Model Configuration Research Articles

New insight in cervical cancer diagnosis using convolution neural network architecture

<p>The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.</p>

Futures of Outcome‐Based Contracts for industrial equipment: A Disaggregative Delphi study

This study explores the dynamic landscape of Outcome-Based Contracts (OBCs) within industrial equipment and investigates how external changes influence OBC feasibility. Employing Disaggregate Delphi involving panels of experts from industry and academia, the research uncovers critical insights of the relationship between OBCs and external changes. The analysis reveals that external changes can significantly impact the feasibility and profitability of OBCs for the supplier. These changes, having direct and indirect impacts, sometimes cascading through chains of events, necessitate proactive management strategies. The study underlines the role of context, in business environment, business model configuration, and product characteristics, in influencing OBC susceptibility to changes. Furthermore, it highlights that parties can inadvertently trigger impacts in response to external change. This research contributes to business model literature by shedding light on the multifaceted impacts of external changes on OBCs. It expands the understanding of how changes can have both positive and negative effects, emphasizing the importance of dynamic business model and contract frameworks. Additionally, the study underscores futures thinking and the significance of proactivity in navigating the evolving landscape of OBCs within industrial equipment OBCs as well as identifies uncertainty as one of the central value propositions of OBCs.

Универсальная централизованная защита от однофазных замыканий на землю в кабельных сетях напряжением 6–10 кВ

Selective protection against single phase earth faults in distribution 6–10 kV cable networks can be performed both with the use of individual (per connection) and centralized devices covering all connections of the protected object. The advantages of centralized devices are reduction of specific (per connection) costs of protection, simplification of its operation and design. Well-known technical solutions in terms of centralized protection devices against this type of damage do not always provide versatility (the possibility to use neutral cables of 6–10 kV in various grounding modes) and high technical perfection (selectivity and stability of operation). Thus, it is relevant to develop a universal centralized protection against single-phase earth faults, ensuring high technical perfection for all types of earth faults in 6–10 kV cable networks with different neutral grounding modes. The authors have used a comprehensive simulation model "cable network – protection device" to study the selectivity and stability of the developed principles of implementation and algorithms for centralized protection against earth faults. The model is designed in SimPowerSystems and Simulink software. The configuration and parameters of the cable network model have been selected considering the key features of 6–10 kV distribution cable networks of industrial and urban power supply systems. A universal centralized protection against all types of earth faults in 6–10 kV cable networks with various neutral grounding modes has been developed. The results of functional tests on the simulation model have confirmed the effectiveness of the developed algorithms of the developed centralized protection for all types of earth faults both in uncompensated and compensated cable networks of 6–10 kV. The proposed technical solution implements a multifunctional multiparametric centralized protection against earth faults. It has such properties as versatility, selectivity, and high stability of operation for all types of earth faults, recognition of types of single-phase earth faults, continuity of action in stable and most dangerous arc intermittent earth faults.

Adjoint-based sensitivity analysis and assimilation of multi-source data for the inference of spatio-temporal parameters in a 2D urban flood hydraulic model

This contribution presents a novel approach for the calibration of distributed parameters in a 2D urban flood hydraulic model. It focuses on the challenging issue of inferring distributed friction parameters from multi-source heterogeneous spatio-temporal observations of their hydraulic signatures in the context of an urban flash flood in a complex street network. A variational data assimilation algorithm is used to infer high-dimensional multi-variate parameters (spatialized friction and inflow discharge time series) using multi-source observations. This method relies on a differentiable 2D shallow water hydraulic model which enables to generate high-resolution sensitivity maps of local gradients and Derivative-based Global Sensitivity Measures (DGSM), enabling to guide adequate definition of parameter spatialization for the data assimilation process. Assimilated data include real local limnigraphic measurements and high-water marks collected after a major flood event, as well as modeled flow velocity used in twin experiments setups. This study is the first to leverage high-water marks with a variational method for the calibration of distributed parameters in an urban flood model. The multi-source data is used to infer inflow hydrographs and distributed friction parameters in setups of varying complexity. In the main setup, the complex structure of the street network, along with the sensitivity maps and hydraulics expertise led to define a model configuration with 45 friction patches. A high-dimensional parameter vector composed of these friction values and an upstream inflow is inferred simultaneously by assimilating real limnigraphic data and high-water marks. This leads to an increase in model fit to observations and satisfying parameter estimates.

