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Aiming at the multi-objective solution problem of the deployment optimization of the hybrid communication network based on PLC, wireless and dual-mode collaborative networking, this paper proposes an algorithm design based on comprehensive performance optimization with business benefits as the orientation. Firstly, according to the non-ideal channel conditions and the low latency service requirements, the cross-layer modeling of the physical layer and MAC layer is adopted. Then, a dynamic weighting mechanism based on different service levels is defined, and a hybrid communication network adaptive access model considering the constraints of business benefits, network performance, and networking costs is designed. The hybrid communication network deployment and access algorithm design based on K-mean clustering and the improved NSGA-II are realized. Finally, the algorithm performance simulation and comparative analysis are carried out. The simulation results show that the proposed algorithm design can effectively balance the two objectives of network benefits and deployment costs under various network constraints and provide diversified deployment strategies in a targeted manner. Full article

(This article belongs to the Special Issue Advances in Networked Systems and Communication Protocols)

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33 pages, 2678 KB

Open AccessArticle

Mechanisms and Pathways of Promoting High-Quality Full Employment Under the Dual Circulation Paradigm: An Evolutionary Simulation Approach Based on System Dynamics

by Cheng Chen, Jinsheng Zhu and Haixia Sun

Systems 2026, 14(7), 737; https://doi.org/10.3390/systems14070737 (registering DOI) - 24 Jun 2026

Abstract

This study investigates the complex and nonlinear interaction between the dual circulation paradigm and high-quality full employment. Moving beyond the limitations of conventional static partial equilibrium frameworks, the analysis conceptualizes this relationship as a system of three interrelated feedback loops. Drawing on system dynamics (SD) theory, a set of nonlinear differential equations is developed, with model parameters calibrated using macroeconomic data from 2010 to 2025. The simulation results yield three main findings. First, international trade, cross-border investment, and technological exchange jointly form a core reinforcing feedback loop that underpins the mutually beneficial interaction between domestic and international circulations. Second, the integrated development of education, technology, and human capital emerges as a critical state variable for overcoming the persistent trade-off between employment quantity and quality. Third, the interplay between horizontal market expansion and vertical technological advancement constitutes a dual driving mechanism that facilitates the system’s transition toward a higher-level equilibrium, with multi-factor interactions generating pronounced nonlinear multiplier effects. Overall, the study provides a quantitative basis for designing adaptive and targeted employment policies within the dual circulation framework. Full article

28 pages, 3794 KB

Open AccessArticle

Mining Weighted Temporal Association Rules in Dynamic Complex Systems via Non-Attributed Graph Sequence with Fuzzy Structure

by Fang Li, Yiman Zhao and Xiao Wang

Systems 2026, 14(7), 735; https://doi.org/10.3390/systems14070735 (registering DOI) - 24 Jun 2026

Abstract

Non-attributed graph sequence offers a powerful formalism for modeling the structural dynamics of complex systems—such as social networks, urban infrastructures, and document transmission pathways—where vertex interactions evolve over time without explicit attribute information. Mining association rules from such sequences to uncover recurring topological patterns have attracted growing interest. Yet two fundamental challenges remain: (1) how to effectively encode edge-level temporal dynamics in non-attributed settings, and (2) how to perform efficient and semantically meaningful temporal association rule mining under structural uncertainty. To address these within a systems-oriented framework, we propose two novel algorithms: the weighted temporal association rule mining algorithm and the fuzzy weighted temporal association rule mining algorithm. The first algorithm introduces time-dependent numerical weights to quantify the strength and persistence of vertex connectivity, integrating them into support and confidence measures to capture both the intensity and evolution of interactions. The second algorithm extends this by incorporating fuzzy set theory, modeling ambiguous or context-sensitive relationships (e.g., indistinct links or weakly correlated vertices) and generating fuzzy-weighted rules that enhance interpretability for real-world system analysis. Evaluated through five comprehensive experiments across diverse datasets and scales using standard metrics (support, confidence, rule count, running time), our methods produce more selective rule sets and achieve lower computational times compared to the classical Apriori algorithm. The proposed approaches thus establish a robust, data-driven foundation for analyzing temporal evolution and structural uncertainty in dynamic complex systems—providing a generalizable methodology applicable beyond domain-specific constraints. Full article

