Search Results (4,371)

Carbon capture and storage (CCS) is essential for achieving net-zero emissions, yet amine-based capture systems face significant challenges including high energy penalties (20–30% of power plant output) and operational costs ($50–120/tonne CO${}_2$). This study develops and validates a novel multi-scale Digital Twin (DT) framework integrating Physics-Informed Neural Networks (PINNs) to address these challenges through real-time optimization. The framework combines molecular dynamics, process simulation, computational fluid dynamics, and deep learning to enable real-time predictive control. A key innovation is the sequential training algorithm with domain decomposition, specifically designed to handle the nonlinear transport equations governing CO${}_2$ absorption with enhanced convergence properties.The algorithm achieves prediction errors below 1% for key process variables (R${}^2$> 0.98) when validated against CFD simulations across 500 test cases. Experimental validation against pilot-scale absorber data (12 m packing, 30 wt% MEA) confirms good agreement with measured profiles, including temperature (RMSE = 1.2 K), CO${}_2$ loading (RMSE = 0.015 mol/mol), and capture efficiency (RMSE = 0.6%). The trained surrogate enables computational speedups of up to four orders of magnitude, supporting real-time inference with response times below 100 ms suitable for closed-loop control. Under the conditions studied, the framework demonstrates reboiler duty reductions of 18.5% and operational cost reductions of approximately 31%. Sensitivity analysis identifies liquid-to-gas ratio and MEA concentration as the most influential parameters, with mechanistic explanations linking these to mass transfer enhancement and reaction kinetics. Techno-economic assessment indicates favorable investment metrics, though results depend on site-specific factors. The framework architecture is designed for extensibility to alternative solvent systems, with future work planned for industrial-scale validation and uncertainty quantification through Bayesian approaches. Full article

Background: Iron plays a crucial role in neurotransmitter synthesis, myelination, and neuronal metabolism. Iron deficiency has been associated with a variety of neurodevelopmental disorders, particularly attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). However, the prevalence, clinical impact, and treatment implications differ between these conditions. Objective: To synthesize current evidence on the prevalence, neurobehavioral consequences, and therapeutic implications of iron deficiency in ADHD and ASD, highlighting convergences and disorder-specific findings. Results: In ADHD, studies using serum ferritin and related peripheral markers show inconsistent associations with core symptom severity, with reported ferritin thresholds for deficiency ranging widely. While some studies suggest links between low ferritin and hyperactivity, inattention, or stimulant response, others report null findings. In contrast, emerging neuroimaging evidence consistently demonstrates reduced brain iron in dopaminergic regions in children. In ASD, the strongest link is between low ferritin and sleep-related motor disturbances, and iron supplementation may improve sleep and motor symptoms. Conclusions: Screening for iron status and targeted supplementation may improve sleep and behavioral outcomes in ADHD and ASD, meriting integration into clinical practice and further randomized controlled trials. Full article

In this paper, we investigate the finite-time consensus problem of a fractional-order multi-agent system with repetitive motion. The system under consideration consists of robotic agents with a leader and a fixed communication topology. A distributed open-closed-loop PDα fractional-order iterative learning control (FOILC) algorithm is proposed. The finite-time uniform convergence of the proposed algorithm is analyzed, and sufficient convergence conditions are derived. The theoretical analysis demonstrates that, as the number of iterations increases, each agent can achieve complete tracking within a finite time by appropriately selecting the gain matrices. Simulation results are presented to verify the effectiveness of the proposed method. Full article

Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration ($\gamma =1$) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling. Full article

