Model Assumptions Research Articles

Exploring rapidity-even dipolar flow in isobaric collisions at RHIC

Employing the AMPT transport model, we investigate the response of the rapidity-even dipolar flow ( v1even ) and its associated Global Momentum Conservation (GMC) parameter K to structural disparities within 96Ru and 96Zr nuclei. We analyze Ru + Ru and Zr + Zr collisions at a center-of-mass energy of sNN = 200 GeV. Our analysis demonstrates that the eccentricity ε 1, v1even and K exhibit subtle yet discernible sensitivity to the input nuclear structure distinctions between 96Ru and 96Zr isobars. This observation suggests that measuring v1even and the GMC parameter in these isobaric collisions could serve as a means to fine-tune the comprehension of their nuclear structure disparities and offer insights to enhance the initial condition assumptions of theoretical models.

Improving Transparency of Decision Models Through the Application of Decision Analytic Models with Omitted Objects Displayed (DAMWOOD).

The coronavirus disease 2019 (COVID-19) pandemic has increased public awareness of the influence of epidemiological and economic decision models on public policy decisions. Alongside this is an increased scrutiny on the development, analysis, reporting and utilisation of decision models for public policy making. Therefore, it is important that model developers can clearly explain and justify to all stakeholders what is included and excluded from a model developed to support decision-making, to both improve transparency and trust in decision-making. Our aim is to provide tools for improving communication between modellers and decision-makers, leading to improved transparency in decision-making. To do so, we extend the recently described directed acyclic graphs with omitted objects displayed (DAGWOOD) approach from Haber et al. (Ann Epidemiol 68:64-71, 2022) to decision analytic models, giving the decision analytic models with omitted objects displayed (DAMWOOD) approach. DAMWOOD is a framework for the identification of objects omitted from a decision model, as well as for consideration of the effects of omissions on model outcomes. Objects omitted from a decision model are classed as either an exclusion (known and unknown confounders), misdirection (alternative model pathways) or structure (e.g. model type, methods for estimating relationships between objects). DAMWOOD requires model developers to use explicit statements and provide illustration of included and omitted objects, supporting communication with model users and stakeholders, allowing them to provide input and feedback to modellers about which objects to include or omit in a model. In developing DAMWOOD, we considered two challenges we encountered in modelling for pandemic policy response. First, the scope of the decision problem is not always made sufficiently explicit by decision-makers, requiring modellers to intuit which policy options should be considered, and/or which outcomes should be considered in their evaluation. Second, there is rarely sufficient transparency to ensure stakeholders can see what is included in models and why. This limits stakeholders' ability to advocate to decision-makers for the prioritisation of specific outcomes and challenge the model results. To illustrate the application of DAMWOOD, we apply it to a previously published COVID-19 vaccine allocation optimisation model. The DAMWOOD diagrams illustrate the ways in which it is possible to improve the communication of model assumptions. The diagrams make explicit which outcomes are omitted and provide information on the expected impact of the omissions on model results. We discuss the usefulness of DAMWOOD for framing the decision problem, communicating the model structure and results and engaging with those making and affected by the decisions the model is developed to inform.

Rtestim: Time-varying reproduction number estimation with trend filtering.

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.

Using joint probability density to create most informative unidimensional indices: a new method using pain and psychiatric severity as examples

