Simple Model Research Articles

Roaming in acetaldehyde.

We investigate roaming in the photodissociation of acetaldehyde (CH3CHO), providing insights into the contrasting roaming dynamics observed for this molecule compared to formaldehyde. We carry out trajectory studies for full-dimensional acetaldehyde, supplemented with an analysis of a two-degree-of-freedom restricted model and obtain evidence for two distinct roaming pathways. Trajectories exhibit roaming at both shorter (9-11.5 au) and larger (14.5-22.9 au) maximum CH3-HCO separations, characterized by differing amounts of HCO rotation. No roaming trajectories were found in the intervening gap region. The roaming dynamics near 14.5-22.9 au are well-reproduced by the restricted model and involve passage through a centrifugal barrier, analogous to formaldehyde roaming. However, the shorter-range 9-11.5 au roaming appears unique to acetaldehyde and is likely facilitated by repulsive interactions absent in the simplified models. Phase space analysis reveals that this additional roaming pathway is inaccessible in the reduced dimensionality system. The findings suggest that acetaldehyde's increased propensity for roaming compared to formaldehyde may arise from the presence of multiple distinct roaming mechanisms rather than solely the higher roaming fragment mass.

Conditions and benefits of X-point radiation for the island divertor

We present a method to geometrically quantify the three magnetic island chains with the poloidal mode numbers m = 4, 5, and 6 (referred to in this paper as high-iota, standard, and low-iota islands, respectively), on which the W7-X divertor relies. The focus is on a comparative study of their detachment performance using a series of models of different physical and geometrical complexity, ranging from one- to three-dimensional (1D to 3D). In particular, it aims to identify the key physical elements behind the correlation between impurity radiation and island geometry and the associated detachment stability. Assuming intrinsic carbon as a radiator, we scan the three island chains with the EMC3-Eirene code based on otherwise identical code inputs. We find that the three islands behave differently in the radiation distribution, in the development of the radiation zones during detachment, and in the ‘radiation costs’, defined as the product of impurity and electron density near the last closed flux surface. While the radiation costs for the iota = 5/4 and 5/5 island chains linearly increase with the total radiation, the low-iota island with iota = 5/6 shows a bifurcation behavior in the sense that the radiation costs initially increase and then decrease when the total radiation exceeds a critical level. Consistent with the numerical trends, stable detachment, which is experimentally easy and robust to achieve with the standard iota = 5/5 island chain, remains an experimental challenge with the low-iota configuration. Dedicated numerical experiments show that the recycling neutrals and the ratio of parallel to perpendicular heat transport, which depends closely on the field line pitch, play a significant role in the formation and evolution of the radiation layer. A deeper understanding of the underlying physics relies on simpler models that explain why and how flux expansion can reduce the radiation costs. From these insights, we derive the conditions in which detached plasmas can benefit from the expansion of flux surfaces around the X-point. We show and explain why the current divertor design limits the actual capability of the high-iota configuration and propose solutions. The work is presented within a theoretical/numerical framework but cites relevant experimental evidence to emphasize its practical significance.

Gauge fixing for sequence-function relationships.

Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation.

Comparison of simplified bone-screw interface models in materially nonlinear μFE simulations

Micro finite-element (μFE) simulations serve as a crucial research tool to assist laboratory experiments in the biomechanical assessment of screw anchorage in bone. However, accurately modelling the interface between bone and screw threads at the microscale poses a significant challenge. Currently, the gold-standard approach involves employing computationally intensive physical contact models to simulate this interface. This study compared nonlinear μFE predictions of deformations, whole-construct stiffness, maximum force and damage patterns of three different computationally efficient simplified interface approaches to the general contact interface in Abaqus Explicit, which was defined as gold-standard and reference model. The μCT images (resolution: 32.8 μm) of two human radii with varying bone volume fractions were utilized and a screw was virtually inserted up to 50% and 100% of the volar-dorsal cortex distance. Materially nonlinear μFE models were generated and loaded in tension, compression and shear. In a first step, the common simplification of using a fully-bonded interface was compared to the general contact interface, revealing overestimations of whole-construct stiffness (19% on average) and maximum force (26% on average), along with inaccurate damage pattern replications. To enhance predictions, two additional simplified interface models were compared: tensionally strained element deletion (TED) and a novel modification of TED (TED-M). TED deletes interface elements strained in tension based on a linear-elastic simulation before the actual simulation. TED-M extends the remaining contact interface of TED by incorporating neighboring elements to the contact area. Both TED and TED-M reduced the errors in whole-construct stiffness and maximum force and improved the replication of the damage distributions in comparison to the fully-bonded approach. TED was better in predicting whole-construct stiffness (average error of 1%), while TED-M showed lowest errors in maximum force (1% on average). In conclusion, both TED and TED-M offer computationally efficient alternatives to physical contact modelling, although the fully-bonded interface may deliver sufficiently accurate predictions for many applications.

