Meteorological satellite stakeholder relationship network based on social network analysis

The meteorological satellite service range is extensive,and science and technology and related industries have become beneficiaries of it.The complex meteorological satellite stakeholder relationship warrants quantitative evaluation.This study investigates the meteorological satellite stakeholder relationship network to provide a new research perspective for meteorological satellites in the field of management.For literature analysis,16 meteorological satellite stakeholders are identified through keyword screening,classified,and coded.A meteorological satellite stakeholder relationship network model is then constructed through social network analysis(SNA).Ego,local,and overall networks are analyzed from three perspectives to measure the network principle and to form a relationship network coordination degree evaluation system.The improved analytic hierarchy process(AHP)-fuzzy comprehensive evaluation method is then used to determine index weights and evaluate the relationship network coordination process design comprehensively.In empirical analysis,data for the meteorological satellite Fengyun-4 are obtained through questionnaire survey and literature analysis.Ucinet6 is used to generate relationship networks and analyze various stakeholder roles and status,stakeholder relationship network coordination degree,and evaluation results.The results demonstrate that the competent meteorological satellite department,the meteorological administration,the National Meteorological Centre,and the government are in the center of the Fengyun-4 stakeholder relationship network,with coordination degree in an“average”state.Thus,establishing a stakeholder coordination mechanism may strengthen connection and promote the development of meteorological undertakings.

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.

An Estimation Method for Relationship Strength in Weighted Social Network Graphs

Previous works mainly focused on estimating direct relationship strength in social networks. If two users are not directly connected in a social network, there is no direct relationship. In order to estimate the relationship strength between two indirectly connected users as well as directly connected users, this paper proposes an estimation method for relationship strength in weighted social network graphs, which is based on the trust propagation strategy and the estimation of direct relationship strength. Our method considers the length of a relationship path, the number of relationship paths and the edge weights (direct relationship strength) along with a relationship path to estimate the strength of indirect relationship. Then it synthesizes the direct and indirect relationship strength to represent the strength of relationship between two users in social net- works. Thus our method can fully estimate the relationship strength between any two users in a social network no matter whether they are directly connected or not.

Contrastive Analysis of Software Networks Based on Different Coupling Relationships

Several software network models are constructed based on the relationships between classes in the object-oriented software systems.Then,a variety of well-known open source software applications are statistically analyzed by using these models.The results show that: (1) Dependency network does play a key role in software architecture;(2) The exponents of in-degree and total-degree distribution functions of different networks differ slightly,while the exponent of out-degree varies obviously;(3) Weak-coupling relationships have greater impact on software architecture than strong-coupling relationships.Finally,a theoretically analysis on these statistical phenomena is proposed from the perspectives of software develop technology,develop process and developer’s habits,respectively.

ARTIFICIAL NEURAL NETWORK MODEL OF CONSTITUTIVE RELATIONSHIP FOR 2A70 ALUMINUM ALLOY

The hat deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over a wide range of temperatures 360-480℃ with strain rates of 0.01-1s^-1 and the largest deformation of 60%, and the true stress of the material was obtained under the above-mentioned conditions. The experimental results shows that 2A70 aluminum alloy is a kind of aluminum alloy with the property of dynamic recovery; its flow stress declines with the increase of temperature, while its flow stress increases with the increase of strain rates. On the basis of experiments, the constitutive relationship of the 2A70 aluminum alloy was constructed using a BP artificial neural network. Comparison of the predicted values with the experimental data shows that the relative error of the trained model is less than ±3% for the sampled data while it is less than ±6% for the nonsampled data. It is evident that the model constructed by BP ANN can accurately predict the flow stress of the 2A70 alloy.

Self-Organized Detection of Relationships in a Network

Multistate operations within a network result in high-dimensional, multivariate temporal data, and are useful for systems, which monitor access to network entities like resources, objects, etc. Efficient self organization of such multi-state network operations stored in databases with respect to relationships amongst users or between a user and a data object is an important and a challenging problem. In this work, a layer is proposed where discovered relationship patterns amongst users are classified as clusters. This information along with attributes of involved users is used to monitor and extract existing and growing relationships. The correlation is used to help generate alerts in advance due to internal user-object interactions or collaboration of internal as well as external entities. Using an experimental setup, the evolving relationships are monitored, and clustered in the database.

