Dual Network Research Articles

This study investigates the elastic and dynamic properties of elastomeric, stoichiometric epoxy networks formed between the telechelic functionalized poly(ethylene glycol) diglycidyl ether (PEGDE) and the linear cross-linker 1,4-diaminobutane across a range of extensional strain rates (107 to 1010 s-1), molar masses (n = 3, 5, 8 repeat units), and two reaction extents determining degree of cross-linking through atomistic simulations and compares them with the experimental n = 8 system. Investigated properties are Young's and shear moduli, the C11 elastic constant, the glass transition temperature, and the network's mean-squared-displacement. Results reveal a notable agreement between simulation-obtained and experimental values of C11 and its experimentally determined Brillouin light scattering (BLS) value and glass transition temperatures, bridging the gap between atomistic and macroscopic length scales. This work contributes to the renewed interest of BLS applied on soft systems and lays the groundwork for computational investigations of complex epoxy architectures, such as dual networks with epoxy covalent and noncovalent bonds.

Ionic conductive hydrogels have broad application prospects in the field of stretchable and flexible electronic products. Among them, salt ion conductive hydrogel has the advantages of high conductivity, simple preparation and low cost, but salt ions are easy to diffuse into the water environment through concentration gradient, and the evaporation of water will lead to salting out. A core-shell strategy is designed, in which the hydrophobic conductive network of polyaniline (PANI) is in situ constructed as a waterproof shell outside the freezing cross-linking and salting out polyvinyl alcohol (PVA) ionic conductive hydrogel (PVA-salt-PANI). This shell effectively locks in water and ions inside, suppressing salt precipitation and ion loss. The obtained PVA-salt-PANI hydrogel has excellent electrical conductivity (5.31 Sm-1), good viscoelasticity, temperature resistance, frost resistance (-40°C unfrozen) and self-regeneration ability. Due to the synergistic effect between the dual network polymer chains, it has excellent fracture strain (1784.01%) and tensile strength (10.17MPa). The environmental stability and versatility of PVA-salt-PANI hydrogel enable it to be used as a human motion sensor, and its waterproof property can reliably sense even in underwater environments. This work provides a novel design strategy for multifunctional sensors that operate reliably under harsh conditions.

Flash floods in urban and karst environments present major modeling challenges due to their complex hydrodynamics, characterized by a rapid urban runoff response and a delayed slower karst groundwater response. This study explores the use of artificial neural networks ANN (multilayer perceptron in particular) to predict flash floods at the downstream of the Las River in Toulon (France). The Las River is fed in a larger proportion by the nearby karst springs and in a smaller proportion by the urban drainage network. In this study, we propose an ensemble modeling strategy to address the system’s double hydrological regime. The initial step was to identify rainfall events in the six-year hydrometeorological database and classify them according to their karst contributions. Two specialized models: urban runoff model (UM) and karst model (KM) were trained solely on each event type (urban runoff and karst events). These models were combined by two methods in an attempt model all events, disregarding of their event type: the first approach was to combine the outputs of the specialized models in an ANN called output combination model (OM), the second approach was to combine the structures of the specialized models and retraining the model parameters called structure combination model (SM). A third more “basic” approach, called bulk model (BM), was to optimize the ANN by selecting the inputs with the best performance improvements. As expected, the specialized models (UM and KM) performed the best on the cross-validation sets and on the test sets on their respective event types but failed to generalize across regimes. The OM was the most robust ensemble strategy across all event types with consistent accuracy on predicting both urban runoff and karst flood events. The BM was better on the karst events while having worst performance on karst events and the SM was the least accurate model. These findings confirm the added value of combining specialized ANNs to model complex hydrological systems. In addition, selecting the right inputs to the models has a bigger impact on the model’s performance than choosing its structure by changing its hyperparameters.

Graph theory offers a conceptual framework for analyzing complex systems, providing complementary insights into the organization, development, and evolution of morphological structures in biological systems. Graphs describe interactions (edges or links) between entities (vertices or nodes) that can be directed or undirected, weighted or unweighted, and cyclic or acyclic. Over the past decade, a growing community of researchers in Argentina, including the authors of this contribution, has applied diverse graph-theoretical approaches to address questions in functional, evolutionary, and developmental morphology. In Latin America, Argentina stands out for incorporating graph theory and new approaches to network analysis into anatomical research. This review highlights the following particular areas where graph theory has been applied: (I) vertex parameters; (II) graph parameters; (III) graph modular organization and hierarchy; (IV) functional interpretations from modularity throughout graph parameters; (V) graph complexity; (VI) adding the temporal dimension to graphs; (VII) Gabriel graph and percolation in geometric networks; (VIII) dual networks; (IX) flow networks and Markov chains. By presenting these applications and original contributions, this work illustrates how graph theory can enrich morphological evo-devo research while reflecting the development of a growing research community in the region.

