Model For Interaction Research Articles

Механизмы разрешения конфликтов в системе взаимодействий политических картин мира

Modern political reality is like an unregulated crossroads where even unspoken norms are no longer respected. This situation leads to difficulties in understanding each other by political actors. The rules of the game become dependent on the initial subjective attitudes and political pictures of the world of political decision-makers. The system of interactions of political pictures of the world is increasingly acquiring a conflict character. Under such conditions, the interpretation and forecasting of political events and situations becomes a difficult task. The issue of formation of scientifically based mechanisms for resolving conflict interactions of political pictures of the world becomes topical. The author analyses such models of interactions from the position of the theory of action and the theory of communicative action. The mechanisms of conflict prevention of political pictures of the world are embodied in a variety of interactive actions manifested in the activities of political institutions. The models of such compensatory interactions are strategic, normative and communicative actions of actors of political pictures of the world.

Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods.

Abstract Data-driven machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements to predictions of energies and forces. As a result, these potentials are only as good as the training data (usually the results of so-called ab initio simulations), and we need to ensure that we have enough information to make a model sufficiently accurate, reliable and transferable. The main challenge stems from the fact that descriptors of chemical environments are often sparse, high-dimensional objects without a well-defined continuous metric. Therefore, it is rather unlikely that any ad hoc method for selecting training examples will be indiscriminate, and it is easy to fall into the trap of confirmation bias, where the same narrow and biased sampling is used to generate training and test sets. We will show that an approach derived from classical concepts of statistical planning of experiments and optimal design can help to mitigate such problems at a relatively low computational cost. The key feature of the method we will investigate is that it allows us to assess the quality of the data without obtaining reference energies and forces - a so-called offline approach. In other words, we are focusing on an approach that is easy to implement and doesn't require sophisticated frameworks that involve automated access to high performance computing (HPC).

Edge‐Optimized Neural Networks for Real‐Time English Corpus Recommendation

ABSTRACTThis paper presents a lightweight framework for real‐time English corpus recommendation optimized for edge computing environments. Traditional recommendation systems, though effective in modeling complex user‐content interactions, often suffer from excessive computational demands, making them unsuitable for deployment on resource‐constrained edge devices. To tackle these limitations, we introduce a novel solution that efficiently balances advanced interaction modeling with computational cost optimization. The framework integrates a robust baseline model that captures rich user‐content interactions while employing knowledge distillation techniques to transfer critical information, ensuring that high‐quality recommendations are maintained even with reduced computational overhead. Our approach leverages real‐time adaptability to edge environments, providing both speed and accuracy. Experimental evaluations on benchmark datasets reveal significant reductions in response time, without compromising the accuracy of recommendations, thus making it ideal for real‐time applications in edge computing scenarios.

Surrogate modeling of pantograph-catenary system interactions

The smooth interaction between the pantograph and the catenary is crucial for the operational safety of railway vehicles. Coupled dynamic models of the pantograph–catenary system (PCS) constructed based on physical principles are important tools for analyzing their interactions; however, these models rely on accurate system parameters (such as stiffness, damping, and mass). Under actual operating conditions, the system parameters of the PCS exhibit time-varying characteristics and are difficult to measure, making it challenging for dynamic models to accurately represent the system’s behavior. Data-driven intelligent algorithms, with powerful feature extraction and nonlinear fitting capabilities, provide new approaches for solving system response prediction and state identification problems of the PCS. However, excessive reliance on large amounts of data for training may lead to poor generalization ability and pose challenges to model robustness, such as sensitivity to input noise or outliers. To address these issues, this paper proposes a surrogate model for the interaction of the PCS by integrating physical information with deep neural networks. The model introduces a novel neural operator that combines Transformers and convolutions (Convs), capable of capturing complex mapping relationships among various parameters within the dynamic model of the PCS in the frequency domain. A residual network incorporating physical information is designed to simulate the intricate correlations among system parameters. Additionally, a dynamic weighting balance algorithm is proposed to adjust the losses of different physical equations dynamically, ensuring the balance of physical information during training. The proposed model effectively performs response prediction and state identification of the PCS. It demonstrates excellent performance on both simulation and real-world data, providing new insights and methodologies for studying PCS interactions.

