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Analysis of Collaborative Governance of Network Public Opinion in Social Circles Based on Blockchain Traceability Strategy: An Evolutionary Game Theory Approach 基于区块链溯源策略的社交圈网络舆情协同治理分析:一种进化博弈论方法
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-24 DOI: 10.1155/cplx/8884816
Zeguo Qiu, Yunhao Chen, Hao Han, Yuchen Yin

In the context of rapid development of social networks, users gather together through social network platforms (SNPs) to form social circles, which become the main carriers for users to disseminate public opinion information and emotions, and it is easy to form the phenomenon of network public opinion polarization in social circles. To prevent the polarization of network public opinions in social circles, the application of blockchain technology to SNPs can provide a credible database for the traceability of public opinions and avoid the spread of false information in social circles. Based on blockchain traceability, this paper establishes a three-party evolutionary game model consisting of government, SNPs, and social circle managers (SCMs). Through model solving and numerical simulation, the influencing factors of each party’s strategy are analyzed. The findings suggest: (1) Although the rising cost of building blockchain platforms may undermine the government’s incentive to regulate, the government will still prioritise support for their construction if they can significantly enhance social welfare. (2) The government could boost SNPs’ willingness to adopt blockchain technology through subsidy measures, but increased privacy risks could undermine this willingness. (3) Blockchain technology realizes the traceability of public opinion and improves the rational guidance tendency of SCMs. Therefore, in order to promote and strengthen the collaborative governance of network public opinion in social circles, SNPs should enhance the use of blockchain technology, while the government should give full play to its regulatory and supervisory roles. This study introduces blockchain technology for public opinion traceability; explores a collaborative network public opinion governance system based on strict regulation by the government, introduction of blockchain technology by SNPs, and rational guidance by SCMs; expands the related research on network public opinion governance; and provides a new idea for the study of network public opinion governance of social circles based on blockchain traceability.

在社交网络快速发展的背景下,用户通过社交网络平台(snp)聚集在一起形成社交圈,成为用户传播舆情信息和情绪的主要载体,极易形成社交圈内网络舆情两极分化的现象。为防止网络舆情在社交圈的两极分化,将区块链技术应用于snp,可以为舆情溯源提供可信的数据库,避免虚假信息在社交圈的传播。基于区块链可追溯性,本文建立了由政府、snp和社交圈管理者(scm)组成的三方进化博弈模型。通过模型求解和数值模拟,分析了各方策略的影响因素。研究结果表明:(1)尽管建设区块链平台的成本上升可能会削弱政府的监管动机,但如果区块链平台能够显著提高社会福利,政府仍将优先支持其建设。(2)政府可以通过补贴措施提高snp采用区块链技术的意愿,但隐私风险的增加会破坏这种意愿。(3)区块链技术实现了舆论的可追溯性,提高了scm的理性引导倾向。因此,为了促进和加强社交圈网络舆情的协同治理,snp应加强区块链技术的使用,而政府应充分发挥其监管和监督作用。本研究引入区块链技术进行舆情溯源;探索政府严格监管、snp引入区块链技术、scm合理引导的协同网络舆情治理体系;拓展网络舆情治理的相关研究;为基于区块链溯源的社交圈网络舆情治理研究提供了一种新的思路。
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引用次数: 0
AI-Powered Precision in Diagnosing Tomato Leaf Diseases 人工智能在番茄叶片疾病诊断中的应用
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-13 DOI: 10.1155/cplx/7838841
MD Jiabul Hoque, Md. Saiful Islam, Md. Khaliluzzaman