(This article belongs to the Section Systems Theory and Methodology)

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41 pages, 5179 KB

Open AccessArticle

IQTN: An Interpretable Quantile Temporal Network for Systems-Oriented Tail-Risk Forecasting and Early Warning in Carbon Allowance Market

by Tianli Huang and Grace T. R. Lin

Systems 2026, 14(7), 734; https://doi.org/10.3390/systems14070734 (registering DOI) - 24 Jun 2026

Abstract

The carbon emission allowance (CEA) market is a complex socio-technical and environmental-management system in which regulatory design, trading activity, liquidity conditions, and price volatility interact dynamically. Accurate systems-level tail-risk forecasting and early warning remain challenging because carbon-market losses are affected by nonlinear dependence, episodic liquidity stress, and time-varying volatility. This study proposes an Interpretable Quantile Temporal Network (IQTN) as a systems-oriented risk-monitoring framework for China’s national CEA market. By integrating a feature-gating mechanism, a causal temporal convolutional encoder, and a non-crossing quantile output layer, IQTN directly models the conditional tail distribution of future carbon-market losses. The framework produces multi-horizon Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) forecasts for 1-day, 5-day, and 10-day horizons and converts predicted tail risk into operational early-warning signals. Compared with historical simulation, EWMA, GARCH-type models, machine-learning quantile models, and deep temporal benchmarks, IQTN achieved the lowest 95% VaR pinball loss across all horizons, with values of 0.1765, 0.3958, and 0.5732. VaR backtesting showed empirical exceedance rates of 5.23%, 6.04%, and 6.94%, closest to the nominal 5% level. Interpretability analysis identified rolling volatility, maximum loss, intraday range, trading value, and illiquidity as key risk drivers. The temporal importance results also show that recent observations dominated the risk forecasts, suggesting that the risk state of the CEA market is highly sensitive to short-term market information. This supports the use of a short-horizon temporal network as a systems-oriented tool for carbon-market tail-risk monitoring and early warning. Full article

19 pages, 1264 KB

Open AccessArticle

Do Vehicle Restrictions on Urban Expressways Reduce Carbon Emissions Across the Urban Road Network? Short-Run and Longer-Run Evidence from Shanghai

by Yizhe Huang, Cunzhuo Liu, Chengying Hua, Yibin Zhang, Alica Kalašová and Shuichao Zhang

Sustainability 2026, 18(13), 6455; https://doi.org/10.3390/su18136455 (registering DOI) - 24 Jun 2026

Abstract

Vehicle restrictions on urban expressways are widely used to relieve traffic congestion and reduce traffic emissions. However, the effects of such restrictions should be assessed over the wider urban road network rather than on expressways alone, and over both short-run and longer-run periods. This study empirically investigates the impacts of vehicle restriction policies on network-level emissions in Shanghai. The network-level vehicle emissions are dynamically estimated using a carbon-emissions macroscopic fundamental diagram (CE-MFD) model based on taxi trajectory data and loop detector data. The effects are then identified using a spatial difference-in-differences (SDID) framework, while geographically weighted regression (GWR) is used to examine spatial heterogeneity in the associated factors. The results show that extending the restriction periods reduced carbon emissions across the urban road network by 9.96% after one month and by 17.93% after one year. The effects are spatially heterogeneous and are associated with population, road-network characteristics, parking supply, and ramp configuration. These findings suggest that the sustainability impacts depend not only on the restrictions themselves, but also on traffic redistribution and local network conditions. Findings provide empirical evidence for designing sustainability-oriented traffic strategies, underscoring the importance of evaluating emissions outcomes across the urban road network over both short-run and longer-run horizons. Full article