The In-Plane Seismic Response of Infilled Reinforced Concrete Frames Using a Strut Modelling Approach: Validation and Applications

Reinforced Concrete (RC) buildings often rely on masonry walls to increase their rigidity and strength, distinguishing them from bare frames. Consequently, the lateral capacity of the RC frames is significantly impacted by the presence or absence of these walls. Numerical models are fundamental to understanding this behavior interaction, but the development of robust simplified models is still scarce. Based on this motivation, the reliability of a simplified numerical modelling approach was examined in this study and compared to several experimental tests. An optimized approach was implemented to determine the strut parameters, rather than relying on existing empirical formulae. The reliability of the initial stiffness, maximum strength, and energy dissipation was studied. From the results, the accuracy of the considered modelling strategy can be observed in different types of masonry elements (strong and weak units) with and without openings. The validated simulation approach reveals that the adopted macro-modelling procedure can accurately represent the behavior of infilled masonry frames. The maximum deviation of the prediction of the initial stiffness and maximum strength was found to be around 23% and 14%, respectively. These findings illustrate that the strut model effectively replicates real behavior with a satisfactory level of accuracy. However, using a consistent formula to define the strut can result in significant errors, particularly in strut width.

A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports.

Although supervised machine learning is popular for information extraction from clinical notes, creating large annotated datasets requires extensive domain expertise and is time-consuming. Meanwhile, large language models (LLMs) have demonstrated promising transfer learning capability. In this study, we explored whether recent LLMs could reduce the need for large-scale data annotations. We curated a dataset of 769 breast cancer pathology reports, manually labeled with 12 categories, to compare zero-shot classification capability of the following LLMs: GPT-4, GPT-3.5, Starling, and ClinicalCamel, with task-specific supervised classification performance of 3 models: random forests, long short-term memory networks with attention (LSTM-Att), and the UCSF-BERT model. Across all 12 tasks, the GPT-4 model performed either significantly better than or as well as the best supervised model, LSTM-Att (average macro F1-score of 0.86 vs 0.75), with advantage on tasks with high label imbalance. Other LLMs demonstrated poor performance. Frequent GPT-4 error categories included incorrect inferences from multiple samples and from history, and complex task design, and several LSTM-Att errors were related to poor generalization to the test set. On tasks where large annotated datasets cannot be easily collected, LLMs can reduce the burden of data labeling. However, if the use of LLMs is prohibitive, the use of simpler models with large annotated datasets can provide comparable results. GPT-4 demonstrated the potential to speed up the execution of clinical NLP studies by reducing the need for large annotated datasets. This may increase the utilization of NLP-based variables and outcomes in clinical studies.

An ANOVA-based test for random effects in a nonlinear mixed model using a Monte Carlo permutation procedure

Nonlinear mixed models are essential for data analysis due to their versatility and adaptability to many research objectives and data formats. Testing misspecification is crucial to selecting a working nonlinear model. A useful and simpler model is often chosen using variance component tests. Nonlinear models are rarely studied in the literature, unlike linear models, which have numerous successful test development attempts. The latter test requires linearity between the answer and the explanatory factors. Thus, we use an existing linearization technique to build a linear model using pseudo-responses. If random effects were needed in the original nonlinear mixed model, the pseudo-responses vector inherits a mixed linear model representation. Simulation experiments and an application to a real-world bioassay data set evaluate this permutation test.

