Deterministic Modeling Techniques Research Articles

Global Health Interventions: The Military, the Magic Bullet, the Deterministic Model—and Intervention Otherwise

“Intervention” is central to global health, but the significance and effects of how intervention is practiced are often taken for granted. This review takes interventions into health and medicine as subjects for ethnographic inquiry. We highlight three lines of anthropological contributions: studies of global health interventions that serve imperial and military objectives, studies of “magic bullet” interventions arising from laboratory science, and studies of interventions based on deterministic modeling techniques. We then outline examples of “intervention otherwise,” in which people build relations of solidarity and care through global health programming, design interventions to be interactive and adaptable, and use data and modeling to support health justice. Whereas many global health interventions reproduce Western power hierarchies, intervention otherwise draws attention to alternative forms of knowledge, action, and expertise. Our analysis of lively and multivalent practices of intervention has implications for debates about the im/possibility of decolonizing global health.

Optimization of a multiperiod refinery planning problem under uncertainty

AbstractUncertainty in refinery planning presents a significant challenge in determining the day‐to‐day operations of an oil refinery. Deterministic modeling techniques often fail to account for this uncertainty, potentially resulting in reduced profit. The stochastic programming framework explicitly incorporates parameter uncertainty in the problem formulation, thus giving preference to robust solutions. In this work, a nonlinear, multiperiod, industrial refinery problem is extended to a two‐stage stochastic problem, formulated as a mixed‐integer nonlinear program. A crude‐oil sequencing case study is developed with binary scheduling decisions in both stages of the stochastic programming problem. Solution via a decomposition strategy based on the generalized Benders decomposition (GBD) algorithm is proposed. The binary decisions are designated as complicating variables that, when fixed, reduce the full‐space problem to a series of independent scenario subproblems. Through the application of the GBD algorithm, a feasible mixed‐integer solution is obtained that is more robust to uncertainty than its deterministic counterpart.

Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed.

A Study of Operators on Fuzzy Sets

Overtime, mathematics had been used as a tool in modeling real life phenomenon. In some cases, these problems cannot fit-into the classical deterministic or stochastic modeling techniques, perhaps due system complexity arising from lack of complete knowledge about the phenomenon or some uncertainty. The uncertainty could either be due to lack of clear boundaries in the description of the object or perhaps due to randomness. In this article, we study a mathematical tool discovered in 1965 by Zadeh suitable for modeling real life phenomenon and examined operations on such a tool. Motivated by the work of Zadeh, we studied operators on Type-1 Fuzzy Sets (T1FSs) and Type-2 Fuzzy sets (T2FSs) and provided examples, one of which is a variant of the Yager complement function for which the complement operator was graphically illustrated. The joint and the meet operators were also studied and examples provided. Non-standard operators were defined on T1FSs and T2FSs and also classified into two groups; the triangular-norm (t-norm) and triangular-conorm (t-conorm). Using t-norm and t-conorm, an example was adopted from Castillo and Aguilar to illustrate the computation of the standard operation on T2FSs. Finally, future research direction was provided based on what is yet to be achieved in fuzzy set theory.

De-risking hydrocarbon exploration in lower Jurassic carbonate systems of Kuwait through forward stratigraphic models