BackgroundDimension reduction methods do not always reduce their underlying indicators to a single composite score. Furthermore, such methods are usually based on optimality criteria that require discarding some information. We suggest, under some conditions, to use the joint probability density function (joint pdf or JPD) of p-dimensional random variable (the p indicators), as an index or a composite score. It is proved that this index is more informative than any alternative composite score. In two examples, we compare the JPD index with some alternatives constructed from traditional methods.MethodsWe develop a probabilistic unsupervised dimension reduction method based on the probability density of multivariate data. We show that the conditional distribution of the variables given JPD is uniform, implying that the JPD is the most informative scalar summary under the most common notions of information. B. We show under some widely plausible conditions, JPD can be used as an index. To use JPD as an index, in addition to having a plausible interpretation, all the random variables should have approximately the same direction(unidirectionality) as the density values (codirectionality). We applied these ideas to two data sets: first, on the 7 Brief Pain Inventory Interference scale (BPI-I) items obtained from 8,889 US Veterans with chronic pain and, second, on a novel measure based on administrative data for 912 US Veterans. To estimate the JPD in both examples, among the available JPD estimation methods, we used its conditional specifications, identified a well-fitted parametric model for each factored conditional (regression) specification, and, by maximizing the corresponding likelihoods, estimated their parameters. Due to the non-uniqueness of conditional specification, the average of all estimated conditional specifications was used as the final estimate. Since a prevalent common use of indices is ranking, we used measures of monotone dependence [e.g., Spearman’s rank correlation (rho)] to assess the strength of unidirectionality and co-directionality. Finally, we cross-validate the JPD score against variance–covariance-based scores (factor scores in unidimensional models), and the “person’s parameter” estimates of (Generalized) Partial Credit and Graded Response IRT models. We used Pearson Divergence as a measure of information and Shannon’s entropy to compare uncertainties (informativeness) in these alternative scores.ResultsAn unsupervised dimension reduction was developed based on the joint probability density (JPD) of the multi-dimensional data. The JPD, under regularity conditions, may be used as an index. For the well-established Brief Pain Interference Inventory (BPI-I (the short form with 7 Items) and for a new mental health severity index (MoPSI) with 6 indicators, we estimated the JPD scoring. We compared, assuming unidimensionality, factor scores, Person’s scores of the Partial Credit model, the Generalized Partial Credit model, and the Graded Response model with JPD scoring. As expected, all scores’ rankings in both examples were monotonically dependent with various strengths. Shannon entropy was the smallest for JPD scores. Pearson Divergence of the estimated densities of different indices against uniform distribution was maximum for JPD scoring.ConclusionsAn unsupervised probabilistic dimension reduction is possible. When appropriate, the joint probability density function can be used as the most informative index. Model specification and estimation and steps to implement the scoring were demonstrated. As expected, when the required assumption in factor analysis and IRT models are satisfied, JPD scoring agrees with these established scores. However, when these assumptions are violated, JPD scores preserve all the information in the indicators with minimal assumption.

Quantifying Plant Signaling Pathways by Integrating Luminescence-Based Biosensors and Mathematical Modeling

Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA.

Characterizing dispersion in bovine liver using ARFI-based shear wave rheometry.


Background: Dispersion presents both a challenge and a diagnostic opportunity in shear wave elastography (SWE). Shear Wave Rheometry (SWR) is an inversion technique for processing SWE data acquired using an acoustic radiation force impulse (ARFI) excitation. The main advantage of SWR is that it can characterize the shear properties of homogeneous soft media over a wide frequency range. SWR was used here to measure the shear properties of bovine liver tissue. Assumptions associated with SWR include tissue homogeneity, tissue isotropy, and axisymmetry of the ARFI excitation.
Objective: Evaluate the validity of the SWR assumptions in ex vivo bovine liver.
Approach: SWR was used to measure the shear properties of bovine liver tissue as a function of frequency, over a large frequency range. Assumptions associated with SWR (homogeneity, isotropy and axisymmetry) were evaluated through measurements performed at multiple locations and probe orientations.
Main results: Measurements of shear properties were obtained over the 25-250 Hz range, and showed a 4-fold increase in shear storage modulus (from 1 to 4 kPa) and over a 10-fold increase in the loss modulus (from 0.2 to 3 kPa) over that decade-wide frequency range. Measurements under different conditions were highly repeatable, and model error was low in all cases.
Significance and Conclusion: SWR depends on modeling the ARFI-induced shear wave as a full vector viscoelastic shear wave resulting from an axisymmetric source; it is agnostic to any specific rheological model. Despite this generality, the model makes three main simplifying assumptions. These results show that the modeling assumptions used in SWR are valid in bovine liver, over a wide frequency band.