Wear Fault Diagnosis of Machinery Based on Neural Networks and Gray Relationships

In this paper, the regular characteristic of -wear particles related to fault type of machines based on condition monitoring of reciprocal machinery is discussed. The typical -wear particles spectrum is established according to the equipment structure , friction and wear rule and the characteristic of 'wear particles; The identification technology of wear particles is proposed based on neural networks and a gray relationship ; an intelligent wear particles identification system is designed. The diagnosis example shows that this system can promote the accuracy and the speed of wear particles identification.

On Exploiting Temporal, Social, and Geographical Relationships for Data Forwarding in Delay Tolerant Networks

Because of unpredictable node mobility and absence of global information in Delay Tolerant Networks (DTNs), effective data forwarding has become a significant challenge in such network. Currently, most of existing data forwarding mechanisms select nodes with high cumulative contact capability as forwarders. However, for the heterogeneity of the transient node contact patterns, these selection approaches may not be the best relay choices within a short time period. This paper proposes an appropriate data forwarding mechanism, which combines time, location, and social characteristics into one coordinate system, to improve the performance of data forwarding in DTNs. The Temporal-Social Relationship and the Temporal-Geographical Relationship reveal the implied connection information among these three factors. This mechanism is formulated and verified in the experimental studies of realistic DTN traces. The empirical results show that our proposed mechanism can achieve better performance compared to the existing schemes with similar forwarding costs (e.g. end-to-end delay and delivery success ratio).

学习型社区赋能教育强国建设——基于在线学习者关系网络分析视角

学习型社区建设有助于加快教育强国落地,社区中在线学习者的各种协作互动行为,促进了学习者关系网络的生成。对该网络进行深入分析,可以挖掘其中蕴含的社区学习的本质特征。该研究提出从社区发现角度对学习者关系网络进行分析,首先设计基于图卷积网络和非负矩阵分解,并集成学习者关系网络信息和文本内容信息的新型学习型社区发现方法,提出四个社区特征度量指标,在真实的学习者关系网络中进行应用分析。结果表明,所提出的分析方法能有效挖掘学习者关系网络存在的学习兴趣主题社区,还可以对社区整体和社区成员个体进行特征分析,并且分析结果可为引导学习者的在线交互协作行为提供决策支持。最后,形成“构建可视化学习型社区—开展社区之间知识分享—促进各社区的互动合作—指标反馈下的反思迭代”的学习型社区赋能策略。通过策略赋能学习型社区高质量发展,以社区新质生产力组合助推教育强国建设。

数字人文视域下20世纪留美作曲家社会网络关系分析

20世纪留美作曲家群体为近现代以来中国音乐的发展做出了不可替代的贡献。文章从数字人文角度出发,梳理多方数据来源,利用社会网络分析软件Ucinet构建20世纪留美作曲家群体社会关系网络,计算网络特征指标,并从师承关系、校友关系、同乡关系三个方面剖析了留美作曲家社会网络关系,具有多重发现,为中国近代音乐史、留学史以及作曲家群体研究提供了新的视角与方法。

商会关系、声誉机制与代理成本

以中国证券市场2004—2021年A股上市公司为研究对象,研究企业家(董事长或总经理)商会关系资本与企业代理成本之间的关系,并进一步考察企业家个人声誉机制对上述关系的潜在影响。研究结果表明:企业家商会关系能够降低企业代理成本,且企业家商会任职层级越高、任职种类越多,抑制作用越显著;当企业家拥有人大代表或政协委员的政治身份以及兼任其他上市公司董事的社会身份等个人声誉时,商会关系对企业代理成本的抑制作用较弱;渠道检验发现,激励机制和监督机制是企业家商会关系作用于企业代理成本的重要渠道;进一步分析发现,企业家行业协会关系、国家及省级商会关系对代理成本的抑制作用更加明显,在非国有企业中企业家商会关系对代理成本的抑制作用更加明显。研究结论丰富和拓展了关系社会资本的微观经济后果及作用机制,为深入理解商会关系文化对微观企业决策行为的作用机制提供了初步的理论分析框架和经验证据。

