Search

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle. [ABSTRACT FROM AUTHOR]

With the goal of forecasting streamflow time series with sufficient lead time, we evaluate the efficiency and accuracy of data-based models ranging from relatively simple to complex. Based on this, we systematically explain the model construction and selection process according to lead time, type and amount of data, and optimization method. This analysis involved optimizing the inputs and hyperparameters of four unique data-driven models: Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Transformer (TRANS), which were applied to the Soyang watershed, South Korea. The type and amount of model inputs are determined through a fine-tuning process that samples based on a correlation threshold, correlation to predictand, and autocorrelation to historical data and evaluates the simulated objective function. Hyperparameters are simultaneously optimized using three conventional optimization methods: Bayesian optimization (BO), particle swarm optimization (PSO), and gray wolf optimization (GWO). The experimental results provide insight into the role of input predictors, data preparations (e.g., wavelet transform), hyperparameter optimization, and model structures. From this, we can provide guidelines for model selection. Relatively simple models can be used when the dataset is small or there are few input variables, when only the near future is predicted, or when the selection of optimization methods is limited. However, a more complex model should be selected if the type and amount of data are sufficient, various optimization methods can be applied, or it is necessary to secure more lead time. More parameters, more complex model structures, and more training materials make this possible. [ABSTRACT FROM AUTHOR]

A data-driven reduced order model (ROM) based on a proper orthogonal decomposition-radial basis function (POD-RBF) approach is adopted in this paper for the analysis of blood flow dynamics in a patient-specific case of atrial fibrillation (AF). The full order model (FOM) is represented by incompressible Navier–Stokes equations, discretized with a finite volume (FV) approach. Both the Newtonian and the Casson's constitutive laws are employed. The aim is to build a computational tool able to efficiently and accurately reconstruct the patterns of relevant hemodynamics indices related to the stasis of the blood in a physical parametrization framework including the cardiac output in the Newtonian case and also the plasma viscosity and the hematocrit in the non-Newtonian one. Many FOM-ROM comparisons are shown to analyze the performance of our approach as regards errors and computational speed-up. [ABSTRACT FROM AUTHOR]

Machine learning models' performance in simulating monthly rainfall–runoff in subtropical regions has not been sufficiently investigated. In this study, we evaluate the performance of six widely used machine learning models, including Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), LASSO Regression (LR), Extreme Gradient Boosting (XGB), and the Light Gradient Boosting Machine (LGBM), against a rainfall–runoff model (WAPABA model) in simulating monthly streamflow across three subtropical sub-basins of the Pearl River Basin (PRB). The results indicate that LSTM generally demonstrates superior capability in simulating monthly streamflow than the other five machine learning models. Using the streamflow of the previous month as an input variable improves the performance of all the machine learning models. When compared with the WAPABA model, LSTM demonstrates better performance in two of the three sub-basins. For simulations in wet seasons, LSTM shows slightly better performance than the WAPABA model. Overall, this study confirms the suitability of machine learning methods in rainfall–runoff modeling at the monthly scale in subtropical basins and proposes an effective strategy for improving their performance. [ABSTRACT FROM AUTHOR]

Reservoir operations are influenced by hydroclimatic variability, reservoir characteristics (i.e., size and purpose), policy regulation, as well as operators' experiences and justification. Data‐driven reservoir operation models based on long‐term historical records shed light on understanding reservoir operation rules and patterns. This study applies generic data‐driven reservoir operation models (GDROMs) developed for 452 data‐rich reservoirs with diversified operation purposes across the CONUS to explore typical operation rules and patterns. We find that the operating policies of any of these reservoirs can be modeled with a small number (1–8) of typical operation modules. The derived modules applied to different conditions of the 452 reservoirs can be categorized into five basic types, that is, constant release, inflow‐driven piecewise constant release, inflow‐driven linear release, storage‐driven piecewise constant release, and storage‐driven nonlinear (or piecewise linear) release. Additionally, a joint‐driven release module, constructed from these five basic types, has been identified. The analysis further shows the module application transition patterns featuring operation dynamics for reservoirs of different operation purposes, sizes, and locations. The typical module types can be used as "Lego" bricks to build operation models, especially for data‐scarce reservoirs. These module types and their application and transition conditions can inform Standard Operation Policy (SOP) and Hedging Policy (HP) with specific inflow, storage, and/or both conditions. Key Points: Five basic types of operation modules are categorized for 452 reservoirsSeasonal patterns for module application transition are identified for reservoirs with different sizes, operation purposes, and locationsThe basic types of modules and operation patterns inform reservoir operation modeling [ABSTRACT FROM AUTHOR]

