Model Assumptions Research Articles

Development of an integrated energy and thermal planner for a series hybrid off-road autonomous tracked vehicle

Electrifying off-road vehicle powertrains enhances energy efficiency and auxiliary power generation but poses control challenges due to extreme temperatures, complex terrain, powerful cooling systems, and high-power demands. This paper presents the Integrated Energy and Thermal Planner (IETP), a unified approach to energy and thermal management for off-road series hybrid tracked vehicles. The IETP addresses challenges posed by extreme ambient temperatures, high-power demands, and complex non-linear thermal dynamics by integrating the control of thermal systems with energy planning. The synergistic operation of the ICE-Generator and thermal actuators reduces battery degradation by up to 29% compared to traditional separated energy and thermal management. Additionally, IETP improves fuel efficiency by at least 10% in power-demanding high-speed driving scenarios. Key contributions include the ’priority-speed’ formulation, which optimizes the ICE-Gen’s operating point in a computationally efficient manner, and a systematic sensitivity analysis to balance planning accuracy with hardware constraints. Real-time planner-in-the-loop application mitigates execution delays through a memory buffer and compensation strategy. Despite uncertainties in modeling and preview assumptions, the IETP demonstrates robustness, with future work aimed at further improving transient compensation and estimation routines. This integrated strategy enhances both the efficiency and durability of hybrid electric vehicles in extreme off-road environments.

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.

Pronoun interpretation is more subject-biased than expected by the Bayesian Model

ABSTRACT A Bayesian Model proposed by Kehler et al. ([2008]. Coherence and coreference revisited. Journal of Semantics, 25(1), 1–44. https://doi.org/10.1093/jos/ffm018) suggests that pronoun production and interpretation are driven by a different set of factors following the Bayes’ rule. Evidence suggests that the Bayesian Model makes better predictions on pronoun interpretation compared to other models that assume that pronoun production and interpretation are influenced by the same set of factors. Yet, it remains unclear precisely to what extent the Bayesian Model can capture this relationship. The current study examines the validity of the Bayesian Model by comparing its performance across three different contexts using a variety of evaluation methods. Our results demonstrate that the Bayesian Model’s performance varied across contexts and consistently underestimated the subject bias in interpretation. We suggest that the underestimation is likely because the subject bias in pronoun production is not sufficient to account for the subject bias in actual interpretation, contra to the assumption of the Bayesian Model. We discuss potential sources of the additional subject bias in interpretation.

Review of the target trial methodological approach on treatment effect estimates in kidney failure: protocol for a systematic assessment.

Patients with kidney failure often lack robust evidence because they are excluded from randomized trials. Trial emulation provides an alternative approach to derive treatment effect estimates when randomized trials cannot be conducted. Critical questions about the comparative efficacy and safety of interventions in kidney failure are now being answered using this approach or parts of it. However, variations and inconsistencies in reporting cast doubt on the reliability and validity of effect estimates not derived from randomized trials. The aim of this methodological systematic review is to understand the extent to which the target study approach is used in kidney failure and the appropriateness of this approach. By identifying and evaluating studies that qualify as emulating a target trial, compared with studies that did not apply the principles. We aim to provide more specific methodological guidance to increase the clarity and reliability of reporting treatment effect estimates when running a trial in kidney failure is not feasible. This protocol is developed in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses Protocols (PRISMA-P) statement. MEDLINE, Embase, and reference lists (backwards citation chasing) will be searched up until 1st July 2023 and the search updated prior to publication to identify all studies evaluating patient outcomes in late-stage kidney disease and failure that use target trial emulation as the primary approach for analysis. Two authors (A. A., P. K.) will select articles based on title and abstract and then full text, with a third reviewer settling disagreements (J. P.). The prespecified variables will be extracted, and the risk of bias will be assessed by at least two authors (A. A., P. K., A. N.) using prespecified data forms. This will enable the determination of the robustness of the methodological quality of observational studies in using the whole or elements of the target trial approach. We will thereby assess their ability to reliably report treatment effect estimates. We will provide specific methodological recommendations on how to design target trials and model assumptions for emulation to get reliable treatment effect estimates for therapeutic interventions in kidney failure. Open Science Framework:Identifier https://doi.org/10.17605/OSF.IO/Z4Y29 .

Getting started with the graded response model: An introduction and tutorial in R.