定位键指数用于含氧多环芳烃对斑马鱼胚胎的急性毒性预测

含氧多环芳烃化合物是一种在多环芳烃苯环上,含有一个或数个羰基氧原子的持久性污染物,它具有比亲代多环芳烃更强的毒性、致癌、致畸和致突变性,因此探究高效的研究方法来建立毒性数据库非常重要.为研究含氧多环芳烃对斑马鱼胚胎的急性毒性与其分子结构之间的定量结构-毒性关系,根据含氧多环芳烃分子中原子空间拓扑结构,提出了一种新的结构指数–定位键指数L,并分别计算了32个含氧多环芳烃化合物分子的电性拓扑态指数Em、电性距离矢量M_(n),分析优化筛选了电性拓扑态指数其中的E16、电性距离矢量其中的M_(14)、M_(21)和M_(32)作为结构描述符,将它们与定位键指数L有机结合,与含氧多环芳烃化合物对斑马鱼胚胎的生物毒性进行回归分析,以5种结构参数为输入变量点价值,神经网络结构采用5-3-1,建构了一种预测含氧多环芳烃急性毒性lgEC_(50)的神经网络模型,该预测模型的总相关系数R_(T)值达到较高的0.9826,对毒性的预测值与实验值两者之间的平均误差仅为0.112;结果表明,含氧多环芳烃的急性毒性与定位键指数、电性拓扑态指数、电性距离矢量等结构参数有良好的非线性关系.研究可为含氧多环芳烃的环境污染及生态风险评估提供理论指导.

中国数字经济空间动态演进的区域现状

数字经济已成为引领我国经济一体化高质量发展的主要驱动力,立足于国家整体经济及区域经济稳步协调发展的战略。文章基于中国2013—2020年省级数据,运用主成分分析法对中国数字经济进行测度,并运用社会网络分析及核密度等方法对我国数字经济动态演化的空间关联及区域差异进行研究。研究发现:(1)2013—2020年我国数字经济发展整体网络密度稳步提升但依旧处于低水平。(2)中国各省份数字经济发展两极分化,东部与西北、东北等边缘地区存在“数字经济鸿沟”,加之整体关联水平低,分化严重,发达地区对边缘地区的辐射效应较低,差距可能愈发扩大。(3)将其划分为四个板块,发现存在“弱关联”板块,板块间溢出关系及关联度差异悬殊,进一步研究各省份数字经济发展的“中间人”作用,发现各省份承担角色更是“极化”严重。研究结果可为我国持续深化数字经济建设,推动经济协调、稳定、高质量发展提供参考。

共享单车与公共交通多元关系分类及阈值研究

为准确评估共享单车和公共交通之间的复杂时空关系,根据替代或补充关系的具体成因类型,结合共享单车出行起讫点的分布情况,定义共享单车与公共交通多元关系;其次,考虑公共汽电车与轨道交通的差异性,提出一种基于弱监督全连接神经网络的共享单车与公共交通多元关系的分类模型,并根据共享单车轨迹数据,计算不同关系分类和交通方式下的公共交通可达范围、共享单车骑行时长及步行接驳距离边界阈值。结果表明:共享单车与公共交通的多元关系可分为接驳补充、空白补充、替代关系1和替代关系2。其中,共享单车与公共汽电车多元关系的三参数划分阈值分别为329.75 m、5.07 min和182.93 m,与轨道交通多元关系的划分阈值分别为816.96 m、10.27 min和653.91 m。共享单车与公共汽电车以替代关系1为主,占总行程的54.98%;共享单车与轨道交通以空白补充关系为主,占总行程的48.90%。共享单车与公共汽电车关系主要为多元替代与接驳补充,与轨道交通主要为多元补充与替代关系2。共享单车与公共汽电车的关系相比于轨道交通会更为复杂。本文可为促进共享单车与公共交通在各自优势距离上的协同发展提供支撑。

合作网络嵌入对企业技术创新绩效的影响——基于知识网络凝聚性视角

基于网络嵌入理论和知识基础理论,利用我国电子信息产业114家企业的面板数据,实证分析了合作网络嵌入对企业技术创新绩效的影响,并进一步探究企业知识网络的凝聚水平对该过程的调节作用。结果表明,合作网络中的关系嵌入对企业技术创新绩效具有正向影响,结构嵌入对技术创新绩效具有负向影响;企业知识网络的全局凝聚性在结构嵌入对企业技术创新绩效的影响中具有正向调节作用,在关系嵌入对技术创新绩效的影响中不具有调节作用;企业知识网络局部凝聚水平在关系嵌入和结构嵌入对技术创新绩效的影响中均具有正向调节作用;合作网络嵌入对东部地区企业的技术创新绩效影响更为显著。