BiGCAN: A novel SRS-based bidirectional graph Convolution Attention Network for dynamic user preference and next-item recommendation

Bidirectional graph modeling represents a significant emerging technology that can be effectively integrated into sequential recommender systems. This innovation enables the explicit modeling of both forward and backward interactions in dynamic user preferences and next-item recommendations. However, existing approaches, particularly unidirectional methods, suffer from critical limitations. Unidirectional models are restricted to processing information in a single direction, which limits their ability to fully capture user interaction patterns, leading to suboptimal recommendation performance. Additionally, simplistic fusion mechanisms in current methods fail to effectively learn the intricate relationships between long-term and short-term user preferences. To address these limitations, we propose a novelty of the Bidirectional Graph Convolution Attention Network (BiGCAN) to enhance recommendation quality through three key innovations. First, bidirectional graph convolution network captures long-term preferences by modeling interactions in both forward and backward directions, allowing for a more comprehensive understanding of user behavior over time. Second, time-aware bidirectional graph attention network focuses on short-term preferences by incorporating temporal dynamics, enabling the model to adapt to recent changes in user preferences. Third, Bidirectional Gated Collaborative Filtering (BiGatedCF) mechanism integrates both long-term and short-term preferences through an advanced gating mechanism that efficiently balances these interactions. The BiGatedCF module combines a bidirectional gated recurrent unit to capture complex sequential dependencies, a deep matrix factorization to learn high-quality latent representations, and a multi-layer perceptron to match scores. Empirical experiments on four benchmark datasets demonstrate that BiGCAN surpasses state-of-the-art methods in terms of Top-N recommendations and performance across varying sparsity levels.

Modeling ground vibration excited by a high-speed train running along a viaduct railway using a new structure-soil interaction method

Ground vibrations generated by high-speed trains running along railways either at grade or on viaduct bridges, are greatly concerned by the railway industry. Considering the train-track-bridge-abutment-pile-soil interactions involved, modeling the problem in an analytical manner is almost impossible, while conventional numerical methods struggle to accurately simulate the dynamic properties of infinitely large soils. Therefore, this study introduces a new substructure methodology for train-track-bridge-abutment-pile-soil interactions that leverages an ingenious combination of the 2.5-dimensional finite element-boundary element method (2.5D FE-BE) for dynamics of infinitely long periodic structures, and a local 3-dimensional finite element model for pile-soil interaction. This innovative method allows for the consideration of detailed pile-soil coupling using a local finite element model, all while retaining the ability to account for the infinite size of the soil structure using the 2.5-dimensional finite element-boundary elements. After a comparison between model prediction and field measurement, the model is employed to explore the environmental vibration characteristics of on-viaduct high-speed railways and analyze the vibration reduction effects of various measures, including rail fasteners, track slab pads, and bridge bearings.

Study on the Mechanical Properties of beta Silicon Nitride Based on Neural Network Potential

Silicon nitride is widely utilized in the manufacture of cutting tools, bearings, and engine components, making accurate predictions of its mechanical and thermal behaviors crucial for engineering design. This study significantly advances the modeling of complex atomic interactions by employing machine learning, specifically Neural Network Potential (NNP), to enhance precision over traditional empirical potentials. Through NNP, predictions of stress-strain curves, thermal conductivity, and grain boundary behavior are achieved. The results demonstrate that NNP not only reduces computational time but also enhances the accuracy of material property predictions. This work provides the mechanical and thermal performance of Si3N4 and opens new avenues for further applications of machine learning in advanced ceramic materials.

Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection.

The Detection Transformer (DETR) has revolutionized the design of CNN-based object detection systems, showcasing impressive performance. However, its potential in the domain of multi-frame 3D object detection remains largely unexplored. In this paper, we present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection by addressing three key aspects specifically tailored for this task. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network, which represents queries as nodes in a graph and enables effective modeling of object interactions within a social context. To solve the problem of missing hard cases in the proposed output of the encoder in the current frame, we incorporate the output of the previous frame to initialize the query input of the decoder. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match. And similar queries are insufficiently suppressed and turn into redundant prediction boxes. To address this issue, our proposed IoU regularization term encourages similar queries to be distinct during the refinement. Through extensive experiments, we demonstrate the effectiveness of our approach in handling challenging scenarios, while incurring only a minor additional computational overhead.

