Unstructured Model Research Articles

Mathematical Modeling of Acetone–Butanol–Ethanol Fermentation with Simultaneous Utilization of Glucose and Xylose by RecombinantClostridium acetobutylicum

Studies on acetone–butanol–ethanol (ABE) fermentation of sugars from lignocellulosic biomass have been actively carried out in recent years, and recombinant strains have been developed to efficiently coutilize glucose and xylose, which are the dominant sugar types in most lignocellulosic biomass. However, there is a lack of mathematical models for describing and predicting the simultaneous utilization of glucose and xylose in ABE fermentation, particularly for the purpose of supporting optimization and control of the process. This study proposes a kinetic model for ABE fermentation with coutilization of glucose and xylose by recombinant Clostridium acetobutylicum. The model is developed based on unstructured models, that is, the Monod equation and the Luedeking–Piret model, and a modified concentration-dependent weighting factor is suggested to describe the simultaneous utilization of glucose and xylose based on the experimental analysis. A systematic identification approach is employed to obtain reliable...

Substrates' and products' inhibition in fructanase production by a new Kluyveromyces marxianus CF15 from Agave tequilana fructan in a batch reactor.

This study focuses on fructanase production in a batch reactor by a new strain isolated from agave juice (K. marxianus var. drosophilarum) employing different Agave tequilana fructan (ATF) concentrations as substrate. The experimental data suggest that the fructanase production may be inhibited or repressed by high substrate (50g/L) and ethanol (20.7g/L) concentrations present in culture medium. To further analyze these phenomena an unstructured kinetic mathematical model taking into account substrate and products inhibition was proposed and fitted. The mathematical model considers six reaction kinetics and the ethanol evaporation, and predicts satisfactorily the biomass, fructan, glucose, fructose, ethanol, and fructanase behavior for different raw material initial concentrations. The proposed model is the first to satisfactorily describe the production of fructanase from branched ATF with a new strain of K. marxianus.

Machine learning in M4: What makes a good unstructured model?

The Makridakis Competitions seek to identify the most accurate forecasting methods for different types of predictions. The M4 competition was the first in which a model of the type commonly described as “machine learning” has outperformed the more traditional statistical approaches, winning the competition. However, many approaches that were self-labeled as “machine learning” failed to produce accurate results, which generated discussion about the respective benefits and drawbacks of “statistical” and “machine learning” approaches. Both terms have remained ill-defined in the context of forecasting. This paper introduces the terms “structured” and “unstructured” models to better define what is intended by the use of the terms “statistical” and “machine learning” in the context of forecasting based on the model’s data generating process. The mechanisms that underlie specific challenges to unstructured modeling are examined in the context of forecasting, along with common solutions. Finally, the innovations in the winning model that allowed it to overcome these challenges and produce highly accurate results are highlighted.

Multi-Objective Optimization of an ABE Fermentation System for Butanol Production as Biofuel

Abstract In this work, a previously reported unstructured kinetic model of Clostridium acetobutylicum ATCC 824, validated with experimental data under different culture conditions, was used to determine the optimal process conditions from an ABE fermentation system for biofuel production. The goal of this work was to simultaneously maximize two conflicting objectives: volumetric productivity and final concentration of butanol considering both Fed-Batch and single-stage CSTR operation regimes using either a free or immobilized cell reactor. The result of the after mentioned strategy was the construction of the Pareto Fronts and optimal trajectories for the inlet solution feeding rate and concentration using a Sequential Quadratic Programming methodology. The obtained results suggest that the maximum concentration and productivity of butanol are achieved in a semi-continuous system operating with immobilized cells, obtaining values of 19.1454 kg m-3 and 0.3655 kg m-3 h-1, respectively, representing an increase of 48 % and 104 % compared to the most recent industrial process reported to date.

Continuous time nonstationary correlation models for sparse longitudinal data

In this paper, we present a new continuous time model for nonstationary correlation structures for longitudinal data. This model, which provides a continuous time analogue to the antedependence model and is thus referred to as the continuous antedependence (CAD) model, is intended to provide more refined correlation models for longitudinal data and to better accommodate sparse (or highly unbalanced) data. A key component of this model is the 'nonstationarity function' which describes nonstationarity as a unidimensional function of time and has an interesting time expansion/contraction interpretation. Focusing on a Markovian version of the model, we develop a novel nonlinear regression model providing nonlinear least square estimators of model parameters. Both unstructured (for nonparametric estimation) and structured versions of the model are presented. We apply the proposed approach to data from the Multicenter AIDS Clinical Study (MACS), with a focus on inference for the nonstationarity function. In simulation studies, we show good properties (low finite sample bias, and high convergence rates and efficiency) of the proposed unstructured model estimator, which compare favorably to those of an alternative maximum likelihood estimator, particularly in sparse data situations.

