Development Of Empirical Model Research Articles

PSVIII-21 Meta-analysis of nutritive factors influencing mean retention time of digesta in the gastrointestinal tract of broiler chickens

Abstract Broiler chicken meat is an important source of food protein for human consumption globally. Over the last several decades, dramatic changes have been made to broiler genetics, management, and nutrition to increase production efficiency. Different feeds with varying nutrient composition have been researched to target goals such as improving growth and feed conversion ratio. Part of understanding and further improving these feeding programs involves understanding the effects of the feed on nutrient digestibility and animal performance. As such, models are a key tool in the wholistic evaluation of diet, genetics, and management interactions. Towards developing a mechanistic digestion model for broilers, knowledge of mean retention time (MRT, min) of digesta in the gastrointestinal tract (GIT) across a variety of diets is required. To understand the relationship between MRT in broilers and their diet, a systematic literature search and meta-analysis was conducted where 34 articles were found to meet the inclusion criteria. MRT in these studies was measured in various parts of the GIT where 7 studies reported MRT in just one segment (e.g. jejunum), 12 studies reported MRT in multiple combined segments (e.g. jejunum + ileum) and 21 studies reported MRT of the entire GIT. Birds used in these studies included those in the starter, grower, and finisher phases as well as breeders. Nutritional composition of the diets was represented by data on metabolizable energy (ME, kcal/kg), crude fiber (% DM), crude fat (% DM), crude protein (% DM) and calcium (% DM) that was consistently reported across studies. A mixed model (PROC MIXED, in SAS), treating study as a random effect, and using a subset of the data collected where MRT was reported for the entire GIT, and limited to grower and finisher phases, was used to examine the relationship between the diet composition and MRT. Only ME (F1,28.1 = 4.25; P = 0.0486) was found to be significantly related to MRT, where an increase in one kcal/kg of ME resulted in a decrease of MRT by 0.091 minutes (within the range of 2,749 to 3,507 kcal/kg ME). Other nutritional components of the diet were found to have no significant relation to MRT (all P > 0.05), though next steps will examine how the source of ME (fat vs starch vs fiber) impacts this relationship. Although other factors (such as age and feed intake) are still to be considered, the initial results from this study provide valuable insights into how nutritional composition of poultry feeds relates to MRT. Further investigation will also quantify this relationship within segment of the GIT. This work not only provides valuable information to nutritionists, but also lays the groundwork for empirical model development to predict MRT of poultry feed, essential to poultry nutritional models.

Development of empirical models for estimation polymer indentation fatigue and validation with finite element simulation models

Development of empirical models for estimation polymer indentation fatigue and validation with finite element simulation models

Development of temperature and mass transfer-based empirical models for estimating reference evapotranspiration in Nigeria

ABSTRACT The empirical models commonly employed as alternatives for estimating evapotranspiration provide constraints and yield inaccurate results when applied to Nigeria. This study aims to develop novel empirical models to enhance evapotranspiration (ET0) estimation accuracy in Nigeria. The coefficients of non-linear equations were optimised using the particle swarm optimisation (PSO) algorithm for the development of the two new ET0 models for Nigeria, Awhari1 (temperature-based) and Awhari2 (mass transfer-based). ERA5 reanalysis data with a 0.1° × 0.1° resolution was used. The models were rigorously assessed against the FAO-56 Penman–Monteith method, resulting in Kling–Gupta efficiency (KGE) and percentage bias (Pbias) values of 0.75, 6.49, and 0.92, 5.67, respectively. The spatial distribution analysis of performance metrics showed both equations exhibited superior accuracy in estimating ET0 across diverse climatic zones in Nigeria. The incorporation of PSO in model development, coupled with spatial analysis, highlights the study's multidimensional approach. The spatial performance of the models indicates that they can be valuable tools for water resource management, irrigation planning, and sustainable agriculture practices in Nigeria.

