Generic Model Research Articles

Financial fragility, regime change, and monetary policy in an open economy – A model and empirical application to emerging market countries

July 20, 2023 (Revised April 15, 2024) The experiences of the 1997–98 Asian Financial Crisis and the 2008–09 Global Financial Crisis encouraged economists to develop new open-economy dynamic finance-macro models. Such models allow for nonlinearities -- to study the effects of contractionary currency devaluation -- in contrast to models of expansionary currency devaluation. The generic dynamic model of the finance-macro link in this paper includes the dynamics of the inflation rates, output gap, and financial variables (credit flows, risk premia, and exchange rates). The theoretical model with nonlinearities shows that there can be resilient or non-resilient outcomes of the dynamics. The level of resilience is however dependent on some empirical vulnerabilities. Relevant empirics show the risk premium is related not only to domestic real macro and financial variables, but also to the exchange rate and the foreign currency reserves. Econometrically, this paper explores the regime-dependent interaction of the output gap and financial variables in a two-regime non-linear Logistic Vector Smooth Transition Auto-Regressive (LVSTAR) model with a logistic-type transition function. This empirical study reveals that the financial variables of emerging economies show regime-dependent responses to external shocks. Therefore, a positive shock to the risk premia in a negative output gap regime entails an exchange rate depreciation, and accelerating contractions. Overall, however, a diversity of EM economies are examined in terms of their response to external shocks and their resilience levels. This research also has implications for international portfolio holdings.

Designing cancer screening trials for reduction in late-stage cancer incidence

ABSTRACT Before implementing a biomarker test for early cancer detection into routine clinical care, the test must demonstrate clinical utility, that is, the test results should lead to clinical actions that positively affect patient-relevant outcomes. Unlike therapeutical trials for patients diagnosed with cancer, designing a randomized controlled trial (RCT) to demonstrate the clinical utility of an early detection biomarker with mortality and related endpoints poses unique challenges. The hurdles stem from the prolonged natural progression of the disease and the lack of information regarding the time-varying screening effect on the target asymptomatic population. To facilitate the study design of screening trials, we propose using a generic multistate disease history model and derive model-based effect sizes. The model links key performance metrics of the test, such as sensitivity, to primary endpoints like the incidence of late-stage cancer. It also incorporates the practical implementation of the biomarker-testing program in real-world scenarios. Based on the chronological time scale aligned with RCT, our method allows the assessment of study powers based on key features of the new program, including the test sensitivity, the length of follow-up, and the number and frequency of repeated tests. The calculation tool from the proposed method will enable practitioners to perform realistic and quick evaluations when strategizing screening trials for specific diseases. We use numerical examples based on the National Lung Screening Trial to demonstrate the method.

Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting.

This article presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data. EnCQR can be applied on top of a generic forecasting model, including deep learning architectures. EnCQR exploits a bootstrap ensemble estimator, which enables the use of conformal predictors for time series by removing the requirement of data exchangeability. The ensemble learners are implemented as generic machine learning algorithms performing quantile regression (QR), which allow the length of the PIs to adapt to local variability in the data. In the experiments, we predict time series characterized by a different amount of heteroscedasticity. The results demonstrate that EnCQR outperforms models based only on QR or conformal prediction (CP), and it provides sharper, more informative, and valid PIs.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate.

Models, Models Everywhere! A practitioners view on the reality of modeling

AbstractThis paper addresses three of the most common real‐world challenges to modeling. The INCOSE Vision 2035 correctly states that the future of SE is Digital and Model Based. Achieving this requires significant development of the state of the art in Digital Engineering and Modeling. The focus of this paper is, however, the significant gap between the current potential of today's models and the actual practice. As practitioners each with over 20+ years of industrial experience, the authors describe problems that plague current modeling practice. The paper: describes the different types of models and their uses; provides a high‐level generic model development approach; and addresses some of the real‐world challenges that modelers and their managers need to address.

