Modeling Approach Research Articles

Projecting impacts of extreme weather events on crop yields using LASSO regression

Extreme weather events are recognized as major drivers of crop yield losses, which threaten food security and farmers’ incomes. Given the increasing frequency and intensity of extreme weather under climate change, it is crucial to quantify the related future yield damages of important crops to inform prospective climate change adaptation planning. In this study, we present a statistical modeling approach to project the changes in crop yields under climate change for eight majorly cultivated field crops in Germany, estimating the impacts of nine types of extreme weather events. To select the most relevant predictors, we apply the least absolute shrinkage and selection operator (LASSO) regression to district-level yield data.The LASSO models select, on average, 62% of the features, which align with well-known biophysical impacts on crops, suggesting that different extremes at various growth stages are relevant for yield prediction. We project on average 2.5-times more severe impacts on summer crops than on winter crops. Under RCP8.5, crop yields experience a mean change from -2.53% to -8.63% in the far future (2069-98) for summer crops and from -0.80% to -2.88% for winter crops, without accounting for CO2 fertilization effects. Heat impacts are identified as the primary driver of yield losses across all crops for 2069-98, while shifting precipitation patterns exacerbate winter and spring waterlogging, and summer and fall drought.Our findings underscore the utility of LASSO regression in identifying relevant drivers for projecting changes in crop yields across multiple crops, crucial for guiding agricultural adaptation. While the present analysis can identify empirical relationships, replicating these findings in biophysical models could provide new insights into the underlying processes.

Data-driven homogenisation of viscoelastic porous elastomers: feedforward versus knowledge-based neural networks

A computational framework is established to implement time-dependent data-driven surrogate constitutive models for the homogenised mechanical response of porous elastomers at large strains. The aim is to enhance the computational efficiency of multiscale analyses through the use of these surrogate models. To achieve this, explicit finite element (FE) simulations are conducted to predict the homogenised response of a cubic unit cell of a porous elastomer, using two different viscoelastic descriptions of the parent material, subject to pseudo-random, multiaxial, non-proportional histories of macroscopic strains. The histories of homogenised variables extracted from each set of FE predictions form a training dataset, which is used to train two different surrogate models, both relying on artificial neural networks (NNs). The first model predicts the increment in macroscopic stress over a simulation step, as a function of the macroscopic stress and strain at the beginning of the step, of the prescribed macroscopic strain increment, and of the corresponding time increment. The second model uses the same inputs and outputs but tests a knowledge-based modelling approach: it relies on the aid of an additional nonlinear elastic constitutive model, which is time- and path-independent and known a priori. This model represents an existing base of knowledge which is augmented and corrected by an NN after training on viscoelastic data. The data-driven surrogate model, therefore, learns the viscoelastic behaviour of the unit cell starting from knowledge of its elastic response. The two surrogate models are found to have comparable and very high accuracies at capturing the homogenised unit cell response to highly complex loading cases across a range of viscoelastic properties. Hyperparameter optimisation shows that the second, knowledge-based model requires a simpler NN and therefore incurs a smaller computational cost.

Optimizing peak shaving operation in hydro-dominated hybrid power systems with limited distributional information on renewable energy uncertainty

The increasing integration of renewable energy sources (RES) in power systems poses challenges for peak shaving operations due to RES uncertainty. However, it is difficult to obtain complete distributional information for uncertainty modeling. This study focuses on optimizing peak shaving in hydro-dominated hybrid power systems under such uncertainty. We utilize limited distributional information of RES forecast errors, specifically the first two moments, to build a moment ambiguity set. Employing distributionally robust chance-constrained programming (DRCCP), we develop a peak shaving model that quantifies the flexibility reserve of hydropower by risk level and the forecast errors. To enhance computational tractability, we apply the Chebyshev inequality to reformulate the moment-based DRCCP model into a mixed-integer linear programming model. Numerical simulations conducted on a provincial power grid in China validate the model's effectiveness. Key findings indicate that: (1) The model effectively leverages hydropower to provide ramping flexibility for peak shaving and quantifies the flexibility reserve needed for RES forecast errors. (2) This uncertainty modeling approach is more practical than probability distribution function-based methods, ensuring reliable peak shaving scheduling and reducing conservatism. (3) Decision-makers can adjust risk level to modify hydropower flexibility reserve, balancing robustness and conservatism of peak shaving scheduling.