A novel methodology for map-based model fitting: A case study with a Dual Source Heat Pump experimental dataset

This paper presents a new methodology to adjust map-based models to experimental data and reports the main results of a comprehensive experimental campaign of a Dual Source Heat Pump (DSHP) prototype. The prototype tested incorporates variable speed components (compressor, circulation pumps, and fan). The novelty of this prototype lies in its ability to select two possible heat sources: air or ground. Thus, it can operate as a geothermal or aerothermal heat pump, as well as a chiller, thanks to the additional capacity to reverse the cycle. Thanks to this hybrid approach, several advantages can be obtained compared to conventional equipment, such as higher efficiency, the requirement of smaller borehole heat exchangers, or the absence of defrost cycles. In a prior study, polynomial models were developed to accurately characterize the DSHP’s performance (i.e., condenser and evaporator capacities and electrical energy consumption). These models were obtained considering the external variables to the unit as independent variables to facilitate their applicability using variables commonly measured in real installations. Due to the complexity of heat pump performance, which in current equipment can be influenced by up to 5 or 6 independent variables, the search for suitable polynomial models required the availability of a complete working map including more than 3000 working points. Thus, this previous work developed these models based only on simulation results. In this sense, this paper concludes the development of these models by focusing on two critical issues concerning empirical model development. The first aspect involves determining the minimum number and location of testing points needed to define the experimental sample for the model adjustment. The reported experimental data were obtained by analyzing the most suitable experimental design methodology to create the experimental matrices in each operating mode of the DSHP. The second aspect focuses on the final adjustment of models using experimental data. A novel fitting approach for empirical models is introduced in the last part of this study. The developed methodology enables the integration of simulation and experimental results for the final fitting of empirical models through a two-step adjustment. The first step involves analyzing and defining polynomial functionals from the complete working maps generated by simulation. Subsequently, in a second step, the polynomial models are refitted to a suitable experimental sample using the methodology presented in this work. The latter allows for the increase of the accuracy of the models and the minimization of experimental costs. This novel approach ensures a robust characterization of systems with many independent variables using a minimum amount of experimental data. Significant benefits can be obtained from its application, such as the reduction of experimental cost and an increase in the model’s accuracy through an effective combination of both experimental and simulated information. Furthermore, it can be considered of general applicability to other engineering problems where the characterization of physical systems influenced by a high number of independent variables is required.

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

Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

Sheared settling in viscoelastic shear‐thinning fluids: Empirical studies and model development

AbstractThe hydrotransport of settling particles using laminar flow as opposed to the more energy‐ and water‐intensive turbulent flow has remained a tentative option for industries due to the complexity in characterizing particle settling dynamics in opaque non‐Newtonian fluids under sheared conditions. To provide insight into this unknown physics, this study focused on viscoelastic shear‐thinning fluids and developed a semi‐empirical model to characterize the batch‐settling dynamics of dilute suspensions (<5 vol% solids concentration) experiencing a shear field. These suspensions consisted of spherical glass microparticles in aqueous xanthan gum solutions. Settling profiles were measured with Electrical Resistance Tomography (ERT) while a motorized belt generated a cross‐shear field. The data acquired by the ERT showed that introducing cross‐shear fields into such suspensions increased settling rates when compared with static settling contexts. Then, a semi‐empirical model describing the “acceleration phase” of the settling process was developed and validated at accuracies between 81% and 97%.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR.

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Updated Predictive Models for Permanent Seismic Displacement of Slopes for Greece and Their Effect on Probabilistic Landslide Hazard Assessment

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope’s yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed.

A Comparative Analysis of the Solar Ultraviolet Spectral Irradiance Measured from Earth and Mars: Toward a General Empirical Model for the Study of Planetary Aeronomy

Accurate estimation of the solar vacuum ultraviolet irradiance between 0.1 and 200 nm is critical for the study of planetary aeronomy. Previous empirical models have relied on a limited number of reference spectra, or on multiple data sets with various degrees of uncertainty, and on an empirical selection of solar proxies. Here we propose a novel method for the development of empirical models based on Fourier transform and least-squares fitting of the long-term measurements from the Solar EUV Experiment on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics mission. A Fourier transform analysis is performed to examine a large number of solar proxies, which reveals that the solar radio flux at 10.7 cm and the solar Lyα flux at 121.6 nm are better proxies for solar irradiance below and above ∼120 nm, respectively. Using these two proxies, a nonlinear empirical model is developed through Fourier transform and least-squares fitting of solar irradiance measurements, which can reproduce the solar irradiance with uncertainties of only ∼1%–2% above ∼120 nm, ∼2%–4% within ∼45–120 nm, and ∼4%–8% below ∼45 nm. Comparison with measurements from the Extreme Ultraviolet Monitor on the Mars Atmosphere and Volatile Evolution mission indicates that the solar irradiance at Mars can be predicted with uncertainties of less than ∼8% by geometric extrapolation of the solar irradiance measured from Earth, provided that the measurements from Earth can be calibrated accurately. Our study provides a general method for the development of empirical models using long-term observations in planetary aeronomy.

