Model For Interaction Research Articles

The Spectra of IceCube Neutrino (SIN) candidate sources

Context. A correlation has been reported between the arrival directions of high-energy IceCube events and γ-ray blazars classified as intermediate- and high-synchrotron-peaked BL Lacs. Subsequent studies have investigated the optical properties of these sources, compiled and analyzed public multiwavelength data, and constrained their individual neutrino emission based on public IceCube point-source data. Aims. We provide a theoretical interpretation of public multiwavelength and neutrino point source data for the 32 BL Lac objects in the sample previously associated with an IceCube alert event. We combined the individual source results to draw conclusions regarding the multimesssenger properties of the sample and the required power in relativistic protons. Methods. We performed particle interaction modeling using open-source numerical simulation software. We constrained the model parameters using a novel and unique approach that simultaneously describes the host galaxy contribution, the observed synchrotron peak properties, the average multiwavelength fluxes, and, where possible, the IceCube point source constraints. Results. We show that a single-zone leptohadronic model can describe the multiwavelength broadband fluxes from all 32 IceCube candidates. In some cases, the model suggests that hadronic emission may contribute a considerable fraction of the γ-ray flux. The required power in relativistic protons ranges from a few percent to a factor of ten of the Eddington luminosity, which is energetically less demanding compared to other leptohadronic blazar models in recent literature. The model can describe the 68% confidence level IceCube flux for a large fraction of the masquerading BL Lacs in the sample, including TXS 0506+056; whereas, for true BL Lacs, the model predicts a low neutrino flux in the IceCube sensitivity range. Physically, this distinction is due to the presence of photons from broad line emission in masquerading BL Lacs, which increase the efficiency of hadronic interactions. The predicted neutrino flux peaks between a few petaelectronvolt and 100 PeV and scales positively with the flux in the gigaelectronvolt, megaelectronvolt, X-ray, and optical bands. Based on these results, we provide a list of the brightest neutrino emitters, which can be used for future searches targeting the 10–100 PeV regime.

Autonomous Vehicle–Pedestrian Interaction Modeling Platform: A Case Study in Four Major Cities

Autonomous Vehicle–Pedestrian Interaction Modeling Platform: A Case Study in Four Major Cities

Learning Heterogeneous Spatial-Temporal Context for Skeleton-Based Action Recognition.

Graph convolution networks (GCNs) have been widely used and achieved fruitful progress in the skeleton-based action recognition task. In GCNs, node interaction modeling dominates the context aggregation and, therefore, is crucial for a graph-based convolution kernel to extract representative features. In this article, we introduce a closer look at a powerful graph convolution formulation to capture rich movement patterns from these skeleton-based graphs. Specifically, we propose a novel heterogeneous graph convolution (HetGCN) that can be considered as the middle ground between the extremes of (2 + 1)-D and 3-D graph convolution. The core observation of HetGCN is that multiple information flows are jointly intertwined in a 3-D convolution kernel, including spatial, temporal, and spatial-temporal cues. Since spatial and temporal information flows characterize different cues for action recognition, HetGCN first dynamically analyzes pairwise interactions between each node and its cross-space-time neighbors and then encourages heterogeneous context aggregation among them. Considering the HetGCN as a generic convolution formulation, we further develop it into two specific instantiations (i.e., intra-scale and inter-scale HetGCN) that significantly facilitate cross-space-time and cross-scale learning on skeleton graphs. By integrating these modules, we propose a strong human action recognition system that outperforms state-of-the-art methods with the accuracy of 93.1% on NTU-60 cross-subject (X-Sub) benchmark, 88.9% on NTU-120 X-Sub benchmark, and 38.4% on kinetics skeleton.

Validation of a PGNAA sensor model for bulk material sorting

Global mineral demand is forecast to increase significantly to achieve the transition to renewable energy. Greater volumes of ore of lower grade will have to be mined to meet demand. Techniques to process large volumes of low-grade ore efficiently are being investigated to reduce the cost and impact of mining. One technique is to use sensor information to sort mined material, allowing waste to be discarded early in mineral processing. Prompt gamma neutron activation analysis (PGNAA) is a sensing technique that can provide information on the multi-elemental composition of a bulk sample which can be used for bulk material sorting. This paper presents the development of a Monte Carlo simulation model of a PGNAA sensor for bulk sorting using the Geant4 toolkit. The GEOSCAN sensor (Scantech Australia) was used as a case-study to demonstrate the application of the model. The sensor responses for a range of pure mineral samples (Fe2O3, SiO2, S, Na2CO3 and MnO2) were measured to validate the developed model. The sensitivity of the simulation results to the hadronic and electromagnetic physics models used was tested. It was determined that the PGNAA sensor model can reproduce measurements obtained from the GEOSCAN sensor. In particular, the model can provide a good reproduction of the overall spectral shape and the locations of distinct characteristic peaks. The differences between simulated and experimental results are within 30% on average. It was found that the Geant4 HP neutron model best reproduces the activation peaks observed in experimental measurements. Additionally, the PGNAA spectrum was found to be insensitive to the choice of electromagnetic model for the photon interactions. The validated sensor model provides a useful tool for investigating PGNAA sensor applications including a bulk sorting strategy for new materials, sensor calibration, improvements in signal analysis and optimised sensor design.