The article provides a comprehensive review of the latest developments in the field of laser surface hardening (LSH) and its modeling techniques. LSH is a crucial process for enhancing the surface properties of metals, particularly their hardness and wear resistance, without compromising their bulk properties. This review highlights the fundamental principles of LSH, the types of lasers used, and the key parameters influencing the hardening process. It delves into various modeling approaches, including finite element method (FEM) simulations, analytical models, and empirical models (using statistical methods), emphasizing the integration of advanced computational techniques such as machine learning and artificial intelligence to improve the accuracy and efficiency of LSH simulations. The review also explores practical applications across different industries, showcasing how LSH models have been used to solve real-world challenges in the automotive, aerospace, and tool manufacturing sectors. Finally, it addresses current limitations and outlines future research directions, suggesting potential areas for further advancements in the modeling and application of LSH processes. [ABSTRACT FROM AUTHOR]

• Predicted power consumption and COP a chiller plant using XGBoost. • Quantified impacts of data intervals and data processing on the accuracy of models. • Improved the predictions compared to the baseline using data smoothing methods. • Demonstrated a guide to develop data-driven chiller power and COP models. In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller power consumption and chiller COP of a water-cooled chiller plant. The 10-second measured data used in this study are from the Intelligent Building Agents Laboratory (IBAL), which includes two water-cooled chillers. Preprocessing, data selection, noise analysis, and data smoothing methods influence the accuracy of these data-driven predictions. The data intervals were changed to 30 s, 60 s, and 180 s using down-sampling and averaging strategies to investigate the impact of data preprocessing methods and data resolutions on the accuracy of chiller COP and power consumption models. To overcome the effect of noise on the accuracy of the models of chiller power consumption and COP, two data smoothing methods, the moving average window strategy and the Savitzky-Golay (SG) filter, are applied. The results show that both methods improve the predictions compared to the baseline, with the SG filter slightly outperforming the moving average. Particularly, the mean absolute percentage error of the chiller COP and power consumption models improved from 4.8 to 4.9 for the baseline to 1.9 and 2.3 with the SG filter, respectively. Overall, this study provides a practical guide to developing XGBoost data-driven chiller power consumption and COP prediction models. [ABSTRACT FROM AUTHOR]

Introduction: Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components. Method: A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy. Results: The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31–60 min in 34%, 61–90 min in 11%, 91–120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%). Conclusion: The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance. [ABSTRACT FROM AUTHOR]

Landslides due to rainfall are among most destructive natural disasters that cause property damages, huge financial losses and human deaths in different parts of the World. To plan for mitigation and resilience, the prediction of the volume of rainfall-induced landslides is essential to understand the relationship between the volume of soil materials debris and their associated predictors. Objectives of this research are to construct a model by utilizing advanced data-driven algorithms (i.e., ordinary least square or Linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), and deep neural network (DNN), K-nearest neighbor (KNN) and Ridge regression (RR)) for the prediction of the volume of landslides due to rainfall considering geological, geomorphological, and environmental conditions. Models were tested on the Korean landslide dataset to observe the best-performing model, and among tested algorithms, the extreme gradient boosting ranked high with the coefficient of determination (R2 = 0.85) and mean absolute error (MAE = 150.421 m3). The volume of landslides was strongly influenced by slope length, drainage status, slope angle, aspect, and age of trees. The anticipated volume of landslide can be important for land use allocation and efficient landslide risk management. [ABSTRACT FROM AUTHOR]

Smart buildings and cities are rapidly emerging as solutions to address the challenges of efficiency, urbanisation, and sustainability in the sector. The study proposes deploying data-driven digital twins for smart buildings by utilising the available building's technology and IT infrastructure to complement and augment existing functions. The digital twin will consist of a core data-driven energy model and a 2D visual representation of the building's systems, with the potential for future evolution into a 3D model. This study aims to present a preliminary investigation into the idea of data-driven digital twins in building management towards enhancing the operations of smart buildings and empowering the concept of smart cities. It is demonstrated on a building on the campus of Qatar University. With an emphasis on the air conditioning systems of the building, considering their substantial contribution to overall energy consumption, the study maintains an open approach to also encompass other energy systems within the buildings, and presents a comparative evaluation between simulation-based and data-driven modelling on the case study, as well as an exploration of various machine learning algorithms that can be used. Furthermore, exploring essential smart applications of the building's data-driven digital twin. Practical Application: The study provides a comprehensive exploration of the practical aspects of deploying data-driven digital twins for smart buildings, addressing challenges related to data collection, model development, integration with building infrastructure, and potential limitations. The paper aims to advance the field of facility management and promote smart and sustainable practices in building operations. By contributing to the existing knowledge in facility services and management, our study offers practical guidance towards optimising building performance, reducing energy consumption, and fostering sustainable urban development. [ABSTRACT FROM AUTHOR]

