Search Results (211)

The rapid evolution of Industry 4.0 technologies has resulted in a scenario in which collaborative networks are essential to overcome the challenges related to their implementation. However, the frameworks to guide such collaborations remain underexplored. This study addresses this gap by proposing Business Rules and Goals Models to operationalize Industry 4.0 solutions through enterprise collaboration. Using the For Enterprise Modeling (4EM) method, the research integrates qualitative insights from expert opinions, including interviews with 12 professionals (academics, industry professionals, and consultants) from Brazilian manufacturing sectors. The Goals Model identifies five main objectives—competitiveness, efficiency, flexibility, interoperability, and real-time collaboration—while the Business Rules Model outlines 18 actionable recommendations, such as investing in digital infrastructure, upskilling employees, and standardizing information technology systems. The results reveal that cultural resistance, limited resources, and knowledge gaps are critical barriers, while interoperability and stakeholder integration emerge as enablers of digital transformation. The study concludes that successfully adopting Industry 4.0 requires technological investments, organizational alignment, structured governance, and collaborative ecosystems. These models provide a practical roadmap for companies navigating the complexities of Industry 4.0, emphasizing adaptability and cross-functional synergy. The research contributes to the literature on collaborative networks by connecting theoretical frameworks with actionable enterprise-level strategies. Full article

With the rapid evolution of social computing, online sentiments have become valuable information for analyzing the latent structure of social networks. Sentiment leaders in social networks are those who bring in new information, ideas, and innovations, disseminate them to the masses, and thus influence the opinions and sentiment of others. Identifying sentiment leaders can help businesses predict marketing campaigns, adjust marketing strategies, maintain their partnerships, and improve their products’ reputations. However, capturing the complex sentiment dynamics from multi-hop interactions and trust/distrust relationships, as well as identifying leaders within sentiment-aligned communities while maximizing sentiment spread efficiently through both direct and indirect paths, is a significant challenge to be addressed. This paper pioneers a challenging and important problem of sentiment leader identification in social networks. To this end, we propose an original solution framework called “SentiRank” and develop the associated algorithms to identify sentiment leaders. SentiRank contains three key technical steps: (1) constructing a sentiment graph from a social network; (2) detecting sentiment communities; (3) ranking the nodes on the selected sentiment communities to identify sentiment leaders. Extensive experimental results based on the real-world datasets demonstrate that the proposed framework and algorithms outperform the existing algorithms in terms of both one-step sentiment coverage and all-path sentiment coverage. Furthermore, the proposed algorithm performs around 6.5 times better than the random approach in terms of sentiment coverage maximization. Full article

The evolution of public opinion on social media affects societal security and stability. To effectively control the societal impact of public opinion evolution, it is essential to study its underlying mechanisms. Public opinion evolution on social media primarily involves two processes: information dissemination and opinion interaction. However, existing studies overlook the bidirectional coupling relationship between these two processes, with limitations such as weak coupling and insufficient consideration of individual heterogeneity. To address this, we propose the SIS-FJ model with a bidirectional coupling mechanism, which combines the strengths of the SIS (Susceptible–Infected–Susceptible) model in information dissemination and the FJ (Friedkin–Johnsen) model in opinion interaction. Specifically, the SIS model is used to describe information dissemination, while the FJ model is used to describe opinion interaction. In the computation of infection and recovery rates of the SIS model, we introduce the opinion differences between individuals and their observable neighbors from the FJ model. In the computation of opinion values in the FJ model, we introduce the node states from the SIS model, thus achieving bidirectional coupling between the two models. Moreover, the model considers individual heterogeneity from multiple aspects, including infection rate, recovery rate, and individual susceptibility. Through simulation experiments, we investigate the effects of initial opinion distribution, individual susceptibility, and network structure on public opinion evolution. Interestingly, neither initial opinion distribution, individual susceptibility, nor network structure exerts a significant influence on the proportion of disseminating and non-disseminating individuals at termination. Furthermore, we optimize the model by adjusting the functions for infection and recovery rates. Full article

Scenario analysis and the modeling of typhoons are fundamental prerequisites for effective emergency decision-making. However, current studies on typhoon scenario modeling lack analyses of cascading effects and users’ concerns, failing to represent cascading disaster impacts and user adaptability. This study constructs a scenario evolution model for typhoons and their cascading disasters through typhoon-related public opinion mining and an analysis of disaster evolution characteristics to address these limitations. Specifically, this study analyzes and extracts information about users’ sentiments and concerns based on public opinion data. Then, public opinion and typhoon evolution progression analyses are conducted, identifying cascading disaster evolution characteristics to determine scenario elements. The scenario model is constructed by calculating scenario node probability distributions using dynamic Bayesian networks (DBNs). In this study, Typhoon Bebinca is selected to verify the proposed scenario model; the results demonstrate that the model is reliable and its evolution process aligns with the impacts of typhoon cascading disasters. This study also reveals two critical insights: (1) Users’ concerns will change with typhoon evolution. (2) Emergency measures for dealing with typhoons and their cascading disasters are fragmented. It is essential to consider their cascading effects when enacting these measures. These findings provide novel insights that could aid government agencies in their decision making. Full article

