Search Results (229)

In the Selection-Stitch Model (SSM), baryonic matter is a $K=4$ remnant trapped in a tetrahedral void of the $K=12$ FCC vacuum lattice. We examine the second interstitial site, the octahedral void, as a candidate dark matter trap. Its bonded subgraph is the complete tripartite graph $K_{2,2,2}$. Four structural properties follow from its symmetry. Two are exact: the defect is self-conjugate, and it has no first-order electric dipole. Two are weaker and are stated as such: the electromagnetic coupling is suppressed at dipole order rather than at all orders, and the mechanism that generates SU(3) color for the tetrahedral baryon has no counterpart here. A closed inclusion–exclusion expansion on $K_{2,2,2}$ gives the structural count $C_{\mathrm{DM}}=25·144-30·10+8·8=3364$. It terminates at third order because the octahedron’s six vertices forbid a four-matching. Under one stated assumption, the standing-information postulate, this corresponds to $m_{\mathrm{DM}}=(3364/1836)\times m_p=1.719$ GeV with the proton mass as the sole calibration input and no cosmological fitting. The same geometry fixes the annihilation channel. Two octahedral defects can meet only along a shared octahedron edge, and of the four cages their interface admits, only the two tetrahedra are products of the merger. The residual therefore has mass $m_p$, and the only channel producing a line gives $E_{\gamma }=1.591$ GeV. A recently reported $1.5$–$1.6$ GeV gamma-ray line has weighted centroid $1.578\pm 0.048$ GeV, which is $0.3\sigma$ away. No observational input enters the derivation. Full article

This study presents a mathematical modeling and simulation framework for pavement crack maintenance under limited work zone energy availability. The framework combines crack evolution, thermal repair, interface behavior, and maintenance decision-making within a unified structural–thermal–energy formulation. A three-dimensional multilayer flexible pavement model is developed with an XFEM-enriched region in the aged asphalt surface layer, a localized repair zone, cohesive interface behavior, and wheel-loading stages before and after maintenance. Rather than assuming constant heating, the repair heat flux is governed by a photovoltaic battery-dependent amplitude function, allowing the model to reflect practical variations in available energy. The simulation generated a dataset containing crack geometry, pavement properties, repair zone dimensions, loading conditions, heat flux intensity, heating duration, bonding quality, XFEM damage status, stress response, temperature distribution, interface damage, energy feasibility, durability, and service life gain. These variables were used to develop the Crack–Energy–Repair Interaction Graph Network (CERIG-Net), which represents each maintenance case as a physics-guided heterogeneous graph and ranks delayed repair, crack sealing, localized patching, thin overlay, and deep repair. The thin overlay strategy reduced final crack length from 94.7 mm to 53.8 mm, lowered maximum stress from 3.85 MPa to 2.22 MPa, achieved a durability index of 0.86, and extended service life by 6.7 years. Full article

Let G be a graph with edge set $E\left(G\right)$. The degree of a vertex w in G is denoted by $d\left(w\right)$. The first-inverse Nirmala (FIN) index and the atom-bond connectivity (ABC) index of the graph G are defined, respectively, as $\mathcal{FIN}\left(G\right)={\sum }_{uv\in E\left(G\right)}\sqrt{{\left(d\left(u\right)\right)}^{-1}+{\left(d\left(v\right)\right)}^{-1}}$ and $\mathcal{ABC}\left(G\right)={\sum }_{uv\in E\left(G\right)}\sqrt{{\left(d\left(u\right)\right)}^{-1}+{\left(d\left(v\right)\right)}^{-1}-2{\left(d\left(u\right)d\left(v\right)\right)}^{-1}}$. The problem of determining the trees that minimize the ABC index over the class ${\mathcal{T}}_n$ of all n-vertex trees required around a decade of research and dozens of publications before it was resolved. In view of the similarity between the definitions of the ABC and FIN indices, one might expect their minimizing trees over ${\mathcal{T}}_n$ to coincide. However, the computational findings of Furtula and Öz published in 2025 for $6\le n\le 20$ demonstrate that this is not the case. The present paper establishes the first theoretical results toward determining the trees that minimize the FIN index over ${\mathcal{T}}_n$. We also extend the computational findings of Furtula and Öz from $6\le n\le 20$ to $6\le n\le 60$. Full article