Blending of partially cracked ammonia with biosyngas is an attractive strategy for improving NH 3 combustion. In practice, products of biomass gasification and those of thermo-catalytic cracking of NH 3 are subject to some compositional uncertainties. Despite their practical importance, so far, the effects of such uncertainties on combustion systems remained largely unexplored. Hence, this paper quantifies the effects of small compositional uncertainties of reactants upon combustion of partially cracked NH 3 /syngas/air mixtures. An uncertainty quantification method, based on polynomial chaos expansion and a data-driven model, is utilised to investigate the effects of uncertainty in fuel composition on the laminar flame speed (S L) and adiabatic flame temperature (T ad) at different inlet pressures (P i). The analysis is then extended to the power output of a combined-cycle gas turbine fuelled by the reactants. It is found that 1.5% fuel compositional uncertainty can cause 12–21% of S L uncertainty depending on the inlet pressure. Furthermore, the effect of compositional uncertainty on T ad increases at higher ratios of H 2 to NH 3. Sensitivity analysis reveals that the uncertainty of CO contribution to S L uncertainty is higher than that of NH 3 , while the trend is reversed for the T ad uncertainty. In addition, the power output from the combined-cycle gas turbine system varies between 4 and 6% with 1.5% of fuel compositional uncertainty. This become more noticeable at elevated P i [5–10 atm], particularly when the fuel mixture contains high H 2 which is the main contributor to T ad variability. • PCE-UQ model to analyse compositional variability effects on flame properties (S L and T ad). • Additional UQ analysis examines the uncertainty impacts on a CCGT system. • H 2 variability is the strongest contributor to the uncertainty in the CCGT power output. • Richer fuel mixtures are most affected (6%) by minor fuel compositional variability. • Uncertainty impact is higher for high H 2 fuel mixtures combustion at elevated pressures. [ABSTRACT FROM AUTHOR]

Cold spray is an additive manufacturing and coating process in which powder particles are accelerated to supersonic speeds without melting them and then deposit on a surface to form a layer of a coating. Process parameters and materials affect the characteristics of manufactured parts and therefore must be chosen with care. Machine learning (ML) techniques have been specifically applied in additive manufacturing for tasks such as predicting and characterizing porosity. Machine learning algorithms can learn how a variation in the input spray parameters affects annotated output data, such as experimentally measured part properties. In this work, a dataset was developed from experiments reported in published academic papers, to train ML algorithms for the porosity prediction of cold spray manufactured parts. Data cleaning steps, such as null value replacement and categorical feature handling, were applied to prepare the dataset for the training of different ML models. The dataset was split into training and testing portions, and floating feature selection and hyperparameter optimization were performed using parts of the training set. A final evaluation of all trained models, using the test portion of the dataset, showed that a prediction accuracy with an average deviation of 0-2% porosity of the predicted values compared to the true values can be achieved. [ABSTRACT FROM AUTHOR]

Among the components of the hydrological cycle, stream flow has a major role in integrated water resources management. Establishing an accurate and reliable forecasting method for prediction of stream flow is very useful. Nowadays, data-driven methods are variously applied for river flow prediction. By hybridizing, one can take advantage of the cons of different methods for the proposed purpose. In the present research, we have combined SETAR and GARCH methods with ANN and also coupled MARS and CANFIS with SPSO to predict the monthly flow of the Maroon River in south west of Iran. Thus, four hybridized data-driven models of ANN–GARCH, ANN–SETAR, MARS–SPSO, and CANFIS–SPSO are developed and compared to see which method has the best performance. Although all the models yielded good results but it was seen that the ANN–SETAR model found more accurate answers in prediction of the stream flow with an average 5% higher accuracy in the results. The IQR of ANN–SETAR model is similar to observed value that this showed the efficiency of the ANN–SETAR for dependable simulation of extreme values of river flow compared to other models. So, it was concluded that the ANN–SETAR model is better than the other methods for forecasting the monthly streamflow. [ABSTRACT FROM AUTHOR]

Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task—accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots. [ABSTRACT FROM AUTHOR]

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description. [ABSTRACT FROM AUTHOR]

Several studies have aimed to predict and control carbon emissions from coal‐fired power plants. However, the highly complex combustion mechanisms in coal‐fired power plant boilers pose a significant challenge in direct modeling and optimization. To tackle this challenge, this study introduced a data‐driven approach along with model‐based process optimization to mitigate NOx emissions from coal‐fired power plants. The process involved collecting a 5‐month operational dataset containing 67 controllable parameters from a 500 MW coal‐fired power plant. Steady‐state data was isolated from the load output using a moving average method, followed by the application of an isolation forest algorithm to detect and remove anomalies. Correlation analysis was then used to evaluate parameter relationships and eliminate redundant ones. Subsequently, a NOx prediction model was developed, combining an extra tree regressor data‐driven prediction model with particle swarm optimization to optimize the most influential controllable parameters for reducing NOx emissions. Testing the proposed model across four different target loads consistently resulted in a reduction of over 20% in NOx emissions by optimizing boiler combustion parameters, representing a significant achievement in optimizing coal‐fired combustion. [ABSTRACT FROM AUTHOR]

