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With increasingly stringent greenhouse gas emission regulations in the shipping industry, there is an urgent need for an efficient energy management strategy for new energy ship power systems. However, existing dispatch models often overlook the dynamic energy-saving potential of active drag reduction technologies and lack effective optimization algorithms capable of handling high-dimensional, multi-constrained problems. To address these problems, this paper proposes a novel integrated dispatch framework for hybrid energy ship power systems that incorporates air lubrication systems. First, a unified multi-energy dispatch model is established, coupling the dynamic operation of air lubrication systems with electrical, thermal, and propulsion energy flows. Second, an Improved Traffic Jam Optimizer algorithm is proposed, which enhances global exploration and local exploitation through a nonlinear parameter adaptation mechanism, differential mutation strategy, and dynamic hybrid search architecture. Convergence analysis based on Markov chain theory is provided to guarantee algorithmic reliability. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of convergence speed, solution accuracy, and stability. Furthermore, integrating air lubrication systems into the ship power system reduces total operating costs and greenhouse gas emissions by up to 20.569% and 6.310%, respectively. Full article

Chaotic itinerancy—irregular switching among metastable collective states—provides a dynamical substrate for flexible social coordination, yet its mechanistic origin in multi-agent systems remains unclear. We present a multi-agent Active Inference model in which chaotic itinerancy emerges from Expected Free Energy minimisation without outcome-level social priors. Agents select actions to minimise Expected Free Energy while updating preferences through a precision-gated learning mechanism modulated by interpersonal trust. Hill-function nonlinearity in state transitions creates bistable “affordance landscapes” that gate behavioural mode switching. Simulations with small number of agents on an Erdos–Rényi trust network reveal spontaneous alternation among multiple metastable behavioural clusters, heavy-tailed dwell-time distributions, and sign-changing finite-time Lyapunov exponents—three hallmarks of chaotic itinerancy. Crucially, replacing Hill-function dynamics with linear transitions reduces the chaotic-itinerancy detection rate from 80% to 20%, demonstrating that nonlinear affordance structure is necessary for generating metastable switching. We further show that agents with simplified internal models of the world sustain richer itinerant dynamics as a group than “perfect-foresight” agents, suggesting that bounded rationality may be functionally advantageous for maintaining behavioural flexibility. These results establish active inference as a principled framework for modelling chaotic itinerancy in social systems and offer a computational account of trust-mediated collective transitions observed in theatre workshops and group dynamics. Full article

Dampers are key energy-dissipating components in structural seismic systems. They can effectively dissipate seismic energy, control structural dynamic responses, and mitigate damage to primary structural members. Thus, they play an important role in improving structural seismic resilience and mitigating seismic hazards. By integrating multiple units with different yield thresholds or energy-dissipating mechanisms, multi-stage energy-dissipating dampers realize sequentially activated energy dissipation under varying seismic intensities and spectral characteristics. They broaden the energy dissipation range under varying seismic intensities and enhance cyclic stability and fatigue resistance. They provide an effective technical approach to overcome the inherent limitations of traditional single-stage dampers, such as insufficient energy dissipation capacity and poor cyclic fatigue performance. This study systematically reviews the recent research progress on multi-stage energy-dissipating dampers, focusing on the structural configurations and seismic performance studies of four typical types: stage-yielding metallic dampers, stage-friction dampers, metal-friction hybrid dampers, and metal-viscoelastic hybrid dampers. Relevant numerical simulation and experimental research results are summarized, and the key issues that require further in-depth exploration in this field are prospected. Full article

Background/Objectives: The main cause of death in patients with chronic kidney disease (CKD) is of cardiovascular origin. Entropy-based analysis of physiological signals reflects system irregularity, complexity, and adaptive capacity. Amplitude-aware permutation entropy (AAPE) is a signal analysis method suitable for assessing complex cardiovascular dynamics, and growing evidence suggests that measures of physiological signal variability and complexity may have prognostic value. This study aimed to evaluate whether AAPE can predict mortality in CKD patients undergoing hemodialysis (HD), with and without diabetes. The aim of this study was to assess whether AAPE analysis of cardiovascular signals following the administration of a glucose bolus directly into the extracorporeal circuit during hemodialysis (HD)—a method originally used to treat intradialytic hypotension and to study the kinetics of glucose, insulin, and C-peptide in patients with and without type 2 diabetes—can predict mortality in patients with chronic kidney disease (CKD) undergoing hemodialysis (HD), both with and without diabetes. Methods: After seven years of follow-up, mortality outcomes were analyzed in relation to AAPE-derived parameters. Results: Higher mortality was associated with smaller differences in AAPE of mean arterial pressure (MAP) and diastolic arterial pressure (DIA) before and after intravenous glucose administration (p = 0.009 and p = 0.016, respectively). Higher tonicity was associated with higher survival (p = 0.01). Additionally, greater reductions in AAPE of systolic arterial pressure (SYS) and larger differences in AAPE of ejection time (EJT) and total peripheral resistance (TPR) were associated with increased mortality. Conclusions: These findings suggest that entropy analysis reflects cardiovascular adaptability and may serve as a prognostic biomarker in HD patients. Full article