IDC-CDR: Cross-domain Recommendation based on Intent Disentanglement and Contrast Learning

Using the user’s past activity across different domains, the cross-domain recommendation (CDR) predicts the items that users are likely to click. Most recent studies on CDR model user interests at the item level. However because items in other domains are inherently heterogeneous, direct modeling of past interactions from other domains to augment user representation in the target domain may limit the effectiveness of recommendation. Thus, in order to enhance the performance of cross-domain recommendation, we present a model called Cross-domain Recommendation based on Intent Disentanglement and Contrast Learning (IDC-CDR) that performs contrastive learning at the intent level between domains and disentangles user interaction intents in various domains. Initially, user–item interaction graphs were created for both single-domain and cross-domain scenarios. Then, by modeling the intention distribution of each user–item interaction, the interaction intention graph and its representation were updated repeatedly. The comprehensive local intent is then obtained by fusing the local domain intents of the source domain and the target domain using the attention technique. In order to enhance representation learning and knowledge transfer, we ultimately develop a cross-domain intention contrastive learning method. Using three pairs of cross-domain scenarios from Amazon and the KuaiRand dataset, we carry out comprehensive experiments. The experimental findings demonstrate that the recommendation performance can be greatly enhanced by IDC-CDR, with an average improvement of 20.62% and 25.32% for HR and NDCG metrics, respectively.

Subducted carbon weakens the forearc mantle wedge in a warm subduction zone

Subducting oceanic plates carry large amounts of carbon into the Earth’s interior. The subducted carbon is mobilized by fluid and encounters ultramafic rocks in the mantle wedge, resulting in changes to the mineral assemblage and mechanical properties of the mantle. Here, we use thermodynamic modeling of interactions between carbon-bearing multi-component fluids and mantle rocks to investigate the down-dip variation in mineral assemblage in the forearc mantle along subduction megathrusts. We found that fluids rich in aqueous carbon are preferentially generated in a warm subduction zone (e.g., Nankai, SW Japan), causing a change in mineral assemblage from serpentine-rich at the mantle wedge corner to talc + carbonate-rich at greater depths. The transition caused by the infiltration of aqueous carbon may influence the depth of the boundary between the seismogenic and aseismic zones, and the down-dip limit of episodic tremor and slip.

Experimental investigation and fluid-structure interaction modelling of the casting of self-compacting concrete for CRTS III slab track in the curved segments

Ensuring the casting quality of self-compacting concrete (SCC) is crucial for the operational safety and durability of China railway track system (CRTS) III slab track. However, the mechanisms of key factors influencing flow behaviors of SCC and structural responses remain insufficiently understood, nor is the relationship between flow behaviors and structural responses during casting. In this study, fluid-structure interaction (FSI) models of SCC casting were developed and verified by full-scale physical tests, comprising slump flow, L-box flow and casting of SCC in an actual track at the field of Laixi-Rongcheng high-speed railway. Through in-depth analysis on the casting process of SCC in curved segments with a super elevation of 80 mm, the relationship between flow behaviors and structural responses was established. Furthermore, the influence mechanisms of key factors on flow behaviors and structural responses were investigated systematically. Results show that during gravity-fed SCC casting, its flow velocity diminishes, thus increasing the static pressure and the forces exerted on the slab by SCC. These vertical and lateral forces increase to 147.16 and 7.83 kN through three stages of development, acceleration and stabilization, resulting in commensurate structural responses. At the end of casting process, the central region of the slab exhibits maximum vertical deformation of 0.95 mm and tensile stress of 1.07 MPa. The tension rods at the side with lower elevation bear greater vertical and lateral forces than the ones at the side with higher elevation. The rise of funnel height from 1.05 to 1.35 m accelerates the casting process but increases the forces exerted by SCC by up to 26 % and augments the associated response of entire structures, whereas the funnel diameter shows little effect. Increasing yield stress of SCC from 9.3 to 49.3 Pa produces a more uniform flow morphology and thus higher static pressure and force by SCC in the middle region of the slab, impacting the structural responses by up to 15 %. The growth of plastic viscosity from 59 to 99 Pa⋅s significantly affects the volume rate and casting time by up to 66 % but has little effect on the flow morphology, fluid pressure and structural responses. In order to ensure high quality casting of SCC, five clamping beams are suggested with two beams positioned at either side of the slab and the other three evenly placed along the middle region of the slab.