Background/Objectives: Cold-smoked salmon (CSS) is a ready-to-eat product with minimal preservation hurdles and a microbiota shaped by raw-material contamination and processing environments. Short breaks in refrigeration commonly occur during shopping and transport, yet their microbiological impact remains unclear. Here, we used ASV-resolved 16S rRNA gene metataxonomics to characterize storage-driven microbiota dynamics in CSS—quantifying ASV-level genetic diversity and phylogeny-aware (UniFrac) community structure—and to evaluate the effect of a brief, consumer-mimicking 2 h room-temperature cold-chain disruption. Methods: Three CSS types (organic, conventional Norwegian, and conventional Scottish) were stored at 5 °C for 35 days. On day 16, half of each batch was exposed to 2 h at room temperature (RT) before analysis; paired controls remained refrigerated. Culture-based counts (total mesophiles, lactic acid bacteria, Photobacterium spp.; indicator/pathogen screens) were performed per ISO methods. Community profiling used 16S rRNA (V3–V4) amplicon sequencing with QIIME 2/DADA2 and SILVA taxonomy. Linear mixed effects modelled alpha diversity; beta diversity by PERMANOVA on UniFrac distances; differential abundance by ANCOM-BC. Results: ASV-resolved 16S rRNA gene profiles of CSS were dominated by Pseudomonadota and Bacillota, with storage-driven shifts and taxon-specific trajectories (e.g., increasing Latilactobacillus). Both time and product type significantly explained phylogeny-aware community structure (unweighted and weighted UniFrac), consistent with storage-driven phylogenetic convergence across products. At day 16, ASV-level genetic diversity (Shannon/Observed features) and genus-level composition did not differ between RT-disrupted and continuously refrigerated samples. Culture-dependent counts increased from baseline to day 16 and largely plateaued by day 35, with lactic acid bacteria in Norwegian CSS continuing to rise; no systematic effect of the 2 h RT exposure was observed in culture-based comparisons. Indicator/pathogen screens detected no unexpected pathogenic species throughout the study period. Conclusions: Refrigerated storage drives pronounced, phylogeny-aware microbiota shifts and cross-product convergence in cold-smoked salmon, whereas a single 2 h RT interruption at mid-storage did not measurably alter ASV-level genetic diversity or community structure under the tested conditions. Integrating culture-based enumeration with ASV-resolved 16S rRNA gene metataxonomics provides complementary insights for shelf-life evaluation and risk assessment in ready-to-eat seafood. Full article

To investigate whether tourism can act as a catalyst for regional economic convergence during the period 2000–2023, this study fills a critical gap in previous research by simultaneously examining the impact of tourism on economic disparities from both static stock and dynamic incremental perspectives, while accounting for spatial dependence. This study analyzes the economic convergence effects of tourism at the Chinese provincial and regional levels using σ convergence and the spatial Durbin model in a conditional β convergence framework. The results confirm the benefits that tourism brings to economic growth and convergence. Spatially, northeastern China exhibits stronger effects, followed by western and eastern China, in contrast to the relatively weaker impacts in central China. Structurally, its direct effect is more pronounced: the convergence effect is stronger for local areas than for neighboring areas. Temporally, the effect is most pronounced in the early (2000–2012) and late (2020–2023) phases, but becomes statistically insignificant in the intermediate period (2013–2019). By moving beyond the question of whether tourism drives growth to reveal for which regions it is most beneficial, this study offers a refined analytical perspective and actionable insights for achieving balanced regional development in China and other countries and regions at a comparable stage of development. The findings also highlight the potential of cultural heritage as a lever for sustainable and equitable regional growth, channeled through tourism. Full article

The probability integral method is the primary technique for predicting mining-induced subsidence in China, and its predictive accuracy strongly depends on the precision of the model parameters. To improve the accuracy and stability of parameter inversion and to overcome the convergence randomness of the Genetic Algorithm (GA) in global search, as well as the tendency of the BFGS quasi-Newton method (BFGS) to converge to local optima in non-convex optimization problems, a hybrid GA–BFGS optimization algorithm is proposed for inverting the parameters of the probability integral model. This hybrid approach combines the global exploration capability of GA with the fast local refinement of BFGS, resulting in a more efficient and robust parameter optimization process. Simulation results under ideal conditions without model error demonstrate that the proposed GA–BFGS algorithm outperforms pattern search (PS), GA, and BFGS in terms of inversion accuracy, convergence stability, and robustness to noise and outliers. In engineering applications, the inversion accuracy is reduced compared with simulation experiments, which can be attributed to complex geological conditions and inherent model uncertainties. Therefore, further improvements in subsidence prediction accuracy require not only refined inversion algorithms but also the development of more accurate prediction models that explicitly account for site-specific geological and mining conditions. Full article

In thick coal seam conditions, the surrounding rock deformation in the longwall mining faces’ along-the-goal roadway is severe, and the support strength struggles to meet roadway retention requirements. A coordinated control strategy, termed “pressure-relief and support,” is proposed, which includes an “Optimization of Roof Cutting in Surrounding Rock Structure, Reinforcement of surrounding rock support, high-strength temporary support, and roadside gangue-blocking support.” A numerical model for roof-cutting pressure relief in thick-seam caving mining gob-side entries was established to simulate various roof-cutting heights and angles. This model analyzes the evolution patterns of stress and displacement under different cutting parameters to identify optimal values. The study presents a coordinated “pressure-relief and support” control scheme for gob-side entries in thick-seam caving mining, with its feasibility validated through numerical simulation analysis and field industrial tests. The findings demonstrate that the selection of the roof-cutting height and angle exerts a significant influence on the deformation behavior of the retained roadway roof. By severing the roof strata, this technique disrupts the load-transfer path from the goaf to the entry, thereby mitigating the adverse effects of overlying strata fracturing and facilitating more effective ground control. As a result, roof-cutting and pressure relief substantially reduce the stress imposed on the supporting structures. The coordinated “pressure-relief & support” control strategy employed in gob-side entry retaining for thick-seam longwall top-coal caving faces notably improves the surrounding rock stress regime and effectively restrains roadway convergence. Full article

