Generic Modeling Approach Research Articles

Pharmacogenomics of Codeine, Morphine, and Morphine-6-Glucuronide

Background and Objective: The analgesic effect of codeine depends on the formation of the opioid metabolites morphine and morphine-6-glucuronide. Different factors have been shown or suspected to affect the safety and efficacy of codeine treatment. The objective of the current study is to assess and quantify the impact of important pharmacokinetic factors, using a mechanistic modeling approach. Methods: By means of a generic modeling approach integrating prior physiologic knowledge, we systematically investigated the complex dependence of opioid exposure on cytochrome P450 2D6 and 3A4 (CYP2D6 and CYP3A4), and uridine diphosphate glucuronosyltransferase 2B7 (UGT2B7) activity, as well as renal function, by means of a virtual clinical trial. Results: First, the known dominant role of CYP2D6 activity for morphine exposure was reproduced. Second, the model demonstrated that mild and moderate renal impairment and co-administration of CYP3A4 inhibitors have only minor influences on opioid exposure. Third, the model showed — in contrast to current opinion — that increased UGT2B7 activity is associated with a decrease in active opioid exposure. Conclusion: Overall, the model-based analysis predicts a wide range of morphine levels after codeine administration and supports recent doubts about safe and efficacious use of codeine for analgesia in non-genotyped individuals.

Parsimonious Multi‐dimensional Impact Assessment

This article develops the conceptual and empirical foundations for a parsimonious, generic modeling approach to multi‐dimensional (i.e., economic, environmental and social) impact assessments of agricultural technologies and environmental change. Joint distributions between technology adoption and outcome variables are characterized, and used to analyze the selection effects of adoption on a general class of impact indicators. The approach is implemented with a generic model that can be parameterized with low‐order moments of outcome variables. A case study of adoption of a high‐yielding maize variety in Kenya illustrates the model's use and confirms theoretical results.

Evaluation of a GIS-Based Integrated Vulnerability Risk Assessment for the Mancha Oriental System (SE Spain)

It is widely recognized that groundwater-vulnerability maps are a useful tool for making decisions on designating pollution-vulnerable areas, in addition to being a requirement of European Directive 91/676/EEC. This study addressed the vulnerability of the Mancha Oriental System (MOS) to groundwater contamination with an integrated Generic and Agricultural DRASTIC model approach. In the MOS, groundwater is the sole water resource for a total population of about 275,000 inhabitants and for 1,000 km2 of irrigated crops. DRASTIC vulnerability maps have been drawn up for two different years (1975 and 2002) in which the potentiometric surface has dropped dramatically (80 m in some areas) due to the considerable expansion of irrigated croplands. The quality of available resources has also deteriorated due to the agricultural practices and the discharge of wastewater effluents. Vulnerability maps are used to test the data on nitrate, sulphate, and chloride contents in groundwater in the Central and El Salobral-Los Llanos hydrogeologic domains of the MOS for 2002. Regardless of the method applied, the dramatic alteration in land use leads to a change in the DRASTIC index and vulnerability to groundwater contamination decreases for the study period. Vulnerability in the MOS increases in areas where the irrigation return flow is notable. The lack of a statistical correspondence between the DRASTIC index and the spatial distribution of nitrate, chloride, and sulphate contents and the distribution of the pollution load suggest that this method does not accurately assess the risk of the MOS to groundwater pollution.

Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models

Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.