Molecular property prediction is a fundamental task in drug discovery and chemical biology, where effective molecular representations are essential for accurate prediction. Learning transferable motif-level representations remains challenging because explicit motif annotations are scarce and existing representations may be altered during downstream supervised optimization. In this study, we propose MSGRL, a motif-driven self-supervised graph representation learning framework for interpretable molecular property prediction. MSGRL represents each molecule through a hierarchical graph structure, consisting of a motif-based graph for inter-motif organization and motif-specific atom-based graphs for intra-motif atomic structure. Its central design is to decouple label-agnostic intra-motif structural learning from label-dependent inter-motif property learning. An MPNN-GRU encoder is pretrained on motif-specific atom-based graphs using a variational motif graph autoencoder (VMGAE), which reconstructs the internal bond topology of motifs in a self-supervised manner. After pretraining, the intra-motif encoder is kept frozen, while the downstream module pools atom-level feature matrices into motif vectors, propagates information over the motif-based graph, and applies attention-based pooling for molecular property prediction. This design keeps the pretrained intra-motif representations fixed while allowing the inter-motif network and prediction head to adapt to individual downstream tasks. Experiments on eight MoleculeNet benchmark datasets show that MSGRL achieves the highest ROC-AUC scores on all five classification datasets and the lowest RMSE on Lipophilicity, while its performance on ESOL and FreeSolv is more mixed. Ablation studies further support the contributions of encoder freezing, motif-based graph construction, and attention-based pooling. Motif-level attribution analyses provide qualitative and dataset-level evidence regarding the substructures emphasized by the model. These results demonstrate the effectiveness of the proposed hierarchical representation strategy, particularly for the evaluated molecular classification tasks. Full article

Hybrid solar–thermal energy harvesting systems are an important advancement in renewable energy technology, enabling simultaneous production of electrical power and useful thermal energy within a single compact platform. However, their performance is often limited by poor heat transfer and inefficient mass transport of working fluids, leading to photovoltaic thermal degradation and significant exergy losses. This study aims to optimize heat and mass transport processes in a hybrid solar–thermal mechanical system to enhance energy recovery and ensure long-term operational reliability. A three-dimensional numerical model based on the finite volume method (FVM) was developed using the governing equations of continuity, momentum, and energy conservation. A Multi-Objective Genetic Algorithm (MOGA) was employed to determine optimal microchannel geometries by analyzing variable cross-section effects on flow behavior and thermal boundary layer disruption. At the Reynolds number of 2000, the optimized configuration increases the average Nusselt number by 43.5% compared to a smooth channel. Consequently, the photovoltaic operating temperature decreases by 12.6 °C, improving electrical efficiency by 9.3%. The system achieves a maximum thermal efficiency and net energy gain of 76.8%, while maintaining an acceptable 16.3% increase in pumping power. The results confirm that optimizing mass transport is essential for effective thermal regulation and improved energy conversion performance, providing a strong foundation for high-efficiency solar collector design. Full article

The Ising model provides a fundamental setting for investigating the emergence of phase transitions from simple interacting degrees of freedom. The current characterization study serves to investigate central requirements for phase transitions in terms of connectivity, i.e., the degree of coupled interactions between interaction sites. The impact of increasing connectivity between 1D-Ising chains mapped onto 2D-lattices with free boundary conditions were studied systematically, using exact free energy calculations. Starting from a reference system of non-interacting 1D-chains, interaction bonds between chains are introduced successively until the fully connected $N\times N$-lattice is obtained. Two distinct construction schemes are analyzed, which differ in the connectivity of the intermediate partially coupled systems. The resulting free energies of the graphs along these paths are evaluated and compared with respect to their convergence behavior as a function of system size. We find that, despite topological differences between the schemes, strikingly, they converge to the same limiting straight line for increasing N when analyzed in terms of residual free energy differences. These findings provide insight into the relationship between interaction structure and thermodynamic behavior and suggest that appropriately chosen construction paths may serve as a basis for efficient extrapolation strategies toward the thermodynamic limit. Full article