Adaptive Gaussian Markov random fields for child mortality estimation.

The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks in mortality relative to their neighbors, which can lead to oversmoothing of U5MR estimates. In this paper, we develop a spatial and temporal smoothing approach based on Gaussian Markov random field models which incorporate knowledge of these expected shocks in mortality. We demonstrate the potential for these models to improve upon alternatives not incorporating knowledge of expected shocks in a simulation study. We apply these models to estimate U5MR in Rwanda at the national level from 1985 to 2019, a time period which includes the Rwandan civil war and genocide.

A spectral approach for the dynamic Bradley–Terry model

SummaryThe dynamic ranking, due to its increasing importance in many applications, is becoming crucial, especially with the collection of voluminous time‐dependent data. One such application is sports statistics, where dynamic ranking aids in forecasting the performance of competitive teams, drawing on historical and current data. Despite its usefulness, predicting and inferring rankings pose challenges in environments necessitating time‐dependent modelling. This paper introduces a spectral ranker called Kernel Rank Centrality, designed to rank items based on pairwise comparisons over time. The ranker operates via kernel smoothing in the Bradley–Terry model, utilising a Markov chain model. Unlike the maximum likelihood approach, the spectral ranker is nonparametric, demands fewer model assumptions and computations and allows for real‐time ranking. We establish the asymptotic distribution of the ranker by applying an innovative group inverse technique, resulting in a uniform and precise entrywise expansion. This result allows us to devise a new inferential method for predictive inference, previously unavailable in existing approaches. Our numerical examples showcase the ranker's utility in predictive accuracy and constructing an uncertainty measure for prediction, leveraging data from the National Basketball Association (NBA). The results underscore our method's potential compared with the gold standard in sports, the Arpad Elo rating system.

Uncertainties too large to predict tipping times of major Earth system components from historical data.

One way to warn of forthcoming critical transitions in Earth system components is using observations to detect declining system stability. It has also been suggested to extrapolate such stability changes into the future and predict tipping times. Here, we argue that the involved uncertainties are too high to robustly predict tipping times. We raise concerns regarding (i) the modeling assumptions underlying any extrapolation of historical results into the future, (ii) the representativeness of individual Earth system component time series, and (iii) the impact of uncertainties and preprocessing of used observational datasets, with focus on nonstationary observational coverage and gap filling. We explore these uncertainties in general and specifically for the example of the Atlantic Meridional Overturning Circulation. We argue that even under the assumption that a given Earth system component has an approaching tipping point, the uncertainties are too large to reliably estimate tipping times by extrapolating historical information.

Dark Matter Distribution in Milky Way analog Galaxies

Our current understanding of how dark matter (DM) is distributed within the Milky Way (MW) halo, particularly in the solar neighborhood, is based on either careful studies of the local stellar orbits, model assumptions on the global shape of the MW halo, or from direct acceleration measurements. In this work, we undertake a study of external galaxies, with the intent of providing insight into the DM distribution in MW-analog galaxies. For this, we carefully select a sample of galaxies similar to the MW, based on maximum atomic hydrogen (H i) rotational velocity ( vmax,HI=200–280 km s−1) and morphological type (Sab–Sbc) criteria. With a need for deep, highly resolved H i, our resulting sample is composed of five galaxies from the VLA Imaging of Virgo in Atomic Gas (VIVA) survey and The H i Nearby Galaxy Survey (THINGS). To perform our baryonic analysis, we use deep mid-IR images at 3.6 and 4.5 μm from the Spitzer Survey of Stellar Structures in Galaxies (S4G). Based on the dynamical three-dimensional modeling software 3D-Based Analysis of Rotating Objects via Line Observations (3D-BAROLO), we construct rotation curves (RCs) and derive the gas and stellar contributions from the galaxy's gaseous and stellar disks' mass surface density profiles. Through a careful decomposition of their RCs into their baryonic (stars, gas) and DM components, we isolate the DM contribution by using a Markov Chain Monte Carlo-based approach. Based on the Sun's location and the MW’s R 25, we define the corresponding location of the solar neighborhood in these systems. We put forward a window for the DM density (ρ dm = 0.21–0.55 GeV cm−3) at these galactocentric distances in our MW analog sample, consistent with the values found for the MW’s local DM density, based on more traditional approaches found in the literature.