This paper discusses the results of an integrated case study using forward stratigraphic modelling of Lower Jurassic complex carbonate reservoirs along the Eastern Arabian Plate. Traditional stochastic or deterministic modelling techniques do not sufficiently capture carbonate reservoir heterogeneities in sectors with restricted datasets. The methodology applied uses a process-based approach that aims to define carbonate architectural and textural/facies heterogeneity and provides a framework to develop a more consistent conceptual geological model for exploration and field development.Through this communication we test the sensitivity of Lower Jurassic reference case model of complex carbonate systems (Al Wazzan et al., 2020) to various framework and sedimentological/ecological parameters as subsidence, eustacy, carbonate production rates, wave energies and evaporation versus precipitation rates. Results of this work help achieve a more refined and data calibrated model that tackles mainly the 3rd and 4th order sequence stratigraphic evolution of the Marrat Formation. Simulation results point to the main impact of differential subsidence and eustatic sea level changes on the architectural and textural variability along Kuwait with dominantly (1) transgressive onlapping packages of intercalated mudstone/wackestone and anhydritic textures in the Lower Marrat, (2) prograding to aggrading packages in the Middle Marrat localized around the southern, northern, northwestern and westernmost part of onshore Kuwait, and (3) an onlapping transgressive system composed of wackestone and lime mudstone with very restricted shoal patches.This work also highlights (1) the good fit with overall tendencies of the recently published Haq (2018) Jurassic global relative sea level curves, (2) the need to reduce the latter amplitudes of high frequency relative sea level drops to fit the G&G dataset, (3) the good anhydritic prediction using evaporation and precipitation predictions of the General Circulation Models (GCMs) (Sellwood and Valdes, 2006) and finally (4) the need for about 20–30% of additional differential subsidence in zones of thinner continental crust. Finally, a more geologically plausible prediction of the spatial and temporal extent of petroleum systems elements unraveled a new under-explored hydrocarbon potential in West and Central Kuwait.

Thermomechanical Analysis of Tungsten Inert Gas Welding Process for Predicting Temperature Distribution and Angular Distortion

In present research work, discussions have been made to predict the bead geometries and shape profiles of weldments using statistical regression modeling and fuzzy logic techniques. However, the regression and fuzzy logic modeling techniques do not take into account the actual physical properties and phenomena that occur in welding. Moreover, techniques such as regression and fuzzy logic modeling are not suitable for predicting the transient temperature distribution and distortion of arc welded joints. To predict the transient temperature distributions, peak temperature distribution, and residual deformation in welding, deterministic modeling techniques such as thermomechanical analysis are preferred. However, while performing thermomechanical analysis of welded joints, size and reinforcement dimensions of the weld bead need to be incorporated into the model for accurate prediction of transient temperature distributions and distortions. In this work, circularly spread moving heat source has been used for transient thermal modeling of tungsten inert gas (TIG) welding process. In the subsequent sections of this article, the weld thermomechanical analyses for TIG square butt joints are discussed to predict the temperature distributions and angular distortion. The weld dimensions such as weld width, weld depression, and weld bulging have shown great influence on the angular distortion patterns. 1. Introduction The present work describes the thermomechanical analysis of an open arc process, i.e., tungsten inert gas (TIG) welding of square butt joints by considering the circularly spread moving heat source to predict the angular distortion and thermal profile (Pandey et al. 2016). TIG welding is commonly used for thin sheet joining (Pandey et al. 2018a, 2018b). Generally, for thin sheets, the TIG welding is performed autogenously such that no filler material is required. In some special cases, such as for fillet and groove welding, a filler rod is used in the TIG welding process. The distortion of the TIG weld square butt joints is primarily dependent on the weld width, bead depression, and bulging (Mahapatra et al. 2006). If these factors of weld bead geometries are within tolerable limits, then, the distortion observed in the weld joint is minimum. In TIG weld butt joints, the angular distortion is more prominent because of the presence of upper bead depression and lower bead bulging. Finite element analysis (FEA) simulation of TIG welding is highly effective in predicting the thermomechanical behavior such as temperature distributions and distortions. In this work, numerical and experimental approaches have been applied to predict the thermal profile and angular distortion in a TIG open arc welding process. An Finite element (FE) model has been developed for 3-D analysis of TIG square butt joints for predicting angular distortion based on circularly spread moving heat source and weld geometry.

Deterministic Modelling Techniques on Strain Influenced by Compressive Strength from All in One Aggregate Concrete

Experts has express strain from various materials on different type of aggregate grade, empirical and experimental analysis has been the concept of generating these parameters, empirical modeling are generated from experimental investigation, the study on these materials was to generate its rate of strength development from all – in- one grave aggregate. The developed strength are reflected on the strain, the values were generated from deterministic modeling techniques, the predictive values were subjected to comparisons using experimental values, both parameters express favorable fits validating the developed model predicting strain from all in one aggregate concrete. The study is imperative because experimental evaluation including empirical approach has been the only method of application or generation of strain thus stress-strain relationship, but deterministic techniques has been developed to predict the strain from all in one aggregate concrete, the strength development from these materials has also become another break through on concrete strength development.