Quantifying the direction, strength and temporal clustering of cross-border financial risk contagion calls for methods that go beyond linear correlation. We suggest a two-layer framework that brings together transfer entropy and a multivariate Hawkes self-exciting point process on a time-varying, directed, weighted network. In the first layer, one-to-one transfer entropies of sovereign credit default swap spreads are estimated with a bias-corrected k nearest neighbour estimator, and this step detects nonlinear and directional information transfer between spreads. The second layer is a multivariate Hawkes process that models how extreme loss events arrive and mutually excite one another across countries, and it gives an excitation intensity matrix, encoding the way a tail event in one country raises the likelihood of an instantaneous hazard occurring in another. By merging these two layers, we obtain a composite, directed, weighted adjacency matrix in which the weights of the edges reflect both information flow and event clustering. We introduce a network-level contagion intensity index and split it into direct, indirect and feedback terms using the graph Laplacian spectrum. Von Neumann graph entropy together with the spectral gap ratio serve as entropy-based measures of the complexity and fragility of the evolving network. We validate the choice of Shannon-type entropy through a Tsallis q-sensitivity analysis, and we verify the nonlinear dependence structure of the data using BDS tests and maximal Lyapunov exponent estimates. Three empirical findings emerge from analysing 20 sovereign CDS markets from January 2015 to December 2025: (i) directional risk spillover signals derived based on transfer entropy are more timely than those derived from variance decomposition; (ii) the Hawkes excitation component amplifies measured contagion intensity by 35 to 58 percent during the COVID-19 shock and the 2022 European energy crisis relative to a transfer-entropy-only baseline; (iii) von Neumann graph entropy reaches historically extreme values 7 to 12 trading days before the peak drawdown in a Global Sovereign Bond Index. These results hold across rolling window lengths, significance thresholds, alternative entropy functionals and alternative Hawkes kernels. Full article

Graph neural networks (GNNs) for crystal property prediction are typically evaluated by a single aggregate error, which can mask where, and for which classes of materials, these models fail. In this study, we present a space-group-, centering-type-, and band gap-resolved error analysis of GNN band gap prediction on the Materials Project dataset. As a channel for this analysis we use Crystal-X, a deliberately simple model: a standard graph convolutional backbone with two minor architectural modifications, an asymmetric edge convolution and a neighbor-feature transformation, that supplement bond information often treated as secondary in node-centric models. Crystal-X is not a state-of-the-art model: it reaches a band gap MAE of 0.256 eV on the MP 2018.6 dataset, behind ALIGNN (0.22 eV) and PotNet (0.20 eV), though ahead of older baselines such as CGCNN (0.39 eV), SchNet (0.415 eV), and MEGNet (0.33 eV) while using only the nine-property CGCNN atomic feature set. Its value here is as a controlled, low-complexity testbed for the error analysis. That analysis reveals systematic patterns that aggregate MAE conceals: errors concentrate in underrepresented band gap ranges and in low-symmetry and non-centrosymmetric space groups; per-group errors for sparsely populated space groups are dominated by sampling noise; and modest, as-yet-unverified gains from edge-aware convolutions appear in monoclinic and non-primitive-centered systems. We argue that this kind of granular, symmetry-resolved evaluation should accompany aggregate benchmarks when assessing crystal GNNs. Full article

The work presented in this study uses Bond Graphs to model and simulate complex urban traffic flow systems consisting of three interconnected, traffic-light-controlled crossroads with heavy traffic demand. Bond Graph models are highly versatile for modeling multi-domain systems and provide a convenient bridge between analytical representations and numerical implementations. In this paper, we exploit Bond Graph model theory and digital logic concepts to develop a structured methodology for deriving Bond Graph switching network models applied to urban traffic flow. A simple traffic-light-controlled crossroad is then modeled and analyzed. Moreover, the application of Bond Graph modeling to traffic flow, illustrated through a real case study of a street network in Sofia, Bulgaria, validates the proposed model-based approach. The obtained results demonstrate the relevance and effectiveness of the proposed Bond Graph model-based macroscopic traffic modeling framework in capturing the fundamental dynamics of traffic flow under signalized control. Beyond the specific case study considered, these results highlight the potential of the approach as a general and extensible tool for modeling more complex urban traffic networks. They open perspectives for future work aimed at assessing the flexibility, scalability, and generalization capability of the framework for heterogeneous intersections and large-scale traffic systems. Full article