In Situ Visualization of Ion Transport Processes in Aqueous Batteries.

Aqueous rechargeable batteries are regarded as one of the most reliable solutions for electrochemical energy storage, and ion (e.g., H+ or OH-) transport is essential for their electrochemical performance. However, modeling and numerical simulations often fall short of depicting the actual ion transport characteristics due to deviations in model assumptions from reality. Experimental methods, including laser interferometry, Raman, and nuclear magnetic resonance imaging, are limited by the complexity of the system and the restricted detection of ions, making it difficult to detect specific ions such as H+ and OH-. Herein, in situ visualization of ion transport is achieved by innovatively introducing laser scanning confocal microscopy. Taking neutral Zn-air batteries as an example and using a pH-sensitive probe, real-time dynamic pH changes associated with ion transport processes are observed during battery operation. The results show that after immersion in the zinc sulfate electrolyte, the pH near the Zn electrode changes significantly and pulsation occurs, which demonstrates the intense self-corrosion hydrogen evolution reaction and the periodic change in the reaction intensity. In contrast, the change in the pH of the galvanized electrode plate is weak, proving its significant corrosion inhibition effect. For the air electrode, the heterogeneity of ion transport during the discharging and charging process is presented. With an increase of the current density, the ion transport characteristics gradually evolve from diffusion dominance to convection-diffusion codominance, revealing the importance of convection in the ion transport process inside batteries. This method opens up a new approach of studying ion transport inside batteries, guiding the design for performance enhancement.

A brief note on the random-effects meta-analysis model and its relationship to other models

Meta-analysis is a statistical method for combining quantitative results across studies. A fundamental decision in undertaking a meta-analysis is choosing an appropriate model for analysis. This is the second of two companion articles which have the joint aim of describing the different meta-analysis models. In the first article, we focused on the common-effect (also known as fixed-effect [singular]) model, and in this article, we focus on the random-effects model. We describe the key assumptions underlying the random-effects model, how it is related to the common-effect and fixed-effects [plural] models, and present some of the arguments for selecting one model over another. We outline some of the methods for fitting a random-effects model. Finally, we present an illustrative example to demonstrate how the results can differ depending on the chosen model and method. Understanding the assumptions of the different meta-analysis models, and the questions they address, is critical for meta-analysis model selection and interpretation.

Modal and non-modal stability for Hagen–Poiseuille flow with non-ideal fluid

Modal and non-modal stability analyses are applied to Hagen–Poiseuille flow with a non-ideal fluid. The non-ideal fluid is defined as a fluid close to its vapor–liquid critical point. In this region, properties of the fluid deviate significantly from the assumptions of the ideal gas model. In this paper, the specific example of CO2 near the critical point is taken as a non-ideal fluid. We studied fluids at supercritical pressure and different wall temperatures so that the centerline temperatures can be lower, equal, and higher than the pseudo-critical temperature. Flow instability is characterized by the Reynolds number, and the product of the Prandtl and Eckert numbers. In modal stability analysis, we observe that there is no unstable mode in Hagen–Poiseuille flow with a non-ideal fluid. Regarding the growth rate, as the axial wavenumber increases, another mode becomes the least stable. The non-modal theory is employed to investigate the optimal response to harmonic external force and transient energy growth. The influence of axial and azimuthal wave numbers, Prandtl and Eckert numbers, and thermodynamic states are also taken into account. In this study, we identify an generalized inflection point in the transcritical base profile, causing the transcritical state to be the most unstable. In non-modal instability, we observe that the optimal response mainly occurs at time invariant axisymmetric disturbance. This suggests that the axisymmetric disturbance could potentially initiate the transition to turbulence.

