Model Assumptions Research Articles

An optimization model to prioritize fuel treatments within a landscape fuel break network.

We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions. Its mathematical formulation is concise and computationally efficient, allowing for customization and expansion to address more complex and challenging fuel break management problems in diverse landscapes. We constructed test cases for Southern California of the United States to understand model outcomes across a wide range of fire and fuel management scenarios. Results suggest optimal fuel treatment layouts within the Southern California's fuel break network responding to various model assumptions, which offer insights for regional fuel break planning. Comparative tests between the proposed optimization model and a rule-based simulation approach indicate that the optimization model can provide significantly better solutions within reasonable solving times, highlighting its potential to support fuel break management and planning decisions.

Effects of Applicants' Use of Generative AI in Personnel Selection: Towards a More Nuanced View?

ABSTRACTGenerative AI (GenAI) has made rapid inroads in assessment, as a growing number of applicants rely on it as a coach in unproctored assessments of various selection procedures. This had led to assertions that applicants' GenAI use undermines key assumptions of the predictive model underlying selection and is thus disruptive for organizations' current unproctored assessments, thereby invoking various strategies of organizations to deter and detect its use. In this provocation article, we present a more nuanced view. To this end, we start by reviewing the recent research related to the effects of applicants' use of GenAI in assessment and discuss the evidence of the potential of applicant GenAI use to disrupt assessment validity. Next, we draw on test coaching frameworks to discuss three scenarios of how applicants' use of GenAI might affect an assessment's mean scores and criterion‐related validity. These perspectives highlight that the use of GenAI might not only exert negative consequences but potentially have also positive consequences for both applicants and organizations. It is pivotal to distinguish among these scenarios because they lead to different strategies for organizations to deal with applicant use of GenAI.

Reassessment of the Conventional: Numerical Validation of a 1D model for the Prediction of Volute Losses

Abstract The traditional one-dimensional (1D) method for predicting losses in a centrifugal compressor volute relies on simple assumptions. However, it is questionable how accurately these assumptions capture the complex flow physics. This study evaluates these 1D predictions using a comprehensive database of CFD calculations for a standalone volute. In these simulations, the diffuser width and inlet conditions were varied to separately analyze the effects of flow angle and flow rate, allowing for a complete assessment of the volute's loss map. Similar to the 1D predictions, the CFD-predicted total pressure losses in the volute's two subcomponents (the scroll and the conical diffuser) are assigned to the respective velocity components at the volute inlet. This allows for validation of the 1D model against CFD results across the volute's entire operating range. The results show that the radial velocity loss coefficient varies significantly, challenging the 1D assumption of a constant value. In contrast, the tangential velocity loss coefficient in the scroll aligns well with CFD results, showing high loss coefficients at high matching coefficients and decreasing towards low matching coefficients. Losses in the conical diffuser depend on both the matching coefficient and the inlet flow angle, contrary to 1D model assumptions. While the 1D model satisfactorily predicts the global loss coefficient characteristics, it generally overestimates losses. Additionally, the CFD results reveal that the matching coefficient for minimum global losses varies with the flow angle, a factor not considered by the 1D model, indicating its limitations for volute matching assessment.

Transport of (Micro)plastic Within a River Cross-Section—Spatio-Temporal Variations and Loads

Despite substantial research, the spatio-temporal dynamics of microplastic fluxes remain underexplored, especially in lower-order rivers. This study aims to quantify microplastic loads using a spatio-temporal sampling approach in a single cross-section of the Lahn River, a typical low-mountain river in Central Germany, over a sampling period from July 2020 to April 2021, covering varying discharge conditions, from low to high flow. A total of 198 plastic particles were detected, averaging 3.67 particles per hour, with a mean microplastic load of 0.03 ± 0.027 particles per cubic metre. Microplastic abundance varied spatially within the river cross-section, with lower concentrations found at deeper sampling positions. The data indicate that higher discharge conditions correlate with increased microplastic loads, predominantly at the water surface, suggesting that hydrological conditions significantly influence plastic transport dynamics. However, it remains unclear whether the microplastics observed at higher discharges originate from additional sources or are reactivated from river sediments. This research highlights the need for further studies to validate model assumptions and better understand the reactivation and transport mechanisms of microplastics in river systems.

