Empirical-based Models Research Articles

Assessment of Shear Strength Models for Squat Rectangular Reinforced Concrete Shear Walls

The shear strength is a critical parameter in the design of Reinforced Concrete (RC) shear walls subjected to lateral loads. Numerous design codes and published studies have proposed formulas for calculating the shear strength of squat RC walls. However, there is a discrepancy between the calculated and experimental results. This study aims to evaluate various models for the calculation of the shear strength of rectangular squat RC walls using 312 databases collected from the literature. The shear strength of the RC walls was calculated using eight code- and empirical-based models, while the input parameters were obtained from the experimental database. The results were evaluated utilizing two statistical indicators: coefficient of determination and root-mean-squared error. The analysis of the results revealed that the model of C. K. Gulec and A. S. Whittaker is the optimal model, followed by the models of S. L. Wood and Eurocode-8 (EC8).

Photovoltaic Modeling: A Comprehensive Analysis of the I–V Characteristic Curve

The I–V curve serves as an effective representation of the inherent nonlinear characteristics describing typical photovoltaic (PV) panels, which are essential for achieving sustainable energy systems. Over the years, several PV models have been proposed in the literature to achieve the simplified and accurate reconstruction of PV characteristic curves as specified in the manufacturer’s datasheets. Based on their derivation, PV models can be classified into three distinct categories: circuit-based, analytical-based, and empirical-based models. However, an extensive analysis of the accuracy of the reconstructed curves for different PV models at the maximum power point (MPP) has not been conducted at the time of writing this paper. The IEC EN 50530 standard stipulates that the absolute errors within the vicinity of MPP should always be less than or equal to 1%. Therefore, this review paper conducts an in-depth analysis of the accuracy of PV models in reconstructing characteristic curves for different PV panels. The limitations of existing PV models were identified based on simulation results obtained using MATLAB and performance indices. Additionally, this paper also provides suggestions for future research directions.

Managing nitrogen in maize production for societal gain.

Highly productive agriculture is essential to feed humanity, but agricultural practices often harm human health and the environment. Using a nitrogen (N) mass-balance model to account for N inputs and losses to the environment, along with empirical based models of yield response, we estimate the potential gains to society from improvements in nitrogen management that could reduce health and environmental costs from maize grown in the US Midwest. We find that the monetized health and environmental costs to society of current maize nitrogen management practices are six times larger than the profits earned by farmers. Air emissions of ammonia from application of synthetic fertilizer and manure are the largest source of pollution costs. We show that it is possible to reduce these costs by 85% ($21.6 billion per year, 2020$) while simultaneously increasing farmer profits. These gains come from (i) managing fertilizer ammonia emissions by changing the mix of fertilizer and manure applied, (ii) improving production efficiency by reducing fertilization rates, and (iii) halting maize production on land where health and environmental costs exceed farmer profits, namely on low-productivity land and locations in which emissions are especially harmful. Reducing ammonia emissions from changing fertilizer types-in (i)-reduces health and environmental costs by 46% ($11.7 billion). Reducing fertilization rates-in (ii)-limits nitrous oxide emissions, further reducing health and environmental costs by $9.5 billion, and halting production on 16% of maize-growing land in the Midwest-in (iii)-reduces costs by an additional $0.4 billion.

Experimental and numerical investigation into the effect of surface roughness on particle rebound