Dynamic determinants of optimal global climate policy

We explore the impact of dynamic characteristics of greenhouse-gas emitting systems, such as inertia, induced innovation, and path-dependency, on optimal responses to climate change. Our compact and analytically tractable model, applied with stylized damage assumptions to derive optimal pathways, highlights how simple dynamic parameters affect responses including the optimal current effort and the cost of delay. The conventional cost-benefit result (i.e., an optimal policy with rising marginal costs that reflects discounted climate damages) arises only as a special case in which the dynamic characteristics of emitting systems are assumed to be insignificant. Our analysis highlights and distinguishes from the (often implicit) assumption in many cost-benefit models, which neglect inertia and assume exogenous technology progress. This tends to defer action. More generally, our model yields useful policy insights for the transition to deep decarbonization, showing that enhanced early action may greatly reduce both damages and abatement costs in the long run.

Effect of Model Fidelity on High-Speed Aeroelastic Behavior of a Cantilever Plate

Turbulence, flow separation, and shock dynamics challenge the modeling and analysis of high-speed aeroelastic behavior. Motivated by this, the importance of modeling the fidelity of the flow is explored in the aeroelastic response of a cantilever plate in an Ma=2.0 separating turbulent flow using unsteady Reynolds-averaged Navier–Stokes (URANS) and URANS-enriched local piston theory (LPT). Structural modeling assumptions are also evaluated using both linear and nonlinear representations. Close agreement in the predicted aeroelastic steady state is observed. However, large discrepancies in the dynamic aeroelastic response predictions are found and ultimately linked to the neglect of deformation-induced cavity pressure fluctuations and dynamic flow separation in the LPT model. Interestingly, the dynamic flow separation induces a fluid-driven limit cycle oscillation in the postflutter regime. Furthermore, structural nonlinearity is not found to have a strong impact on the conditions and configurations considered.

GOLD PRICE PREDICTION IN INDONESIA BASED ON INTEREST RATE USING DISTRIBUTED LAG ALMON TRANSFORMATION

Gold is valued for its safety and profitability, driven by steady price changes and influenced by interest rates. Accurately predicting gold prices is very important to make the right investment decisions. This study aims to build a gold price model in Indonesia using the Almon transformation lag distribution and see gold price predictions based on the model that has been built. We used the data on gold prices and interest rates from January 2016 to December 2023. Based on the results of the analysis, the best Almon transformation model used in this study is the Almon model with a maximum lag length of 16 and the second polynomial degree. The prediction results have a MAPE of 16.49%, which shows that the Almon model can predict gold prices well for one year. This study contributes to the understanding of gold price dynamics amid economic variations. However, limitations in the model assumptions should be considered.

Economic evaluation of the "paramedics and palliative care: bringing vital services to Canadians" program compared to the status quo.

Based on programs implemented in 2011-2013 in three Canadian provinces to improve the support paramedics provide to people receiving palliative care, the Canadian Partnership Against Cancer and Healthcare Excellence Canada provided support and funding from 2018 to 2022 to spread this approach in Canada. The study objectives were to conduct an economic evaluation of "the Program" compared to the status quo. A probabilistic decision analytic model was used to compare the expected costs, the quality-adjusted life years (QALYs) and the return on investment associated with the Program compared to the status quo from a publicly funded healthcare payer perspective. Effectiveness data and Program costs, expressed in 2022 Canadian dollars, from each jurisdiction were supplemented with literature data. Probabilistic sensitivity analyses varying key model assumptions were conducted. Analyses of 5416 9-1-1 calls from five jurisdictions where paramedics provided support to people with palliative care needs between April 1, 2020 and March 31, 2022 indicated that 60% of the 9-1-1 calls under the Program enabled people to avoid transport to the emergency department and receive palliative care at home. Treating people at home saved paramedics an average of 31min (range from 15 to 67). The Program was associated with cost savings of $2773 (95% confidence interval [CI] $1539-$4352) and an additional 0.00069 QALYs (95% CI 0.00024-0.00137) per 9-1-1 palliative care call. The Program return on investment was $4.6 for every $1 invested. Threshold analyses indicated that in order to be cost saving, 33% of 9-1-1 calls should be treated at home under the Program, the Program should generate a minimum of 97 calls per year with each call costing no more than $2773. The Program was cost-effective in the majority of the scenarios examined. These results support the implementation of paramedic-based palliative care at home programs in Canada.

