Data-based Modelling Approach Research Articles

Current status and usefulness of therapeutic drug monitoring implementation of theophylline in elderly patients based on a nationwide database study and modeling approach.

Theophylline is used in the treatment of chronic obstructive pulmonary disease but readily causes symptoms of intoxication and exhibits high risks in elderly patients. However, there have only been a few recent reports on the significance of therapeutic drug monitoring (TDM) implementation, especially in elderly patients. To examine the usefulness of theophylline TDM, we evaluated the current status of prescriptions containing theophylline and its side effects and assessed the influence of aging, sex, drug formulation, and concurrent drugs use on theophylline exposure using data from various nationwide databases and a pharmacokinetic modeling approach. We utilized sampling data from the National Database of Health Insurance Claims and Specific Health Checkups of Japan. Using the data of patients aged ≥80 years, we conducted an association analysis of theophylline and concurrent drugs. The transition in plasma theophylline concentration levels of elderly patients was estimated based on a previously reported physiological-based pharmacokinetic model. Altogether, 3973 patients using theophylline were registered in our dataset, and about 50% were over 70 years old and used theophylline. Therapeutic drug monitoring implementation was confirmed in only 1.13% of patients. The association analysis confirmed a frequent co-occurrence with allopurinol and famotidine, which increase theophylline exposure, in elderly patients aged ≥80 years. The physiologically based pharmacokinetic model indicated that theophylline trough concentrations were 1.65-fold higher in elderly patients aged 80 years compared to those aged 30 years and 1.35-fold higher in females compared to males. This study effectively combined information on the nationwide health care database and modeling approach, indicating the importance of proactive TDM and dose justification for female and elderly patients.

Investigating the role of relational factors on buyer-supplier relationship commitment to enhance firm performance through supply chain integration

PurposeThis study assesses how relational factors and buyer-supplier relationship commitment (BSRC) influence supply chain integration (SCI) and firm performance in Bangladesh's apparel manufacturing sector. Firm performance includes operational performance and innovation performance.Design/methodology/approachGrounded in the social exchange theory, a survey data-based structural equation modeling (SEM) approach is applied. Based on two experts and four executives' opinions and an in-depth literature review, 28 measurement items were identified in the close-ended questionnaire design. Further, 144 valid questionnaires from the manufacturer-supplier dyads in Bangladesh were collected and used for SEM analysis.FindingsOur study reveals that relational factors positively influence BSRC. BSRC directly impacts SCI, operational, and innovation performance, whereas SCI is significantly related to operational and innovation performance. Besides, SCI mediates the two relationships: BSRC and operational performance; and BSRC and innovation performance.Originality/valueOur results contribute to the literature and offer a new way to understand relationships that connect relational factors of BSRC, BSRC, and outcomes not only by examining the focal firm but also by examining its dyadic supplier partner separately. Separate assessment in the dyad displays some similar and dissimilar results. Moreover, we suggest practical implications for managers to enhance firm performance by focusing on the significance of linking relational factors, BSRC, and SCI.

Revolutionising Egyptian pavement design: a comprehensive E* database and advanced modeling approach for contextually informed performance predictions

ABSTRACT This research assesses the dynamic modulus (E*) as a pivotal rheological attribute for characterizing viscoelastic behaviour in various indigenous asphalt mixtures across diverse scenarios. An exhaustive investigation involving 32 laboratory-designed asphalt mixes and two field mixes explores the influence of traffic loading patterns, air voids, binder sources, and climatic conditions on E*. Utilizing Superpave gyratory compaction, 68 E*-specimens are compacted and subjected to E*-testing, leading to master curve development. Evaluation of NCHRP 1-37A and NCHRP 1-40D Witczak models demonstrates their fair to excellent accuracy in predicting E* for Egyptian mixes. The NCHRP 1-40D model stands out with an R² of 0.94, showcasing superior accuracy and minimal bias. This study integrates AASHTOW are Pavement ME Design (PMED) to analyse pavement sections, revealing the impact of design factors, characterized by measured E*, on predicted pavement performance, encompassing asphalt concrete rutting, alligator cracking, longitudinal cracking, and terminal international roughness index. The investigation highlights the marginal impact of substituting predicted E* for measured E* on AASHTOWare pavement performance indicators for Egyptian mixes under default binder characteristic values. This research contributes valuable insights into the interplay of E* and pavement performance, facilitating wellinformed decisions in pavement design and analysis across diverse conditions in the Egyptian context.