Recursive Kronecker-Based Vector Autoregressive Identification for Large-Scale Adaptive Optics

This brief presents an algorithm for the recursive identification of Vector AutoRegressive (VAR) models of large dimensions. We consider a VAR model where the coefficient matrices can be written as a sum of Kronecker products. The algorithm proposed consists of recursively updating the Kronecker factor matrices at each new time step using alternating least squares. When the number of terms in the Kronecker sum is small, a significant reduction in computational complexity is achieved with respect to the recursive least squares algorithm on an unstructured VAR model. Numerical validation of nonstationary atmospheric turbulence data, both synthetic and experimental, is shown for an adaptive optics application. Significant improvements in accuracy over batch identification methods that assume stationarity are observed while both the computational complexity and the required storage are reduced.

Increased production of ganoderic acids by overexpression of homologous farnesyl diphosphate synthase and kinetic modeling of ganoderic acid production in Ganoderma lucidum

BackgroundGanoderic acids (GAs), derived from the medicinal mushroom Ganoderma lucidum, possess anticancer and other important pharmacological activities. To improve production of GAs, a homologous farnesyl diphosphate synthase (FPS) gene was overexpressed in G. lucidum. Moreover, the influence of FPS gene overexpression on GA production was investigated by developing the corresponding mathematical models.ResultsThe maximum levels of total GAs and individual GAs (GA-T, GA-S, and GA-Me) in the transgenic strain were 2.76 mg/100 mg dry weight (DW), 41 ± 2, 21 ± 5, and 28 ± 1 μg/100 mg DW, respectively, which were increased by 2.28-, 2.27-, 2.62-, and 2.80-folds compared with those in the control. Transcription levels of squalene synthase (SQS) and lanosterol synthase (LS) genes during GA biosynthesis were upregulated by 2.28- and 1.73-folds, respectively, in the transgenic G. lucidum. In addition, the developed unstructured models had a satisfactory fit for the process of GA production in submerged cultures of G. lucidum. Analysis of the kinetic process showed that FPS gene overexpression had a stronger positive impact on GA production compared with its influence on cell growth. Also, FPS gene overexpression led to a higher non-growth-associated-constant β (1.151) over the growth-associated-constant α (0.026) in the developed models.ConclusionsFPS gene overexpression is an effective strategy to improve the production of GAs in G. lucidum. The developed mathematical models are useful for developing a better GA production process in future large-scale bioreactors.

Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China

Bridge piers on river channels can cause obstacles for flood flow by reducing the cross-sectional area and inducing local eddy currents and high flow velocities, which may destroy hydraulic structures. A two-dimensional numerical model was used to investigate the effects of bridge piers on river flood hazards in the Jialing River, China. For the modeling, Mike 21 FM was used, which is an unstructured mesh and finite volume model that solves the shallow water equations. The numerical model was validated with collected historical flood traces, and sensitivity analyses identified the effects of the Manning coefficient and the dependence on the grid size. The influence of backwater effects on the flow field was analyzed by comparing numerical results with and without piers. The results showed that the most significant impacts were caused by the Fengxian Bridge. The maximum water level rise was about 1 m and the maximum velocity near this bridge decreased by 22.77% for a 10-year flood. We found that the top elevations of planned levees near the bridges must be increased by 0.15–0.36 m. The influence of bridge piers on the flood velocity field is more complex. These findings will help flood hazard management in this river and provides a reference for similar projects.

Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines

In the variable rotor speed and variable blade pitch wind-turbines system, the pitch controller is crucial in the high wind speed range to adjust the generator speed so as to produce the rated power. The main challenges in designing the pitch controller are the wind turbine’s nonlinearities, constraints on pitch-angle, the variations in wind-speed, and the unstructured model dynamics. From this perspective, a new pitch controller is proposed by employing partial offline quasi-min-max fuzzy model-predictive control to investigate the variable-speed wind turbine performance. Based on the fuzzy modeling, the online optimization problem is simplified as a partial offline optimization problem (offline design and online synthesis). The key advantage of this controller is the guaranteed stability with actuator constraints using LMI constraints and less computational burden. Also, this controller is compared with standard gain scheduled-PI (proportional integral) controller, which has been utilized profusely in the wind-turbine industry. Furthermore, a typical 5 MW benchmark wind-turbine is employed to validate the results from the nonlinear mathematical model. Several case studies are made to prove the proposed controller effectiveness. The results show the superiority of the proposed controller over the gain scheduled-PI controller and two other advanced control techniques.