基于卷积神经网络的医疗护理实体关系抽取

针对医疗护理领域知识复杂性强、数据量大以及对准确度要求较高的问题,该研究提出一种基于卷积神经网络的医疗护理学实体关系抽取方法,实现对护理学语义关系的细粒度文本挖掘。该研究构建了医疗护理学语料标注系统,通过将医疗语料转化为向量特征矩阵,实现了对医疗语料的自动过滤和标注。通过向神经网络模型嵌入所构建的医疗关系语料库,一定程度上提高了模型疾病分类的准确度。在医疗护理学数据集上的实验表明,基于卷积神经网络的模型在指标精确度、召回率、F1值可达到89.78%、87.59%、89.77%。综上所述,该研究提出的基于卷积神经网络的医疗护理学实体关系抽取方法能够有效地抽取医疗语料数据中的实体关系,优于传统的实体关系抽取模型。

融合用户社交关系的自适应图卷积推荐算法

为了缓解推荐系统中不同用户社交空间与兴趣空间的内在信息差异和忽视高阶邻居的问题,提出了一种融合用户社交关系的自适应图卷积推荐算法(adaptive graph convolutional recommendation algorithm integrating user social relationships,AGCRSR)。首先,模型在嵌入层使用映射矩阵将初始特征向量转换为自适应嵌入;其次,引入注意力机制聚合不同方面的用户嵌入,通过图卷积网络来线性学习用户和项目的潜在表示;最后,通过自适应模块聚合用户表示并利用内积函数预测用户对项目的最终推荐结果。在数据集LastFM和Ciao上与其他基线算法进行了对比实验,实验结果表明AGCRSR的推荐效果较其他算法有显著提升。

基于BP神经网络的胶结砂砾石应力-应变关系预测

在前期宏观试验基础之上,采用离散元模拟和BP神经网络相结合的方法获取不同胶凝材料掺量和围压下胶结砂砾石的应力-应变关系。根据前期胶凝材料掺量分别为20、40、60、80、100 kg/m3的胶结砂砾石三轴排水剪切试验结果,开展离散元数值模拟。以试验数据为学习样本,开展BP神经网络模型训练,预测胶凝材料掺量分别为30、50、70、90 kg/m3的胶结砂砾石应力-应变关系,并将预测结果和离散元模拟结果进行对比。研究结果表明,BP神经网络能够实现胶结砂砾石应力-应变关系的预测,并在较低围压下具有较好的精度。

基于细胞模型和网络药理学探究儿茶素抗炎、抗癌的构效关系

目的:系统揭示茶叶儿茶素的抗炎、抗癌构效关系,并初步探究其背后的分子作用机制。方法:利用脂多糖诱导的小鼠巨噬细胞RAW264.7和人结肠癌细胞HCT116为体外炎症、癌症模型,采用格里斯试剂法与四甲基偶氮噻唑蓝比色法分析比较儿茶素的体外抗炎、抗癌活性,借助网络药理学预测各儿茶素抗炎、抗癌的关键靶点与通路,并通过分子对接技术模拟各儿茶素与关键靶点蛋白的相互作用,比较各单体与关键靶点的结合能力。结果:8种儿茶素单体均有较强的体外抗炎、抗癌活性,酯型儿茶素的没食子酰基、焦酚型儿茶素B环的邻苯三酚结构和反式儿茶素C_(2)、C_(3)的反式构象使其具有更强的抗炎、抗癌活性,但酯型、焦酚型儿茶素的没食子酰基与其B环的邻苯三酚结构间存在拮抗效应;8种儿茶素通过多靶点、多通路发挥抗炎、抗癌活性,且没食子酰基有利于儿茶素与其关键抗炎、抗癌靶蛋白IL6、TNF、AKT1通过氢键结合。本文揭示了儿茶素的体外抗炎、抗癌活性及构效关系,为系统、深入阐释儿茶素的功能特性与分子结构等关系提供了理论和技术借鉴。

基于潜层关系增强的实体和关系联合抽取

为充分发掘文本序列中潜层语义关系信息,提出一种实体和关系联合抽取的潜层关系增强模型SREM(text subtext relationship enhancement model)。在潜层关系表示层利用结构化对齐的方式,获取并保持文本序列中的语义信息结构。在融合注意力机制的关系网络层中对数据进行建模,提高模型对文本词汇间关系信息的捕获能力。结合注意力机制获取细粒度语义信息,对上下文信息进行选择过滤。实验结果表明,在数据集NYT和WebNLG上取得的F1值分别为92.40%和92.52%,验证了模型的有效性。