Framework Of Model Research Articles

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.

Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

Identifying signals of memory from observations of animal movements

AbstractIncorporating memory (i.e., some notion of familiarity or experience with the landscape) into models of animal movement is a rising challenge in the field of movement ecology. The recent proliferation of new methods offers new opportunities to understand how memory influences movement. However, there are no clear guidelines for practitioners wishing to parameterize the effects of memory on moving animals. We review approaches for incorporating memory into step-selection analyses (SSAs), a frequently used movement modeling framework. Memory-informed SSAs can be constructed by including spatial-temporal covariates (or maps) that define some aspect of familiarity (e.g., whether, how often, or how long ago the animal visited different spatial locations) derived from long-term telemetry data. We demonstrate how various familiarity covariates can be included in SSAs using a series of coded examples in which we fit models to wildlife tracking data from a wide range of taxa. We discuss how these different approaches can be used to address questions related to whether and how animals use information from past experiences to inform their future movements. We also highlight challenges and decisions that the user must make when applying these methods to their tracking data. By reviewing different approaches and providing code templates for their implementation, we hope to inspire practitioners to investigate further the importance of memory in animal movements using wildlife tracking data.

Performance analysis of position sensorless control of antagonistic shape memory alloy actuators

Abstract This paper presents a comprehensive performance analysis of position sensorless control of antagonistic shape memory alloy actuators (ASMAA), focusing on enhancing the sensorless position sensing capabilities and servo performance. The mechanisms and influencing factors of ASMAA self-sensing characteristics are comprehensively analyzed. A self-sensing model framework based on the constitutive model of ASMAA is proposed, and a self-sensing model based on differential resistance feedback is developed. An experimental platform based on the ASMAA motion mechanism is established to test and analyze SMA wires of two different materials under various pretension force conditions. The experimental results show that the antagonistic configuration mitigates hysteresis and improves linearity. A simple polynomial fitting is employed to establish the resistance-displacement relationship, achieving good accuracy with minimal residuals. The system identification is employed to avoid the parameter complexity and time-varying in the constitutive model. Based on the identified transfer function, a traditional PID controller is designed for position sensorless servo control. However, the inherent nonlinear hysteresis of ASMAA is difficult to suppress using a linear PID controller. Furthermore, a compound control strategy based on the Duhem model and PID controller is proposed, which utilizes the particle swarm optimization (PSO) algorithm to identify the Duhem inverse model controller. Experimental results demonstrate that the compound controller significantly enhances position tracking accuracy and response speed compared to a standalone PID controller.

Abstract A040: A novel stroma-mediated positive interaction between resistant and sensitive cells in non-small cell lung cancer facilitates drug resistance

Abstract Resistance to targeted therapies, particularly those inhibiting oncogenic tyrosine kinases (TKIs), presents a significant challenge in the treatment of advanced non-small cell lung cancer (NSCLC). Despite initial robust clinical responses, tumors often relapse due to the emergence of resistant cell populations. The origins of this resistance—whether it is pre-existing in a subset of tumor cells or arises de novo during treatment—remains a critical question in understanding and combating therapeutic failure. To investigate this, we developed in silico mathematical models that integrate both pre-existing and de novo resistance mechanisms within a framework that explicitly integrates the stromal niche. Our study focused on the evolutionary dynamics of NSCLC in response to therapy, using a hybrid agent-based spatial modeling framework (HAL) to simulate tumor behavior under treatment. These models were designed to simulate the complex interactions between resistant and sensitive tumor cells, mediated by the stroma, and to predict the timing of relapse based on these interactions. Our findings suggest that stroma-mediated interactions play a crucial role in the evolutionary dynamics of therapy responses, influencing the fitness and survival of both resistant and sensitive cells. Specifically, our models predict that pre-existing resistance is unlikely to be compatible with the long remission periods observed in a substantial subset of patients treated with advanced anaplastic lymphoma kinase (ALK) inhibitors. This conclusion was supported by the observed relapse times and volumetric tumor growth data from subsequent animal models. Moreover, our analyses highlight the importance of ecological factors within the tumor microenvironment in shaping the response to therapy. The spatial models we developed reveal that the stroma can foster positive interactions between resistant and sensitive cells, thereby accelerating the emergence of resistance. These insights underscore the need to consider the stromal niche when developing new therapeutic strategies and when interpreting the dynamics of drug resistance. In conclusion, our study provides new insights into the role of stroma in mediating resistance to TKIs in NSCLC. By combining mathematical modeling with experimental data, we have elucidated how stroma-mediated interactions between tumor cells can influence the evolution of resistance, challenging the assumption that pre-existing resistance alone drives relapses in TKI-treated patients. Our findings emphasize the need for adaptive therapeutic approaches that anticipate and disrupt these microenvironmental interactions to improve patient outcomes. Citation Format: Rishi M. Shah, Sagnik Yarlagadda, Mark Robertson-Tessi, Bina Desai, Tatiana Miti, Daria Miroshnychenko, David Basanta, Andriy Marusyk, Alexander Anderson. A novel stroma-mediated positive interaction between resistant and sensitive cells in non-small cell lung cancer facilitates drug resistance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A040.