24 pages, 8059 KB

Open AccessArticle

Information-Theoretic Channel Selection and Spatiotemporal Deep Learning for Early Fault Detection in Microsatellite Thermal Control Systems

by Weijian Pang, Jun Zhou, Jingwen Xu and Xinian Zhi

Entropy 2026, 28(7), 725; https://doi.org/10.3390/e28070725 (registering DOI) - 24 Jun 2026

Abstract

Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1–2 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability. Full article

(This article belongs to the Section Signal and Data Analysis)

24 pages, 1314 KB

Open AccessArticle

An Online Detection and Rejection Method for Consecutive Outliers in Underwater Long-Baseline Positioning Based on Kinematic Constraints

by Le Wang, Jun Su, Runze Mao and Sha Wang

Sensors 2026, 26(13), 4013; https://doi.org/10.3390/s26134013 (registering DOI) - 24 Jun 2026

Abstract

To address the issue of persistent high-magnitude outlier interference affecting long-baseline (LBL) positioning systems in complex marine environments, this paper proposes a kinematic constraint-based Robust Interacting Multiple Model Kalman Filter algorithm. Combined with anchor point initialization and multi-step historical observations, the algorithm constructs a spatial Euclidean distance discriminant criterion. By further incorporating the maximum velocity constraint of the Autonomous Underwater Vehicle (AUV), dynamic decision thresholds are established, and final detection decisions are output to the positioning system. Within the Kalman Filter recursion process, a measurement mask matrix is introduced to instantly isolate measurement outliers, preventing abnormal data from participating in state updates and model probability evolution. Simulation results demonstrate that, compared with standard LBL positioning, conventional single outlier detection, and the conventional maximum correntropy criterion-based Kalman filter (MCC-KF) algorithm, the proposed approach enhances outlier identification and suppression—particularly under consecutive anomaly conditions—thereby improving the positioning accuracy of maneuvering targets in complex underwater scenarios. Full article

(This article belongs to the Special Issue Intelligent Optimization for Marine Vehicle Navigation and Positioning)

56 pages, 18066 KB

Open AccessReview

Distributed Deep Learning and Intelligent Soil–Water Analytics in Precision Agriculture: A Comprehensive Review

by Polina Lemenkova

Land 2026, 15(7), 1125; https://doi.org/10.3390/land15071125 (registering DOI) - 24 Jun 2026

Abstract

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

(This article belongs to the Special Issue Sustainable and AI-Driven Approaches to Managing the Soil-Water Complex in Agriculture)

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22 pages, 3137 KB

Open AccessArticle

Fault-Tolerant Attitude Control of Flexible Spacecraft via Reinforcement Learning

by Zhuoyue Peng and Qiang Shen

Aerospace 2026, 13(7), 571; https://doi.org/10.3390/aerospace13070571 (registering DOI) - 24 Jun 2026

Abstract

This paper proposes an integrated attitude control framework for flexible spacecraft subject to external disturbances, rigid–flexible dynamic coupling, and actuator faults. The control framework combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with an adaptive fault-tolerant (AFT) compensator. First, a rigid–flexible coupling dynamic model is formulated using Modified Rodrigues Parameters. Second, an observer-based TD3 attitude controller is designed, where a hierarchical reward function incorporating the observer-estimated flexible modal displacement

\hat{η}

is constructed to train the agent for simultaneous attitude convergence and vibration suppression. Third, a composite fault-tolerant control structure is developed by integrating the trained TD3 policy with an adaptive sliding mode compensator that handles both partial loss-of-effectiveness faults and time-varying additive faults. The proposed framework is evaluated under a progressive five-scenario uncertainty evaluation framework encompassing measurement noise, parameter mismatch, external disturbances, and actuator faults. Simulation results demonstrate that (i) the

\hat{η}

-augmented reward enables substantial improvements in vibration suppression over the baseline reward, achieving a better balance between pointing accuracy and vibration attenuation; (ii) under the most demanding fault scenario, the AFT compensator proves essential for precise convergence, and the composite TD3+AFT architecture achieves the best overall performance among the four compared control schemes. Full article