Assessing human exposure to organic contaminants in fish: A modeling approach integrating chemical bioconcentration and food thermal processing

Assessing human exposure to organic contaminants in fish: A modeling approach integrating chemical bioconcentration and food thermal processing

A New Sensing Channel Modeling Approach Based on Ray Tracing and Stochastic Methods for Vehicle-to-Everything Applications

A New Sensing Channel Modeling Approach Based on Ray Tracing and Stochastic Methods for Vehicle-to-Everything Applications

A novel modelling approach for realistically-wrapped state recycled aggregates and its application in meso-scale tensile behaviour simulation of recycled concrete

A novel modelling approach for realistically-wrapped state recycled aggregates and its application in meso-scale tensile behaviour simulation of recycled concrete

Dynamic modeling of post-combustion carbon capture process based on multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder network

Solvent-based post-combustion carbon capture (PCC) technology is a promising, near-term solution for decarbonizing power generation and industrial facilities. Model-based process simulation is crucial for the optimal design and operation of the PCC process. Recently, data-driven models have gained attention due to their adaptability, efficient computation and high accuracy. However, the nonlinearity, strong couplings and multi-time scale features of the PCC process pose significant challenges for model identification. To this end, this paper proposes a multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder (MMoE-DAED) network for dynamic modeling of the PCC process under wide operating conditions. An encoder-decoder composed of long short-term memory (LSTM) network is employed to extract features from the time-dependent input data and learn the complex dynamic interactions caused by the inertial and delay properties of the process. Dual-stage attention mechanism is incorporated into the encoder and decoder respectively to select the most relevant input features and their correlations within the time series data. To enhance multi-output prediction accuracy, multi-gate mixture-of-experts (MMoE) framework that considers correlations of multitask learning is implemented. Simulation results using operating data from a PCC experimental setup indicate that the proposed modeling approach accurately predicts the steady-state values and dynamic trends of the CO2 capture rate and stripper bottom temperature over a wide operating range. The RMSE, MAPE and R2 indices for the CO2 capture rate are 2.1592, 0.0295, 0.9641, respectively, and for the stripper bottom temperature are 0.1491, 0.0003, 0.9833, respectively. Validations on a PCC simulator further verify the accuracy and efficiency of the MMoE-DAED model, which enables an 80.87% reduction in computation time compared to the simulator. This paper points to a new direction for the data-driven dynamic modeling of complex energy conversion processes.

Deciphering the CO2 hydrates formation dynamics in brine-saturated oceanic sediments using experimental and machine learning modelling approach

One of the potential strategies for limiting global warming is capturing excess amounts of industry CO2 emissions and sequestrating it inside the oceanic sediments as CO2 hydrates. The seabed consists of sediments with different porosity and sizes, which can significantly impede the kinetics of CO2 hydrates. To address these challenges, in this novel work the CO2 hydrate kinetics and morphologies have been investigated in brine-rich [3.5 wt% NaCl] coarse sediments [porosity = 0.28, Ø =0.5-1.5 mm], granule sediments [porosity = 0.43, Ø =1.5-3 mm] and dual sediments [coarse + granules] inside a high-pressure reactor [3.5 MPa, 1-2 oC] with artificial seabed. According to the key experimental results, the water-to-hydrate conversion [%] was estimated to be 67.24 (± 3.02) % for coarse sediments, 49.58 (± 4.97) % for dual sediments (coarse + granules), and 27.68 (± 5.21) % for granules. In-situ Raman spectroscopy was used to evaluate the real-time solubility of CO2 [0.023 mol/mol], and image processing thresholding was used to identify CO2 hydrate distribution patterns. A mathematical model was proposed as a major contribution to predict CO2 hydrates kinetics in sediments, using 94,203 data points. The model was trained via a supervised machine-learning [ML] algorithm and can predict CO2 hydrate kinetics with an AARD of 7.54-8.47%.

