Search Results (6,017)

Silk fibroin (SF)–polyphenol systems have emerged as a versatile class of gels and hydrogels in which supramolecular interactions and dynamic crosslinking regulate network formation, responsiveness, and multifunctional performance. Polyphenols interact with SF through hydrogen bonding, hydrophobic interactions, π–π stacking, metal coordination, and covalent crosslinking, thereby modulating conformational transitions, gelation behavior, structural stability, and interfacial functionality. These interaction mechanisms enable the development of SF–polyphenol gel systems with tunable mechanical properties, wet adhesion, antioxidant activity, self-healing capability, and stimuli responsiveness. This review summarizes recent advances in SF–polyphenol gels and hydrogels, with particular emphasis on molecular interaction mechanisms, gelation and fabrication strategies, responsive behaviors, and structure–property relationships. Representative preparation approaches, including solution blending, electrospinning, impregnation–adsorption, enzymatic crosslinking, metal–phenolic coordination, and photo-initiated processing, are systematically discussed in relation to their effects on network architecture and functional output. The responsive behaviors of these systems under pH, redox, electrical, thermal, and optical stimuli are also analyzed from the perspective of dynamic gel networks and adaptive material design. Emerging applications of SF–polyphenol gels in bioadhesives, delivery platforms, flexible bioelectronics, wound-related materials, and sustainable functional systems are highlighted. Current limitations associated with polyphenol instability, formulation sensitivity, reproducibility, and scale-up are critically discussed, together with future opportunities for predictive design of gel-based natural polymer systems. This review provides a comprehensive framework for understanding SF–polyphenol gelation and for guiding the development of next-generation multifunctional gels and hydrogels. Full article

Understanding the development and differentiation of cardiac progenitor cells during the initial stages of embryogenesis is central to a complete understanding of vertebrate heart development. In zebrafish, cardiac specification begins during gastrulation; however, the single-cell transcriptional dynamics of initial cardiac lineage commitment remain not fully defined. In this case, we integrated single-cell RNA sequencing datasets of zebrafish embryos at 4 and 6 h post-fertilisation (hpf) to investigate early cardiac lineage specification. The unsupervised clustering of the integrated dataset identified 12 distinct cell clusters, which made it possible to identify a transcriptionally distinct population of cells characterised by the coordinated expression of transcription factors associated with cardiac development. A further subclustering of the cells expressing cardiac-associated transcription factors showed a significant level of early diversification of the cardiac progenitor group. A projection onto low-dimensional embedding revealed a structured transcriptional organisation of the cardiac subclusters, marked by the differential expression of key cardiac transcription factors, including Gata5, Gata6, Hand2, Nkx2.5, and Tbx5a. A pseudotemporal trajectory analysis uncovered a continuous developmental progression within the cardiac lineage and indicated the gene-specific dynamic regulation and temporal hierarchy of cardiac transcriptional programs. Collectively, these results indicate that zebrafish cardiac progenitors are transcriptionally diverse and acquire cardiac fate through a sustained, continuous regulatory process rather than an abrupt fate transition. This work provides an informative, high-resolution model of early cardiac lineage specification and highlights the power of single-cell transcriptomics for analysing dynamic events in vertebrate embryogenesis. Full article

The persistence of an immunosuppressive microenvironment remains a formidable challenge for cancer immunotherapy, particularly in tumors with immune-excluded or immune-desert phenotypes. Increasing evidence indicates that chronic inflammation and tumor progression are intrinsically linked through shared signaling hubs, including NF-κB and PI3K/Akt. Osthole, a natural coumarin compound, has been reported to exhibit both potent anti-inflammatory and antitumor activities; however, whether these effects reflect a coordinated regulation of the inflammation–cancer axis remains unclear. In this study, we deployed an integrative framework founded on network pharmacology, molecular docking, and rigorous molecular dynamics simulations, complemented by literature-based evidence synthesis, to computationally explore the potential mechanisms underlying Osthole’s dual activities. Our analysis revealed that Osthole’s predicted targets are significantly enriched in signaling pathways bridging inflammatory and oncogenic processes, most notably the PI3K/Akt, NF-κB, and TGF-β/Smad pathways. Crucially, MD simulations provided supportive computational evidence, suggesting that Osthole forms stable, energetically favorable complexes with core protein hubs (AKT1, RELA, and TGFB1) under the simulated conditions. Evidence from representative inflammatory and tumor models supports the biological plausibility of these predictions, including suppression of pro-inflammatory signaling, mitigation of maladaptive tissue remodeling, and induction of apoptosis. Furthermore, in hepatocellular carcinoma models, Osthole-mediated apoptosis appeared linked to HMGB1-related inflammatory signaling, highlighting its potential to modulate the local immune niche. Collectively, this convergence of systems-level predictions and dynamic structural evidence identifies Osthole as a promising multi-target candidate for the coordinated regulation of inflammation-associated tumor progression, providing a robust rationale for further experimental validation. Full article

