Framework Of Model Research Articles

Spatial interactions modulate tumor growth and immune infiltration

Direct observation of tumor-immune interactions is unlikely in tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen evolve as a mechanism of immune escape, but the exact nature of immune-collagen interactions is poorly understood. Spatial data quantifying collagen fiber alignment in squamous cell carcinomas indicates that late-stage disease is associated with highly aligned fibers. Our computational modeling framework discriminates between two hypotheses: immune cell migration that moves (1) parallel or (2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-extracellular matrix interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. Here, computational modeling provides important mechanistic insights by defining a kernel cell-cell interaction function that considers a spectrum of local (cell-scale) to global (tumor-scale) spatial interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interaction kernels drives tumor growth and infiltration.

Non-single dating app use and the cognitive and psychological mechanisms of infidelity: gender differences

The increasing prevalence of dating apps has reshaped the dynamics of romantic relationships, with many users already in committed partnerships. However, the link between encountering a large pool of potential alternatives and predicting current relationship outcomes, particularly infidelity and possible gender differences, has not been fully explored. Drawing on the investment model framework, this study examines the cognitive and psychological mechanisms underlying online and offline infidelity tendencies among dating app users in relationships. Data from non-single dating app users in Taiwan reveal a positive relationship between the quantity and quality of alternatives, suggesting that exposure to many profiles may prompt users to rely on heuristics and focus on distinctive characteristics to quickly assess potential alternatives. Furthermore, having many choices may satisfy the need for short-term encounters. The quality of alternatives is positively linked to partner choice regret, which negatively impacts commitment. Gender differences emerge, with males showing a higher likelihood of infidelity due to partner choice regrets, particularly in offline contexts. Despite maintaining a strong commitment to their primary partner, males exhibit a greater inclination towards offline infidelity, highlighting a scenario where commitment and infidelity may coexist for male dating app users. Theoretical implications of digital matchmaking in contemporary relationships are discussed.

Analysis of Influence Factors on Acceptance of the Access by KAI System using Technology Acceptance Model Framework

Analysis of Influence Factors on Acceptance of the Access by KAI System using Technology Acceptance Model Framework

High-Altitude-UAV-Relayed Satellite D2D Communications for 6G IoT Network

High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language Model (LLM) framework is introduced that dynamically predicts optimal HA-UAV connectivity for IoT devices, enhancing network performance and adaptability. The study emphasizes HA-UAVs’ operational efficiency, broad coverage, and potential to transform global communications, particularly in remote and underserved areas. Our proposed satellite-HA-UAV-IoT architecture with LLM optimization demonstrated substantial improvements, including a 25% increase in network throughput (from 20 Mbps to 25 Mbps at a 20 km distance), a 40% reduction in latency (from 25 ms to 15 ms), and a 28% enhancement in energy efficiency (from 0.25 μJ/bit to 0.18 μJ/bit), significantly advancing the performance and adaptability of next-generation IoT networks. These advancements pave the way for unprecedented connectivity and set the stage for future communication technologies.

A Low-Impact Development-Based Modeling Framework for Flood Mitigation in a Coastal Community

Urbanization is known to increase the volume of stormwater runoff and peak flow rates, which leads to changes in the natural flow regime and increases the likelihood of flooding. Low-impact development (LID) practices seek to reduce runoff volume and peak flow and are generally considered to be a more sustainable solution for urban stormwater management. In this study, we present a systematic approach to address nuisance flooding issues in small cities and communities. As an application, the effectiveness of two LID practices, rain barrels and permeable pavements, were explored in mitigating the urban flooding problem of a highly urbanized small coastal watershed in Alabama, USA. The EPA Stormwater Management Model (SWMM) was first calibrated for water depth using data collected at multiple sites within the watershed during the 2014–2015 period. The calibrated model was then used to first identify the areas prone to flooding using design storms with 1, 2, 5-, 10-, 50-, and 100-year return periods. Floodplain maps were generated for those design storms with HEC-RAS. Next, LID options upstream of those flood-prone areas were assessed to potentially minimize the flooding risks. The results indicate that LID controls can have considerable benefits for stormwater management by reducing runoff volume (1–24%), peak flow rates (18–25%), and water depth (5–15%), potentially returning watersheds to their natural flow regimes, thereby minimizing the flooding risk in urbanized areas. However, the effectiveness of LIDs, especially for the runoff volume, quickly diminishes as the return periods of the storms increase. Rain barrels were identified as the most economical and effective LID within the drainage system.