Silver fir stand productivity is enhanced when mixed with Norway spruce: evidence based on large-scale inventory data and a generic modelling approach

Questions: How to evaluate the mixture effect on basal area increment in two-species forest stands? Is a mixed Norway spruce–silver fir stand more productive than pure adjacent stands of either species? How to develop generic modelling approaches to assess mixture effects in forest stands? Location: In addition to a case study on Norway spruce–silver fir stands in French mountain forests, the generic approach used goes beyond local applications. Methods: We took advantage of National Forest Inventory data to develop a unique stand basal-area-increment model for pure and mixed stands of Norway spruce and silver fir that responds to ecological site conditions. The database was made up of 284 pure Norway spruce stands, 196 pure silver fir stands, and 323 mixed stands of these species. Results: Pure silver fir basal area increment is strongly influenced by spring climatic conditions, whereas pure Norway spruce is more influenced by soil conditions. The mixture of these species has a positive effect on silver fir, which decreases as the proportion of fir increases. In contrast, the mixture has no noticeable effect on Norway spruce. Conclusion: We developed a stand basal-area-increment model evidencing an advantage of the mixture on silver fir basal area increment, but not on Norway spruce. The mathematical formulation of the model developed is generic and can be used in all two-species mixture situations. It also makes it possible to compare different mixture situations with each other.

The Engineering Hydrodynamics of Viscoplastic Suspensions

From a practical engineering design and operation perspective, accurate hydrodynamic modeling of viscoplastic suspension behavior is fundamentally important. Since the hydrodynamics of process suspension flow behavior is strongly influenced by the suspension rheology, it is essential to accommodate the suspension rheology in any hydrodynamic modeling of the suspension's flow behavior. The objective of this article is to present the overarching modeling approach developed and to compare this with empirical suspension hydrodynamic behavior over wide ranges of fluid properties and applications. The relevant literature is reviewed, and the inability of dimensional analysis to accommodate the common yield stress rheological models applicable to industrial suspensions is discussed. This approach is evaluated in the context of hydrodynamics problems relevant to the process industry: straight pipe flow, energy losses in pipe fittings and valves, free surface flows, and centrifugal pump derating. Comparison with experimental data shows that this generic modeling approach leads to accurate establishment of dynamic similarity, which is a fundamental precursor to efficient engineering design for the process industry.

Measurement and tracking control of the Z‐tilts error compensating stage of the nano‐measuring machine

PurposeThe purpose of this paper is to develop the multi‐degree‐of‐freedom measurement system to test, verify, and control the nano‐measuring machine.Design/methodology/approachA generic differential model approach is constructed to numerically describe the hysteresis effects of piezoelectric actuators. Based on the generic differential model, a feedforward compensator with a proportional integral (PI) type controller is designed to compensate for the hysteresis nonlinearity of a piezoelectric actuated three degree‐of‐freedom coplanar nanostage which can provide high‐precision applications.FindingsThe Z‐tilts (z, pitch, and roll motion) error compensation stage of the nano‐measuring machine is accomplished. Moreover, a high‐resolution laser interferometer is used to measure position accurately.Originality/valueThis paper contributes to develop a tracking control design method for the piezoelectric motion platform which combines a closed‐loop feedforward compensator with a PI type controller.

Determinants of household energy consumption in India

Improving access to affordable modern energy is critical to improving living standards in the developing world. Rural households in India, in particular, are almost entirely reliant on traditional biomass for their basic cooking energy needs. This has adverse effects on their health and productivity, and also causes environmental degradation. This study presents a new generic modelling approach, with a focus on cooking fuel choices, and explores response strategies for energy poverty eradication in India. The modelling approach analyzes the determinants of fuel consumption choices for heterogeneous household groups, incorporating the effect of income distributions and traditionally more intangible factors such as preferences and private discount rates. The methodology is used to develop alternate future scenarios that explore how different policy mechanisms such as fuel subsidies and micro-financing can enhance the diffusion of modern, more efficient, energy sources in India.