Correct detection of plant diseases is critical for enhancing crop yield and quality. Conventional methods, such as visual inspection and microscopic analysis, are typically labor-intensive, subjective, and vulnerable to human error, making them infeasible for extensive monitoring. In this study, we propose a novel technique to detect tomato leaf diseases effectively and efficiently through a pipeline of four stages. First, image enhancement techniques deal with problems of illumination and noise to recover the visual details as clearly and accurately as possible. Subsequently, regions of interest (ROIs), containing possible symptoms of a disease, are accurately captured. The ROIs are then fed into K-means clustering, which can separate the leaf sections based on health and disease, allowing the diagnosis of multiple diseases. After that, a hybrid feature extraction approach taking advantage of three methods is proposed. A discrete wavelet transform (DWT) extracts hidden and abstract textures in the diseased zones by breaking down the pixel values of the images to various frequency ranges. Through spatial relation analysis of pixels, the gray level co-occurrence matrix (GLCM) is extremely valuable in delivering texture patterns in correlation with specific ailments. Principal component analysis (PCA) is a technique for dimensionality reduction, feature selection, and redundancy elimination. We collected 9014 samples from publicly available repositories; this dataset allows us to have a diverse and representative collection of tomato leaf images. The study addresses four main diseases: curl virus, bacterial spot, late blight, and Septoria spot. To rigorously evaluate the model, the dataset is split into 70%, 10%, and 20% as training, validation, and testing subsets, respectively. The proposed technique was able to achieve a fantastic accuracy of 99.97%, higher than current approaches. The high precision achieved emphasizes the promising implications of incorporating DWT, PCA, GLCM, and ANN techniques in an automated system for plant diseases, offering a powerful solution for farmers in managing crop health efficiently.

正确检测植物病害对提高作物产量和品质至关重要。传统的方法,如目视检查和显微分析,通常是劳动密集型的,主观的,容易受到人为错误的影响,使它们不适合广泛的监测。在这项研究中,我们提出了一种新的技术,通过四个阶段的管道有效地检测番茄叶片疾病。首先,图像增强技术处理光照和噪声问题,以尽可能清晰准确地恢复视觉细节。随后,包含疾病可能症状的感兴趣区域(roi)被准确捕获。然后将roi输入到k均值聚类中,该聚类可以根据健康和疾病分离叶子部分,从而允许诊断多种疾病。在此基础上,提出了三种方法的混合特征提取方法。离散小波变换(DWT)通过将图像的像素值分解到不同的频率范围来提取病变区域中隐藏和抽象的纹理。通过像素的空间关系分析,灰度共生矩阵(GLCM)在提供与特定疾病相关的纹理模式方面具有非常重要的价值。主成分分析是一种用于降维、特征选择和冗余消除的技术。我们从公开的存储库中收集了9014个样本;这个数据集允许我们有一个多样化和代表性的番茄叶片图像集合。该研究针对四种主要疾病:卷曲病毒、细菌性斑疹、晚疫病和Septoria斑疹。为了严格评估模型,数据集被分为70%、10%和20%,分别作为训练、验证和测试子集。所提出的技术能够达到99.97%的惊人准确率,高于目前的方法。实现的高精度强调了在植物病害自动化系统中结合DWT, PCA, GLCM和ANN技术的前景,为农民有效管理作物健康提供了强大的解决方案。
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引用次数: 0
Scale-Dependent Inverse Temperature Features Associated With Crashes in the US and Japanese Stock Markets 与美国和日本股市崩盘相关的尺度依赖的逆温度特征
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-10 DOI: 10.1155/cplx/9451788
Peter Tsung-Wen Yen, Siew Ann Cheong