A minimal physiologically-based pharmacokinetic modeling platform to predict intratumor exposure and receptor occupancy of an anti-LAG-3 monoclonal antibody.

In oncology drug development, measuring drug concentrations at the tumor site and at the targeted receptor remains an ongoing challenge. Positron emission tomography (PET)-imaging is a promising noninvasive method to quantify intratumor exposure of a radiolabeled drug (biodistribution data) and target saturation by treatment doses invivo. Here, we present the development and application of a minimal physiologically-based pharmacokinetic (mPBPK) modeling approach to integrate biodistribution data in a quantitative platform to characterize and predict intratumor exposure and receptor occupancy (RO) of BI 754111, an IgG-based anti-lymphocyte-activation gene 3 (LAG-3) monoclonal antibody (mAb). Specifically, calibration and qualification of the predictions were performed using 89Zr-labeled BI 754111 biodistribution data, that is, PET-derived intratumor drug concentration data, tumor-to-plasma ratios, and data from Patlak analyses. The model predictions were refined iteratively by the inclusion of additional biological processes into the model structure and the use of sensitivity analyses to assess the impact of model assumptions and parameter uncertainty on the predictions and model robustness. The developed mPBPK model allowed an adequate description of observed tumor radioactivity concentrations and tumor-to-plasma ratios leading to subsequent adequate prediction of LAG-3 RO at different dose levels. In the future, the developed model could be used as a platform for the prediction and analysis of biodistribution data for other mAbs and may ultimately support dose optimization by identifying dosages resulting in saturated RO.

Diversification in Business Valuation

Abstract The importance of the diversification and the related diversification factor in business valuation has not been studied in depth. In CAPM, due to the model assumptions, it is implied that an investor holds a perfectly diversified portfolio, which answers the question of diversification. The objective of this paper is to clarify the definition of diversification and to show how diversification effects can be incorporated into business valuation. Our research contribution entails demonstrating methods to capture the risk situation of an investor based on its asset allocation and then deriving the diversification effect from this assessment. As a methodological approach, we have opted for one that encompasses the most diverse degrees of diversification. Our analysis shows that this approach captures all non-hedged risks and thus performs a stand-alone valuation without diversification effects. If the diversification effect of a specific portfolio is to be included in the valuation, the analyst must shift from a stand-alone approach to a portfolio based approach, where the diversification factor cannot simply be equated with the correlation coefficient but must be calculated using various aggregation methods. Given that the conventional variance-covariance method excludes extreme risks, we propose the use of the copula approach. This approach can enhance risk modeling and yield more accurate results in diversification effect calculations. Our analysis demonstrates that it is possible to effectively model different diversification effects in business valuation. However, this entails substantial data expenses and demands a high degree of methodological expertise from the analyst.

Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b

Located 10.888 pc from Earth, COCONUTS-2b is a planetary-mass companion to a young (150–800 Myr) M3 star, with a wide orbital separation (6471 au) and a low companion-to-host mass ratio (0.021 ± 0.005). We have studied the atmospheric properties of COCONUTS-2b using newly acquired 1.0–2.5 μm spectroscopy from Gemini/Flamingos-2. The spectral type of COCONUTS-2b is refined to T9.5 ± 0.5 based on comparisons with T/Y dwarf spectral templates. We have conducted an extensive forward-modeling analysis, comparing the near-infrared spectrum and mid-infrared broadband photometry of COCONUTS-2b with 16 state-of-the-art atmospheric model grids developed for brown dwarfs and self-luminous exoplanets near the T/Y transition. The PH 3 -free ATMO2020++, ATMO2020++, and Exo-REM models best match the specific observations of COCONUTS-2b, regardless of variations in the input spectrophotometry. This analysis suggests the presence of disequilibrium chemistry, along with a diabatic thermal structure and/or clouds, in the atmosphere of COCONUTS-2b. All models predict fainter Y-band fluxes than observed, highlighting uncertainties in the alkali chemistry models and opacities. We determine a bolometric luminosity of log(Lbol/L⊙)=−6.18 dex, with a 0.5 dex wide range of [−6.43, −5.93] dex that accounts for various assumptions of atmospheric models. Using several thermal evolution models, we derive an effective temperature of Teff=483−53+44 K, a surface gravity of log(g)=4.19−0.13+0.18 dex, a radius of R=1.11−0.04+0.03 R Jup, and a mass of M = 8 ± 2 M Jup. Various atmospheric model grids consistently indicate that COCONUTS-2b’s atmosphere likely has subsolar or near-solar metallicity and C/O. These findings provide valuable insights into COCONUTS-2b’s formation history and the potential outward migration to its current wide orbit.

