Model Assumptions Research Articles

COMAP Pathfinder – Season 2 results

The Carbon monOxide Mapping Array Project (COMAP) Pathfinder survey continues to demonstrate the feasibility of line-intensity mapping using high-redshift carbon monoxide (CO) line emission traced at cosmological scales. The latest COMAP Pathfinder power spectrum analysis is based on observations through the end of Season 2, covering the first three years of Pathfinder operations. We use our latest constraints on the CO(1–0) line-intensity power spectrum atz~ 3 to update corresponding constraints on the cosmological clustering of CO line emission and thus the cosmic molecular gas content at a key epoch of galaxy assembly. We first mirror the COMAP Early Science interpretation, considering how Season 2 results translate to limits on the shot noise power of CO fluctuations and the bias of CO emission as a tracer of the underlying dark matter distribution. The COMAP Season 2 results place the most stringent limits on the CO tracer bias to date, at ⟨T b⟩ < 4.8 μK, which translates to a molecular gas density upper limit ofρH2< 1.6 × 108M⊙Mpc−3atz~ 3 given additional model assumptions. These limits narrow the model space significantly compared to previous CO line-intensity mapping results while maintaining consistency with small-volume interferometric surveys of resolved line candidates. The results also express a weak preference for CO emission models used to guide fiducial forecasts from COMAP Early Science, including our data-driven priors. We also consider directly constraining a model of the halo–CO connection, and show qualitative hints of capturing the total contribution of faint CO emitters through the improved sensitivity of COMAP data. With continued observations and matching improvements in analysis, the COMAP Pathfinder remains on track for a detection of cosmological clustering of CO emission.

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.

Complementary use of analytical equations and numerical models for composite liner designs

The finite element (FE) method is used to identify limitations and extend the applicability of an analytical solution for leakage through holed wrinkles in composite liners. The limiting assumption in the analytical model is relaxed based on FE results, providing a means of obtaining good agreement through the two methods over a wide spectrum of cases. Despite the flexibility of the FE, the analytical model has the advantage of readily accommodating a wide range of dimensions and material properties that, even with a very large mesh, may be difficult to analyze using FE. In addition, the analytical equation is well-suited for Monte Carlo simulation. This is illustrated by the use of the modified analytical equation and the Monte Carlo simulations to compare the performance of two types of composite liners currently approved for use in Chinese landfills with a generic design in Canada and other parts of the world. It is shown that even with a relatively small number of holed wrinkles leakage through the geomembrane composite liner can differ by up to two orders of magnitude between composite liners considered to be equivalent. The paper also notes the need to revise regulations to reflect the evolution of knowledge.

Terrain parameter identification for wheeled mobile robots using deep neural networks

This paper introduces an innovative approach to soil parameter estimation using deep artificial neural networks (ANN) tailored to estimate a wide range of soil types. Previous research in this field has relied on limited datasets and oversimplified assumptions in semi-empirical models. In this work, the sensitivity of stress distribution is analyzed with respect to soil parameters, revealing significant errors resulting from the simplifications in semi-empirical models and emphasizing the need for more accurate estimation methods with minimal reliance on simplifying assumptions. Training an ANN requires a comprehensive dataset, which has been a challenge due to limited access to diverse soil samples. Addressing this issue, the paper conducts simulations of a single wheel’s movement across a wide range of soil parameter combinations to generate the required dataset. These simulations are done using a dynamic motion-compliant semi-empirical model detailed in the article. Subsequently, the network’s hyperparameters are fine-tuned through a grid search, followed by extensive training of the optimized ANN model. In the end, the ANN demonstrates promising performance in accurately estimating soil parameters, validated through simulation results of both single wheel and Mars rover motion.

On the estimation of bivariate conditional transition rates

Recent literature has found conditional transition rates to be a useful tool for avoiding Markov assumptions in multi-state models. While the estimation of univariate conditional transition rates has been extensively studied, the intertemporal dependencies captured in the bivariate conditional transition rates still require a consistent estimator. We provide an estimator that is suitable for censored data and emphasize the connection to the rich theory of the estimation of bivariate survival functions. Bivariate conditional transition rates are necessary for various applications in the survival context but especially in the calculation of moments in life insurance mathematics.