Binding affinity prediction is about estimating the degree to which a drug binds to a protein. Predicting the binding affinity between a drug and a protein in a computational process helps researchers filter huge libraries of compounds before performing expensive biochemical lab experiments. Currently, there is interest in predicting binding affinity through computational pattern recognition or machine learning methods instead of the classical physics-inspired methods, which are computationally intractable except for tiny chemical compounds. In the last five years, several machine learning-based methods have been presented, whose experimental validations have achieved increasing Pearson coefficients while trained and tested in the PDBBind 2016 and CASF 2016 databases, respectively. These methods have an important diversity of architectures that provide different properties. The aim of this paper is to discern which binary properties (existence or absence) of these methods make them return higher Pearson coefficients. Basically, the properties introduced are related to the level of structural knowledge, the presence of 3D information, and the introduction of the relationship between the drug and the protein in the input of the model. The t-test confirms that the important binary properties for having a high Pearson coefficient are the protein (or part of the protein) being represented and introduced into the computational model as a graph, the pocket and the drug–protein interaction being part of the input, and incorporating the distance between atoms and the type of chemical bonds into the model. Full article

Machine-learned molecular force fields require many-body geometry, but obtaining it through Clebsch–Gordan tensor products is computationally expensive. For a strong no-Clebsch–Gordan backbone such as GotenNet, we ask whether the limitation in handling three-body geometry is one of representational capacity or one of training supervision, and separate the two factors with three controlled probes on a single-seed, paper-aligned rMD17 aspirin split. (i) While frame projection of tensor features is comparable to scalar cos-angle triplet cross-attention (SCTA) at pilot scale, algebraically its diagonal scalar collapses to a frame-independent inner product and the remaining channel is parity-odd, making SCTA’s cos-angle input the principled $\mathrm{O}\left(3\right)$ scalar choice. (ii) SCTA matches GotenNet’s converged force accuracy within ∼0.4% without independent gain, indicating that three-body representational capacity is not the binding constraint. (iii) A graph-level auxiliary loss on bond-angle and dihedral statistics gives the best force mean absolute error (MAE; $0.1280$ vs. $0.1303$ kcal/mol/Å) and reduces epochs-to-validation-target by 26–55%. Cross-molecule probes do not extend this finding; a paired salicylic acid comparison shows a directional degradation that, under a configuration-level paired block bootstrap, is significant and opposite in sign to the aspirin effect. Across three random seeds, the auxiliary force-MAE gain is small and seed-dependent but consistently reduces seed-to-seed variance and accelerates convergence, indicating that low-cost three-body supervision can be a more effective lever than added three-body capacity. Full article

Molecular property prediction is a fundamental task in drug discovery and materials design. While graph neural networks (GNNs) and SMILES-based Transformers have made significant strides, the former are often limited by local message-passing bottlenecks such as over-squashing, while the latter frequently lack explicit topological constraints and suffer from severe vocabulary imbalance. In this work, we revisit the granularity of molecular modeling and propose a representation learning framework built upon bond-level sequences. Our framework models molecules as sequences of directed bond tokens and introduces a structure-aware hybrid attention mechanism. By imposing hard topological constraints on a subset of attention heads to reinforce local connectivity while preserving global receptive fields in the remaining heads, the design is intended to separate short-range chemical bonding from long-range contextual dependencies. For pre-training, we implemented a multi-scale consistency learning paradigm, which utilizes an atom-centric group masking strategy to induce a hierarchical loss of local structural information and employs contrastive and triplet losses to ensure identity consistency across varying scales of structural degradation. Furthermore, by incorporating macro-scale physicochemical descriptors (e.g., LogP, TPSA) as global anchors, we examined how the inclusion of global attribute bias can provide weak physicochemical priors during pre-training, while its effect during downstream fine-tuning remains task-dependent. Experimental results demonstrate that our lightweight model, with approximately 3.5 million parameters, exhibits a dataset-dependent performance profile across MoleculeNet benchmarks and shows promising behavior on selected topology-sensitive tasks, particularly MUV. Ablation studies further analyze the contribution of bond-level connectivity, the stage-dependent dynamics of global attribute bias, structured masking, and pre-training configurations. Ultimately, this work provides an alternative representation design for molecular modeling, offering a parameter-efficient option for future molecular learning systems alongside traditional SMILES-based and graph-based formulations. Full article

