Explicit Mathematical Model Research Articles

Development of energy consumption model of abrasive machining process by a combined evolutionary computing approach

Abrasive machining is employed for improving surface characteristics of components used in oil and gas applications. Optimization of power consumed in abrasive machining process is vital from environmental standpoint that requires the formulation of the generalized and an explicit mathematical model. In the present work, we propose to study the power consumption in abrasive machining process using a combined evolutionary computing approach based on Multi-Adaptive Regression Splines (MARS) and Genetic Programming (GP) techniques. Sensitivity and parametric analysis have also been conducted to capture the dynamics of process by unveiling dominant input variables and hidden non-linear relationships. It is concluded that selection of optimal machining time and abrasive is necessary for achieving better environmental performance of abrasive machining process.

A spatially explicit model for flowering time in bamboos: long rhizomes drive the evolution of delayed flowering

Summary Clonal plants produce semi‐autonomous modules (ramets). The spatial distribution of ramets affects lifetime reproductive success via self‐pollination, kin competition and competition among genotypes over space and nutrients. These, in turn, influence the evolution of the timing of flowering in monocarps (flowering is fatal), such as bamboos. The vegetative growth period (flowering interval) in bamboos shows a geographic cline from tropical (short‐interval) to temperate (long‐interval) regions. However, the mechanisms underlying the emergence of this geographic trend in flowering interval remain elusive. We hypothesized that differences in flowering interval are determined by ecological features that affect the spatial arrangement of ramets and the degree of kin competition. Using a spatially explicit mathematical model, we examined the effects of rhizome elongation, seed and pollen dispersal ranges and inbreeding depression on the evolution of flowering interval. Flowering interval became longer as rhizome length increased but was shorter as seed dispersal range increased. However, flowering interval was less affected when these ranges were extremely large. These results could be caused by intragenet competition due to the spatial structure of genets. If the rhizome is short, each genet forms a clumping structure by vegetative growth and is subject to high intragenet competition. Early flowering may reduce this competition. When seed dispersal range is shorter, kin competition among seeds becomes greater and delayed flowering is a better strategy. When these parameters are extremely large, the effects of this type of competition are reduced, and flowering interval does not change. The effect of inbreeding depression depends on the spatial distribution of ramets formed by vegetative growth. Inbreeding depression reduces reproductive success, and hence, delayed flowering is selected for when both rhizome length and pollen dispersal range are short, and inbreeding with neighbouring siblings is unavoidable. Synthesis. The evolutionary consequences of flowering interval in clonal plants are affected by the spatial distribution of genets. Geographic patterns in flowering interval of bamboos can be explained by differences in rhizome length. Interspecific comparisons among species with different rhizome lengths should be examined by collecting long‐term demographic data on the spatial arrangement of ramets.

Evolution of pathogen virulence across space during an epidemic.

We explore pathogen virulence evolution during the spatial expansion of an infectious disease epidemic in the presence of a novel host movement trade-off, using a simple, spatially explicit mathematical model. This work is motivated by empirical observations of the Mycoplasma gallisepticum invasion into North American house finch (Haemorhous mexicanus) populations; however, our results likely have important applications to other emerging infectious diseases in mobile hosts. We assume that infection reduces host movement and survival and that across pathogen strains the severity of these reductions increases with pathogen infectiousness. Assuming these trade-offs between pathogen virulence (host mortality), pathogen transmission, and host movement, we find that pathogen virulence levels near the epidemic front (that maximize wave speed) are lower than those that have a short-term growth rate advantage or that ultimately prevail (i.e., are evolutionarily stable) near the epicenter and where infection becomes endemic (i.e., that maximize the pathogen basic reproductive ratio). We predict that, under these trade-offs, less virulent pathogen strains will dominate the periphery of an epidemic and that more virulent strains will increase in frequency after invasion where disease is endemic. These results have important implications for observing and interpreting spatiotemporal epidemic data and may help explain transient virulence dynamics of emerging infectious diseases.

