Interaction Modeling Framework Research Articles

Identifying Research Hotspots and Trends in Psoriasis Literature: Autotuned Topic Modeling with Agent.

The rapid expansion of psoriasis research literature presents challenges for efficient analysis and trend identification, necessitating advanced approaches. We propose AgenTopic, an interactive topic modeling framework that integrates BERT embeddings, dimensionality reduction, clustering, and a language model feedback loop to analyze psoriasis research literature from 2000 to 2023. Applied to PubMed articles, AgenTopic extracted 158 psoriasis-related topics across 8 categories, outperforming traditional methods in handling complex medical literature. Further trend analysis using multiple modeling techniques, including an SVR-Linear model, revealed non-linear patterns in research growth across categories (R2 values 0.75-0.97). Key trends identified include focus on nail psoriasis and spondyloarthritis, shift from TNF-α to IL-17 in pathogenesis understanding, rapid development of biologics and small molecule inhibitors, and increased attention to comorbidities. We developed an interactive web tool to facilitate literature retrieval and trend identification. To our knowledge, this application of an agent-based interactive topic modeling framework to dermatological literature is previously unreported. Using only topic-modeled data, our framework achieved comparable performance to expert manual review in identifying research trends. AgenTopic performed better than several state-of-the-art topic modeling methods and demonstrated the potential of AI in advancing medical literature analysis.

Digitally enhanced development of customised lubricant: Experimental and modelling studies of lubricant performance for hot stamping

Digitally enhanced technologies are transforming every aspect of the manufacturing sector towards the era of digital manufacturing. Traditional lubricant development methods involving systematic but time-consuming iterative processes is still extensively used in the metal forming industry. In the present study, a novel digitally enhanced lubricant development scheme was proposed by leveraging a mechanism-based interactive friction modelling framework and quantitative and comprehensive evaluation of lubricant performance via the data-centric lubricant limit diagrams. By predicting transient lubricant behaviour following the complex contact condition evolution experienced in actual forming operations, a close association and quantified relation between the lubricant performance and its properties such as viscosity, evaporation rate and fraction of dry matter was established. This can facilitate the optimisation efficiency of lubricant parameters and minimise the experimental cost for iterative lubricant trials. A case study was conducted in this work to develop a customised lubricant using this digitally enhance scheme for the target hot stamping process based on a benchmark lubricant as a reference. Further industrial forming tests of an automotive component were conducted to validate the ideal performance of the customised lubricant.

Three-dimensional trajectories of irregular-shaped tunnel lining fragments in the flow environment caused by high-speed trains

High-speed railway tunnel lining fragments can cause collisions with trains and track blockages, severely affecting train operation. When a train passes through a tunnel where lining fragment is likely to occur, the train wind effect may significantly affect the trajectory of the lining fragment, making the location where the lining fragment is difficult to predict. For safety purpose, this study aims to analyze the impact of the initial circumferential position and shape of irregular-shaped lining fragments on their aerodynamic performance. Using on-site scanning and mathematical statistical methods, the shape characteristics and probability distribution of actual lining fragments in the tunnel are obtained. The aerodynamic behavior of irregular-shaped lining fragments with different initial positions and three typical aspect ratios (ARs) are investigated based on the overset grid method and the dynamic fluid–body interaction model framework as a high-speed train passes. The study found that the most representative lining fragments with an AR of three have a mass of 1.5 kg and are located 2.5 m from the tunnel centerline. The flight behavior of lining fragments shows distinct three-dimensional features, with both translation and rotation significantly affected by the aerodynamic effects of the train and the geometric shape of the fragments. The longitudinal and lateral translational distances of lining fragments at the top of the train decrease as their initial position moves further from the tunnel's centerline. With an increase in AR, both the longitudinal and lateral flight distances and average flight velocities of the fragments increase. The macroscopic flow field within the tunnel directly influences the motion characteristics of the lining fragments. Complex flow separation and circulation phenomena near the fragments result in uneven pressure differences acting on the smooth and rough surfaces of the lining fragments, causing irregular motion. The conclusions of this study provide a theoretical basis for assessing and preventing the impact of lining fragments on the operational safety of trains.