Thermo-Mechanical Stability of Hydrocarbon-Based Pemion® Proton Exchange Membranes

Considering the environmental concerns about the disposal of perfluorosulfonic acid (PFSA) membranes as well as the growing interest in higher temperature operability of polymer electrolyte membrane fuel cells (PEMFCs), non-fluorinated hydrocarbon-based proton exchange membranes (PEMs) with aromatic backbones have become an increasingly active area of research. Low reactant cross-over, tunable electrochemical properties, and differentiated chemistries and potentially safer and cost-reducing synthesis procedures, are additional favourable features of hydrocarbon-based PEMs for prospective use in PEMFCs [1]. Vulnerable linking units, however, are known to be the main disadvantages of these polymers in the oxidative environment of fuel cells. During PEMFC operation, destructive radical species such as hydroxyl (HO•) are produced, causing polymer degradation and thinning in the membrane [2]. Recently, sulfo-phenylated polyphenylenes (sPPPs) have shown outstanding oxidative stability due to a polymer backbone comprising only aryl-aryl bonds, with mitigated chemical degradation in ex-situ and in-situ durability studies [3, 4]. Another concern is regarding the thermo-mechanical stability of PEMs. Thus, a systematic thermo-mechanical stability study is required to confirm the potential of sPPP-based PEMs to replace conventional PFSAs, especially for high temperature PEMFC, i.e., 110-120 °C.The present research objective is to assess the thermo-mechanical stability of a commercial reinforced hydrocarbon-based PEM, Pemion® (PF1-HLF8-15-X, 15 µm thick, reinforced), as well as a mechanically-reinforced PFSA-based reference membrane, across a wide range of temperature (30-120 °C) and relative humidity (RH) (10-90%) conditions that includes the crucial high temperature window of interest for future PEMFCs [6]. To this end, a comprehensive design of experiment yielded 19 tensile tests at various hygrothermal conditions with dynamic mechanical analyzer (DMA 850; TA Instruments) equipped with an external environmental chamber accessory (TA Instruments, RH Accessory). Important mechanical properties such as Young’s modulus, ultimate tensile stress, yield stress, maximum elongation at break, strain hardening, and modulus of resilience were extracted and discussed as well. Datapoints were fitted for empirical model development and mechanical properties were estimated for high temperature and RH conditions beyond the capability of the instrument. This method can be employed to assess if the mechanical properties of membrane materials can be retained at higher temperatures, regardless of polymer chemistry and type. Storage modulus and loss modulus were separately measured in a dynamic mode to observe the impact of temperature on the mechanical response of the hydrophobic backbone and hydrophilic ionic clusters, respectively.Overall, Pemion® demonstrates tough tensile properties in the stress-strain tests due to its sterically encumbered polyphenylene backbone (Figure 1a), whereas the reference PFSA (Figure 1b) shows elastomer-like behavior with much lower Young’s modulus, yield stress, and strain hardening (slope of the curve in the plastic deformation region). Figure 1c-f show the main viscoelastic properties extracted from the tensile tests at room (30 °C, 50% RH) and high temperature fuel cell conditions (110 °C, 50% RH). The modulus of elasticity and strain hardening of Pemion® membrane are almost temperature-independent, whereas these properties for the reinforced PFSA material undergo a significant decay at high-temperature ambience. Pemion® maintains good yielding strength even at high temperature and RH conditions (110-120 °C and 80% RH). Nevertheless, small mechanical stress values as low as 1 MPa can cause spontaneous yielding in the PFSA reference material above 110 °C. The modulus of resilience for the reinforced PFSA is also predicted to be zero at elevated hygrothermal conditions (i.e., 90 °C and 70% RH). This is interpreted as an approach to the material’s glass transition condition, where it loses its mechanical integrity and therefore, is potentially unsuitable for higher temperature PEMFC operation. Moreover, the dynamic mechanical thermal analysis revealed that Pemion® retains its robustness at hygrothermal ambience close to high-temperature PEMFCs, i.e., 110-120 °C and 40-50% RH, whereas the backbone of the PFSA material gradually loses its strength. Acknowledgements This project was financially supported by Natural Sciences and Engineering Research Council of Canada (NSERC), Ionomr Innovations Inc, Canada Foundation for Innovation (CFI), British Columbia Knowledge Development Fund (BCKDF), Western Economic Diversification Canada (WD), and Canada Research Chairs (CRC).