Reduced-order models (ROMs) have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics, such as instabilities and oscillations, at a considerably reduced computational cost. In contrast, the reduced-order modelling of non-Newtonian viscoelastic fluid flows remains relatively unexplored. This work leverages the sparse identification of nonlinear dynamics (SINDy) algorithm to develop interpretable ROMs for viscoelastic flows. In particular, we explore a benchmark oscillatory viscoelastic flow on the four-roll mill geometry using the classical Oldroyd-B fluid. This flow exemplifies many canonical challenges associated with non-Newtonian flows, including transitions, asymmetries, instabilities, and bifurcations arising from the interplay of viscous and elastic forces, all of which require expensive computations in order to resolve the fast timescales and long transients characteristic of such flows. First, we demonstrate the effectiveness of our data-driven surrogate model to predict the transient evolution and accurately reconstruct the spatial flow field for fixed flow parameters. We then develop a fully parametric, nonlinear model capable of capturing the dynamic variations as a function of the Weissenberg number. While the training data are predominantly concentrated on a limit cycle regime for moderate [Formula: see text], we show that the parametrized model can be used to extrapolate, accurately predicting the dominant dynamics in the case of high Weissenberg numbers. The proposed methodology represents an initial step in applying machine learning and reduced-order modelling techniques to viscoelastic flows.

In the field of water supply management, multi-steps water demand forecasting plays a crucial role. While there have been many studies related to multi-steps water demand forecasting based on deep learning, little attention has been paid to the interpretability of forecasting models. Aiming to improve both the forecasting accuracy and interpretability of the model, a novel urban water demand forecasting neural network (UWDFNet) was presented in this paper. Compared with traditional deep learning models, it innovatively considered domain-specific prior knowledge from water supply management and incorporated the correlation relationship between different input variables into the design of the neural network structure, and verified the consistency between the knowledge learned by the model and prior knowledge through interpretability analysis. Additionally, a systematic performance evaluation was conducted and proved that UWDFNet possesses better accuracy and stability compared to other baseline models(e.g., gated recurrent unit network (GRUN), GRUN with a corrected Network (GRUN+CORRNet), GRUN+PID, GRUN+Kmeans).

This study aimed to develop an accurate and reliable model for predicting suspended sediment load (SL) in river systems, which is crucial for water resource management and environmental protection. While Xtreme Gradient Boosting (XGB), a powerful ensemble machine learning (ML) model, has been employed in previous studies, the novelty of this research lies in the introduction of a hybrid approach that synergistically combines XGB with the bio-inspired Marine Predators Algorithm (XGB-MPA) to estimate SL in the Yeşilirmak River (Turkey). To this end, streamflow (Q) and sediment concentration (SC) values as well as their lag times (1 to 3 month lag times) were fed as input variables – under 9 scenarios – into ML models. A time series of datasets from March 1973 to December 2011 and January 2012 to March 2023 were used for training and testing of ML models, respectively. The superiority of the proposed model (XGB-MPA) compared to two other hybrid models, including XGB-PSO (Particle Swarm Optimization) and XGB-GWO (Grey Wolf Optimization) was also investigated. According to the results, the simultaneous application of Q and SC lag time values as inputs has led to the best SL estimates by XGB-MPA, with XGB-MPA9 (RMSE = 103.7 ton/day; NSE = 0.96) exhibiting the lowest error rates. In addition, XGB-MPA has performed better than XGB in all scenarios, with the lowest and highest reduction in RMSE being 19.3% (scenario 5) and 97.4% (scenario 1), respectively. When comparing the performance of hybrid models, the proposed XGB-MPA model has performed best with MAE, RMSE and NSE of 40.94, 103.7 and 0.96, respectively, in comparison with 816.02, 1063.74 and −2.94 for XGB-PSO and 693.16, 981.68 and −2.37 for XGB-GWO. Further research can include the use of time series of efficient variables extracted from satellite images (e.g. land cover, river morphology, etc.) as model inputs.Highlights Improvement of SL estimation by coupling MPA with XGB modelUsing delayed combinations of streamflow and sediment concentration as model inputsSuperiority of MPA compare to PSO and GWO in SL estimationGreater variations in SHAP caused by sediment concentration compared to streamflowFurther studies required on the effects of hydrological and topographical features