A knowledge interchange broker composition modeling framework for simulating water, energy, and water-energy nexus systems

Understanding the dynamics of complex systems requires developing and combining different kinds of models that can be simulated separately and together. Modeling the interactions as separate models contributes to building flexible hybrid simulation frameworks. In this research, a Discrete Event System Specification–based Interaction Model (DEVS-IM) framework is developed based on the Knowledge Interchange Broker (KIB) approach. This KIB-based RESTful modeling composition framework is shown to enable systematic modeling and simulation of interactions between disparate simulatable models. It supports storing IMs developed for componentized Water Evaluation and Planning System (WEAP) and Low Emissions Analysis Platform (LEAP) tools. It generates the skeleton of DEVS-IMs stored in database for DEVS-Suite simulator. An exemplar model consisting of water, energy, and IMs demonstrates this methodology for developing nexus models of water–energy systems.

Self-Paced Multi-Grained Cross-Modal Interaction Modeling for Referring Expression Comprehension.

As an important and challenging problem in vision-language tasks, referring expression comprehension (REC) generally requires a large amount of multi-grained information of visual and linguistic modalities to realize accurate reasoning. In addition, due to the diversity of visual scenes and the variation of linguistic expressions, some hard examples have much more abundant multi-grained information than others. How to aggregate multi-grained information from different modalities and extract abundant knowledge from hard examples is crucial in the REC task. To address aforementioned challenges, in this paper, we propose a Self-paced Multi-grained Cross-modal Interaction Modeling framework, which improves the language-to-vision localization ability through innovations in network structure and learning mechanism. Concretely, we design a transformer-based multi-grained cross-modal attention, which effectively utilizes the inherent multi-grained information in visual and linguistic encoders. Furthermore, considering the large variance of samples, we propose a self-paced sample informativeness learning to adaptively enhance the network learning for samples containing abundant multi-grained information. The proposed framework significantly outperforms state-of-the-art methods on widely used datasets, such as RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame datasets, demonstrating the effectiveness of our method.

Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning

Vertical forest structure is closely linked to multiple ecosystem characteristics, such as biodiversity, habitat, and productivity. Mixing tree species in planted forests has the potential to create diverse vertical forest structures due to the different physiological and morphological traits of the composing tree species. However, the relative importance of species richness, species identity and species interactions for the variation in vertical forest structure remains unclear, mainly because traditional forest inventories do not observe vertical stand structure in detail. Terrestrial laser scanning (TLS), however, allows to study vertical forest structure in an unprecedented way. Therefore, we used TLS single scan data from 126 plots across three experimental planted forests of a large-scale tree diversity experiment in Belgium to study the drivers of vertical forest structure. These plots were 9–11 years old young pure and mixed forests, characterized by four levels of tree species richness ranging from monocultures to four-species mixtures, across twenty composition levels. We generated vertical plant profiles from the TLS data and derived six stand structural variables. Linear mixed models were used to test the effect of species richness on structural variables. Employing a hierarchical diversity interaction modelling framework, we further assessed species identity effect and various species interaction effects on the six stand structural variables. Our results showed that species richness did not significantly influence most of the stand structure variables, except for canopy height and foliage height diversity. Species identity on the other hand exhibited a significant impact on vertical forest structure across all sites. Species interaction effects were observed to be site-dependent due to varying site conditions and species pools, and rapidly growing tree species tend to dominate these interactions. Overall, our results highlighted the importance of considering both species identity and interaction effects in choosing suitable species combinations for forest management practices aimed at enhancing vertical forest structure.

