Empirical Model Research Articles

Exploring opportunities for public sector organisations to connect wellbeing to resource loops in a regional circular economy

ABSTRACT This paper contributes towards our understanding of the role of public sector organisations in delivering circular economy (CE) goals, suggesting that such organisations seek to deliver social value when implementing CE activities. Through empirical evidence from Wales, and a programme designed to deliver circular outcomes via increased collaborative working in public sector organisations, the paper shows that public sector actors interpret the CE as a means to deliver well-being outcomes. The paper shows that public sector organisations, in Wales, are seeking to exploit the maximum social value from CE activities. This, the paper argues, requires a nuanced attention to the context and circumstances in which public service delivery is implemented. A legislative framework, the Wellbeing Future Generations Act (2015), that promotes collaborative working with people and communities to prevent persistent problems such as poverty, health inequalities and climate change, is supporting the creation of a definition of a CE that connects people and wellbeing to resource loops. The key contribution of the paper is to provide an empirical model demonstrating how public sector organisations in Wales are enacting CE in Wales, mediating between and integrating the social, material and spatial elements of a CE.

The impact of environmental variables on surface Conductance: Advancing simulation with a nonlinear Machine learning model

Surface conductance (Gs) is a key factor in the Penman-Monteith (PM) equation; the interaction between environmental variables such as CO2 concentration, air temperature (TA), vapor pressure deficit (VPD), soil water content (SWC), and net radiation (R) affects Gs, evapotranspiration and thus impacts the hydrological cycle. These interactions are highly nonlinear and vary among different vegetation types. However, conventional Gs simulation models use fixed interactions between environmental variables in their equations for all vegetation types. Moreover, the characterisation and parameterisation of conventional Gs models is highly uncertain due to the high spatiotemporal variability in key environmental variables and plant parameters, which inhibits their generalisation. This study investigates whether Gs could be estimated more accurately by nonlinear statistical techniques that capture the multiple interactions between the environmental variables that affect Gs for each vegetation type. We compare mixed generalized additive model (MGAM) for Gs simulation with semi-empirical and empirical models at 20 eddy covariance flux tower sites with four different vegetation types at daily and monthly timescales. The results show that the Nash-Sutcliffe Efficiency (NSE) in Gs simulation increased by up to 50% in MGAM model in comparison to the semi-empirical and empirical models. The MGAM model highlighted the interactive effects of CO2, VPD, and SWC for crops and grasses. The interactive effects of CO2, VPD, and TA were important for trees and grasses. The results from this study expand our understanding of the ability of Gs simulation models to identify and include the interactive effects of crucial environmental variables on plant transpiration and hydrological processes.

New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

Predictive models for photosynthetic active radiation irradiance in temperate climates

This research evaluated 10 different empirical models designed for predicting Photosynthetically Active Radiation (PAR) at higher latitudes, addressing atmospheric conditions specific to these regions. The research introduces the Musleh-Rahman (MR) model, which substitutes Diffuse Horziontal Irradiance (DHI) with Clear Direct Normal Irradiance (DNI), Ozone and Aerosol Optical Depth at 550 nm (AOD550) sourced for satellite reanalysis data, achieving a Mean Bias Deviation (MBD) of 0.19 % and Root Mean Square Error (RMSE) of 12.42 W/m2. Furthermore, when applied to six untested locations, results demonstrate that the MR model outperformed the best performing empirical model with an MBD improvement of 3.68 % and an RMSE of 4.28 W/m2, whereas, when compared to machine learning models, the Light Gradient Boost Model (LGBM), had an MBD of −3.85 %. The MR model also maintained consistency across seasonal and density evaluations, attaining an R2 value as high as 0.9709, thereby highlighting the significant benefits of integrating satellite-sourced atmospheric data into PAR prediction models. Moreover, the research illustrated that substituting DHI with Clear DNI, Ozone, and AOD550 not only reduces MBD and boosts R2 values but also amplifies the model's applicability and accuracy in capturing early PAR peaks and reducing overestimations through precise adjustments in Ozone and AOD550 levels. This highlights the benefits of incorporating satellite-derived atmospheric data into PAR predictions models.