On-line inspection of lattice structures and metamaterials via in-situ imaging in Additive Manufacturing

As advanced production capabilities are moving towards novel types of geometries as well as higher customization demands, a new and more efficient approach for process and part qualification is becoming an urgent need in industry. The layerwise nature of additive manufacturing (AM) potentially allows anticipating qualification tasks in-line and in-process, aiming at reducing the time and costs devoted to post-process inspections, enabling at the same time an early detection of defects since their onset stage. Such opportunity is particularly attractive in the presence of highly complex shapes like lattice structures or metamaterials, which have been increasingly investigated for industrial adoption in various sectors, aiming to achieve enhanced mechanical properties and innovative functionalities. This paper presents a novel methodology to inspect the geometry of lattice structures while the part is being built. The method is specifically designed to tackle the natural variability affecting layerwise images gathered in laser powder bed fusion. To this aim, it combines the segmentation of in-situ powder bed images of solidified layers with a data modelling approach to synthesize the 3-D shape of each unit cell into a 1-D profile representation. Such low-dimensional representation is suitable to quickly detect undesired distortions that may have a detrimental impact on final quality and performance. By using post-process X-ray computed tomography as ground truth reference, this study shows the effectiveness of the proposed approach for in-line inspection, opening a novel and cost-efficient way to address complex shape qualification for lattice structures in AM.

A Hybrid Modelling Approach Coupling Physics-based Simulation and Deep Learning for Battery Electrode Manufacturing Simulations

Lithium-ion battery (LIB) performance is significantly influenced by its manufacturing process. Manufacturing of an optimized electrode can incur high production costs such as high energy consumption, high scrap rates and emissions. This is due to the process that consists of a series of manufacturing steps presenting a complex interrelationship, thus limiting the understanding of performance as a function of manufacturing parameters. While several empirical and computational methods are employed for optimization, they are demanding in terms of resources such as materials or computational effort. By leveraging Deep Learning (DL), we can enhance our understanding of the complex manufacturing processes and accelerate its optimization. We propose a data-driven supervised DL methodology to complement physics-based LIB cathode manufacturing simulations. The trained DL-based predictive model integrates well into the manufacturing simulation framework to forecast cathode slurry microstructures. The DL model demonstrates robust predictive performance for LIB NMC-111 and LiFePO4–based slurries and slurries for a solid-state battery NMC-622/argyrodite composite electrode preparation. While the current work is focused on the cathode slurry process, the proposed methodology has potential for application to drying and calendering steps. This approach will be helpful in streamlining lab-scale electrode manufacturing, and reducing errors, waste and resource consumption.

Numerical Investigation of the Cohesive Strength Regime of the Bilobated Arrokoth after the Sky-crater-forming Impact Event

In 2019, NASA’s New Horizons mission, using the Long Range Reconnaissance Imager, revealed Arrokoth’s bilobated shape and a large impact-crater-like region (“Sky”) on the small lobe, which is ∼7 km wide and ∼1 km deep. Given that this depression takes up ∼7% of the entire volume of the small lobe, Arrokoth’s neck, the most structurally sensitive area to failure, might have been subject to substantial structural modification if the Sky-crater-forming event occurred after the bilobate shape had formed. Using the π-scaling law, we quantified the linear momentum imparted to the small lobe by the Sky-crater-forming event, which was in the range of (2.4–4.0) × 1013 kg m s−1, depending on Arrokoth’s bulk density of 250–500 kg m−3 and impact speeds of 100 m s−1, 300 m s−1, and 1 km s−1. If the linear momentum was fully transferred to Arrokoth’s small lobe, it would have given the small lobe an impulse velocity of approximately 0.1 m s−1 relative to the large lobe. To assess the structural impact of this event, we used a finite-element modeling approach to simulate post-impact stress fields driven by the estimated impulse velocity on the small lobe and constrained the critical cohesive strength required to prevent structural failure. Based on the current parameter space, our results suggest that the Sky-crater-forming event could have required the critical cohesive strength of up to ∼20 kPa for Arrokoth’s neck to avoid structural failure, which is higher than the typical cohesive strength estimated for small bodies (usually less than 1 kPa for asteroids and comets).