The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To address these challenges, this work develops an integrated framework for both global task allocation and real-time replanning in complex three-dimensional operational environments. First, for the combinatorial optimization of task execution sequences across multiple aircraft, a static task assignment method is proposed. This method employs a Hybrid-encoding Constrained Black-winged Kite Algorithm (HCBKA), which incorporates optimization metrics such as mission execution time, completion rate, and load-balancing symmetry among aircraft. The HCBKA aims to find a task assignment scheme that achieves a comprehensive optimum across multiple objectives through efficient model solving. Second, to handle potential real-time dynamic changes during mission execution, a rapid-response and generalizable replanning mechanism is developed. This mechanism utilizes an event-triggered strategy based on a Time-window aware Dynamic Auction Algorithm (TDAA). It ensures that the system can promptly initiate and execute online task reallocation in response to contingencies such as changing mission requirements or losses within its own drone swarm, thus maintaining the adaptability and robustness of the overall plan. Simulation results show that the proposed framework produces high-quality global solutions and maintains strong robustness under dynamic changes. The approach provides an effective and scalable solution for coordinated multi-aircraft swarm airdrop missions. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Cancer exhibits pronounced heterogeneity at both spatial and cellular levels, contributing to variability in therapeutic responses and the emergence of treatment resistance. This heterogeneity is underscored by the diverse genetic, epigenetic, and phenotypic variations found within tumor cell populations. Cancer stem cells (CSCs), although representing a minor fraction of tumor cells, possess the capacity to self-renew and differentiate, thereby driving the dynamic evolution of tumor heterogeneity. CSCs interact intricately with various elements of the tumor microenvironment (TME), further amplifying this heterogeneity. Recent advancements in organoid technology have facilitated the development of CSC-derived organoid models that more faithfully recapitulate the TME and intratumoral heterogeneity, which conventional 2D culture systems fail to replicate. These CSC-derived organoid systems not only preserve the structural and genomic characteristics of tumors, but they also enable the exploration and evaluation of therapeutic strategies that reflect tumor complexity. However, CSC-derived organoid systems face several challenges, such as the rarity of CSCs, lack of standardized culture conditions, absence of TME components, limited predictive accuracy, and insufficient modeling of tumor heterogeneity. This review discusses these limitations and explores potential solutions, including the use of artificial intelligence (AI) to enhance treatment predictability. These innovations may improve the utility of organoid models for therapeutic evaluation and for targeting tumor heterogeneity. Ultimately, CSC-derived organoids may serve as a valuable platform for advancing precision medicine and cancer research. Full article

Arid agricultural systems face increasing sustainability challenges due to water scarcity, climate variability, and structural resource constraints. Although Artificial Intelligence (AI) is widely promoted as a key enabler of sustainable agriculture, empirical evidence on its long-term effects on agriculture-related Sustainable Development Goals (SDGs), particularly in arid regions, remains limited. This study investigates the role of AI in supporting sustainable agricultural development in Saudi Arabia’s Ha’il region. Using annual data from 1995 to 2025, AI adoption—proxied by SDG9 indicators that reflect AI-enabling digital infrastructure and innovation readiness rather than observed on-farm AI deployment—is examined in relation to a composite Sustainable Agricultural Development Goals index (SADGH), which integrates SDG2 (food security), SDG6 (water management), SDG8 (economic performance), SDG12 (responsible production), SDG13 (climate action), and SDG15 (land sustainability). Econometric analysis based on a Vector Error Correction Model (VECM) reveals a stable long-run relationship between AI adoption and agricultural sustainability, with approximately 32% of short-term disequilibrium corrected annually. In the short run, AI adoption is positively associated with food security, economic performance, and land sustainability, while water- and climate-related indicators adjust more gradually. Dynamic analyses suggest that AI-related shocks may generate cumulative effects over time. In addition, deep learning models using Long Short–Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are applied within an exploratory framework to capture potential nonlinear dynamics and generate indicative forecasts. The GRU model shows lower prediction errors; however, results should be interpreted with caution, given the limited sample size. Overall, the findings suggest that AI may contribute to sustainable agricultural development in arid regions, while highlighting the need for further research based on larger datasets. Full article