A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance

Determining the optimum input parameter settings (temperature, rotational velocity and feed rate) in optimizing the properties (strength and time) of the nano-drilling process can result in an improvement in its environmental performance. This is because the rotational velocity is an essential component of power consumption during drilling and therefore by determining its appropriate value required in optimization of properties, the trial-and-error approach that normally results in loss of power and waste of resources can be avoided. However, an effective optimization of properties requires the formulation of the generalized and an explicit mathematical model. In the present work, the nano-drilling process of Boron Nitride Nanosheet (BNN) panels is studied using an explicit model formulated by a molecular dynamics (MD) based computational intelligence (CI) approach. The approach consists of nano scale modeling using MD simulation which is further fed into the paradigm of a CI cluster comprising genetic programming, which was specifically designed to formulate the explicit relationship of nano-machining properties of BNN panel with respect to process temperature, feed and rotational velocity of drill bit. Performance of the proposed model is evaluated against the actual results. We find that our proposed integrated CI model is able to model the nano-drilling process of BNN panel very well, which can be used to complement the analytical solution developed by MD simulation. Additionally, we also conducted sensitivity and parametric analysis and found that the temperature has the least influence, whereas the velocity has the highest influence on the properties of nano-drilling process of BNN panel.

The Effect of Alcohol Hangover on Choice Response Time

The effect of alcohol hangover on cognitive processing has received little attention. We explored the effect of alcohol hangover on choice response time (RT), a dominant dependent variable in cognitive research. Prior research of the effect of hangover on RT has produced mixed findings; all studies reviewed relied exclusively on estimates of central tendency (e.g., mean RT), which has limited information value. Here we present novel analytical methods by going beyond mean RT analysis. Specifically, we examined performance in hangover conditions across the whole RT distribution by fitting ex-Gaussian models to participant data, providing a formal description of the RT distribution. This analysis showed detriments to performance under hangover conditions at the slower end of the RT distribution; RT variance was also increased under hangover conditions. We also fitted an explicit mathematical process model of choice RT — the diffusion model — to participant data, which estimates parameters reflecting psychologically-meaningful processes underlying choice RT. This analysis showed that hangover reduced information processing efficiency during response selection, and increased response caution; changes in these parameters reflect hangover affecting core decisional-components of RT performance. The implications of the data as well as the methods used for hangover research are discussed.

A Pre-Compensation Fuzzy Logic Algorithm Designed for the Dynamic Compensation Robotic System

This paper deals with the issue of non-model-based position regulation for the dynamic compensation robotic system (DCRS), which has been proposed for cooperating with the existing main robotic systems, such as the common serial robotic arms, to accomplish high-speed and accurate manipulations. The dynamic compensation concept is realized by fusing a high-speed & light-weight compensation actuator as well as endpoint closed loop (ECL) configured high-speed cameras. Within the context of the DCRS, the coarse motion, which is realized by the main robotic system, usually gives rise to negative dynamic impact on the compensation actuator that is configured to accomplish the fine motion. Through the analysis of a simplified model for the coupled two-plant system, relative velocity information between the two plants is found to play a role in the first order derivative of the displacement error. With the use of the relative position information from high-speed visual feedback, this paper proposes a new pre-compensation fuzzy logic control (PFLC) approach for control of the compensation actuator. The PFLC method is model-independent and is realized with a cascade fuzzy inference structure that conveniently integrates the relative velocity term between the two plants into the error regulation, and therefore realizes the partial counteraction of the disturbance from the main robot easily without knowing the explicit mathematical models of the system. Comparison works between the proposed PFLC and approaches that take no consideration of the relative velocity information, such as proportional-derivative (PD) control and conventional fuzzy logic control, are conducted. Simulations and experiments show the consistent effectiveness of the proposed approach.

Models of dataset size, question design, and cross-language speech perception for speech crowdsourcing applications

AbstractTranscribers make mistakes. Workers recruited in a crowdsourcing marketplace, because of their varying levels of commitment and education, make more mistakes than workers in a controlled laboratory setting. Methods for compensating transcriber mistakes are desirable because, with such methods available, crowdsourcing has the potential to significantly increase the scale of experiments in laboratory phonology. This paper provides a brief tutorial on statistical learning theory, introducing the relationship between dataset size and estimation error, then presents a theoretical description and preliminary results for two new methods that control labeler error in laboratory phonology experiments. First, we discuss the method of crowdsourcing over error-correcting codes. In the error-correcting-code method, each difficult labeling task is first factored, by the experimenter, into the product of several easy labeling tasks (typically binary). Factoring increases the total number of tasks, nevertheless it results in faster completion and higher accuracy, because workers unable to perform the difficult task may be able to meaningfully contribute to the solution of each easy task. Second, we discuss the use of explicit mathematical models of the errors made by a worker in the crowd. In particular, we introduce the method of mismatched crowdsourcing, in which workers transcribe a language they do not understand, and an explicit mathematical model of second-language phoneme perception is used to learn and then compensate their transcription errors. Though introduced as technologies that increase the scale of phonology experiments, both methods have implications beyond increased scale. The method of easy questions permits us to probe the perception, by untrained listeners, of complicated phonological models; examples are provided from the prosody of English and Hindi. The method of mismatched crowdsourcing permits us to probe, in more detail than ever before, the perception of phonetic categories by listeners with a different phonological system.