The Omega-3 Index and Long-term Walnut Consumption in Elderly Participants of the WAHA Study (P08-107-19)

ObjectivesTo determine if consuming walnuts daily for 2 years would improve the omega-3 index in healthy elderly compared to habitual diet without walnuts. MethodsDesign: Secondary analysis of the Walnuts and Healthy Aging (WAHA) study, a 2-year, randomized controlled clinical trial, conducted in Loma Linda University, CA, USA and Hospital Clinic, Barcelona, Spain. Subjects were randomly assigned to either a walnut or a control group. Study PopulationIncluded 193 WAHA study subjects from the Loma Linda center only, whose blood samples were collected and analyzed for RBC fatty acids. Statistical analysisTo assess whether the changes of the Omega-3 index from baseline to year 1 and baseline to year 2 are significantly different between the walnut and control groups. Unstructured covariance model best fitted the data and was utilized. Then, repeated measures analyses were conducted to assess the delta change differences based on generalized least squares model. ResultsOmega-3 index did not significantly change between baseline and year 1 in either group. The index significantly increased by 1.5% (P < 0.001) in walnut group and by 1.8% (P < 0.001) in control group between baseline and year 2. This between-group difference was not statistically significant (P > 0.05). ConclusionsPreliminary findings suggest that consuming walnuts, a rich source of ALA, may not be adequate to improve the omega-3 index compared to a walnut free habitual diet. Background intake of EPA and DHA may have a more pronounced influence on the index and needs to be further explored. Funding SourcesCalifornia Walnut Commission, Folsom, CA.

An efficient data structure for calculation of unstructured T-spline surfaces

To overcome the topological constraints of non-uniform rational B-splines, T-splines have been proposed to define the freeform surfaces. The introduction of T-junctions and extraordinary points makes it possible to represent arbitrarily shaped models by a single T-spline surface. Whereas, the complexity and flexibility of topology structure bring difficulty in programming, which have caused a great obstacle for the development and application of T-spline technologies. So far, research literatures concerning T-spline data structures compatible with extraordinary points are very scarce. In this paper, an efficient data structure for calculation of unstructured T-spline surfaces is developed, by which any complex T-spline surface models can be easily and efficiently computed. Several unstructured T-spline surface models are calculated and visualized in our prototype system to verify the validity of the proposed method.

A Partial Mastery, Higher-Order Latent Structural Model for Polytomous Attributes in Cognitive Diagnostic Assessments

The latent attribute space in cognitive diagnosis models (CDMs) is often assumed to be unstructured or saturated. In recent years, the number of latent attributes in real tests has often been found to be large, and polytomous latent attributes have been advocated. Therefore, it is preferable to adopt substantive theories to connect seemingly unrelated latent attributes, to replace the unstructured or saturated latent structural models (LSMs) with structured or parsimonious ones, with simplified parameter estimation. In the present study, we developed a partial mastery, higher-order LSM for polytomous attributes, which was built upon the framework of adjacent-category logit models to account for a higher-order latent structure of multiple polytomous attributes. The proposed model can be incorporated into many existing CDMs. We conducted simulations to evaluate the psychometric properties of the proposed model and obtained good parameter recovery. We then provided an empirical example to demonstrate the applications and the advantages of the proposed model.

Transformation boosting machines

The broad class of conditional transformation models includes interpretable and simple as well as potentially very complex models for conditional distributions. This makes conditional transformation models attractive for predictive distribution modelling, especially because models featuring interpretable parameters and black-box machines can be understood as extremes in a whole cascade of models. So far, algorithms and corresponding theory was developed for special forms of conditional transformation models only: maximum likelihood inference is available for rather simple models, there exists a tailored boosting algorithm for the estimation of additive conditional transformation models, and a special form of random forests targets the estimation of interaction models. Here, I propose boosting algorithms capable of estimating conditional transformation models of arbitrary complexity, starting from simple shift transformation models featuring linear predictors to essentially unstructured conditional transformation models allowing complex nonlinear interaction functions. A generic form of the likelihood is maximized. Thus, the novel boosting algorithms for conditional transformation models are applicable to all types of univariate response variables, including randomly censored or truncated observations.