Growing concerns over food security and greenhouse gas emissions present a dual challenge, as mitigation strategies for one often intensify the other. Nitrification inhibitors (NIs) have emerged as a promising approach to simultaneously reduce nitrous oxide (N₂O) emissions and enhance crop productivity. However, their effectiveness is highly dependent on environmental conditions. To systematically evaluate the environmental controls and the economic trade-offs associated with NI application, this study presents a systematic data synthesis of 196 peer-reviewed articles, assessing the performance of three widely used NIs: dicyandiamide (DCD), 3,4-dimethylpyrazole phosphate (DMPP), and nitrapyrin. The analysis quantifies the influence of key environmental factors (e.g., temperature, soil pH, soil moisture, and soil organic carbon) on NI biodegradability, nitrogen dynamics, and N₂O emissions. The results indicate that soil organic carbon has a limited effect on NI performance, whereas temperature emerges as the dominant controlling factor. Among the NIs evaluated, DCD and DMPP demonstrate the highest mitigation efficiencies, achieving N₂O emission rates as low as 10⁻⁶ and 10⁻⁵ kg ha⁻¹ d⁻¹, respectively. An integrated economic analysis further evaluates the cost-effectiveness of NI application across major cropping systems, including corn, rice, and wheat. The findings show that DMPP and nitrapyrin applications yield the highest net economic returns in corn and rice systems (up to 860 USD and 880 USD, respectively), while wheat systems without NI application remain less profitable (approximately 330 USD). Ultimately, this study demonstrates that the practical viability of NIs depends heavily on balancing input costs with crop-specific yield gains, rather than environmental benefits alone. While NIs offer substantial greenhouse gas mitigation potential, their widespread adoption requires careful, site-specific economic evaluation to ensure that yield improvements sufficiently offset the added application costs to achieve truly sustainable agricultural practices. Full article

Anthropogenic polymers have become an increasingly important class of emerging contaminants in terrestrial ecosystems. While extensive research has focused on microplastics in aquatic environments, their interactions with soil systems and particularly with soil organic matter (SOM) remain insufficiently understood. Soil represents a major environmental sink for polymer residues originating from agricultural practices, urban activities, and atmospheric deposition. Accordingly, associations between polymers and SOM, including humic substances, may significantly influence the retention, mobility, and transformation of carbon in soil systems. This review synthesizes current knowledge on the influence of synthetic polymers on soil organic matter dynamics. A bibliometric and qualitative literature analysis based on publications indexed in Web of Science and Scopus from 1979 to 2025 was conducted to identify major research trends and knowledge gaps. The results indicate that polymer particles can alter soil structure, microbial activity, and sorption processes, thereby affecting the stability and cycling of soil organic carbon. Interactions between polymer surfaces and humic substances may modify aggregation processes and influence the persistence and mobility of both polymers and organic carbon compounds. Despite the rapid growth of research on microplastics, studies addressing polymer–SOM interactions remain limited and methodologically heterogeneous. Greater integration between polymer research, soil science, and land use studies is necessary to better understand the implications of polymer contamination for soil quality and carbon cycling. The findings highlight the need for standardized analytical approaches and interdisciplinary research frameworks to assess the long-term effects of polymers in soil ecosystems. Full article

In a context marked by the Anthropocene, the climate crisis, and the contemporary blockage of political and projective imagination, utopias and dystopias re-emerge as fundamental critical instruments for architecture. Far from constituting evasive or unrealizable exercises, these constructions operate as epistemological and projective devices capable of exploring possible futures, revealing latent tensions, and questioning the ideological frameworks that shape the built environment. This article examines speculative design as a contemporary updating of the utopian and dystopian tradition in architecture, understood not as a normative model but as a critical method for imagining radical transformations of dwelling in response to the current ecological, social, and geopolitical urgencies. Drawing on a series of projects developed within the university context, it analyses how architectural speculation, enhanced by artificial intelligence tools, enables the exploration of alternative scenarios of urbanization, adaptive habitats, and new relationships between architecture, territory, and nature. The cases analysed show that the combination of utopia, dystopia, and emerging technologies fosters an understanding of architecture as an open, dynamic, and relational system capable of responding to contexts of high uncertainty. The article argues that the return of utopian imagination, now mediated by speculative practices and digital tools, constitutes a relevant contribution to the contemporary debate on new forms of urbanization, flexible megastructures, and sustainable architectural futures. Full article