Cost-utility analysis for sublingual versus intravenous edaravone in the treatment of amyotrophic lateral sclerosis

BackgroundEdaravone has been widely used in amyotrophic lateral sclerosis (ALS) treatment, and a sublingual (SL) tablet has been developed to offer a more convenient alternative for injection. We present a cost-utility analysis to comprehensively evaluate the costs and health outcomes of oral and intravenous edaravone for the treatment of ALS in Chinese medical context.MethodsCost-utility analysis of SL tablets of edaravone versus intravenous edaravone at home was performed by constructing a 20-year Markov model of ALS stage 1–4 and death. The data were extracted from the literature with model assumptions. Typical sensitivity analysis and scenario analysis for administering SL tablets at home versus intravenous tablets at the hospital were performed.ResultsIn the base case analysis, with SL tablets and intravenous injections both at home, the model estimated an additional cost of ¥12,670.04 and an additional 0.034 QALYs over 20 years (life time) of modeling analysis, and the ICER was ¥372,648.24 per QALY. However, in the scenario of intravenous administration at the hospital, SL tablet was demonstrated dominance to intravenous injection.ConclusionsUsing 3 times the GDP per capita of China in 2023 as the threshold, the SL tablet edaravone was not cost-effective in the context of home treatment for both formulationst, but was dominance to intravenous injection in hospital treatment. The results highlighted the importance of treatment context for health economic analysis.

2D BAO vs. 3D BAO: Solving the Hubble Tension with Bimetric Cosmology

Ordinary 3D Baryon Acoustic Oscillations (BAO) data are model-dependent, requiring the assumption of a cosmological model to calculate comoving distances during data reduction. Throughout the present-day literature, the assumed model is ΛCDM. However, it has been pointed out in several recent works that this assumption can be inadequate when analyzing alternative cosmologies, potentially biasing the Hubble constant (H0) low, thus contributing to the Hubble tension. To address this issue, 3D BAO data can be replaced with 2D BAO data, which are only weakly model-dependent. The impact of using 2D BAO data, in combination with alternative cosmological models beyond ΛCDM, has been explored for several phenomenological models, showing a promising reduction in the Hubble tension. In this work, we accommodate these models in the theoretically robust framework of bimetric gravity. This is a modified theory of gravity that exhibits a transition from a (possibly) negative cosmological constant in the early universe to a positive one in the late universe. By combining 2D BAO data with cosmic microwave background and type Ia supernovae data, we find that the inverse distance ladder in this theory yields a Hubble constant of H0=(71.0±0.9)km/s/Mpc, consistent with the SH0ES local distance ladder measurement of H0=(73.0±1.0)km/s/Mpc. Replacing 2D BAO with 3D BAO results in H0=(68.6±0.5)km/s/Mpc from the inverse distance ladder. We conclude that the choice of BAO data significantly impacts the Hubble tension, with ordinary 3D BAO data exacerbating the tension, while 2D BAO data provide results consistent with the local distance ladder.

6.G. Scientific session: The importance of using country-specific severity distributions in burden of disease indicators

Abstract One of the most commonly used health gap summary measures in the field of public health is the Disability-adjusted life year (DALY). It has become the key metric for quantifying the burden of disease (BoD) and is used as an information source for setting priorities for health care and health service planning. The DALY metric quantifies gaps between a theoretical population in perfect health and the current health status related to both morbidity and mortality. It is the sum of the number of healthy life years lost due to disability (YLD) and death (YLL). The YLD summarises the loss of healthy life time using the frequency of diseases differentiated by severities or related health states (including sequelae). Depending on each health state, it converts the time spent with diseases into YLD by weighting each year with health limitation with so-called disability weights. This allows the two indicators YLL and YLD to be added to the DALY. Consequently, severity distributions are a means of describing the range of health loss along specific health states, which enables an accurate mapping with disability weights. For most of the diseases, these distributions are usually categorised as mild, moderate, severe, or no health loss (asymptomatic). Conditions with higher severity levels contribute more to the YLD and thus, to the overall disease specific BoD. The severity distributions are highly dependent on utilization at the individual level, but also on available health care services at the society level, which indicates that differences by settings are to be expected. Changes over time in the share of severe cases, for instance, may reflect the effectiveness of health interventions and policies. This underlines the importance of severity distributions, as they also reflect information about the quality of healthcare for those affected. The Global Burden of Disease (GBD) study applies for a significant proportion of their considered diseases uniform severity distributions across countries and broader regions. They acknowledge concerns over applying estimates of severity distributions based on limited data, noting that it is the only available information that they were able to use. As the YLD estimates are often utilized to develop and prioritize health policy interventions, there is a necessity for country-specific estimates that are evidence based and less susceptible to model assumptions. In most cases, the estimation of severity distributions involves complex methodology as data for such assessments are very limited or hardly reliable. In the following workshop, general concepts and country cases from Germany, Scotland, and Belgium will be presented covering a variety of diseases. The examples underline the importance of estimating national and even regional severity distributions when identifying the importance of diseases or comparing the health conditions of populations across time. Key messages • As the Burden of disease indicators are predominantly used to prioritize health interventions and allocate resources, it is crucial to use accurate information in the severity levels of disease. • Severity distributions reflect the health care services and vary across different settings. Utilising country-specific severity distributions when estimating Burden of Disease indicators is essential.