Fluid–structure interaction in a follow-up study of arterio-venous fistula maturation

Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysed patients. AVF is created surgically using the patient’s artery and vein. Once the connection (anastomosis) is made, the maturation process begins. Studies have shown that most AVFs do not survive beyond one year. This study presents fluid–structure interaction (FSI) modelling of non-Newtonian blood flow through an end-to-side radio-cephalic AVF, investigated weekly during a 15-week follow-up period and 1.5 years postoperatively using ultrasound methods. The aim was to collect qualitative and quantitative data regarding changes in hemodynamics and alterations in the walls of AVF vasculature. Different material properties were assigned to the artery, suture zone (anastomosis), and vein, while the stiffening of the venous arm over time was also modelled. The proposed FSI methodology can be implemented in future follow-up studies involving groups of patients. The main findings revealed: a) counter-rotating vortices in the anastomosis cross-section affecting local pressure conditions; b) different temporal progression of vorticity, shear strain rate, and turbulent kinetic energy and similarity of the temporal progression of WSS obtained under the assumptions of the rigid-walled and FSI; c) a negligible low-WSS zone in the presented thrombosis-free AVF; d) migration of the zone of maximal temporal wall deformation over time.

Dynamic response analysis of monopile-supported offshore wind turbine on sandy ground under seismic and environmental loads

Monopiles are the most widely adopted foundation type for Offshore Wind Turbines (OWTs) in shallow waters. With the expansion of the construction of OWT, the number of OWT farms in seismic regions increases globally including the coastal areas of Japan and China. It is necessary to evaluate the impact of earthquakes including the vibration and soil liquefaction on the OWTs supported by the monopile foundation, while the effects of liquefaction on offshore structures, especially for OWTs with monopiles, have not been sufficiently studied. This study investigates the seismic response of the monopile-supported OWTs with the use of an advanced soil model. A three-dimensional numerical model is built, and dynamic analyses are carried out using the OpenSees framework. The pressure-dependent multi-yield (PDMY03) constitutive model is used to simulate the dynamic soil behavior. The applicability of the large-diameter pile modeling method for proper soil-pile interaction modeling in this numerical analysis is first validated through centrifuge tests on monopiles subjected to lateral loading. The dynamic analyses are then carried out to demonstrate the seismic response of the entire OWT system. The numerical results indicate that the contribution of higher modes of vibration is becoming of increased importance for large wind turbines and soil-structure interaction plays a significant role in the dynamic response. Moreover, the monopile-supported OWT in dense sand deposits experiences substantial lateral displacement and rotation under the combined action of wind and earthquake loads when liquefaction occurs.

New aspects concerning <i>Pediculus capitis</i> (Anoplura: Pedicullidae) infestation in Iran

Pediculosis caused by the Pediculus capitis (De Geer, 1767), is a major public health concern worldwide, including in Iran. Policymakers must be well-informed on the relevant evidence through comprehensive studies. No study has been done concerning trends of Pediculus capitis infestation in provinces of Iran, geographic and climatic regions, seasons, location of meridians and tropics areas, together with the distribution and stratification. Therefore, a study of the mentioned objectives was conducted. The data on P. capitis infestation levels and factor impacts on P. capitis infestation levels in Iranian people were extracted and summarized from 126 articles. The trends of P. capitis infestation have increased in about half of the country’s provinces over the previous two decades. These provinces are located mostly in the southern Caspian Sea shores, the central plateau, and the south coast. Pediculus capitis infestations are in the range of -ꝏ–-0.40, -0.40–-0.20, and -0.20–0.00 in the regression coefficient regarding the distribution and stratification. For the first time in this study, the impact of the meridians and the tropics that pass through Iran was also evaluated on P. capitis infestation. Based on the intensity of P. capitis infestation, the order of the influence of the factors that affect infestation is estimated. Interaction modeling demonstrated that primary school has a synergistic impact on time trends. In addition, the Western mountains, the central plateau, and the southeast showed synergistic effects with time trends on the incidence of the outcome. The findings provide the national baseline data to initiate steps for national pediculosis intervention programs.

Interformer: an interaction-aware model for protein-ligand docking and affinity prediction.

In recent years, the application of deep learning models to protein-ligand docking and affinity prediction, both vital for structure-based drug design, has garnered increasing interest. However, many of these models overlook the intricate modeling of interactions between ligand and protein atoms in the complex, consequently limiting their capacity for generalization and interpretability. In this work, we propose Interformer, a unified model built upon the Graph-Transformer architecture. The proposed model is designed to capture non-covalent interactions utilizing an interaction-aware mixture density network. Additionally, we introduce a negative sampling strategy, facilitating an effective correction of interaction distribution for affinity prediction. Experimental results on widely used and our in-house datasets demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by accurately modeling specific protein-ligand interactions. Encouragingly, our approach advances docking tasks state-of-the-art (SOTA) performance.

