Complexity最新文献

Purpose: Safety production supervision is the core of the sustainable and healthy development of the traditional Chinese medicine (TCM) industry. This study aims to construct an evaluation system for safety production supervision of TCM enterprises to promote the sustainable development of the industry.

Method: A multidimensional evaluation model was established using a combination of analytic hierarchy process (ANP) and fuzzy comprehensive evaluation method, and empirical analysis was conducted on domestic TCM enterprises.

Result: (1) Overall, the safety production supervision of TCM in China is relatively sound; however, further efforts are needed to enhance environmental protection in TCM production areas, improve the construction of quality standardization systems, and promote the development of authentic TCM cultivation and breeding; (2) There are significant gaps in the quality control of TCM production enterprises, the innovation and inheritance of classic formulas, and the construction of a traceability system for the circulation of TCM; (3) Listed TCM companies generally exhibit better performance in safety production supervision compared to their nonlisted companies, indicating that a strict and comprehensive regulatory system helps to improve the level of safety production supervision for TCM.

The controllability of temporal networks has been one of the most important challenges in this type of network over the last decade. The main goal of network controllability processes is to find the minimum set of control nodes in such a way that all network nodes can be controlled by them. This problem is NP-hard in the temporal networks. In this paper, a controllability method is proposed to improve the efficiency of the controllability process on temporal networks. In the proposed method, a population method based on the ant colony optimization (ACO) algorithm is proposed, which is compatible with temporal networks. Due to the temporal nature of the controllability processes in temporal networks, the ACO algorithm is adapted temporally. Also, due to the time-consuming controllable processes in temporal networks and in order to increase the efficiency of the ACO algorithm, a backpropagation neural network has been used, which finds the minimum driver node set of the network based on the layered model in order to fully control the network nodes. The results of the implementation of the proposed method on real-world datasets demonstrate that the proposed ACO-BPNN method works stably and with high efficiency on high-volume datasets. By comparing the efficiency of the proposed method with conventional controllability methods, it is found that the proposed method has performed better in terms of the speed of execution and the length of the minimum driver node set.

Employing the lens of Actor-Network Theory (ANT), this paper explores university educators’ perceptions of Gen-AI and its potential impact on education through a qualitative research approach. It focuses on how educators perceive generative AI technologies and how these technologies influence their teaching practices and role identification. The study reveals that multiple factors shape teachers’ acceptance and utilization of AI, encompassing personal attitudes, student responses, teaching environments, and institutional policies. The interaction between teachers and AI is conceptualized as part of a human-machine interaction network, where AI transcends being merely a teaching tool and is attributed human-like qualities such as a wise mentor and learning companion. This research underscores that the realization of human-AI integration necessitates a synergistic development between educators and technology, coupled with the concerted efforts of other actors within the educational ecosystem. The paper concludes by discussing the implications and challenges of human-AI integration for educational reform.

This study details the substantial technological progress experienced in the last few decades, its impact on engineering, and how machine learning along with data science can contribute to solving human problems. The objective here is to establish the principles of “complexity engineering,” based on the works of Edgar Morin, and to demonstrate how these principles are suitable for engineering to deal with complex systems and the wicked problems linked to them. Thus, initially, a history of the events, discoveries, and disruptive inventions that marked engineering in recent centuries is made. Conceptual considerations and practical applications of complexity engineering in different areas of knowledge are also shown. The idea is to provide a historical perspective of engineering development that started before the advent of scientific methodological contributions and is based on accurate observation of natural behaviors. Since Galileo and Newton’s objective vision, scientific progress strongly influenced engineering progress, leading to the creation of unthinkable wonders, allowing spatial trips, and mainly providing a more comfortable daily life. However, two important new issues have emerged: ways to relate this progress with life on Earth and techniques to use the big data available to improve methodological engineering attitude. In this study, these questions are discussed, and we have shown that objectively obtained big data can be used to address subjective human problems, creating a new discipline called complexity engineering. The applications of complexity engineering to real problems are presented using examples of research developed by the authors involving urban planning, mental health, and landscape ecology in problems that require massive use of data.