An articulated shape model to predict paediatric lower limb bone geometry using sparse landmarks

Creating musculoskeletal models in a paediatric population currently involves either creating an image-based model from medical imaging data or a generic model using linear scaling. Image-based models provide a high level of accuracy but are time-consuming and costly to implement, on the other hand, linear scaling of an adult template musculoskeletal model is faster and common practice, but the output errors are significantly higher. An articulated shape model incorporates pose and shape to predict geometry for use in musculoskeletal models based on existing information from a population to provide both a fast and accurate method. From a population of 333 children aged 4–18 years old, we have developed an articulated shape model of paediatric lower limb bones to predict bone geometry from eight bone landmarks commonly used for motion capture. Bone surface root mean squared errors were found to be 2.63 ± 0.90 mm, 1.97 ± 0.61 mm, and 1.72 ± 0.51 mm for the pelvis, femur, and tibia/fibula, respectively. Linear scaling produced bone surface errors of 4.79 ± 1.39 mm, 4.38 ± 0.72 mm, and 4.39 ± 0.86 mm for the pelvis, femur, and tibia/fibula, respectively. Clinical bone measurement errors were low across all bones predicted using the articulated shape model, which outperformed linear scaling for all measurements. However, the model failed to accurately capture torsional measures (femoral anteversion and tibial torsion). Overall, the articulated shape model was shown to be a fast and accurate method to predict lower limb bone geometry in a paediatric population, superior to linear scaling.

Experiment-guided tuning of muscle–tendon parameters to estimate muscle fiber lengths and passive forces

The workflow to simulate motion with recorded data usually starts with selecting a generic musculoskeletal model and scaling it to represent subject-specific characteristics. Simulating muscle dynamics with muscle–tendon parameters computed from existing scaling methods in literature, however, yields some inconsistencies compared to measurable outcomes. For instance, simulating fiber lengths and muscle excitations during walking with linearly scaled parameters does not resemble established patterns in the literature. This study presents a tool that leverages reported in vivo experimental observations to tune muscle–tendon parameters and evaluates their influence in estimating muscle excitations and metabolic costs during walking. From a scaled generic musculoskeletal model, we tuned optimal fiber length, tendon slack length, and tendon stiffness to match reported fiber lengths from ultrasound imaging and muscle passive force–length relationships to match reported in vivo joint moment–angle relationships. With tuned parameters, muscle contracted more isometrically, and soleus’s operating range was better estimated than with linearly scaled parameters. Also, with tuned parameters, on/off timing of nearly all muscles’ excitations in the model agreed with reported electromyographic signals, and metabolic rate trajectories varied significantly throughout the gait cycle compared to linearly scaled parameters. Our tool, freely available online, can customize muscle–tendon parameters easily and be adapted to incorporate more experimental data.

Assessing Reliability, Resilience and Vulnerability of Water Supply from SuDS

In recent decades, the impacts of urbanization on the hydrological cycle have led to an increase in the frequency and magnitude of urban flooding events, and this is also amplified by the effects of climate change. Sustainable Drainage Systems (SuDS) provide a revolutionary change in this field, improving the sustainability and resilience of cities. This research explores the integration of different SuDS with the aim of significantly reducing both the flow volume and celerity of floods in a residual urban catchment area of the metropolitan city of Querétaro (Mèxico), where extreme rainfall frequently occurs. This catchment is a representative suburb of urban pressure and environmental degradation problems. Currently, managing storm water under climate uncertainty through a multi-disciplinary approach is a major concern in this urban area. A 1D–2D coupling model of shallow water equations, the finite volume method, an unstructured meshing method, and a hybrid parallel computing application defined the optimal configuration of SuDS at catchment scale to reduce the flood vulnerability in Querétaro. Specifically, in this paper, we explore the management issues of the proposed SuDS configuration that acts as a water resource system with multiple purposes. A generic simulation model called MODSIM was applied to simulate the designed urban drainage system under a balanced IPCC future climate scenario in terms of reliability, resilience and vulnerability against water scarcity. The proposed hierarchical Reliability–Resilience–Vulnerability approach appears to be effective in evaluating the system performance, showing that the complete satisfaction of non-essential water uses in Querétaro can be assured at a 65% rate of reliability for a large range of reservoir storage conditions.