Molecular trajectory prediction is fundamental to computational chemistry, drug discovery, and materials simulation, enabling insights into dynamics, reaction pathways, and conformational stability. Its natural alignment with graph-structured spatiotemporal data has made it a key frontier in GNN research. However, current mainstream spatiotemporal GNNs, while enforcing E(3)-equivariance, treat atoms as unconstrained point masses and lack explicit rigid geometric constraints, often yielding unphysical deformations that compromise predictive interpretability. To address this challenge, we propose STEGMN—the first spatiotemporal graph architecture for molecular trajectory prediction that explicitly encodes rigid constraints. Inspired by Graph Mechanics Networks, we design a constraint-preserving equivariant spatiotemporal attention mechanism that captures temporal dependencies while rigorously maintaining both E(3)-equivariance and rigid-body constraints. Additionally, we introduce a constraint-preserving equivariant pooling module that generates future states by performing a learnable weighted aggregation of historical angular velocities, followed by forward kinematics mapping. This ensures that all outputs simultaneously satisfy E(3)-equivariance and strict bond-length conservation. Evaluated on real-world molecular dynamics datasets, STEGMN consistently outperforms strong baselines. On the rMD17 benchmark, it achieves an average ∼40% reduction in prediction MSE relative to representative spatiotemporal graph models (ST-GNN, ST-GCN, and ST-EGNN) across eight small-molecule systems, highlighting the critical value of explicit constraint modeling for physically stable trajectory prediction. Full article

In the present study, we built several models based on three-dimensional molecular descriptors to predict the glass transition temperature using a data set of 117 polymers. A data set division was established (training and test data) and consequently the models were developed and validated. Finally, the prediction/screen of the top models were compared. Three main descriptors were obtained with excellent predictions: E2 (E2u and E2s), which encodes angular and radial information about atomic configuration, usually in relation to two atoms; TDB (TDB10u, TDB10e, TDB10s) describes the relationship between the average three-dimensional (Euclidean) distance and the topological distance (path length, or number of bonds) between possible atom pairs in a molecule; and RDF (RDF25i, RDF65u, RDF25u) describes the density of atoms at different distances from a reference atom, capturing information about the local structure of the molecule. An initial exploratory GA-LDA classification analysis using 3D descriptors revealed only partial discrimination between polymers with distinct T_g behavior, indicating that simplified 3D structural representations alone are limited for robust T_g prediction. Consequently, graph-based (2D) descriptors models were created and the prediction of the T_g was successfully achieved. Overall, the most influential variables are predominantly graph-based (2D) descriptors associated with molecular connectivity patterns (e.g., autocorrelation-type descriptors such as ATS2*), topological/shape-related indices (TSC* family), and ring-related terms. This attribution profile is consistent with the expected physicochemical determinants of the glass transition: polymer repeat units with higher structural rigidity, constrained connectivity, and increased ring/unsaturation content that typically exhibits reduced segmental mobility and, therefore, higher T_g. Full article

4-Methyl- (1) and 4-ethyl-8-hydroxyquinoline (2) crystallize from a mixture of diethyl ether and chloroform in the triclinic space group P$\overline{1}$. X-ray analysis reveals that both compounds form discrete molecular dimers stabilized by intermolecular O-H∙∙∙N and C-H∙∙∙O hydrogen bonds, resulting in $R_2^2\left(5\right)$ cyclic synthons. This pattern of hydrogen bonds is further stabilized by intramolecular O-H∙∙∙N bonds so that the quinoline nitrogen atom acts as a bifurcated binding site. The dimers exhibit a planar geometry and arrange into layer-like structures held together by π∙∙∙π stacking and van der Waals forces. While the fundamental bonding motifs are similar, the increased steric demand of the ethyl group in compound 2 induces a shift in the crystallographic orientation of the layers and alters the degree of π-overlap compared to the methyl-substituted analogue 1. Full article