Modeling fouling of hollow fiber membrane using response surface methodology

Hollow fiber membranes have been widely employed for potable water treatment, wastewater treatment and reclamation, and desalination pretreatment. They have advantages such as small footprint, ease of operation, and high removal of particles and pathogens. Nevertheless, membrane fouling is one of the most serious issues in operating hollow fiber membrane systems. Although there have been numerous researches for understanding and forecasting fouling, it is still challenging to predict fouling based on explicit mathematical models. A more practical approach is to apply statistical models, which allows more accurate fouling prediction. In this context, this study attempts the development of a statistical model to analyze fouling of hollow fiber membranes. A response surface experimental design was used to optimize and investigate the influence of process variables such as foulant types (kaolin, colloidal silica, NOM, and alginate), foulant concentration, and imposed flux on the fouling rate. The results obtained from the experiments were evaluated by multiple regression analysis method and empirical relationship between the response and independent variables. Empirical models were developed to understand the interactive correlation between the responses and process variables.

Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. Artificial neural networks (ANN) have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces some other problems. For this purpose, one method that has been identified as a possible alternative for ANN in hydrology and water resources problems is the adaptive neuro-fuzzy inference system (ANFIS). Nevertheless, the data arising from the monitoring stations and experiment might be corrupted by noise signals owing to systematic and non-systematic errors. This noisy data often made the prediction task relatively difficult. Thus, in order to compensate for this augmented noise, the primary objective of this paper is to develop a technique that could enhance the accuracy of rainfall prediction. Therefore, the wavelet decomposition method is proposed to link to ANFIS and ANN models. In this paper, two scenarios are employed; in the first scenario, monthly rainfall value is imposed solely as an input in different time delays from the time (t) to the time (t-4) into ANN and ANFIS, second scenario uses the wavelet transform to eliminate the error and prepares sub-series as inputs in different time delays to the ANN and ANFIS. The four criteria as Root Mean Square Error (RMSE), Correlation Coefficient (R2), Gamma coefficient (G), and Spearman Correlation Coefficient (ρ) are used to evaluate the proposed models. The results showed that the model based on wavelet decomposition conjoined with ANFIS could perform better than the ANN and ANFIS models individually.

Blind source separation filters-based-fault detection and isolation in a three tank system

Fault detection and Isolation takes a strategic position in modern industrial processes for which various approaches are proposed. These approaches are usually developed and based on a consistency test between an observed state of the process provided by sensors and an expected behaviour provided by a mathematical model of the system. These methods require a reliable model of the system to be monitored which is a complex task. Alternatively, we propose in this paper to use blind source separation filters (BSSFs) in order to detect and isolate faults in a three tank pilot plant. This technique is very beneficial as it uses blind identification without an explicit mathematical model of the system. The independent component analysis (ICA), relying on the assumption of the statistical independence of the extracted sources, is used as a tool for each BSSF to extract signals of the process under consideration. The experimental results show the effectiveness and robustness of this approach in detecting and isolating faults that are on sensors in the system.

Mathematical Modeling of Eukaryotic Cell Migration: Insights Beyond Experiments

A migrating cell is a molecular machine made of tens of thousands of short-lived and interacting parts. Understanding migration means understanding the self-organization of these parts into a system of functional units. This task is one of tackling complexity: First, the system integrates numerous chemical and mechanical component processes. Second, these processes are connected in feedback interactions and over a large range of spatial and temporal scales. Third, many processes are stochastic, which leads to heterogeneous migration behaviors. Early on in the research of cell migration it became evident that this complexity exceeds human intuition. Thus, the cell migration community has led the charge to build mathematical models that could integrate the diverse experimental observations and measurements in consistent frameworks, first in conceptual and more recently in molecularly explicit models. The main goal of this review is to sift through a series of important conceptual and explicit mathematical models of cell migration and to evaluate their contribution to the field in their ability to integrate critical experimental data.