Determinants of students' learning motivation in Islamic boarding schools: A structural equation modeling approach

Motivation plays a key role in the learning process, as it serves as a driving force or positive influence for students. This study aims to explore factors affecting students' learning motivation by examining teacher professionalism, family environment, school environment, and academic stress using the Structural Equation Modeling (SEM) method. Data was collected through questionnaires distributed to students at Baitur Rohmah Muhammadiyah Islamic Boarding School, with responses from 201 students. The findings show that teacher professionalism (β: 0.345, P-value: 0.044), family environment (β: 0.088, P-value: 0.010), and academic stress (β: 0.415, P-value: 0.042) positively influence learning motivation. However, the school environment does not significantly affect learning motivation (β: 0.156, P-value: 0.224). Additionally, teacher professionalism does not have a significant impact on students' academic stress levels (β: 0.156, P-value: 0.224).

Mesoscopic analysis on the coral aggregate reinforced concrete column under axial and eccentric compression

Coral aggregate concrete (CAC) has been identified as a promising building material for reef engineering construction, yet its practical application remains challenging due to incomplete research on the mechanical behaviors of the CAC beam and column components. In this study, a 3D mesoscale modeling approach considering the heterogeneity of CAC was utilized to investigate the mechanical performance of coral aggregate reinforced concrete columns (CARCCs) under axial and eccentric compression. Through numerical delineation of failure processes and damage mechanisms at the mesoscale, comprehensive parametric analysis integrating both numerical simulations and experimental validations were executed to assess the load-bearing capacity of CARCC. Results indicate that the mesoscale model has significant reliability in simulating the deformation and cracking failure behaviors and analyzing the load-displacement relationships of CARCC under axial and eccentric compression loads. When under axial loads, macroscopic cracks in CARCC primarily develop in an oblique manner, precipitating significant concrete spalling and extensive cracking failure along the longitudinal axis. Under eccentric loads, failure advances from crack initiation at load points to abrupt vertical fractures in stressed zones, manifesting a decrement in the load-bearing capacity with escalating eccentricity. Furthermore, the strength grade and reinforcement ratio exert a profound influence on the load-bearing capacity, with disparate impacts under varied loading conditions. It is concluded that reinforcement strategies tailored to specific loading conditions have significant implications for the design and safety of reinforced concrete structures in reef engineering projects.

Effectiveness-MTU modeling approach for hydrogen separation with dense metallic membranes

As hydrogen gains interest as energy vector, so it happens for its purification techniques. In particular, dense metallic membranes are a promising technology for many high-temperature applications. Modeling of hydrogen permeation is a fundamental support to their commercial development. Permeation through metallic membranes, in particular Pd-alloys, is regulated by a generalized form of the Richarson’s equation, where driving force for permeation is due to the difference in hydrogen partial pressure at the membrane surfaces. From the flux expression it is possible to develop a model for a mass exchanger, in analogy with the mathematical description of heat exchangers. For the latter, a well-known method, called , is used to simplify heat transfer problem. The same approach applies to mass exchangers, where the method had already been transferred to reverse osmosis. In this article, method is applied to ideal mass exchangers for hydrogen separation. Effectiveness showed to be influenced by inlet hydrogen fraction, exponent of partial pressures and pressure ratio. Moreover, a procedure to include concentration polarization losses is presented. A rule-of-thumb approach is proposed for an estimation of hydrogen recovery, validated to reproduced an experimental result with less that 7% relative error. shows to be a fundamental support to membrane module design.

Projecting Untruncated Climate Change Effects on Species' Climate Suitability: Insights From an Alpine Country.

