Search Results (5,508)

The rational design of supported ruthenium catalysts for sustainable energy applications requires precise control over metal nanoparticle size, dispersion, and metal–support interactions. This study investigates the influence of mesoporous silica support topology—SBA-15 (2D hexagonal, cylindrical pores), SBA-12 (3D hexagonal structure), and SBA-3 (2D hexagonal)—on the structure and catalytic performance of 1 wt% ruthenium catalysts in CO₂ methanation and gas-phase toluene hydrogenation. Comprehensive characterization by nitrogen physisorption, low- and high-angle X-ray diffraction (XRD), H₂ temperature-programmed reduction (H₂-TPR), CO chemisorption, and transmission electron microscopy (TEM) revealed that support pore architecture dictates ruthenium particle size (1.2 nm for Ru/SBA-15, 2.8 nm for Ru/SBA-3, 4.3 nm for Ru/SBA-12) and dispersion (80%, 35%, 23%, respectively) through geometric confinement effects. Catalytic testing demonstrated contrasting structure–activity relationships: CO₂ methanation exhibited strong structure sensitivity with turnover frequency (TOF) increasing with particle size (Pearson’s r = 0.96), favoring Ru/SBA-3 and Ru/SBA-12 with near-optimal 3–4 nm particles, while toluene hydrogenation showed weaker structure sensitivity, with Ru/SBA-12 achieving the highest TOF owing to its larger particle size and higher crystallinity. These findings underscore the critical importance of tailoring mesoporous support topology to match reaction-specific structure sensitivity, providing fundamental insights for the design of bifunctional catalysts for hydrogenation reactions. Full article

The development of lithium-ion batteries (LIBs) capable of extreme fast charging (XFC) while preserving safety, durability, and practical energy density remains a central challenge for next-generation electric transportation and grid-scale storage. Conventional graphite anodes are fundamentally limited at high current densities by sluggish intercalation kinetics, which cause lithium plating, motivating the exploration of alternative insertion materials. This review provides a comprehensive and internally consistent assessment of titanium-based oxide anodes, encompassing TiO₂ polymorphs, lithium titanate (Li₄Ti₅O₁₂), and Wadsley–Roth titanium niobium oxides, through the combined lenses of crystal topology, diffusion pathways, redox chemistry, interfacial behavior, and resource scalability. By systematically comparing structural frameworks and electrochemical mechanisms across these material classes, we demonstrate that fast-charging performance is governed not by nano-structuring alone, but by the intrinsic coupling between operating potential, framework rigidity, and multi-electron redox activity. While Li₄Ti₅O₁₂ establishes the benchmark for safety and cyclability, and TiO₂ polymorphs provide structural versatility, titanium niobium oxides uniquely reconcile high theoretical capacity with minimal lithiation strain and open diffusion channels, positioning them as highly promising candidates for sub-10 min charging without catastrophic degradation. This review highlights the persistent obstacles these materials suffer, such as limited round-trip energy efficiency (RTE), interfacial gas evolution, poor dopant stability, and unsustainable extraction, while simultaneously exploring targeted design strategies to overcome them. Finally, this review provides a materials design and comparison framework for the development of safe, high-power, and commercially viable ultrafast-charging LIBs. Full article

We propose a reformulated notion of Levitin–Polyak (abbreviated as LP) well posedness for fuzzy optimization problems formulated in the fuzzy order-preserving (FOP) setting, where minimizing sequences are governed by a total ordering defined on fuzzy intervals. Under this formulation, we present verifiable sufficient conditions that guarantee LP well-posed behavior. These conditions are derived using ranking mechanisms that maintain interval order relations and ensure solution comparability. One central contribution is an equivalence-based theoretical characterization of LP well posedness obtained through an examination of the topological properties of the approximate solution mapping, particularly its closed-graph structure and upper semicontinuity. In addition, convergence of approximating solution sequences is investigated under the upper Hausdorff metric, leading to stability results for the associated solution sets. The established criteria provide a comprehensive framework for analyzing the convergence performance of algorithms designed for fuzzy optimization environments. Full article