This tutorial introduces the graded response model (GRM), a tool for testing measurement precision within the item response theory (IRT) paradigm, which is useful for informing researchers about the item and person properties of their measurement. The tutorial aims to guide applied researchers through a unidimensional GRM analysis in the R environment, using the psych, mirt and ggmirt packages. GRM is specifically designed to examine the psychometric properties of psychological scales with polytomous (Likert-style) items. The tutorial illustrates the procedure using data from the Open Psychometrics Database on the right-wing authoritarianism (RWA) scale, outlining the theoretical underpinnings of GRM and steps for data preparation, testing model assumptions, model fitting, plotting item parameters and interpretation of results.

Coupled infectious disease and behavior dynamics. A review of model assumptions.

To comprehend the dynamics of infectious disease transmission, it is imperative to incorporate human protective behavior into models of disease spreading. While models exist for both infectious disease and behavior dynamics independently, the integration of these aspects has yet to yield a cohesive body of literature. Such an integration is crucial for gaining insights into phenomena like the rise of infodemics, the polarization of opinions regarding vaccines, and the dissemination of conspiracy theories during a pandemic. 
We make a threefold contribution. First, we introduce a framework to describe models coupling infectious disease and behavior dynamics, delineating four distinct update functions. Reviewing existing literature, we highlight a substantial diversity in the implementation of each update function. This variation, coupled with a dearth of model comparisons, renders the literature hardly informative for researchers seeking to develop models tailored to specific populations, infectious diseases, and forms of protection.
Second, we advocate an approach to comparing models' assumptions about human behavior, the model aspect characterized by the strongest disagreement. Rather than representing the psychological complexity of decision-making, we show that "influence-response functions'' allow one to identify which model differences generate different disease dynamics and which do not, guiding both model development and empirical research testing model assumptions. 
Third, we propose recommendations for future modeling endeavors and empirical research aimed at selecting models of coupled infectious disease and behavior dynamics. We underscore the importance of incorporating empirical approaches from the social sciences to propel the literature forward.

A General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates.

In many trials and experiments, subjects are not only observed once but multiple times, resulting in a cluster of possibly correlated observations (e.g., brain regions per patient). Observations often do not fulfill model assumptions of mixed models and require the use of nonparametric methods. In this article, we develop and present a purely nonparametric rank-based procedure that flexibly allows the unbiased and consistent estimation of the Wilcoxon-Mann-Whitney effect in clustered data designs. Compared with existing methods, we allow flexible weights to be used in effect estimation. Additionally, we develop global and multiple contrast test procedures to test null hypotheses formulated regarding the generalized Mann-Whitney effects and for the computation of range-preserving simultaneous confidence intervals in a unified way. Extensive simulation studies show that these methods control the type-I error rate well and have reasonable power to detect alternatives in various situations.

Analysis and optimization of the efficient press shop operation

Abstract. In the last decade, stamping and forming manufacturing methods have been widely used in the manufacturing industry, and the stamping and forming industry has gradually shifted from the competition of scale and quantity to the competition of high quality, cost-effectiveness, and multi-category. Therefore, it is of far-reaching significance to study the efficient operation method of the press shop. In this paper, the efficient operation modeling method, digital simulation platform, and production line improvement of the press shop are reviewed. According to analysis, it can be concluded that the dynamic data-driven modeling method is perfect, but the theoretical basis involved in the practical application process is too complicated, therefore, press shop managers may be required to have a certain degree of theoretical basis so that the method can be effectively realized. Besides, the virtual-real combination of digital simulation platforms is easy to operate in the press shop, but the model assumptions analyzed in this paper are too idealized, and potential unresolved problems may arise in the practical application of the press shop with a larger scope and wider coverage. Finally, the production-based transformation of the shop floor production line is a more suitable management method for older factories that are ready for upgrading.

Dynamic spatial network simulation accounting for multiple ecological factors provides practical recommendations for biosecurity early detection and rapid response (EDRR) strategies.

Globally the spread of invasive pests is being facilitated by increased human mobility and climate change. Simulation modelling can help assess biosecurity strategies for early detection and rapid response (EDRR), but has struggled to account for important factors in the invasion process, such as spatial and temporal variability in habitat suitability and connectivity; population dynamics; and multiple dispersal pathways. We developed a novel dynamic spatial network simulation approach based on spatial network theory that enables integration of a wider range of spatio-temporal factors than previous studies, calibrated it against extensive historical trapping data, and applied it to comprehensively analyse the EDRR strategy for Oriental fruit fly (Bactrocera dorsalis; OFF) in northern Australia. Simulations indicated that the chance of OFF reaching the mainland in the next 20 years could be up to 20% under the current EDRR strategy, depending on how optimistic or pessimistic model assumptions are, and highlighted possible improvements to the EDRR strategy for further consideration. Simulations under optimistic assumptions indicate that transport via wind is most important in OFF reaching the mainland, but under pessimistic assumptions transport via people carrying infected fruit becomes more important. Our new dynamic spatial network simulation approach can account for a wide range of spatio-temporal ecological factors to provide practical real-world recommendations. At a minimum, this approach only requires weather and population data, both of which are available globally from a variety of free and open sources, making it broadly applicable to assessing the EDRR strategies in place for different species in other locations. © 2024 Society of Chemical Industry.