Review of the creep constitutive models for rocks and the application of creep analysis in geomechanics

AbstractThe creep behavior of rocks has been broadly researched because of its extensive application in geomechanics. Since the time-dependent stability of underground constructions is a critical aspect of geotechnical engineering, a comprehensive understanding of the creep behavior of rocks plays a pivotal role in ensuring the safety of such structures. Various factors, including stress level, temperature, rock damage, water content, rock anisotropy, etc., can influence rocks’ creep characteristics. One of the main topics in the creep analysis of rocks is the constitutive models, which can be categorized into empirical, component, and mechanism-based models. In this research, the previously proposed creep models were reviewed, and their main characteristics were discussed. The effectiveness of the models in simulating the accelerated phase of rock creep was evaluated by comparing their performance with the creep test results of different types of rocks. The application of rock’s creep analysis in different engineering projects and adopting appropriate creep properties for rock mass were also examined. The primary limitation associated with empirical and classical component models lies in their challenges when it comes to modeling the tertiary phase of rock creep. The mechanism-based models have demonstrated success in effectively simulating the complete creep phases; nevertheless, additional validation is crucial to establish their broader applicability. However, further investigation is still required to develop creep models specific to rock mass. In this paper, we attempted to review and discuss the most recent studies in creep analysis of rocks that can be used by researchers conducting creep analysis in geomechanics.

Does capital efficiency influence economic growth in Bangladesh? Application of the Harrod-Domar model

PurposeThe main purpose of this paper is to analyse the influence of capital efficiency on the economic growth of Bangladesh using the Harrod-Domar (H-D) model.Design/methodology/approachWe use annual data from 1980 to 2019 for this paper. Three steps are taken in the data analysis. First, to check the existence of a unit root, we use the augmented Dickey-Fuller (ADF) test and to determine co-integration among the variables, we use the Johansen-Juselius co-integration test. Next, for long-run estimation, we use the dynamic ordinary least square (DOLS) estimator. The sensitivity of the long-run estimations is further checked by the fully modified OLS (FMOLS) and autoregressive distributed lag (ARDL) estimators. Lastly, we use the Granger causality test to determine the long-run causality among the variables.FindingsThe long-run co-integration test validates the co-integrating relationship among the variables. DOLS estimations reveal that the economic growth of Bangladesh is negatively associated with the incremental capital output ratio (ICOR), validating the notion that capital efficiency matters for achieving higher economic growth. On average, an increase in ICOR by a unit tends to reduce economic growth in the long term by 0.75 percent. Our results also reveal no significant relationship between savings and economic growth when the model is extended. Finally, causality results indicate unidirectional causality between ICOR and economic growth.Practical implicationsBased on the results obtained, we argue that the enhancement of capital productivity could bring efficiency because ICOR is an inverse of capital productivity. Since Bangladesh’s capital productivity is considerably low compared with other neighbouring countries, it is suggested that firms should gradually move towards technological advancement and enhance economies of scale, etc. in the long run. Moreover, policies in favour of continuous skill development programmes could be highly effective in increasing capital productivity given that capital follows a vintage structure.Originality/valueThis is the first paper to analyse the economic growth pattern of Bangladesh using the traditional H-D model by incorporating variables such as savings and ICOR and also by relaxing the assumption of time-invariant (i.e. fixed) data of the variables. Moreover, this paper extends the traditional H-D empirical model by introducing key indicators and time breaks for Bangladesh’s economy through a stepwise regression process.

