development of forecasting methods.     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.     

红山诸文化"神鸟"崇拜与萨满"鸟神"

鸟崇拜是人类原始图腾崇拜的普遍形式.距今8000～5000年的红山诸文化曾经有过十分发达的“神鸟“崇拜.在考古发现的遗物中有许多各类鸟形纹样、鸟形雕塑和以石、陶、泥、木、玉等材质制成的鸟形器和佩饰物.鸟是北方诸族萨满的主神或主要保护神,在北方古代游牧民族中鸟崇拜和鸟祖神话十分流行.红山诸文化的“神鸟“崇拜与北方民族的“鸟神“崇拜,特别是萨满“鸟神“有诸多相似之处.     

江西龙宫洞不同季节土壤微生物群落结构比较

采用非培养法和传统培养法相结合,利用Illumina高通量测序技术对江西省九江市彭泽县龙宫洞微生物的16S rDNA和ITS相关区域进行测序及生物信息学分析,通过实时荧光定量PCR(qPCR)对洞穴土壤中细菌和真菌含量进行绝对定量,探究不同季节对洞穴土壤微生物群落结构的影响,同时观察相应可培养微生物分布情况.结果表明:...