Real-time control of a soft manipulator based on reduced order extended position-based dynamics

Soft robots are high-dimensional nonlinear systems coupled with both geometric and material nonlinearity. Control of such a system is complex and time-consuming. In this study, a real-time trajectory tracking control framework is established based on the reduced order extended position-based dynamics. In contrast to the common nonlinear model order reduction methods that require to collect a large number of data to create the motion subspace, this article's motion subspace is constructed based on the model configuration and material properties. The linear modes of the model and the related modal derivatives provide the reduced order matrix, which streamlines and increases the efficiency of model construction. Then, coupled with the instantaneous optimal control, a real-time reduced order model-based control framework of soft robots can be constructed. Experiments on trajectory tracking of a soft manipulator are conducted to verify the accuracy and efficiency of the proposed controller. The average error of all experiments is within 1 cm; and the single-step calculation time of the controller is about 0.057 s, which is less than the sampling period 0.1 s.

Impact of charge and non-minimal fluid-geometry coupling on anisotropic interiors

This paper explores the interior configuration of various charged anisotropic compact models within the framework of f(R,T,RληTλη) gravity. The chosen model in this theory is represented by R+γRληTλη , where γ is a real-valued parameter. We adopt a static spherical metric to describe the interior of compact strange stars and derive the corresponding field equations. The solution to these equations is then obtained using the Finch-Skea metric and a linear equation of state. Taking the radius and mass of the compact star model 4U 1820-30 as a case study, we investigate the impact of charge and modified corrections on its internal distribution. The stability of the resulting model is also determined within a specific range of γ. Additionally, we determine the values of the model parameter through the vanishing radial pressure constraint, aligning with the experimental data of eight different stars. Our findings indicate that the resulting model adheres to the conditions necessary for physically relevant interiors, particularly in the case of lower charge.

Daylighting and energy performance optimization of anidolic ceiling systems for tropical office buildings

A simple window is often unable to ensure standard illumination to the deep parts of large office spaces, and advanced daylighting systems are necessary to ensure the level of interior illuminance. Anidolic ceiling systems are one of the less popular daylighting strategies designed to redirect and redistribute daylight deeper, which can reduce the demand for artificial lighting and energy use for lighting purposes. This study shows how a parametric approach can be applied to optimize the daylighting and energy performance of an anidolic ceiling system in a tropical climate. Multi-objective optimization (MOO) techniques were applied to combine different design variables to establish multiple design alternatives and find the optimal design by comparing their performances. A simulation study of an anidolic ceiling system was conducted with test models primarily developed by Rhinoceros and ClimateStudio, and optimization of performance metrics was conducted using Grasshopper and Octopus. Data exportation was done using the TT toolbox, while data analysis was done through mathematical and statistical analyses and cross-checked by Design Explorer. By incorporating an optimum anidolic ceiling system into the case office space, spatial daylight autonomy (sDA) increased 20.51 %∼68.18 %, annual sunlight exposure (ASE) decreased 15.83 %∼8.52 %, mean illuminance increased 221 lux–3027 lux and energy use intensity (EUI) reduced 189.3 kWh/(m2y)∼174 kWh/(m2y) for different configurations of test models. The results demonstrated a MOO methodology that successfully finds the optimum solutions for the anidolic ceiling systems in a deep plan office space to enhance daylighting and energy performance.

Square & H metasurfaces for SPR Increasing in long Wave-IR absorber

In the long-wave infrared (LWIR) spectrum, this paper suggests an electromagnetic (EM) waveband absorber design based on metamaterials. Germanium, gold, and magnesium oxide layers are arranged in a layered structure from top to bottom in the suggested model. Our proposed metamaterial structure’s upper surface is composed of metallic metasurfaces with H and square forms from various studies. The finite element method is used to evaluate the metamaterials’ electromagnetic properties in terms of absorbance and reflectance. It is observed that there is a particular size of the metamaterial at which extremely localized electromagnetic resonance occurs. Quantitative findings indicate that the suggested metamaterial design’s average absorption reaches 90 % in the 10 μm to 14 μm range across a wide variety of incidence angles (00 to 400 & 00 to 800) for both transverse electric (TE) and transverse magnetic (TM) polarization. It is evident from these data that the suggested model configuration has broad potential applications in manyoptoelectronic fields of study.