Model of dimensions and variables of corporate social responsibility updated through structural equations.

Corporate social responsibility (CSR) is a constantly evolving concept that reflects changes in society and the expectations of stakeholders in a process that leads companies to adapt to respond to new challenges and demands. Similarly, the theory and practice of CSR have moved from regulatory compliance to a more holistic approach that requires more sophisticated models that allow for a deeper understanding. Following the fact that companies now have access to a greater amount of information related to their social and environmental performance, as well as comparative data from other organizations, which is why it is necessary to have a management tool that allows collecting, process, and generating more accurate and meaningful metrics that more effectively reflect a company's impact on society. Consequently, the present work aims to identify a system of variables and dimensions representative of the management of organizations that allows the validation of a generic model of corporate social responsibility determined through factorial and structural modeling capable of reflecting more accurate and up-to-date dimensions and metrics of model variables and dimensions. To carry out the study, a sample of 667 middle and senior managers from medium and large companies in Guayas, Ecuador, were randomly contacted in their respective business contexts by trained interviewers. The data processing was carried out in stages. First, the exploratory factorial analysis method of the items was applied to form relevant factors. Then, the dimensions' structural modeling was formulated and ratified through the pertinent goodness-of-fit indices. The results determined a system of correlated factors whose items present estimators of commonality and high and significant factorial loads, excluding variables that did not exceed the sensitization criteria applied. Finally, a model made up of 15 factors and 66 variables ratified using the comparative adjustment indices, the chi-square likelihood ratio, and the mean square error of approximation is introduced, confirming the proposed corporate social responsibility model.

Real-time hardware-in-loop system based validation of vehicle-grid interface for bidirectional power flow control

In the context of modern sustainable transportation technology, the terms Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) holds significant value. During V2G and G2V modes, vehicle and grid exchange power using various power converters to modulate the power for a sustainable electrical energy ecosystem. With V2G, the EVs can also perform the role of a peak power contributor, reserve power source, and an efficient system to improve power quality issues by working as power factor corrector, reactive power compensator, and active power filter, to name a few during the V2G mode. The on-board power converter in the vehicle, working as the charger for the battery, is the component required to facilitate the intended bidirectional power exchange between the vehicle and grid and also modulates the power as and when required. The modality for operating the onboard charger as a power exchange tool depends on the type of control strategy adopted as well as on the battery and grid parameters. In this paper, a bidirectional power converter (on-board converter) is introduced, which is controlled by adopting a d-q axis-based control strategy. The MATLAB/SIMULINK-based system simulation model is created, and the Hardware-in-Loop (HIL) tool Opal-RT 4510 is used to validate the simulation results on the intended hardware and real-time scenario. The control strategy, when integrated with the adopted model, contributes to enhancing the power-transferring capability of the converter, which is assessed using the simulation and subsequent HIL-based implementation. The paper also focuses on narrating the steps to implement the vehicle-grid interface using HIL and forming a generic reference model for a bidirectional power flow converter, such that it can be used for other circuits and its implementation in real-time scenarios.

Deviation entropy-based dynamic multi-model ensemble interval prediction method for quantifying uncertainty of building cooling load

Interval prediction is a promising method that can reveal the uncertainty of building load and has been shown to effectively manage building energy systems. Previous studies focused on improving the performance of three types of generic single models (quantile regression, bootstrap, and lower upper bound estimation models). Multi-model ensemble methods have the advantages of reducing overfitting risk, improving stability. However, in the field of building cooling load interval prediction, there is a research gap that the performance of the multi-model ensemble method has not been conducted extensively. To compensate for this gap, firstly, referring to the information entropy in information theory, we proposed deviation entropy, which is a new concept that can be used to calculate the weight of a generic single model. Based on this, a static multi-model ensemble interval prediction method was developed. Then, we introduced a sliding time window to further upgrade the static method into a dynamic method. Finally, we used actual data to evaluate the performance of the proposed dynamic multi-model ensemble interval prediction method. The study results show that the multi-model ensemble method outperforms traditional generic single model, and the dynamic method can improve the prediction reliability by 8.5% with only 0.76% loss of prediction accuracy.