K-Surfaces: Bézier-Splines Interpolating at Gaussian Curvature Extrema

K-surfaces are an interactive modeling technique for Bézier-spline surfaces. Inspired by k -curves by [Yan et al. 2017], each patch provides a single control point that is being interpolated at a local extremum of Gaussian curvature. The challenge is to solve the inverse problem of finding the center control point of a Bézier patch given the boundary control points and the handle. Unlike the situation in 2D, bi-quadratic Bézier patches may exhibit none, one, or several extrema, and finding them is non-trivial. We solve the difficult inverse problem, including the possible selection among several extrema, by learning the desired function from samples, generated by computing Gaussian curvature of random patches. This approximation provides a stable solution to the ill-defined inverse problem and is much more efficient than direct numerical optimization, facilitating the interactive modeling framework. The local solution is used in an iterative optimization incorporating continuity constraints across patches. We demonstrate that the surface varies smoothly with the handle location and that the resulting modeling system provides local and generally intuitive control. The idea of learning the inverse mapping from handles to patches may be applicable to other parametric surfaces.

A review of the housing market-clearing process in integrated land-use and transport models

The land-use/transport interaction (LUTI) modeling framework has become the current state of best practice for analyzing the interdependency between the land-use and transportation systems. This paper presents a comprehensive review of the housing market-clearing mechanisms used in operational LUTI models. Market clearing is a critical component of modeling housing markets, but a systematic review and critique of the current state of the art have not previously been undertaken. In the review paper, the theoretical foundations for modeling household location choice are reviewed, including bid-rent and random utility theories. Five LUTI models are discussed in detail: two equilibrium models, MUSSA and RELU-TRAN, and three dynamic disequilibrium models, UrbanSim, ILUTE, and SimMobility. The discussion focuses on the following key points: the assumptions embedded in the models, the aggregation level of households and locations, computational cost and operationalization of the models. One of the challenges is that there are rarely any empirical studies that compare the performance of equilibrium and dynamic models in the same study context. Future research is recommended to empirically investigate the pros and cons of the two modeling approaches and compare the model performances for their representativeness of real-world behavior, computational efficiencies, and abilities for policy analysis. More sophisticated studies about the impacts of agents’ behavior on the housing market-clearing process are also recommended.

An interactive agent-based modelling framework for assessing COVID-19 transmission risk on construction site

The outbreak of COVID-19 has impacted the world and society in its entirety. The labour-intensive construction industry is especially disrupted by COVID-19 and construction workers have a higher chance of exposure to COVID-19. Despite the extensiveness of qualitative and quantitative research around the impact of COVID-19 on the construction industry, it is observed that very limited proportion of such research actually investigated the COVID-19 dynamics within a specific construction site as well as the effectiveness of the corresponding safety control measures. Given this context, this study developed an interactive agent-based modelling framework embedded with a modified susceptible-exposed-infectious-recovered (SEIR) model for assessing COVID-19 transmission risk on construction sites, with the application of five safety control measures (i.e., SCM including face covering, vaccination, ventilation, social distance and isolation). This study afterwards set up 108 SCM scenarios based on the five SCM and sensitivity analyses were conducted in order to generate robust results. Based on the simulation results, the efficacy of the 5 SCM in preventing COVID-19 spread was assessed. Therefore, the results of the 108 scenarios are a useful scientific reference for stakeholders or policymakers when making decisions regarding mitigating the spread of COVID-19 and other infectious diseases within the construction sector.

A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions

Understanding the effects of ambient turbulence (expressed often in terms of the turbulence intensity It) is critical to the development of predictive models for the performance of Ocean Current Turbine (OCTs). This paper describes a new, wake interaction modeling framework capable of capturing the detailed effects of turbulence on various performance parameters associated with OCTs that may be arranged in any arbitrary configuration. The model accounts for the effects of turbulence on the structure of the turbine wakes, specifically the extents of near- and far-wake regions, and the dependence of the transition point between the two regions on It. The analytical description for turbine wake is combined with an existing wake interaction model, the Unrestricted Wind Farm Layout Optimization (UWFLO) model to predict the global power output from an array of OCTs. The resulting modelling framework accurately captures the effect of inlet turbulence on the OCT farm power and efficiency, and can be applied to any array configuration. Results from the model are validated against Large Eddy Simulations (LES) in which the OCTs are modeled using the Blade Element Momentum (BEM) model, while the inlet flow is superposed with a synthetic turbulence field designed to approximate turbulence properties obtained from observational measurements of the Gulf Stream. The simulations show that OCT wakes recover faster at higher levels of inlet turbulence due to the enhanced entrainment and mixing between ambient flow and the wake, an effect that is captured by the modified UWFLO model.