Some complex systems (e.g., an ecosystem) in direct contact with an environment can be assigned the temperature of the environment. Other complex systems, such as human beings, can maintain a core temperature of 36.5°C in environments with different temperatures, at least for a short period of time. Finally, for complex systems such as financial markets, whose environments we understand very little of, is there even a reasonable way to define a temperature? It is clear that human beings are almost never in thermal equilibrium with their surroundings, but can financial markets achieve detailed balance independently at all scales, or is information flow in such systems different at different scales? If we combine the information-theoretic picture with the thermodynamics picture of entropy, temperature is the driving force for changes in information content of a system. From an interactions point of view, the information content of a financial market can be computed from the cross correlations between its stocks. In their 2015 paper, Ye et al. (2015) constructed the normalized graph Laplacians in different time periods based on strong cross correlations between stocks listed on the New York Stock Exchange. By writing the partition function in terms of polynomials of the normalized graph Laplacian, Ye et al. computed the average energy E, entropy S, and inverse temperature β = 1/kBT. This led us to an information-based definition of the inverse temperature. In this work, we investigated the inverse temperature β(ϵ,  n) at different times n and scales ϵ for two mature financial markets, using the S&P 500 and Nikkei 225 cross sections of stocks from January 2007 to May 2023. In the dynamics of β, the most prominent features are peaks at various times. We identified five esoteric and seven characteristic peaks and studied how they change with scale ϵ. The latter consists of a negative power-law dip followed by a positive power-law rise, with exponents narrowly distributed between 0.3–0.4. In addition, we constructed heat maps of β that reveal positive-, negative-, and infinite-slope cascades that hint at their possible exogenous and endogenous origins. Notably, the heat map of β confirmed that the 2007−2009 Global Financial Crisis was an endogenous crash in the US market, which in turn caused an exogenous crash in the Japanese stock market. To better understand the evolution of β, we analyzed ΔJ (the difference in the number of links) and ΔQ (the difference in the number of triangles) and found they oscillate in time. Occasionally, very intense swings of ΔQemerge over all scales, suggesting significant market-level reconstructions at these times.

一些与环境直接接触的复杂系统(如生态系统)可以被赋予环境的温度。其他复杂系统,如人类,可以在不同温度的环境中至少在短时间内保持36.5°C的核心温度。最后,对于像金融市场这样的复杂系统,我们对其环境了解甚少,是否存在一种合理的方法来定义温度?很明显,人类几乎从来没有与周围环境处于热平衡状态,但金融市场是否可以在所有尺度上独立地实现详细的平衡,或者在不同的尺度上,这种系统中的信息流是不同的?如果我们把信息论的图像和熵的热力学图像结合起来,温度就是系统信息含量变化的驱动力。从相互作用的角度来看,金融市场的信息含量可以通过股票之间的相互关系来计算。Ye et al.(2015)在其2015年的论文中,基于纽约证券交易所上市股票之间的强相互关联,构建了不同时间段的归一化图拉普拉斯算子。通过将配分函数写成归一化图拉普拉斯函数的多项式,Ye等人计算出平均能量E、熵S和逆温度β = 1/kBT。这使我们得到了逆温度的基于信息的定义。在这项工作中,我们使用2007年1月至2023年5月的标准普尔500指数和日经225指数的股票横截面,研究了两个成熟金融市场在不同时间n和尺度上的逆温度β(λ, n)。在β动力学中,最显著的特征是不同时间的峰值。我们确定了5个深奥峰和7个特征峰,并研究了它们是如何随刻度柱变化的。后者由负幂律下降和正幂律上升组成,指数狭窄地分布在0.3-0.4之间。此外,我们构建了β的热图,揭示了正斜率、负斜率和无限斜率级联,暗示了它们可能的外源性和内源性起源。值得注意的是,β热图证实了2007 - 2009年全球金融危机是美国市场的内生崩溃,这反过来又导致了日本股市的外生崩溃。为了更好地理解β的进化,我们分析了ΔJ(链接数量的差异)和ΔQ(三角形数量的差异),发现它们在时间上振荡。偶尔,ΔQemerge在所有尺度上都有非常剧烈的波动,这表明在这些时候市场层面出现了重大的重建。
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引用次数: 0
Prevention of EMT-Mediated Metastasis via Optimal Modulation Strategies for the Dysregulated WNT Pathway Interacting With TGF-β 通过与TGF-β相互作用的失调WNT通路的最佳调节策略预防emt介导的转移
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-04 DOI: 10.1155/cplx/9007322
Sooyoun Choi, Yunil Roh, Yong Dam Jeong, Il Hyo Jung