With the rapid development of the Internet, the speed and scope of sudden public events disseminating in cyberspace have grown significantly. Current methods of quantifying public opinion heat often neglect emotion-driven factors and user interaction behaviors, making it difficult to accurately capture fluctuations during dissemination. To address these issues, first, this study addressed the complexity of interaction behaviors by introducing an approach that employs the information gain ratio as a weighting indicator to measure the “interaction heat” contributed by different interaction attributes during event evolution. Second, this study built on SnowNLP and expanded textual features to conduct in-depth sentiment mining of large-scale opinion texts, defining the variance of netizens’ emotional tendencies as an indicator of emotional fluctuations, thereby capturing “emotional heat”. We then integrated interactive behavior and emotional conflict assessment to achieve comprehensive heat index to quantification and dynamic evolution analysis of online public opinion heat. Subsequently, we used Hodrick–Prescott filter to separate long-term trends and short-term fluctuations, extract six key quantitative features (number of peaks, time of first peak, maximum amplitude, decay time, peak emotional conflict, and overall duration), and applied K-means clustering algorithm (K-means) to classify events into three propagation patterns, which are extreme burst, normal burst, and long-tail. Finally, this study conducted ablation experiments on critical external intervention nodes to quantify the distinct contribution of each intervention to the propagation trend by observing changes in the model’s goodness-of-fit ($R^2$) after removing different interventions. Through an empirical analysis of six representative public opinion events from 2024, this study verified the effectiveness of the proposed framework and uncovered critical characteristics of opinion dissemination, including explosiveness versus persistence, multi-round dissemination with recurring emotional fluctuations, and the interplay of multiple driving factors. Full article

The development of artificial intelligence has enabled the automation of an increasing number of processes and actions in the online environment, from creating unique and engaging content to simulating user behaviors (likes, comments, reviews). This automation has brought several positives to the online environment, including reduced working time and better results, among others. However, at the other end of the spectrum, consumer trust is starting to decline. Before the advent of artificial intelligence, reviews were often the opinions of other customers who had tried the product or service in question. With the evolution of these reviews, providers can now automatically post them to create a favorable image. Given the increasing concern among young people about environmental issues, this study investigates how AI-generated content affects their trust in sustainability-related online reviews and how this trust influences their pro-environmental purchasing decisions. Quantitative research was conducted in the article, based on which a conceptual model of the degree of trust users have in online reviews and reactions in the context of artificial intelligence was developed. The research methodology involved conducting quantitative research and constructing variables based on the data collected. The results revealed significant links between the evolution of artificial intelligence and the degree of trust users place in general feedback found in online environments. Full article

With frequent interactions between social media platforms, the dissemination of information and the interaction of opinions on the internet have become increasingly complex and diverse. This increase in information complexity not only affects the formation of public opinion but may also exacerbate the spread of diseases. Based on multilayer complex networks and combined with the Deffuant-I model, this paper explores the dual impact of information complexity and individual characteristics on both information and disease propagation. Through systematic simulation experiments, this paper analyzes the mechanisms of information complexity, individual compromise, and cognitive ability in the evolution of propagation. This study shows that the interactive effects of individual characteristics and information complexity have a significant impact on disease spread. This research not only provides a new theoretical perspective for understanding complex information dissemination but also offers valuable insights for public policymakers in promoting social harmony and addressing public health emergencies. Full article