Theoretical and numerical investigation into roles of geofluid flow in ore forming systems: Integrated mass conservation and generic model approach

Mass and energy should be conservative in nature and ore forming systems within the upper crust of the earth are no exception. Thus, the mass conservation law is valid not only for a closed system, but for an open system as well. In the latter case, exchange between the system and its surroundings must be considered. Based on the mass conservation law, this paper considers a number of different roles of geofluid flow in ore forming systems. Due to the concurrence of these roles, interactions between fluid flow, heat transfer, mass transport and chemical reactions need to be considered in a comprehensive manner. Through theoretical analysis and computational simulations of several typical generic models for ore forming systems, it can be demonstrated that for an ore forming system in chemical equilibrium, the aqueous species of which are produced by the chemical dissolution reactions, the approximate form (i.e. the rock alteration index and the improved rock alteration index) of the mineralization rate can be used to predict mineralization patterns in the ore forming system. However, for a chemically non-equilibrated ore forming system, the detailed interaction between solute advection, solute diffusion/dispersion and chemical kinetics needs to be considered to determine potential mineralization patterns in the ore forming system.

Probabilistic approach to the condition monitoring of aerospace engines

The provision of TotalCare® styled service offerings by original equipment manufacture (OEM) suppliers of high-integrity assets is intended to provide improved levels of system availability to the operator. A key element of such service offerings is the ability to minimize unplanned equipment downtime, and the utilization of advanced diagnostic and prognostic monitoring tools is a significant component in achieving this. Monitoring methods, founded on novelty detection technologies, are now a well-established condition monitoring technique. This approach is particularly appropriate for monitoring high-integrity plant where fault conditions arise with extremely low levels of probability. The approach described in this article is to establish empirically based models of normality that are guided by engineering knowledge and utilize key features normally used by expert engineers. However, rather than consider generic modelling approaches, it is proposed that application of models that adapt their sensitivity to the operation of individual assets offer greater prognostic efficiency. This article demonstrates how this can be achieved by considering asset-specific models that adapt the threshold of alerting in accordance with the observed normal running of the plant.

Orthogonal-Least-Squares Forward Selection for Parsimonious Modelling from Data

The objective of modelling from data is not that the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, interpretability and ease for knowledge extraction. All these desired properties depend crucially on the ability to construct appropriate parsimonious models by the modelling process, and a basic principle in practical nonlinear data modelling is the parsimonious principle of ensuring the smallest possible model that explains the training data. There exists a vast amount of works in the area of sparse modelling, and a widely adopted approach is based on the linear-in-the-parameters data modelling that include the radial basis function network, the neurofuzzy network and all the sparse kernel modelling techniques. A well tested strategy for parsimonious modelling from data is the orthogonal least squares (OLS) algorithm for forward selection modelling, which is capable of constructing sparse models that generalise well. This contribution continues this theme and provides a unified framework for sparse modelling from data that includes regression and classification, which belong to supervised learning, and probability density function estimation, which is an unsupervised learning problem. The OLS forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic parsimonious modelling approach from data.

Modeling variable‐speed pump operations for target hydraulic characteristics

Conventional hydraulic models can be used to analyze operational scenarios for a variable‐speed pump (VSP) delivering target hydraulic characteristics of either the fixed hydraulic head or the desired pump flow. However, modelers must repeatedly adjust relative speed factors for achieving the target hydraulic performance, which can be a time‐consuming task. A new solution method has been developed for directly calculating the required VSP speed to provide a fixed hydraulic head. However, this method needs to be extended as a generic modeling approach for practical applications. This article presents an enhanced VSP solution method that extends the previously developed VSP hydraulic network solver to automatically calculate the relative pump‐speed coefficient for attaining the prescribed hydraulic head or pumping the preferred amount of flow. Using this improved method, engineers can model not only a single VSP but also multiple VSPs with rule‐based logic controls. This allows the target control head to be specified at any location or for the target flow to be specified for VSPs of different capacities. The extended method provides a flexible, robust modeling approach for engineers to analyze a variety of VSP configurations in water and wastewater collection systems.

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.