Erosion damage and particle deposition are crucial wear phenomena in gas turbine engines. As a result, compressor efficiency decreases, stability margin reduces, and maintenance cost increases. Hence, predicting these phenomena in an accurate manner is of paramount importance for a cost-efficient, safe, and sustainable operation. Erosion and particle deposition in the annulus are affected by particle transportation in the fluid and particle-wall interaction. The latter involves the particle impact, the potential damage of the surface and/or the particle, and the particle rebound. Particle rebounds are statistical in nature due to the target surface roughness, the variability in particle sizes, and superimposed effects caused by particle shapes as well as particle rotation and particle break-up during contact. Multiple studies investigated the statistics of particle rebound, providing empirical-based models for median and spread. However, modeling the particle-wall interaction and its data spread on a transparent physical basis allows separating the effect of target roughness from superimposed effects. The presented article pursues this objective by assessing the statistical spread of particle rebound data through multiple techniques and utilizing their interdependencies. It combines experimental, numerical, and analytical considerations. For the first time, coherent boundary conditions for the experimental, numerical and analytical setup allow the distinction of the effect of roughness from the integral effect of the superimposed phenomena. A sandblast test rig equipped with laser measurement equipment was used to measure particle rebound from flat titanium and stainless steel plates at different angles. The numerical setup is developed under OpenFOAM 6 using a RANS solver for transient simulations with compressible media in combination with one-way coupled particle flows. The numerical model includes the rebound spread model proposed by Altmeppen et al. combined with the quasi-analytical rebound model proposed by Bons et al. The statistical spread of particle rebound is investigated for roughness levels that are similar to the ones of deteriorated high-pressure compressor blades as discussed by Gilge et al. The measured surface roughness of the experimentally investigated targets is used as input parameters to the numerical framework. The rebound statistics obtained in the numerical simulation are compared to the rebound data measured in the experiment. Based on this study, conclusions are drawn about which part of the rebound spread is attributable to surface roughness and which is caused by superimposed effects. It was found that the effect of surface roughness as characterized by Altmeppen et al. contributes in the order of 46 % to the rebound spread for small impact angles. However, the share in spread due to roughness gradually decreases with increasing global impact angles to a level of 13 % for angles close to 90°. The remaining percentage of rebound spread is attributed to superimposing phenomena. In addition to the rolling and sliding of aspherical particles, further phenomena such as plastic deformation and erosion of the roughness peaks during contact and the associated dissipation of energy gain in importance for steeper impact angles. Therefore, the effect of surface roughness should not be neglected in numerical simulations of particle-laden flows. Modeling the superimposed phenomena which are observed to be dominating at high impact angles opens up a further field of research.

Evaluation of Data-driven Hybrid Machine Learning Algorithms for Modelling Daily Reference Evapotranspiration

ABSTRACT Reference evapotranspiration (ET0) is one of the crucial variables used for irrigation scheduling, agricultural production, and water balance studies. This study compares six different models with sequential inclusion of six meteorological input variables such as minimum temperature (Tmin), maximum temperature (Tmax), mean relative humidity (RH), wind speed (SW), sunshine hours (HSS), and solar radiation (RS), which are necessarily used in physical or empirical-based models to estimate ET0. Each model utilized three variants of machine learning algorithms, i.e. Additive Regression (AdR), Random Subspace (RSS), M5 Pruning tree (M5P) independently and four novel permutated hybrid combinations of these algorithms. To evaluate the efficacy of these hybridizations and the stability of machine learning models, a comprehensive evaluation of independent and hybrid models was performed. With more input variables, the model performances were found to be superior in terms of prediction accuracies. The model AdR6 that included all the 6 selected meteorological variables outperformed other models during the testing period, exhibiting statistical performance of MAPE (1.30), RMSE (0.07), RAE (2.41), RRSE (3.10), and R 2 (0.998). However, the AdR algorithm, alone, was found to capture about 86% of variance in the observed data conforming to the 95% confidence band across all models irrespective of the number of input variables used to predict ET0. The RSS algorithm, in comparison to other algorithms, failed to capture the observed trends even with all the input variables. The hybrid combinations of algorithms with AdR as a constituent were better performers in terms of their prediction accuracies but remained inferior to AdR as an individual performer. All the algorithms are better predictors of the higher values of ET0 that included values beyond the 75% quartile.

How does the digital innovation process unfold in practice? A novel third-generation and empirical-based need–solution pairing model

PurposeThere is a lack of empirical-based models derived from practice to explain the digital innovation process. The authors investigate how the digital innovation process unfolds in practice.Design/methodology/approachThe authors undertake an exploratory and phenomenological study of 21 Malaysian small and medium enterprises (SMEs) in the information and communication technology (ICT) sector.FindingsThe findings show that the delineation between digital innovation process and outcome is blurred in practice, due to the process' iterative nature. Under this process, customers' role has changed from being passive receivers of innovative products to active reviewers, testers, influential decision-makers, initiators and co-creators at different review points in the innovation process. Enterprises' role has expanded from being the initiator of the innovation process to being a cogitative actor by seeking and absorbing knowledge from customer reviews into the digital innovation process. Market analysis is often the initiator of the digital innovation process, and the findings shed light on the underlying causative mechanisms of the initiation stage, which are understudied and not well understood in the existing literature.Originality/valueThe study contributes to academic knowledge by answering scholars' call for developing third-generation practice-based innovation models, which accounts for enterprises' context-specificities and internal and external environments, and for exploring the suitability of the need–solution fit approach for the digital innovation process. Such models have only been conceptually advocated in the literature. The study also informs practitioners on the organizational and operational activities involved in managing and strategizing for the digital innovation process.