Cancer metastasis is one of the leading causes of death in cancer patients. Dysregulation of the WNT signaling pathway is known to increase the risk of cancer metastasis by leading to excessive accumulation of β-catenin, which activates epithelial–mesenchymal transition (EMT) mechanisms that induce cell motility. Although mono and combination therapies are being developed to prevent metastasis by controlling the abnormally elevated levels of β-catenin, there are limitations in comparing and predicting the treatment effects due to the complexity of cell signaling pathways. In addition, uncertainty exists in determining the optimal combination ratio of each therapy in combination treatments. In this study, we aim to address these challenges by investigating optimal modulation strategies to minimize β-catenin concentration, using a mathematical model that comprehensively describes the interactions between the WNT signaling pathway and transforming growth factor-β (TGF-β) involved in EMT, along with optimal control theory. We analyze the efficacy of monotherapy strategies to prevent the hyperactivation of β-catenin and quantitatively determine the optimal combination ratio for preventing EMT, based on the E-cadherin biomarker as an indicator of EMT. Furthermore, we identify the optimal therapy protocol that minimizes patient burden while maximizing therapeutic efficacy by incorporating considerations of control sequences and delay times. Our findings are expected to not only enhance the understanding of the complex signaling pathways underlying cancer metastasis but also contribute to the development of novel therapeutic approaches.

癌症转移是癌症患者死亡的主要原因之一。众所周知,WNT信号通路的失调会导致β-catenin的过度积累,从而增加癌症转移的风险,β-catenin会激活诱导细胞运动的上皮-间质转化(EMT)机制。尽管通过控制β-catenin的异常升高水平来预防转移的单药和联合治疗正在开发中,但由于细胞信号通路的复杂性,在比较和预测治疗效果方面存在局限性。此外,在确定联合治疗中各疗法的最佳联合比例时,存在不确定性。在本研究中,我们的目标是通过研究最小化β-catenin浓度的最佳调制策略来解决这些挑战,使用一个数学模型,全面描述WNT信号通路与EMT中涉及的转化生长因子-β (TGF-β)之间的相互作用,以及最优控制理论。我们分析了单药治疗策略预防β-catenin过度激活的效果,并基于E-cadherin生物标志物作为EMT的指标,定量确定了预防EMT的最佳组合比例。此外,我们通过考虑控制序列和延迟时间,确定了最大限度地减少患者负担,同时最大限度地提高治疗效果的最佳治疗方案。我们的研究结果不仅有助于加深对癌症转移的复杂信号通路的理解,而且有助于开发新的治疗方法。
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引用次数: 0
Detection of Effective Devices in Information Dissemination on the Complex Social Internet of Things Networks Based on Device Centrality Measures 基于设备中心性测度的复杂社会物联网网络信息传播有效设备检测
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-04 DOI: 10.1155/cplx/2919169
Wei Deng, Junqi Deng, Peyman Arebi

The Complex Social Internet of Things (CSIoT) integrates the connectivity of IoT with the relational dynamics of complex social networks, creating systems where devices autonomously form and manage relationships. The centrality measures specify the topological characteristics of each node in terms of local and global information of the node in the network. The detection of effective devices in disseminating information across CSIoT networks is critical for optimizing communication, improving network performance, and ensuring efficient resource utilization. In this paper, temporal centrality measures are used to identify influential devices in information dissemination. For this purpose, first, the centrality measures for SIoT network devices have been redefined, and then, using the SIR model, each of the measures has been evaluated in terms of the success rate in identifying effective devices in information dissemination. The results have shown that in SIoT networks that have a high clustering coefficient, the centrality measures of closeness and betweenness have a better performance in identifying influential devices that are effective in spreading information. Also, for networks that have a high degree of heterogeneity, the device coreness centrality and device Katz centrality measures perform better than other measures. Finally, the results show that mobile devices play a more important role in disseminating information than static devices.