We discuss the spread of epidemics caused by two viruses which cannot infect the same individual at the same time. The mathematical modeling of this epidemic leads to a kind of SIIRR model with two groups of infected individuals and two groups of recovered individuals. An additional assumption is that after recovering from one of the viruses, the individual cannot be infected by the other virus. The mathematical model consists of five equations which can be reduced to a system of three differential equations for the susceptible and for the recovered individuals. This system has analytical solutions for the case when one of the viruses infects many more individuals than the other virus. Cases which are more complicated than this one can be studied numerically. The theory is applied to the study of waves of popularity of an individual/groups of individuals or of an idea/group of ideas in the case of the presence of two opposite opinions about the popularity of the corresponding individual/group of individuals or idea/group of ideas. We consider two cases for the initial values of the infected individuals: (a) the initial value of the individuals infected with one of the viruses is much larger than the initial values of the individuals infected by the second virus, and (b) the two initial values of the infected individuals are the same. The following effects connected to the evolution of the numbers of infected individuals are observed: 1. arising of bell-shaped profiles of the numbers of infected individuals; 2. suppression of popularity; 3. faster increase–faster decrease effect for the peaks of the bell-shaped profiles; 4. peak shift in the time; 5. effect of forgetting; 6. window of dominance; 7. short-term win–long-term loss effect; 8. effect of the single peak. The proposed SIIRR model is used to build a mathematical theory of popularity waves where a person or idea can have positive and negative popularity at the same time and these popularities evolve with time. Full article

Textual analysis is grounded in conceptual schemes of traditional qualitative and quantitative content analysis techniques that have led to the hybridization of methodological styles widely used across social scientific fields. This paper delivers an extensive review of the origins and evolution of text analysis within the domains of traditional content analysis. Emphasis is given to the conceptual schemas and operational structure of latent semantic analysis, and its capacity to detect topical clusters of large corpora. Further, I describe the operations of Entity–Aspect Sentiment Analysis which are designed to measure and assess sentiments/opinions within specific contextual domains of textual data. Then, I conceptualize and elaborate on the potential of streamlining latent semantic and Entity–Aspect Sentiment Analysis complemented by Correspondence Analysis, generating an integrated operational scheme that would detect the topic structure, assess the contextual sentiment/opinion for each detected topic, test for statistical dependence of sentiments/opinions across topical domains, and graphically display conceptual maps of sentiments in topics space. Full article

Under extreme operating conditions such as high temperatures, strong corrosion, and cyclic thermal shocks, key equipment in solar tower power plants (STPPs) is prone to severe accidents and results in significant losses. To systematically quantify potential failure risks and address the methodological gaps in existing research, this study proposes a risk assessment framework combining a novel scenario propagation model covering triggering factors, precursor events, accident scenarios, and response measures with an interval type-2 fuzzy set (IT2FS) Bayesian network. This framework establishes equipment failure evolution pathways and consequence evaluation criteria. To address data scarcity, the methodology integrates actual case data and expert elicitation to obtain assessment parameters. Specifically, an IT2FS-based similarity aggregation method quantifies expert opinions for prior probability estimation. Additionally, to reduce computational complexity and enhance reliability in conditional probability acquisition, the IT2FS-integrated best–worst method evaluates the relative importance of parent nodes, combined with a leakage-weighted summation algorithm to generate conditional probability tables. The model was applied to a western Chinese STPP and the results show the probabilities of receiver blockage, pipeline blockage, tank leakage, and heat exchanger blockage are 0.061, 0.059, 0.04, and 0.08, respectively. Under normal operating conditions, the occurrence rates of level II accident consequences for all four equipment types remain below 6%, with response measures demonstrating significant suppression effects on accidents. The research results provide critical decision-making support for risk management and mitigation strategies in STPPs. Full article

Current methods for public opinion propagation prediction struggle to jointly model temporal dynamics, structural complexity, and dynamic node influence in evolving social networks. To overcome these limitations, this paper proposes a public opinion dissemination prediction model based on the integration of dynamic time-weighted Rényi entropy (DTWRE) and graph neural networks. By incorporating a time-weighted mechanism, the model devises two tiers of Rényi entropy metrics—local node entropy and global time-step entropy—to effectively quantify the uncertainty and complexity of network topology at different time points. Simultaneously, by integrating DTWRE features with high-dimensional node embeddings generated by Node2Vec and utilizing GraphSAGE to construct a spatiotemporal fusion modeling framework, the model achieves precise prediction of link formation and key node identification in public opinion dissemination. The model was validated on multiple public opinion datasets, and the results indicate that, compared to baseline methods, it exhibits significant advantages in several evaluation metrics such as AUC, thereby fully demonstrating the effectiveness of the dynamic time-weighted mechanism in capturing the temporal evolution of public opinion dissemination and the dynamic changes in network structure. Full article