Determination of Rainfall Thresholds for Landslide Prediction Using an Algorithm-Based Approach: Case Study in the Darjeeling Himalayas, India

Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall–rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010–2016. The results signify that a rainfall event of 48 hours with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system.

Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network

Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.

An Explicit Creep-Fatigue Model for Engineering Design Purposes

Background: Creep-fatigue phenomena are complex and difficult to model in ways that are useful from an engineering design perspective. Existing empirical-based models can be difficult to apply in practice, have poor accuracy, and lack economy. Need: There is a need to improve on the ability to predict creep-fatigue life, and do so in a way that is applicable to engineering design. Method: The present work modified the unified creep-fatigue model of Liu and Pons by introducing the parameters of temperature and cyclic time into the exponent component. The relationships between them were extracted by investigating creep behavior, and then a reference condition was introduced. Outcomes: The modified formulation was successfully validated on the materials of 63Sn37Pb solder and stainless steel 316. It was also compared against several other models. The results indicate that the explicit model presents better ability to predict fatigue life for both the creep fatigue and pure fatigue situations. Originality: The explicit model has the following beneficial attributes: Integration—it provides one formulation that covers the full range of conditions from pure fatigue, to creep fatigue, then to pure creep; Unified—it accommodates multiple temperatures, multiple cyclic times, and multiple metallic materials; Natural origin—it provides some physical basis for the structure of the formulation, in its consistency with diffusion-creep behavior, the plastic zone around the crack tip, and fatigue capacity; Economy—although two more coefficients were introduced into the explicit model, the economy is not significantly impacted; Applicability—the explicit model is applicable to engineering design for both manual engineering calculations and finite element analysis. The overall contribution is that the explicit model provides improved ability to predict fatigue life for both the creep-fatigue and pure-fatigue conditions for engineering design.

A simulation-based probabilistic framework for lithium-ion battery modelling

State-of-the-art researches on the modelling of lithium-ion batteries for electric vehicle have been conducted based on the physics and empirical-based models to estimate their states. However, less attention has been paid to evaluating the mechanical strength of the batteries when the battery pack is subjected to sudden external impact or crash. The present work, therefore, proposes a simulation-based probabilistic framework that combines artificial neural network and a moment-based uncertainty evaluation technique utilising the finite element model of a lithium-ion battery to evaluate its mechanical strength. The study was based on the following inputs: displacement, temperature and strain rate of the battery, and their uncertainties when the battery is subjected to sudden impact. The artificial neural network outperforms other well-known modelling methods, such as the radial basis function neural network and polynomial regression, for the global mechanical strength modelling, and the probability distribution obtained from the proposed uncertainty evaluation procedure is shown to be accurate. Further analysis employing the framework reveals that the mean mechanical strength of the battery decreases with increasing temperature, but increases with increasing displacement and strain rate. It was also found that the displacement and temperature have similarly high influence on the mechanical strength of the battery compared to the strain rate. The proposed framework and presented findings can help battery manufacturers improve the road safety of electric vehicles.

Deficiencies in 2D Simulation: A Comparative Study of 2D Versus 3D Simulation of Multi-seam Longwall Mining