The analysis of super-dyadic relations through hypergraphs is gradually gaining attention, with its community structure analysis playing a crucial role in computational social science. However, few scholars have paid attention to the impact of hyperedge diversity on the community structure of hypergraphs, especially the impact generated by heterogeneous hyperedges. This paper expands hypergraphs into signed hypergraphs and proposes a framework for community structure in signed hypergraphs along with a variant of modularity. Simultaneously, an optimization algorithm is introduced in this paper to detect potential communities by maximizing modularity. Experimental results reveal that the proposed method can effectively optimize the objective function and detect community structures.

The complexity of brain tumors highlights the critical need for advanced computer-aided diagnosis (CAD) tools to support surgeons in clinical decision-making and improve patient outcomes. This paper introduces a novel deep learning model for the multiclass classification of brain tumors using magnetic resonance imaging (MRI), offering significant advancements in feature extraction and classification accuracy. The proposed model comprises three key components: (1) a fine-tuned EfficientNetB7 convolutional neural network (CNN), adapted through transfer learning by freezing the initial layers and retraining subsequent layers to optimize feature extraction from MR images; (2) a channel attention module that refines extracted feature maps, emphasizing essential features for accurate tumor detection; and (3) a fully connected classifier, optimized through grid search, to achieve precise multiclass tumor classification. Additionally, hyperparameter tuning and data augmentation techniques enhance generalization and model robustness. Experimental results confirm the model’s superior performance, outperforming recent approaches in multiclass and binary classification scenarios, underscoring its potential to advance brain tumor diagnosis and treatment.

The United Nations peacekeeping has recently played a more proactive role in maintaining global peace and security. However, the majority of the literature lacks a comprehensive, visualized bibliometric analysis and forecast for emerging fields. This study employed CiteSpace to conduct a bibliometric analysis of 937 publications between 2000 and 2022, aiming to gain a comprehensive understanding of the research status and development trend of the UN peacekeeping research. Via the fundamental and in-depth analysis, we find that the overall publication trend has experienced a nonlinear but steady upward evolution due to the mature and stable cooperation research network established at the country, institution, and author levels; representative keywords such as peace, intervention, and civil war illustrate the dynamic evolution characteristics of hotspots and frontiers. This article is innovative because we use fundamental and in-depth analysis to depict a panorama of this field; identify the hotspots through macro-, meso-, and microlevels; and provide the knowledge framework with future research characteristics, which help scholars continue future research effectively.

In this publication, we present a model that simulates the spread of monkeypox virus. The model takes into account the effect of interactions between human and rodent populations, as well as certain realistic factors such as the complex network with a heterogeneous mean field and certain disease control measures such as vaccination and disease therapy. Although vaccines do not offer total protection against the disease, they can reduce the transmission of monkeypox, which could help reduce the spread of the virus in a population, as our results show. So to develop efficient strategies for epidemic control and prevention, we studied global sensitivity analysis to determine the parameters most influential on disease spread. The existence and overall stability of the disease-free equilibrium are also discussed. The basic reproduction number R₀ of the model is calculated. The results show that if R₀ < 1, the disease-free equilibrium of the model is globally asymptotically stable. In addition, the model’s key parameters are calculated using a least-mean-squares curve fit to the real data.

Controllability in temporal networks is considered one of the most important challenges in this type of network. Network controllability methods try to fully control the network with the minimum number of control nodes. This type of network is always exposed to internal and external attacks and failures. Therefore, controllability processes need recovery mechanisms to be resistant to various types of failures. The high volume of temporal networks causes the recovery controllability processes to be disrupted. In the paper, a novel method of recovery controllability in temporal networks is proposed to improving controllability network robustness. To restore controllability of the network, the RCTE framework is proposed, in which a temporal network is converted into snapshots at discrete times and then its dimensions are reduced by using network embedding. Finally, using link prediction based on local and global similarity, links that are subject to failure are identified. The effectiveness of the proposed method against various network attacks has been evaluated and compared with other conventional methods. The results show that the RCTE framework performed better than other conventional methods. Also, the proposed method has more controllability and tolerance against malicious attacks compared to other recovery methods.