Process development in Quality‐by‐Design paradigm for anti‐solvent aided crystallization: impact of crystallization parameters on particle morphology and dissolution behaviour of Dexlansoprazole active pharmaceutical ingredient

AbstractBACKGROUNDIn this work, a process development study for the crystallization step of dexlansoprazole API has been demonstrated under the Quality‐by‐Design (QbD) paradigm. Dexlansoprazole (crystalline) samples prepared under different crystallization conditions of reactor temperature and addition time of anti‐solvent were subjected to solid‐state characterization with variety of analytical techniques, including X‐ray powder diffraction (XRPD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), dynamic vapour sorption (DVS), scanning electron microscopy (SEM), particle size distribution (PSD) by laser diffraction and intrinsic dissolution rate (IDR).RESULTSThe crystalline samples were observed to have identical polymorphic phase integrity but differed in their physical characteristics. The particle size distribution and microscopy data collectively elucidated the particle growth kinetics and the extent of agglomeration could be correlated to crystallization conditions, namely reactor temperature and addition time of anti‐solvent. The intrinsic dissolution rate of samples was in good agreement with the morphological properties.CONCLUSIONAn empirical model has been proposed for intrinsic dissolution rate correlating with crystallization process parameters. In conclusion, it was possible to elucidate the impact of crystallization parameters on the solid‐state behaviour of dexlansoprazole for development of appropriate pharmaceutical dosage forms. © 2024 Society of Chemical Industry (SCI).

Investigation on the Collection Mechanism of van der Waals Force in Air Filtration by Numerical Simulation and Empirical Model Development

Investigation on the Collection Mechanism of van der Waals Force in Air Filtration by Numerical Simulation and Empirical Model Development

A Numerical Time Integration Procedure for Secondary Dendrite Arm Spacing in Hyper-Peritectic Steel Alloys

AbstractA numerical time integration procedure for the calculation of the secondary dendrite arm spacing (SDAS) in FeC hyper-peritectic alloys is presented, a preferred group of low-carbon casting steel. The procedure incorporates a three-stage thin arm dissolution model, which is solved at each time step using Newton–Raphson iteration. This is coupled to a coarsening model based on the integral forms of the dissolution model, which are solved using Gaussian quadrature, as well as a growth model for solid fraction evolution. The procedure can easily be embedded into numerical models for solidification, in which the space–time evolution of the SDAS is required for determining the dynamic permeability in the mushy zone. Higher order approximations for both growth and solute concentration evolution can easily be incorporated. Temperature dependence of thermophysical parameters is taken into account using inter-dendritic solidification empirical models, and an alloy-specific peritectic reaction constant is used to determine the isothermal peritectic holding time. The procedure is validated against experimental data presented in literature. Various cases of SDAS as a function of local solidification time, cooling rate and carbon composition are investigated. The method is compared to experimental results of SDAS obtained from test castings of a hyper-peritectic steel alloy and can be used to iteratively determine the alloy-specific peritectic reaction constant by comparing the solid fraction evolution during the peritectic reaction with that found from the experimental cooling curve.

The role of renewable energy and total factor productivity in reducing carbon emissions: A case of top-ranked nations in the renewable energy country attractiveness index

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results.20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates.The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent.

Divisia Monetary Aggregates for India

Abstract In this paper, we are motivated by the fast growing literature that investigates the performance of Divisia monetary aggregates. We construct Divisia monetary aggregates for India using monthly data form January 2001 to March 2020 and present a comprehensive comparison across the Indian Divisia monetary aggregates at four levels of monetary aggregation, M1, M2, M3, and M4. We do so in the context of three classes of empirical models. In particular, we compute correlations between the cyclical components of the Divisia monetary aggregates and the cyclical component of the industrial production index. We test for Granger causality running from the Divisia monetary aggregates to industrial production. We also test for time-varying Granger causality. We find that the levels of the Divisia monetary aggregates Granger cause economic activity in India during normal times, but the causal link broke during and in the aftermath of the extremely unusual circumstances of the Covid-19 crisis.

A data-driven exploration of the complexity of the phosphorus-centered pnictogen bonds

Phosphorus-centered pnictogen bonds, similar to well-studied halogen bonds, play a vital role in molecular recognition and assembly. This study aims to explain how pnictogen bonds interact with other noncovalent interactions. A model system, phosphines (PH3), which is also present in phosphorus cycles and reduces planetary atmospheres, is examined. The investigation of the phosphorus trifluoride (PF3) molecule explores its substituent effects. To generate a broad spectrum of molecular configurations, a specially tailored protocol for sampling and optimisation was implemented. The configurations were refined to uncover primary interaction patterns, and a clustering algorithm revealed unique interaction patterns. This report presents the energy stability and distribution of all the clusters. Our findings verify the prevalent presence of pnictogen bonds, identified by their geometric characteristics and co-occurrence with hydrogen bonds, which show an almost linear correlation. Another significant discovery is the correlation between various energy decomposition elements, especially regarding the electrostatic energy and overall binding energy. These results are anticipated to significantly contribute towards our comprehension of non-covalent interactions among phosphorus-containing molecules and the formulation of empirical models having physical interpretations.