Taphonomy and labour at the Indus Valley site of Harappa (3700–1300 BC)

The emergence of early cities required new agricultural practices and archaeobotanical crop-processing models have been used to investigate the social and economic organisation of urban ‘consumer’ and non-urban ‘producer’ sites. Archaeobotanical work on the Indus Valley has previously identified various interpretations of labour and subsistence practices. Here, the authors analyse a large archaeobotanical assemblage from Harappa, Pakistan (3700–1300 BC), questioning some of the assumptions of traditional crop-processing models. The ubiquity of small weed seeds, typically removed during the early stages of crop processing, is argued to result from dung burning. This additional taphonomic consideration adds nuance to the understanding of Harappa's labour organisation and food supply with implications for crop-processing models in other contexts.

Sustainable Economic Growth And Industry Insights: Nonlinear Interaction Analysis of GDP Vs FDI in Malaysia Using SCILAB Programming

Objective: To investigate the nonlinear relationship between Gross Domestic Product (GDP) and Foreign Direct Investment (FDI) in Malaysia, with the aim of providing insights into their bidirectional interactions. Theoretical Framework: The main concepts and theories that underpin the research are nonlinear regression techniques and economic growth models. These frameworks provide a solid basis on understanding the dynamic interaction between GDP and FDI in the context of Malaysia’s economic. Method: The methodology comprises writing Scilab coding to analyze nonlinear regression models. Malaysia's economic data on GDP and FDI were utilized as inputs for the analysis. The study involves modeling the dynamics between these indicators and evaluating their relationship over time. Results and Discussion: The results obtained revealed a significant nonlinear relationship between GDP and FDI in Malaysia. These results are contextualized within the theoretical framework, highlighting the bidirectional nature of the relationship. Possible discrepancies and limitations, including data constraints and assumptions of the nonlinear models, are also considered. Research Implications: This research offers valuable insights for policymakers, helping shape economic planning,and investment strategies. It highlights the importance of FDI in promoting sustainable economic growth and industrial development towards United Nation SDG 8 and SDG 9. Originality/Value: This study contributes by using nonlinear regression techniques and Scilab programming to analyze the complex relationship between GDP and FDI, an approach not widely applied to Malaysia's context. The relevance and value of this research are evidenced by its potential impact on economic policy and its contributions to a deeper understanding of Malaysia's economic development.

Detecting departures from the conditional independence assumption in diagnostic latent class models: a simulation study

BackgroundLatent class models can be used to estimate diagnostic accuracy without a gold standard test. Early studies often assumed independence between tests given the true disease state, however this can lead to biased estimates when there are inter-test dependencies. Residual correlation plots and chi-squared statistics have been commonly utilized to assess the validity of the conditional independence assumption and, when it does not hold, identify which test pairs are conditionally dependent. We aimed to assess the performance of these tools with a simulation study covering a wide range of scenarios.MethodsWe generated data sets from a model with four tests and a dependence between tests 1 and 2 within the diseased group. We varied sample size, prevalence, covariance, sensitivity and specificity, with 504 combinations of these in total, and 1000 data sets for each combination. We fitted the conditional independence model in a Bayesian framework, and reported absolute bias, coverage, and how often the residual correlation plots, G2 and χ2 statistics indicated lack-of-fit globally or for each test pair.ResultsAcross all settings, residual correlation plots, pairwise G2 and χ2 detected the correct correlated pair of tests only 12.1%, 10.3%, and 10.3% of the time, respectively, but incorrectly suggested dependence between tests 3 and 4 64.9%, 49.7%, and 49.5% of the time. We observed some variation in this across parameter settings, with these tools appearing to perform more as intended when tests 3 and 4 were both much more accurate than tests 1 and 2. Residual correlation plots, G2 and χ2 statistics identified a lack of overall fit in 74.3%, 64.5% and 67.5% of models, respectively. The conditional independence model tended to overestimate the sensitivities of the correlated tests (median bias across all scenarios 0.094, 2.5th and 97.5th percentiles -0.003, 0.397) and underestimate prevalence and the specificities of the uncorrelated tests.ConclusionsResidual correlation plots and chi-squared statistics cannot be relied upon to identify which tests are conditionally dependent, and also have relatively low power to detect lack of overall fit. This is important since failure to account for conditional dependence can lead to highly biased parameter estimates.