Complexation Mechanisms of Aqueous Amylose: Molecular Dynamics Study Using 3-Pentadecylphenol.

Molecular dynamics (MD) simulations of linear amylose fragments containing 10 to 40 glucose units were used to study the complexation of the prototypical compound, 3-pentadecylphenol (PDP)─a natural product with surfactant-like properties─in aqueous solution. The amylose-PDP binding leverages mainly hydrophobic interactions together with excluded volume effects. It was found that while the most stable complexes contained PDP inside the helical structure of the amylose in the expected guest-host (inclusion) complexation manner, at higher temperatures, the commonly observed PDP-amylose complexes often involved more nonspecific interactions than inclusion complexation. In the case where a stoichiometric excess of PDP was added to the simulation box, self-aggregation of the small molecule precluded its ability to enter the internal helical part of the oligosaccharide, and as a result, inclusion complexation became ineffective. MD simulation trajectories were analyzed preliminarily using cluster analysis (CA), followed by more rigorous solvent accessible surface area (SASA) determination over the temperature range spanning from 277 to 433 K. It was found that using the SASA of PDP corrected for its intrinsic conformational changes, together with a generic hidden Markov model (HMM), an adequate quantification of the different types of PDP-amylose aggregates was obtained to allow further analysis. The enthalpy change associated with the guest-host binding equilibrium constant (Kgh) in aqueous solution was estimated to be -75 kJ/mol, which is about twice as high as one might expect based on experimentally measured values of similar complexes in the solid state where the (unsolvated) helical structure of amylose remains rigid. On the other hand, the nonspecific binding (Kns) enthalpy change associated with PDP-amylose interactions in the same solution environment was found to be about half of the inclusion complexation value.

EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction

BackgroundRecognition of enhancer–promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources. Therefore, more and more studies are being done on developing computational techniques, particularly using deep learning and other machine learning techniques, to address such problems. Unfortunately, the majority of current computational methods are based on convolutional neural networks, recurrent neural networks, or a combination of them, which don’t take into consideration contextual details and the long-range interactions between the enhancer and promoter sequences. A new transformer-based model called EPI-Trans is presented in this study to overcome the aforementioned limitations. The multi-head attention mechanism in the transformer model automatically learns features that represent the long interrelationships between enhancer and promoter sequences. Furthermore, a generic model is created with transferability that can be utilized as a pre-trained model for various cell lines. Moreover, the parameters of the generic model are fine-tuned using a particular cell line dataset to improve performance.ResultsBased on the results obtained from six benchmark cell lines, the average AUROC for the specific, generic, and best models is 94.2%, 95%, and 95.7%, while the average AUPR is 80.5%, 66.1%, and 79.6% respectively.ConclusionsThis study proposed a transformer-based deep learning model for EPI prediction. The comparative results on certain cell lines show that EPI-Trans outperforms other cutting-edge techniques and can provide superior performance on the challenge of recognizing EPI.

Comparative didactics: Toward a generic model for analyzing content-specific dimensions of teaching quality

Since the 2000s, the Joint Action framework in Didactics (JAD) has been developed in the context of the expansion of the French-speaking research in Comparative didactics. Studies carried out with this framework typically investigate how knowledge content develop in teacher-student classroom interactions. From a quite different perspective, classroom research on teaching quality has also developed considerably in recent years. Despite the many efforts to better understand relations between generic versus subject-specific dimensions of teaching, this dichotomy remains strong in the debates on teaching quality. This contribution explores a comparative model for characterizing the quality of teaching practices from the conceptual categories of the Joint Action framework in Didactics (JAD-MTQ). Based on the comparison of teaching and learning practices in two school subjects—physics and contemporary dance—this paper pursues a twofold objective: (1) proposing a provisional set of generic criteria for capturing content-specific dimensions of teaching quality; and (2) highlighting certain methodological conditions for ensuring qualitative analyses of these dimensions of teaching quality.