Symmetry energy in holographic QCD

We study the symmetry energy (SE), an important quantity in nuclear physics, in the Witten-Sakai-Sugimoto model and in a much simpler hard-wall model of holographic QCD. The SE is the energy contribution to the nucleus due to having an unequal number of neutrons and protons. Using a homogeneous Ansatz representing smeared instantons and quantizing their isospin, we extract the SE and the proton fraction assuming charge neutrality and beta-equilibrium, using quantization of the isospin zeromode. We also show the equivalence between our method adapted from solitons and the usual way of the isospin controlled by a chemical potential at the holographic boundary. We find that the SE can be well described in the WSS model if we allow for a larger ’t Hooft coupling and lower Kaluza-Klein scale than is normally used in phenomenological fits.

Numerical simulation of steel-concrete composite beams and slabs at elevated temperatures

Steel-concrete composite beams with shear studs providing composite action between the concrete slabs and steel beams are widely used in modern steel-framed building construction. Three-dimensional finite element modelling has been a viable option for investigating the fire behaviour of composite beams. Although many finite element models are available in the open literature, most of them are simplified models verified by limited test data. Meanwhile, early test data often did not have all the required information for finite element simulation, and some assumptions need to be made. But more detailed test data are now available in the open literature for steel-concrete composite beams, which can be used to develop a generalised finite element model. In addition, National Institute of Standards and Technology has proposed new stress-strain models for steels at elevated temperatures, which can be compared with the more widely adopted Eurocode 4 steel material models in simulating composite beams. Accordingly, this paper selected 22 specimens, including two reinforced concrete slabs, 12 composite beams with reinforced concrete slabs, two composite slabs with profiled steel sheeting, two steel-concrete composite push test specimens with profiled steel sheeting, and four composite beams with profiled steel sheeting, from nine references to verify the developed finite element model. The proposed finite element model can capture different failure modes of composite beams, such as steel yielding/local buckling, concrete cracking/crushing, debonding of profiled steel sheeting, and stud fracture, which is crucial to comprehend the composite beam behaviour at elevated temperatures.

Balancing High-performance and Lightweight: HL-UNet for 3D Cardiac Medical Image Segmentation

Cardiac magnetic resonance imaging is a crucial tool for analyzing, diagnosing, and formulating treatment plans for cardiovascular diseases. Currently, there is very little research focused on balancing cardiac segmentation performance with lightweight methods. Despite the existence of numerous efficient image segmentation algorithms, they primarily rely on complex and computationally intensive network models, making it challenging to implement them on resource-constrained medical devices. Furthermore, simplified models designed to meet the requirements of device lightweighting may have limitations in comprehending and utilizing both global and local information for cardiac segmentation. Therefore, we propose a novel 3D high-performance lightweight medical image segmentation network, HL-UNet, for application in cardiac image segmentation. Specifically, in HL-UNet, we propose a novel residual-enhanced Adaptive attention (REAA) module that combines residual-enhanced connectivity with an adaptive attention mechanism to efficiently capture key features of input images and optimize their representation capabilities, and integrates the Visual Mamba (VSS) module to enhance the performance of HL-UNet. Compared with large-scale models such as TransUNet, HL-UNet increased the Dice of the right ventricular cavity (RV), left ventricular myocardia (MYO) and left ventricular cavity (LV), the key indicators of cardiac image segmentation, by 1.61%, 5.03% and 0.19%, respectively. At the same time, the Params and FLOPs of the model decreased by 41.3M and 31.05G, respectively. Compared to lightweight models such as the MISSFormer, the HL-UNet improves the Dice of RV, MYO, and LV by 4.11%, 3.82%, and 4.33%, respectively, when the number of parameters and computational complexity are close to or even lower.

Forecasting realized volatility: Does anything beat linear models?

We evaluate the performance of several linear and nonlinear machine learning (ML) models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset that includes past values of the RV and additional predictors, including lagged returns, implied volatility, macroeconomic and sentiment variables. We compare these models to widely used heterogeneous autoregressive (HAR) models. Our main conclusions are that (i) the additional predictors improve the out-of-sample forecasts at the daily and weekly forecast horizons; (ii) we find no evidence that nonlinear ML models can statistically outperform linear models in general; and (iii) in terms of the economic value that an investor would derive from monthly RV forecasts to build volatility-timing portfolios, simpler models without additional predictors work better.