Effect of the rotor blade geometry on particle size and energy demand for knife-milled beech chips: Experimental identification and mathematical modelling

Rotor blade geometry (linear and screw) was experimentally identified for particle size characteristics and specific energy demand for knife-milled beech chips (7.8 wt % moisture content). The operational conditions of peripheral speed (6.8–20.4 m s−1) and screen sieve openings (0.75–6 mm) were considered for the knife milling of beech chips. The Rossin-Rammler-Sperling-Bennet model precisely described particle size distribution of comminuted beech chips with its parameters depending on screen sieve, rotor speed and blade geometry in a linear relationship. This relationship allowed the development and calibration of a unique empirical linear model. The model predicted particle size under the conditions considered for each blade geometry with accuracy. Energy demand values between 6.97 and 101.6 kW h t−1 were identified for D50 particle sizes between 0.29 and 2.60 mm. Energy demand was independent of the rotor blade geometry, and it was mainly affected by particle size characteristics, even though speed was also impactful. The linear model obtained allowed the prediction of energy requirements using speed and final particle size with an R2 value of 0.97.

Development of empirical models to predict stope wall stability in open stope mines using gene-expression programming

Development of empirical models to predict stope wall stability in open stope mines using gene-expression programming

Bibliometric investigation on green human resource management research

PurposeThe paper aims to present the comprehensive picture on green human resource management (GHRM) based upon bibliometric investigation, including different criteria, such as key authors, documents, keywords, bibliographic coupling (BC), etc.Design/methodology/approachBibliometric technique has been used with the application of VOSviewer. Further, the Scopus database has been utilised to extract the research articles.FindingsThe present study has exposed the due relevance of GHRM in the area of human resource management (HRM) for researchers as well as practitioners. The study has revealed that number of citations in the area is high from the researchers of China. Also, Jabbour, C.J.C., has been identified as the lead contributor and author from the area with 43 publications and 1,352 citations. The journal of cleaner production has been identified as the journal with the largest number of publications from the area. Also, prominent keywords used in the area are sustainability, GHRM, environmental management, sustainable environment and HRM.Research limitations/implicationsThe study has offered the comprehensive publication analysis on the area which will assist the researchers in gaining review on topic and development of empirical models. Further, with the utilisation of keywords criterion they can easily explore the publications done in the area. Also, study will help the scholars and researchers to identify the region and countries along with the institutions and universities to pursue research on the GHRM topic.Practical implicationsThe present study will help the mangers to focus on GHRM with more focus to acquire positive consequences for human resource management.Originality/valueThe present paper has presented various new and recent facets associated with the area of GHRM. Previously the similar analysis with bibliometric technique is rarely done with the present Scopus database.

Novel experimental technique to evaluate soil thermal conductivity in a transient state

novel experimental technique to evaluate soil thermal conductivity in a transient state

Variability of response spectra of single-path ground-motion record pairs

To improve the accuracy of seismic hazard analysis, understanding the uncertainty of the prediction of the ground-motion amplitude for a specific site owing to a specific source is important. In this study, the variability of the acceleration response spectra of single-path ground motion was evaluated using record pairs obtained from two earthquakes at the same site with the same magnitude, location, and focal mechanism, recorded by high-density strong-motion observation networks in Japan. The natural log standard deviations of the single-path ground motions varied with the period and were distributed in the range of approximately 0.3–0.5. Even if an empirical ground-motion prediction model that accurately models the characteristics of single-path ground motions could be developed, avoiding this variability in the prediction is difficult. The standard deviation of the within-event residuals was smaller at larger distances, and the period of the maximum standard deviation tended to increase as the earthquake magnitude increased. These features may be attributed to differences in the source characteristics of each earthquake. These findings will be useful for the development of advanced empirical ground-motion models and for improving the accuracy of seismic hazard analysis.