(This article belongs to the Special Issue Intelligent Guidance, Navigation, and Control for Next-Generation Spacecraft)

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20 pages, 4461 KB

Open AccessArticle

Immunogenetic and Transcriptomic Evidence Implicating the NKG2D-MICA/MICB Axis in CALR-Mutated Myeloproliferative Neoplasms

by Velizar Shivarov, Gergana Tsvetkova, Ilina Micheva, Evgueniy Hadjiev, Jasmina Petkova, Galia Madjarova and Milena Ivanova

Cancers 2026, 18(13), 2052; https://doi.org/10.3390/cancers18132052 (registering DOI) - 24 Jun 2026

Abstract

Background/Objectives: Immune surveillance is increasingly recognized as a modifier of myeloproliferative neoplasm (MPN) initiation and evolution, yet the contribution of the NKG2D receptor and its ligands MICA/MICB to CALR-mutated disease remains unclear. Methods: We performed high-resolution next-generation sequencing genotyping of MICA and MICB in 43 patients with CALR-mutated MPN (WHO 2022 criteria) and compared the allele and haplotype distributions with those of 156 healthy Bulgarian controls and 85 patients with JAK2 V617F-positive MPN. Associations were tested using age- and sex-adjusted additive generalized linear models; bi-locus haplotypes were evaluated using haplotype score methods. In a genotyped subgroup (35 CALR-mutated MPN patients and 105 controls), functional KLRK1 (NKG2D) polymorphisms were analyzed for haplotype-level associations. We also performed 700 ns molecular dynamics simulations of selected MICA variants in complex with NKG2D and reanalyzed publicly available single-cell RNA-sequencing data (GSE117826) and RNA-sequencing data from CRISPR/Cas9-edited CALR-mutant iPSC-derived megakaryocytes to evaluate MICA/MICB expression. Results: MICA*004:001 was significantly associated with CALR-mutated MPN versus controls (p = 0.004; Bonferroni-adjusted p = 0.047), while MICB*008:001 showed only nominal association. Exploratory haplotype analyses identified a MICA*009:01-MICB*004:001 haplotype associated with CALR-mutated status (p = 0.008) and a KLRK1 G-A-G-T haplotype (rs1049174-rs2617160-rs2246809-rs2617170) associated with increased CALR-mutated MPN risk (OR = 3.61; p = 0.029). Transcriptomic reanalysis indicated a higher fraction of CALR-mutant stem and progenitor cells expressing detectable MICA/MICB transcripts, and heterozygous CALR-mutant megakaryocytes exhibited higher MICA expression than the wild type. Conclusions: Together, these data support an exploratory immunogenetic and transcriptomic link between the NKG2D-MICA/MICB axis and CALR-mutated MPN, but direct protein-level and functional studies are required before mechanistic or therapeutic conclusions can be drawn. Full article

(This article belongs to the Special Issue Novel Advances in the Molecular Understanding of Myeloproliferative Neoplasms (MPNs))

20 pages, 670 KB

Open AccessArticle

Fractional-Order SEIRS-V Dynamics of Worm Propagation in Wireless Sensor Networks: Semi-Analytical and Numerical Study with Stability and Uniqueness Insights

by Mahmoud M. Mokhtar and H. M. Hamouda

Fractal Fract. 2026, 10(7), 427; https://doi.org/10.3390/fractalfract10070427 (registering DOI) - 24 Jun 2026

Abstract

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

(This article belongs to the Section Numerical and Computational Methods)

21 pages, 11344 KB

Open AccessArticle

Simultaneous Determination of CH₄, C₂H₆ and C₂H₄ Mixtures Using MCPSO-Optimized DKELM

by Pengcheng Gu, Meixuan Zhao, Xinyu Tian and Yuwang Han

Spectrosc. J. 2026, 4(3), 12; https://doi.org/10.3390/spectroscj4030012 (registering DOI) - 24 Jun 2026