Hierarchical forest management with anticipation: an application to tactical–operational planning integration

This paper examines the problem of harvest capacity planning at a tactical level. Annual capacity planning allows planners to determine the number of contractors to hire per period throughout the year and to define the duration of their contracts. In practice, this process usually involves the analysis of historical data regarding the operational use of capacity and aggregated demand forecast, the output of which then serves to plan harvest operations. Although this form of hierarchical planning reduces the complexity of the task, the decomposition into subproblems that must be successively resolved can lead to infeasibility or poor use of harvesting capacity. The specific problem addressed here resides in how one can consider the operational impact of harvesting decisions taken at the tactical level to ensure a plan’s feasibility at the operational level. We present a tactical planning process based on Schneeweiss’ generic hierarchical modeling approach. A computational experiment demonstrates how a tactical planning process is influenced by the input of the operational level anticipation model. The anticipation approach we propose appears to be a valid method to better integrate key operational-level decisions into tactical plans.

Bias Modelling in Evidence Synthesis

Policy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal bias) and relevance (or lack of external bias) in evidence synthesis. The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies. Many were historically controlled, only one was a randomized trial and doses, populations and outcomes varied between studies and differed from the target UK setting. Using elicited opinion, we construct prior distributions to represent the biases in each study and perform a bias-adjusted meta-analysis. Adjustment had the effect of shifting the combined estimate away from the null by approximately 10%, and the variance of the combined estimate was almost tripled. Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.

Navigating from the heights of technical rigour to the swampy reality: Lessons from New Zealand in population health outcomes monitoring

Background: The objective of this paper is to describe and discuss two documents produced by the NewZealand Ministry of Health concerning the monitoring of outcomes of public health programs.The New Zealand Government is increasingly expecting planners and managers of publicly funded services toshift their focus from the delivery of ‘outputs’ towards achievement of ‘outcomes’. Intervention logic modelsand outcomes monitoring are promoted by central government agencies as suitable management methodsfor implementing the change. [1-3]Methods: To help managers design and implement comprehensive, effective and measurable populationhealth programmes the Ministry of Health recently published two guidance documents. The first documentprovided guidance about how to plan programmes using a generic logic model approach.[4] The second setout in detail a process on how to monitor population health programmes.[5] The intent of the documents wasto help managers navigate between the heights of technical rigour and the swamps of reality in the deliveryof population health programmes.[6]Results: A number of issues and implications for how population health programmes are planned monitoredand performance assessed have been identified by the guidance documents. Issues include the problem ofsmall numbers, understanding the difference between outcomes monitoring and traditional forms ofevaluation, and outcomes monitoring being seen as a tool for punitive performance management rather than‘continuous programme improvement’. Implications include more time spent on the design of programmes.Planners will need to focus upon better sequencing of activities, setting more specific and time limited goals,and to be more informed about how to use research to inform the selection of interventions.Conclusions: The guidance documents promoted by the Ministry of Health have provided useful advice abouthow to develop and use logic models and outcomes monitoring in the planning of population healthprogrammes. Practitioners particularly welcomed the inclusion of a glossary that included explanatorycomments and examples. Two key issues have been identified with the application of outcomes monitoringthat need to be managed. The first is the issue of statistically small numbers associated with relatively rarehealth events. The second is to promote outcomes monitoring as a tool for continuous programmeimprovement, rather than as a potentially punitive.

A Bayesian Hierarchical Model for Photometric Red Shifts

Summary The Sloan digital sky survey is an extremely large astronomical survey that is conducted with the intention of mapping more than a quarter of the sky. Among the data that it is generating are spectroscopic and photometric measurements, both containing information about the red shift of galaxies. The former are precise and easy to interpret but expensive to gather; the latter are far cheaper but correspondingly more difficult to interpret. Recently, Csabai and co-workers have described various calibration techniques aiming to predict red shift from photometric measurements. We investigate what a structured Bayesian approach to the problem can add. In particular, we are interested in providing uncertainty bounds that are associated with the underlying red shifts and the classifications of the galaxies. We find that quite a generic statistical modelling approach, using for the most part standard model ingredients, can compete with much more specific custom-made and highly tuned techniques that are already available in the astronomical literature.