ABSTRACTData-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

We propose to use a conventional simulator, formulated on the topology of a coarse volumetric 3D grid, as a data-driven network model that seeks to reproduce observed and predict future well responses. The conceptual difference from standard history matching is that the tunable network parameters are calibrated freely without regard to the physical interpretation of their calibrated values. The simplest version uses a minimal rectilinear mesh covering the assumed map outline and base/top surface of the reservoir. The resulting CGNet models fit immediately in any standard simulator and are very fast to evaluate because of the low cell count. We show that surprisingly accurate network models can be developed using grids with a few tens or hundreds of cells. Compared with similar interwell network models (e.g., Ren et al., 2019, 10.2118/193855-MS), a typical CGNet model has fewer computational cells but a richer connection graph and more tunable parameters. In our experience, CGNet models therefore calibrate better and are simpler to set up to reflect known fluid contacts, etc. For cases with poor vertical connection or internal fluid contacts, it is advantageous if the model has several horizontal layers in the network topology. We also show that starting with a good ballpark estimate of the reservoir volume is a precursor to a good calibration. [ABSTRACT FROM AUTHOR]

Higher Himalayan catchments are often poorly monitored for hydrological activities involving flood flow prediction for the safety of riverside communities and the successful operation of hydropower projects. This study aimed to estimate the comparative performance of artificial neural network (ANN) based flow prediction models using 10 years of daily river flow data of Kaligandaki catchment at Kotagaun, Nepal, which is a snow-fed catchment in the Himalayan region. The flow prediction models were trained and tested at a hydrological station using the previous 3 days' river flow data to predict the 1-day ahead flow data. Eight different training functions were employed in an ANN model for comprehensive statistical assessment of accuracy and precision of each training function. The most significant and validated result obtained in this study is the comprehensive comparison of various training functions' performance, and identification of the most efficient training function for the study case. Among the training functions investigated, the Levenberg-Marquardt backpropagation function exhibits the best performance for the model having Nash-Sutcliffe efficiency, root mean square error and mean absolute error values of 0.866, 209.578 and 75.422, respectively. This study provides a fundamental basis for accurate flow prediction of topographically challenged catchments where hydrological monitoring and data collection may be limited. In particular, this model will help to improve early warning system, hydrological planning, and the safety of riverside communities in the Himalayan region. [ABSTRACT FROM AUTHOR]

The abnormal aggregation of extracellular amyloid‐β(Aβ)$$ \left(A\beta \right) $$ in senile plaques resulting in calcium Ca+2$$ \left({Ca}^{+2}\right) $$ dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving Aβ$$ A\beta $$ deposition and Ca+2$$ {Ca}^{+2} $$ dysregulation. Importantly, synaptic impairments, neuronal loss, and cognitive failure in AD patients are all related to the buildup of intraneuronal Aβ$$ A\beta $$ accumulation. Moreover, increasing evidence show a feed‐forward loop between Aβ$$ A\beta $$ and Ca+2$$ {Ca}^{+2} $$ levels, that is, Aβ$$ A\beta $$ disrupts neuronal Ca+2$$ {Ca}^{+2} $$ levels, which in turn affects the formation of Aβ$$ A\beta $$. To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between Aβ$$ A\beta $$ and Ca+2$$ {Ca}^{+2} $$ using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modeling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi‐state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from 2$$ 2 $$‐year visits for AD patients, we employ this method to investigate the interplay between Aβ$$ A\beta $$ and Ca+2$$ {Ca}^{+2} $$ levels at various disease development phases. Incorporating the ADNI data in our physics‐based Bayesian model, we discovered that a sufficiently large disruption in either Aβ$$ A\beta $$ metabolism or intracellular Ca+2$$ {Ca}^{+2} $$ homeostasis causes the relative growth rate in both Ca+2$$ {Ca}^{+2} $$ and Aβ$$ A\beta $$, which corresponds to the development of AD. The imbalance of Ca+2$$ {Ca}^{+2} $$ ions causes Aβ$$ A\beta $$ disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of Ca+2$$ {Ca}^{+2} $$ ion transportation and deposition. This suggests that altering the Ca+2$$ {Ca}^{+2} $$ balance or the balance between Aβ$$ A\beta $$ and Ca+2$$ {Ca}^{+2} $$ by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy. [ABSTRACT FROM AUTHOR]

Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery's capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models' architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation's main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision. [ABSTRACT FROM AUTHOR]

Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management. [ABSTRACT FROM AUTHOR]

The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios. [ABSTRACT FROM AUTHOR]

Abstract Seasonal precipitation forecasts are essential for water resource management, agricultural activities, and the operational planning of hydropower systems. Any methodological advancement that enhances the accuracy of precipitation predictions will yield considerable societal benefits. In this context, this study proposes and evaluates two approaches for refining seasonal precipitation forecasts in Brazil, using simple data-based models, such as multiple linear regression (MLR) and nonlinear support vector machine (SVM). These models employ climate indices related to different teleconnection patterns that affect seasonal precipitation in Brazil, the unified gauge-based analysis of global daily precipitation from the Climate Prediction Center (CPC), and the precipitation forecasts from the Seasonal Forecast System 5 (SEAS5) as input variables. Both MLR and SVM models were validated from Jan-2017 to Dec-2020 using precipitation from the CPC as ground truth. The results suggest that, compared to SEAS5, MLR and SVM models enhance predictive accuracy and reduce bias in precipitation forecasts for the Southeast, Midwest, and North regions of Brazil during the austral summer. However, the performance of the models was found to be on par with the original predictions of SEAS5 in the Northeast and South regions, sectors of Brazil where the climate is significantly influenced by the El Niño-Southern Oscillation.

Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-observed systems. Notably, data-driven models consistently outperform physics-based models for systems with few observable degrees of freedom; e.g. hydrological systems. Here, we provide an operator-theoretic explanation for this empirical success. To predict a partially-observed system’s future behavior with physics-based models, the missing degrees of freedom must be explicitly accounted for using data assimilation and model parametrization. Data-driven models, in contrast, employ delay-coordinate embeddings and their evolution under the Koopman operator to implicitly model the effects of the missing degrees of freedom. We describe in detail the statistical physics of partial observations underlying data-driven models using novel maximum entropy and maximum caliber measures. The resulting nonequilibrium Wiener projections applied to the Mori-Zwanzig formalism reveal how data-driven models may converge to the true dynamics of the observable degrees of freedom. Additionally, this framework shows how data-driven models infer the effects of unobserved degrees of freedom implicitly, in much the same way that physics models infer the effects explicitly. This provides a unified implicit-explicit modeling framework for predicting partially-observed systems, with hybrid physics-informed machine learning methods combining both implicit and explicit aspects.

Optimizing vessel hull resistance is pivotal for enhancing maritime performance and minimizing environmental impacts. Traditional methods combine expert intuition with Data-Driven Models (DDMs), relying on parametrization to predict and optimize hull geometries using Experimental Fluid Dynamics (EFD) or Computational Fluid Dynamics (CFD) data. However, these conventional approaches are hampered by several limitations: they require significant human input, are computationally intensive and costly, and lack flexibility in adapting to new families of geometries or parameters beyond predefined ranges. Addressing these challenges, our research introduces a novel method that significantly reduces the need for human intervention, computational resources, and costs, while also improving the model’s adaptability. By proposing a new a parametrization technique that accurately encompasses the Delft Systematic Yacht Hull Series (DSYHS), we demonstrate that DDMs can be effectively trained directly on EFD datasets. This eliminates the dependency on extensive CFD simulations or the generation of new EFD data tailored to a specific investigation. Our approach matches the performance of leading-edge CFD models, even in extrapolating conditions, with physical plausibility and minimal human oversight. The validation of our method under various and increasingly complex extrapolating scenarios, employing statistical analyses on the DSYHS EFD dataset and comparisons with state-of-the-art CFD models, underscores the effectiveness of our proposal. Furthermore, we demonstrated that our model can successfully optimize hull resistance when navigating geometric parameters outside the confines of the DSYHS validating our results through leading-edge CFD simulations. This work addresses the limitations of existing methodologies by offering a novel approach more accurate, efficient, cost-effective, flexible, automated, and robust to extrapolation for hull resistance optimization.

The present study aims to thoroughly review GWL depletion monitoring studies completed between 2000 and 2023 based on data-driven models and GIS approaches from a global perspective. The review summarizes the details of the reviewed papers, including location, period, time scale, key objective, input parameter, applied model, performance metrics, research gaps, limitations, and depletion rate. The mean rate of GWL depletion varied worldwide from 2.9 ± 1.56 to 1100 ± 33.76 mm/yr using data-driven models and from 7.6 ± 2.98 to 2046 ± 45.27 mm/yr using GIS-based approaches. This study assesses the strength of relationships between various keywords and analyzed co-author networks using the Vos-viewer. It proposes a groundwater development strategy based on the evaluated papers to provide a long-term solution to the water scarcity problem. Overall, this review highlights the existing research gaps and suggests potential future research paths to boost the associated new knowledge and increase the accuracy of the GWL depletion monitoring approaches.

Inappropriate scheduling plans can result in additional economic losses and the safety of water distribution network (WDN). Optimizing manual experience based scheduling plans can help water utilities rationally allocate water plants and pump stations, ensuring the safety, stability, and economy of the water supply system. However, there is a lack of real-time, rational, and optimized scheduling methods. To address this, we proposed a novel intelligent scheduling framework based on deep learning. In this framework, two neural network models, multi-heads convolutional gated recurrent unit network (MH-CGRU) and multi-head gated recurrent unit network (MH-GRU), can effectively extract key features from the WDNs. Operating data were used as decision variables to predict and generate scheduling orders for water plants and pump stations, respectively. The rationality of the orders is verified by combining a high precision online hydraulic model and the evaluation of the operational status of the WDNs. This system has been deployed in a real WDN and put into practical application. From June to November of 2022, the total adoption rate of all orders reached 96.29%, with the average deviation between predicted and actual control targets being less than 5%, and energy consumption decreased by 3.05% compared to the previous year. HIGHLIGHTS Proposed an optimized, real-time, and secure intelligent control method for water supply networks based on deep learning algorithms.; Presented a data evaluation approach for selecting high-quality samples from monitoring data in the water supply system.; Developed an intelligent verification mechanism that combines a high-precision hydraulic model with scheduling orders for improved control reliability.;