ABSTRACT A series of incidents showed that many security threats caused by Twitter spam tweets can reach far beyond the social media platform and impact the real world. Many studies have applied machine learning techniques to classify spam tweets to alleviate such threats. However, Twitter spam detection faces significant challenges due to class imbalance and the evolving nature of spam techniques. This paper proposes a heterogeneous ensemble approach combining dual LSTM networks with a meta-classifier to address imbalanced Twitter spam detection. Our architecture integrates a similarity-based LSTM processing user behavioral features with a word embedding LSTM analyzing semantic textual patterns, followed by an XGBoost meta-classifier trained on disagreed instances. Experiments on two benchmark datasets (1KS10KN and HSPAM) demonstrate that our model outperforms existing baseline models, achieving F1 scores of 0.952 and 0.945, respectively. The precision-focused approach achieves effective performance suitable for social media content moderation requirements while maintaining practical deployment feasibility. The heterogeneous ensemble framework shows strong potential for application to other imbalanced text classification domains beyond spam detection, providing a robust foundation for cybersecurity and content moderation systems.

Hydrophobic associations and cellulose nanofibers reinforced PVA/PAM multi-network conductive hydrogel with high sensitivity, fast response, and excellent mechanical properties.

Dual interaction network with cross-image attention for medical image segmentation

Synergistic dual and efficient additive attention network for No-Reference Image Quality Assessment

Accordion-shaped MXene/PANI-Pd loaded with dual aptamer rolling circle-amplified 3D DNA networks for enhanced sensitivity in circulating tumor cell detection.

Predicting deformation and stress-strain behaviour of lattice truss structures under compression using dual Graph Neural Network

A dual spatial temporal neural network for bottleneck prediction in manufacturing systems

A cross dual branch guidance network for salient object detection

Enhanced magnetic resonance imaging feature extraction for precise brain tumor classification using dual deep convolutional networks

Dual attention focus network for few-shot skeleton-based action recognition

Dual Branch Network Based on Multi-Directional Feature Cross Fusion for Carotid Plaque and Vessel Co-Segmentation

Excellent thermal resistance and mechanical properties polyimide resin with dual crosslinking network structure for thermal oxidation aging

SpaOmicsVAE: A deep learning framework for integrative analysis of spatial multi-omics data.

Cell structure design and bond-cell synergistic impact resistance response mechanism in dual network silicone rubber/PBS composite foam

Rigid-flexible dual network hydrogel photoelectrodes for ultrafast and stable catalytic degradation of bisphenol A with minimal metal leaching

As tourism increasingly drives the revitalization of traditional villages, rural spaces are undergoing a transformation from functional living areas to spaces for cultural display and leisure. This shift has amplified the spatial usage discrepancies between multiple stakeholders, such as tourists and villagers, highlighting conflicts in spatial resource allocation and behavior path organization. Using Wulin Village, a typical example of a Minnan overseas Chinese village, as a case study, this paper introduces social network analysis to construct a “spatial–behavioral” dual network model. The model integrates both architectural and public spaces, alongside behavior path data from villagers and tourists, to analyze the spatial structure at three scales: village-level network completeness, district-level structural balance, and point-level node vulnerability. The study integrates two dimensions—architectural space and public space—along with behavioral path data from both villagers and tourists. It reveals the characteristics of spatial structure under the intervention of multiple behavioral agents from three scales: village-level network completeness, district-level structural balance, and point-level node vulnerability. The core research focus of the spatial network includes the network structure of architectural and public spaces, while the behavioral network concerns the activity paths and behavior patterns of tourists and villagers. The study finds that, at the village scale, Wulin Village’s spatial network demonstrates good connectivity and structural integrity, but the behavior paths of both tourists and villagers are highly concentrated in core areas, leading to underutilization of peripheral spaces. This creates an asymmetry characterized by “structural integrity—concentrated behavioral usage.” At the district scale, the spatial node distribution appears balanced, but tourist behavior paths are concentrated around cultural nodes, such as the ancestral hall, visitor center, and theater, while other areas remain inactive. At the point scale, both tourist and villager activities are highly dependent on a few high-degree, high-cluster nodes, improving local efficiency but exacerbating systemic vulnerability. Comparison with domestic and international studies on cultural settlements shows that tourism often leads to over-concentration of spatial paths and node overload, revealing significant discrepancies between spatial integration and behavioral usage. In response, this study proposes multi-scale spatial optimization strategies: enhancing accessibility and path redundancy in non-core areas at the village scale; guiding behavior distribution towards multifunctional nodes at the district scale; and strengthening the capacity and resilience of core nodes at the point scale. The results not only extend the application of behavioral network methods in spatial structure research but also provide theoretical insights and practical strategies for spatial governance and cultural continuity in tourism-driven cultural villages.