Book review : The active modeler : mathematical modeling with Microsoft® Excel by E. Neuwirth and D. Arganbright

A recently-published work in by two well-known authors in the field of spreadsheet modeling is reviewed.

Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions

Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects’ ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT–RR and a trivariate QT–RR–RESP or QT–RR–EDR model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects’ data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT–RR–RESP and QT–RR–EDR) gave significantly better fit value (p < 0.05) than that of found from the QT–RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.

RiboSys, a high-resolution, quantitative approach to measure the in vivo kinetics of pre-mRNA splicing and 3'-end processing in Saccharomyces cerevisiae

We describe methods for obtaining a quantitative description of RNA processing at high resolution in budding yeast. As a model gene expression system, we constructed tetON (for induction studies) and tetOFF (for repression, derepression, and RNA degradation studies) yeast strains with a series of reporter genes integrated in the genome under the control of a tetO7 promoter. Reverse transcription and quantitative real-time-PCR (RT-qPCR) methods were adapted to allow the determination of mRNA abundance as the average number of copies per cell in a population. Fluorescence in situ hybridization (FISH) measurements of transcript numbers in individual cells validated the RT-qPCR approach for the average copy-number determination despite the broad distribution of transcript levels within a population of cells. In addition, RT-qPCR was used to distinguish the products of the different steps in splicing of the reporter transcripts, and methods were developed to map and quantify 3′-end cleavage and polyadenylation. This system permits pre-mRNA production, splicing, 3′-end maturation and degradation to be quantitatively monitored with unprecedented kinetic detail, suitable for mathematical modeling. Using this approach, we demonstrate that reporter transcripts are spliced prior to their 3′-end cleavage and polyadenylation, that is, cotranscriptionally.

An effective VAR planning to improve dynamic voltage profile of distribution networks with distributed wind generation

This paper proposes an effective VAR planning based on reactive power margin for the enhancement of dynamic voltage stability in distribution networks with distributed wind generation. The analysis is carried over a distribution test system representative of the Kumamoto area in Japan. The detailed mathematical modeling of the system is also presented. Firstly, this paper provides simulation results showing the effects of composite load on voltage dynamics in the distribution network through an accurate time-domain analysis. Then, a cost-effective combination of shunt capacitor bank and distribution static synchronous compensator (D-STATCOM) is selected to ensure fast voltage recovery after a sudden disturbance. The analysis shows that the proposed approach can reduce the size of compensating devices, which in turn, reduces the cost. It also reduces power loss of the system.

Impact of high wind penetration on the voltage profile of distribution systems

In this paper, simulation results showing the effect of lower and higher penetration of distributed wind generation on the voltage profile in distribution systems have been presented. The analysis is carried out over two distribution test systems. The detailed mathematical modeling of the system is also presented. It also investigates the small-signal stability of distribution systems using eigenvalue approach. The analyses show that voltage variation problems occur in different nodes of the distribution networks with an increase of penetration level. However, proper selection of dispersion level can improve the voltage profile of the distribution systems

Fifteen to twenty percent of HIV substitution mutations are associated with recombination

HIV undergoes high rates of mutation and recombination during reverse transcription, but it is not known whether these events occur independently or are linked mechanistically. Here we used a system of silent marker mutations in HIV and a single round of infection in primary T lymphocytes combined with a high-throughput sequencing and mathematical modeling approach to directly estimate the viral recombination and mutation rates. From >7 million nucleotides (nt) of sequences from HIV infection, we observed 4,801 recombination events and 859 substitution mutations (≈1.51 and 0.12 events per 1,000 nt, respectively). We used experimental controls to account for PCR-induced and transfection-induced recombination and sequencing error. We found that the single-cycle virus-induced mutation rate is 4.6 × 10(-5) mutations per nt after correction. By sorting of our data into recombined and nonrecombined sequences, we found a significantly higher mutation rate in recombined regions (P = 0.003 by Fisher's exact test). We used a permutation approach to eliminate a number of potential confounding factors and confirm that mutation occurs around the site of recombination and is not simply colocated in the genome. By comparing mutation rates in recombined and nonrecombined regions, we found that recombination-associated mutations account for 15 to 20% of all mutations occurring during reverse transcription.

Numerical modeling of interactions between a macro-crack and a cluster of micro-defects

The interactions between a macro-crack and a cluster of micro-defects are studied numerically by using a series of special finite elements each containing a defect. These special finite elements, which contain defects such as holes, cracks, and inhomogeneities, are developed based on the hybrid displacement, complex potential and conformal mapping techniques. These hybrid-type elements can be used together with the conventional finite elements without any difficulty. Thus, simple finite element models can be devised to study the interactions between a macro-crack and a cluster of micro-defects. In this paper, the mathematical and finite element modeling procedures for the study of the above-mentioned problems are presented.

Predictive modeling of student competency

Explores how machine learning techniques can be used to build effective student modeling systems with constrained development and operational overheads, by integrating top-down and bottom-up initiatives. Emphasizes feature-based modelling.