Metal Bioaccumulation in Aquatic Species: Quantification of Uptake and Elimination Rate Constants Using Physicochemical Properties of Metals and Physiological Characteristics of Species

Mechanistic bioaccumulation models are powerful tools in environmental risk assessment as they provide insight in varying accumulation patterns across species, contaminants, and conditions, and they are applicable beyond tested cases. In these models key parameters, as absorption and elimination rate constants, are predicted based on chemical specific properties and physiological characteristics. However, due to the complex environmental behavior of metals, the development of mechanistic bioaccumulation models has lagged behind that for organic chemicals. Absorption and elimination rate constants of organic substances have long been linked to their octanol-water partition coefficient, yet no equivalent quantitative relationships exist for metals. In the present study, we successfully related metal absorption rate constants to a metal specific property, the covalent index, and a species-characteristic, the ventilation rate. This quantitative relationship holds for a wide range of organisms and metals, i.e., 17 aquatic species and 10 metals, suggesting that a generic modeling approach of metal uptake kinetics is feasible for aquatic organisms. In contrast, elimination rate constants show no metal - specific character. Average, weight-corrected elimination rate constants are relatively similar among metals and species, suggesting that a single weight-corrected elimination rate constant can be used in bioaccumulation studies on aquatic species.

Wildlife Contraception, Individuals, and Populations: How Much Fertility Control is Enough?

Author(s): Cowan, David P.; Massei, Giovanna | Abstract: The resolution of conflicts between human and wildlife interests often involves lethal control to reduce problem wildlife populations. However, lethal control has always had its limitations, the acceptable methods are becoming fewer, and public opposition is on the increase. Fertility control offers a potential alternative approach that is widely regarded as being inherently more benign. Furthermore, in some circumstances fertility control may have specific advantages over culling. The development of “single-shot” injectable immunocontraceptive vaccines that inhibit the fertility of individual animals for several years is leading to practical applications that exploit this novel technology. Further advances can be expected to lead to the emergence of a new generation of wildlife management tools. A key issue in this process is predicting what the population consequences will be for a particular species, given a specific level of induced infertility. Here, we use population modelling techniques to explore how much fertility control is enough to achieve different levels of population reduction, how long it will take to realise these reductions, and to understand how these effects are shaped by the population biology of the target species. This offers some generic conclusions, with low levels of infertility having little impact on species with high population turnover rates, while modest levels of infertility may yield useful population reductions for species with low intrinsic rates of increase, although such effects take longer to be realised in long-lived species. We also observed that there is potential for optimising the intensity of induced infertility, in terms of the proportion of breeding animals rendered infertile, and the frequency of application; so, for instance, biennial application could be more efficient than annual application for some forms of fertility control in certain species. There is increasing evidence from field studies that the survival of infertile animals is enhanced, probably because they do not incur the costs of reproduction. Our model predicts that this effect will be of limited importance for short-lived species with high intrinsic rates of increase, but it is more likely to compromise population reduction in long-lived species. We suggest that the generic modelling approach can help develop an evidence-based platform for discussing when fertility control can be regarded as a feasible, desirable, and sustainable option to manage problem wildlife.

Electromechanical impedance modeling of PZT transducers for health monitoring ofcylindrical shell structures

This paper presents an electromechanical impedance (EMI) model for health monitoring ofcylindrical shell structures. By investigating the interaction between the piezoceramic (PZT)transducers and a typical cylindrical shell structure, the EMI of the PZT transducers isobtained. In this study, a generic two-dimensional impedance modeling approach is adoptedto obtain the admittance (inverse of impedance) of the PZT transducers. Thep-Ritz method, due to its effectiveness and common utility for various boundary conditions,is employed to calculate the mechanical impedance of the cylindrical shell structure. Anexperimental test is carried out on an aluminum cylindrical shell specimen and the PZTadmittance signatures are measured by an impedance analyzer. The experimental resultsare compared with the theoretical ones to verify the developed EMI model for thePZT–shell system under undamaged and damaged conditions. The feasibilityof the EMI method for damage detection of cylindrical shell structures is alsodiscussed.