Enhanced Indirect Field-Oriented Control of Single-Phase Induction Motor Drive Using H∞ Current Controller

In this paper, the enhanced indirect rotor field-oriented control (IRFOC) of single-phase induction motor (SPIM) drive is proposed. Its optimal stator currents are ensured by the multi-input and multi-output H∞ stator current controller, which is designed through solving the weighted mixed sensitivity criterion by the improved loop-shaping two-Riccati formulae. Moreover, the SPIM’s rotor speed is also ensured by the PID controller, which is designed from solving the mean square error criterion using the genetic algorithm. The SPIM’s actual behavior is modeled by the unstructured uncertainty model where significant variations in its stator and rotor resistors are taken into accounts. Therefore, an optimal trade-off between nominal performance and robust stability should be attained by the proposed IRFOC strategy while respecting some requirements such as a good tracking dynamic of the reference stator trajectories, as well as the reference rotor speed, a good decoupling behavior of the measured stator currents, a good rejection dynamic of plant uncertainties and a good suppression dynamic of prospective sensor noises. The modeling and control framework of the SPIM drive are simulated in MATLAB®/Simulink using Simpower systems library, in which the enhanced IRFOC performances are compared with those given by the standard IRFOC based upon the hysteresis current controller.

Modelling evolution of virulence in populations with a distributed parasite load

Modelling evolution of virulence in host-parasite systems is an actively developing area of research with ever-growing literature. However, most of the existing studies overlook the fact that individuals within an infected population may have a variable infection load, i.e. infected populations are naturally structured with respect to the parasite burden. Empirical data suggests that the mortality and infectiousness of individuals can strongly depend on their infection load; moreover, the shape of distribution of infection load may vary on ecological and evolutionary time scales. Here we show that distributed infection load may have important consequences for the eventual evolution of virulence as compared to a similar model without structuring. Mathematically, we consider an SI model, where the dynamics of the infected subpopulation is described by a von Förster-type equation, in which the infection load plays the role of age. We implement the adaptive dynamics framework to predict evolutionary outcomes in this model. We demonstrate that for simple trade-off functions between virulence, disease transmission and parasite growth rates, multiple evolutionary attractors are possible. Interestingly, unlike in the case of unstructured models, achieving an evolutionary stable strategy becomes possible even for a variation of a single ecological parameter (the parasite growth rate) and keeping the other parameters constant. We conclude that evolution in disease-structured populations is strongly mediated by alterations in the overall shape of the parasite load distribution.

Assessment of air flow distribution and hazardous release dispersion around a single obstacle using Reynolds-averaged Navier-Stokes equations

The flow around a cubical building, with a pollution source at the central point of the top of the cube, is studied. The Reynolds-averaged Navier-Stokes and species concentration equations are solved for Reynolds number, Re = 40,000, is based on the height of the cube. The predictions obtained with the standard, the Kato-Launder, and the low-Reynolds number k-epsilon models are examined with various wall functions for the near wall treatment of the flow. Results are compared against Martinuzzi and Tropea measurements (J. of Fluids Eng., 115, 85–92, 1993) for the flow field and against Li and Meroney (J. of Wind Eng. and Industrial Aerodynamics, 81, 333–345, 1983) experiments and Gaussian models for the concentration distribution. It is found that the present unstructured mesh model performs similarly to the structured mesh models. Results from the Kato-Launder model are closer to the experimental data for the flow patterns and contaminant distribution on the cube's roof. However, the Kato-Launder model has an over-prediction for the recirculation zone and the contaminant distribution windward of the cube. The standard k-epsilon and the low-Reynolds number k-epsilon models predict similar flow patterns and are closer to the experimental data of the cube's windward and side face.

Analysis of the bias induced by voxel and unstructured mesh Monte Carlo models for the MCNP6 code in orthovoltage applications