Extended metric validation of a semi-physical Space Weather Modeling Framework conductance model on field-aligned current estimations

In this study, a detailed metric survey on the “Galaxy 15” (April 2010) space weather event is conducted to validate MAGNetosphere–Ionosphere–Thermosphere (MAGNIT), a semi-physical auroral ionospheric conductance model characterizing four precipitation sources, against AMPERE measurements via field-aligned current (FAC) characteristics. As part of this study, the comparative performance of three ionosphere electrodynamic specifications involving auroral conductance models, MAGNIT, Ridley Legacy Model (RLM) (empirical), and Conductance Model for Extreme Events (CMEE) (empirical), within the Space Weather Modeling Framework (SWMF), is demonstrated. Overall, MAGNIT exhibits marginally improved predictions; root mean square error values in upward and downward FACs of MAGNIT predictions compared to AMPERE data are smaller than those of CMEE and Ridley Ionosphere Model (RIM) by ∼12.7% and ∼6.24% before the storm, ∼4.52% and ∼2.13% better during the main phase, ∼1.98% and ∼1.27% worse during the second minimum, and better by ∼1.84% and ∼1.49% by the beginning of the recovery, respectively. In all three model configurations, the dusk and night magnetic local time (MLT) sectors over-predict throughout the storm, while the day and dawn MLT sectors under-predict in response to interplanetary magnetic field (IMF) conditions. In addition to accuracy and bias, similar results and conclusions are drawn from additional metrics, including in the categories of correlation, precision, extremes, and skill, and recommendations are made for the best-performing model configuration in each metric category. Visual data–model comparisons conducted by studying the FAC location and latitude/MLT spread throughout various phases of the storm suggest that the spatial extent of the FACs is captured relatively well in the night-side auroral oval, unlike in the day-side oval. The spread in latitude of the FACs matches that in the previous literature on other model performances. This information on auroral precipitation sources and their weight on FACs, along with metrics from model–data comparisons, can be used to modify MAGNIT settings to optimize SWMF model performance.

From the quantum breakdown model to the lattice gauge theory

The one-dimensional quantum breakdown model, which features spatially asymmetric fermionic interactions simulating the electrical breakdown phenomenon, exhibits an exponential U(1) symmetry and a variety of dynamical phases including many-body localization and quantum chaos with quantum scar states. We investigate the minimal quantum breakdown model with the minimal number of on-site fermion orbitals required for the interaction and identify a large number of local conserved charges in the model. We then reveal a mapping between the minimal quantum breakdown model in certain charge sectors and a quantum link model which simulates the U(1) lattice gauge theory and show that the local conserved charges map to the gauge symmetry generators. A special charge sector of the model further maps to the PXP model, which shows quantum many-body scars. This mapping unveils the rich dynamics in different Krylov subspaces characterized by different gauge configurations in the quantum breakdown model.

Automatic Poetic Metre Detection for Czech Verse

Metrical analysis of verse is an essential and challenging task in the research on versification consisting of analysing a poem and deciding which metre it is written in. Thanks to existing corpora, we can take advantage of data-driven approaches, which can be better suited to the specific versification problems at hand than rulebased systems. This work analyses the Czech accentual-syllabic verse and automatic metre assignment using the vast and annotated Corpus of Czech Verse. We define the problem as a sequence tagging task and approach it using a machine learning model and many different input data configurations. In comparison to this approach, we reimplement the existing data-driven system KVĚTA. Our results demonstrate that the bidirectional LSTM-CRF sequence tagging model, enhanced with syllable embeddings, significantly outperforms the existing KVĚTA system, with predictions achieving 99.61% syllable accuracy, 98.86% line accuracy, and 90.40% poem accuracy. The model also achieved competitive results with token embeddings. One of the most interesting findings is that the best results are obtained by inputting sequences representing whole poems instead of individual poem lines.

Comparison of non-reactive solute transport models for the evaluation of fluid flow in packed beds with implications for heap leaching practice

This study investigated the effects of mean particle size fraction, bottom particle size, particle porosity and wettability on solution scale preferential flow behaviour via step input tracer tests in drip irrigated, narrowly and mixed-sized beds under steady state fluid flux. Nine solute transport models were used to quantify this behaviour reflected in the residence time distribution (RTD) profiles. Four were empirical models: three compartmental model configurations (CM-1, CM-2, CM-3) and tanks-in-series (TIS) model. The remainder five models were semi-empirical: advection dispersion (AD), piston exchange (PE), piston exchange - diffusion variant (PE-D), piston dispersion and exchange (PDE) and piston dispersion and exchange - diffusion variant (PDED). The model fit results showed that the mono-porosity TIS, AD and CM-2 models were the worst performers, while the dual porosity PDE and novel PDE-D models achieved the lowest average error values across the various systems. Higher levels of particle wettability coupled with capillary effects produced peculiar RTD curves that were relatively more difficult for the mono-porosity models to simulate. The model parameters investigated included the longitudinal dispersion coefficient (Dds), dead to total volume fraction (VD/VT), dynamic to total saturation fraction (βd/βT), overall mass transfer coefficient (Kma) and maximum diffusional pore length (X). The results showed that an increase in the average particle size within the beds led to higher VD/VT, Dds and X values, but lower βd/βT and Kma values. These indicate an overall increase in solution scale preferential flow behaviour. Decreased capillary suction and connectivity between particle pores and inter-particle voids were deemed responsible for the results. Higher levels of particle porosity acted as a buffer against these effects. Overall, the results highlight the benefit of the addition of fines (0.1–1 mm particles) during the agglomeration process in heaps to help reduce solution scale preferential flow behaviour and increase liquid hold-up. This is more necessary when the ore has low to moderate levels of porosity (surface area: <2 m2/g) and will also increase the modelling options available as most models performed better fitting data from such beds.