Many of life’s biggest dilemmas can be summed up as a tension between holding on and letting go. The very language evokes a notion of intentionality which, for the most part, has evaded scientific understanding. How might we even get a window into it? Important insights have come from a seemingly simple task: wiggling one’s fingers to and fro to the beat of a metronome. As the metronome pace increases to some critical frequency, one coordinative pattern becomes unstable and switches spontaneously to another. Such transitions are typically preceded by critical fluctuations, a predicted feature of self-organization in complex, dynamical systems. Here we address the nature and source of these fluctuations, usually assumed to be: (1) random; (2) of external origin; and (3) of fixed magnitude. We performed an experiment in which participants were instructed to oscillate their fingers in either an in-phase or anti-phase pattern in time with a metronome and instructed them to either “hold-on” or “let-go” should they feel the pattern begin to change, yielding a 2 by 2 within-subjects design. We observed that as the metronome frequency was increased from 1.00 to 3.00 Hz, fluctuations in the relative phase between the fingers were significantly altered both by the starting coordinative pattern as well as the participant’s intention to “hold it on” or “let it go”. Specifically, the intention to hold on to the anti-phase pattern delayed the spontaneous transition to in-phase, an effect that was paired with increased fluctuations beyond the critical frequency. These observations were analyzed under the extended Haken–Kelso–Bunz (HKB) model which describes the non-linear stochastic dynamics of the order parameter (relative phase) as a gradient descent on a certain potential. Our analysis, in line with experimental results, suggests that intention transforms the HKB potential not only by stabilizing unstable coordination states but also (paradoxically) by increasing fluctuations around them. Such findings may offer new interpretative light on the relation between intention and fluctuations in the coordination dynamics of living things. Full article

As a key indicator of low-carbon economic transformation, the influencing factors of carbon productivity (CP) have attracted considerable academic attention. However, the study of the role of artificial intelligence (AI) technology innovation is comparatively confined. Using China’s prefecture-level-and-above cities as the sample, this study measures regional AI technology innovation based on AI patent stocks and empirically examines its impact on carbon productivity. The principal findings of this paper are as follows: (1) AI technology innovation boosts urban carbon productivity through three channels: enhancing green innovation, reducing transaction costs, and increasing AI attention. (2) The regional heterogeneity analysis shows that this positive impact of AI technology innovation on carbon productivity exerts a stronger facilitating effect on eastern regions, resource-dependent cities, and central cities. The heterogeneity analysis at the technological level further provides evidence of the effect of AI technology innovation on carbon productivity varying along different tiers of technological development, innovation mode, and innovation role. (3) The analysis identifies the energy structure as a pivotal threshold variable governing the efficacy of AI innovation in bolstering carbon productivity. Notably, crossing the threshold of clean energy penetration triggers an escalating positive feedback loop between AI innovation and carbon productivity. (4) Estimation of temporal effect dynamics via non-parametric panel model shows that the impact of AI technology innovation on CP exhibits phased characteristics. The coefficient became significantly positive in 2010 and peaked in 2015, after which its effect gradually weakened. This study provides comprehensive empirical evidence for understanding the relationship between AI technology innovation and CP and provides policy references for the use of AI technology to promote the coordinated achievement of economic growth and carbon reduction. Full article