复杂社交物联网(CSIoT)将物联网的连接性与复杂社交网络的关系动态相结合,创建了设备自主形成和管理关系的系统。中心性度量根据网络中节点的局部和全局信息指定每个节点的拓扑特征。检测跨CSIoT网络传播信息的有效设备对于优化通信、提高网络性能和确保有效的资源利用至关重要。本文采用时间中心性度量来识别信息传播中的影响因素。为此,首先对SIoT网络设备的中心性度量进行了重新定义,然后使用SIR模型,根据识别信息传播中有效设备的成功率对每个度量进行了评估。结果表明,在具有高聚类系数的SIoT网络中,接近度和中间度的中心性度量在识别有效传播信息的有影响力设备方面具有更好的性能。此外,对于具有高度异质性的网络,设备核心中心性和设备Katz中心性度量比其他度量表现更好。最后,研究结果表明,移动设备在传播信息方面比静态设备发挥更重要的作用。
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引用次数: 0
EIM: A Novel Evolutionary Influence Maximizer in Complex Networks 复杂网络中一种新的进化影响最大化器
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-04 DOI: 10.1155/cplx/9973872
Vahideh Sahargahi, Vahid Majidnezhad, Saeid Taghavi Afshord, Yasser Jafari

This study addresses influence maximization in complex networks, aiming to identify optimal seed nodes for maximal cascades. Greedy methods, though effective, prove inefficient for large-scale social networks. This article introduces a double-chromosome evolutionary algorithm to tackle this challenge efficiently. This method introduces a smart operator for stochastic selection based on the node degree to initialize the primary solutions. A novel smart approach was also employed to improve the convergence of the proposed method by ranking the nodes existing in the current solution and using a blacklist to reduce the probability of selecting the nodes that might be influenced by the selected nodes. Moreover, a novel local search operator with appropriate efficiency was proposed to increase influence. To maintain solution diversity, a population diversity retention operator is integrated. Experimental evaluations on six real-world networks revealed the algorithm’s superiority in terms of influence rates, consistently outperforming the DPSO algorithm and ranking second to CELF with minimal margin according to statistical analysis using the Friedman test. For runtime efficiency, the proposed method demonstrated significantly shorter execution times compared to CELF and DPSO, showcasing its scalability and robustness. These results underscore the method’s effectiveness for applications requiring accurate identification of influential nodes.

本研究解决了复杂网络中的影响最大化问题,旨在识别最大级联的最佳种子节点。贪婪的方法虽然有效,但对于大规模的社会网络来说是低效的。本文介绍了一种双染色体进化算法来有效地解决这一挑战。该方法引入了一种基于节点度的随机选择智能算子来初始化初级解。采用一种新颖的智能方法对当前解中存在的节点进行排序,并使用黑名单来降低选择可能受所选节点影响的节点的概率,从而提高所提方法的收敛性。在此基础上,提出了一种具有适当效率的局部搜索算子,以提高算法的影响。为了保持解的多样性,集成了种群多样性保持算子。在六个现实世界网络上的实验评估显示,该算法在影响率方面具有优势,根据使用Friedman测试的统计分析,该算法始终优于DPSO算法,并以最小的差距排名第二,仅次于CELF。在运行时效率方面,与CELF和DPSO相比,该方法的执行时间明显缩短,显示了其可扩展性和鲁棒性。这些结果强调了该方法在需要准确识别影响节点的应用中的有效性。
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引用次数: 0
On Novel Design Methods of Fixed-Time State Observation and Consensus Control for Linear Leader–Follower Multiagent System 线性Leader-Follower多智能体系统固定时间状态观察与共识控制的新设计方法
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-22 DOI: 10.1155/cplx/6615172
Hongliang Li, Fan Liu, Yilin Liu, Wuneng Zhou, Kaili Liao