The reliable prediction and management of mining-induced surface subsidence is one of the environmentally challenging issues for the coal mining industry. Because coal mining companies operate under strict environmental accountability, the absence of robust and reliable analysis tools may significantly affect the industry’s ability to gain approval and licenses when significant surface subsidence issues are involved. This issue becomes even more critical in multi-seam mining conditions, where high-stress concentration and large amounts of surface subsidence are expected to generate during multi-seam mining, hence could affect the feasibility and safety of all seams being mined. To obtain mining approval, it is, therefore, imperative to understand the geomechanical effect of mining in one seam on the mining of the underlying/overlying seams, and to accurately predict the magnitude and profile of surface subsidence. Various computer programs using empirical or numerical approaches have been developed to estimate the stresses at pillars and coal seams during multi-seam mining (Bigby et al. 2007; Ellenberger et al. 2003; Mark et al. 2007; SCT 2010; Sears and Heasley 2013). However, empirical-based models have severe limitations, which often make them inapplicable for assessing the feasibility of multi-seam mining at green field sites. Instead, numerical simulations are widely employed for this purpose. Due to the complexity of the problems and the computational times, researchers and engineers generally resort to twodimensional (2D) simulations. The present study assesses the performance of 2D and 3D numerical simulations and presents comparisons of subsidence profiles and stresses in pillars obtained during multi-seam mining. We modeled two different seams, each with four mining panels, using an in-house, 3D, finite element code called COSFLOW (Adhikary et al. 1996; Adhikary and Guo 2002). A unique feature of COSFLOW is the incorporation of Cosserat continuum theory in its formulation (Cosserat and Cosserat 1909). In the Cosserat model, interlayer interfaces (i.e., joints, bedding planes) are considered to be smeared across the mass. In other words, the effects of the interfaces are incorporated implicitly in the choice of stress–strain model formulation. The Cosserat model incorporates the bending rigidity of individual layers in its formulation, unlike other conventional implicit models. COSFLOW produced very accurate results when simulating surface subsidence due to longwall mining at Appin Colliery in New South Wales in Australia (Guo et al. 2004).

Empirical-based models for predicting head-fire rate of spread in Australian fuel types

SummaryThe knowledge of a free-burning fire’s potential rate of spread is critical for safe and effective bushfire control and use. A number of models for predicting the head-fire rate of spread in various types of Australian vegetation have been developed over the past 60 years or so since Alan G. McArthur began his pioneering research into bushfire behaviour. Most of the major Australian vegetation types have had more than one model developed for operational use. These include grassland, shrubland, both dry and wet eucalypt forests, and pine plantation fuel types. A better understanding of the technical basis for each of these models and their utility is essential for the correct selection and application of the most appropriate models. This review provides a systematic overview of 22 models of the rate of fire spread and their applicability in prescribed burning and wildfire operations.Background information and a description of each model is given. This includes information on the data used in the model development that defines the bounds of its application. The mathematical equations that represent each model are given along with a discussion of model form and behaviour, the main input variables and their influence, and evaluations of model performance undertaken to date.This review has enabled the identification of those models that constitute the current state of knowledge with respect to bushfire behaviour science in Australia. We recommend the models that should underpin best practice in the near term in the operational prediction of fire behaviour and those that should no longer be used, and provide reasons for these recommendations.

Towards aesthetics of image: A Bayesian framework for color harmony modeling

Color harmony is one of the most important features that determine the aesthetics quality of images. The existing color harmony models can be roughly divided into two groups: empirical based models defined by artists and designers and learning based models established by discovering underlying patterns from the collected samples. However, these two types of methods treat the model of color harmony from two distinct aspects and no consolidated framework exists to ensure that the benefits can be easily reaped from each other. To overcome this problem, we proposed a Bayesian framework for constructing the color harmony model, in which the empirical rules defined by the artists or designers serve as a prior and the patterns discovered by machine learning methods from the training images are modeled as the likelihood. Particularly, under this framework, we integrate two empirical (Matsuda and Moon–Spencer) color harmony models into a latent Dirichlet allocation (LDA) based learning procedure to train the color harmony model. The experimental results on a public dataset show that the proposed Bayesian based color harmony model is superior to the conventional color harmony models in respect of the image aesthetics assessment.

Computer-aided conversion of an engine from diesel to methane

The paper proposes an analytical methodology that uses empirical based models and CFD simulations to efficiently evaluate design alternatives in the conversion of a diesel engine to either CNG dedicated or dual fuel engines. The procedure is performed in five steps. Firstly, a database of different combustion chambers that can be obtained from the original piston is obtained. The chambers in the database differ for the shape of the bowl, the value of the compression ratio, the offset of the bowl and the size of the squish region. The second step of the procedure is the selection, from the first database, of the combustion chambers able to resist to the mechanical stresses due to the pressure and temperature distribution at full load. For each combination of suitable combustion chamber shape and engine control parameters (ignition/injection crank angle, EGR, etc.), a CFD simulation is used to evaluate the combustion performance of the engine. Then, a post-processing procedure is used to evaluate the detonation tendency and intensity of each combination. All the tools developed for the application of the method have been linked in the ModeFrontier optimization environment in order to perform the final choice of the combustion chamber.The overall process requires not more of a week of computation on the four processor servers considered for the optimization. Moreover, the selected chambers can be obtained from the original piston of the engine. Therefore, the conversion cost of the engine is quite small compared with the case of a completely new piston. The paper also describes the application of the procedure to two different engines.