Rising protectionism and foreign direct investment

AbstractThis paper investigates the impact of the changing international investment regime on foreign direct investment (FDI). Since the late 2000s, hundreds of international investment agreements (IIAs) have been renegotiated or terminated. We study the impact of these shifts in the global investment regime using a novel FDI dataset based on transaction-level investment data and detailed information on IIA provisions. Relying on a stand-alone partial equilibrium modeling framework, we hypothesize that rising protectionism in IIAs increased investment frictions and harmed FDI. We then use a parsimonious three-way gravity framework to test the hypothesis. The empirical evidence shows that reformed and newly enforced IIAs have no impact on FDI flows. Decreasing investment openness in recently implemented IIAs is the driving mechanism behind these findings. We also show that the effects of IIA termination differ by FDI type, being more pronounced for brownfield than greenfield FDI and larger for horizontal than vertical FDI. Lastly, we show significant treatment heterogeneity across industries, with capital-intensive sectors benefiting more from IIAs than labor-intensive ones. Our findings highlight the need to reconsider the benefits and costs of IIAs under an increasingly protective international investment regime.

Implications of Asymptomatic Carriers for Tuberculosis Transmission and Control in Thailand: A Modelling Approach

Tuberculosis (TB) is a regional and global bacterial illness that has been expanding and affecting individuals in every generation. An unknown percentage of asymptomatic hosts have TB, and the infection can spread while exhibiting no symptoms. These asymptomatic TB carriers, who contribute to the spread of the illness yet go mostly undetected, may make it more difficult to prevent transmission. In this study, we utilized the concept of symmetry to construct a manageable disease modelling framework for TB transmission and control. We developed a TB model to investigate the potential influence of asymptomatic carriers, symptomatic infections, and the entirety of TB prevalence on different approaches to treatment and prevention in Thailand. Annual TB incidence data from Thailand from 2000 to 2022 were used to calibrate the model parameters. We assessed the potential for reaching conflicting results about the management and spread of tuberculosis in Thailand. Our results showed that some TB strategies that were thought to be effective in reducing transmission may have the opposite impact, or that an intervention’s effectiveness might be overestimated, making it seem unfeasible in certain scenarios. For example, the objective of TB treatment, which attempts to decrease the occurrence of symptomatic TB infections, is to decrease the TB infection and propagation rates if the relative carrier (η) is less than one. Nonetheless, our results indicate that this strategy may increase the frequency of asymptomatic TB patients, symptomatic TB viral infections, and overall TB prevalence if η has been sufficiently understated. We also found that reducing only the progression rate of symptomatic TB infections cannot stop asymptomatic TB carriers and total TB prevalence, even when the relative infection of carriers (η) is less than unity. Our research provides a better understanding of the role of asymptomatic patients in spreading TB and highlights the need to accurately include bearers in models that guide Thailand’s TB control strategy.

Incremental model transformations with triple graph grammars for multi-version models and multi-version pattern matching

AbstractLike conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In previous work, we have introduced multi-version models for model-driven software engineering, which allow checking well-formedness and finding merge conflicts for multiple versions of the same model at once. However, situations where different models are linked via automatic model transformations also have to be handled for multi-version models. In this paper, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model. This enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis. Our approach is based on the well-known formalism of triple graph grammars and the aforementioned encoding of model version histories called multi-version models. In addition to batch transformation of an entire history, the technique covers incremental synchronization of changes in the framework of multi-version models. Our solution is complemented by a dedicated pattern matching technique for multi-version models. We show the correctness of our approach with respect to the standard semantics of triple graph grammars and conduct an empirical evaluation to investigate the performance of our technique regarding execution time and memory consumption. Our results indicate that the proposed solution affords lower memory consumption and may improve execution time for batch transformation of large version histories, but can also come with computational overhead in unfavorable cases.