This paper concentrates on fractional-order fuzzy cellular neural networks (FOFCNNs) with bounded uncertainties and transmission delays. To better capture real-world dynamic behaviors, the fuzzy AND and OR operators are employed to construct drive-response systems. For the fixed-time synchronization task of the systems, a novel multi-module feedback controller incorporating three functional terms is designed. These terms aim to eliminate delay effects, ensure fixed-time convergence, and reduce parameter conservativeness. Leveraging the properties of fractional-order operators and our multi-module control scheme, new synchronization criteria of the studied drive-response systems can be established within a predefined time. An upper bound on the settling time is derived, depending on the system size and control parameters, but independent of the initial conditions. A significant corollary is derived for the case of no uncertainties under the nonlinear controller. Numerical experiments discuss the impact of uncertainties and delays on synchronization, and confirm the validity of the results presented in this study. Full article

Plant immunity research is being reshaped by integrative multi-omics approaches that connect transcriptomic, proteomic, and interactomic data to build systems-level views of plant–pathogen interactions. This review outlines the scope and methodological landscape of these approaches, with particular emphasis on how transcriptomic and proteomic insights converge through network-based analyses to elucidate defense regulation. Transcriptomics captures infection-induced transcriptional reprogramming, while proteomics reveals protein abundance changes, post-translational modifications, and signaling dynamics essential for immune activation. Network-driven computational frameworks including iOmicsPASS, WGCNA, and DIABLO enable the identification of regulatory modules, hub genes, and concordant or discordant molecular patterns that structure plant defense responses. Interactomic techniques such as yeast two-hybrid screening and affinity purification–mass spectrometry further map host–pathogen protein–protein interactions, highlighting key immune nodes such as receptor-like kinases, R proteins, and effector-targeted complexes. Recent advances in machine learning and gene regulatory network modeling enhance the predictive interpretation of transcription–translation relationships, especially under combined or fluctuating stress conditions. By synthesizing these developments, this review clarifies how integrative multi-omics and network-based frameworks deepen understanding of the architecture and coordination of plant immune networks and support the identification of molecular targets for engineering durable pathogen resistance. Full article

This investigation evaluates the viscosity and flow performance of cationic polyacrylamide (CPAA) solutions by assessing the effect of CPAA concentrations, shear rate, temperature, and electrolyte salt types. The study aims to characterize the flow behavior of CPAA solutions for different industrial utilizations under some challenging conditions of high salinity of two different electrolytes and high-temperature environments. In addition, the study addresses the critical shear rate thresholds at which the transition from shear-thinning to shear-thickening occurs. An Anton Paar rotational rheometer was employed to evaluate the flow behavior of cationic polyacrylamide solutions over the range of 20–80 °C at 20 °C intervals. Polymer samples were prepared from CPAA powder in a concentration range of 500–5000 ppm. To determine the electrolyte effects, NaCl and CaCl₂ were incorporated into the polymer solutions with a concentration range of 0–10 Wt.%. This study revealed that shear stress is vastly sensitive to CPAA concentration at shear rates less than 200 s⁻¹, whereas this sensitivity reduces at higher shear rates where the resulting profiles converge. Moreover, a considerable decrease in shear stress was reported with temperature as a result of the thermal influence on the molecular interaction forces. Rheological analysis of the CPAA solutions shows they exhibit strong non-Newtonian shear-thinning behaviors with viscosity decreasing significantly as the shear rate approaches 200 s⁻¹. On the contrary, a transition to a shear-thickening profile is observed at a shear rate above this limit of 200 s⁻¹. The results show that the dynamic viscosity of the CPAA solutions rises significantly as the concentration increases from 500 to 5000 ppm. At a shear rate of 10 s⁻¹, the dynamic viscosity increased from 2.4 to 33.8 mPa·s as the CPAA concentration increased from 500 to 5000 ppm (exactly 2.4, 11.8, 16.6, and 33.8 mPa.s for 500, 1500, 2500, and 5000 ppm, respectively). Additionally, increasing the temperature from 20 to 80 °C exerts a strong negative influence on dynamic viscosity. Specifically, for the 5000 ppm concentration at a shear rate of 10 s⁻¹, the dynamic viscosity decreased from 33.8 to 18.3 mPa.s as the temperatures rose from 20 to 80 °C (recorded as 33.8, 27.9, and 18.3 mPa.s at 20, 40, and 80 °C, respectively). Furthermore, the introduction of different electrolytes, such as NaCl and CaCl₂, significantly reduces the viscosity flow profiles. Full article