Investigation of thermodynamic process in a humidifier of humidification–dehumidification system based on a computational fluid dynamics model

Humidification–dehumidification is well-suited for solar-powered operation. Packed bed humidifier is an essential system component for water evaporation. Computational fluid dynamics (CFD) simulation of the packed bed humidifier is a challenging multiscale task. The simultaneous capture of micro flow properties and macro transfer performance by existing CFD models is inadequate. This paper presents a novel CFD model for a structured packed bed humidifier. The Eulerian method is applied to the multiphase flow and several simplified models are proposed to evaluate the influence of the microstructure on the transfer properties. The model can predict the overall transfer performance and partially reflect the micro flow characteristics. It is found that the air temperature increases rapidly when the packing height is < 0.3 m, while the liquid temperature decreases rapidly when the height is > 1.65 m or <0.45 m. Under different conditions, the outlet relative humidity of the air is all between 95 % and 99 %. The maximum evaluation error of gained output ratio of the humidification–dehumidification system can reach 10 % under the saturated air assumption. With a maximum of 10.85 % and a minimum of 3.46 %, the proportion of sensible heat transfer in the total energy transfer decreases with the packing height.

Simplified and Detailed Evaluations of the Uncertainty of the Measurement of Microbiological Contamination of Pharmaceutical Products.

The control of the microbial contamination of pharmaceutical products (PP) is crucial to ensure their safety and efficacy. The validity of the monitoring of such contamination depends on the uncertainty of this quantification. Highly uncertain quantifications due to the variability of determinations or the magnitude of systematic effects affecting microbial growth or other analytical operations make analysis unfit for the intended use. The quantification of the measurement uncertainty expressing the combined effects of all random and systematic effects affecting the analysis allows for a sound decision about quantification adequacy for their intended use. The complexity of the quantification of microbial analysis uncertainty led to the development of simplified ways of performing this evaluation. This work assesses the adequacy of the simplified quantification of the uncertainty of the determination of the microbial contamination of PP by log transforming microbial count and dilution factor of the test sample whose uncertainty is combined in a log scale using the uncertainty propagation law. This assessment is performed by a parallel novel bottom-up and accurate evaluation of microbial analysis uncertainty involving the Monte Carlo method simulation of the Poisson log-normal distribution of counts and of the normally distributed measured volumes involved in the analysis. Systematic effects are assessed and corrected on results to compensate for their impact on the determinations. Poisson regression is used to predict precision affecting determinations on unknown test samples. Simplified and detailed models of the uncertainty of the measurement of the microbial contamination of PP are provided, allowing objective comparisons of several determinations and those with a maximum contamination level. This work concludes that triplicate determinations are required to produce results with adequately low uncertainty and that simplified uncertainty quantification underevaluates or overevaluates the uncertainty from determinations based on low or high colony numbers, respectively. Therefore, detailed uncertainty evaluations are advised for determinations between 50 and 200% of PP's maximum admissible contamination value. User-friendly tools for detailed and simplified evaluations of the uncertainty of the measurement of microbial contamination of PP are provided together with the understanding of when simplifications are adequate.

From Dawn to Dusk: High-Resolution Tree Shading Model Based on Terrestrial LiDAR Data