Inference of host-pathogen interaction matrices from genome-wide polymorphism data.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype x genotype (GxG) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of host and pathogen genomes (co-GWAs) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an Approximate Bayesian Computation method to pinpoint loci with relevant GxG interactions and to infer their underlying interaction matrix. In a combined SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (GxG) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of GxG interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key GxG interactions underpinning HCV adaptation to the European human population after a recent expansion.

Simplified soil–structure interaction modeling techniques for the dynamic assessment of end shield bridges

In this paper, the dynamic behavior of four railway bridges with integrated retaining walls, considering the effect of soil–structure interaction (SSI), is investigated both numerically and experimentally. Among these bridges, two are single-span, and the remaining two are three-span. Each bridge is equipped with numerous accelerometers and is excited by a hydraulic actuator across various frequencies. Full 3D solid Finite Element (FE) models incorporating the railway bridges and surrounding soils are developed and calibrated using the Frequency Response Functions (FRFs) from each accelerometer. Furthermore, simplified 3D solid and 2D beam models are created for each railway bridge, incorporating springs and dashpots to account for the effect of surrounding soils. The values for these springs and dashpots are obtained from simple equations, except for the impact of the backfill soil in the simplified 2D beam models, which are derived from the impedance functions of the soil medium. The performance of these simplified models is then compared to the calibrated 3D models in terms of modal properties of the first bending mode and the maximum acceleration response during high-speed train passages. The results indicate that the simplified models closely align with the calibrated models in terms of modal properties and high-speed train passage response and can be used as simple and efficient alternatives for practical usage in bridge design.

Why does a cell function? New arguments in favor of quantum effects

In this study, the complexities of intracellular processes have been analyzed, including DNA folding, alternative splicing, mitochondrial function, and enzyme transport in lysosomes. Based on a previously proposed hypothesis (Levinthal's generalized paradox), a conclusion is made that all abovementioned processes cannot be realized with sufficient accuracy and in a realistic timeframe within the framework of classical physics. It is unclear why the cell functions at all. For the cell to function, its internal environment must be highly structured. In this regard, the cell shares similarities with computational devices (computers). In this study, quantum models of interactions between biologically important molecules were constructed, taking into account the long-range effects. One significant aspect of these models is the special role of the phase of the wavefunction, which serves as a controlling parameter. Experiments have been proposed that may confirm or refute these models.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Attitude, Habit Strength, and the Relationship Between Intention and Leisure-Time Physical Activity Behavior Among College Students

ABSTRACT According to the dual-process theories, many scholars proposed that physical activity behavior tends to be governed by an interplay of intentional planning and habitual action. The major purpose of this study was to determine the moderation effects of habit strength on the relationship between leisure time moderate-to-vigorous physical activity (MVPA) intention and behavior and the impacts of cognitive and affective attitude on PA habit strength, utilizing a latent interaction modeling approach. A total of 1803 undergraduate students completed this study. All variables were measured using validated self-report questionnaires. Structural equation modeling and Hayes’ PROCESS program (model 1) were conducted to address the research questions. Habit strength significantly and positively moderated the effects of intention on leisure time MVPA behavior (β = .08, p < .01). The association between intention and PA behavior became stronger as habit strength increased. Affective attitude positively and significantly influenced habit strength (β = .71, p < .01). The effect of cognitive attitude on habit strength was significant and negative with a small effect size (β = –.08, p < .01). Findings indicated that habit strength positively moderated the relationship between PA intention and behavior, suggesting that stronger habit strength may amplify the association between PA intention and actual PA behavior. Affective attitude toward PA significantly influenced habit strength, whereas cognitive attitude exhibited minimal negative impact on habit strength. These findings contribute to our understanding of the interaction effects between deliberative and automatic processes in regulating PA behavior, as well as the nuanced relationship between attitude and habit development.

Water-tank metabarrier for seismic Rayleigh wave attenuation.