Two Dimensional Flows on Degree of Porosity and Permeability Influences on Radial Flow in Lateritic and Silty Formation Patani Coastal Area of Niger Delta

This paper is monitor the deposition of radial flow applying deterministic modelling techniques, the system observed porosity and permeability to express lots of influences determined by geological setting from the soil. This expression were found imperative on the system in other to ensure that two dimensional phase of radial flowstudy are carried out. Permeability and porosity influences were observed to pressure it deposition in various soil formation in lateritic and silty formation. The influences are from the structural disintegration from porous rocks, the rates of permeation were observed to be paramount parameters deposited in two dimensional flow phase of the system. Investigation carried out express these stated parameters as one of the formation influences that may have pressured the variation of radial flow in lateritic and silty formation. The developed modelling approach were found imperative, the expressed model were generated base on the developed system through these variables, experts in soil engineering will applied these concepts as one way to monitor two dimensional radial flow in unconfined bed including other engineering purpose.

Modelado de sistemas bioquímicos: De la Ley de Acción de Masas a la Aproximación Lineal del Ruido

In the last decade we have witnessed a growing application of engineering techniques to biology. Areas such as Systems Biology or, more recently, Synthetic Biology, get more and more attention from the engineers. Specifically, modeling in these fields makes possible the generation of new experimentally verifiable hypothesis, and new ways of biological intervention, as well as more or less mechanistic explanations of experimental results. A model-based approximation requires the consideration of the biochemical reactions dynamics and their regulation. The first part of this tutorial describes the deterministic modeling and model reduction techniques, as applied to the class of biochemical reactions specific to molecular cell biology.Noise plays a crucial role in the biological circuitry dynamics. In the field of automatic control there is a long tradition of modeling using linear stochastic differential equations, under the simplifying assumption that noise has a magnitude independent of the state. This assumption is not valid in biological circuits. The second part of the tutorial describes the most widely used methods for stochastic modeling in molecular cell biology, paying special attention to the so-called linear noise approximation.

Application of Speech Signals to Deterministic Signal Modeling Techniques

Signal modeling is concerned with the representation of signals. The modeled signal consists of parameters, using which the original signal can be reconstructed or recovered. When once it is possible to accurately model a signal, then it becomes possible to perform important signal processing tasks such as signal compression, interpolation, prediction. The models used are AR (Auto Regressive) or All-Pole model, MA (Moving Average) or All-Zero model, ARMA (Auto Regressive Moving Average) or Pole-Zero model. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Shank, Autocorrelation, Covariance techniques. In this paper, these techniques are applied for speech signals and comparisons are carried out. The comparisons are entirely based on the value of the coefficients obtained.

Using New Hybrid Integrators to Design IIR Fractional-Order Integrators via Impulse Invariance Method

This paper presents a method for designing fullband and non-fullband IIR digital integrators with linear phase that has been used to approximate fractional-order integrators with IIR filters. Several numerical integration rules have been considered in this study, namely, Euler, Simpson, Schneider, bilinear, and F012. The main idea in our design is based on interpolating the well-known integration rules (Euler, Bilinear and Simpson) proposed by Al-Alaoui combined with deterministic signal modeling techniques. Numerical examples are presented to illustrate the performance of the proposed integrators. It was found that the Euler–Simpson integrator gives better approximation accuracy than existing integrators.

