Complexity最新文献

{"title":"Social Human Complexity and Global Challenges: Theoretical, Methodological and Empirical Contributions for Transformative Change","authors":"David S. Byrne,&nbsp;Emma Uprichard,&nbsp;Gill Callaghan","doi":"10.1155/cplx/1588585","DOIUrl":"https://doi.org/10.1155/cplx/1588585","url":null,"abstract":"<p>This paper advances a framework for complexity-informed action research as a mode of governance for addressing the contemporary polycrisis in socio-ecological systems. It argues that knowledge is not merely representational but possesses causal power in shaping future system trajectories and that research and governance are inseparable, recursive practices. Transformation involves imagining desired futures, and transformative knowledge is created in action within complex socio-ecological systems. Building on complexity theory and participatory action research, the paper identifies <i>organic intellectuals</i> within governance institutions as key agents of transformation and positions grey literature as a primary and underutilised knowledge base for understanding and intervening in real governance processes. We argue for action research based on collaboration between individual and collective actors in civil society through the construction of scenarios built on narratives. Scenario narratives of path-dependent pasts and desired futures are causally effective fields of action that structure possibilities for collective agency. Narrative scenario construction is shown to operate through recursive bottom-up and top-down dynamics, enabling the co-production of desired futures across institutional and civil-society actors. The paper proposes the city-region as a critical governance scale at which participatory scenario building can most effectively integrate situated knowledge, professional expertise and institutional capacity. Narrative scenario-based action research is therefore presented not as a supplement to policy or technical modelling, but as a distinct governance practice capable of reshaping attractor landscapes and opening pathways towards more equitable and sustainable socio-ecological futures.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/1588585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Path Simulation and Evolution Mechanism of Risk Contagion in Construction Project Portfolios","authors":"Jielin Wei,&nbsp;Fengtao Liu","doi":"10.1155/cplx/6286835","DOIUrl":"https://doi.org/10.1155/cplx/6286835","url":null,"abstract":"<p>The construction projects are characterized by long cycles, large investments, and complex risks, which are also influenced by resource allocation, and risks exhibit certain diffusion effects. This study focuses on the production characteristics of construction projects. The risk causation involved in the whole life cycle stage is analyzed, and a total of 16 quantitative risk indicators in four dimensions are sorted out. The risk transmission characteristics of construction projects are clarified through risk matrix calculations. Secondly, under the analysis framework of system dynamics, a risk transmission combination model for construction projects is constructed, and a simulation study on the formation and diffusion effects of the risk transmission network under different conditional factors is conducted. The simulation results showed that indicators such as “project interaction benefits,” “project manager risk appetite,” and “average network density” have a positive influence on risk contagion. Finally, corresponding management recommendations are proposed, providing research support for multiproject portfolio risk analysis in cross-resource environments.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/6286835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148166931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Annual CO2 Emissions Forecasting in Vietnam Using a PCA-Stacked Bidirectional LSTM Model","authors":"Ha Thi Thu Nguyen","doi":"10.1155/cplx/4362724","DOIUrl":"https://doi.org/10.1155/cplx/4362724","url":null,"abstract":"<p>Vietnam’s rapid economic growth has brought substantial improvements in living standards, industrial capacity, and infrastructure. However, it has also resulted in significant environmental stress, primarily driven by increased energy consumption for production and consumption activities. This study proposes a principal component analysis (PCA)–stacked bidirectional long short-term memory (PCA-stacked BiLSTM) framework, evaluated using an expanding-window forecasting strategy. Drawing on a complex adaptive systems (CAS) perspective, PCA is used to transform highly interdependent macroeconomic predictors into latent components, thereby improving model stability. The expanding-window approach ensures that forecasts are generated sequentially using only historical information, thereby providing a realistic assessment of out-of-sample performance. With annual data spanning 1990–2023 for Vietnam, the proposed model is compared with benchmark methods, including ARIMA, support vector regression, KNN, and unidirectional LSTM models. The results indicate that while classical time-series models remain highly competitive in small-sample settings, the stacked BiLSTM improves forecasting robustness relative to standard LSTM architectures and machine learning baselines. This study contributes a methodologically grounded framework that integrates complexity theory, multicollinearity mitigation, and realistic time-series validation, offering more reliable long-horizon CO₂ emissions forecasts in data-constrained environments.