This study employs evolutionary game theory to analyze the tripartite interaction among government regulators, media publishers, and self-media participants in emergency public opinion management. We establish an evolutionary game model incorporating strategic motivations and key influencing factors; then, we validate the model through systematic simulations. Key findings demonstrate the following: ① the system exhibits dual stable equilibria: regulated equilibrium and autonomous equilibrium. ② Sensitivity analysis identifies critical dynamics: ① self-media behavior is primarily driven by penalty avoidance ($g_3$) and losses ($w_2$); ② media participation hinges on revenue incentives ($m_2$) versus regulatory burdens ($k$); ③ government intervention efficacy diminishes on emergencies when resistance ($v_1$ + $v_3$) exceeds control benefits. The study reveals that effective governance requires the following: ① adaptive parameter tuning of punishment–reward mechanisms; ② dynamic coordination between information control and market incentives. This framework advances emergency management by quantifying how micro-level interactions shape macro-level opinion evolution, providing actionable insights for balancing stability and information freedom in digital governance. Full article

This study examines the dynamics of information dissemination and opinion formation in Indonesian social media through a comprehensive analysis of a high-profile youth violence case. Using social network analysis (SNA), we analyzed 264,155 activities from 83,097 accounts on platform X (formerly Twitter) to understand the patterns of information flow, cluster formation, and inter-group interactions. The analysis revealed four distinct clusters with unique characteristics: a dominant support cluster (40.12%), a context-focused cluster (26.93%), a mainstream media cluster (14.14%), and a peripheral engagement cluster (6.05%). This study found significant patterns in information dissemination, with retweets dominating at 68% of total activities and strategic hashtag usage at 28%. Cross-cluster interactions comprised 20% of total activities, challenging assumptions about echo chambers in digital discourse. The network showed high resilience with 85% path reliability and demonstrated a consistent multiplier effect with a 1:5:15 ratio in message amplification. Bridge nodes (10–15% of accounts) played crucial roles in facilitating cross-cluster dialogue and maintaining network cohesion. The temporal evolution of discourse showed distinct phases, from initial factual reporting to later systemic analysis, with each phase characterized by different engagement patterns and narrative focuses. These findings extend existing theoretical frameworks while highlighting the need for more culturally nuanced approaches to understanding digital discourse in contexts of collectivist cultural dimensions. This study’s results have significant implications for digital literacy education, social media intervention strategies, and youth violence prevention efforts, suggesting the need for sophisticated, network-aware approaches that consider both structural dynamics and cultural contexts. Full article

This research focuses on groundwater mapping for the Çumra and Beyşehir Basins in Konya, a semi-arid region in Turkey that plays a crucial role in agriculture and the food industry. Geographic information systems (GIS), the analytical hierarchical process (AHP), and the multi-population-based differential evolution algorithm (MDE) were combined to identify potential groundwater zones. Since direct data on groundwater presence are costly to obtain, thematic maps created from groundwater conditioning factors (such as aquifer, slope, permeability, alluvial soil, soil quality, lithology, precipitation, temperature, salinity, and stone density) can be used to estimate groundwater potential. In this study, these factors were assigned weights using the AHP technique in Model 1 and the MDE technique in Model 2. The TOPSIS (technique for order preference by similarity to ideal solution) method was then employed to simulate groundwater potential, using weights from both techniques. The performance metrics of both models were as follows: Model 1 (RMSE: 114.219, MSE: 13,046.091, and MAE: 99.663) and Model 2 (RMSE: 114.209, MSE: 13,043.785, and MAE: 99.652). The proposed method addresses issues of consistency and bias that might arise from relying on expert opinions through the use of heuristic techniques. Moreover, this approach, which does not require direct data on groundwater availability, enables the creation of accurate predictions while overcoming the challenges of obtaining expensive data in underdeveloped and developing countries. It provides a scientifically sound way to identify and conserve water resources, reducing drilling and other related costs in watershed management and planning. Full article

The research literature presents divergent opinions regarding the role of dissipation in living systems, with views ranging from it being useless to it being essential for driving life. The implications of universal thermodynamic evolution are often overlooked or considered controversial. A higher rate of entropy production indicates faster thermodynamic evolution. We calculated enzyme-associated dissipation under steady-state conditions using minimalistic models of enzyme kinetics when all microscopic rate constants are known. We found that dissipation is roughly proportional to the turnover number, and a log-log power-law relationship exists between dissipation and the catalytic efficiency of enzymes. “Perfect” specialized enzymes exhibit the highest dissipation levels and represent the pinnacle of biological evolution. The examples that we analyzed suggested two key points: (a) more evolved enzymes excel in free-energy dissipation, and (b) the proposed evolutionary trajectory from generalist to specialized enzymes should involve increased dissipation for the latter. Introducing stochastic noise in the kinetics of individual enzymes may lead to optimal performance parameters that exceed the observed values. Our findings indicate that biological evolution has opened new channels for dissipation through specialized enzymes. We also discuss the implications of our results concerning scaling laws and the seamless coupling between thermodynamic and biological evolution in living systems immersed in out-of-equilibrium environments. Full article