Light availability and distribution play an important role in every ecosystem as these affect a variety of ecosystem processes and functions. To estimate light availability and distribution, light simulations can be used. Many previous models were based on highly simplified tree models and geometrical assumptions about tree form, or were sophisticated and computationally demanding models based on 3D data which had to be acquired in every season to be simulated. The aim of this study was to model the shadow cast by individual trees at high spatial and temporal resolution without the need for repeated data collection during multiple seasons. For our approach, we captured trees under leaf-off conditions using terrestrial laser scanning and simulated leaf-on conditions for individual trees over the remainder of the year. The model was validated against light measurements (n=20,436) collected using 60 quantum sensors underneath an apple tree (Malus domestica Borkh.) on a sunny and cloudless summer day. On this day, the leaves and the shadow were simulated with a high spatial (1 cm) and temporal resolution (1 min). The simulated values were highly correlated with the measured radiation at r=0.84. Additionally, we simulated the radiation for a whole year for the sample apple tree (tree height: 6.6 m, crown width: 7.6 m) with a resolution of 10 cm and a temporal resolution of 10 min. Below the tree, an area of 49.55 m² is exposed to a radiation reduction of at least 10%, 17.74 m² to at least 20% and only 0.12 m² to at least 30%. The model could be further improved by incorporating branch growth, curved leaf surfaces, and gravity to take the weight of the foliage into account. The presented approach offers a high potential for modelling the light availability in the surroundings of trees with an unprecedented spatial and temporal resolution.

MODELING THERMAL BEHAVIOR IN HIGH-POWER SEMICONDUCTOR DEVICES USING THE MODIFIED OHM’S LAW

This paper addresses the challenge of thermal management in high-power semiconductor devices, where increasing power densities and complex operating environments demand more accurate thermal prediction methods. Traditional approaches often rely on simplified models that do not account for the crucial factor of temperature-dependent resistance variations. This limitation leads to inaccurate device temperature predictions, potentially compromising device reliability. This work proposes a novel approach for thermal management by introducing the first empirical application of a Modified Ohm’s Law. This modified law incorporates an exponential term to account for the non-linear relationship between temperature, current, and resistance. The paper demonstrates through simulations and empirical validation that the Modified Ohm’s Law offers a more accurate representation of thermal behavior compared to the standard version. This translates to more precise predictions of device temperature, especially during periods of rapid temperature changes. The validation process goes beyond simply establishing the Modified Ohm’s Law. It provides valuable insights into the thermal dynamics of the device, allowing for the refinement of simulation parameters used to assess various cooling strategies. These strategies include simulating different heat sink geometries and materials, modifying airflow rates over the device’s surface, and exploring the impact of Thermal Interface Materials (TIMs) between the device and the heat sink. By incorporating these elements, the simulations provide a more comprehensive picture of the device’s thermal behavior under various operating conditions and cooling configurations. Ultimately, this paper not only advances the theoretical understanding of thermal management but also offers practical benefits. Through enabling more accurate thermal predictions, the Modified Ohm’s Law model paves the way for informed decision-making in device design and optimization.

Evaluating the Performance of Classification Algorithms on the UNSW-NB15 Dataset for Network Intrusion Detection

Network intrusion detection is a critical aspect of cybersecurity, aiming to distinguish between normal and malicious network activities. This study evaluates the performance of various machine learning algorithms on the UNSW-NB15 dataset for binary classification of network traffic into normal and attack categories. We employed several preprocessing steps, including handling missing values, encoding categorical features, and addressing class imbalance using a mix of Synthetic Minority Over-sampling Technique (SMOTE) and undersampling. The models evaluated include k-Nearest Neighbors (k-NN), Naive Bayes, Logistic Regression, Support Vector Machines (SVM), and Neural Networks. Our experimental results show that complex models like Neural Networks and SVMs significantly outperform simpler models. The Neural Network model achieved the highest accuracy of 92%, with a precision of 91%, recall of 93%, and an F1-score of 92%. SVM also performed robustly with an accuracy of 90%. Simpler models, while less effective, still achieved respectable performance, with Logistic Regression and k-NN reaching accuracies of 88% and 85%, respectively. The study highlights the importance of comprehensive preprocessing and the implementation of advanced machine learning techniques for effective network intrusion detection. The results suggest that while complex models offer superior detection capabilities, simpler models can still be valuable in resource-constrained environments. Future research should focus on applying these models to real-world data, exploring more advanced neural network architectures, and implementing cost-sensitive learning techniques to further enhance detection performance and efficiency.