IMPACT OF THE SHIP WAVES AND TIDAL FORCES ON THE SEDIMENT RESUSPENSION IN INLAND WATERWAYS

Ship movement in narrow intertidal waterways generate complex hydrodynamics and sediment resuspension along with tides. In order to investigate the complex hydrodynamics and induced sediment concentration, field measurements were performed at the Falta stretch of the Hooghly river, India. The low frequency primary waves and high frequency secondary waves are dominant in this region as ships navigate in subcritical range due to complex river bathymetry. The time-frequency analysis is a useful tool to separate the ship-generated waves with the tides. The modest performance of the existing empirical models led to the development of the new empirical models for the prediction of ship-generated primary waves. The tides are the dominant factor for the sediment resuspension during the spring period whereas the ship-induced waves are dominant in neap period. The overall contribution of the ship waves on sediment resuspension during the tenure of field measurement was 24.66percent.

Development of Empirical and Artificial Neural Network Model for the Prediction of Sorption Time to Assess the Potential of CO2 Sequestration in Coal.

Geological sequestration of CO2 in a coal seam is considered an attractive option to reduce the carbon footprint. It has an additional advantage of enhancing the recovery of coalbed methane, which has less sorption affinity toward coal in comparison to CO2. Desorption of gases from coal is controlled by various parameters, including reservoir depth and coal rank. A representative factor for desorption and diffusion in coal is the sorption time. It is an indicator which helps in estimation and evaluation of gas movement in the coal seam. Coals exhibiting high sorption time allow greater quantities of CO2 injection and hold potential for CO2 sequestration. Therefore, reliable and cost-effective estimation of sorption time is very important prior to investment in projects related to CO2 sequestration. Generally, proximate and gas content analyses are part of the preliminary analysis of coal for the assessment of its potential as a coal-bed methane reservoir. In this study, data generated using these analyses were found very useful for estimating the sorption time and CO2 sequestration potential of coal. The coal samples were collected from different depths of the Mand Raigarh coalfield for testing, and an empirical equation and artificial neural network (ANN)-based model have been developed to predict the sorption time of coal. The developed empirical equation predicts the sorption time with a coefficient of determination value of 0.88 and a root mean squared error value of ±1.07 days. Furthermore, the developed ANN model has been found to be very efficient in prediction with a correlation coefficient value of 0.97.

Shear resistance of welded studs in deck slab ribs transverse to beams

Accurate and reliable predictions of the shear resistance of welded headed studs in deck slab ribs transverse to supporting beams are essential for achieving safe and economical designs of steel–concrete composite floors. This paper presents the development of a new empirical design model for computing the stud resistance, based on machine learning methods combined with regression models, and its calibration via reliability analyses according to the Eurocodes and US design practice. As an alternative to using the new equations directly, reduction factors have also been developed from the proposed model, which can be applied to three existing resistance models for studs in solid slabs. Comparisons of the proposed model with seven existing models indicate that the former outperformed the latter. At the same time, the proposed model produces higher shear resistances while satisfying the reliability requirements for many combinations of parameters, especially for lower concrete strengths common in composite floors, thicker decks, and through-hole stud welding. It was also found that the proposed model is insensitive to the following parameters considered important by the existing models: the concrete elastic modulus, the normal force on the slab face (ranging from 0 to 20% of the longitudinal shear force), stud welding type (through-deck or holes), location of slab reinforcement, and deck yield strength. The proposed model produces shear stud predictions that can be confidently used in design whilst providing considerably higher resistances for certain parameter combinations than those computed with the existing models, including those adopted by Eurocode 4 and AISC 360.

A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-Based Model Development

Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence. This study presents a largely unsupervised qualitative data extraction framework for ABM development. Using semantic and syntactic Natural Language Processing tools, our methodology extracts information on system agents, their attributes, and actions and interactions. In addition to expediting information extraction for ABM, the largely unsupervised approach also minimizes biases arising from modelers’ preconceptions about target systems. We also introduce automatic and manual noise-reduction stages to make the framework usable on large semi-structured datasets. We demonstrate the approach by developing a conceptual ABM of household food security in rural Mali. The data for the model contain a large set of semi-structured qualitative field interviews. The data extraction is swift, predominantly automatic, and devoid of human manipulation. We contextualize the model manually using the extracted information. We also put the conceptual model to stakeholder evaluation for added credibility and validity.