Practical Modeling Approach Research Articles

Population analysis of myelosuppression profiles using routine clinical data after the ICE (ifosfamide/carboplatin/etoposide) regimen for malignant gliomas

We propose a simple and practical modeling approach for analysis of the data for myelosuppression after cancer chemotherapy, which can be applied when pharmacokinetic data are not available and several anticancer drugs were simultaneously administered. The model equation is based on the probability density function for the Erlang distribution. The data for cell counts of leukocytes (white blood cell, WBC), platelets (PLT), and reticulocytes (RET) obtained in routine clinical laboratory tests after the ICE (ifosfamide/carboplatin/etoposide) regimen for cancer chemotherapy were retrospectively collected from 28 patients, and a population analysis was applied. The time course profiles could be well explained by the proposed model. The individual values of the time to reach the nadir were obtained by the Bayesian method, and their medians (days) were 16.8 for WBC, 12.8 for PLT, and 8.2 for RET. Such information would be useful to determine the day of visit for outpatients especially for additional treatment to prevent side effects such as infections. The model is simple and applicable to explain the time course profiles for myelosuppression irrespective of cell types, and also practical because it requires only the data from routine clinical laboratory tests without any additional burden to patients. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:4402–4412, 2009

A practical method for proper modeling of structural damping in inelastic plane structural systems

This study addresses some of the pitfalls of conventional numerical modeling of Rayleigh-type damping in inelastic structures. A practical modeling approach to solve these problems is proposed. Conventional modeling of Rayleigh-type damping for inelastic structures generates responses in which unrealistic damping forces are present that results in underestimation of peak displacement demands, overestimation of peak strength demands, and underestimation of buildings' collapse potential. The approach proposed in this paper avoids these problems by modeling each structural element with an equivalent combination of one elastic element with stiffness-proportional damping, and two springs at its two ends with no stiffness proportional damping.

A practical approach for exploration and modeling of the design space of a bacterial vaccine cultivation process

A licensed pharmaceutical process is required to be executed within the validated ranges throughout the lifetime of product manufacturing. Changes to the process, especially for processes involving biological products, usually require the manufacturer to demonstrate that the safety and efficacy of the product remains unchanged by new or additional clinical testing. Recent changes in the regulations for pharmaceutical processing allow broader ranges of process settings to be submitted for regulatory approval, the so-called process design space, which means that a manufacturer can optimize his process within the submitted ranges after the product has entered the market, which allows flexible processes. In this article, the applicability of this concept of the process design space is investigated for the cultivation process step for a vaccine against whooping cough disease. An experimental design (DoE) is applied to investigate the ranges of critical process parameters that still result in a product that meets specifications. The on-line process data, including near infrared spectroscopy, are used to build a descriptive model of the processes used in the experimental design. Finally, the data of all processes are integrated in a multivariate batch monitoring model that represents the investigated process design space. This article demonstrates how the general principles of PAT and process design space can be applied for an undefined biological product such as a whole cell vaccine. The approach chosen for model development described here, allows on line monitoring and control of cultivation batches in order to assure in real time that a process is running within the process design space.

Procedure for reducing the risk of delayed deliveries in make-to-order production

Make-to-order production is generally operated in a very unpredictable and competitive environment, where the key factors to succeed are to provide high service levels and flexibility while at the same time offering inexpensive products. To receive customers, production companies must often promise short lead-times and the option of adjustable order quantities and delivery dates. Coping with uncertainty and variable demand is a challenging task. With the additional challenge of cutting down the production costs to be able to provide inexpensive products, proper planning and scheduling of the production becomes very difficult and crucial for success. It is therefore of crucial importance to develop systematic methods to address the problem of planning and scheduling under uncertainty in order to create efficient and reliable plans and thereby reduce the risk of delayed deliveries of customer orders. This study introduces the subject of creating robust production plans and schedules in the typical modern production environment characterised by several important sources of uncertainty. We introduce an efficient and practical modelling approach for creating robust production plans under uncertain and varying demand conditions. As an inspiration we have a large real-world problem originating from a complex pharmaceutical enterprise.