Analysis of the Influence of Vehicle Dynamic Parameters on Tire Marks

ABSTRACT Tire marks play a central role in the reconstruction of traffic accidents, since they can provide valuable information about the vehicle's trajectory, initial speed, or the steering and braking input of the driver. The research project described in this paper focuses on the analysis of tire marks under controlled conditions using a monowheel setup to enable a selective variation of different vehicle dynamic parameters without mutual influence. The long-term goal is to find a model for the development of tire marks to predict the influence of specific vehicle dynamic parameters on the generation of tire marks. This model may be applied in accident reconstruction tools. A literature review has been performed to find evidence for the development of tire marks and to identify relevant parameters for their generation. Currently, no explicit physical or mathematical model showing the influence of tire forces or slip on the generation of tire marks is available. In the literature, it is often assumed that tire marks occur at the limit of traction. A physically motivated formula has been developed to calculate the friction force within the contact patch as a function of the tire forces, the longitudinal slip, and the side slip angle. The main hypothesis deduced from this formula is that the intensity of a tire mark depends on the magnitude of this friction force independent of the varying parameter. To verify this hypothesis, experiments have been conducted with variations in longitudinal slip, side slip angle, and tire type. First results agree with the model, showing a correlation between the intensity of tire marks and friction force, depending on the tribological and optical tire and road properties. This correlation is introduced as tire-marking sensitivity.

Spatial instabilities untie the exclusion-principle constraint on species coexistence

Using a spatially explicit mathematical model for water-limited vegetation we show that spatial instabilities of uniform states can lead to species coexistence under conditions where uniformly distributed species competitively exclude one another. Coexistence is made possible when water-rich patches formed by a pattern forming species provide habitats for a highly dispersive species that is a better competitor in uniform settings.

The Ecology of Human Papillomavirus–Induced Epithelial Lesions and the Role of Somatic Evolution in Their Progression

Human papillomavirus (HPV) infection frequently induces hyperproliferation of epithelial cells, leading to both benign (warts) and malignant tumors (cervical cancer). We seek to understand how HPV may achieve these changes by modulating cellular population dynamics. Furthermore, HPV-induced lesion progression is generally understood as a series of molecular changes. As a complement to this approach, we investigate the role of phenotypic changes produced by natural selection during lesion progression. We develop and numerically analyze spatially and evolutionarily explicit mathematical models of HPV-induced epithelial lesions. Infection of basal cells is a requirement for persistent infection. Increasing the maximum tissue density at which HPV-infected cells can divide and decreasing infected cell migration rate leads to large increases in the number of HPV-infected cells, and consequently, virions shed from the skin surface per day. Evolution by natural selection in an autonomous population of cells leads to tissue changes that are qualitatively similar to those observed during lesion progression. HPV modulates cell population dynamics, which can be characterized by specific ecological parameters. As an unintended consequence, this ecological strategy of the virus may be successfully co-opted by autonomous host cells and play a role in lesion progression.

Modelling the dynamics of migrations for large herbivore populations in the Amboseli National Park, Kenya

The spread of human activity, settlement and land fragmentation threatens the migrations of large migratory ungulates in Africa. Modelling the migrations gives conservationists a tool for building scenarios of the threats and containment options. We propose a simple spatially explicit mathematical model of ungulate migrations based on the seasonal distribution of vegetation quantity and quality and allometric models of diet. We use the seasonal movements of selected migrants in relation to vegetation in the Amboseli ecosystem, Kenya. Parameters estimation was done by fitting the model to long term movement patterns by minimizing total least square errors. The model suggests that the migrants broadly track the shifting patterns of vegetation growth and senescence according to body size.

Agri-environmental policies for biodiversity when the spatial pattern of the reserve matters

The aim of this paper is to compare different environmental policies for cost-effective habitat conservation on agricultural lands, when the desired spatial pattern of reserves is a random mosaic. We use a spatially explicit mathematical programming model which studies the farmers' behavior as profit maximizers under technical and administrative constraints. Facing different policy measures, each farmer chooses the land-use on each field, which determines the landscape at the regional level. A spatial pattern index (Ripley L function) is then associated to the obtained landscape, indicating on the degree of dispersion of the reserve. We compare a subsidy per hectare of reserve with an auction scheme and an agglomeration malus. We find that the auction is superior to the uniform subsidy for cost-efficiency. The agglomeration malus does better than the auction for the spatial pattern but is more costly.