How to Measure Qualitative Understanding of DC-Circuit Phenomena - Taking a Closer Look at the External Representations of 9-Year-Olds

Pupils’ qualitative understanding of DC-circuit phenomena is reported to be weak. In numerous research reports lists of problems in understanding the functioning of simple DC-circuits have been presented. So-called mental model surveys have uncovered difficulties in different age groups, and in different phases of instruction. In this study, the concept of qualitative understanding, and the content or position of reported mental models of DC-circuit phenomena are discussed. On the grounds of this review, new tools for investigating qualitative understanding and analysing external representations of DC-circuit phenomena are presented. According to this approach, the external representations of DC-circuit phenomena that describe pupils’ expressed conceptions of the topic should include both empirical-based models and theoretical explanations. In the empirical part of this study, third-graders (9-year-olds) learning DC-circuit phenomena in a comprehensive school in a small group were scrutinised. The focus of the study is the external representations manifested in the talk of the small group. The study challenges earlier studies, which claim that children exhibit a wide range of qualitative difficulties when learning DC-circuit phenomena. In this study it will be shown that even in the case of abstract subject matter like DC-circuit phenomena, small groups that highlight empirical-based modelling and activate talk can be a fruitful learning environment, where pupils’ qualitative understanding really develops. Thus, the study proposes taking a closer look at pupils’ external representations concerning DC-circuit phenomena.

Performance Analysis of IEEE 802.15.4 Compliant Wireless Devices for Heterogeneous Indoor Home Automation Environments

The influence of topology as well as morphology of complex indoor scenarios in the deployment of wireless sensor networks and wireless systems applied to home and building automation systems is analyzed. The existence of loss mechanisms such as material absorption (walls, furniture, etc.) and strong multipath components as well as the increase in the number of wireless sensors within indoor scenarios increases the relevance in the configuration of the heterogeneous wireless systems. Simulation results by means of empirical-based models are compared with an in-house 3D ray launching code as well as measurement results from wireless sensor networks illustrate the strong influence of the indoor scenario in the overall performance. The use of adequate radioplanning strategies lead to optimal wireless network deployments in terms of capacity, quality of service, and reduced power consumption.

Evaluation of the suitability of empirically-based models for predicting energy performance of centrifugal water chillers with variable chilled water flow

This study evaluates the performance prediction ability and model suitability of eleven empirically-based performance models for centrifugal water chillers. Specifically, this study uses over 2000 datasets with a constant or variable chilled water flow rate for fixed or variable speed drive centrifugal liquid chillers. The best regression coefficients for each empirical-based model were obtained using the ordinary least squares (OLSs) method. The model prediction accuracy of each empirical-based model is based on the coefficient of variation of root-mean-square error (CV). The evaluation for model suitability is based on the considerations of prediction ability, the complexity in training datasets, the effort needed to calibrate, the generality of the model, and its ability to physically interpret the model regression coefficients in this study. Results show that among the eleven empirical-based models, the BQ (CV=0.54%), MP (CV=0.61%), SMP (CV=0.70%), and MDOE-2 (CV=0.63%) models have overall prediction CV values under 1% for all kinds of datasets and achieve extremely good prediction accuracy. Because the MDOE-2 model has a more complicated datasets training process than the BQ, MP, and SMP models, and it has no ability to physically interpret the model regression coefficients, the BQ, MP, and SMP models have the best suitability. The results of this study provide important reference values for selecting empirically-based performance models for energy analysis, optimal operating control, energy efficiency measurement and verification (M&V), and the development of fault detection and diagnosis (FDD) systems in centrifugal water chillers.