Hybrid AI modeling techniques for pilot scale bubble column aeration: A comparative study

With increased accessibility of process data from the production lines in chemical and biochemical production plants, the use of data-based modeling methods is gaining interest. In this work, three different data-based modeling approaches are applied for modeling aeration in a pilot scale bubble column. In all three modeling approaches the same serial hybrid-model structure is used, combining a species conservation balance based on first-principles with different data-based models for the overall volumetric mass transfer coefficient (KLa). Simple empirical correlations with parameters fit to process data provide transparent models but lack the accuracy of Artificial Neural Networks (ANNs). ANNs provide models with high accuracy within the operation regimes used for training, however, the models are prone to overfitting, and their black-box nature results in models that are difficult to interpret. As an alternative, a symbolic regression-inspired technique is used for discovering symbolic equations, resulting in interpretable models with accuracy that is comparable to that of the ANN.

Energy demand and manufacturing system performance – a data-based modelling approach towards deeper understanding and integrated improvement

From both economic as well as environmental perspective energy demand is of strong and even more increasing relevance for manufacturing nowadays. But typically energy demand and manufacturing system performance (e.g. output, time demands, quality) are considered separately while actually being inherently connected in technical systems. Against this background, the paper presents a data-based approach to analyze and model those important aspects and interdependencies in an integrated manner. The developed theoretical framework and defined methodology enable deeper understanding of energy related impacts of operative influencing factors and eventually to derive fields of action for improvement. The approach was exemplary applied in an industrial case study. Results underline the feasibility, applicability, and potential benefits of the approach.

Quantifying the natural flood management potential of leaky dams in upland catchments, Part I: A data-based modelling approach

A novel application of data-based time series methods is proposed in this study to help overcome barriers to quantifying the impacts of Natural Flood Management measures from hydrological timeseries data which hitherto have prevented accurate assessment of the effectiveness of interventions. To demonstrate the value of this method, a transfer function noise model was fitted to stage data from a three year before-after-control-impact style monitoring study of leaky dams in an upland catchment in North Yorkshire, England. Using the data-based time series method, uncertainties associated with stage data were overcome. The models were able to simulate the peaks of flood events on one stream to within ±2 cm accuracy for 95% of events recorded during the baseline monitoring period. These simulations are used in this study’s companion paper to quantify leaky dam impacts on flood peak magnitude. The level of accuracy achieved in this study provides proof of concept for application of the approach to data from other environments and natural flood management interventions, which is crucial if natural flood management is to be used as a mainstream flood risk management measure.

Data-based instantaneous conditional progress variable dissipation rate modeling for turbulent premixed combustion