An Instrumental Case Study of Blended Preservice Early Childhood Preparation

Blended and other collaborative models of early childhood personnel preparation center on the belief that they can improve the quality and availability of inclusive services for children with diverse abilities and their families. Little is known, however, as to their relative efficacy to impact the inclusive practice of graduates. Further, current understanding of this approach is complicated by a lack of common terminology, conceptions, and a dated, primarily descriptive literature base. To provide a contemporary empirical contribution, we applied a conceptual framework derived from activity systems theory coupled with a research framework for collaborative models to examine one preparation program as a system through qualitative case study. Findings outline parameters of practice specific to collaborative program dimensions, elements of harmony and tension within the system, and cultural tools specific to the program’s attempts to meet its desired outcome. Implications for current and future collaborative early childhood personnel preparation are discussed.

Future land use maps for the Netherlands based on the Dutch One Health Shared Socio-economic Pathways.

To enable detailed study of a wide variety of future health challenges, we have created future land use maps for the Netherlands for 2050, based on the Dutch One Health Shared Socio-economic Pathways (SSPs). This was done using the DynaCLUE modelling framework. Future land use is based on altitude, soil properties, groundwater, salinity, flood risk, agricultural land price, distance to transport hubs and climate. We also account for anticipated demand for different land use types, historic land use changes and potential spatial restrictions. These land use maps can be used to model many different health risks to people, animals and the environment, such as disease, water quality and pollution. In addition, the Netherlands can serve as an example for other rapidly urbanising deltas where many of the health risks will be similar.

Deciphering twist-3 chiral-even GPDs in the light-front quark-diquark model

We investigate quantum chromodynamics (QCD) in this study by computing chiral-even generalized parton distributions (GPDs) at twist-3 in the framework of the light-front quark-diquark model, particularly in the zero skewness scenario. We provide a detailed examination of twist-3 chiral-even GPDs, illustrating their behavior through extensive two-dimensional (2-D) and three-dimensional (3-D) visualizations, which demonstrate their dependence on the longitudinal momentum fraction (x) and the momentum transfer (t). Our investigation also reveals the intricate relationships between these GPDs and other distribution functions such as generalized transverse-momentum dependent distributions, transverse momentum-dependent parton distributions, and parton distribution functions (PDFs). Our study also includes the connected form factors at this twist, which are crucial in understanding the internal structure of hadrons. Additionally, we provide impact parameter-dependent PDF plots to offer insights into the spatial distribution of partons. Published by the American Physical Society 2024

Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes.

Body mass index (BMI) trajectories have been used to assess the growth of children with respect to their peers, and to anticipate future obesity and disease risk. While retrospective BMI trajectories have been actively studied, models to prospectively predict continuous BMI trajectories have not been investigated. Using longitudinal BMI measurements between birth and age 10 y from a mother-offspring cohort, we leveraged a multi-task Gaussian process approach to develop and evaluate a unified framework for modeling, clustering, and prospective prediction of BMI trajectories. We compared its sensitivity to missing values in the longitudinal follow-up of children, compared its prediction performance to cubic B-spline and multilevel Jenss-Bayley models, and used prospectively predicted BMI trajectories to assess the probability of future BMIs crossing the clinical cutoffs for obesity. MagmaClust identified 5 distinct patterns of BMI trajectories between 0 to 10 y. The method outperformed both cubic B-spline and multilevel Jenss-Bayley models in the accuracy of retrospective BMI trajectories while being more robust to missing data (up to 90%). It was also better at prospectively forecasting BMI trajectories of children for periods ranging from 2 to 8 years into the future, using historic BMI data. Given BMI data between birth and age 2 years, prediction of overweight/obesity status at age 10 years, as computed from MagmaClust's predictions exhibited high specificity (0.94), negative predictive value (0.89), and accuracy (0.86). The accuracy, sensitivity, and positive predictive value of predictions increased as BMI data from additional time points were utilized for prediction. MagmaClust provides a unified, probabilistic, non-parametric framework to model, cluster, and prospectively predict childhood BMI trajectories and overweight/obesity risk. The proposed method offers a convenient tool for clinicians to monitor BMI growth in children, allowing them to prospectively identify children with high predicted overweight/obesity risk and implement timely interventions.

Relationship between Molecular Structure and Surface Activity of Ionic Surfactants.

A new model is developed to quantify the surface tension of ionic surfactants from surface affinity and ionization equilibrium. The model successfully predicts two important molecular structure-surface activity factors: the length of single-branch homologues and the nature of counterions. The modeling results also clarify the underlying mechanisms of the two processes. Changing the counterion only affects the ionization, not the affinity. On the other hand, increasing carbon length dramatically increases the affinity while having a small effect on ionization. The modeling framework consistently resolves structure-activity observations, some of which have been reported since the 19th century. The model can be extended for surfactants with more than one ionic state and surfactant/electrolyte mixtures.