Efficient 3D nonlinear Winkler model for shallow foundations

This paper presents a new practical modeling approach, based on the beam-on-a-nonlinear Winkler foundation (BNWF) model, to simulate the 3D rocking, vertical and horizontal responses of shallow foundations using structural elements that are readily available in the element library of commercially available structural analysis programs. An assemblage of a moment-rotation hinge, shear hinge connected in series with an elastic frame member attached to the bottom end of ground story columns was proposed to model the response of the footing under combined action of vertical, horizontal and moment loading. To couple the responses of these hinges, two bounding surfaces equations were introduced and derived mathematically: a surface that defines the interaction between the rocking and vertical capacities of the footing along its width and length; and a surface that defines the interaction between the horizontal capacities of the footing along its width and length. Simple calculation steps to evaluate the geometric and mechanical properties of the proposed assemblage of structural elements are provided. The proposed modeling approach was verified using experimental results from large scale model foundations subjected to cyclic loading. Based on this study, it was found that the proposed assemblage can be reliably used in modeling the rocking and horizontal responses of shallow foundations under cyclic loading.

Two New Methods for Predicting Effects of Landcover-Related Stressors on Stream Biotic Integrity at the Catchment Scale

ABSTRACT. Effective landuse planning to protect stream ecosystems requires quantitative tools that link catchment landuse to stream biotic integrity. This study explores two practical catchment-scale modeling approaches to this problem. Both predict levels of sediment or nutrient stressors in stream reaches based on landcover and other geospatial data, then predict impacts of these stressor levels on metrics of stream biotic integrity using fitted stressor-response functions (SRFs). Both approaches can be applied to large numbers of catchments using only readily available geospatial data and types of biological data commonly collected by state environmental agencies. The two approaches differ in the types of stressors they address and in details of how stressor loads of catchments are predicted. Stressor variables include total sediment and dissolved, particulate, and total nitrogen and phosphorus. Biological response variables include various metrics of community structure and pollution tolerance or sens...

A case-based and practical approach for multivariate modeling of grinding process

Grinding process is claimed to be the only possible cost-effective means of shaping engineering part(s) or component(s) into finished products with required surface topology. However, because of the complicated relationship(s) between several phenomena and entities involved in a grinding process, determining functional relationship between relevant input(s), in-process variable(s), and output quality characteristic [or response] remains a critical and difficult task for researchers and practitioners. The complexity of the problem further increases as number of responses considered for modeling is more than one, and the situation is so-called multivariate or multiresponse surface modeling. Although various mechanistic and empirical modeling approaches have been proposed in literature, they are found to be of limited validity, with applied functional approximation and simplistic assumption(s). In addition, empirical models are sometimes based on off-line designed experimental data, which are expensive or uneconomical in many line layout type mass-manufacturing situations. This paper attempts to provide a case-based practical approach for practitioners, with detailed steps involved in multivariate modeling, based on conventional regression technique, and unconventional artificial neural network-based technique, using real-time production data. The two case examples selected to verify the approaches are from an automobile engine manufacturing unit located in eastern India.

From single cell model to battery pack simulation for Li-ion batteries

A practical universal modeling and simulation approach is presented in this paper to show that accurate battery pack simulation can be achieved if cell-to-cell variations were taken into account. A generic equivalent circuit model was used in the approach with parameters deduced from cell testing with proper protocols, which could come from live cell monitoring in a control circuitry. Using a single cell model, which was validated against experimental data and demonstrated with validity of high accuracy in predicting cell performance, we showed that such a high accuracy in single cell model is essential for a high fidelity pack simulation. It is also important to derive statistical confidence intervals accurately from experiments to characterize intrinsic cell-to-cell variations in capacity and internal resistance, which need to be considered in the pack model. If parameters for each individual cell were correctly approximated and used in the pack model, the accuracy in the prediction of pack performance could be significantly improved.

Geochemical Modeling of Reactions and Partitioning of Trace Metals and Radionuclides during Titration of Contaminated Acidic Sediments

Many geochemical reactions that control aqueous metal concentrations are directly affected by solution pH. However, changes in solution pH are strongly buffered by various aqueous phase and solid phase precipitation/dissolution and adsorption/desorption reactions. The ability to predict acid-base behavior of the soil-solution system is thus critical to predict metal transport under variable pH conditions. This studywas undertaken to develop a practical generic geochemical modeling approach to predict aqueous and solid phase concentrations of metals and anions during conditions of acid or base additions. The method of Spalding and Spalding was utilized to model soil buffer capacity and pH-dependent cation exchange capacity by treating aquifer solids as a polyprotic acid. To simulate the dynamic and pH-dependent anion exchange capacity, the aquifer solids were simultaneously treated as a polyprotic base controlled by mineral precipitation/ dissolution reactions. An equilibrium reaction model that describes aqueous complexation, precipitation, sorption and soil buffering with pH-dependent ion exchange was developed using HydroGeoChem v5.0 (HGC5). Comparison of model results with experimental titration data of pH, Al, Ca, Mg, Sr, Mn, Ni, Co, and SO4(2-) for contaminated sediments indicated close agreement suggesting that the model could potentially be used to predictthe acid-base behavior of the sediment-solution system under variable pH conditions.