Manifold-based turbulent combustion modeling reduces computational cost considerably by projecting the thermochemical state onto a lower-dimensional space of a few manifold variables and reconstructing the state from a set of manifold equations. In turbulent premixed combustion, a one-dimensional space is defined by the progress variable, and the progress variable dissipation rate appears as a parameter in the resulting one-dimensional manifold equations. Closure of the manifold equations requires a model for the dependence of this dissipation rate on the progress variable. Existing models for these profiles are often ad hoc, derived analytically with crude assumptions, and assumed to be spatially and temporally ‘universal’ — yet the resulting thermochemical state can be sensitive to these profiles. To evaluate the assumptions in traditional models, instantaneous conditional progress variable dissipation rate profiles are extracted from DNS databases of turbulent premixed jet flames. At low Karlovitz number, the instantaneous conditional progress variable dissipation rate profiles exhibit some variance but are relatively well-characterized by a ‘universal’ model. However, a large variance in the instantaneous conditional progress variable dissipation rate profiles is observed at high Karlovitz number, indicating the form of the conditional dissipation rate is certainly not ‘universal’. To capture this variation at high Karlovitz number, a data-based model for the instantaneous conditional progress variable dissipation rate profiles is generated, and the resulting model predictions demonstrate excellent agreement with profiles extracted from DNS and far less error than ‘universal’ models. The sensitivity of the solutions of the one-dimensional premixed manifold equations to the dissipation rate profile is demonstrated a priori, and the error associated with the observed variance in the instantaneous conditional progress variable dissipation rate profiles (i.e., relative to the DNS mean) indicates that an instantaneous modeling approach is necessary. A variable importance study is conducted, and the conditional velocity gradient is identified as the critical quantity that characterizes the observed variance in the instantaneous conditional progress variable dissipation rate profiles, providing further motivation for a data-based modeling approach.

Pursuing supply chain ecosystem health under environmental turbulence: a supply chain learning approach

Although supply chain ecosystem health (SCE Health) is receiving attention in relation to environmental uncertainty, its conception and measurement are largely undocumented, and how to pursue SCE Health under environmental turbulence is unclear. Supply chain learning (SCL) is an important way to build dynamic capabilities, and whether it can empower the achievement of SCE Health is worthy of investigative study. Therefore, grounded in the dynamic capabilities theory, a survey data-based structural equation modelling (SEM) approach is employed. Based on four experts’ opinions and an in-depth literature review, 47 measurement items (11 for SCL, 28 for SCE Health, and 8 for environmental turbulence) were identified in the questionnaire design. Further, 208 valid questionnaires from the field survey of supply chain management (SCM)-related firms in China were collected and used for SEM analysis. The results show that the internal learning of SCL stimulates its external learning. SCL empowers the pursuit of SCE Health, which is strengthened under higher environmental turbulence. The theoretical framework and results also derive practical insights and support from 11 interviewees of five companies.

Characterizing sediment load variability in the red river system using empirical orthogonal function analysis: Implications for water resources management in data poor regions

This paper introduces a novel data-based modelling approach, Empirical Orthogonal Function (EOF), to characterize the sediment load variability at the downstream section of the Red River system in Vietnam. We modified the original EOF and performed it on the demeaned datasets of monthly total sediment load. Rating curves between the discharge and sediment load were used to validate the performance of EOF analysis. The results showed significant similarity between simulated and observed patterns. A dramatic decrease in sediment load has been observed via both the EOF and rating curve methods across the observation period (∼1958–2021), with two dramatic decreasing phases identified (1988–2008 and 2009-present). The variation in the sediment load was mainly related to the seasonal and inter-annual hydrological conditions in the basin, as well as the dam-reservoir impact. Before 1988, river discharge and rainfall were well correlated to the first EOF mode, indicating that the natural forces were the main driving factor for the sediment load. However, anthropogenic pressure (dam-reservoir impact) emerged as an important driver to the declining of sediment load since the end of 1988 and became more severe towards the end of 2008. We conclude that the modified EOF method developed in this study successfully demonstrates a simple and efficient means of reconstructing and evaluating the sediment load variability over time in data-poor regions, influenced by complex natural and anthropogenic – drivers. It can, thus, be useful for water resources management in ungauged basins in the region.

Parameters, architecture and emergent properties of the Pseudomonas aeruginosa LasI/LasR quorum-sensing circuit.