Potential distribution of rice thrips (Stenchaetothrips biformis) in India under changing climate

Sudden outbreaks of agricultural pests are increasing in India due to the growing prominence of environmental issues like global warming, the occurrence of weather extremes, and hydrological extremes. Climate change impacts the developmental rates and population dynamics of agricultural pests. As rice is a staple food widely used in India, it often suffers intensive damage by various pests, leading to a considerable decrease in crop yield. One of the most common pests in rice fields in India and neighboring countries is the rice thrips (Stenchaetothrips biformis). However, the degree of pest infestation of rice crops under climate change scenarios is poorly known. Therefore, this study aimed to understand the potential geographical distribution of the rice thrips in India for future scenarios using the MaxEnt modeling framework. Future projections from global climate models (GCMs), such as HadGEM3-GC31 and MPI-ESMI-2, are used to predict the most affected regions in 2050 under three greenhouse gas emission scenarios: SSP1-2.6, SSP3-4.5, and SSP5-8.5. The results from the MaxEnt indicate that the southern and northeastern parts of India will be highly affected by rice thrips in 2050. The affected number of districts are found to be 256, 330 and 313 under SSP1-2.6, SSP3-4.5, and SSP5-8.5 scenarios, respectively. It has been observed that the maximum temperature is the most influential climatic parameter for the geographical distribution of rice thrips, followed by the annual mean and minimum temperatures. It can be concluded from these results that the elevated temperature enhances the growth and distribution of rice thrips.

Строение центральной части палеопротерозойского Лосевского террейна по данным плотностного моделирования

The results of detailed three-dimensional density modeling of the upper crust of the central part of the Losevsky terrane located in the northern part of the Voronezh crystal massif are considered. The distribution of absolute crustal density values of to a depth of 16 km was obtained as a result of the inversion of local anomalies of the gravitational field in the Bouguer reduction. The solution of the inverse problem of gravimetry was implemented within the framework of the initial model, which was formed using digital geological and geophysical data from the study area: the regional density model of the East European Platform lithosphere and its corresponding regional gravity field; the field of local gravity anomalies, representing the difference between the observed and regional fields of the lithosphere model; generalized information on the density of sedimentary cover rocks and the crystal basement; the thickness scheme of the «gravity-active» layer, obtained on the basis of statistical analysis of the anomalous field; geological and topographic information. Quantitative interpretation of the gravimetry data allowed us to obtain fundamentally new information about the geological structure of the upper crust of the study area. The reliability of the obtained results is confirmed by the consistency of a priori data and the correspondence of the total model (local plus regional) and observed gravitational fields.

An integrated approach toward digital design and simulation of the automated overbraiding process for composite manufacturing

The study presents a new integrated hybrid modeling framework that combines kinematic models with Finite Element Analysis (FEA) to simulate the overbraiding process on non-circular and variable cross-section mandrels in composite manufacturing. By combining the computational efficiencies of kinematic models with the detailed physical insights from FEA simulations, this hybrid method provides a holistic solution for designing and prototyping overbraided structures. Specifically applied to a complex rectangular-to-square mandrel, the approach accurately predicts braid angles, validated against physical braiding experiments covering a range of target angles. The achieved results highlight the model's accuracy and emphasize its potential to boost the efficiency and precision of overbraiding in industrial settings, thereby reducing the need for costly and time-consuming physical braiding iterations.