Multiaxial fatigue life prediction based on modular neural network pretrained with uniaxial fatigue data

PurposeData-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.Design/methodology/approachDrawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.FindingsBased on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.Originality/valueThe proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

Development of an empirical model for the prediction of the sound absorption coefficient for thin and low-density fibrous materials

Currently, FEA software such as ABAQUS uses empirical models to predict the sound absorption coefficient of poroelastic materials. However, based on a recent review of the literature it was found that the current sound absorption empirical models are inadequate for accurate prediction of thin (t < 20 mm), low-density materials (ρB < 50 kg/m3). Therefore, the predictions of the sound pressure levels in vehicle cabins, using such software, will be inaccurate since the trim materials are thin and have a low density. Thus, this research aimed to develop an empirical model that can accurately predict the sound absorption coefficient of these materials. Hence, polypropylene fibres consisting of four different diameters were manufactured and converted into nonwovens. Thereafter, airflow resistivity and impedance tube experimental testing were performed on the specimens. Subsequently, statistical analysis of the data was performed using SAS software. SAS was used to identify which independent variables should be included in the models to be developed. The empirical models were developed using the regression analysis toolbox in Microsoft Excel. Once the models were developed, various checks were performed to validate the assumptions of linear regression. The software NumXL was used to perform Cook’s distance tests. Thereafter, the models were validated against the validation dataset, where it was found that the developed exponential model performed best. Finally, the exponential model was compared to existing models using two data sets i.e. an internal dataset, and an external dataset derived from the literature. The developed model outperformed all the historic models on both datasets.

Morphological changes and color development during cookie baking-Kinetic, heat, and mass transfer considerations.

Color and shape are important quality attributes in baked goods, particularly cookies. Composition and processing conditions determine and influence color development and morphological changes in these baked goods. The objective of this study was to systematically evaluate the evolution of color and shape during baking to determine useful correlations that can be implemented during the assessment and modeling of the baking process. Cookies (AACC-I standard protocol 10-53.01) were baked at 185, 205, and 225°C. Moisture content, water activity, surface temperature, characteristic dimensions (radius and thickness), and color indexes (lightness, redness, blueness, and browning index [BI]) were monitored at different locations on the cookie surface and baking times. Relationships among the tested conditions were explored using correlation analysis. The cookies' dimensions and color indexes were strongly correlated with changes in moisture content over time, and those relationships were characterized using empirical models. The temperature dependence of the kinetic parameters of the changes in lightness and BI was also described and deemed independent of the location on the cookie surface. This study provides insights into the influence of heat and mass transfer on the physical and physicochemical changes of cookies during baking. The kinetic and secondary models developed in this study can serve as important components for establishing a comprehensive approach for coupling heat transfer, mass transfer, and reaction kinetics to estimate and optimize cookie-baking processes. PRACTICAL APPLICATION: The findings from this study provide valuable information for better understanding the morphological changes and color developments during the cookie-baking process. The quantitative data and models generated in this study will allow identifying baking conditions for better quality development.