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/4362724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148166930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semisupervised Classification of Brain Tumor Images Using Gradient Adversarial Consistency on Mixup","authors":"Mohammad Saber Iraji,&nbsp;Marzieh Iraji,&nbsp;Mohammad Bagher Iraji","doi":"10.1155/cplx/3116792","DOIUrl":"https://doi.org/10.1155/cplx/3116792","url":null,"abstract":"<p>The scarcity of labeled brain MRI datasets presents a significant challenge, as manual labeling is resource-intensive. This lack of data can impede the training and generalization of convolutional neural network (CNN) models, increasing the risk of overfitting and diminishing performance on real-world clinical data. By incorporating labeled and unlabeled data, semisupervised learning techniques aim to build precise models. Effectively using unlabeled data to boost the accuracy of models trained on limited labeled data remains a crucial challenge. To address this, we propose a gradient adversarial consistency on Mixup (GAM), a semisupervised framework. GAM uses a Mixup of labeled images to shift the classifier’s decision boundary toward the interclass region. It also applies the GAM of unlabeled data to smooth the boundary. This allows GAM to leverage unlabeled patterns and learn robust features, improving brain tumor classification. The research evaluates a semisupervised CNN-13 classifier on a combined brain dataset from three imaging studies, exploring the impact of generating novel augmentation as gradient noise on the Mixup of unlabeled images. GAM achieved statistically significant accuracy improvements (<i>p</i> &lt; 0.05), reaching 0.8154 and 0.8352 with 50 and 100 labeled images per class, respectively, averaged over five runs, compared to baseline accuracies of 0.7833 and 0.8225. Experimental results demonstrate that GAM outperforms previous techniques, particularly in enhancing performance near decision boundaries. The findings indicate that GAM effectively leverages both labeled and unlabeled data, resulting in improved classification accuracy for brain tumor detection, especially in situations where labeled data are scarce in medical imaging applications.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/3116792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148092804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Simulation of Generalized Fractional Thermoelasticity With Two Temperature and Phase Lags in a Half Space Using GLA-PINN","authors":"Fazl Ullah Fazal,&nbsp;Muhammad Sulaiman,&nbsp;Mustafa Ahmed Ali","doi":"10.1155/cplx/5821866","DOIUrl":"https://doi.org/10.1155/cplx/5821866","url":null,"abstract":"<p>This research manuscript presents a model of heat conduction incorporating two-phase lags and time fractional derivatives to precisely identify the nonsimple behaviors of thermoelastic materials. Generalized fractional differentiation operators with nonsingular kernels are considered. The model incorporates the two-temperature idea and a two-stage delay technique to account for microstructure effects. Thermoelastic relationships between isotropic substances and the exterior body forces were investigated as a real-world application of the new idea. There is some analysis of the impact of the fractional differential operators. Furthermore, an innovative physics-informed neural network (PINN) technique named GLA-PINN has been developed to investigate the behavior of the fractional thermoelasticity model. The system of fractional partial differential equations is solved through the proposed novel technique. The graphical representations generated by computational outputs were utilized to illustrate the researched physical fields’ behavior. The residual error graphs and the loss graphs indicate the validation and accuracy of the developed technique. The extended model of fractional heat conduction is shown to be suitable for giving temperature forecasting. The results concluded that the suggested framework could be beneficial in solving problems in anomalous transport, heat conduction, and analysis of the other branches of engineering.