Accounting for adaptation when projecting climate change impacts on health: a review of methods

Abstract Exposure to high and low temperatures can cause harm to health. Temperature related health effects are likely to increase in the future, due to global warming, unless populations can adapt to a warming environment. Adaptation occurs through multiple mechanisms, however, the amount that populations may adapt to rising temperatures is unclear. There are currently a variety of methods used to include adaptation in projections, but it is unclear how underlying assumptions are made and whether they are based on evidence. With increasing interest from decisionmakers around implementation of adaptation strategies, an important step in responding to climate change will be to understand how adaptation can statistically be incorporated when estimating future health burdens. A systematic scoping review was conducted to identify all quantitative methods to include adaptation in studies projecting future temperature-related health impacts under climate change. Approaches employed in studies were coded into methodological categories. Advantages and disadvantages of each method were also explored, along with the empirical basis for model assumptions. Eight methodological categories were identified from the included fifty-nine studies. More recent methods of including adaptation in projections use a combination of approaches or modelling adaptation based on specific adaptation strategies or socioeconomic conditions. The most used approaches in the literature to model adaptation are heat threshold shifts and reductions in the exposure-response slope. Just under 20% of studies were categorised as using an empirical basis for statistical assumptions. Including adaptation in projections considerably reduced the projected temperature-mortality burden in the future. Researchers should ensure that all future impact assessments include adaptation uncertainty in projections and assumptions are based on empirical evidence where possible. Key messages • Including adaptation in future projections considerably affects temperature-related mortality numbers. • All future impact assessments should include adaptation uncertainty and assumptions should be based on evidence where possible.

Estimation of the friction coefficient by identifying the evolution of rough surface topography

The complexity and randomness of surface roughness make the origin of friction always puzzling. Is there a potential correlation mechanism between the evolution of friction force and surface topography, and is it possible to estimate the friction coefficient from the topography evolution? Here, we explore the evolution of surface topography in metallic materials during friction by defining the characteristic orientation of the surface and analyzing its changes over time, which closely mirrors the behavior of friction forces. Then, we propose a simple relationship that relates the characteristic orientation and friction force via the friction coefficient. Therefore, the friction coefficient can be evaluated based on topography evolution without relying on any theoretical modeling assumptions. These results reveal the mechanism between complex surface topography and friction force through a simple formula and provide a method to estimate the friction coefficient. This method has promising applications when the friction coefficient is difficult to measure, such as in large-scale landslides and geologic faults.

Calculating potential impact fractions to assess the impact of tobacco control policies

Abstract This presentation will provide a straightforward and accessible method for evaluating the effectiveness of various tobacco control policies in reducing the smoking-associated disease burden. To estimate numbers and proportions of potentially avoidable disease cases under different policy intervention scenarios (such as cigarette tax/price increases, comprehensive marketing ban, and plain packaging), this approach entails the calculation of age- and sex-specific potential impact fractions, representing the percentage change in disease risk following changes in exposure to smoking. The approach can account for lag and latency periods between reductions in smoking prevalence and declines in disease excess risks. The future disease burden is estimated by combining data on the incidence or mortality of the disease of interest, published effect sizes of tobacco control policies, and national smoking prevalence data. Intervention scenarios are compared against a status quo scenario that assumes the continuation of recent smoking trends. The presentation will outline the approach, using the example of smoking-associated cancer, and discuss model assumptions, required data, and sensitivity analyses. This straightforward modelling framework enables the comparison of different health policy measures’ impacts, providing valuable insights for policymakers and public health officials regarding the potential public health impact of tobacco control policy measures.