Mercury in jarosites is crucial for environmental management and metallurgy. These minerals can incorporate highly toxic heavy metals from mining waste into their structure. This study analyzes the decomposition of mercury jarosite in a Ca(OH)₂ medium, focusing on its topological, kinetic, and modeling characteristics. Topological analysis, XRD and SEM−EDS were performed. ICP−OES was used to analyze the mercury and sulfur ions diffusing from the mercury jarosite into the Ca(OH)₂ solution. The kinetic model that best fit the data was that of spherical particles of constant size with an unreacted core under chemical control. The XRD results did not show new crystallographic phases. SEM−EDS showed a partially decomposed particle indicating a halo and core. The experimental conditions included temperatures from 298.15 to 333.15 K, concentrations of 0.0071–0.23210 mol L⁻¹ Ca(OH)₂, particle diameters of 25–53 µm, and pH of 11.12–12.85. During the induction period, reaction orders of 1.04 and 0.44 were obtained, along with an activation energy of 77.580 kJ mol⁻¹. For the progressive conversion period, the reaction orders were 0.59 and 0.15, with an activation energy of 52.124 kJ mol⁻¹. The overall kinetic modeling showed favorable results, supporting the evolutionary process of the mercury jarosite decomposition reaction in an alkaline medium under different conditions. This allows prediction of when mercury could be released back into the environment in alkaline soils or lime barriers. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

The framework of the research whose part of results are published in this work is the category of real vector bundles over finite dimensional differentiable manifolds. The objects of studies are gauge structures on these vector bundles. We are interested in the dynamical properties of the holonomy groups of Koszul connections as well as on their topological properties, i.e., properties that are of homological nature. For the most part the context is the subcategory of Lie algebroids. In addition to other investigations, three open problems are studied in detail. (P1-Affine Geometry): When is a Koszul connection an affine connection? (P2-Riemannian Geometry): When is a Koszul connection a metric connection? (P3-Fedosov Geometry): When is a Koszul connection a symplectic connection? In the category of tangent Lie algebroids our homological approach leads to deep relations of our homological ingredients with the open problem of how to produce labeled foliations the most studied of which are Riemannian foliations. On a Lie algebroid we define two families of differential equations, the family of differential Hessian equations and the family of differential gauge equations. The solutions of these differential equations are implemented to construct homological ingredients which are key tools for our studies of open problems we are concerned with. We introduce Koszul Homological Series. This notion is a machine for converting obstructions whose nature is vector space into obstructions whose nature is homological class. We define the property of Degeneracy and the property Nondegeneracy of Koszul homological Series. The property of Degeneracy is implemented to solve problems (P1), (P2), and (P3). In the abundant literature on Riemannian foliations, we have only cited references directly related to the open problems which are studied using the tools which are introduced in this work. Thus, the property of nondegeneracy is implemented to give a complete solution of the problem posed by E. Ghys, (P4-Differential Topology): How does one produce Riemannian foliations? See our Theorems 12 and 13, which are fruits of a happy conjunction between gauge geometry and differential topology. Full article

This paper presents a comprehensive review of the intelligent understanding of power grid drawings, with the aim of enabling reliable and executable grid modeling. First, a unified pipeline is established to describe the transformation from drawings to grid models, covering visual understanding, topology inference, and consistency validation. Second, existing methods are systematically analyzed within this framework, where visual understanding extracts components and textual information and topology inference reconstructs electrical connectivity and network structure. Third, model generation methods are investigated as a critical yet underexplored component, focusing on topology correctness and physical constraint verification. Compared with existing review studies that primarily focus on perception-level tasks such as detection and recognition, this paper explicitly emphasizes the reliability of the resulting models. It highlights that errors in connectivity inference and the lack of validation mechanisms significantly limit practical deployment. Key challenges, including connectivity ambiguity, error propagation, and the absence of standardized validation frameworks, are analyzed. Furthermore, emerging directions such as topology-aware learning and physics-constrained validation are discussed. This review provides a structured perspective on transforming power grid drawings into reliable models and offers insights for future research into power system digitalization. Full article