Shor’s algorithm does not factor large integers in the presence of noise

We consider Shor’s quantum factoring algorithm in the setting of noisy quantum gates. Under a generic model of random noise for (controlled) rotation gates, we prove that the algorithm does not factor integers of the form pq when the noise exceeds a vanishingly small level in terms of n—the number of bits of the integer to be factored, where p and q are from a well-defined set of primes of positive density. We further prove that with probability 1 − o(1) over random prime pairs (p, q), Shor’s factoring algorithm does not factor numbers of the form pq, with the same level of random noise present.

Novel methods for monitoring low chlorophyll-a concentrations in the large, oligotrophic Lake Malawi/Nyasa/Niassa

The use of remote sensing for monitoring chlorophyll-a (chla) and modelling eutrophication has advanced over the last decades. Although the application of the technology has proven successful in ocean ecosystems, there is a need to monitor chla concentrations in large, nutrient-poor inland water bodies. The main objective of this study was to explore the utility of publicly available remotely sensed Sentinel-2 (S2) imagery to quantify chla concentrations in the nutrient-deficient Lake Malawi/Niassa/Nyasa (LMNN). A secondary objective was to compare the S2 derived chla with the Global Change Observation Mission–Climate (GCOM-C) chla product that provides uninterrupted data throughout the year. In situ chla data (n = 76) from upper, middle and lower sections of LMNN served as a reference to produce remote sensing-based quantification. The line-height approach method built on color index, was applied for chla concentrations below 0.25 mg/m3. Moderate Resolution Imaging Spectroradiometer 3-band Ocean Color (MODIS-OC3) – was adopted when chla concentration exceeded 0.35 mg/m3. The MODIS-OC3 algorithm had generic model coefficients that were calibrated for each in situ sample by using GCOM-C Level 3 chla product. A weighted sum of the two algorithms was applied for chla concentrations that fell between 0.25 and 0.35 mg/m3. The above methods were then applied to the S2 data to estimate chla at each pixel. S2 showed a promising accuracy in distinguishing chla levels (MSE = 0.18) although the chla range in the lake was relatively narrow, particularly using the locally calibrated coefficients of the OC3 algorithm. Chla distribution maps produced from the S2 data revealed limited spatial variation across the LMNN with higher concentrations identified in the coastal areas. S2-derived chla and GCOM-C chla comparison showed fairly good similarity between the two datasets (MSE = 0.205). Accepting this similarity, monthly chla dynamics of the lake was profiled using the temporally reliable GCOM-C data that showed oligotrophic conditions (1.7 mg/m3 to 3.2 mg/m3) in most parts of the lake throughout the year. The study's findings advance the potential for both remote sensing approaches to provide vital information at the required spatial and temporal resolution for evidence-based policymaking and proactive environmental management in an otherwise very data deficient region.