New methodological approach for the mechanical testing of aerial lime-based binders and mortars: the effect of PEG-1000 addition

So far, the mechanical testing at the macro scale of aerial lime-based materials has mainly been focused on the analysis of mortars, while binder specimens have not been studied as much. In the present work an alternative, material-effective and low-waste methodology to evaluate the mechanical properties of aerial lime binders and mortars is proposed. The core of this methodology is based on specimen size reduction, minimizing quantities of material required during development and testing. In particular, the proposed methodology is aimed at studying the interaction of different quantities of a phase change material (PCM) with aerial lime binders and mortars. The mechanical properties of pure aerial lime binders were successfully characterized, whereas mortar specimens provided less detailed information. The mechanical characterization showed that aerial lime binders, and consequently mortars, are weakened in the presence of Poly(ethylene glycol) - PEG, a PCM. It was observed that 8 % of PEG in the binder lower the resistance to compression by 36 %, resistance to scratch on 76 % and flexural resistance by 32 %. Finally, this study also proposes a model for the physical interaction between the PCM and the aerial lime-binder to explain the observed weakening mechanism: PEG can hinder the correct crystalline interlocking between the calcite crystals in the carbonated binder, and act as a lubricant promoting their sliding under compressive forces.

One-way fluid–structure interaction modeling and multi-objective optimization of horizontal-axis wind turbine blades equipped with winglets

ABSTRACT This study aims to investigate the effects of backward-directed winglets applied on the NREL Phase-II reference wind turbine geometry on structural and aerodynamic characteristics and to reveal optimized configurations. The aerodynamic pressure distributions on the blades were imposed as boundary conditions for the FEM analyses since the structural characteristics of the turbine were analyzed by adopting a one-way Fluid Structure Interaction (FSI) approach. The Taguchi analysis with an L27 orthogonal array revealed the descending order of importance of design variables on the coefficient of power ( C P ): height, cant angle, toe angle, curvature radius, and finally twist angle. For von-Mises stress, the descending order of importance is height, cant angle, toe angle, twist angle, and curvature radius. A multi-objective genetic algorithm using artificial neural network (ANN) models was used to optimize the objectives C P and von-Mises stress. Pareto-optimal solutions with off-design points were obtained, and their performances were compared to the reference NREL Phase-II geometry. As a result, it was observed that there was an increase in C P from 8.07% to 15.56% and the von-Mises stress showed a considerable increase up to 231.13%. The study demonstrates the aerodynamic benefits and structural drawbacks of various winglet designs in horizontal axis wind turbines.

Dynamic analysis in polar exploration: Fluid-structure interaction modeling of projectile colliding with floating ice during water entry

In polar resource exploration, the interaction between polar detectors and floating ice, as well as their water entry mechanisms, are crucial for ensuring effective detector operation and data collection. This study developed a fluid-structure interaction (FSI) model to simulate the water entry of the projectile in a multidegree motion state upon collision with the floating ice, and the numerical method was validated through experiments. This study analyzes the mechanisms of cavity evolution and the laws of cavity pinch-off. This analysis further explores the motion states and dynamic characteristics under the interaction between the projectile and the floating ice. Additionally, this study also considers the influence of structural parameters of the floating ice, including thickness (Lt), width (Lw), and collision position (Sd), on the water entry process. The study reveals that increasing the submergence depth of the floating ice enhances the stability between the floating ice and water, and can mitigate flow separation phenomena generated by passive motion under inertial effects. Variations in the floating ice thickness significantly affect the cavity evolution and the projectile's underwater motion state. Conversely, variations in the floating ice width notably affect the liquid level disturbances, the development of splash crowns, and the evolution of passive water entry cavities. In specific multidegree motion states, various collision positions do not alter the evolution form of water entry cavities, yet the variation in collision positions notably affects floating ice displacement. As the collision position shifts from the center to the side edge of the floating ice, both the hydrodynamic forces on the projectile and the stress on the floating ice gradually decrease, with the decrease in hydrodynamic forces being the most significant, reaching up to 58%. This study is important for enhancing multi-body fluid-structure interaction algorithms and advancing polar exploration engineering development.