Euclid and KiDS-1000: Quantifying the impact of source-lens clustering on cosmic shear analyses

Cosmic shear is a powerful probe of cosmological models and the transition from current Stage-III surveys such as the Kilo-Degree Survey (KiDS) to the increased area and redshift range of Stage IV surveys such as will significantly increase the precision of weak lensing analyses. However, with increasing precision, the accuracy of model assumptions needs to be evaluated. In this study, we quantify the impact of the correlated clustering of weak lensing source galaxies with the surrounding large-scale structure, known as source-lens clustering (SLC), which is commonly neglected. We include the impact of realistic scatter in photometric redshift estimates, which impacts the assignment of galaxies to tomographic bins and increases the SLC . For this, we use simulated cosmological datasets with realistically distributed galaxies and measure shear correlation functions for both clustered and uniformly distributed source galaxies. Cosmological analyses are performed for both scenarios to quantify the impact of SLC on parameter inference for a KiDS-like and a setting. We find for Stage III surveys such as KiDS, SLC has a minor impact when accounting for nuisance parameters for intrinsic alignments and shifts of tomographic bins, as these nuisance parameters absorb the effect of SLC, thus changing their original meaning. For KiDS ( the inferred intrinsic alignment amplitude $A_ IA $ changes from $0.11_ $) for data without SLC to $0.28_ $) with SLC. However, fixed nuisance parameters lead to shifts in $S_8$ and $ m $, emphasizing the need for including SLC in the modelling. For we find that $ m $, and $w_0$ are shifted by 0.19, 0.12, and 0.12 sigma , respectively when including free nuisance parameters and by 0.20, 0.16, and 0.32 sigma when fixing the nuisance parameters. Consequently, SLC on its own has only a small impact on the inferred parameter inference when using uninformative priors for nuisance parameters. However, SLC might conspire with the breakdown of other modelling assumptions, such as magnification bias or source obscuration, which could collectively exert a more pronounced effect on inferred parameters.

Viscosity of aqueous ammonium nitrate–organic particles: equilibrium partitioning may be a reasonable assumption for most tropospheric conditions

Abstract. The viscosity of aerosol particles determines the critical mixing time of gas–particle partitioning of volatile compounds in the atmosphere. The partitioning of the semi-volatile ammonium nitrate (NH4NO3) might alter the viscosity of highly viscous secondary organic aerosol particles during their lifetimes. In contrast to the viscosity of organic particles, data on the viscosity of internally mixed inorganic–organic aerosol particles are scarce. We determined the viscosity of an aqueous ternary inorganic–organic system consisting of NH4NO3 and a proxy compound for a highly viscous organic, sucrose. Three techniques were applied to cover the atmospherically relevant humidity range: viscometry, fluorescence recovery after photobleaching, and the poke-flow technique. We show that the viscosity of NH4NO3–sucrose–H2O with an organic to inorganic dry mass ratio of 4:1 is 4 orders of magnitude lower than the viscosity of the aqueous sucrose under low-humidity conditions (30 % relative humidity (RH), 293 K). By comparing viscosity predictions of mixing rules with those of the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients Viscosity (AIOMFAC-VISC) model, we found that a mixing rule based on mole fractions performs similarly when data from corresponding binary aqueous subsystems are available. Applying this mixing rule, we estimated the characteristic internal mixing time of aerosol particles, indicating significantly faster mixing for inorganic–organic mixtures compared to electrolyte-free particles, especially at lower RH. Hence, the assumption in global atmospheric chemistry models of quasi-instantaneous equilibrium gas–particle partitioning is reasonable for internally mixed single-phase particles containing dissolved electrolytes (but not necessarily for phase-separated particles), for most conditions in the planetary boundary layer. Further data are needed to see whether this assumption holds for the entire troposphere at midlatitudes and at RH > 35 %.