Commitment and Dispatch With Uncertain Wind Generation by Dynamic Programming

Fluctuating wind production over short time periods is balanced by adjusting generation from thermal plants to meet demand. Thermal ramp rates are limited, so increased variation in wind output as wind penetration increases can add to system operating costs because of the need for more thermal operating reserves. Traditional deterministic modeling techniques fail to fully capture these extra costs. We propose a stochastic dynamic programming (SDP) approach to unit commitment and dispatch, minimizing operating costs by making optimal unit commitment, dispatch, and storage decisions in the face of uncertain wind generation. The SDP solution is compared with two other solutions: 1) that of a deterministic dynamic program with perfect wind predictions to find the cost of imperfect information, and 2) that of a simulation model run under a decision rule, derived from Monte Carlo simulations of the deterministic model, to assess the cost of suboptimal stochastic decision making. An example Netherlands application shows that these costs can amount to several percent of total production costs, depending on installed wind capacity. These are the conclusions of a single simplified case study. Nonetheless, the results indicate that efforts to improve wind forecasting and to develop stochastic commitment models may be highly beneficial.

On the self-similarity of 1/fβ sequences synthesized by recursive filtering

This paper describes an approximate method for synthesizing sequences of statistically self-similar processes and analyses its performance to generate sample sequences with this statistical property. The method is based upon approximating the infinite dimensional difference equation which describes the FARIMA(0,α,0) model by a finite dimensional difference equation. The parameters estimation for parameterizing the binomial coefficients is performed by using deterministic signal modeling techniques. The three techniques considered are: Prony, Steiglitz MacBride, and Shaw methods. In addition to allow considerable savings in memory requirements and great reduction in computation time, the performance analysis results show that the generated sequences are statistically self-similar in the sense that the estimated Hurst parameter is very close to that imposed in the sequence generator.

Simulating the formation of keratin filament networks by a piecewise-deterministic Markov process

Keratin intermediate filament networks are part of the cytoskeleton in epithelial cells. They were found to regulate viscoelastic properties and motility of cancer cells. Due to unique biochemical properties of keratin polymers, the knowledge of the mechanisms controlling keratin network formation is incomplete. A combination of deterministic and stochastic modeling techniques can be a valuable source of information since they can describe known mechanisms of network evolution while reflecting the uncertainty with respect to a variety of molecular events. We applied the concept of piecewise-deterministic Markov processes to the modeling of keratin network formation with high spatiotemporal resolution. The deterministic component describes the diffusion-driven evolution of a pool of soluble keratin filament precursors fueling various network formation processes. Instants of network formation events are determined by a stochastic point process on the time axis. A probability distribution controlled by model parameters exercises control over the frequency of different mechanisms of network formation to be triggered. Locations of the network formation events are assigned dependent on the spatial distribution of the soluble pool of filament precursors. Based on this modeling approach, simulation studies revealed that the architecture of keratin networks mostly depends on the balance between filament elongation and branching processes. The spatial distribution of network mesh size, which strongly influences the mechanical characteristics of filament networks, is modulated by lateral annealing processes. This mechanism which is a specific feature of intermediate filament networks appears to be a major and fast regulator of cell mechanics.

Using the FORE-SCE model to project land-cover change in the southeastern United States

A wide variety of ecological applications require spatially explicit current and projected land-use and land-cover data. The southeastern United States has experienced massive land-use change since European settlement and continues to experience extremely high rates of forest cutting, significant urban development, and changes in agricultural land use. Forest-cover patterns and structure are projected to change dramatically in the southeastern United States in the next 50 years due to population growth and demand for wood products [Wear, D.N., Greis, J.G. (Eds.), 2002. Southern Forest Resource Assessment. General Technical Report SRS-53. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, 635 pp]. Along with our climate partners, we are examining the potential effects of southeastern U.S. land-cover change on regional climate. The U.S. Geological Survey (USGS) Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States, providing ecoregion-by-ecoregion estimates of the rates of change, descriptive transition matrices, and changes in landscape metrics. The FORecasting SCEnarios of future land-cover (FORE-SCE) model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land-cover change through 2050 for the southeastern United States. Prescriptions for future proportions of land cover for this application were provided by ecoregion-based extrapolations of historical change. Logistic regression was used to develop relationships between suspected drivers of land-cover change and land cover, resulting in the development of probability-of-occurrence surfaces for each unique land-cover type. Forest stand age was initially established with Forest Inventory and Analysis (FIA) data and tracked through model iterations. The spatial allocation procedure placed patches of new land cover on the landscape until the scenario prescriptions were met, using measured Land Cover Trends data to guide patch characteristics and the probability surfaces to guide placement. The approach provides an efficient method for extrapolating historical land-cover trends and is amenable to the incorporation of more detailed and focused studies for the establishment of scenario prescriptions.