A Review of Comprehensive Guidelines for Computational Fluid Dynamics (CFD) Validation in Solar Chimney Power Plants: Methodology and Manzanares Prototype Case Study

This review provides a comprehensive examination of CFD modeling procedures for SCPP, with an emphasis on the detailed methodologies and a case study of the Manzanares prototype in Spain. The introduction delineates the historical context and physical modeling principles of solar chimneys, while highlighting their potential in industrial applications. The governing equations are meticulously discussed, covering assumptions in both 2D and 3D CFD modeling, the continuity and momentum equations, and the selection and accuracy of turbulence models, particularly the k-ε equations. The review also delves into heat transfer modeling, encompassing the energy equation and radiation modeling. Analytical evaluations of turbine pressure drop ratios and performance metrics for power generation efficiency are critically analyzed. The establishment of boundary conditions in solar chimney applications, including sky temperature assessments and distinctions between 2D and 3D boundary conditions, is extensively explored. Mesh generation techniques for both 2D and 3D CFD models are presented, supported by case studies. Parametric studies and experimental investigations are scrutinized to elucidate their impact on the performance of solar chimneys. The temperature–entropy diagram for an idealized Brayton cycle is introduced as a conceptual framework for efficiency analysis. Validation of the CFD codes, both 2D and 3D, against experimental data is performed to ensure model accuracy. The review further examines energy balance approaches in modeling solar chimneys, presenting state-of-the-art CFD results and discussing their implications in both 2D and 3D contexts. The synthesis of these findings culminates in a comprehensive conclusion, offering insights into the future directions and potential advancements in the CFD modeling of solar chimneys. This work aims to serve as a definitive reference for researchers and practitioners in the field, providing a robust foundation for the development and optimization of SCPP technology.

A novel simulation of space charge decay dynamics after removing the voltage in epoxy resin composites

Abstract Epoxy resin serves as a critical insulating component in ultra-high voltage dry direct current bushings. However, the accumulation of space charges within the epoxy resin, a byproduct of charge mobility, poses a significant risk to the reliability and operational safety of bushings. Conventional space charge attenuation models, especially after voltage removal, have limited use in simulations aimed at understanding this phenomenon. This study introduces an improved model integrating the bipolar charge transport mechanism with space charge decay based on the hopping conduction mechanism and Schottky’s theorem, establishes a theoretical framework for predicting the space charge behavior following voltage removal, and conducts a simulation to investigate the decay process within the internal structure of epoxy resin and measure the residual charges after voltage removal using the pulsed electro-acoustic method. The experimental data validate the accuracy of the proposed model and theoretical assumptions. The findings show that the remaining negative charges after voltage removal are not enhanced by the field enhancement; the anodic positive and cathodic positive charges are distributed in two-segment discontinuous traps, whereas the negative charges are distributed in one-segment traps. The model identifies two distinct trapping sites for anodic and cathodic positive charges, and one trapping site for negative charges. After voltage removal, when the field strength exceeded 40 kV mm−1, positive charges exist near the upper and lower electrodes, and negative charges exist near the center of the specimen. The consistency between the simulated predictions and experimental data proves the effectiveness of the proposed model in accurately simulating the space charge decay in epoxy materials after voltage removal.

A measurement scale for material deprivation: A model‐based approach

The prevailing practice of defining classes of material deprivation based on the raw sum of a relatively small number of binary items has obvious advantages in terms of communication and ease of use. Do two people with the same raw score always have the same level of deprivation? This paper examines the methodological foundations of the raw score, comparing classical test theory with model‐based test theory such as Rasch measurement and item response theories. After discussing the merits and limitations of the different approaches, the paper legitimizes the raw score when the assumptions of the Rasch model are met. Based on the analytical procedure of the Rasch model and using the pre‐Covid data from the Survey on Income and Living Conditions, the paper also suggests how to develop a European scale for measuring material deprivation that ensures comparability across European countries.

Theoretical analysis of water-droplet condensation and evaporation in prescribed environment with radiation

Abstract A quasi-explicit algebraic solution of the problem of quasi-steady water droplet heat- and mass-transfer with condensation or evaporation in a prescribed environment including radiation has been obtained. The solution is based on the mass-fraction form of Fick’s law of species diffusive mass transport, which allows accounting for variation in gas density through the diffusion layer surrounding the droplet. A new term, the average vapor supersaturation through the diffusion layer, has been included that allows the model to be extended to environmental conditions significantly outside those for which it is theoretically derived (dilute or near-saturation), even to conditions of relatively high temperature, low relative humidity, and low pressure. The latter two have atmospheric significance because evaporative cooling can induce significant decrease in droplet temperature and possibly induce freezing. Basic modeling assumptions are confirmed by comparison with time-dependent temperature and evaporation-rate measurements of a laboratory laser-heated droplet. Accuracy of the new model is assessed by comparison with exact equations. For temperatures between −30 and 0°C and negligible radiation, relative-error magnitudes in droplet temperature and evaporation rate are less than 5%, even for relative humidity down to 10% and pressure down to 60 kPa. This represents an improvement in accuracy over the conventional model, in which property variations of density and supersaturation through the diffusion layer are not taken into account. Differences in predicted evaporation rates between the mass-fraction and the more common partial-density Fick’s laws, which can be significant (>10% above 70% relative humidity) in coupled, implicit equations, are shown to be made negligibly small (<0.5%) by linearizing about the saturation state.