Empirical mathematical model for absorption and evaporation phenomena of organic solvent in electrically-sensitive wood

Empirical mathematical model for absorption and evaporation phenomena of organic solvent in electrically-sensitive wood

Optimizing the cleaning frequency of solar photovoltaic (PV) systems using numerical analysis and empirical models

The significant reduction in the solar panels’ efficiency due to soiling can be mitigated through periodic cleaning, however, this comes at the expense of increased operational costs. Soiling rates are influenced by factors such as geographical location, season, system configuration, weather, and meteorological conditions. Optimizing cleaning frequency based on specific experimental conditions has limitations. Alternatively, numerical analysis can be employed to generalize and incorporate significant factors into the dust deposition model, thereby enhancing the applicability of the optimized model. This study aims to determine the optimum cleaning frequency that maximizes power gain while minimizing cleaning costs, utilizing numerical analysis to model the dust deposition rate. An optimization model is developed to determine the cleaning intervals based on surrounding conditions dynamically. The model minimizes the sum of cleaning costs and revenue loss due to soiling. The decline in photovoltaic efficiency from soiling is modeled using empirical equations in terms of dust deposition rate, derived through regression analysis from numerical results. The model is validated using a case study from the literature and then applied to a more generalized scenario. Results reveal that the cleaning interval dynamically varies throughout the year as surrounding conditions change, illustrating the significant contribution of this approach.

Effective approach to assess higher heating value of biomass from ultimate and proximate analysis

AbstractFor proper design and operation of biomass‐based energy systems, it is important to determine the higher heating value (HHV) of biomass. In this paper, two machine learning (ML) approaches, namely extra trees (ET) and least squares support vector machine (LSSVM), are used to predict the value of HHV associated with biofuels. The data required for HHV calculation, including proximate and ultimate analyses datasets, were collected from the literature. The performances of these two ML approaches for predicting biomass HHV were then compared with other smart models available in the literature. Even though the available empirical models can predict the biomass HHV with acceptable precision, it was found that our proposed ML techniques have a superior performance based on the error analysis; the proposed approaches also consider all key biomass characteristics in the developed models. In addition, the ET model proved to be slightly more accurate compared to the LSSVM model. Additionally, the developed proximate‐based ET model showed better performance compared to the ultimate‐based ET model. The most influential parameters in the developed ET models for the proximate and ultimate approaches were determined to be ash fraction and carbon fraction, respectively. Finally, it was concluded that the smart modelling techniques can be utilized as a robust and reliable alternative predictive methodology to replace direct laboratory measurement of the biomass HHV.

Intangible Capital in Factor Models

The transition from a traditional manufacturing-based economy to a knowledge- and service-based economy over recent decades resulted in a considerable rise in intangible capital, most of which is not reported on companies’ balance sheets. As a result, balance sheet-based valuation ratios, investment measures, and other firm characteristics that do not incorporate off-balance sheet (OBS) intangible capital suffer from significant measurement error problems. We incorporate a new measure of OBS intangible capital into firm characteristics, such as book to market, investment, and profitability, to address these measurement errors. These OBS intangible adjustments improve the performance of the Fama–French three- and five-factor models and the q-factor model, especially during recent decades. We further find that the value factor is no longer redundant in these empirical factor models. This paper was accepted by Lukas Schmid, finance. Funding: The authors are grateful for financial support from the 2019 EDHEC Scientific Beta “Advanced ESG & Factor Investing” Research Chair. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01261 .

Field-driven polyelectrolyte-polymer collisions in nanochannels.

Even though dilute (unentangled) polymer solutions cannot act as gel-like sieving media, it has been shown that they can be used to separate DNA molecules in capillary electrophoresis. The separation then comes from sporadic, independent DNA-polymer collisions. We study polymer-polymer collisions in nanochannels (i.e., channels that are smaller than the normal size of the polymers), a situation where a polyelectrolyte is forced to migrate "through" isolated uncharged molecules during electrophoresis. We use Langevin dynamics simulations to explore the nature of these collisions and their effect on the net motion of the two polymer chains. We identify several types of collisions, including some that are unique to nanochannels. When the uncharged polymer is much larger than the polyelectrolyte, the system is reminiscent of gel electrophoresis, and we propose a modified empirical reptation model to explain the data, with an orientation factor that depends on the tube diameter. We also observe that the duration of a collision is a non-monotonic function of the polymer size ratio when the two chains are of comparable size, a surprising resonance-like phenomenon, which, combined with the asymmetric nature of molecular conformations during collision, suggests possible ratchet-like mechanisms that could be used to sort polyelectrolytes in nanodevices.