As a strategic node of the Silk Road Economic Belt and a prototypical valley-type city, Lanzhou is subject to the dual constraints of rapid urbanization and an inherently fragile ecological foundation, making the coordination between urban expansion and ecosystem services a critical issue for regional sustainability. Drawing upon multi-temporal land use remote sensing datasets provided by the Chinese Academy of Sciences Resource and Environment Science Data Center, in conjunction with soil, meteorological, and socio-economic data, this study integrates a land use transition matrix, the InVEST model, a modified coupling coordination degree model, and the geographic detector to comprehensively examine land use dynamics, the spatiotemporal evolution of urban expansion, and the spatial heterogeneity of ecosystem services (i.e., carbon storage, water yield, habitat quality, and soil conservation) in Lanzhou. In addition, the coupling coordination relationship and its underlying driving mechanisms are systematically explored. The results demonstrate the following: (1) Between 1980 and 2020, urban land area in Lanzhou increased from 103.87 km² to 286.83 km², accounting for 2.17% of the total area, with cropland constituting the dominant source of expansion and exhibiting a fluctuating “high–low–high” conversion trajectory. (2) Ecosystem services exhibit pronounced spatial heterogeneity, with carbon storage and habitat quality displaying a pattern of “low in the southeast and high in the northwest”, water yield showing an increasing gradient from southeast to northwest, and soil conservation characterized by “lower values in central areas and higher values in peripheral regions”; (3) Urban expansion has accelerated significantly, with Yongdeng County and Gaolan County emerging as principal expansion hotspots during 2010–2020. (4) The dominant driving mechanism gradually shifted from natural factors to the synergistic interaction between natural and socioeconomic factors, and the interaction among driving factors markedly enhanced the explanatory power for ecosystem service evolution. (5) The coupling coordination degree has transitioned from widespread imbalance to a spatially differentiated pattern, characterized by relatively coordinated conditions in peripheral areas and persistent imbalance within the central urban core. These findings provide a robust scientific basis for territorial spatial optimization and the synergistic development of ecological and economic systems in valley-type cities, and offer important implications for sustainable development in arid and semi-arid regions. Full article

In this article, we use truncated M-fractional derivatives to analyze the bifurcation and chaotic behavior of and traveling-wave solutions to the Zhiber–Shabat equation. By introducing truncated M-fractional derivatives, the equation exhibits richer dynamic properties. Based on phase diagram analysis and dynamical system theory, the bifurcation behavior of the equilibrium point of a two-dimensional dynamical system is discussed. At the same time, the dynamical behavior of a two-dimensional dynamical system with periodic disturbances is considered, revealing the complex chaotic phenomena of the system under specific parameters. A planar phase diagram, a three-dimensional phase diagram, a sensitivity analysis, and a maximum Lyapunov exponent diagram of the perturbed two-dimensional dynamical system were employed. Furthermore, various forms of accurate analytical solutions were obtained through traveling-wave transformation and numerical simulation. The three-dimensional, two-dimensional, density, and polar coordinates of the solutions were plotted using mathematical software. The results indicate that the fractional order and system parameters have a significant impact on the morphology and chaotic characteristics of the solution. This study provides new theoretical insights into the nonlinear dynamics of fractional-order Zhiber–Shabat equations. Full article

Understanding how sustainable urban competitiveness evolves within megaregions has become a central concern in urban and regional studies, particularly under the pressures of carbon neutrality, spatial inequality, and network-driven urbanization. This study develops a multidimensional framework to assess the sustainable competitiveness of cities in the Beijing–Tianjin–Hebei “2 + 36” urban agglomeration and examines its spatio-temporal evolution and relational structure. Using a 30-indicator system grounded in factor foundations, economic performance, innovation capacity, openness, and environmental livability, we construct a composite competitiveness index through principal component analysis (PCA). Kernel density estimation reveals a pattern of overall improvement accompanied by widening disparities, characterized by selective agglomeration and the emergence of a pronounced high-value tail. Spatial autocorrelation consistently indicates significant spatial dependence, while LISA analysis identifies persistent low–low clusters and limited spillover absorption around core cities. A modified gravity model further uncovers a transition from a linear, corridor-based linkage structure to a more polycentric and networked competitiveness system, albeit with enduring peripheral weak nodes. The study contributes theoretically by conceptualizing sustainable urban competitiveness as a multidimensional nexus shaped jointly by territorial attributes and relational network structures. It demonstrates that competitiveness dynamics in megaregions emerge from the interplay of hierarchical consolidation, spatial divergence, and network reconfiguration—challenging the traditional assumption of simple core-to-periphery diffusion. The findings offer broader global implications, showing that the Beijing–Tianjin–Hebei case mirrors worldwide megaregional patterns, where proximity alone is insufficient to ensure functional integration, and where coordinated governance, network embeddedness and sustainability transitions increasingly determine regional competitiveness. This research provides a comprehensive analytical foundation for understanding and governing megaregional competitiveness in the era of sustainable development. Full article