Do rising interest rates matter for bank profitability? Evidence from Portuguese banks

The purpose of this paper is to analyse the profitability determinants of seventeen banks operating in Portugal from 2013 to 2023. The banking market has changed significantly, particularly since 2021, when Euribor grew rapidly to control inflation target. Methodologically, a hypothetical-deductive approach was used based on panel data collected from banks' published accounts. To generate results, grouped ordinary least squares were applied, as the Breusch-Pagan test confirms homoscedasticity, an essential assumption in this regression model. Internal variables considered include credit quality, capital adequacy, management quality, financial margin, and bank size, alongside an external variable, the Euribor. The findings reveal that credit risk, capital adequacy, management capacity, and Euribor are the most statistically significant for both return on equity and return on assets, with Euribor emerging as the greatest statistically significant variable. The analysis of Euribor as an explanatory variable represents the key contribution of this study relative to the existing and reviewed literature.

Determination of solid-liquid adhesion work on flat surfaces in a direct and absolute manner

There is a long-standing need for a direct determination of solid-liquid work of adhesion due to its central role in several scientific and industrial fields. Here, a method is introduced to determine the value of adhesion work on flat surfaces directly, without the need of any model assumptions, and independently from uncertainties of contact angles. The presented method enables to determine the adhesion work separately both for advancing and receding situations. The directly measured adhesion work values were benchmarked successfully against the prediction of the Young–Dupré equation in the 0°–120° contact angle range. That is, the Young–Dupré equation was experimentally proven to be valid separately for both advancing and receding cases. The method provides an absolute, thermodynamic quantity, hence it is insensitive to the measurement parameters, moreover the resulting values can be used directly in further calculations.

Learning in Associative Networks Through Pavlovian Dynamics

Abstract Hebbian learning theory is rooted in Pavlov’s classical conditioning While mathematical models of the former have been proposed and studied in the past decades, especially in spin glass theory, only recently has it been numerically shown that it is possible to write neural and synaptic dynamics that mirror Pavlov conditioning mechanisms and also give rise to synaptic weights that correspond to the Hebbian learning rule. In this letter we show that the same dynamics can be derived with equilibrium statistical mechanics tools and basic and motivated modeling assumptions. Then we show how to study the resulting system of coupled stochastic differential equations assuming the reasonable separation of neural and synaptic timescale. In particular, we analytically demonstrate that this synaptic evolution converges to the Hebbian learning rule in various settings and compute the variance of the stochastic process. Finally, drawing from evidence on pure memory reinforcement during sleep stages, we show how the proposed model can simulate neural networks that undergo sleep-associated memory consolidation processes, thereby proving the compatibility of Pavlovian learning with dreaming mechanisms.

Smoothed Estimation on Optimal Treatment Regime Under Semisupervised Setting in Randomized Trials.

A treatment regime refers to the process of assigning the most suitable treatment to a patient based on their observed information. However, prevailing research on treatment regimes predominantly relies on labeled data, which may lead to the omission of valuable information contained within unlabeled data, such as historical records and healthcare databases. Current semisupervised works for deriving optimal treatment regimes either rely on model assumptions or struggle with high computational burdens for even moderate-dimensional covariates. To address this concern, we propose a semisupervised framework that operates within a model-free context to estimate the optimal treatment regime by leveraging the abundant unlabeled data. Our proposed approach encompasses three key steps. First, we employ a single-index model to achieve dimension reduction, followed by kernel regression to impute the missing outcomes in the unlabeled data. Second, we propose various forms of semisupervised value functions based on the imputed values, incorporating both labeled and unlabeled data components. Lastly, the optimal treatment regimes are derived by maximizing the semisupervised value functions. We establish the consistency and asymptotic normality of the estimators proposed in our framework. Furthermore, we introduce a perturbation resampling procedure to estimate the asymptotic variance. Simulations confirm the advantageous properties of incorporating unlabeled data in the estimation for optimal treatment regimes. A practical data example is also provided to illustrate the application of our methodology. This work is rooted in the framework of randomized trials, with additional discussions extending to observationalstudies.

Designing work for healthy sleep: A multidimensional, latent transition approach to employee sleep health.