An innovative concept of metabarrier is presented for seismic Rayleigh wave attenuation, which consists of a periodic array of cylindrical water tanks acting as resonant units above the soil surface. A pertinent theoretical framework is developed and implemented in COMSOL Multiphysics. The framework treats the dynamics of the water tank by a well-established three-dimensional linear, pressure-based model for fluid-structure interaction under earthquake excitation, accounting for the flexibility of the tank wall; furthermore, the soil is idealized as a homogeneous and isotropic medium. Floquet-Bloch dispersion analysis of the unit cell demonstrates the presence of relevant band gaps in the low-frequency range below 20 Hz and in the higher frequency range as well. The dispersion analysis is validated by comparison with the frequency-domain analysis of a soil domain with a finite array of water tanks. The band gaps are of interest to attenuate seismic Rayleigh waves and, more generally, Rayleigh waves caused by other ground vibration sources such as road or railway traffic. The water-tank resonant units are readily tunable by varying the water level, which allows changing opening frequencies/widths of the wave attenuation zones. This is a remarkable advantage over alternative seismic metamaterials that, in general, are not designed to be tunable.This article is part of the theme issue 'Current developments in elastic and acoustic metamaterials science (Part 2)'.

Mechanisms of RORα-dependent effects of melatonin

The transcription factor RORα has not traditionally been attributed a fundamental role in the development of Th17 cells, but recent studies have shown that it is necessary for the formation of a pathogenic variant of Th17 cells, the so-called Th1-polarized Th17 (Th17.1). Since the transcriptional activity of RORα depends on ligand binding, the search for such ligands is highly relevant, and in this regard, melatonin is of particular interest. The question of the ability of RORα to directly bind melatonin remains open today; data on this problem are extremely contradictory. In 1995, I. Wiesenberg and colleagues identified RORα as a nuclear receptor for melatonin, demonstrating the hormone’s ability to enhance the binding of this factor to DNA and determining the dissociation constant for the interaction of RORα with melatonin using classical Scatchard analysis. In 2011, P.J. Lardone and colleagues “rediscovered” RORα as a receptor for melatonin by demonstrating the coprecipitation of melatonin with RORα. And in 2016, A.J. Slominski and colleagues published a paper that cast doubt on the possibility of melatonin binding to RORα based on molecular modeling of ligand-receptor interactions supported by functional studies. However, a careful analysis of these data indicates the ambiguity of this conclusion, allowing us to speak, rather, of medium or low binding affinity of the hormone to RORα, but not of its absence. This conclusion is also supported by the fact that RORα mediates many of the effects of melatonin, both physiological and pharmacological, including the regulation of circadian rhythms and oxidative metabolism, neuro- and cardioprotection, and control of the immune response. In general, the data available today allow us to consider the transcription factor RORα as a receptor for melatonin with medium affinity, although indirect regulation of this factor by the hormone is not excluded, and RORα-dependent mechanisms should contribute to the cellular response to melatonin, both under physiological conditions and in the case of pharmacological use of the hormone.

Heat and mass transfer behavior in CMT plus pulse arc manufacturing

The metal transfer mode, temperature distribution, flow behavior, and microstructure characteristic in the process of cold metal transfer plus pulse (CMT+P) arc manufacturing were investigated by the in-situ observation and numerical simulation. A novel coupling model for droplet-pool interaction under the CMT+P arc was established, along with a new heat source model that considered Marangoni forces on droplets. These innovations provided significant insights into the heat and mass transfer mechanisms in the CMT+P hybrid arc manufacturing process. The results show that the droplets generated by the CMT+P hybrid arc exhibited a mixed transfer mode: short-circuit transfer in the CMT period and projected transfer of one droplet per pulse during the pulse period. The short-circuit transfer was more stable than the projected transfer, attributed to the smooth transfer of droplets to the molten pool through the liquid metal bridge, dominated by surface tension and Marangoni forces. The main energy of the molten pool was provided by droplets, which had a dominant ability to melt the substrate. However, the liquid metal bridge in the CMT period slowed down the heat transfer to the molten pool and had significant oscillating and stirring effects. During the pulse period, the droplet was accelerated and projected transfer by gravity and electromagnetic force. After solidification, the microstructure exhibited an alternating distribution of columnar and equiaxed grains from the bottom-up in the monolayer deposited layer, with a deflected fusion interface towards the scanning direction. At the top of the deposited layer, coarse columnar grains were distributed obliquely or laterally, inhibiting the growth of equiaxed grains and forming a distinct boundary with the equiaxed grains below.

