Unstructured Model Research Articles

Valorization of environmental-burden waste towards microalgal metabolites production

The present study develops a novel concept of using waste media as an algal nutrient resource compared to the usual growth media with the aid of growth kinetics study and metabolite production abilities. Food- and agri-compost wastes are compact structures with elemental compounds for microbial media. As a part of the study, environ-burden wastes (3:1) as a food source for photosynthetic algae as a substitute for the costly nutrient media were proposed. The environment-burden waste was also envisaged for macromolecule production, i.e., 99200 μg/ml lipid, 112.5 μg/ml protein, and 8.75 μg/ml carbohydrate with different dilutions of agri-waste, bold basal media (BBM), and Food waste, respectively. The fabricated growth kinetics and dynamics showcased the unstructured models of different photosynthetic algal growth phases and the depiction of productivity and kinetic parameters. The theoretical maximum biomass concentration (Xp) was found to be more (0.871) with diluted agricompost media than the usual BBM (0.697). The XLim values were found to be 0.362, 0.323 and 0.209 for BBM, diluted agri-compost media and diluted food waste media, respectively. Overall, the study proposes a cleaner approach of utilizing the wastes as growth media through a circular economy approach which eventually reduces the growth media cost with integrated macromolecule production capabilities.

Unstructured kinetic models with time-averaged growth approach in the biodegradation of triclosan by activated sludge culture

The concentration of triclosan in wastewater is expected to rise dramatically as a consequence of the COVID-19 pandemic. A modeling analysis of the growth kinetics of microbial culture during triclosan degradation was necessary in order to establish effective wastewater treatment. The kinetic parameters are used by engineers to aid in the design and process control of biological processes. Studies were conducted using triclosan-acclimated culture to examine biomass growth and associated substrate degradation at different initial substrate concentrations (0.35–4.9 mg L−1) to this end. Substrate inhibition was calculated from experimental growth parameters using unstructured kinetic models. Unlike other model studies, a time-averaged specific bacterial growth rate in the log phase was considered for kinetic models in this study. Overestimation of the conventional log phase calculation for unstructured kinetic model constants was eliminated when the slowdown growth part of the log growth phase was taken into account. The Haldane Model was more accurate to fitting experimental data in an excellent manner. In this case, the time-averaged specific growth rate, saturation constant, and inhibition constant were 0.56 h−1, 12.77 mg L−1, 0.52 mg L−1, respectively. A yield coefficient of 0.404 mgX.mgS−1 was calculated. The critical triclosan concentration was 2.57 mg L−1. Wastewater treatment plants can be more sensitive to the value of the critical triclosan concentration. The value for time-averaged critical specific growth rate was 0.051 h−1. Pre-or post-treatment requirements can be estimated using time-averaged critical growth rate values as a benchmarking tool in biological treatment systems.

Scalable self attraction and loading calculations for unstructured ocean tide models

Self attraction and earth-loading effects are important for accurately modeling global tides. A common approach of handling this forcing is to expand mass anomalies into spherical harmonics, which are scaled by load Love numbers to account for elastic earth deformation. We investigate two different approaches to perform these calculations for ocean models that employ unstructured meshes and distributed memory parallelization. The first approach leverages a highly efficient spherical harmonics library, but requires all-to-one and one-to-all communications and interpolation operations between the unstructured and a structured mesh. This approach is compared to a parallel algorithm that computes the spherical harmonic transformations directly on the unstructured mesh with an all-reduce communication. Our results show that although the unstructured mesh calculations are more expensive, the scalability of the unstructured mesh approach allows for more efficient spherical harmonics transforms for high-resolution meshes and large processor counts. This methodology enables the efficient inclusion of tidal dynamics large-scale Earth system model simulations.