Healthy sleep is essential to employee well-being and productivity, but many modern workers do not obtain adequate sleep. Are technology-related changes to job design (i.e., computer use, sedentary work, nontraditional work schedules) related to long-term worsening of employee sleep health? The present study seeks to address this question using nationally representative data from the Midlife in the United States study, which includes detailed information on sleep duration, regularity, sleep onset latency, insomnia symptoms, napping, and daytime tiredness from full-time workers (N = 1,297) at two time points separated by approximately 10 years. Using latent transition analysis to consider how these sleep health dimensions co-occur, we identify three multidimensional sleep health phenotypes at both time points: good sleepers, catch-up sleepers, and insomnia sleepers. Sedentary work is linked to the insomnia sleeper phenotype. Nontraditional work schedules are linked to the catch-up sleeper phenotype. These findings test assumptions of modern models of job design regarding the impact of technology on employee sleep health and advance measurement of sleep health in the organizational sciences to be multidimensional and dynamic. Further, results point to specific sleep needs in the working adult population and identify potential points of intervention via job design. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stability Analysis of SIR Mathematics Model in Shopeepay Later Addiction Case

This research discusses the case of Shopeepay Later addiction using the SIR mathematics model. Therefore, the purpose of this research is to build, analyze and find out the basic reproduction number (????0) and simulation of the SIR model of Shopeepay Later addiction. The research begins by building the assumptions of the SIR model of Shopeepay Later addiction, finding the equilibrium point, analyzing the stability of the equilibrium point using the jacobian matrix, finding the basic reproduction number (????0), and doing a simulation using Google Colab. The parameters used in this SIR model include the transmission rate parameter (????), the recovery rate parameter (????) the self-control parameter (????1), the promotion parameter (????2), and the application stop parameter (????3). The result of this study obtained the SIR model of Shopeepay Later addiction, one endemic equilibrium point is stable and the basic reproduction number ????0 = −37,29 that is, there is no transmission. Then to get expected conditions from the simulation result, namely given parameter values ???? = 0.5, ???? = 0.4, and ????1, ????2, ????3 = 0.9.

Effect of powder and absorptivity assumptions in the high-fidelity modeling of Laser Powder Bed Fusion processes

Laser Powder Bed Fusion (LPBF) technology represents the most promising metal additive manufacturing process to be scaled up to an industrial level for producing full batches of parts within the automotive aerospace and health sectors, among others. Despite this higher technological readiness, imperfections such as warping, porosities or undesired non-equilibrium microstructures can arise unexpectedly due to the complex thermal histories inherent to LPBF. Being able to anticipate to such imperfections is thus of high importance. With this purpose, numerical modeling strategies can be employed to significantly reduce experimental work on the melt pool scale. This allows to quantify the odds and magnitude of these process unwanted effects. More precisely, Computational Fluid Dynamics (CFD) enables high-fidelity models on the laser-matter interaction during LPBF processing. In this work, a modeling approach is presented in which the driving forces for melt formation are included in a CFD workspace, where multiple reflections of the laser on the melt pool Liquid-Gas Interface (LGI) are considered by means of an absorptivity function of the LGI depth. This simplification and the use of adaptive mesh strategies allows the model to be computed within a reasonable time. Results and applicability of the model on the determination of the melting mode (conduction or keyhole) are displayed and compared with experimental measurements of the melt pool width and depth for 316L and Ti64 single tracks produced on a LPBF machine. Likewise, further aspects such as total absorbed power are assessed.

Towards Effective Clustered Federated Learning: A Peer-to-Peer Framework With Adaptive Neighbor Matching

In federated learning (FL), clients may have diverse objectives, and merging all clients' knowledge into one global model will cause negative transfer to local performance. Thus, clustered FL is proposed to group similar clients into clusters and maintain several global models. In the literature, centralized clustered FL algorithms require the assumption of the number of clusters and hence are not effective enough to explore the latent relationships among clients. In this paper, without assuming the number of clusters, we propose a peer-to-peer (P2P) FL algorithm named PANM. In PANM, clients communicate with peers to adaptively form an effective clustered topology. Specifically, we present two novel metrics for measuring client similarity and a two-stage neighbor matching algorithm based Monte Carlo method and Expectation Maximization under the Gaussian Mixture Model assumption. We have conducted theoretical analyses of PANM on the probability of neighbor estimation and the error gap to the clustered optimum. We have also implemented extensive experiments under both synthetic and real-world clustered heterogeneity. Theoretical analysis and empirical experiments show that the proposed algorithm is superior to the P2P FL counterparts, and it achieves better performance than the centralized cluster FL method. PANM is effective even under extremely low communication budgets.