A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting

Accurate and reliable incoming flood forecasting is an important prerequisite for flood warning, flood risk analysis and reservoir flood control operation. This paper proposes a hybrid model for real-time flood forecasting that couples process-driven hydrological models (HMs) with data-driven models (DDMs). The generic hybrid model framework adds DDMs as the post-processing procedure for residual correction to the original results of HMs, and considers multiple uncertainties in input data, parameter and model structure simultaneously. The performance of the hybrid model is evaluated comprehensively in terms of deterministic forecast accuracy, interval forecast reliability, and the reliability and sharpness of probabilistic forecast. Taking the multireservoir system at the east Pi River as a study case, the results indicate that: (1) Compared to the benchmark model (ensemble XAJ model), the hybrid model with additional residual analysis show a significant improvement. The average continuous ranked probability score (CRPS) metric values calculated by the Stacking-Hybrid model improved by 71.5 %, 67.0 % and 38.1 % in the three data samples. Furthermore, the adaptability of the Stacking-Hybrid model for residual correction during short-duration intense rainfall events has been validated, with the relative error of the peak discharge improved to within ± 10 %. (2) The Stacking-Hybrid model, which also takes into account structure uncertainty, is able to better exploit the combined advantages and improve the stability of the model performance compared to those that only apply a single DDM. (3) When the number of iterations within the BOA reaches 300, the parameter optimization process is capable to search for the hyperparameters that bring out the best performance of the DDMs. (4) When the ensemble size reaches 200, the uncertainty of HM parameters can be fully defined, and the consumed computational resources can be controlled within an acceptable bound while ensuring stable model performance. Overall, the hybrid model that takes into account multiple sources of uncertainty generates both interval and probabilistic forecast in addition to deterministic forecast, which can provide richer risk information for subsequent flood warning and reservoir operation, making the flood prevention decisions more reliable and scientific.

Application of a generic PBK model for beef cattle: Tissue/fluid correlations of paracetamol and NSAIDs

Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) and paracetamol can be administered off-label to cattle. Since the use of these veterinary medicines in cattle may pose a public health risk after meat consumption, it is important to translate measured concentrations in urine and tissues into concentrations in meat for human consumption. A generic physiologically-based kinetic (PBK) model for cattle can enable this translation. In this work, a beef cattle PBK model was applied to calculate the relationships between concentrations in different bovine tissues and those were compared to measured concentrations in different matrices. Sixty-seven kidney samples, the corresponding urine and meat samples, and available 19 serum samples were analysed. Overall, 70% of the PBK model predictions are within a 2-fold factor and relationships for kidney/meat, urine/meat, and plasma/meat ratios were established. The conversions of measured kidney concentrations into meat concentrations were mostly within a factor two, while those based on plasma and urine were underpredicted. Based on these ratios, plasma and urine could be used as an appropriate surrogate matrix for a fast, simple in vivo sample screening test under field conditions, such as in local farms and slaughterhouses, to predict a maximum residue level exceedance in meat, reducing the number of test samples.

On immiscibility preservation conditions of material interfaces in the generic five-equation model

Interfaces separating pure materials and mixtures tend to be severely smeared with interface-capturing methods for compressible multi-material flows, necessitating the requirement of various interface-sharpening techniques. However, these techniques have various problems related to consistency, conservation, and thermodynamic compatibility. In this work, we derive a general theoretical formulation of interface-sharpening techniques for the generic five-equation model. This theoretical formulation is not only conservative in mass, momentum, and total energy but also asymptotically compatible with the thermodynamic mixture laws of the mixture model upon which it is constructed, and is independent of various specific numerical algorithms. We further propose a general numerical method to solve this theoretical formulation. The proposed method is consistent and conservative, and it prevents spurious errors at the interfaces. Examples of one- and two-dimensional multimaterial compressible flow problems, including shocks and interfaces, are considered to verify the analysis and demonstrate the efficiency of the method.

A Simple Generic Model of Elastin-Like Polypeptides with Proline Isomerization.

A generic model of elastin-like polypeptides (ELP) is derived that includes proline isomerization (ProI). As a case study, conformational transition of a -[valine-proline-glycine-valine-glycine]- sequence is investigated in aqueous ethanol mixtures. While the non-bonded interactions are based on the Lennard-Jones (LJ) parameters, the effect of ProI is incorporated by tuning the intramolecular 3- and 4-body interactions known from the underlying all-atom simulations into the generic model. One of the key advantages of such a minimalistic model is that it readily decouples the effects of geometry and the monomer-solvent interactions due to the presence of ProI, thus gives a clearer microscopic picture that is otherwise rather nontrivial within the all-atom setups. These results are consistent with the available all-atom and experimental data. The model derived here may pave the way to investigate large scale self-assembly of ELPs or biomimetic polymers ingeneral.