Abstract

Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments. Full article

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21 pages, 5583 KB

Open AccessReview

Nutrition as the Intelligent Nexus: Integrating Precision Farming into Sustainable Ruminant Systems

by Luis O. Tedeschi, Egleu D. M. Mendes and Marcia H. M. R. Fernandes

Agriculture 2026, 16(13), 1379; https://doi.org/10.3390/agriculture16131379 (registering DOI) - 24 Jun 2026

Abstract

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

(This article belongs to the Section Farm Animal Production)

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26 pages, 4104 KB

Open AccessArticle

Multiplexity and Disruption Propagation in Global Container Liner Shipping Networks: From the Perspective of Carriers’ Geopolitical Affiliations

by Huanyu Ren, Xiaozhen Lian, Qiong Chen, Ziheng Lin, Zonghui Jiang and Zhenglong Li

Entropy 2026, 28(7), 723; https://doi.org/10.3390/e28070723 (registering DOI) - 24 Jun 2026

Abstract

Global container liner shipping networks (GCLSNs) underpin world trade, yet their organization is increasingly reshaped by geopolitical fragmentation. Existing studies often model GCLSNs as single-layer networks, overlooking how carriers’ geopolitical affiliations structure both connectivity and disruption risk. This study constructs a weighted carrier–geopolitical multiplex network in which layers are defined by carriers’ geopolitical affiliations and coupled through shared port calls. Structural analysis reveals pronounced asymmetry in layer size, cohesion, and inter-layer dependence, with overlap concentrated in a limited set of shared hubs. Using the Red Sea crisis as an empirical stress-test scenario, we develop a load–capacity propagation model, incorporating intra-layer load redistribution, rerouting to substitute shared hubs, and inter-layer resource squeeze at same-port layer copies. Results show that direct losses concentrate in corridor-exposed layers, while indirect losses propagate selectively through bridge hubs, especially Singapore, Shanghai, Shenzhen, and Port Klang. Sensitivity analysis indicates nonlinear amplification when low tolerance, strong inter-layer squeeze, and elevated rerouting pressure coincide. These findings show that multiplexity does not imply resilience by itself; cross-layer connectivity buffers disruption only when spare capacity is distributed but amplifies vulnerability when it converges on a narrow set of shared hubs. The paper contributes a carrier–geopolitical perspective to shipping network analysis and a dynamic framework for studying disruption propagation in complex logistics systems. Full article

(This article belongs to the Special Issue Complexity of Social Networks)

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27 pages, 2131 KB

Open AccessArticle

Stage-Dependent Behavioral Patterns in MOOC Dropout: An Explainable Learning Analytics Study

by Xinyu Xiang, Jiayue Song, Shukai Duan, Lidan Wang and Jia Yan

Educ. Sci. 2026, 16(7), 999; https://doi.org/10.3390/educsci16070999 (registering DOI) - 24 Jun 2026

Abstract

The high dropout rate in massive open online courses (MOOCs) continues to limit their potential in promoting inclusive and sustainable learning. Although many prediction models have been used to identify potential dropouts, most studies view dropout as a static classification problem and fail to clearly reveal the dynamic trajectory of learner participation over time. Therefore, this study introduces a phased analysis perspective, treating MOOC dropout as a process that continuously evolves at different stages. On the basis of the KDDCUP2015 dataset, we constructed behavioral characteristics at three time points: the first week, the third week, and the fifth week. By combining robust feature analysis and interpretable models, we systematically examined the changing patterns of dropout modes. The results revealed significant differences across the different stages. In the early stage of the course, dropout was related mainly to the unstable interaction behaviors of learners, such as restricted access to resources and irregular participation rhythms. In the middle and late stages, task-oriented behaviors, especially those related to video-based learning activities, gradually became key factors. Notably, high-frequency video participation does not always reduce the risk of dropout; when video activity is high but the overall interaction rate is low, it is more likely to indicate an increase in the risk of dropout. These results indicate that the combination of behaviors is more crucial than mere activity levels. By revealing the changing characteristics of behaviors at different stages, this study helps support the design of more practical early warning methods. Full article

(This article belongs to the Special Issue AI in Higher Education: Advancing Research, Teaching, and Learning)

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