Discrimination of mnemonic similarity is associated with short-term and long-term memory precision.

Remembering specific memories with precision relies on the differentiation of similar memory contents - a process commonly referred to as pattern separation and behaviorally operationalized as lure discrimination in the mnemonic similarity task. Although pattern separation is typically investigated in the context of long-term memory (LTM), recent research extends these findings to short-term memory (STM) within a mixture model framework, emphasizing the distinction between memory quality and quantity. According to this framework, pattern separation is associated with memory precision across STM and LTM, regardless of the overall memory likelihood. However, these associations among memory quality measures may persist without the mixture model assumption. In an alternative model, a unitary memory strength measure quantified as a discrimination score (d') may also capture the association between pattern separation and memory quality, as pattern separation has been previously linked with strength-based memory performance. We tested these possibilities based on individual differences among 132 participants who underwent tasks measuring mnemonic pattern separation and STM/LTM quality. We found that behavioral estimates of pattern separation are significantly correlated with STM and LTM precision, irrespective of the likelihood of STM/LTM recall success. However, these associations are absent when considering the correlation between pattern separation and memory strength under a unitary model framework. By leveraging individual differences to constrain our understanding of cognitive models, our data unravel the intricate relationship between pattern separation and memory quality across timescales. These findings may therefore contribute to identifying sensitive behavioral measures for detecting subtle memory deficits in older adults or clinical populations.

Accelerometer-assisted computer vision data fusion framework for structural dynamic displacement reconstruction

Accurate displacement measurement provides crucial information for evaluating the structural health condition. It is important to note that both direct and indirect displacement measurement methods have their own limitations, which can be remedied by leveraging each other’s strengths. In this study, a novel accelerometer-assisted computer vision data fusion framework is developed for accurately reconstructing structural dynamic displacement. The framework does not require a specific mathematical model or prior knowledge about the data’s characteristics, allowing it to be applied more broadly across different applications without the constraints of model assumptions. The core of this framework is to integrate successive variational mode decomposition (SVMD) and Bayesian optimization approaches to adaptively determine the weight factors of the fusion components. Importantly, an enhanced optical flow approach is presented for converting pixel movement to structural displacement from natural targets. This approach can effectively reduce the selection of mis-matched points within the ROI (Region of Interest), thereby reducing drift errors. The developed framework is verified via shaking table tests of a reinforced concrete frame structure under seismic excitation. Results indicate that the developed framework excels in accurately estimating structural dynamic displacement. Compared to single-vision displacement identification results, the proposed framework demonstrates lower peak error (< 1.65 mm) and normalized root mean square error (< 0.30). Meanwhile, the reconstructed displacement, by introducing dynamic displacement component from acceleration measurement, presents a wider frequency range than the vision-based displacement.

A data-driven prediction for concrete crack propagation path based on deep learning method

Accurate and efficient prediction of concrete crack propagation path facilitates timely maintenance. To avoid errors arising from assumptions in physical models and parameter measurements, a data-driven prediction method for concrete crack propagation path is proposed. The spatio-temporal prediction approach is presented by unifying the conditional Generative Adversarial Network and Long Short-Term Memory Network (UcGLS-Net). The UcGLS-Net has the advantage of enabling temporal (by Long Short-Term Memory Network, LSTM) and spatial information (by the conditional Generative Adversarial Network, cGAN) in the concrete crack propagation process to be treated simultaneously and distinctly thus improving prediction accuracy. Notably, results demonstrate that UcGLS-Net still works in crack propagation path prediction even with unknown aggregate distribution. Moreover, the effects of historical input quantity and prediction intervals on prediction accuracy of the UcGLS-Net are studied and the network framework is optimized. Due to high efficiency, this study provides a new adaptable and real-time prediction method for concrete crack propagation path, offering support for the design, damage prediction, assessment, and maintenance of concrete structures.

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.