Weighted Attribute-Based Proxy Re-Encryption Scheme with Distributed Multi-Authority Attributes

Existing attribute-based proxy re-encryption schemes suffer from issues like complex access policies, large ciphertext storage space consumption, and an excessive authority of the authorization center, leading to weak security and controllability of data sharing in cloud storage. This study proposes a Weighted Attribute Authority Multi-Authority Proxy Re-Encryption (WAMA-PRE) scheme that introduces attribute weights to elevate the expression of access policies from binary to multi-valued, simplifying policies and reducing ciphertext storage space. Simultaneously, the multiple attribute authorities and the authorization center construct a joint key, reducing reliance on a single authorization center. The proposed distributed attribute authority network enhances the anti-attack capability of cloud storage. Experimental results show that introducing attribute weights can reduce ciphertext storage space by 50%, proxy re-encryption saves 63% time compared to repeated encryption, and the joint key construction time is only 1% of the benchmark scheme. Security analysis proves that WAMA-PRE achieves CPA security under the decisional q-parallel BDHE assumption in the random oracle model. This study provides an effective solution for secure data sharing in cloud storage.

Examining the objectives of ethnic parties: A comprehensive analysis of the dynamics of ethnic party competition beyond the constraints of the outbidding model

AbstractThis study scrutinises the core assumptions of Donald Horowitz's outbidding model, which defines ethnic party competition as a rivalry between extremist groups focussed solely on ethnic politics, deeming other socio‐economic issues insufficient for voter mobilisation and electoral success (Horowitz, 1985). Through a comprehensive analysis of ethnic parties in Iraqi Kurdistan, this study demonstrates how these parties extend their focus beyond ethnic concerns to include broader administrative matters, such as social and economic issues within the local governance framework. Utilising content analysis of Kurdish newspapers and a comparative perspective, this study challenges the prevailing notion of ethnic outbidding, revealing the multifaceted strategies employed by Kurdish parties.

Hydrodynamic Porosity: A New Perspective on Flow through Porous Media, Part I

Pore-scale flow velocity is an essential parameter in determining transport through porous media, but it is often miscalculated. Researchers use a static porosity value to relate volumetric or superficial velocities to pore-scale flow velocities. We know this modeling assumption to be an oversimplification. The variable fraction of porosity conducive to flow, what we define as hydrodynamic porosity, , exhibits a quantifiable dependence on the Reynolds number (i.e., pore-scale flow velocity) in the Laminar flow regime. This fact remains largely unacknowledged in the literature. In this work, we quantify the dependence of on the Reynolds number via numerical flow simulation at the pore scale for rectangular pores of various aspect ratios, i.e., for highly idealized dead-end pore spaces. We demonstrate that, for the chosen cavity geometries, decreases by as much as 42% over the Laminar flow regime. Moreover, exhibits an exponential dependence on the Reynolds number, Re = . The fit quality is effectively perfect, with a coefficient of determination (R2) of approximately 1 for each set of simulation data. Finally, we show that this exponential dependence can be easily fitted for pore-scale flow velocity through use of only a few Picard iterations, even with an initial guess that is 10 orders of magnitude off. Not only is this relationship a more accurate definition of pore-scale flow velocity, but it is also a necessary modeling improvement that can be easily implemented. In the companion paper (Part 2), we build upon the findings reported here and demonstrate their applicability to media with other pore geometries: rectangular and non-rectangular cavities (circular and triangular).