DIDER: Discovering Interpretable Dynamically Evolving Relations

Effective understanding of dynamically evolving multiagent interactions is crucial to capturing the underlying behavior of agents in social systems. It is usually challenging to observe these interactions directly, and therefore modeling the latent interactions is essential for realizing the complex behaviors. Recent work on Dynamic Neural Relational Inference (DNRI) captures explicit inter-agent interactions at every step. However, prediction at every step results in noisy interactions and lacks intrinsic interpretability without post-hoc inspection. Moreover, it requires access to ground truth annotations to analyze the predicted interactions, which are hard to obtain. This letter introduces DIDER, Discovering Interpretable Dynamically Evolving Relations, a generic end-to-end interaction modeling framework with intrinsic interpretability. DIDER discovers an interpretable sequence of inter-agent interactions by disentangling the task of latent interaction prediction into sub-interaction prediction and duration estimation. By imposing the consistency of a sub-interaction type over an extended time duration, the proposed framework achieves intrinsic interpretability without requiring any post-hoc inspection. We evaluate DIDER on both synthetic and real-world datasets. The experimental results demonstrate that modeling disentangled and interpretable dynamic relations improves performance on trajectory forecasting tasks.

Transitioning to low-GWP alternatives with enhanced energy efficiency in cooling non-residential buildings of China.

The electricity demand for space cooling in the non-residential building (NRB) sector of China is growing significantly and is becoming increasingly critical with rapid economic development and mounting impacts of climate change. The growing demand for space cooling will increase global warming due to emissions of hydrofluorocarbons used in cooling equipment and carbon dioxide emissions from the mostly fossil fuel-based electricity currently powering space cooling. This study uses the Greenhouse Gas and Air Pollution Interaction and Synergies (GAINS) model framework to estimate current and future emissions of hydrofluorocarbons and their abatement potentials for space cooling in the NRB sector of China and assess the co-benefits in the form of savings in electricity and associated reductions in greenhouse gas (GHG), air pollution, and short-lived climate pollutant emissions. Co-benefits of space cooling are assessed by taking into account (a) regional and urban/rural heterogeneities and climatic zones among different provinces; (b) technical/economic energy efficiency improvements of the cooling technologies; and (c) transition towards lower global warming potential (GWP) refrigerants under the Kigali Amendment. Under the business-as-usual (BAU) scenario, the total energy consumption for space cooling in the NRB sector will increase from 166 TWh in 2015 to 564 TWh in 2050, primarily due to the rapid increase in the floor space area of non-residential buildings. The total GHG mitigation potential due to the transition towards low-GWP refrigerants and technical energy efficiency improvement of cooling technologies will approximately be equal to 10% of the total carbon emissions from the building sector of China in 2050.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11027-022-10021-w.

Aeroelastic Analysis of a Single Element Composite Wing in Ground Effect Using Fluid–Structure Interaction

Abstract This work focuses on an advanced coupling of computational fluid dynamics (CFD) and structural finite element analysis (FEA) on the aeroelastic behavior of a single element inverted composite wing with the novelty of including the ground effect. The front wing of the formula one (F1) car can become flexible under the fluid loading due to elastic characteristics of composite materials, resulting in changing the flow field and eventually altering overall aerodynamics. The purpose of this study is to setup an accurate fluid–structure interaction (FSI) modeling framework and to assess the influence of elastic behavior of the wing in ground effect on the aerodynamic and structural performance. Different turbulence models are studied to capture better the changes of the flow field and variation of ride heights are considered to investigate the influence of ground effect on aerodynamic phenomena. A steady-state two-way coupling method is exploited to run the FSI numerical simulations using ansys, which enables simultaneous calculation by coupling CFD with FEA. The effect of various composite structures on the wing performance is extensively studied concerning structure configuration, ply orientation, and core materials. The numerical results generally represent good agreement with the experimental data, however, discrepancy, especially in the aerodynamic force, is presented. This may be a consequence of a less effective angle of attack due to the wing deflection and deterioration of vortex-induced effect. For the structural analysis, the woven structure gives rise to more stable structural deflection than the unidirectional structure despite the associated weight penalty.