Open string stub as an auxiliary string field

Witten’s open string field theory with a generalized version of stubs is reformulated as a cubic string field theory using an auxiliary string field. The gauge symmetries and equations of motion as well as the associative algebra of the resulting theory are investigated. Integrating out either the original or auxiliary field is shown to recover the conventional cubic theory. Our analysis demonstrates that deformations due to the stubs can be described as a homotopy transfer purely in the context of strong deformation retract. We also discuss to what extent the vertex regions resulting from stubs provide a model for the elementary interactions of closed string field theory.

Mathematical Modeling of Tumor Immune Interactions: The Role of Anti-FGFR and Anti-PD-1 in the Combination Therapy.

Bladder cancer poses a significant global health burden with high incidence and recurrence rates. This study addresses the therapeutic challenges in advanced bladder cancer, focusing on the competitive mechanisms of ligand or drug binding to receptors. We developed a refined mathematical model that integrates the dynamics of tumor cells and immune responses, particularly targeting fibroblast growth factor receptor 3 (FGFR3) and immune checkpoint inhibitors (ICIs). This study contributes to understanding combination therapies by elucidating the competitive binding dynamics and quantifying the synergistic effects. The findings highlight the importance of personalized immunotherapeutic strategies, considering factors such as drug dosage, dosing schedules, and patient-specific parameters. Our model further reveals that ligand-independent activated-state receptors are the most essential drivers of tumor proliferation. Moreover, we found that PD-L1 expression rate was more important than PD-1 in driving the dynamic evolution of tumor and immune cells. The proposed mathematical model provides a comprehensive framework for unraveling the complexities of combination therapies in advanced bladder cancer. As research progresses, this multidisciplinary approach contributes valuable insights toward optimizing therapeutic strategies and advancing cancer treatment paradigms.

Coarse-grained modeling of DNA-protein interactions helps elucidate DNA compaction

Coarse-grained modeling of DNA-protein interactions helps elucidate DNA compaction

A semi-empirical life-prediction model for multiaxial ratchetting-fatigue interaction of SUS301L stainless steel tubular welded joint

Based on the strain measured in the SUS301L stainless steel tubular welded joint in the asymmetrically stress-controlled multiaxial low-cycle fatigue tests by using the digital image correlation (DIC) method, the dependence of fatigue life on the loading level and loading path is well explained by discussing the multiaxial ratchetting-fatigue interaction in the fatigue failure zone of welded joint. To reflect the contribution of mean stress to the non-proportionality of loading path, a new non-proportionality factor is defined. And then the path-dependent damage parameters of pure fatigue damage and additional ratchetting one are proposed in a strain form and validated. Finally, a multiaxial life-prediction model is constructed through addressing the concurrent efforts of additional ratchetting damage and pure fatigue one to predict the multiaxial fatigue life of welded joint. The results demonstrate that the predicted multiaxial fatigue lives are well expected and all life data fall in the twice error bands regarding to the experimental ones.

A relativistic scalar model for fractional interaction between dark matter and gravity

Abstract In a series of recent papers we put forward a “fractional gravity”&amp;#xD;framework striking an intermediate course between a modified gravity theory and an&amp;#xD;exotic dark matter (DM) scenario, which envisages the DM component in virialized&amp;#xD;halos to feel a non-local interaction mediated by gravity. The remarkable success&amp;#xD;of this model in reproducing several aspects of DM phenomenology motivates us to&amp;#xD;look for a general relativistic extension. Specifically, we propose a theory, dubbed&amp;#xD;Relativistic Scalar Fractional Gravity or RSFG, in which the trace of the DM stressenergy tensor couples to the scalar curvature via a non-local operator constructed with&amp;#xD;a fractional power of the d’Alembertian. We derive the field equations starting from&amp;#xD;an action principle, and then we investigate their weak field limit, demonstrating that&amp;#xD;in the Newtonian approximation the fractional gravity setup of our previous works is&amp;#xD;recovered. We compute the first-order post-Newtonian parameter γ and its relation&amp;#xD;with weak lensing, showing that although in RSFG the former deviates from its GR&amp;#xD;values of unity, the latter is unaffected. We also perform a standard scalar-vectortensor-decomposition of RSFG in the weak field limit, to highlight that gravitational&amp;#xD;waves propagate at the speed of light, though also an additional scalar mode becomes&amp;#xD;dynamical. Finally, we derive the modified conservation laws of the DM stress energy&amp;#xD;tensor in RSFG, showing that a new non-local force emerges, and hence that the DM&amp;#xD;fluid deviates from the geodesic solutions of the field equations.