Complexity最新文献

Rapid urbanization, coupled with inadequate pedestrian infrastructure, has contributed to an increase in pedestrian fatalities within major Bangladeshi cities. This study employs analytical hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), and simple additive weighting (SAW) models to evaluate pedestrian road crossing behaviors and associated crash risk factors in Khulna City. Findings reveal group crossing (R_i = 0.16), mixed and perpendicular crossing patterns, and mobile-phone usage pose the highest risk at zebra crossings, while young pedestrians (A_i = 0.87), hand gestures (R_i = 0.00), and running are identified as the most significant risks at nonzebra crossings. The multimodel approach provides comprehensive insights. Specifically, TOPSIS identifies group crossing and hand gestures as the riskiest behaviors at respective intersection types and SAW highlighting perpendicular crossing (A_i = 0.88) and young pedestrians as particularly vulnerable. These findings offer evidence-based guidance for the development of safer and more sustainable urban environments, aligning with the journal’s focus on resilient urban environments, smart transportation, and social aspects. Urban planners can utilize these findings to enhance pedestrian infrastructure, promote clean mobility, and foster livable communities. By quantifying risk factors and implementing decision support systems, this research advances the design of sustainable, socially resilient cities and contributes to creating healthy, equitable urban societies.

Nodes are important elements of the cyberspace skeleton map visualization process. However, the quality parameters of the node importance index and topological potential index are difficult to obtain, and skeleton map visualization rarely accounts for the spatial distribution characteristics of nodes. The index synthesis and cluster distribution methods are adopted to solve these problems in this paper. The results are as follows: (1) According to the SIR propagation model, the maximum numbers of recoveries and infections for both the ARPA network and social network equal the TPDomiH maximum, and the TPDomiH index has the largest correlation coefficient. All the results show that the proposed TPDomiH index has certain advantages. (2) Regarding the center, the clustering results obtained for a social network are almost unchanged, whereas the original results exhibit large changes. For the center of gravity, the clustering results decrease gradually. The differences relative to the original results are small. With respect to the information entropy and the maximum amount of geometric information, the clustering results are larger than the original results. As the retention ratio increases, all the differences between the clustering results and the original results gradually narrow. These results indicate that the cyberspace skeleton map obtained after clustering is better than the original map. This research can provide a reference for the development of the field of cyberspace map visualization.

This study presents a holistic level of sustainability in the context of green architecture supply chains by integrating GSCM and GHRM through an integrated decision-making framework. The study applies a hybrid approach of neutrosophic fuzzy TOPSIS and fault tree analysis (FTA) with data mining to identify and rank critical barriers relating to sustainability, including insufficient government funding and technology challenges. This model offers practical recommendations for policymakers and practitioners aiming to improve green architecture considering GSCM and GHRM on supply chains. The analysis underscores the need for enhanced targeted policies and engagement from various stakeholders and the integration of digital technologies to strengthen sustainability efforts in the industry.

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.