Epistemic commitments have no “Off” button: on the embodiment of commitments by way of model formulation

The current paper examines how a commitment to a principle, adhered to by an individual agent, becomes an accepted standard of an epistemic community. Addressing this question requires three steps: first, to define the terms used throughout the paper, and especially the characteristics of commitments to a principle. The second step is to find a mechanism through which such epistemic commitments are introduced to an epistemic community and in certain cases are adopted as the standard by the community. While there could be several such mechanisms, the current paper focuses on the practice of model formulation. The third step is to demonstrate the analytical framework developed in the first two steps in a case study. The case study chosen for this paper is the unique approach to feedback analysis adopted by the ecologist and population geneticist Richard Levins. In what follows I will show that part of the features that made Levins’ approach unique was his Marxist commitments, and his attempt to embody those commitments in feedback analysis by formally representing them as modeling assumptions.

Search for Pauli Exclusion Principle violations with Gator at LNGS

The Pauli Exclusion Principle (PEP) appears from fundamental symmetries in quantum field theories, but its physical origin is still to be understood. High-precision experimental searches for small PEP violations permit testing key assumptions of the Standard Model with high sensitivity. We report on a dedicated measurement with Gator, a low-background, high-purity germanium detector operated at the Laboratori Nazionali del Gran Sasso, aimed at testing PEP-violating atomic transitions in lead. The experimental technique, relying on forming a new symmetry state by introducing electrons into the pre-existing electron system through a direct current, satisfies the conditions of the Messiah-Greenberg superselection rule. No PEP violation has been observed, and an upper limit on the PEP violation probability of β2/2<4.8·10-29\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta ^2/2 < 4.8 \\cdot 10^{-29}$$\\end{document} (90% CL) is set. This improves the previous constraint from a comparable measurement by more than one order of magnitude.

Multiwavelength Study of Extreme High-energy Peaked BL Lac Source 1ES 0229+200 Using Ultraviolet, X-Ray, and γ-Ray Observations

We present a comprehensive analysis of the broadband spectral energy distribution (SED) of the extreme high-energy peaked BL Lac source, 1ES 0229+200. Our study utilizes near-simultaneous data collected at various epochs between 2017 September and 2021 August (MJD 58119−59365) from different instruments, including AstroSat−UVIT, Soft X-ray focusing Telescope, LAXPC, Swift−UVOT, Fermi-LAT, and MAGIC. We investigate the one-zone synchrotron and synchrotron self-Compton (SSC) model, employing diverse particle distributions such as the log parabola, broken power law, power law with a maximum electron energy γ, energy-dependent diffusion (EDD), and energy-dependent acceleration (EDA) models to fit the broadband SED of the source. Our findings indicate that both peaks in the SED are well described by the one-zone SSC model across all particle distribution models. We estimate the jet power for different particle distributions. The estimated jet power for broken power law particle distributions is found to be on the order of 1047 (1044) erg s−1 for a minimum electron energy γmin ∼10 (104). However, for intrinsically curved particle energy distributions (e.g., log parabola, EDD, and EDA models), the estimated jet power is ∼1044 erg s−1. The SED fitting at five epochs enables us to explore the correlation between the derived spectral parameters of various particle distribution models. Notably, the observed correlations are inconsistent with the predictions in the power-law with a maximum γ model, although the EDD and EDA models yield the correlations as expected. Moreover, the estimated physical parameter values are consistent with the model assumptions.

Mass modeling and kinematics of galaxy clusters in modified gravity

The chameleon screening mechanism has been constrained many a time using dynamic and kinematic galaxy cluster observables. Current constraints are, however, insensitive to different mass components within galaxy clusters and have been mainly focused on a single mass density profile, the Navarro-Frenk-White mass density model. In this work, we extend the study of the Chameleon screening mechanism in galaxy clusters by considering a series of mass density models, namely: generalized-Navarro-Frenk-While, b-Navarro-Frenk-While, Burket, Isothermal and Einasto. The coupling strength (β) and asymptotic value of the chameleon field (ϕ ∞) are constrained by using kinematics analyses of simulated galaxy clusters, generated both assuming General Relativity and a strong chameleon scenario. By implementing a Bayesian analysis we comprehensively show that the biases introduced due to an incorrect assumption of the mass model are minimal. Similarly, we also demonstrate that a spurious detection of evidence for modifications to gravity is highly unlikely when utilizing the kinematics of galaxy clusters.