Quorum sensing is a widespread process in bacteria that controls collective behaviours in response to cell density. Populations of cells coordinate gene expression through the perception of self-produced chemical signals. Although this process is well-characterized genetically and biochemically, quantitative information about network properties, including induction dynamics and steady-state behaviour, is scarce. Here we integrate experiments with mathematical modelling to quantitatively analyse the LasI/LasR quorum sensing pathway in the opportunistic pathogen Pseudomonas aeruginosa. We determine key kinetic parameters of the pathway and, using the parametrized model, show that quorum sensing behaves as a bistable hysteretic switch, with stable on and off states. We investigate the significance of feedback architecture and find that positive feedback on signal production is critical for induction dynamics and bistability, whereas positive feedback on receptor expression and negative feedback on signal production play a minor role. Taken together, our data-based modelling approach reveals fundamental and emergent properties of a bacterial quorum sensing circuit, and provides evidence that native quorum sensing can indeed function as the gene expression switch it is commonly perceived to be.

Modeling streamflow in headwater catchments: A data-based mechanistic grounded framework

Study RegionThe Lenguazaque River Basin is a 290 km2 headwater catchment of the Fuquene Lake Watershed in the Colombian Andes. Regional stakeholders are optimizing water allocations for all users, given numerous quantity related issues such as its simultaneous use for conservation, agriculture, and coal mining. Study FocusDeveloping hydrological models in headwater catchments is challenging, especially in developing countries where technical resources and data are limited. This study addresses these challenges by proposing strategies to develop reliable yet mathematically simple models, requiring fewer inputs than data-intensive alternatives. Three stages are proposed, focused on 1) assessing hydrological data, 2) preparing datasets for modeling, and 3) developing models in daily and sub-daily resolutions. The last stage is grounded on a data-based mechanistic modeling approach and includes a novel combination of baseflow separation with digital filters and multi-objective optimization principles. Having a hydrophysical meaning, obtained models are suitable for simulating alternative scenarios. New hydrological insights for the regionResulting daily models achieved an acceptable performance, significantly better than a semi-distributed model. The performance of sub-daily models was sub-optimal, yet it can be improved by enhancing the quality of sub-daily datasets and the efficiency of computational algorithms. Due to their mathematical nature, the proposed strategies are expected to be applicable to other headwater catchments where improving water management is an imperative need.

Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows

Abstract. Models and simulation tools for gravitational mass flows (GMFs) such as snow avalanches, rockfall, landslides, and debris flows are important for research, education, and practice. In addition to basic simulations and classic applications (e.g., hazard zone mapping), the importance and adaptability of GMF simulation tools for new and advanced applications (e.g., automatic classification of terrain susceptible for GMF initiation or identification of forests with a protective function) are currently driving model developments. In principle, two types of modeling approaches exist: process-based physically motivated and data-based empirically motivated models. The choice for one or the other modeling approach depends on the addressed question, the availability of input data, the required accuracy of the simulation output, and the applied spatial scale. Here we present the computationally inexpensive open-source GMF simulation tool Flow-Py. Flow-Py's model equations are implemented via the Python computer language and based on geometrical relations motivated by the classical data-based runout angle concepts and path routing in three-dimensional terrain. That is, Flow-Py employs a data-based modeling approach to identify process areas and corresponding intensities of GMFs by combining models for routing and stopping, which depend on local terrain and prior movement. The only required input data are a digital elevation model, the positions of starting zones, and a minimum of four model parameters. In addition to the major advantage that the open-source code is freely available for further model development, we illustrate and discuss Flow-Py's key advancements and simulation performance by means of three computational experiments. Implementation and validation. We provide a well-organized and easily adaptable solver and present its application to GMFs on generic topographies. Performance. Flow-Py's performance and low computation time are demonstrated by applying the simulation tool to a case study of snow avalanche modeling on a regional scale. Modularity and expandability. The modular and adaptive Flow-Py development environment allows access to spatial information easily and consistently, which enables, e.g., back-tracking of GMF paths that interact with obstacles to their starting zones. The aim of this contribution is to enable the reader to reproduce and understand the basic concepts of GMF modeling at the level of (1) derivation of model equations and (2) their implementation in the Flow-Py code. Therefore, Flow-Py is an educational, innovative GMF simulation tool that can be applied for basic simulations but also for more sophisticated and custom applications such as identifying forests with a protective function or quantifying effects of forests on snow avalanches, rockfall, landslides, and debris flows.

Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches

Hybrid kinetic models represent a promising alternative to describe and evaluate the effect of multiple variables in the performance of complex chemical processes, since they combine system knowledge and extrapolability of the (semi-)mechanistic models in a wide range of reaction conditions with the adaptability and fast convergence of data-based approaches (e.g., artificial neural networks—ANNs). For the first time, a hybrid kinetic model for the direct DME synthesis was developed consisting of a reactor model, i.e., balance equations, and an ANN for the reaction kinetics. The accuracy, computational time, interpolation and extrapolation ability of the new hybrid model were compared to those of a lumped and a data-based model with the same validity range, using both simulations and experiments. The convergence of parameter estimation and simulations with the hybrid model is much faster than with the lumped model, and the predictions show a greater degree of accuracy within the models’ validity range. A satisfactory dimension and range extrapolation was reached when the extrapolated variable was included in the knowledge module of the model. This feature is particularly dependent on the network architecture and phenomena covered by the underlying model, and less on the experimental conditions evaluated during model development.

Analysis of Driver Behavior and Intervehicular Collision: A Data-Based Traffic Modeling and Simulation Approach

The emergence of intelligent connected vehicles (ICVs) is expected to contribute to resolving traffic congestion and safety problems; however, it is inevitable that ICV safety issues in mixed traffic (involving ICVs and human driven vehicles) will be a critical challenge. The numerical simulation of scenarios involving a mix of different driving profiles is expected to be an important safety assessment tool in the process of testing and validating ICVs, especially regarding extreme scenarios, including car collisions, which are rarely captured in real-world datasets. In this study, we propose a novel approach for car collision generation in numerical simulations based on the assumption that car collision occurrences are mostly associated with certain specific driver profiles. Using a dataset provided by the Next Generation Simulation (NGSIM) project, NGSIM 101 dataset, we identify three different driver profiles: aggressive, inattentive, and normal drivers. We then replicate car collision occurrences by varying the percentages of these three driver profiles in the simulated environment, allowing us to establish a relationship between driver profiles and car collision occurrences. We also investigate the severity of car collisions and classify them with respect to the driver profiles of the cars involved in the collisions. Our approach of replicating car collision occurrences in numerical simulations will facilitate the testing and validation of ICVs in the future, especially regarding the testing of ICV functionalities in dealing with traffic accidents.

A machine learning framework for LES closure terms

In the present work, we explore the capability of artificial neural networks (ANN) to predict the closure terms for large eddy simulations (LES) solely from coarse-scale data. To this end, we derive a consistent framework for LES closure models, with special emphasis laid upon the incorporation of implicit discretization-based filters and numerical approximation errors. We investigate implicit filter types that are inspired by the solution representation of discontinuous Galerkin and finite volume schemes and mimic the behavior of the discretization operator, and a global Fourier cutoff filter as a representative of a typical explicit LES filter. Within the perfect LES framework, we compute the exact closure terms for the different LES filter functions from direct numerical simulation results of decaying homogeneous isotropic turbulence. Multiple ANN with a multilayer perceptron (MLP) or a gated recurrent unit (GRU) architecture are trained to predict the computed closure terms solely from coarse-scale input data. For the given application, the GRU architecture clearly outperforms the MLP networks in terms of accuracy, whilst reaching up to 99.9% correlation between the networks' predictions and the exact closure terms for all considered filter functions. The GRU networks are also shown to generalize well across different LES filters and resolutions. The present study can thus be seen as a starting point for the investigation of data-based modeling approaches for LES, which not only include the physical closure terms, but account for the discretization effects in implicitly filtered LES as well.