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.

Baseline-to-loaded changes in regional tibial cartilage thickness, T1ρ and T2: Utilization of an MRI compatible loading device.

The objective of the study was to evaluate tibial cartilage thickness (TCT), T1ρ and T2 values within both loaded and baseline configurations in a cadaveric knee model using a 3D bone based tibial coordinate system. Ten intact cadaveric knees were mounted into an magnetic resonance imaging (MRI) compatible loading device. Morphologic and quantitative MRI (qMRI) images were acquired with the knee in a baseline configuration and after application of 50% body weight. The morphologic images were evaluated for cartilage degeneration using a modified Noyes scoring system. A 3D bone-based tibial coordinate system was utilized to evaluate regional changes of tibial T1ρ, T2, and cartilage thickness values among regions covered and uncovered by the meniscus. Inter-regional differences in medial and lateral MRI outcomes were found between loaded and baseline configurations. Cartilage regions covered by the meniscus demonstrated disparate qMRI and TCT results as compared to cartilage regions not covered by the meniscus. The regions covered by meniscus experienced a ~3.5%, ~0.5%, and ~5.5% reduction of T1ρ (p < 0.05, medial and lateral compartments), T2 and TCT, respectively, in both compartments while regions not covered by the meniscus experienced larger reductions of ~10%, ~2%, and ~10.5% reduction of T1ρ (p < 0.05, medial and lateral compartments), T2 and TCT (p < 0.05, lateral compartment only), respectively, in both compartments. T1ρ and T2 decreases following application of 50% body weight load were substantially larger in the tibial regions with modified Noyes grade 3 (n = 2) compared to either healthy regions (n = 85, p < 0.0.003) or regions with modified Noyes grade 2 (n = 13, p < 0.004). Interregional differences in MRI outcomes reflect variations in structure and function, and largely followed a pattern in cartilage regions that were covered or not covered by the meniscus. Results of the current study suggest that ΔT1ρ and ΔT2 values may be sensitive to superficial fissuring, more than baseline or loaded T1ρ or T2 values, or TCT alone, however future studies with additional specimens, with greater variability in OA grade distribution, may further emphasize the current findings.

Advancing Air Quality Prediction: An Evaluation of WRF Model Configurations and Their Impact on Meteorological Parameters in Hungary

Advancing Air Quality Prediction: An Evaluation of WRF Model Configurations and Their Impact on Meteorological Parameters in Hungary

Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy

The paper is devoted to the development of the method of laser shock peening (LSP) of metals. To optimize the mode of LSP for Ti-6Al-4V specimens a deep learning model for predicting residual stresses by laser shock peening was developed. A numerical-experimental method was used to carry out the model training, in which an experimental study of the effect of different processing mode on the depth and distribution of residual stresses was carried out. The Johnson-Cook model was used as the governing relationship for modeling the dynamic deformation process. At the second stage, the problem of static equilibrium of a body with a plastically deformed area was numerically solved to determine residual stresses. The results of research on determination of the optimal configuration of the deep learning model showed that when using sinusoidal activation function of the neural network with 4 hidden layers and the number of neurons 10, the best level of accuracy in solving the problem is achieved. The obtained model allows us to optimally determine the LSP mode according to the given limitations of values and depth of residual stresses.