Many operators of water distribution networks (WDNs) are unable to meet the increasing demand for water. Utility operators in such situations resort to rationing the supply as a partial solution to this problem; this, in turn, may lead to disproportionate allocation of water or inequity in supply. In this study, we propose a mixed integer non-linear program formulation and an efficient solution approach to minimize the inequity in supply, subject to hydraulic constraints and additional constraints on hours of supply and valve operation. Further, we show that the schedule can be obtained using a data-driven approach based on flow and level measurements, which eliminates the modelling effort and uncertainty associated with the use of hydraulic models. We demonstrate the proposed approaches through simulations of a real WDN, and experiments conducted on a topologically similar laboratory-scale network. HIGHLIGHTS A technique is proposed for scheduling intermittent WDNs that can be implemented in the field.; The operational objective is equity in supply and constraints on implementation are incorporated.; The technique is demonstrated on a simulated and laboratory scale of a real network.;

A hull and propeller biofouling assessment framework is presented and demonstrated using a bulk carrier as a case study corresponding to an operational period of two and a half years. The aim is to support the decision-making process for optimizing maintenance related to hull and propeller cleaning actions. For the degradation assessment, an appropriate key performance indicator is defined comparing the expected shaft power required with the measured power under the same operational conditions. The power prediction models are data-driven based on machine learning algorithms. The process includes feature engineering, filtering, and data smoothing, while an evaluation of regression algorithms of the decision tree family is performed. The extra trees algorithm was selected, presenting a mean absolute percentage error of 1.1%. The analysis incorporates two prediction models corresponding to two different approaches. In the first, the model is employed as a reference performance baseline representing the clean vessel. When applied to a dataset reflecting advanced stages of biofouling, an average power increase of 11.3% is predicted. In the second approach, the model entails a temporal feature enabling the examination of scenarios at different points in time. Considering synthetic data corresponding to 300 days since hull cleaning, it was derived that the fouled vessel required an average 20.5% increase in power.

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle.

Machine learning models’ performance in simulating monthly rainfall–runoff in subtropical regions has not been sufficiently investigated. In this study, we evaluate the performance of six widely used machine learning models, including Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), LASSO Regression (LR), Extreme Gradient Boosting (XGB), and the Light Gradient Boosting Machine (LGBM), against a rainfall–runoff model (WAPABA model) in simulating monthly streamflow across three subtropical sub-basins of the Pearl River Basin (PRB). The results indicate that LSTM generally demonstrates superior capability in simulating monthly streamflow than the other five machine learning models. Using the streamflow of the previous month as an input variable improves the performance of all the machine learning models. When compared with the WAPABA model, LSTM demonstrates better performance in two of the three sub-basins. For simulations in wet seasons, LSTM shows slightly better performance than the WAPABA model. Overall, this study confirms the suitability of machine learning methods in rainfall–runoff modeling at the monthly scale in subtropical basins and proposes an effective strategy for improving their performance.

In this work, a purely data-driven approach to mapping out the state of a dynamical system over a set of chosen parameters is presented and demonstrated along a case study using real-world experimental data from a friction brake system. Complex engineering systems often exhibit a rich bifurcation behavior with respect to one or several parameters, which is difficult to grasp using experimental approaches or numerical simulations. At the same time, the growing need for energy-efficient machines that can operate under varying or extreme environmental conditions also calls for a better understanding of these systems to avoid critical transitions. The proposed method combines machine learning techniques with synthetic data augmentation to create a complete state map for a dynamical system. First, a machine learning model is trained on experimental data, picking up hidden mechanisms and complex parametric relations of the underlying dynamical system. The model is then exploited to assess the state of the system for a set of synthetically generated data to obtain a state map over the complete space spanned by the chosen parameters. In addition, an extension of the concept to a probability state map is introduced. The results indicate that the proposed method can uncover hidden variables which drive dynamical transitions between different states of a system that were previously inaccessible. [ABSTRACT FROM AUTHOR]

Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxin production. Monitoring coastal eutrophication is crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the tourist sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in recent decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modeling study, two different types of artificial neural networks (ANNs) are developed based on in situ data collected from stations located in the coastal waters of Cyprus. These ANNs aim to model the eutrophication phenomenon based on two different data-driven modeling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters' (specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen, and electrical conductivity) interactions with the Chlorophyll-a (Chl-a) parameter. The SOM model enables us to visualize the monitored parameters' relationships and to comprehend complex biological mechanisms related to Chl-a production. A second feed-forward ANN model is also developed for predicting the Chl-a levels. The feed-forward ANN managed to predict the Chl-a levels with great accuracy (MAE = 0.0124; R = 0.97). The sensitivity analysis results revealed that salinity and water temperature are the most influential parameters on Chl-a production. Moreover, the sensitivity analysis results of the feed-forward ANN captured the winter upwelling phenomenon that is observed in Cypriot coastal waters. Regarding the SOM results, the clustering verified the oligotrophic nature of Cypriot coastal waters and the good water quality status (only 1.4% of the data samples were classified as not good). The created ANNs allowed us to comprehend the mechanisms related to eutrophication regarding the coastal waters of Cyprus and can act as useful management tools regarding eutrophication control. [ABSTRACT FROM AUTHOR]

In recent years, the use of deep learning methods has rapidly increased in many research fields. Similarly, they have become a powerful tool within the climate scientific community. Deep learning methods have been successfully applied for different tasks, such as the identification of atmospheric patterns, weather extreme classification, or weather forecasting. However, due to the inherent complexity of atmospheric processes, the ability of deep learning models to simulate natural processes, particularly in the case of weather extremes, is still challenging. Therefore, a thorough evaluation of their performance and robustness in predicting precipitation fields is still needed, especially for extreme precipitation events, which can have devastating consequences in terms of infrastructure damage, economic losses, and even loss of life. In this study, we present a comprehensive evaluation of a set of deep learning architectures to simulate precipitation, including heavy precipitation events (>95th percentile) and extreme events (>99th percentile) over the European domain. Among the architectures analyzed here, the U‐Net network was found to be superior and outperformed the other networks in simulating precipitation events. In particular, we found that a simplified version of the original U‐Net with two encoder‐decoder levels generally achieved similar skill scores than deeper versions for predicting precipitation extremes, while significantly reducing the overall complexity and computing resources. We further assess how the model predicts through the attribution heatmaps from a layer‐wise relevance propagation explainability method. Plain Language Summary: With the increasing success of machine learning methods in Earth Sciences, deep learning is becoming a promising tool for building data‐driven models for meteorological applications. Yet, predicting extreme events, such as heavy rainfall, is still challenging. Here, we present an intercomparison of deep learning models to assess the ability of different architectures to predict precipitation events. While most of the models perform relatively well, we show that U‐Net‐based architectures outperformed the rest of the models. We additionally applied explainability methods to quantify which input features are the most important to predict precipitation events. Overall, the relative humidity showed the highest relevance values, followed by both wind components, particularly in western and southern Europe. Key Points: We present an intercomparison of deep learning models to assess the ability of different architectures to predict precipitation eventsU‐Net‐based architectures outperformed the rest of the modelsA layer‐wise relevance propagation explainability method quantify the most important feature to predict precipitation extreme [ABSTRACT FROM AUTHOR]

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. Recognizing the limitations of isolated methodologies - namely, the lack of domain understanding in data-driven models, the subjective nature of empirical knowledge, and the idealized assumptions in first-principles simulations, we explore their synergetic integration. We showed the potential risk of biased results when using data-driven models without causal analysis. Through a case study on energy consumption in building design, we demonstrate how causal analysis significantly enhances the modeling process, mitigating biases and spurious correlations. We concluded that: (a) Sole data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Integrating causal analysis results aid to first-principles simulation design and parameter checking to avoid cognitive biases. We advocate for the routine integration of causal inference within data-driven models in engineering practices, emphasizing its critical role in ensuring the models' reliability and real-world applicability.

This research aimed at modelling and predicting the antioxidant activities of Amaranthus viridis seed extract using four (4) data-driven models. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest Neighbour (k-NN), and Decision Tree (DT) were used as modelling algorithms for the construction of a non-linear empirical model to predict the antioxidant properties of Amaranthus viridis seed extract. Datasets for the modelling operation were obtained from a Box Behnken design while the hyperparameters of the ANN, SVM, k-NN and DT were determined using a 10-fold cross-validation technique. Among the Machine Learning algorithms, DT was observed to exhibit excellent performance and outperformed other Machine Learning algorithms in predicting the antioxidant activities of the seed extract, with a sensitivity of 0.867, precision of 0.928, area under the curve of 0.979, root mean square error of 0.184 and correlation coefficient of 0.9878. It was closely followed by ANN which was used to analyze and explain in detail the effect of the independent variables on the antioxidant activities of the seed extracts. This result affirmed the suitability of DT in predicting the antioxidant activities of Amaranthus viridis.

Introduction: Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components. Method: A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy. Results: The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31–60 min in 34%, 61–90 min in 11%, 91–120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%). Conclusion: The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.