Molecular representation learning (MRL) has garnered significant attention due to its pivotal role in downstream applications such as molecular property prediction and drug discovery. In most MRL approaches, molecules are encoded into 2D topological graphs via graph neural network (GNN), which suffers from over-smoothing issues and limited receptive fields. Furthermore, most GNN models fail to utilize the 3D spatial structural information that determines molecular physicochemical properties and biological activity. To this end, here we propose multimodal contrast-enhanced molecular representation learning (MCMRL). This approach utilizes both the 2D topological information and 3D structural information of molecules for contrastive learning to enhance molecular graph representations. Further, it integrates additional molecular fingerprint information and feature fusion techniques to incorporate multimodal knowledge, yielding more reliable and generalizable molecular representations. MCMRL is pre-trained on ~10 million unlabeled molecules from PubChem, followed by various downstream benchmark tasks. Experimental results demonstrate that MCMRL achieves superior performance in 9 out of 13 benchmark tests for molecular property prediction, validating its effectiveness in molecular representation learning. Furthermore, potential molecular drugs binding to biological target protein DRD2 screened by MCMRL representation show promising affinity score, which also demonstrates the efficacy of the proposed method. Full article

Camellia oleifera is an economically vital woody oil crop. Its productivity and oil quality are severely compromised by various diseases. Implementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection. Despite recent progress, existing segmentation methods struggle with three primary challenges: semantic ambiguity arising from evolving pathological stages, blurred boundaries due to overlapping lesions, and the high omission rate of micro-lesions. To address these issues, this paper presents TB-DLossNet (Text-Conditioned Boundary-Aware Network with Dynamic Loss Reweighting), a novel segmentation framework based on semantic-visual multi-modal fusion. Leveraging VMamba as the visual backbone, the proposed model innovatively integrates BERT-encoded structured text as an auxiliary modality to resolve visual ambiguities through cross-modal semantic guidance. Furthermore, a boundary enhancement branch is incorporated alongside a multi-scale deep supervision strategy to mitigate boundary displacement and ensure the topological continuity of lesion structures. To tackle the detection of small-scale targets, we designed a dynamic weight loss function conditioned on lesion area, significantly bolstering the model’s sensitivity to minute pathological features. Additionally, to alleviate the scarcity of high-quality data, we curated a comprehensive multi-modal dataset encompassing seven typical diseases of Camellia oleifera. Experimental results demonstrate that TB-DLossNet achieves a Mean Intersection over Union (mIoU) of 87.02%, outperforming the state-of-the-art unimodal VMamba and multimodal Lvit by 4.9% and 2.59%, respectively. Qualitative evaluations confirm that our model exhibits lower false-negative rates and superior boundary-fitting precision in heterogeneous field scenarios. Finally, generalization tests on an apple disease dataset further validate the robustness and transferability of the proposed framework. Full article

Silicene, an atomically thin monolayer allotrope of silicon, had emerged as a prominent topic in condensed matter physics and material science due to its novel properties and promising potential applications. Although challenges exist in fabricating freestanding silicene because of its sensitivity to the conventional environment, its theoretical study continues to develop intensively. This short review highlights the progress made in the ab initio simulations of silicene, such as geometry optimization of silicene and its electrical structure and physical characteristics including optical properties, topological properties and mechanical behavior. The theories and methods used for the theoretical studies of silicene could provide a framework for investigating other one-atom-thick two-dimensional materials with Archimedean lattice structures. Full article

The structural characterization of GG-rich DNA sequences in presence of metal ions provides essential insight into quadruplex stability and ion-dependent conformational specifics. We report the crystal structure of the GG-quadruplex formed by the sequence GGGGTTTTGGGG in the presence of Zn²⁺, K⁺, and Na⁺. It was deposited in the RCSB Protein Data Bank under the accession code 9FTA. The structure was determined by single-crystal X-ray diffraction at a resolution of 2.49 Å in the space group P2₁2₁2₁. It reveals a parallel-stranded, two-G-tetrad stabilized by K⁺ ions within the central channel, while Na⁺ and Zn²⁺ occupy peripheral and groove-associated sites. Zn²⁺ ions are engaged in noncanonical coordination interactions with phosphate oxygens and structured water molecules, contributing to lattice stabilization and subtle adjustments in groove dimensions. The T4 loop forms a compact, ordered motif that contributes to crystal packing rather than intramolecular G4 stabilization. The presence of mixed cations produces a sole lattice architecture mediated by ions that provides structural insight into how bivalent and monovalent metals mutually modulate G-quadruplex topology. These results suggest a basis for understanding the specific ion effects on G4 structures and may direct the design of metal open DNA architectures. Full article