Leveraging RALI‐THINICE Observations to Assess How the ICOLMDZ Model Simulates Clouds Embedded in Arctic Cyclones

AbstractDespite their essential role in the high‐latitude climate, the representation of mixed‐phase clouds is still a challenge for Global Climate Models (GCMs)'s cloud schemes. In this study we propose a methodology for robustly assessing Arctic mixed‐phase cloud properties in a climate model using airborne measurements. We leverage data collected during the RALI‐THINICE airborne campaign that took place near Svalbard in August 2022 to evaluate the simulation of mid‐level clouds associated with Arctic cyclones. Simulations are carried out with the new limited‐area configuration of the ICOLMDZ model which combines the recent icosahedral dynamical core DYNAMICO and the physics of LMDZ, the atmospheric component of the IPSL‐CM Earth System Model. Airborne radar and microphysical probes measurements are then used to evaluate the simulated clouds. A comparison method has been set‐up to guarantee as much as possible the spatiotemporal co‐location between observed and simulated cloud fields. We mostly focus on the representation of ice and liquid in‐cloud contents and on their vertical distribution. Results show that the model overestimates the amount of cloud condensates and exhibits a poor cloud phase spatial distribution, with too much liquid water far from cloud top and too much ice close to it. The downward gradual increase in snowfall flux is also not captured by the model. This in‐depth model evaluation thereby pinpoints priorities for further improvements in the ICOLMDZ cloud scheme.

Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia

Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast.

Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)

Abstract. The calculation of the radiative transfer is a key component of global circulation models. In this article, we describe the most recent updates of the radiation infrastructure in the Modular Earth Submodel System (MESSy). These updates include the implementation of the PSrad radiation scheme within the RAD submodel. Furthermore, the radiation-related submodels CLOUDOPT (for the calculation of cloud optical properties) and AEROPT (for the calculation of aerosol optical properties) have been updated and are now more flexible in order to deal with different sets of shortwave and longwave bands of radiation schemes. In the wake of these updates, a new submodel (ALBEDO), which features solar-zenith-angle-dependent albedos and a new satellite-based background (white sky) albedo, was created. All of these developments are backward compatible, and previous features of the MESSy radiation infrastructure remain available. Moreover, these developments mark an important step in the use of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model, as the update of the radiation scheme was a key aspect in the development of the sixth generation of the European Centre for Medium-Range Weather Forecasts – HAMburg (ECHAM6) model from ECHAM5. The developments presented here are also aimed towards using the MESSy infrastructure with the ICOsahedral Non-hydrostatic (ICON) model as a base model. The improved infrastructure will also aid in the implementation of additional radiation schemes once this should be needed. We have optimized the set of free parameters for two general circulation model-type (GCM-type) setups for pre-industrial and present-day conditions: one with the radiation scheme that was used to date (i.e. the radiation scheme of ECHAM5) and one with the newly implemented PSrad radiation scheme. After this parameter optimization, we performed four model simulations and evaluated the corresponding model results using reanalysis and observational data. The most apparent improvements related to the updated radiation scheme are the reduced cold biases in the tropical upper troposphere and lower stratosphere and the extratropical lower stratosphere and a strengthened polar vortex. The former is also related to improved stratospheric humidity and its variability if the new radiation scheme is employed. Using the multiple radiation call capability of MESSy, we have applied the two model configurations to calculate instantaneous and stratospheric-adjusted radiative forcings related to changes in greenhouse gases. Overall, we find that for many forcing experiments the simulations with the new radiation scheme show improved radiative forcing values. This is in particular the case for methane radiative forcings, which are considerably higher when assessed with the new radiation scheme and thus in better agreement with reference values.

Development of a continuum-based, meshless, finite element modeling approach for representation of trabecular bone indentation

Implant subsidence into the underlying trabecular bone is a common problem in orthopaedic surgeries; however, the ability to pre-operatively predict implant subsidence remains limited. Current state-of-the-art computational models for predicting subsidence have issues addressing this clinical problem, often resulting from the size and complexity of existing subject-specific, image-based finite element (FE) models. The current study aimed to develop a simplified approach to FE modeling of subject-specific trabecular bone indentation resulting from implant penetration.Confined indentation experiments of human trabecular bone with flat- and sharp-tip indenters were simulated using FE analysis. A generalized continuum-level approach using a meshless smoothed particle hydrodynamics (SPH) approach and an isotropic crushable foam (CF) material model was developed for the trabecular bone specimens. Five FE models were generated with CF material parameters calibrated to cadaveric specimens spanning a range of bone mineral densities (BMD). Additionally, an alternative model configuration was developed that included consideration of bone marrow, with bone and marrow material parameters assigned to elements randomly according to bone volume (BV%) measurements of experimental specimens, owing to the non-uniform nature of trabecular bone tissue microstructure.Statistical analysis found significant correlation between the shapes of the numerical and experimental force-displacement curves. FE models accurately captured the bone densification patterns observed experimentally. Inclusion of marrow elements offered improved response prediction of the flat-tip indenter tests.Ultimately, the developed approach demonstrates the ability of a generalizable continuum-level SPH approach to capture bone variability using clinical bone imaging metrics without needing detailed image-based geometries, a significant step towards simplified subject-specific modeling of implant subsidence.