Surface coverage of filter medium in deep bed filtration : mathematical modelling and experiments

This paper highlights the importance of surface coverage in modeling the removal of particles in deep bed filtration. A model that considers the saturation of sites on which particle deposition occurs is used. Experimental results obtained with monodispersed suspensions of 0.46 and 0.816 μm latex particles at different influent concentrations and ionic strengths were used to calculate the fraction of filter grain surface (β1) on which actual particle deposition occurs. This will be useful in evaluating the filter performance in terms of the utilization of available surface area of the filter medium. Further, the level of dendrite formation of particles on filter grains during filtration is expressed in terms of β1 and the specific surface coverage, θT (the fraction of a filter grain surface that is covered by particles at time T, assuming that the filter grain is covered by a monolayer of particles). This can be used to compare the contribution of deposited particles in the removal efficiency of deep bed filtration for suspensions with different physical and chemical characteristics.

A monotonicity index for the monotone fuzzy modeling problem

In this paper, the problem of maintaining the (global) monotonicity and local monotonicity properties between the input(s) and the output of an FIS model is addressed. This is known as the monotone fuzzy modeling problem. In our previous work, this problem has been tackled by developing some mathematical conditions for an FIS model to observe the monotonicity property. These mathematical conditions are used as a set of governing equations for undertaking FIS modeling problems, and have been extended to some advanced FIS modeling techniques. Here, we examine an alternative to the monotone fuzzy modeling problem by introducing a monotonicity index. The monotonicity index is employed as an approximate indicator to measure the fulfillment of an FIS model to the monotonicity property. It allows the FIS model to be constructed using an optimization method, or be tuned to achieve a better performance, without knowing the exact mathematical conditions of the FIS model to satisfy the monotonicity property. Besides, the monotonicity index can be extended to FIS modeling that involves the local monotonicity problem. We also analyze the relationship between the FIS model and its monotonicity property fulfillment, as well as derived mathematical conditions, using the Monte Carlo method.

Modeling malware propagation in smartphone social networks

Smartphones have become an integral part of our everyday lives, such as online information accessing, SMS/MMS, social networking, online banking, and other applications. The pervasive usage of smartphones also results them in enticing targets of hackers and malware writers. This is a desperate threat to legitimate users and poses considerable challenges to network security community. In this paper, we model smartphone malware propagation through combining mathematical epidemics and social relationship graph of smartphones. Moreover, we design a strategy to simulate the dynamic of SMS/MMS-based worm propagation process from one node to an entire network. The strategy integrates infection factor that evaluates the propagation degree of infected nodes, and resistance factor that offers resistance evaluation towards susceptible nodes. Extensive simulations have demonstrated that the proposed malware propagation model is effective and efficient.

Expert systems modeling for assessing climate change impacts and adaptation in agricultural systems at regional level

Australian agriculture is very susceptible to the adverse impacts of climate change, with major shifts in temperature and rainfall projected. In this context, this paper describes a research methodology for assessing potential climate change impacts on, and formulating adaptation options for, agriculture at regional level. The methodology was developed and applied in the analysis of climate change impacts on key horticultural commodities—pome fruits (apples and pears), stone fruits (peaches and nectarines) and wine grapes—in the Goulburn Broken catchment management region, State of Victoria, Australia. Core components of the methodology are mathematical models that enable to spatially represent the degree of biophysical land suitability for the growth of agricultural commodities in the region of interest given current and future climatic conditions. The methodology provides a sound analytic approach to 1) recognise regions under threat of declines in agricultural production due to unfolding climatic changes; 2) identify alternative agricultural systems better adapted to likely future climatic conditions and 3) investigate incremental and transformational adaptation actions to improve the problem situations that are being created by climate change.

Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method

Ionic polymer conductive network composite (IPCNC) actuators are a class of electroactive polymer composites that exhibit some interesting electromechanical characteristics such as low voltage actuation, large displacements, and benefit from low density and elastic modulus. Thus, these emerging materials have potential applications in biomimetic and biomedical devices. Whereas significant efforts have been directed toward the development of IPMC actuators, the establishment of a proper mathematical model that could effectively predict the actuators' dynamic behavior is still a key challenge. This paper presents development of an effective modeling strategy for dynamic analysis of IPCNC actuators undergoing large bending deformations. The proposed model is composed of two parts, namely electrical and mechanical dynamic models. The electrical model describes the actuator as a resistive-capacitive (RC) transmission line, whereas the mechanical model describes the actuator as a system of rigid links connected by spring-damping elements. The proposed modeling approach is validated by experimental data, and the results are discussed. © 2014 Elsevier B.V. All rights reserved.

Theoretical modeling and experimental validation of surface stress in thrombin aptasensor

Adsorption of target molecules on the immobilized microcantilever surface produced beam displacement due to the differential surface stress generated between the immobilized and non-immobilized surface. Surface stress is caused by the intermolecular forces between the molecules. Van der Waals, electrostatic forces, hydrogen bonding, hydrophobic effect and steric hindrance are some of the intermolecular forces involved. A theoretical framework describing the adsorption-induced microcantilever displacement is derived in this paper. Experimental displacement of thrombin aptamer-thrombin interactions was carried out. The relation between the electrostatic interactions involved between adsorbates (thrombin) as well as adsorbates and substrates (thrombin aptamer) and the microcantilever beam displacement utilizing the proposed mathematical model was quantified and compared to the experimental value. This exercise is important to aid the designers in microcantilever sensing performance optimization.