Mapping and assessing the future provision of lake ecosystem services in Lithuania

Lakes supply multiple ecosystem services (ES), key to supporting socio-ecologic systems and human well-being. In the context of future land use and climate changes, it is imperative to anticipate potential impacts on lake ES supply. Hence, studies that deal with future lake ES, such as mapping, are lacking. In this work, we mapped and assessed the future supply of three ES: (1) maintenance of nursery conditions (nursery ES), (2) maintenance of chemical conditions of freshwaters (nutrient regulation ES), and (3) direct and indirect cultural outputs (recreation ES) in Lithuania. Four future scenarios were utilised, integrating land use and climate changes: A0 - business as usual; A1-urbanisation; A2: land abandonment and afforestation; and A3 - agricultural intensification. The projected year was 2050, following the intermediate Representative Concentration Pathway (RCP 4.5). The future scenarios were simulated using the open-source software Dinamica EGO based on a 6-step modelling framework. Statistical differences among the scenarios and ES were analysed by applying a Kruskal-Wallis ANOVA test. Spatial analysis was done by performing a Moran's I and Getis-Ord (Gi∗) hotspot analysis. The results showed significant differences in nursery and nutrient regulation ES. The highest supply in nursery ES was observed for the A0 scenario. For nutrient regulation ES, the lowest ES supply was identified for the A1 scenario and for recreation ES, the highest was found in the A2 scenario. The eastern and northeastern regions of Lithuania showed a high ES supply. Hot spots were only identified in the eastern region. These regions are associated with a high area covered by forests and protected areas. The central region shows a low ES supply, identified as a cold spot where the agricultural landscape dominates. The results of the PCA analysis revealed an association between nursery and recreation ES. Nutrient regulation was not associated with the other two ES. Mapping and assessing the impact of future scenarios is vital to anticipating the potential dynamics of lake ES, especially in the context of climate change. This information is essential in the context of environmental management, helping decision-makers to ensure a sustainable ES supply and contributing to human wellbeing.

Modeling and analysis of heterogeneous traffic flow with mixed regular and connected human-driven vehicles considering driver stochasticity

This paper presents a novel microscopic modeling framework to explore the impact of driver stochasticity and other related factors on heterogeneous traffic flows, consisting of both regular human-driven vehicles (RHDVs) and connected human-driven vehicles (CHDVs). We apply the stochastic optimal velocity car-following model for RHDVs, while an extended version of the stochastic continuous car-following model, taking into account optimal velocity guidance and driver compliance, is devised for CHDVs. The stability of CHDV car-following model is examined theoretically and numerically. Simulation experiments are conducted to analyze the stochastic heterogeneous traffic flow properties (i.e. stability and safety) with the proposed microscopic modeling framework under diverse parameter settings including CHDV penetration rate, driver compliance, and driver stochasticity. Results show that traffic flow stability and safety improve with the increase in CHDV penetration rate and driver compliance but deteriorate as the driver’s stochasticity strength increases. CHDVs significantly enhance traffic stability when the CHDV penetration rate or driver compliance exceeds a certain threshold, typically ranging between 0.5 and 0.7. Additionally, there is an interaction between driver stochasticity and CHDV penetration rate in their effect on traffic flow stability, where the contribution of stochasticity to traffic oscillations first increases and then decreases as the penetration rate rises. When the penetration rate is between 0 and 0.4, the traffic risk in emergency situations can be significantly reduced as the penetration rate increases. These findings may guide the management and control strategies of heterogeneous traffic flow.

Affliction class moderates the dementing impact of adipokines.

Biomarker-specific interventions (e.g., for dementia) will necessitate an individualized approach to treatment. We have constructed a psychometric classifier to identify persons adversely impacted by plasma adipokines. The subjects (N = 1,737) of the Alzheimer's Disease Neuroimaging Initiative were assigned to groups "afflicted" by versus "resilient" against the unique effect of plasma adipokines using a classifier derived by confirmatory factor analysis in a structural equation model framework. The impact of affliction class above and beyond observed biomarker levels and covariates was tested by multivariate regression using CDR "Sum of Boxes" as the dependent variable. The affliction class' moderation of adipokines' effect was tested by chi-square difference. The effect of affliction class on prospective conversion risk was tested by Cox's proportional hazards models. Seven hundred four out of the 1,737 subjects (40.53%) were assigned to the afflicted class. The afflicted subjects had greater dementia severity, lower (adverse) Adipokines factor composite scores (by analysis of variance, F(1, 1,735) 2619.68, p < .001) and higher observed levels of plasma adipokines (by Tukey's honestly significant difference test, all p < .001). Adipokines' association with dementia severity was moderated by affliction class. The effect persisted at 48 months. Afflicted cases were more likely to convert to Alzheimer's disease in that timeframe, by Cox's F: F(234, 286) = 3.89, p < .001. Our approach could guide precision interventions against specific biomarkers. This classifier could be administered by telephone, making class assignment feasible without direct patient contact or biomarker assessment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Using Chronic Kidney Disease as a Model Framework to Estimate Healthcare-Related Environmental Impact.