Simulating Robots Without Conventional Physics: A Neural Network Approach

The construction of physics-based simulators for use in Evolutionary Robotics (ER) can be complex and time-consuming. Alternative simulation schemes construct robotic simulators from empirically-collected data. Such empirical simulators, however, also have associated challenges. This paper therefore investigates the potential use of Artificial Neural Networks, henceforth simply referred to as Neural Networks (NNs), as alternative robotic simulators. In contrast to physics models, NN-based simulators can be constructed without requiring an explicit mathematical model of the system being modeled, which can simplify simulator development. The generalization abilities of NNs, along with NNs’ noise tolerance, suggest that NNs could be well-suited to application in robotics simulation. Investigating whether NNs can be effectively used as robotic simulators in ER is thus the endeavour of this work. Two robot morphologies were selected on which the NN simulators created in this work were based, namely a differentially steered robot and an inverted pendulum robot. Accuracy tests indicated that NN simulators created for these robots generally trained well and could generalize well on data not presented during simulator construction. In order to validate the feasibility of the created NN simulators in the ER process, these simulators were subsequently used to evolve controllers in simulation, similar to controllers developed in related studies. Encouraging results were obtained, with the newly-evolved controllers allowing experimental robots to exhibit obstacle avoidance, light-approaching behaviour and inverted pendulum stabilization. It was thus clearly established that NN-based robotic simulators can be successfully employed as alternative simulation schemes in the ER process.

Explicit Water Quality Model Generation and Rapid Multiscenario Simulation

AbstractEmerging applications in water distribution systems place new challenges on water quality modeling tools, including the need for an explicit mathematical representation and simulation of large ensembles of contamination scenarios. We present a computational framework, referred to as Merlion, that creates an explicit mathematical model for water quality in drinking water distribution systems that is appropriate for embedding within other numerical applications (e.g., optimization). This model is efficiently generated for large water distribution systems and represents an all-to-all mapping (inputs include injections at all possible nodes and time steps, and outputs include concentrations at all possible nodes and time steps), which is necessary for many security applications. The Merlion framework is compared to water quality simulations using EPANET on a set of network models ranging in size from 10 to approximately 13,000 nodes. The simulation results show excellent agreement. Furthermore, the ex...

Continuum determination in spectroscopic data by means of topological concepts and Fourier filtering

A novel implementation of a convex hull minimization algorithm for the determination of the continuum in x-ray and γ-ray spectroscopic data is presented in this paper. The method is semi-automatic, performed in three successive steps and requires user intervention regarding the values of two parameters. The first controls the low pass filtering and the second the reduction of the estimated points which construct the background. On the other hand, it does not rely on an explicit mathematical model of the background or the signal (FWHM, lineshapes) and keeps the original spectrum data unaltered.

Model structure exploration for index flood regionalization

AbstractAlthough the nonlinear power form model structure is widely accepted by practitioners in the flood regionalization modelling, there is a lack of studies on whether there is a room for further improvement, and if the answer is yes, what should be done to explore alternative model structures. A framework is proposed in this study towards investigating this issue by the following steps: (i) a universal data‐driven model is utilized to see if there is a room for improvement compared with the conventional model, and (ii) if improvement is achieved, this means that there should exist more effective model structures than the current form. However, because the universal data‐driven models are usually opaque, more explicit model structures should be explored, which are convenient for practical usage. In this study, the proposed framework is applied in a case study using the catchment characteristics from the Flood Estimation Handbook in conjunction with the gamma test, support vector machine (SVM) and genetic programming (GP). First, the gamma test is used for the purpose of input variables selection where no model structure needs to be defined as a priori, and therefore, the result can be applied to any model structures for model building. Second, an SVM, which is a powerful data‐driven nonlinear model capable of modelling a variety of nonlinear systems, is applied to the index flood model for the first time. Once the best model is determined using those two data‐driven tools, GP is employed to find an alternative model structure. As the SVM is not formulated for producing explicit model functional form, the GP offers an advantage at this point where it can infer an explicit mathematical model functional form. Copyright © 2012 John Wiley & Sons, Ltd.