THE EFFICIENCY RESEARCH OF MIMO SYS-TEMS WITH CORRELATED ELEMENTS OF A CHANNEL TRANSMISSION MATRIX

Currently, there is an active development of wireless data transmission systems. A solution widely used in improving existing and creating new wireless communication technologies, which allows increasing spectral efficiency and transmission speed, is the use of systems with Multiple-Input-Multiple-Output, MIMO. In this regard, research into the potential capabilities of MIMO systems under various operating conditions is an urgent task. In particular, the issue of the influence of the correlation of transmission matrix elements on the efficiency of the communication system is important. We will further consider cases of multipath propagation typical for urban conditions. We will also consider the signals transmitted by different antennas to be narrowband and independent of each other. For the two currently most commonly used MIMO radio channel models, the influence of the correlation between the elements of the transmission channel matrix on the efficiency of the MIMO system was studied using statistical tests. Dependences of the error probability per bit of transmitted information on the signal-to-noise ratio were obtained for channels with transmission matrices whose covariance matrices have varying degrees of difference from the diagonal (unit) matrix. When generating channel matrices with correlated elements, the unstructured (general correlation) model and the Kronecker model are used.

EFFICIENCY OF MIMO SYSTEMS WITH TRANSMISSION CHANNEL MATRIX CORRELATION ELEMENTS

Researching the potential capabilities of MIMO systems under various operating conditions is an urgent task. In particular, the issue of the influence of the correlation of the elements of the transmission matrix on the efficiency of the communication system is important. For the currently most commonly used MIMO radio channel model, the influence of the correlation between the elements of the transmission channel matrix on the efficiency of the MIMO system was studied using statistical tests. The dependences of the error probability per bit of transmitted information on the signal-to-noise ratio were obtained for channels with covariance matrices that have varying degrees of difference from the covariance matrix with uncorrelated elements. When generating channel matrices with correlated elements, the unstructured (general correlation) model and the Kronecker model are used.

Structured prediction of sparse dependent variables for traffic state estimation in large-scale networks

Currently, one of the biggest challenges in modern traffic engineering is related to traffic state estimation (TSE). Although many machine learning and domain models can be used for TSE, they do not consider the sparsity and spatial dependence of traffic state variables. In this paper, we propose a hybrid soft computing model of two Gaussian conditional random field (GCRF) models for the inference of traffic speed, which is a relevant variable for TSE and travel information systems. The proposed model can infer the traffic state variables in large-scale networks whose nodes are geographically dispersed. Moreover, by combining a Gaussian conditional random field binary classification model (GCRFBC), which classifies traffic regimes as free-flow or potentially congested, and a regression GCRF model for the prediction of traffic speed in potentially congested traffic regimes, the model addresses two specifics of the problem: sparsity in traffic data, and the fact that observations are not independent. The proposed model was tested on two large-scale real-world networks in Serbia, namely an arterial E70-E75 335 km long highway stretch and the major ski resort Kopaonik with 55 km of ski slopes. In addition, the proposed model showed better prediction performance than several other unstructured and structured models.

Process Mining: Auditing Approach Based on Process Discovery Using Frequency Paths Concept

In the company environment, the management team is responsible for producing normative models. The normative model is considered a standard model that aims at auditing all business processes in the same context. In this regard, the audit operation encompasses four process mining activities, in a hybrid evaluation (offline and online), which are the detect, the check, the compare, and the promote activities. This is still well performed for structured business processes. Otherwise, complex processes may deviate from the initial defined normative model context. Indeed, the latter must be refined for more precise results. Therefore, the combination of human knowledge, control-flow discovery algorithms, and process mining activities is required. To this end, we present a technique for reducing the complexity of unstructured process models (Spaghetti process models) into structured ones (Lasagna process models). This framework outputs a refined normative model for improving the future Business Process (BP) auditing operations. Moreover, this work introduces the sustainability advantage that can occur using process mining techniques.

Typhoon Forecasts with Dynamic Vortex Initialization Using an Unstructured Mesh Global Model