A Low-Order Wake Interaction Modeling Framework for the Performance of Ocean Current Turbines Under Turbulent Conditions

A Low-Order Wake Interaction Modeling Framework for the Performance of Ocean Current Turbines Under Turbulent Conditions

Simplifying Computer Aided Ergonomics: A User-Product Interaction-Modeling Framework in CAD Based on a Taxonomy of Elementary Affordances

OCCUPATIONAL APPLICATIONS This contribution provides a framework for modeling user-product interactions (in CAD) for in-depth ergonomic analysis of product design, using digital human models. The framework aims to be applicable to a wide range of different products while being suitable for designers – especially those who do not have specialized ergonomic expertise or training in human behavior – by providing an intuitive, standardized, and time-efficient modeling procedure. The framework contains 31 elementary affordances, which describe mechanical dependencies between product geometries and human end effectors. These elementary affordances serve as a tool for interaction modeling. Additionally, the paper provides a taxonomy of elementary affordances, which can be used to formalize / abstract the nature of user-product interactions and to describe them as elementary affordances. Furthermore, an implementation of the interaction-modeling framework is presented in a CAD environment and provides an example of how the framework could be used in terms of a computer aided ergonomics tool.

Idealization through interactive modeling and experimental assessment of 3D-printed gyroid for trabecular bone scaffold.

Porous scaffolds assisted bone tissue engineering is a viable alternative for reconstruction of large segmental bone defects caused by bone pathologies or trauma. In the current study, we intend to develop trabecular bone scaffolds using gyroid architecture. An interactive modeling framework is developed for the design of three-dimensional gyroid scaffolds using advanced generative tools including K3DSurf, MeshLab, and Netfabb. The suggested modeling approach resulted in uniform and interconnected pores. Subsequently, fused deposition modeling 3D-printing is employed to fabricate the scaffolds using poly lactic acid material. The pores interconnectivity, porosity, and surface finish of the fabricated scaffolds are characterized using micro-computer tomography and scanning electron microscopy. Additionally, to assess the performance of scaffolds as a bone substitute, compression, and in-vitro biocompatibility tests on sterilized scaffolds are conducted. Compression tests reveal mechanical strength in the range of native bone while human adipose-derived mesenchymal stem cells show high proliferation after 72 h of incubation. Based on these results, the fabricated gyroid scaffolds can be said to possess favorable properties for trabecular bone scaffold.

Potential and Pitfalls of Big Transport Data for Spatial Interaction Models of Urban Mobility

Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and use shared resources are being produced by cities. Harnessing these ever-increasing data streams is crucial for understanding urban dynamics. Within the context of transportation modeling, it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes. Therefore, in this article, the usefulness of a recently available big transport data set—the New York City taxi trip data—is evaluated within a spatial interaction modeling framework. This is done by first comparing parameter estimates from a model using the taxi data to parameter estimates from a model using a traditional commuting data set. In addition, the high temporal resolution of the taxi data provides an exciting means to explore potential dynamics in movement behavior. It is demonstrated how parameter estimates can be obtained for temporal subsets of data and compared over time to investigate mobility dynamics. The results of this work indicate that a pitfall of big transport data is that it is less useful for modeling distinct phenomena; however, there is a strong potential for modeling high-frequency temporal dynamics of diverse urban activities.

A Comprehensive Engineering Analysis of Left Heart Dynamics After MitraClip in a Functional Mitral Regurgitation Patient.