While the economic and clinical burden of chronic diseases are well documented, their environmental impact remains poorly understood. We developed a framework to estimate the environmental impact of a disease care pathway using chronic kidney disease (CKD) as an example. A life cycle assessment framework was developed for the CKD care pathway and validated by experts. Life cycle stages were characterised for resource utilisation based on a literature review and ecoinvent database inputs, in ten countries. The ReCiPe impact assessment method was used to calculate impacts across multiple environmental dimensions. At CKD stage5, kidney replacement therapies (KRT) have highest impact; emissions ranged between 3.5 and 43.9kg carbon dioxide equivalents (CO2e) per session depending on dialysis modality, and 336-2022kgCO2e for kidney transplant surgery, depending on donor type. Hospitalisations have a substantial environmental impact: a 1-day intensive care stay had highest impact (66.4-143.6kgCO2e), followed by a 1-day hospital stay (28.8-63.9kgCO2e) and an 8-h emergency room visit (14.4-27.5kgCO2e). Patient transport to and from healthcare sites was a key driver of environmental impact for all life cycle stages, representing up to 99.5% of total CO2e emissions. Full care pathways should be analysed alongside specific healthcare processes. Application of this framework enables quantification of the environmental benefits of preventative medicine and effective management of chronic diseases. For CKD, early diagnosis, and proactive management to reduce the need for KRT and hospitalisations could improve patient outcomes and reduce environmental burden.

A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework

Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model’s reliability, with a lower mean uncertainty (7.14 × 10−8) and standard deviation of uncertainty (6.48 × 10−8) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework’s broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide.

Diffusion Monte Carlo calculation of fully heavy pentaquarks

Using a diffusion Monte Carlo algorithm, we calculated the spectra of all possible S-wave fully heavy pentaquarks within the framework of the quark model. Our aim was to compare the masses of different spin-color configurations corresponding to the same spatial wave function for each quark composition. In particular, we computed the masses of the configuration with the maximum number of undistinguishable quarks and those of all the possible baryon+meson splittings compatible with the total number of b’s and c’s for a given pentaquark. What we found was that, in all cases, compact baryon+meson configurations had lower masses than their more symmetric counterparts. Moreover, those “molecular” associations were unstable (with larger masses) that their infinitely separated noninteracting pieces except in the case of (ccb)(cb¯) and (bbc)(bc¯) pentaquarks. In any case, even for unstable arrangements, the excess masses are so small (∼20–60 MeV) as to make those compact molecular structures serious candidates to be experimentally detected. Published by the American Physical Society 2024

Frictional Abrasion of Rubber: Transition from Sliding to Rolling

ABSTRACT To develop applicable friction and wear models on tire scale, reliable test data are required. Consequently, friction tests on block level are requested because the distribution of contact pressure as well as slip velocity is nearly homogeneous at the contact surface of the sliding rubber block. However, wear mechanism and energy intensity levels of sliding rubber blocks and rolling rubber wheels or tires differ significantly. Consequently, linking both sliding and rolling frictional abrasion is required; thus, a wear model for rubber material is introduced to consider both deformation slip and sliding. The model input for sliding friction and resulting wear rate is derived from linear friction test experiments using sliding rubber blocks at different loading. A unique and sophisticated re-mesh algorithm ensures proper mesh modification due to abrasion of the structure. A wear energy evolution approach is developed to consider low abrasion with small sliding distances to predict wear at rolling rubber wheels. The simulation framework of abrasion modeling is successfully validated using laboratory abrasion tests.

Optimal roadworks schedule in multi-agent transportation models

In this paper we consider the problem of the optimal roadworks scheduling in the road traffic network optimization. The main goal of this research is to find the optimal order of roadworks and minimize their negative impact on the transportation network throughput. In order to model the dynamics of a transportation system, we propose a large-scale, multi-agent vehicle routing modelling framework for traffic simulation and impact of roadworks on traffic throughput. We present a quick and effective heuristic algorithm for streamlining the road repairs. Finally, the numeric experiment in a real-world setting is conducted. The obtained results indicate the flexibility of the proposed algorithm, which can be effectively applied by decision makers who need to carry out roadworks with limited time and resources.