Efficient Generation of $1/f^{\beta}$ Noise Using Signal Modeling Techniques

This paper presents a new procedure for generating 1/fbeta noise sequences by filtering Gaussian white noise through a recursive filter approximating a discrete-time fractional order integrator. Power series expansion and deterministic signal modeling techniques were combined to determine a rational transfer function to approximate the ideal discrete fractional transfer function resulting from Al-Alaoui's rule raised to the power of the fractional order of integration. The proposed approach is computationally more efficient and more accurate than infinite impulse response truncation. Numerical results are provided to demonstrate the performance of the proposed method.

The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling

A wide variety of ecological applications require spatially explicit, historic, current, and projected land use and land cover data. The U.S. Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States. The newly developed FORE-SCE model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land cover change through 2020 for multiple plausible scenarios. Projected proportions of future land use were initially developed, and then sited on the lands with the highest potential for supporting that land use and land cover using a statistically based stochastic allocation procedure. Three scenarios of 2020 land cover were mapped for the western Great Plains in the US. The model provided realistic, high-resolution, scenario-based land-cover products suitable for multiple applications, including studies of climate and weather variability, carbon dynamics, and regional hydrology.

Computation of Fractional Order Derivative and Integral via Power Series Expansion and Signal Modelling

The three techniques of s-to-z transform, power series expansion (PSE) and signal modelling are combined to develop a new procedure for efficiently computing the fractional order derivatives and integrals of discrete-time signals. A mapping function between the s-plane and the z-plane is first chosen, and then a PSE of this mapping function raised to fractional order is performed to get the desired infinite impulse response of the ideal digital fractional operator. Finally, the desired impulse response is modelled as the impulse response of a linear invariant system whose rational transfer function is determined using deterministic signal modelling techniques. Three non-iterative techniques, namely Pade, Prony and Shanks’ methods have been considered in this paper. Using Al-Alaoui’s rule as s-to-z transform, computation examples show that both Prony and Shanks’ method can achieve more accurate fractional differentiation and integration than Pade method which is equivalent to continued fraction expansion technique.

Comparative Analysis of Data-Driven and GIS-Based Conceptual Rainfall-Runoff Model

Modeling of the rainfall-runoff process is important in hydrology. Historically, researchers relied on conventional deterministic modeling techniques based either on the physics of the underlying processes, or on the conceptual systems which may or may not mimic the underlying processes. This study investigates the suitability of a conceptual technique along with a data-driven technique, to model the rainfall-runoff process. The conceptual technique used is based on the Xinanjiang model coupled with geographic information system (GIS) for runoff routing and the data-driven model is based on genetic programming (GP), which was used for rainfall-runoff modeling in the recent past. To verify GP’s capability, a simple example with a known relation from fluid mechanics is considered first. For a small, steep-sloped catchment in Hong Kong, it was found that the conceptual model outperformed the data-driven model and provided a better representation of the rainfall-runoff process in general, and better predictio...

Runoff modelling using radar data and flow measurements in a stochastic state space approach

In urban drainage the estimation of runoff with the help of models is a complex task. This is in part due to the fact that rainfall, the most important input to urban drainage modelling, is highly uncertain. Added to the uncertainty of rainfall is the complexity of performing accurate flow measurements. In terms of deterministic modelling techniques these are needed for calibration and evaluation of the applied model. Therefore, the uncertainties of rainfall and flow measurements have a severe impact on the model parameters and results. To overcome these problems a new methodology has been developed which is based on simple rain plane and runoff models that are incorporated into a stochastic state space model approach. The state estimation is done by using the extended Kalman filter in combination with a maximum likelihood criterion and an off-line optimization routine. This paper presents the results of this new methodology with respect to the combined consideration of uncertainties in distributed rainfall derived from radar data and uncertainties in measured flows in an urban catchment within the Emscher river basin, Germany.