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/5821866","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148092808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Pollution to Integration: How Environmental Quality Drives Urban–Rural Synergy in China","authors":"Ying Song,&nbsp;Yuanping Cao,&nbsp;Zhiyi Zhuo","doi":"10.1155/cplx/3616234","DOIUrl":"https://doi.org/10.1155/cplx/3616234","url":null,"abstract":"<p>Urban–rural synergy (URS) is a key policy goal in China, yet environmental quality (EQ) is usually treated as an outcome rather than a driver of coordination. This study reverses that perspective. Using provincial panel data for 31 Chinese provinces from 2011 to 2023, we construct a multidimensional URS index and measure EQ with a composite air-pollution indicator. We estimate fixed-effects models, a spatial Durbin model, and threshold regressions with digital inclusive finance (DIF) as the threshold variable. Poorer EQ, measured by an inversely coded EQ proxy, is associated with lower overall URS. The pattern is strongest in the economic and demographic dimensions, while evidence for the social, spatial, and ecological dimensions is less consistent. Coastal–inland analysis shows that the positive role of environmental improvement is stronger in inland provinces. Spatial results indicate cross-provincial interdependence. Threshold analysis points to nonlinear heterogeneity across DIF regimes rather than a simple amplification effect. These findings link environmental governance more closely to balanced urban–rural development.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/3616234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148087582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multistructure Novel Stochastic Soliton Solutions for the (2+1)-Dimensional Breaking Wave Equation Within the Fractional Derivative Influence","authors":"Fatma Nur Kaya Sağlam,&nbsp;Volkan Ala,&nbsp;Bahadır Kopçasız,&nbsp;Ercan Çelik","doi":"10.1155/cplx/4775274","DOIUrl":"https://doi.org/10.1155/cplx/4775274","url":null,"abstract":"<p>In this paper, we investigate the exact stochastic solutions of the (2 + 1)-dimensional stochastic fractional-space breaking soliton equation (SFSBSE) involving the truncated M-fractional derivative in space. This equation models a range of physical phenomena, including fluid wave propagation, shallow water dynamics, and plasma physics, under the influence of stochastic fluctuations. To derive analytical solutions, we apply the Kumar–Malik approach, which transforms the SFSBSE into an ordinary differential equation via a stochastic wave transformation. A wide class of exact solutions is obtained, including Jacobi elliptic, hyperbolic, trigonometric, and exponential function solutions. The effects of multiplicative noise and the fractional-order operator on the solution behavior are analyzed both analytically and graphically. Several 3D, 2D, and contour plots are provided to illustrate the qualitative dynamics of the stochastic soliton profiles. In particular, we highlight the transition from deterministic to stochastic behavior as the noise intensity increases and emphasize the role of the fractional order in modulating the soliton structure. To the best of our knowledge, this is the first time the Kumar–Malik method has been implemented to obtain exact stochastic solutions of the SFSBSE with a truncated M-fractional derivative. The correctness of all obtained solutions is verified symbolically using Maple. The presented framework is expected to provide new insights into the dynamics of stochastic fractional soliton models.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/4775274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148083977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting of the Role of CD8+ T-Cells in Vaccine-Induced Protection Against SARS-CoV-2 in Macaques via Mathematical Modeling","authors":"Malik Muhammad Ibrahim,&nbsp;Jong Hyuk Byun,&nbsp;Malik Muhammad Naeem Mannan,&nbsp;Kwang Su Kim","doi":"10.1155/cplx/9930076","DOIUrl":"https://doi.org/10.1155/cplx/9930076","url":null,"abstract":"<p>Experimental studies have demonstrated that CD8⁺ T-cells play an important role in vaccine-induced protection against SARS-CoV-2; however, a quantitative understanding of how CD8⁺ T-cell activity dynamically regulates viral load following vaccination remains limited. In particular, the mechanisms by which CD8⁺ T-cell–mediated immune responses influence viral clearance across different anatomical compartments have not been systematically quantified. In this study, we developed a mathematical model of SARS-CoV-2 viral dynamics incorporating CD8⁺ T-cell–mediated immune effects and validated it using experimental viral load data from vaccinated and CD8⁺ T-cell–depleted rhesus macaques. The model captures interactions between target cells, infected cells, viral replication, and immune effector activity, enabling quantitative comparison of infection dynamics under distinct immunological conditions. Viral load data from nasal swabs and bronchoalveolar lavage (BAL) samples were used to assess how variations in immune effector strength influence viral kinetics. Our results show that CD8⁺ T-cell activity substantially accelerates viral clearance and reduces infection duration, with vaccination-induced CD8⁺ T-cell responses leading to markedly lower viral loads compared to CD8⁺ T-cell–depleted scenarios. Analysis of viral load area under the curve (AUC) reveals threshold such as behavior, whereby increases in immune effector strength beyond critical levels result in disproportionately large reductions in total viral burden. Sensitivity analysis further identifies CD8⁺ T-cell–associated parameters as key determinants of viral load dynamics. Overall, this study provides a quantitative framework demonstrating that robust CD8⁺ T-cell responses are critical for effective vaccine-induced control of SARS-CoV-2 infection, offering mechanistic insights into immune thresholds required for optimal viral suppression.