Multi-objective evolutionary optimization faces significant challenges due to guidance mismatch under complex Pareto-front geometries. This paper proposes a dynamic self-organizing map-assisted evolutionary algorithm (D-SOMA), a manifold-aware framework that harmonizes knowledge-informed priors with unsupervised objective-space characterization. Specifically, a knowledge-informed guided resampling strategy is formulated to bridge stochastic initialization and targeted exploitation. By distilling spatial distribution priors from the decision-variable boundaries of early-stage elite solutions, it establishes a high-quality starting population biased towards promising regions. To capture the intrinsic geometry of the evolving population, a self-organizing map (SOM)-based adaptive subregion characterization strategy leverages the topological preservation of self-organizing maps to extract latent modeling parameters. This strategy adaptively determines subregion centers and influence radii, enabling a data-driven partitioning that respects the underlying manifold structure. Furthermore, a density-driven phase-responsive scale adjustment strategy is introduced. By synthesizing spatial density feedback and temporal evolutionary trajectories, it dynamically modulates the characterization granularity K, thereby maintaining a rigorous balance between geometric modeling fidelity and computational overhead. Extensive experiments on 50 benchmark problems from the DTLZ, WFG, MaF and RWMOP suites demonstrate that D-SOMA is statistically superior to seven state-of-the-art algorithms, exhibiting robust convergence and superior diversity across diverse problem landscapes. Full article

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches. Full article

Cells rely heavily on DNA repair networks to survive genomic damage. For repairing double-strand breaks, Non-Homologous End Joining (NHEJ) remains the primary pathway, which is largely controlled by the DNA-dependent protein kinase catalytic subunit (DNA-PKcs). Researchers have long studied how phosphorylation drives this kinase. However, recent data point to an important additional layer of control. Drawing on evidence accumulated over the past two decades, we propose a “Spatiotemporal Logic Circuit” model for DNA-PKcs regulation. In this model, SUMO-associated interactions may help stabilize synaptic assembly, HUWE1-mediated neddylation may facilitate kinase activation at Lys4007, and K48-linked ubiquitination—potentially involving RNF144A—may contribute to the turnover of persistent repair complexes. Importantly, we frame these UBL-mediated events within the broader autophosphorylation-driven conformational cycle of DNA-PKcs, which remains central to NHEJ progression. Additionally, we highlight the structural interface where activation and degradation signals may converge and the extraction barrier posed by the massive DNA-PKcs scaffold. From a translational perspective, we argue that the exceptional size of DNA-PKcs (~470 kDa) and its topological entrapment on DNA render it an unusually challenging PROTAC target—one that may require p97/VCP-assisted extraction before proteolysis can proceed. We also highlight the underappreciated risk that E3 ligase loss-of-function, already documented in BET-PROTAC resistance, may similarly undermine DNA-PKcs degrader strategies. Full article

Machining feature recognition is a key enabling technology in intelligent manufacturing that extracts manufacturing semantics from the boundary representation (B-Rep) of 3D CAD models to bridge design and process planning. Recent advances in deep learning have accelerated data-driven feature recognition methods. Among these, graph neural networks (GNNs) have gained significant attention due to their natural compatibility with the non-Euclidean, hierarchical topological structure of B-Rep data, enabling efficient and lossless encoding of geometric and topological attributes. However, existing GNN-based methods primarily leverage the topological structure and geometric attributes of B-Rep models, often neglecting the inherent geometric relationships present in the B-Rep data structure. To address this gap, we propose a dual-graph fusion network (Topo-Geom DualGNN) that integrates a topological attribute adjacency graph and a geometric relationship graph. Our approach employs a GatedGCN-based graph encoder and an FiLM-based cross-stream fusion mechanism to jointly encode topological and geometric information from the B-Rep model. Evaluations on open-source synthetic datasets, including MFInstSeg and MFRCAD, demonstrate that the proposed method achieves competitive comprehensive recognition performance and exhibits promising capability in recognizing machining features in complex parts. Full article