Predicting the Non-Linear Conveying Behavior in Single-Screw Extrusion: A Comparison of Various Data-Based Modeling Approaches used with CFD Simulations

Abstract The traditional approach to modeling the polymer melt flow in single-screw extruders is based on analytical and numerical analyses. Due to increasing computational power, data-driven modeling has grown significantly in popularity in recent years. In this study, we compared and evaluated databased modeling approaches (i. e., gradient-boosted trees, artificial neural networks, and symbolic regression models based on genetic programming) in terms of their ability to predict – within a hybrid modeling framework – the three-dimensional non-linear throughput-pressure relationship of metering channels in single-screw extruders. By applying the theory of similarity to the governing flow equations, we identified the characteristic dimensionless influencing parameters, which we then varied to create a large dataset covering a wide range of possible applications. For each single design point we conducted numerical simulations and obtained the dimensionless flow rate. The large dataset was divided into three independent sets for training, interpolation, and extrapolation, the first being used to generate and the remaining two to evaluate the models. Further, we added two features derived from expert knowledge to the models and analyzed their influence on predictive power. In addition to prediction accuracy and interpolation and extrapolation capabilities, we evaluated model complexity, interpretability, and time required to learn the models. This study provides a rigorous analysis of various data-based modeling approaches applied to simulation data in extrusion.

Manual operation simulation using motion-time analysis toward labor productivity estimation: A case study of concrete pouring operations

Based on the labor-intensive nature of construction tasks and high costs associated with labor operations, reliable estimation of construction labor productivity is crucial. However, owing to the dynamic nature and variety of tasks involved in a construction activity, obtaining accurate and reliable productivity data has proven to be difficult. Herein, a motion data-based modeling approach for estimating the labor productivity is proposed. The method consists of the following: estimating the standard motion time of the tasks, measuring the unit workload of an operation cycle using 3D models, and quantifying the production rates based on the cycle times and unit workloads. Motion-data analysis is also integrated into simulation modeling to incorporate the impact of jobsite conditions. To validate the proposed approach, a case study is applied for estimating the production rate of concrete placement operations. The results demonstrate that the proposed approach can reliably estimate the productivity of the operations.

Nonlinear Structure based Artificial Neural Computing for Upstream Flow Functional Models

Most of the real world systems are nonlinear and complex and it is challenging to model these types of systems for analyzing and forecasting the hidden behaviour of the systems. In the paradigm of vague complex systems, data-based time series modeling approaches of intelligent systems showed its applicability for coping with the problems of hidden noise and dynamicity which are encapsulated in the data. Getting from nature is one of the humans’ features and they are striving to produce the intellectual schemes by coping rare features of cognitions and intellect of the brain. In this paper, nonlinear autoregressive structure based modeling of the brain (i.e. Artificial Neural Network) is the aim of this study that suggest various Dynamic Neural Network (DNN) models by using time deferred autoregressive configurations, for the stream-flow of Sukkur barrage on lower Indus river basin. The suitability of the models for training, validation and testing stages, are evinced on assessment metrics which demonstrate the accuracy and sufficiency of the models which may be beneficial for water-resource management.

An interpretation of the relationship between dominant rainfall-runoff processes and the shape of flow duration curve by using data-based modeling approach

An interpretation of the relationship between dominant rainfall-runoff processes and the shape of flow duration curve by using data-based modeling approach

Machine Learning based System Identification Tool for data-based Energy and Resource Modeling and Simulation

Generated machine data is often not fully utilized in modern power production, although it could provide new approaches to significantly increase productivity, flexibility and resource efficiency as well as energy efficiency in production. Data-based models, which can be created with the help of machine learning algorithms, can map the system’s behavior accurately and thus provide a basis for a better system understanding for further energy und resource optimization approaches. The objective of this paper is to develop a generic system identification tool that uses the above-mentioned data-based modeling approach to optimize the electrical power and resource consumption for a given load, regardless of the considered plant or machine. Therefore, the system identification tool autonomously preprocesses the data, compares different hyperparameters for neural networks to reproduce the system’s behavior and finally selects the best-suited regression algorithm with the corresponding hyperparameters.