A multi-step calibration strategy for reliable parameter determination of salt rock mechanics constitutive models

The storage of renewable hydrogen in salt caverns requires fast injection and production rates to cope with the imbalance between energy production and consumption. This raises concerns about the mechanical stability of salt caverns under such operational conditions. The use of appropriate constitutive models for salt mechanics is an important step in investigating this issue, therefore many constitutive models with several parameters have been presented in the literature. However, a robust calibration strategy to reliably determine which model and parameter set represents the given rock, based on stress–strain data sets, remains an unsolved challenge. In this paper, for the first time in the community, we present a multi-step strategy to determine a single parameter set based on many deformation data sets for salt rocks. Towards this end, we first develop a comprehensive constitutive model able to capture all relevant nonlinear deformation physics of transient, reverse, and steady-state creep. The determination of the single set of representative material parameters is then achieved by framing the calibration process as an optimization problem, for which the global Particle Swarm Optimization algorithm is employed. To allow for dynamic data integration, a multi-step calibration strategy is developed for a situation where experiments are included one at a time, as they become available. Additionally, due to the existing mild heterogeneity in the experimental rock samples, our optimization strategy is made flexible to allow for this slight heterogeneity. The devised optimization strategy, based on the multi-physics comprehensive constitutive modeling framework, results in a single set of representative material properties of all the deformation data sets. As a rigorous mathematical analysis for the presented method and the lack of relevant experimental data sets, we consider a wide range of synthetic experimental data sets, inspired by the existing sparse relevant data in the literature. The results of our performance analyses show that the proposed calibration strategy is robust. Moreover, the results become increasingly more accurate as more data sets become available.

Why More Biologists Must Embrace Quantitative Modeling.

Biology as a field has transformed since the time of its foundation from an organized enterprise cataloging the diversity of the natural world to a quantitatively rigorous science seeking to answer complex questions about the functions of organisms and their interactions with each other and their environments. As the mathematical rigor of biological analyses has improved, quantitative models have been developed to describe multi-mechanistic systems and to test complex hypotheses. However, applications of quantitative models have been uneven across fields, and many biologists lack the foundational training necessary to apply them in their research or to interpret their results to inform biological problem-solving efforts. This gap in scientific training has created a false dichotomy of "biologists" and "modelers" that only exacerbates the barriers to working biologists seeking additional training in quantitative modeling. Here, we make the argument that all biologists are modelers and are capable of using sophisticated quantitative modeling in their work. We highlight four benefits of conducting biological research within the framework of quantitative models, identify the potential producers and consumers of information produced by such models, and make recommendations for strategies to overcome barriers to their widespread implementation. Improved understanding of quantitative modeling could guide the producers of biological information to better apply biological measurements through analyses that evaluate mechanisms, and allow consumers of biological information to better judge the quality and applications of the information they receive. As our explanations of biological phenomena increase in complexity, so too must we embrace modeling as a foundational skill.

Defect related anomalous mobility of small polarons in dielectric oxides at the example of congruent lithium niobate

Polarons play a major role in the description of optical, electrical and dielectrical properties of several ferroelectric oxides. The motion of those particles occurs by elementary hops among the material lattice sites. In order to compute macroscopic transport parameters such as charge mobility, normal (i.e. Fickian) diffusion laws are generally assumed. In this paper we show that when defect states able to trap the polarons for long times are considered, significant deviations from the normal diffusion behaviour arise. As an example of this behavior, we consider here the case of lithium niobate (LN). This can be considered as a prototypical system, having a rich landscape of interacting polaron types and for which a significant wealth of information is available in literature. Our analysis considers the case of a stoichiometric, defect-free lithium niobate containing a certain concentration of small electron polarons hopping on regular Nb sites, and compares it to the material in congruent composition, which is generally found in real-life applications and which is characterized by a large concentration of antisite NbLi defects. While in the first case the charge carriers are free polarons hopping on a regular Nb sublattice, in the second case a fraction of polarons is trapped on antisite defects. Thus, in the congruent material, a range of different hopping possibilities arises, depending on the type of starting and destination sites. We develop a formalism encompassing all these microscopic processes in the framework of a switching diffusion model which can be well approximated by a mobile–immobile transport model providing explicit expressions for the polaron mobility. Finally, starting from the Marcus–Holstein’s model for the polaron hopping frequency we verify by means of a Monte Carlo approach the diffusion/mobility of the different polarons species showing that, while free polarons obey the laws for normal diffusion as expected, bound polarons follow an anomalous diffusion behaviour and that in the case of the congruent crystal where mixed free and bound polaron transport is involved, our expressions indeed provide a satisfactory description.