In this paper, the fixed-time consensus problem for leader–follower multiagent systems with directed graphs is discussed. First, a new fixed-time state observer of the multiagent system is designed. In the new state observer, an auxiliary matrix is introduced which can be theoretically obtained by solving a linear matrix inequality. The important role of the auxiliary matrix is that it makes the upper bound of the fixed time for the state observation of multiagent system solvable and more accurate, which enable the observation error system converges faster. Based on the new fixed-time state observer, a sufficient condition is given with which the observation of the state of multiagent system can be reached in fixed time. Second, a new fixed-time control protocol is designed for the leader–follower multiagent system. In the new controller, another auxiliary matrix is introduced which can also be theoretically obtained with linear matrix inequality. With the new control protocol, the closed-loop leader–follower multiagent system is theoretically shown that the fixed-time consensus can be reached in fixed time by means of the concept of fixed-time stability, Lyapunov stability theory, and LaSalle’s invariance principle. The important role of the new control protocol is that it also makes the fixed time for the consensus multiagent system solvable and then the closed-loop multiagent system converges faster. Finally, some numerical simulations are presented to demonstrate convincingly the superiority of the method and results obtained in this paper. From the simulations, it can be seen that in comparison with some existing works, the estimate of fixed time for the consensus problem may be more accurate and faster.

讨论了具有有向图的多智能体系统的定时一致性问题。首先,设计了一种新的多智能体系统定时状态观测器。在新的状态观测器中,引入了一个辅助矩阵,该辅助矩阵可以通过求解线性矩阵不等式从理论上得到。辅助矩阵的重要作用是使多智能体系统状态观测的固定时间上界可解且更精确,从而使观测误差系统收敛得更快。基于新的固定时间状态观测器,给出了多智能体系统在固定时间内达到状态观测的充分条件。其次,针对领导-随从多智能体系统设计了一种新的固定时间控制协议。在新的控制器中,引入了另一个辅助矩阵,该辅助矩阵也可以用线性矩阵不等式在理论上得到。利用新的控制协议,利用定时稳定性概念、Lyapunov稳定性理论和LaSalle不变性原理,从理论上证明了闭环leader-follower多智能体系统在固定时间内可以达到定时一致性。新控制协议的重要作用是使共识多智能体系统的固定时间可解,从而使闭环多智能体系统收敛得更快。最后,给出了数值模拟,令人信服地证明了本文方法和结果的优越性。从仿真中可以看出,与现有的一些工作相比,对于共识问题的固定时间估计可能更加准确和快速。
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引用次数: 0
Complex Dynamics and Chaos Control of Discrete Prey–Predator Model With Caputo Fractional Derivative 具有Caputo分数阶导数的离散捕食-捕食模型的复杂动力学与混沌控制
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-21 DOI: 10.1155/cplx/4415022
Rowshon Ara, Sohel Rana S. M.

This work examines a discrete prey–predator model using the fractional derivative. The conditions for the existence and stability of the fixed points in the model are identified. The analysis is centered on exploring various bifurcations at the positive fixed point to understand their ecological implications. Using bifurcation theory, bifurcations related to period doubling, Neimark–Sacker, and strong resonances are studied. Lastly, the analytical results are confirmed through numerical simulations using the MATLAB package MatContM, and a controller is applied to relieve the extreme instability within the system.

这项工作检查了一个离散的猎物-捕食者模型使用分数阶导数。确定了模型中不动点存在和稳定的条件。分析的重点是探索在正不动点上的各种分岔,以了解它们的生态含义。利用分岔理论,研究了与周期加倍、内马克-萨克和强共振有关的分岔。最后,利用MATLAB软件包MatContM对分析结果进行数值仿真验证,并采用控制器来缓解系统内部的极端不稳定性。
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引用次数: 0
Deep Recognition of Public Opinion Reversals in Critical Incidents Based on Hypernetwork Architecture 基于超网络架构的重大事件舆情逆转深度识别
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-16 DOI: 10.1155/cplx/6858524
Xuna Wang, Lifan Zhang