An Absolute Mass, Precise Age, and Hints of Planetary Winds for WASP-121A and b from a JWST NIRSpec Phase Curve

We have conducted a planetary radial velocity measurement of the ultrahot Jupiter WASP-121b using JWST NIRSpec phase curve data. Our analysis reveals the Doppler shift of the planetary spectral lines across the full orbit, which shifts considerably across the detector (∼10 pixels). Using cross-correlation techniques, we have determined an overall planetary velocity amplitude of K p = 215.7 ± 1.1 km s−1, which is in good agreement with the expected value. We have also calculated the dynamical mass for both components of the system by treating it as an eclipsing double-line spectroscopic binary, with WASP-121A having a mass of M ⋆ = 1.330 ± 0.019 M ⊙, while WASP-121b has a mass of M p = 1.170 ± 0.043 M Jup. These dynamical measurements are ∼3× more precise than previous estimates and do not rely on any stellar modeling assumptions that have a ∼5% systematic floor mass uncertainty. Additionally, we used stellar evolution modeling constrained with a stellar density and parallax measurement to determine a precise age for the system, found to be 1.11 ± 0.14 Gyr. Finally, we observed potential velocity differences between the two NIRSpec detectors, with NRS1 lower by 5.5 ± 2.2 km s−1. We suggest that differences can arise from day/night asymmetries in the thermal emission, which can lead to a sensitivity bias favoring the illuminated side of the planet, with planetary rotation and winds both acting to lower a measured K P. The planet’s rotation can account for 1 km s−1 of the observed velocity difference, with 4.5 ± 2.2 km s−1 potentially attributable to vertical differences in wind speeds.

Tailor-Designed Models for the Turbulent Velocity Gradient through Normalizing Flow.

Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for nonlocal pressure and viscous contributions. The fidelity of the resulting models depends on the accuracy of the underlying modeling assumptions. Here, we discuss an alternative data-driven approach leveraging machine learning to derive a velocity gradient model which captures its statistics by construction. We use a normalizing flow to learn the velocity gradient probability density function (PDF) from direct numerical simulation (DNS) of incompressible turbulence. Then, by using the equation for the single-time PDF of the velocity gradient, we construct a deterministic, yet chaotic, dynamical system featuring the learned steady-state PDF by design. Finally, utilizing gauge terms for the velocity gradient single-time statistics, we optimize the time correlations as obtained from our model against the DNS data. As a result, the model time realizations resemble the time series from DNS statistically closely.

Toward first principles-based simulations of dense hydrogen

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g., planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange–correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principles PIMC as well as in DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions that are based on first principles.

Long live the cat: Ocelot population viability in a planned reintroduced population in Texas, USA

AbstractReintroductions are often needed to recover carnivore populations and restore ecological processes. Felids are common subjects of reintroduction efforts, but published population models informing felid reintroduction plans are uncommon, and poor planning has sometimes caused issues in felid reintroduction programs. In the United States, ocelots (Leopardus pardalis pardalis) are classified as endangered, and recovery requires population expansion into historic habitat. A multi‐organization effort is underway to establish a new ocelot population in Texas by releasing ocelots into an area of 478 km2 of suitable habitat in ocelots' historic but now unoccupied range. In this study, we used population viability analyses to compare different ocelot reintroduction strategies for the identified reintroduction area. Based on a potential ocelot breeding program's limitations, we modeled reintroduction using a founding population of no more than six ocelots and no more than four ocelots released per year for no more than 15 subsequent years. Within these limitations, we assessed projected population abundances and extinction risks after 30 years for 20 different reintroduction strategies. We found that long‐term releases are necessary to establish a viable population; under conservative model assumptions, releasing six ocelots in the initial year and then releasing four individuals annually for an additional 10–15 years is necessary for attaining a projected population greater than 36.62 ocelots (baseline) with &lt;6% extinction risk. We also found that ocelot population abundance is about equally sensitive to post‐release mortality and inbreeding depression. This highlights the importance of not only supporting reintroduced ocelots' survival but also managing for high genetic diversity in the reintroduction program. Further, we found that realistic but more liberal assumptions on the carrying capacity of the reintroduction area and the age of first reproduction for ocelots increase projected population abundances (53.95 individuals and 61.26 individuals, respectively), and thus reintroduction success. The model's sensitivity to carrying capacity suggests that long‐term habitat protection and expansion are among the most important management actions to support ocelot reintroduction. Our study establishes the first population viability model for an ocelot reintroduction plan anywhere across the species' wide geographic range, and it reinforces several key considerations for wildlife reintroduction efforts worldwide.