Percutaneous edge-to-edge mitral valve (MV) repair using MitraClip has been recently established as a treatment option for patients with heart failure and functional mitral regurgitation (MR), which significantly expands the number of patients that can be treated with this device. This study aimed to quantify the morphologic, hemodynamic and structural changes, and evaluate the biomechanical interaction between the MitraClip and the left heart (LH) complex of a heart failure patient with functional MR using a fluid-structure interaction (FSI) modeling framework. MitraClip implantation using lateral, central and double clip positions, as well as combined annuloplasty procedures were simulated in a patient-specific LH model that integrates detailed anatomic structures, incorporates age- and gender-matched non-linear elastic material properties, and accounts for mitral chordae tethering. Our results showed that antero-posterior distance, mitral annulus spherecity index, anatomic regurgitant orifice area, and anatomic opening orifice area decreased by up to 28, 39, 52, and 71%, respectively, when compared to the pre-clip model. MitraClip implantation immediately decreased the MR severity and improved the hemodynamic profile, but imposed a non-physiologic configuration and loading on the mitral apparatus, with anterior and posterior leaflet stress significantly increasing up to 210 and 145% during diastole, respectively. For this patient case, while implanting a combined central clip and ring resulted in the highest reduction in the regurgitant volume (46%), this configuration also led to mitral stenosis. Patient-specific computer simulations as used here can be a powerful tool to examine the complex device-host biomechanical interaction, and may be useful to guide device positioning for potential favorable clinical outcomes.

Transapical mitral valve repair with neochordae implantation: FSI analysis of neochordae number and complexity of leaflet prolapse.

Transapical mitral valve repair with neochordae implantation is a relatively new minimally invasive technique to treat primary mitral regurgitation. Quantifying the complex biomechanical interaction and interdependence between the left heart structures and the neochordae during this procedure is technically challenging. The aim of this parametric computational study is to investigate the immediate effects of neochordae number and complexity of leaflet prolapse on restoring physiologic left heart dynamics after optimal transapical neochordae repair procedures. Neochordae implantation using three and four sutures was modeled under three clinically relevant prolapse conditions: isolated P2, multi-scallop P2/P3, and multi-scallop P2/P1. A fluid-structure interaction (FSI) modeling framework was used to evaluate the left heart dynamics under baseline, prerepair, and postrepair states. Despite immediate restoration of leaflet coaptation and no residual mitral regurgitation in all postrepair models, the average and peak stresses in the repaired scallop(s) increased >40% and >100%, respectively, compared with the baseline state. Additionally, anterior mitral leaflet marginal chordae tension increased >30%, while posterior mitral leaflet chordae tension decreased at least 30%. No marked differences in hemodynamic performance, in native and neochordae forces, and in leaflet stress were found when implanting three or four sutures. We report, to our knowledge, the first set of time-dependent in silico FSI human neochordae tension measurements during transapical neochordae repair. This work represents a further step towards an improved understanding of the biomechanical outcomes of minimally invasive mitral valve repair procedures.

Spatial interaction effects on inland distribution of maritime flows

The relationship between the regional economy and the infrastructure endowment has long been discussed in the literature. In this context, it can be said that ports play an important role for regions, as they affect their competitiveness, while the regional economic activity has an effect on port traffic. Both circumstances influence the inland distribution of maritime traffic and are interconnected by the configuration of the hinterland of ports. The hypothesis of this paper is that spatial interaction effects, both exogenous and endogenous, shape the inter-port distribution of maritime traffic. The endogenous interaction effect arises when the inter-port distribution of the flows is influenced by those of nearby regions, whereas the exogenous interaction effect appears when the circumstances of these neighbours affect the flows generated by the region considered.The inter-port distribution of Spanish maritime flows of foreign trade for the period 1995–2015 was analysed in order to confirm the validity of this hypothesis. To that end, the Spatial Econometric Interaction Modelling (SEIM) framework was followed, and a Spatial Durbin Model (SDM) to origin-destination flows was applied. The results confirm that endogenous and exogenous interaction effects impact the inland distribution of the Spanish container flows and reveal the existence of direct and network effects regarding the variables analysed.