The Evolution of Population Pharmacokinetic Models to Describe the Enterohepatic Recycling of Mycophenolic Acid in Solid Organ Transplantation and Autoimmune Disease

With the increasing use of mycophenolic acid (MPA) as an immunosuppressant in solid organ transplantation and in treating autoimmune diseases such as systemic lupus erythematosus, the need for strategies to optimize therapy with this agent has become increasingly apparent. This need is largely based on MPA's significant between-subject and between-occasion (within-subject) pharmacokinetic variability. While there is a strong relationship between MPA exposure and effect, the relationship between drug dose, plasma concentration and exposure (area under the concentration-time curve [AUC]) is very complex and remains to be completely defined. Population pharmacokinetic models using various approaches have been proposed over the past 10 years to further evaluate the pharmacokinetic and pharmacodynamic behaviour of MPA. These models have evolved from simple one-compartment linear iterations to complex multi-compartment versions that try to include various factors, which may influence MPA's pharmacokinetic variability, such as enterohepatic recycling and pharmacogenetic polymorphisms. There have been major advances in the understanding of the roles transport mechanisms, metabolizing and other enzymes, drug-drug interactions and pharmacogenetic polymorphisms play in MPA's pharmacokinetic variability. Given these advances, the usefulness of empirical-based models and the limitations of nonlinear mixed-effects modelling in developing mechanism-based models need to be considered and discussed. If the goal is to individualize MPA dosing, it needs to be determined whether factors which may contribute significantly to variability can be utilized in the population pharmacokinetic models. Some pharmacokinetic models developed to date show promise in being able to describe the impact of physiological processes such as enterohepatic recycling. Most studies have historically been based on retrospective data or poorly designed studies which do not take these factors into consideration. Modelling typically has been undertaken using non-controlled therapeutic drug monitoring data, which do not have the information content to support the development of complex mechanistic models. Only a few recent modelling approaches have moved away from empiricism and have included mechanisms considered important, such as enterohepatic recycling. It is recognized that well thought-out sampling schedules allow for better evaluation of the pharmacokinetic data. It is not possible to undertake complex absorption modelling with very few samples being obtained during the absorption phase (which has often been the case). It is important to utilize robust AUC monitoring which is now being propagated in the latest consensus guideline on MPA therapeutic drug monitoring. This review aims to explore the biological factors that contribute to the clinical pharmacokinetics of MPA and how these have been introduced in the development of population pharmacokinetic models. An overview of the processes involved in the enterohepatic recycling of MPA will be provided. This will summarize the components that complicate absorption and recycling to influence MPA exposure such as biotransformation, transport, bile physiology and gut flora. Already published population pharmacokinetic models will be examined, and the evolution of these models away from empirical approaches to more mechanism-based models will be discussed.

Tensile properties of jute fibres

One hundred tensile tests were undertaken at each of five distinct fibre lengths (6, 10, 20, 30 and 50 mm) on a single batch of jute fibres from South Asia. The Young's moduli were found to be independent of length. The ultimate stress (fracture strength) and fracture strains were found to decrease with increasing fibre length. The variation in mechanical properties at each fibre length was characterised using Weibull statistics based on a maximum likelihood estimate; referred to as point estimates. Two empirical based models (a linear and a natural logarithmic interpolation model) have been developed to estimate the fracture properties at any length between 6 and 50 mm. These two interpolation models were also developed based on maximum likelihood estimates. The point estimates were used to benchmark the performance of the two models. The natural logarithmic model was found to be superior to the linear model.

Dynamic modelling of a TRMS using analytical and empirical approaches

This investigation presents 1 degree of freedom (1DOF) dynamic modelling of an experimental aerodynamic test rig, a twin rotor multi-input–multi-output system (TRMS) using analytical as well as empirical approaches. The TRMS is a highly nonlinear system which can be considered as an experimental model of a complex air vehicle. Such vehicles are required to be modelled precisely to ensure satisfactory control performance to meet the demand for automation and sophistication. The TRMS is modelled in terms of vertical and horizontal 1DOF dynamics using Newtonian and Lagrangian methods based analytical approaches and neural networks based empirical approaches. The analytical modelling is carried out in two phases. In the first phase the interface circuit, DC motors and propulsive forces due to these motors are modelled. Thereafter, the dynamic equations for the remaining parts are formulated taking all the effective forces into account. Two neural network based models are developed using Levenberg–Marquardt (LM) and gradient descent (GD) algorithms. The responses of all the analytical and empirical based models are compared with that of the real TRMS to validate the accuracy of the models. The performances of the models are also compared with respect to each other.