A generalized vector-field framework for mobility

Given the identification with travel demand and its relevance for transportation and urban planning, the estimation of trip flows between areas is a fundamental metric for human mobility. Previous models focus on flow intensity, disregarding the information provided by the local mobility orientation. A field-theoretic approach can overcome this issue and handle both intensity and direction at once. Here we propose a general vector-field representation starting from individuals’ trajectories valid for any type of mobility. We also show with simplified models how individuals’ choices determine the mesoscopic properties of the mobility field. Distance optimization in long displacements and random-like local exploration are necessary to reproduce empirical field features observed in Chinese logistic data and in New York City Foursquare check-ins. Our framework is able to capture hidden symmetries in mesoscopic urban mobility and opens the doors to the use of field theory in a wide spectrum of applications.

Assessing the use of Fisher knowledge in oceanic eco-social networks of Peru: A comprehensive approach to sustainable fisheries management and governance

This study is the first attempt to determine the local stability and resilience of the highly exploited oceanic ecosystem of Peru, integrating the use of fisher knowledge (FK) by government institutions (GI), which could increase sustainable governance and improve the design of management strategies. To do this, five simplified semi-quantitative eco-social models with different levels of complexity were constructed. The Routh-Hurwitz local stability criterion 1 (RH1) showed that all models (with and without the link between FK and GI) exhibited the highest percentages of transient stable states in the scenario of demand and fishers in self-negative feedback (D- & F-). Only the least complex eco-social models (models 1 and 2) exhibited an increase in the average values of Fs (stability) and Fn (resilience) when connecting FK with GI, whereas the more complex models showed the opposite trend. Although the link that connects FK by GI would allow increasing the number of stable transient states in the most complex eco-social models (models 3, 4 and 5), however FK would be self-controlled, that is, an evaluation of the quantity and quality of the information available is required. Since the models constructed are simplifications of the eco-social system studied, they allowed us to identify the conditions of stable/resilient (sustainable) scenarios, which would help us in decision-making and governance based on the use of FK in Peruvian fisheries management and monitoring.

Exploring critical drivers of global innovation: A Bayesian Network perspective

This study adopts a Bayesian Belief Network (BBN) methodology to explore relationships among innovation indicators within the Global Innovation Index (GII) framework using 2023 data. The analysis identifies 'research and development’, 'online creativity’, and 'knowledge creation' as pivotal factors influencing innovation outcomes. The BBN model demonstrates a predictive accuracy of 90.9%. For high-performing countries, 'information and communication technologies’, 'regulatory environment’, and 'education' are critical, whereas 'investment' shows a low performance in driving innovation excellence. In low-performing countries, 'innovation linkages' and 'research and development' are strongly associated with the low innovation performance of these countries. Sensitivity analysis highlights 'knowledge workers’, 'online creativity’, and 'institutional environment' as significant drivers, illustrating the complex nature of innovation dynamics. This study enhances our understanding of innovation within the GII framework and offers actionable insights for policymakers and stakeholders. Existing methods often rely on simplistic linear models that fail to capture the complex interdependencies among innovation indicators. This study addresses this limitation by leveraging the BBN approach to provide a better understanding of innovation dynamics.

All-atom modeling of methacrylate-based multi-modal chromatography resins for Langmuir constant prediction of peptides

In downstream processing, the intricate nature of the interactions between biomolecules and adsorbent materials presents a significant challenge in the prediction of their binding and elution behaviors. This complexity is further heightened in multi-modal chromatography (MMC), which employs two distinct binding mechanisms. To gain a deeper understanding of the involved interactions, simulating the adsorption of biomolecules on resin surfaces is a focal point of ongoing research. However, previous studies often simplified the adsorbent surface, modeling it as a flat or slightly curved plane without including a realistic backbone structure. Here, we introduce and validate two novel workflows aimed at predicting peptide binding behaviors in MMC, specifically targeting methacrylate-based resins. Our first achievement was the development of an all-atom model of a commercial MMC resin surface, incorporating its polymethacrylic backbone. Furthermore, we established and tested a workflow for rapid calculations of binding free energies (ΔG) with 10 linear peptides as target molecules. These ΔG calculations were effectively used to predict Langmuir constants, achieving a high coefficient of determination (R²) of 0.96. In subsequent benchmarking tests, our model outperformed established, simpler resin surface models in terms of predictive capabilities.