Boltz-1 Democratizing Biomolecular Interaction Modeling.

Understanding biomolecular interactions is fundamental to advancing fields like drug discovery and protein design. In this paper, we introduce Boltz-1, an open-source deep learning model incorporating innovations in model architecture, speed optimization, and data processing achieving A lpha F old 3-level accuracy in predicting the 3D structures of biomolecular complexes. Boltz-1 demonstrates a performance on-par with state-of-the-art commercial models on a range of diverse benchmarks, setting a new benchmark for commercially accessible tools in structural biology. By releasing the training and inference code, model weights, datasets, and benchmarks under the MIT open license, we aim to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling.

Intraoperative interaction modeling between surgical instruments and soft tissues in neurosurgery based on energy functions

A physical model of soft tissue that provides realistic and real-time haptic and visual feedback is crucial for neurosurgical procedures. This paper investigates the interaction between surgical instruments and soft brain tissue, proposing a soft tissue deformation simulation method based on the principle of energy minimization and constrained energy function. The model includes a permanent deformation energy function induced by friction and a volume preservation energy function to more accurately depict tissue response during procedures such as resection of convex meningiomas and evacuation of intracerebral hematomas. Experimental results show that the proposed method meets the requirements of neurosurgical simulation.

The heat transfer characteristics of semi-molten wide sieving dilute phase particles between vertical heating surfaces

The high-temperature slag produced during the steelmaking process presents a significant opportunity for waste heat recovery and material utilization. Centrifugal granulation is acknowledged as an effective treatment method for managing this slag. However, the compact design of centrifugal granulation equipment poses a challenge in achieving the necessary cooling rates for high-temperature slag relying solely on gas-solid heat transfer within a confined space. This paper presents a physical model for the gas-solid two-phase interaction of an ultra-dilute high-temperature particle cluster and analyzes its heat exchange with vertical heating surfaces. The research indicates that radiative heat transfer is the primary mode of heat exchange between the particles and the heated surface, constituting more than 50 %. Moreover, heat is transferred from the particles through the air to the heated surface. Significantly, lowering the air inlet temperature and the particle velocity greatly improves the heat transfer performance and overall efficiency. Additionally, increasing the air flow rate can offset the decrease in contact thermal conductivity and radiative heat transfer that may occur due to increased spacing between the vertical tube bundle heating surfaces, thus promoting particle heat transfer. This study is crucial for guiding the improvement of heat transfer techniques for high-temperature molten particles in confined spaces.

The TRIM33 Bromodomain Recognizes Histone Lysine Lactylation.

Histone lysine lactylation (Kla) regulates inflammatory gene expression in activated macrophages and mediates the polarization of inflammatory (M1) to reparative (M2) macrophages. However, the molecular mechanisms and key protein players involved in Kla-mediated transcriptional changes are unknown. As Kla is structurally similar to lysine acetylation (Kac), which is bound by bromodomains, we hypothesized that bromodomain-containing proteins bind histone Kla. Here, we screened 28 recombinantly expressed bromodomains for binding to histone Kla peptides via AlphaScreen assays. TRIM33 was the sole bromodomain tested that bound histone Kla peptides. TRIM33 attenuates inflammatory genes during late-stage macrophage activation; thus, TRIM33 provides a potential link between histone Kla and macrophage polarization. Orthogonal biophysical techniques, including isothermal titration calorimetry and protein-detected nuclear magnetic resonance, confirmed the submicromolar binding affinity of the TRIM33 bromodomain to both Kla and Kac histone post-translational modifications. Sequence alignments of human bromodomains revealed a unique glutamic acid residue within the TRIM33 binding pocket that we found confers TRIM33 specificity for binding Kla compared with other bromodomains. Molecular modeling of interactions of Kla with the TRIM33 bromodomain binding pocket and site-directed mutagenesis of glutamic acid confirmed the critical role of this residue in the selective recognition of Kla by TRIM33. Collectively, our findings implicate TRIM33, a bromodomain-containing protein, as a novel reader of histone Kla, potentially bridging the gap between histone Kla and macrophage polarization. This study enhances our understanding of the regulatory role of histone Kla in macrophage-mediated inflammation and offers insights into the underlying structural and biophysical mechanisms.

A GPU-accelerated two-phase flow model for fluid-solid interaction using the sharp interface immersed boundary method

A GPU-accelerated two-phase flow model for fluid-solid interaction using the sharp interface immersed boundary method