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/9930076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148082166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3-Dimensional Convolutional Neural Network for Detection of an Anterior Cruciate Ligament Injury in Knee Magnetic Resonance Imaging Scans","authors":"Abdullah Kamran,&nbsp;Khalid Ansari,&nbsp;Faisal Amin,&nbsp;Muhammad Jawad Khan,&nbsp;Ahmed Fraz,&nbsp;Falah Alanazi,&nbsp;Asim Waris,&nbsp;Syed Omer Gilani","doi":"10.1155/cplx/8122774","DOIUrl":"https://doi.org/10.1155/cplx/8122774","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Problem</h3>\u0000 \u0000 <p>Accurate diagnosis of anterior cruciate ligament (ACL) injuries from knee magnetic resonance imaging (MRI) is clinically essential but remains challenging due to subtle anatomical variations, limited labeled data, and severe class imbalance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Aim</h3>\u0000 \u0000 <p>This study aims to develop a lightweight and computationally efficient deep learning model for automated detection of complete ACL tears from knee MRI scans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A custom 3D convolutional neural network (3D-CNN) with approximately 153 k trainable parameters was proposed. Two publicly available datasets included are Stanford ML Group and Clinical Hospital Centre Rijeka, which were combined to improve generalization. To address dataset heterogeneity and class imbalance, standardized preprocessing, conservative geometric data augmentation, and class weighting were employed. The model was trained using 5-fold stratified cross-validation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The proposed model achieved an average classification accuracy of 97.6% and an average AUC of 0.993, outperforming several state-of-the-art deep learning architectures while maintaining a small model size (∼2 MB) and short training time (∼10 min per fold).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The results demonstrate that a lightweight 3D-CNN can accurately and efficiently detect complete ACL tears from knee MRI scans, highlighting its potential for clinical decision support and deployment in resource-constrained environments.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/8122774","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148073723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multistage Two-Sided Matching Model in Interval-Valued Triangular Fuzzy Environments","authors":"Jian-min Qiao,&nbsp;Wan-bao Wu,&nbsp;Wo-yuan Li","doi":"10.1155/cplx/2837397","DOIUrl":"https://doi.org/10.1155/cplx/2837397","url":null,"abstract":"<p>In two-sided matching problems, accurately quantifying preference intensity using precise numerical values remains a significant challenge due to the inherent complexity and fuzziness of real-world decision-making. Moreover, existing research rarely considers the staged dynamics of matching processes across multiple time periods. To address these gaps, this study proposes a novel multistage dynamic two-sided matching method based on interval-valued triangular fuzzy sets. First, dynamic stage weights are determined by maximizing the comprehensive mean of optimal matching information at each period. Second, an uncertain dynamic interval triangular fuzzy weighted geometric (UDITFWG) operator is introduced to aggregate multistage preference information, where the geometric aggregation characteristic effectively captures the noncompensatory nature of evaluation criteria. Third, a one-to-one two-sided matching optimization model is constructed with the objective of maximizing the overall comprehensive mean. A numerical example of venture capital investment matching demonstrates the feasibility and effectiveness of the proposed method. Sensitivity analysis reveals that both the intermediary’s risk preference and the decision power of matching parties significantly influence the optimal matching schemes. Comparative analysis shows that the UDITFWG operator yields more balanced matching results compared to arithmetic aggregation approaches, particularly when decision criteria exhibit “shortboard effects.” This research contributes to the theoretical development of two-sided matching in fuzzy environments and offers practical implications for resource allocation in areas such as investment, human resources, and supply chain management.</p>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2026 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/cplx/2837397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148072834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}