ABSTRACTThe use of Monte Carlo methods for the set up of Treatment Planning Systems (TPS) in radiotherapy applications is a current standard. The most advanced modeling techniques aim at directly link the output of CT scans to a patient-specific model build up instead of standard phantoms and look-up tables. This link represents a critical step since even the most accurate segmentation and organ volume definition must be translated into a suitable input for the Monte Carlo code. During that step, the segmented volume is usually mapped on a regular geometrical lattice (voxels). A more sophisticated option appears to be a volume description based on an Unstructured Mesh (UM) geometry typical of current finite element codes. In this paper, we compared the two approaches analyzing the bias induced by the different choices. Starting from anonymous patient DICOM files coming from a CT scan, suitable segmentation and volume definition have been carried out and voxel and UM-based equivalent models for the Monte Carlo code MCNP6 have been built. Some computational phantoms, covering some significant portion of the human body (head and lower limb), have been used as a benchmark of the dose distribution obtained from X-ray sources commonly used in orthovoltage applications. The specific clinical application has been chosen because, despite being used on a growing number of patients and with multi-Gy doses, treatment planning is still based on poorly characterized dose mapping systems such as look-up tables or low-resolution computational phantoms for MC codes. Also, experimental measurements on phantom slabs irradiated by an X-ray source were carried out preliminarily to validate the simulated radiation source. As shown in the results, the UM computational phantoms (built through the Simpleware SCAN-IP™ tool) can reduce the bias induced by the regularity of the classical cubic voxel geometry, give a more accurate description of volumes and complex surfaces and, thanks to an optimized discretization of the volumes, are also able to reduce the computational work. For the same UM models, various comparisons with different voxel sizes have been produced to investigate the dose distributions and evaluate the voxelization effects. The comparison shows a convergence pattern of the voxel model to the UM one. It has been possible to verify that the most significant bias occurs where the dose gradient is higher, along the beam borders and at tissue interfaces. The simulations showed that the UM can be really effective and reliable to compute the dose distributions within computational anthropomorphic phantoms obtained from the patient’s CT scans.

High level extracellular production of recombinant human interferon alpha 2b in glycoengineered Pichia pastoris: culture medium optimization, high cell density cultivation and biological characterization.

The present study was aimed at design of experiments (DoE)- and artificial intelligence-based culture medium optimization for high level extracellular production of a novel recombinant human interferon alpha 2b (huIFNα2b) in glycoengineered Pichia pastoris and its characterization. The artificial neural network-genetic algorithm model exhibited improved huIFNα2b production and better predictability compared to response surface methodology. The optimized medium exhibited a fivefold increase in huIFNα2b titre compared to the complex medium. A maximum titre of huIFNα2b (436mgl-1 ) was achieved using the optimized medium in the bioreactor. Real-time capacitance data from dielectric spectroscopy were utilized to model the growth kinetics with unstructured models. Biological characterization by antiproliferative assay proved that the purified recombinant huIFNα2b was biologically active, exhibiting growth inhibition on breast cancer cell line. Culture medium optimization resulted in enhanced production of huIFNα2b in glycoengineered P. pastoris at both shake flask and bioreactor level. The purified huIFNα2b was found to be N-glycosylated and biologically active. DoE-based medium optimization strategy significantly improved huIFNα2b production. The antiproliferative activity of huIFNα2b substantiates its potential scope for application in cancer therapy.

Dark fermentative biohydrogen production from synthetic cheese whey in an anaerobic structured-bed reactor: Performance evaluation and kinetic modeling

This study investigated the potential of dark fermentative hydrogen production from cheese whey in an acidogenic anaerobic structured-bed reactor (ASTBR). The reactor was operated at 25 °C with an organic loading rate (OLR) of 24 kg COD m−3 d−1 and a hydraulic retention time (HRT) of 24 h. The ASTBR had an average volumetric hydrogen production rate (VHPR) of 1.6 ± 0.7 L H2 L−1 d−1 and an average hydrogen yield (HY) of 1.4 ± 0.7 moL H2 mol−1 of consumed lactose. Batch experiments were performed with biomass from the ASTBR to characterize the main metabolic transformations involved in hydrogen production. An ADM1-based, unstructured kinetic model was developed and fitted to the obtained temporal profiles. The model allowed a satisfactory description of the process and indicated that hydrogen production from lactate and acetate could explain 74.5% of the total hydrogen volume produced. Because lactic-acid-producing bacteria are expected to ultimately colonize acidogenic reactors treating cheese whey, optimizing electron flow through this pathway may be a suitable strategy to enable long-term hydrogen production from cheese whey using mixed-culture fermentation.

Optimal sustainable harvesting of populations in random environments

We study the optimal sustainable harvesting of a population that lives in a random environment. The novelty of our setting is that we maximize the asymptotic harvesting yield, both in an expected value and almost sure sense, for a large class of harvesting strategies and unstructured population models. We prove under relatively weak assumptions that there exists a unique optimal harvesting strategy characterized by an optimal threshold below which the population is maintained at all times by utilizing a local time push-type policy. We also discuss, through Abelian limits, how our results are related to the optimal harvesting strategies when one maximizes the expected cumulative present value of the harvesting yield and establish a simple connection and ordering between the values and optimal boundaries. Finally, we explicitly characterize the optimal harvesting strategies in two different cases, one of which is the celebrated stochastic Verhulst Pearl logistic model of population growth.