Underwater image enhancement is critical for marine robotic perception, yet existing methods often face a persistent trade-off between restoration quality, structural reliability, and deployment efficiency. Although lightweight enhancement networks are attractive for resource-constrained underwater platforms, many of them mainly rely on empirical architectural simplification and appearance-oriented objectives, with limited mathematical analysis of complexity reduction, semantic regularization, and optimization coordination. To address this issue, this paper proposes MobileGAN, a lightweight underwater image enhancement framework equipped with dual-reference regularization and a theoretical analysis module. The proposed generator adopts a compact encoder–bottleneck–decoder architecture based on depthwise separable convolutions, which substantially reduces convolutional redundancy while preserving effective restoration capability. A dual-reference feature consistency formulation is introduced to simultaneously constrain the enhanced image toward the high-quality target representation and the degraded-input semantic anchor. In addition, an edge-aware regularization term and a stage-wise dynamic weighting mechanism are incorporated to improve local structure recovery and multi-objective optimization behavior. Beyond architectural design, we provide a mathematical analysis of the proposed framework from three aspects: computational complexity reduction, geometric interpretation of dual-reference regularization, and piecewise optimization properties of stage-wise weighted training. Extensive experiments on the UIEB benchmark demonstrate that MobileGAN achieves a favorable trade-off between enhancement quality and computational efficiency. The proposed method maintains real-time inference with a compact model size while providing competitive structural consistency and detail restoration. These results indicate that MobileGAN is not only a practical deployment-oriented enhancement framework but also an interpretable optimization model with analyzable structural properties. Full article

This narrative review explores the potential role of cognitive–motor dual-task training (CMDT) approaches within training methods used in sports clubs, with particular emphasis on soccer clubs, to support performance enhancement, recovery, and injury prevention; improve agility, decision making, and functional readiness; and enhance training quality and specificity. The review discusses how CMDT may be integrated as part of the broader and more comprehensive planning of the club’s full training program, including during the preseason period, as part of preparation for training and competition, within recovery sessions, during periods of high load, and throughout the rehabilitation process and the transition back to team training and contact exposure, while also potentially contributing to variety, mental stimulation, enjoyment, and player engagement. The review also emphasizes the importance of implementing CMDT within a coordinated professional framework, through collaboration and synchronization within the professional and medical staff of the club, and in broad alignment with club goals, player characteristics, and sport-specific demands. The key insight is that CMDT has the potential to serve as a practical, complementary approach that helps bridge the gap between controlled training and rehabilitation settings and the dynamic demands of soccer participation. Based on this review, practical recommendations and future research directions are presented, while emphasizing that CMDT should be applied with caution, through gradual and context-specific progression, and in line with established training, recovery, and rehabilitation principles. Full article

For autonomous vehicles, high-speed cornering can easily lead to degraded handling stability and increased risks of sideslip or even rollover. Therefore, vehicle phase-plane stability-region analysis has become an important topic in active safety-control research. However, most existing studies still construct phase-plane stability regions mainly based on simplified vehicle models, without sufficiently considering the influence of vertical load transfer during cornering on tire lateral forces and stability boundaries. To address this issue, this paper proposes a hierarchical control strategy based on phase-plane analysis for active inward tilt vehicles. This method adopts a three-degree-of-freedom vehicle dynamics model and a tire model. By carefully comparing the phase-plane stability regions of active inward tilt and passive roll vehicles and by further analyzing the state-trajectory convergence characteristics of active inward tilt vehicles under different longitudinal speeds, front wheel steering angles, and road adhesion coefficients, the effects of active inward tilt on stability-region expansion and vehicle-state convergence are revealed. Subsequently, a hierarchical control strategy is proposed as an integrated solution to improve vehicle handling stability. The upper-level controller dynamically adjusts the reference values and objective weights according to whether the vehicle state is located in the stable, critical, or dangerous region. The lower-level NMPC controller optimizes the front wheel steering angle and active suspension forces to achieve coordinated trajectory tracking and stability control. Double lane-change simulation results show that active inward tilt can improve the left–right vertical load distribution and expand the lateral stability region. Compared with passive roll and conventional active inward tilt control, the proposed strategy reduces the phase-plane state convergence area by 68% and 75%, respectively, thereby improving vehicle handling stability and active safety under extreme conditions. Full article