The torque ripples of robotic permanent magnet synchronous motors (PMSMs) degrade motion smoothness and positioning accuracy of the system, while inevitable load transients in robotic tasks further complicate torque ripple attenuation. To address this issue, this paper develops an event-triggered torque ripple attenuation method that explicitly distinguishes torque ripple from dynamic load transients. First, a sliding-mode torque observer is constructed to obtain real-time torque information, whose stability is rigorously analyzed using a Lyapunov function. Second, frequency-selective torque ripple extraction schemes are proposed to accurately isolate steady-state high-frequency torque ripple from the estimated torque signal. In particular, two specially designed filtering structures are developed and compared, one of which is selected to preserve ripple-related frequency content during test, ensuring robust and accurate ripple identification under varying operating conditions in robotics. Third, a torque-ripple-regulation-based compensation strategy is used within a vector-controlled PMSM drive, in which the extracted torque ripple is processed by a dedicated ripple regulator to generate voltage compensation signals. This strategy achieves effective steady-state torque ripple attenuation with low implementation complexity, while avoiding performance degradation during dynamic load transients. Finally, experimental results are provided to validate the effectiveness of the proposed methods. Full article

Partially liquid-filled rotor systems subjected to lateral excitation exhibit pronounced fluid–structure interaction, leading to complex and highly sensitive vibration responses. To enable efficient probabilistic prediction under parametric uncertainty, this study develops a deterministic–data-driven framework for a rigid hollow rotor partially filled with liquid. Based on small-perturbation flow theory, the liquid-induced feedback forces are analytically derived and incorporated into the coupled rotor–liquid dynamic equations, yielding a closed-form steady-state solution. The results reveal that lateral excitation in one direction induces coupled vibration in the orthogonal direction, resulting in an elliptical whirl trajectory of the rotor center. The vibration characteristics depend jointly on excitation frequency and rotor angular velocity, and for a given angular velocity, two critical excitation frequencies are identified at which the response amplitude increases sharply. Surrogate models based on a backpropagation neural network (BPNN) and a support vector machine (SVM) are constructed and validated, with the BPNN demonstrating superior predictive accuracy. Uncertainty analysis further shows that the maximum vibration amplitude exhibits asymmetric, non-Gaussian distributions even under normally distributed inputs, and excessive amplification may occur beyond certain uncertainty levels. The proposed framework provides a robust tool for probabilistic vibration assessment and uncertainty-informed design of partially liquid-filled rotor systems. Full article

To solve the problems of low seeding precision and the poor operational adaptability of traditional no-till seeders under dense planting mode, and meet the agronomic requirements for high maize yield, this study carried out optimization and experimental research on the no-till precision fertilizer-seed co-sowing system for maize with wide-narrow row dense planting, relying on the experimental base of the Science and Technology Courtyard for Super High-Yield Cropping Systems in Qihe, China Agricultural University. Through modular integration and the optimization of key components, precise row spacing adjustment and improved sowing depth consistency in complex plots were achieved. A tractor-implement integrated a kinematic model and a dynamic model of the seed metering tube, which were constructed to quantify the correlation between operational parameters and motion states, providing theoretical support for structural parameter optimization. Field tests showed that all operational quality indicators of the system met the local high-yield requirements for no-till dense planting; the comprehensive performance was optimal at a density of 75,000 plants·ha⁻¹, with the best seeding uniformity (coefficient of variation: 5.65%), seedling emergence and seedling uniformity, which is well adapted to the agronomic characteristics of the wheat–maize rotation areas in the Huang-Huai-Hai Plain. Subsequent optimization by reducing the operating speed and increasing the spring stiffness can further improve the operational quality, realize the deep integration of agronomy and agricultural machinery, provide agricultural machinery support for high-yield and high-quality maize cultivation, and is of great significance for improving agricultural production efficiency and resource utilization. Full article

Bursting is a special dynamic mode of neurons that consists of several consecutive spikes separated by a period of quiescence. This paper considers the synchronization of two non-identical neurons capable of both spiking and bursting behavior connected by a unidirectional pulse coupling. Unlike most studies, which focus on purely mathematical modeling and numerical simulations, we use simulation modeling of a neuron based on a phase-locked loop (PLL) in addition to the conventional solving of ODEs. Using these two approaches, we demonstrate the fundamental possibility of synchronizing two neurons operating in different dynamic modes. Synchronization regions are constructed in the system parameter space. It is shown that synchronization is achieved both for two spiking neurons and for neurons generating bursting activity. The results obtained are of interest from either a fundamental perspective, as an example of a complex nonlinear system with rich dynamic behavior, or from an applied perspective. In particular, the proposed model can be used in the field of neuromorphic electronics, where not only biological plausibility is important but also the possibility of simple hardware implementation. Differences between the simulation model and the results of numerical solution of ODEs are also demonstrated, revealing the importance of proper simulation frameworks at different design levels of neuromorphic electronics. Full article