Federated learning (FL)-based vehicular edge computing networks (VECNs) are emerging as a key enabler of intelligent transportation systems, as their privacy-preserving and distributed architecture can safeguard vehicle data while reducing latency and energy consumption. However, conventional roadside units face processing bottlenecks in dense traffic and at the network edge, motivating the adoption of unmanned aerial vehicle (UAV)-assisted VECNs. To address this challenge, this paper proposes a UAV-assisted VECN framework with FL, aiming to improve model accuracy while minimizing latency and energy consumption during computation and transmission. Specifically, a reputation-based client selection mechanism is introduced to enhance the accuracy and reliability of federated aggregation. Furthermore, to address the channel dynamics induced by high vehicle mobility, we design a robust reinforcement learning-based resource allocation scheme. In particular, an asynchronous parallel deep deterministic policy gradient (APDDPG) algorithm is developed to adaptively allocate computation and communication resources in response to real-time channel states and task demands. To ensure consistency with real vehicular communication environments, field experiments were conducted and the obtained measurements were used as simulation parameters to analyze the proposed algorithm. Compared with state-of-the-art algorithms, the developed APDDPG algorithm achieves 20% faster convergence, 9% lower energy consumption, a FL accuracy of 95.8%, and the most robust standard deviation under varying channel conditions. Full article

Underground coal mining-induced subsidence threatens farmland resources and ecological sustainability in coal–grain overlapping regions with high groundwater tables, making concurrent mining and reclamation a critical management need. Previous studies have not systematically compared the integrated effects of mining sequence, extraction method, and panel optimization on subsidence control and reclamation efficiency in such regions. This study designed six mining schemes, integrating these three technical factors to investigate spatiotemporal subsidence evolution and the performance of deep digging–shallow filling reclamation. Findings reveal that mining design synergistically regulates short-to-mid-term subsidence: deep–thin seam-first skip mining eliminates initial severe subsidence damage, while shallow-thick seam-first sequential mining induces the most severe early-stage ecological disturbance. After a full extraction of both coal seams, long-term surface damage converges to 2374 ha (1509 ha severe damage), dictated by total extracted coal volume and inherent geological conditions. Reclamation efficiency depended on earthwork availability and terrain adaptability, with the optimal scheme achieving a reclamation rate of 65.00%. The findings identify mining strategies that balance subsidence mitigation and farmland restoration, providing actionable insights for sustainable mining in high-groundwater coal–grain overlapping regions. Full article

In recognising both the emerging industrial applications of multi-link robotic manipulators and the inherent challenges of modelling and controlling their highly complex nonlinear dynamics, this work proposes a completely model-free terminal sliding mode control (MFTSMC) design approach to reduce the sensitivity and complexity often associated with model-based routines. Consequently, the proposed design achieves strong robustness, simplicity, and good operation tuning by eliminating the need for system modelling and enabling direct operator–machine interaction. Simulink simulations on a 3-link case subjected to different disturbance conditions (free, low-frequency, high-frequency, and mixed) show rapid dynamic convergence, good tracking precision, and strong disturbance rejection. The system reaches the sliding surface within 0.07 s, maintains steady-state errors around ${10}^{-2}$, and achieves a smooth torque response with low energy costs. The benchmark results confirm the finite-time convergence and demonstrate that the proposed framework is practical and scalable for multi-DOF systems and has potential for underactuated manipulators. It should be noted that a generalised dynamic model for a planar n-link manipulator is presented in the study for (1) the ground truth of the manipulator in simulation (not for the MFTSMC design), (2) the model-based controller designs in comparison to the MFTSMC, and (3) understanding the dynamic characteristics. Full article

This work addresses the challenges of inadequate data acquisition and the limited availability of labeled samples for shearer rocker arm bearing faults by developing a data augmentation methodology that synergistically incorporates the Genetic Algorithm-optimized Wavelet Transform Symmetrical Dot Pattern (GA-WT-SDP) with a Wasserstein Conditional Generative Adversarial Network (WCGAN). In the initial step, the Genetic Algorithm (GA) is employed to refine the mapping parameters of the Wavelet Transform Symmetrical Dot Pattern (WT-SDP), facilitating the transformation of raw vibration signals into advanced and discriminative graphical representations. Thereafter, the Wasserstein distance in conjunction with a gradient penalty mechanism is introduced through the WCGAN, thereby ensuring higher-quality generated samples and improved stability during model training. Experimental results validate that the proposed approach yields accelerated convergence and superior performance in sample generation. The augmented data significantly bolsters the generalization ability and predictive accuracy of fault diagnosis models trained on small datasets, with notable gains achieved in deep architectures (CNNs, LSTMs). The research substantiates that this technique helps overcome overfitting, enhances feature representation capacity, and ensures consistently high identification accuracy even in complex working environments. Full article