With the development of social media and online platforms, the speed of dissemination and influence of emergencies in cyberspace have increased significantly. The rapid change of public opinion, especially the reversal of public opinion, may have a significant impact on social stability and government credibility. The hypernetwork structure has complex multilevel and multidimensional characteristics, and it is of great significance to analyze the multiple participating subjects of public opinion evolution and their complex relationships based on the hypernetwork theory, and to further identify the public opinion reversal for the public opinion response and guidance of emergencies. According to the complex interaction between the participants of emergencies and internal and external factors, this paper constructs a hypernetwork model including four subnets of users, time series, opinions, and emotions, and analyzes the network structure in detail. On this basis, the method steps of emergency public opinion inversion recognition are proposed. Taking the public opinion event caused by Hongxing Erke donation during the rainstorm in Henan Province of China as an example, the empirical analysis is carried out. The research shows that the proposed emergency hypernetwork model provides effective support for the identification of public opinion inversion, and the identification method of public opinion inversion based on the hypernetwork is helpful to find the trend of public opinion evolution, so as to infer the tendency of public opinion inversion, which provides new ideas for the related research of public opinion monitoring and emergency response.

随着社交媒体和网络平台的发展,突发事件在网络空间的传播速度和影响力大幅提升。舆情的快速变化,尤其是舆情的逆转,可能对社会稳定和政府公信力产生重大影响。超网络结构具有复杂的多层次、多维度特征,基于超网络理论分析舆情演变的多元参与主体及其复杂关系,进一步识别舆情反转对突发事件的舆论应对和引导具有重要意义。根据突发事件参与主体与内外部因素的复杂互动关系,本文构建了包括用户、时序、舆情、情绪四个子网的超网络模型,并对网络结构进行了详细分析。在此基础上,提出了突发事件舆情反转识别的方法步骤。以河南暴雨中红星二客捐款引发的舆情事件为例,进行了实证分析。研究表明,所提出的突发事件超网络模型为舆情反转识别提供了有效支撑,基于超网络的舆情反转识别方法有助于发现舆情演变趋势,从而推断舆情反转的倾向性,为舆情监测与应急处置的相关研究提供了新思路。
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引用次数: 0
Predicting Course Grades Through Comprehensive Modeling of Students’ Learning Behavioral Patterns 通过学生学习行为模式的综合建模预测课程成绩
IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-07 DOI: 10.1155/cplx/8851264
Danial Hooshyar, Yeongwook Yang

While modeling students’ learning behavior or preferences has been found to be a crucial indicator for their course achievement, very few studies have considered it in predicting the achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior models constitute a comprehensive student’s learning behavioral pattern which is later used for the prediction of their course achievement. Lastly, using a multiple-criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with high accuracy through modeling their learning behavior during online courses.

虽然已经发现学生的学习行为或偏好建模是学生课程成绩的一个重要指标,但很少有研究在预测学生的在线课程成绩时考虑到这一点。本研究旨在对学生在线学习行为进行建模,并据此预测学生的课程成绩。首先,在课程中使用聚合的动作日志来开发特征向量。其次,将其中一些特征向量量化为三个数值,用于模拟学生的学习行为,即访问学习资源(content access)、与同伴互动(engagement)和参加评估测试(assessment)。学生的特征向量和行为模型构成了一个全面的学生学习行为模式,并用于预测学生的课程成绩。最后,采用多准则决策方法(即TOPSIS),确定了不同规模课程的最佳分类方法。我们的研究结果表明,所提出的概化方法可以成功地预测学生在不同人数和不同特征的课程中的成绩,显示了该方法的稳定性。决策树和AdaBoost分类方法在不同的数据集上表现优于其他现有方法。此外,我们的研究结果证明,通过建模学生在网络课程中的学习行为来准确预测学生的课程成绩是可行的。
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引用次数: 0
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Complexity
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