Climate projections for continental Europe indicate drier summers, increased annual precipitation, and less snowy winters, which are expected to cause shifts in species' distributions. Yet, most regions/countries currently lack comprehensive climate-driven biodiversity projections across taxonomic groups, challenging effective conservation efforts. To address this gap, our study evaluated the potential effects of climate change on the biodiversity of an alpine country of Europe, Switzerland. We used a state-of-the art species distribution modeling approach and species occurrence data that covered the climatic conditions encountered across the full species' ranges to help limiting niche truncation. We quantified the relationship between baseline climate and the spatial distribution of 7291 species from 12 main taxonomic groups and projected future climate suitability for three 30-year periods and two greenhouse gas concentration scenarios (RCP4.5 and 8.5). Our results indicated important effects of projected climate changes on species' climate suitability, with responses varying by the taxonomic and conservation status group. The percentage of species facing major changes in climate suitability was higher under RCP8.5 (68%) compared to RCP4.5 (66%). By the end of the century, decreases in climate suitability were projected for 3000 species under RCP8.5 and 1758 species under RCP4.5. The most affected groups under RCP8.5 were molluscs, algae, and amphibians, while it was molluscs, birds, and vascular plants under RCP4.5. Spatially, by 2070-2099, we projected an overall decrease in climate suitability for 39% of the cells in the study area under RCP8.5 and 10% under RCP4.5, while projecting an increase for 50% of the cells under RCP8.5 and 73% under RCP4.5. The most consistent geographical shifts were upward, southward, and eastward. We found that the coverage of high climate suitability cells by protected areas was expected to increase. Our models and maps provide guidance for spatial conservation planning by pointing out future climate-suitable areas for biodiversity.

Effect of patient specificity on predicting knee cartilage degeneration in obese adults: Musculoskeletal finite-element modeling of data from the CAROT trial.

Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. Inthisstudy,we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren-Lawrence (KL)and Boston-Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve(AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.

Efficient dual-stream neural networks: A modeling approach for inferring wild mammal behavior from video data

Efficient dual-stream neural networks: A modeling approach for inferring wild mammal behavior from video data

Predicting the intention to install solar photovoltaic panels in emerging market: The role of consumer innovativeness, knowledge, and support for government incentives

Promoting the installation of solar photovoltaic panels at the residential level in Vietnam may effectively mitigate the electricity overload on the national grid and support the country’s sustainable objectives. However, the installation rates of this system in urban areas in Vietnam remains low despite the favorable atmosphere and the provision of several incentives. Based on the Diffusion of Innovation and Theory of Planned Behavior, this study explores the role of consumer innovativeness, knowledge, and support for government incentives on the diffusion and social acceptance of solar photovoltaic panels. A questionnaire survey was conducted to collect data for analysis. With a sample of 339 citizens in Vietnam, the partial least squares structural equation modeling approach was employed to evaluate the results. The findings revealed that consumer innovativeness was linked to social norms and knowledge. Furthermore, it was discovered that the knowledge of consumers can significantly enhance the support for government incentives and the intention to install solar photovoltaic panels. Additionally, social norms significantly influenced attitudes, ultimately resulting in an increase in the intention to install. The findings of this study could aid the nation's policymakers and institutions in formulating strategies to grow the solar energy sector, therefore facilitating the achievement of sustainable energy goals.

A modelling approach combining swat with Gis-based DRASTIC techniques to assess aquifer vulnerability evolution in highly anthropised aquifers

A modelling approach combining swat with Gis-based DRASTIC techniques to assess aquifer vulnerability evolution in highly anthropised aquifers

Modeling Nitrogen Recovery and Water Transport in Gas-Permeable Membranes

This study presents a new modeling approach for nitrogen recovery for gas-permeable membrane (GPM) contactors, including both ammonia and water transport dynamics. A distinct feature of the model is its capacity to model water transport across the membrane, which has been overlooked in most publications. Osmotic pressure differences are used to predict the behavior of ammonia and water transport in the GPM. Experiments carried out to develop, test and calibrate the model examined the dynamics of ammonia and water transport through the GPM at various nitrogen concentrations. Specifically, the GPM contactor was tested for nitrogen recovery from high-strength synthetic wastewaters (2.4-10.6 g N/L) at 35°C and at pH 9. The initial volume of the trapping solution (diluted H2SO4) was 10 times lower than that of the synthetic wastewater, aiming to concentrate the recovered nitrogen. The estimated ammonia transport constant (Km) ranged between (1.2 – 2.1)·10-6 m/s and water transport constant Kw between (2.8 – 8.2)·10-10 m/(s bar). Numerical determination of the model parameters revealed high R² values, demonstrating strong agreement with experimental data.