Sieve Maximum Likelihood Estimation for Regression Models With Covariates Missing at Random

Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of the regression coefficients. These estimators are shown to be consistent and asymptotically normal with their asymptotic covariance matrix that achieves the semiparametric efficiency bound. A bootstrap approach is used to estimate the asymptotic covariance matrix. Some practical modeling approaches for high-dimensional covariates are proposed. Extensive simulation studies are conducted to examine the finite-sample properties of the estimates, and a real data set from a liver cancer clinical trial is analyzed using the proposed method.

Practical Modeling of Heterogeneous Bundles of Single-Walled Carbon Nanotubes for Adsorption Applications: Estimating the Fraction of Open-Ended Nanotubes in Samples

A practical approach for adsorption modeling of heterogeneity of single-walled carbon nanotube (SWNT) bundles has been developed. The method integrates experimental analysis with grand canonical Monte Carlo (GCMC) simulation of a small probe molecule, such as nitrogen at 77 K. Using this method, it is possible for one to separately estimate adsorption inside the nanotubes, adsorption on the external surface of the bundles, and adsorptive contributions from the impurities present in samples. By introducing a scaling parameter for adsorption in the internal porous volume of the bundles, the predicted adsorption isotherm results in a near replication of the experimental N2 adsorption isotherm. We refer to this parameter as the volume fraction of open-ended nanotubes. Our GCMC-assisted experimental characterization method has been applied successfully to several commercial samples obtained from different suppliers, such as MER Corp., Carbon Nanotechnologies Inc., Carbon Solutions Inc., Carbolex Inc., and Buck...

An Autocalibrating Inductance Model for Switched Reluctance Motor Drives

Development of a precise dynamic model is a critical step in design and analysis of optimal control strategies for switched-reluctance machines (SRM). This paper is focused on important issues concerning the development of such models and their subsequent use in designing control strategies for SRM drives. The main goal in modeling is to provide a good accuracy over the entire speed and torque range. To achieve this objective, the following requirements need to be met: 1) a good accuracy in matching the inductance of each stator phase; 2) inclusion of mutual effects when significant overlap among phases exists; 3) inclusion of short flux-path operation in each electrical cycle when significant overlap among adjacent phases exists; and 4) capability for autocalibration to cope with parameter variations incurred by manufacturing imperfections and operational conditions. In this paper, in addition to an in-depth discussion of the above factors, a practical modeling approach along with an autocalibration strategy is presented. A simple test collects the necessary data in developing the proposed model. Inherent separation among mechanical, electrical, and control time constants has been used to develop the autocalibration process. Experimental results are presented to validate the proposed method.

Stochastic modeling of financial electricity contracts

We discuss the modeling of electricity contracts traded in many deregulated power markets. These forward/futures type contracts deliver (either physically or financially) electricity over a specified time period, and is frequently referred to as swaps since they in effect represent an exchange of fixed for floating electricity price. We propose to use the Heath–Jarrow–Morton approach to model swap prices since the notion of a spot price is not easily defined in these markets. For general stochastic dynamical models, we connect the spot price, the instantaneous-delivery forward price and the swap price, and analyze two different ways to apply the Heath–Jarrow–Morton approach to swap pricing: Either one specifies a dynamics for the non-existing instantaneous-delivery forwards and derives the implied swap dynamics, or one models directly on the swaps. The former is shown to lead to quite complicated stochastic models for the swap price, even when the forward dynamics is simple. The latter has some theoretical problems due to a no-arbitrage condition that has to be satisfied for swaps with overlapping delivery periods. To overcome this problem, a practical modeling approach is analyzed. The market is supposed only to consist of non-overlapping swaps, and these are modelled directly. A thorough empirical study is performed using data collected from Nord Pool. Our investigations demonstrate that it is possible to state reasonable models for the swap price dynamics which is analytically tractable for risk management and option pricing purposes, however, this is an area of further research.

Compact modeling of on-chip ESD protection devices using Verilog-A

A practical approach for the compact modeling of electrostatic discharge (ESD) protection devices, using the behavioral language Verilog-A, is presented. Models of the NMOS transistor, the vertical n-p-n transistor, the diode, and the resistor have been developed, suitable for circuit-level simulation. Large-signal and small-signal models are provided for transient and alternating current (ac) simulation, respectively. A self-heating model is included for accurate simulation of the device ON-resistance under transient high-current conditions.