Population-level impact of switching to 1-dose human papillomavirus vaccination in high-income countries: examining uncertainties using mathematical modeling.

A concern in high-income countries is that switching to 1-dose human papillomavirus (HPV) vaccination could cause a rebound in HPV infection and cervical cancer if 1-dose efficacy or duration were inferior to 2 doses. Using mathematical modeling and up-to-date trial-based data, we projected the population-level effectiveness of switching from 2-dose to 1-dose vaccination under different vaccine efficacy and duration assumptions in high-income countries. We used HPV-ADVISE (Agent-based Dynamic model for VaccInation and Screening Evaluation), a transmission-dynamic model of HPV infection and cervical cancer, varying key model assumptions to identify those with the greatest impact on projections of HPV-16 and cervical cancer incidence over time: 1) 1-dose vaccine efficacy and vaccine duration, 2) mechanisms of vaccine efficacy and duration over time, 3) midadult (>30 years of age) sexual behavior, 4) progression to cervical cancer among midadults, and 5) vaccination coverage and programs. In high-income countries, 1-dose vaccination would cause no appreciable rebound in HPV-16 infection, except for a limited rebound under the most pessimistic assumptions of vaccine duration (average, 25 years), because 1) the switch would occur when HPV prevalence is low because of high 2-dose vaccination coverage and 2) individuals would be protected during their peak ages of sexual activity (<35 to 40 years of age). Our model projects a more limited rebound in cervical cancer because of a shift to older age at infection, resulting in fewer life-years left to potentially develop cancer. Projections were robust when varying key model assumptions. High protection during peak ages of sexual activity in high-income countries would likely mitigate any potential rebounds in HPV infection and cervical cancer under the most pessimistic assumptions of 1-dose efficacy and duration.

GIS-based solar radiation modelling for photovoltaic potential in cities: A sensitivity analysis for the evaluation of output variability range

Currently, there is increasing attention to matching the buildings’ energy demand with energy produced by renewable sources. This approach serves to both reduce the environmental footprint, the target of several European policies and initiatives and ensure access to energy for everyone worldwide, aligning with one of the aims of the United Nations Agenda 2030. Urban environments are suitable for the installation of photovoltaic panels, which can be easily integrated within roofs to minimise land use and installation costs. Multiple studies focused on solar radiation as the most critical element affecting the estimation of the photovoltaic potential, in particular by resorting to Geographic Information Systems (GIS). Among different tools, which are useful for integrating layers with different spatial resolutions, the r.sun function embedded in GRASS GIS has been selected as the most suitable to capture complex variations resulting from weather and geographical parameters. However, a gap in the literature has emerged as no study is available to define the precision degree of the tool when inputting parameters derived from various data sources based on different model assumptions. The scope of this research is to collect open-access weather inputs and quantify the solar radiation output along with its variability range. Based on 28 test plans, resulting from the combination of input parameters from alternative data sources, this research quantified the output variability range to be ±20 %, thus requiring additional processing to increase the precision. The paper concludes with a critical assessment of the strengths and weaknesses of the current research, emphasizing the importance of validating the results with measured values in forthcoming research, to move from precision to accuracy evaluation.

Household Durable Goods Spending in Urban Areas: A New Keynesian Micro Perspective

In the New Keynesian model, durable goods are typically assumed to be more responsive to monetary policy than non-durable goods. While this assumption is generally based on macroeconomic policy studies, this research aims to identify whether monetary authorities need to consider household microeconomic aspects such as dependents and long-term saving behavior when formulating policies to stimulate durable goods spending. The study employs two inverse semilogarithmic equations with micro data on urban households. Research findings confirm the existing assumption of the New Keynesian model. Durable goods spending remains sensitive to macroeconomic policies, particularly interest rate adjustments by monetary authorities. Furthermore, the research provides evidence supporting the life-cycle hypothesis for durable goods consumption among urban households. Therefore, this study underscores the pivotal role of durable goods expenditures in ensuring sustainable economic stability.