A Modelling Framework for Rapid Evaluation of Speed Limitations during Extrusion of Aluminium Profiles

A Modelling Framework for Rapid Evaluation of Speed Limitations during Extrusion of Aluminium Profiles

Dynamic Simulation of Street-Level Carbon Emissions in Megacities: A Case Study of Wuhan City, China (2015-2030)

Dynamic simulation of carbon emissions (CE) in megacities is crucial for regional carbon reduction management, however, limited simulation accuracy hinders its application in carbon reduction policies. An integrated modeling framework was developed based on high-resolution multi-source data to analyze the street-level carbon emissions in Wuhan from 2015 to 2030. First, we conducted principal components analysis on the 5 driving factors of carbon emissions (including point of interests, electricity consumption, gross domestic product, population, and nighttime light) to delineate single carbon emission character region (CECR). Then, a machine learning method was used to simulating CE and explaining the contributions of different CECRs. Multi-scenarios CE accountings also were conducted with CECR simulations and an improved cellular automata model. Results shows that: (1) CE in Wuhan showed strong aggregation during the historical period. The five CECRs formed a near-concentric circle. The changes of CECR reflected the enhancement of human activity. (2) CE gradually shifted from the central urban areas to the surrounding regions during the scenario period, showing significant spatial spillover effects. Administrative districts with lower CE density exhibited greater scenario variation in total carbon emissions, indicating a higher potential for carbon reductions. (3) The total CE of Wuhan (148.11 Mt) in 2030 is projected to increase by 42.6% compared to 2015 in the baseline scenario, representing 105% of the low scenario and 91% of the high scenario. The growth rate of total CE in Wuhan significantly slows down (<1 %) under all scenarios. The high-resolution dynamic simulation of CE will provide an important scientific basis for low-carbon city management in China.

CoMPers: A configurable conflict management framework for personalized collaborative modeling

Context:Modeling is an activity in the software development life cycle where experts and stakeholders collaborate as a team. In collaborative modeling, adhering to the optimistic versioning paradigm allows users to make concurrent changes to the same model, but conflicts may arise. To achieve an integrated and consistent merged model, conflicts must be resolved. Objective:The primary objective of this study was to provide a customizable and extensible framework for conflict management in personalized change propagation during collaborative modeling. Methods:We propose CoMPers, a customizable and extensible conflict management framework designed to address various conflicts encountered in collaborative modeling. We present the duel algorithm for automatically detecting and resolving conflicts according to user preferences. The framework utilizes personalized change propagation to customize collaboration and supports the conflict management process by executing the duel algorithm based on user preferences. As a proof-of-concept, we have implemented the CoMPers framework and extended the EMF.cloud modeling framework to demonstrate its applicability. Results:We have constructed a proof-of-concept implementation and conducted a real-world case study, a benchmark experiment, and a user experience evaluation. Our findings demonstrate that: (1) CoMPers enables collaborators to configure propagation strategies according to their habits; (2) CoMPers successfully identifies all anticipated conflicts and achieves a 100% accuracy in conflict handling; (3) The majority of participants agreed that CoMPers is user-friendly for collaborative modeling. Conclusion:This paper presents the CoMPers framework, which is based on personalized change propagation, and helps collaborators customize conflict management activities. The results confirm the feasibility and advantages of consistent and concurrent modeling within the collaborative CoMPers platform, with an acceptable functionality for approximately ten collaborators.

Multiscale Analysis of Impact-Resistance in Self-Healing Poly(ethylene-co-methacrylic acid) (EMAA) Plain Woven Composites

A study combining multiscale numerical simulation and low-velocity impact (LVI) experiments was performed to explore the comprehensive effects on the impact-resistance of EMAA filaments incorporated as thermoplastic healing agents into a plain woven composite. A multiscale micro–meso–macro modeling framework was established, sequentially propagating mechanical performance parameters among micro–meso–macro models. The equivalent mechanical parameters of the carbon fiber bundles were predicted based on the microscopic model. The mesoscopic representative volume element (RVE) model was crafted by extracting the actual architecture of the monolayer EMAA filaments encompassing the plain woven composite. Subsequently, the fiber and matrix of the mesoscopic model were transformed into a monolayer-equivalent cross-panel model containing monolayers aligned at 0° and 90° by local homogenization, which was extended into a macroscopic equivalent model to study the impact-resistance behavior. The predicted force–time curves, energy–time curves, and damage profile align closely with experimental measurements, confirming the reliability of the proposed multiscale modeling approach. The multiscale analysis reveals that the EMAA stitching network can effectively improve the impact-resistance of plain woven composite laminates. Furthermore, there exist positive correlations between EMAA content and both impact-resistance and self-healing efficiency, achieving a self-healing efficiency of up to 98.28%.

Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy.

Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST). A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario. The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16). Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

A fast computational framework for the linear peridynamic model

A fast computational framework for the linear peridynamic model

Framework for the Sustainable Modeling of Electric Truck Fleet Usage

Framework for the Sustainable Modeling of Electric Truck Fleet Usage

Development and validation of an infectious disease control competency scale for public health professionals

BackgroundInfectious diseases persistently pose global threats, and it is imperative to accelerate the professionalization of public health workforce. This study aimed to develop and validate the infectious disease control competency scale (IDCCS) for public health professionals to fill a theoretical gap and elevate practical capabilities by informing public health professionals’ development goals.MethodsThe initial item pool was generated through a literature review, and categorized into three dimensions (knowledge, practical skills, and leadership) based on the competency iceberg model and public health leadership framework. A two-round Delphi process was conducted to determine indicators within the scale. A pilot survey was utilized for item analysis and exploratory factor analysis (EFA). A formal survey was employed for confirmatory factor analysis (CFA). The weight value of each indicator was calculated using the analytic hierarchy process.ResultsAn initial scale with three primary items, 14 secondary items, and 81 tertiary items was generated. Twenty experts participated in the two rounds of the Delphi process. Authority coefficients exceeded 0.9 in both rounds. Kendall's W was 0.29 and 0.19, respectively (both P < 0.001). Item analysis presented a Cronbach's Alpha of 0.98, with corrected item-total correlation coefficients ranging from 0.33 to 0.78. EFA demonstrated that cumulative variance explanations for the four primary dimensions (knowledge, practical skills, leadership, and personal quality) were 77.463%, 73.976%, 81.174%, and 68.654%, respectively. CFA indicated that all composite reliability values and average variance extracted surpassed 0.8 and 0.5, respectively. The standardized factor loadings of the items ranged from 0.630 to 0.977. Among the seven model fit indices, each of the four dimensions satisfied at least five criteria. A final three-level scale comprising four primary items, 14 secondary items, and 64 tertiary items was constructed. The weight values for the four primary items were 0.4064, 0.2878, 0.2082, and 0.0981, respectively.ConclusionsThe IDCCS was established to evaluate the competencies of knowledge, practical skills, leadership, and personal quality for public health professionals in infectious disease control. This scale demonstrates good reliability and validity, and can be used for performance evaluation, recruitment processes, curriculum development, and individual self-assessment.

China's provincial power decarbonization transition in a carbon neutral vision

The power sector plays a crucial role in achieving the China's carbon neutrality target. However, there remain knowledge gaps regarding provincial emission reduction efforts toward carbon neutrality and the impact of carbon flows embodied in electricity transmissions. This study built a modeling framework, linking a bottom-up multi-regional power system model and Quasi-Input-Output approach, to investigate the provincial-level power system transition. Multiple combined scenarios based on shared socio-economic developments and different carbon mitigation policies were designed for a comprehensive assessment. Results indicates that the transition of the power system by 2060, under various scenarios, necessitates 7–7.8 TW of wind and solar photovoltaic power, 4810–6309 TWh of electricity transmissions, and 140–262 GW of coal power retrofits. Notably, carbon emission flows will accumulatively reach 12.7–18.7 Gt from 2020 to 2060. Inner Mongolia will bear a cumulative of more than 5 Gt of carbon flows from North China. When considering the indirect emissions, load centers such as Shandong will have higher peak emissions, and Shandong will replace Inner Mongolia as the largest emitter province. In addition, provincial emission factors under the influence of complex electricity transmissions and carbon flows are estimated.