Development of a plastic injection molding training system using Petri nets and virtual reality

In this study, Virtual Reality (VR)-based plastic injection molding training system (VPIMTS), which can be modeled as an integrated system with a task planning module, an intelligent instruction module, a simulation module, and virtual environment (VE) module, was developed. Presented in this paper are an architecture of VPIMTS, a practical knowledge modelling approach for modelling the training scenarios of the system by using Petri nets formalism and key techniques (FEM, injection molding procedure modelling) which have been developed independently. The utilization of the Petri net model realized the environment where the trainee can behave freely, and also made it possible to equip the system with the function of showing the next action of the trainee whenever he wants. The overall system is a powerful approach for highly improving the trainee’s comprehension and injection molding study-efficiency by building digital, intelligent, knowledgeable, and visual aids.

Statistical Challenges in Evaluating Dose-Response Using Epidemiological Data

Statistical Challenges in Evaluating Dose-Response Using Epidemiological Data

CONVECTIVE BOILING IN PARALLEL MICROCHANNELS

Experiments were performed with clear water and with surfactant flowing in parallel triangular microchannels. The study is based on systematic measurements of temperature and flow pattern by infrared radiometry and high-speed digital video imaging. Different flow patterns were observed simultaneously in various microchannels at a fixed value of water or surfactant flow rates. Depending on flow and heat flux, pressure and temperature instabilities in the heated microchannels were studied. This work develops a practical modeling approach for two-phase microchannel heat sinks and also considers effect of surfactant on convective boiling in microchannels.

The HG Diagram Model for Induction Motors Analysis: Theoretical Approach and Applications

This article presents theory and applications of a practical approach for induction motors modeling under temperature and saturation effects. This approach is based on the so-called HG diagram that is established from the induction motor measurement analysis of the active and reactive power consumption for each operating point. In practice, this diagram is a circle plotted in the complex plane. The distinct feature of this circle is its diameter that is time-varying with the induction motor temperature and saturation states. In fact, these two effects are both interfering and conjugate, and only an online plotted circle will allow taking them into account. Since the HG diagram is characterized by three parameters, especially the rotor ones, it constitutes an interesting tool to achieve the identification of online motor parameters. This possibility has led us to apply this diagram for induction motor control and faults diagnosis, especially in the rotor. Moreover, it can easily be applied for modeling saturation, which will refine the control and diagnosis processes. Computer simulations and experimental tests, carried out on a 4-kW and a 0.75 four-pole squirrel cage induction motors, provide an encouraging validation of the HG diagram applications. Theresults obtained clearly point out the distinct features, simplicity and accuracy, of the proposed approach.

Two-phase flow patterns in parallel micro-channels

Micro-channel heat sinks with two-phase flow can satisfy the increasing heat removal requirements of modern micro-electronic devices. Some of the important aspects associated with two-phase flows in micro-channels, is to study the bubble behavior and flow regimes in diabatic, parallel micro-channels. Most of the reports in the literature present data of only a single channel and mostly adiabatic. This does not account for flow mixing and hydrodynamic instability that occurs in parallel micro-channels, connected by common inlet and outlet collectors. In the present study, experiments were performed for air–water and steam–water flow in parallel triangular micro-channels. The experimental study is based on systematic measurements of temperature and flow pattern by infrared radiometry and high-speed digital video imaging. In air–water flow different flow patterns were observed simultaneously in the various micro-channels at a fixed values of water and gas flow rates. In steam–water flow, instability in uniformly heated micro-channels was observed. This work develops a practical modeling approach for two-phase micro-channel heat sinks and considers the discrepancy between flow patterns of air–water and steam–water flow in parallel micro-channels.

Generalized Linear Additive Smooth Structures

This article proposes a practical modeling approach that can accommodate a rich variety of predictors, united in a generalized linear model (GLM) setting. In addition to the usual ANOVA-type or covariatelinear (L) predictors, we consider modeling any combination of smooth additive (G) components, varying coefficient (V) components, and (discrete representations of) signal (S) components. We assume that G is, and the coefficients of V and S are, inherently smooth—projecting each of these onto B-spline bases using a modest number of equally spaced knots. Enough knots are used to ensure more flexibility than needed; further smoothness is achieved through a difference penalty on adjacent B-spline coefficients (P-splines). This linear re-expression allows all of the parameters associated with these components to be estimated simultaneously in one large GLM through penalized likelihood. Thus, we have the advantage of avoiding both the backfitting algorithm and complex knot selection schemes. We regulate the flexibility of each component through a separate penalty parameter that is optimally chosen based on cross-validation or an information criterion.