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines. Full article

Addressing the challenges of topological design and the limitations of global optimization for large-scale photovoltaic (PV) plants in complex terrains, this paper proposes a topology optimization method based on mixed-integer linear programming (MILP). The innovation of the proposed method lies in its use of a MILP framework to integrate complex terrain modeling, quantification of construction difficulty, and coordinated configuration of conductor cross-sections into a single equivalent annual cost optimization model. First, equivalent mathematical models tailored to diverse environmental features—including flat, mountainous, and hilly terrains—are developed to enable accurate spatial identification. Second, aimed at minimizing the total equivalent annual cost (EAC), a MILP model is formulated. This model comprehensively incorporates physical construction difficulties and strict electrical constraints, such as active power flow balance, cable current-carrying capacity, and node voltage deviations. A high-performance solver is then utilized to achieve global optimization for radial topologies. Furthermore, the cross-sectional areas of the conductors are dynamically configured to compensate for power quality losses caused by path detours. Case studies demonstrate that the proposed method significantly reduces the EAC and enhances the overall economic benefits of PV plants while ensuring strict electrical safety across various complex environments. Full article

This study evaluates structure-preserving neural network architectures for learning holonomically constrained mechanical dynamics in Cartesian coordinates. In contrast to methods using reduced coordinates, the full ambient phase space ${\mathbb{R}}^{2n}$ is retained with explicit algebraic constraints $C_i\left(\mathbf{q}\right)=0$ to provide a test bed for constraint-aware learning. The Constraint-Aware Hamiltonian Neural Network (CA-HNN) is proposed, which augments the standard HNN with a dedicated multiplier network ${\lambda }_{\varphi }(\mathbf{q},\mathbf{p})$ for Lagrange multipliers and a composite loss function evaluated on predicted rollouts. The theoretical framework is grounded in the geometry of constrained Hamiltonian systems: the extended phase space ${\mathbb{R}}^{2n+m}$ carries a degenerate antisymmetric structure where an m-dimensional kernel encodes constraint directions, while the symplectic structure emerges on the $2(n-m)$-dimensional reduced manifold $\Sigma$. It is proven that the physical Hamiltonian is conserved on the constraint surface under augmented flow. Benchmarks on a pendulum ($C=x^2+y^2-l^2$), double pendulum, and bead on a parabola ($C=y-x^2$) demonstrate that CA-HNN reduces constraint violations $\parallel C\left(\mathbf{q}\right)\parallel$ by $5\times$ to $2400\times$ compared to standard HNNs. While the best energy conservation is achieved by PINNs, these findings clarify the roles of architectural inductive bias, constraint augmentation, and soft physics regularization. Full article

Chemotherapy-induced metabolic reprogramming of glioblastoma multiforme (GBM) cells increases intracellular levels of reductive and energetic carriers, thereby fueling drug-relocation and retention systems and enhancing GBM drug-resistance. We have previously shown the role of this process in the adaptation of poly(morpho)nuclear “giant” cells (PGCs) in T98G populations to doxorubicin (DOX)-induced stress. Here, we addressed the role of a “resistance triad”, which coordinates metabolic T98G reprogramming with the activation of the drug-relocation and drug-retention axis, in the recovery of GBM populations from chemotherapeutic stress. A combination of proteomic analyses with metabolic and phenotypic profiling of pulse DOX-treated T98G cells revealed the significance of mitochondrial dynamics for the efficiency of the T98G “resistance triad”. DOX-induced mobilization of ATP-generating systems and ATP-dependent anabolic pathways was accompanied by the formation of DOX-negative, “mosaic” mitochondrial networks and the upregulation of mitofusin-2 (MFN2) in T98G PGCs. Transient MFN2 down-regulation correlated with the respiratory capacity of T98G cells, while impairing cell welfare in the absence and presence of DOX. However, minute fractions of PGCs, which withstood combined MFN2 down-regulation and pulse DOX treatment, retained mitochondrial networks and displayed efficient ABC transporter-/V-type channel-dependent lysosomal DOX retention. Collectively, a “triad” of mitochondrial activation, ABC transporter-dependent perinuclear redistribution and V-type channel-mediated lysosomal DOX compartmentalization determines DOX resistance of T98G cells. Whereas MFN2-dependent mitochondrial rearrangements may contribute to these processes, complementary adaptative mechanisms can compensate MFN2 dysfunction, limiting its potential as a therapeutic target. Full article