Abstract A dynamical vortex initialization (DVI) scheme is implemented on unstructured meshes for the global model MPAS for typhoon forecasts. The DVI extracts the departure vortex within a specified radius of the vortex center and implants this vortex at the observed vortex location in continuously cycled 1-h integrations of the model. The cycling integration is stopped when either the simulated central sea level pressure or maximum wind speed of the typhoon has reached the value in the best track data, denoted as P-match or V-match, respectively. The DVI may spin up the initial vortex with a more contracting eyewall, but still keeping the same size of the outer vortex. Forecasts for 16 typhoons over the western North Pacific in 2015–20 are investigated. Predictions from the experiments with the 60–15-km variable-resolution MPAS mesh show that both P-match and V-match significantly improve the track forecasts, where V-match mostly requires less cycle runs than P-match. Cycling results with P-match or V-match are also dependent on the choice of physics suites within MPAS. Positive impacts are larger for V-match than P-match using the mesoscale reference physics suite, with significantly improved track forecasts and earlier intensity forecasts. Intensity differences resulting from the DVI have gradually decreased with forecast time, which are closely correlated to the differences in the averaged tropospheric potential vorticity of the inner vortex. The DVI with the 60–15–3-km variable-resolution mesh also works well and improves intensity forecasts. The DVI can also help produce asymmetric structures and spin up inner vortex cores for typhoons near high topography, which leads to improved intensity forecasts.

Monitoring the Recombinant Adeno-Associated Virus Production using Extended Kalman Filter

The recombinant adeno-associated virus (rAAV) is a viral vector technology for gene therapy that is considered the safest and most effective way to repair single-gene abnormalities in non-dividing cells. However, improving the viral titer productivity in rAAV production remains challenging. The first step to this end is to effectively monitor the process state variables (cell density, GLC, GLN, LAC, AMM, and rAAV viral titer) to improve the control performance for an enhanced productivity. However, the current approaches to monitoring are expensive, laborious, and time-consuming. This paper presents an extended Kalman filter (EKF) approach used to monitor the rAAV production using the online viable cell density measurements and estimating the other state variables measured at a low frequency. The proposed EKF uses an unstructured mechanistic kinetic model applicable in the upstream process. Three datasets were used for parameter estimation, calibration, and testing, and the data were collected from the production of rAAV through a triple-plasmid transfection of HEK293SF-3F6 cells. Overall, the proposed approach accurately estimated metabolite concentrations and the rAAV production yield. Therefore, the approach has a high potential to be extended to an online soft sensor and to be classified as a cost-effective and fast approach to the monitoring of rAAV production.

Model-aided targeted volatile fatty acid production from food waste using a defined co-culture microbial community

The production of volatile fatty acids (VFA) is gaining momentum due to their central role in the emerging carboxylate platform. Particularly, the production of the longest VFA (from butyrate to caproate) is desired due to their increased economic value and easier downstream processing. While the use of undefined microbial cultures is usually preferred with organic waste streams, the use of defined microbial co-culture processes could tackle some of their drawbacks such as poor control over the process outcome, which often leads to low selectivity for the desired products. However, the extensive experimentation needed to design a co-culture system hinders the use of this technology. In this work, a workflow based on the combined use of mathematical models and wet experimentation is proposed to accelerate the design of novel bioprocesses. In particular, a co-culture consisting of Pediococcus pentosaceus and Megaphaera cerevisiae is used to target the production of high-value odd- and even‑carbon VFA. An unstructured kinetic model was developed, calibrated and used to design experiments with the goal of increasing the selectivity for the desired VFA, which were experimentally validated. In the case of even‑carbon VFA, the experimental validation showed an increase of 38 % in caproate yield and, in the case of enhanced odd‑carbon VFA experiments, the yield of butyrate and caproate diminished by 62 % and 94 %, respectively, while propionate became one of the main end products and valerate yield value increased from 0.007 to 0.085 gvalearte per gconsumed sugar. The workflow followed in this work proved to be a sound tool for bioprocess design due to its capacity to explore and design new experiments in silico in a fast way and ability to quickly adapt to new scenarios.

SEMANTICALLY ENRICHED VOXELS AS A COMMON REPRESENTATION FOR COMPARISON AND EVALUATION OF 3D BUILDING MODELS

Abstract. 3D city models are fast becoming a key factor for planning, design and analysis in the AEC industry, and urban informatics. Data are being acquired from a variety of sources and each consequent representation contains inherent information about its location, geometry, structure and usability. These digital representations can range from unstructured models with very little semantic data such as 3D point clouds, and 3D mesh models or they can be highly structured and semantically enriched models following CityGML and IFC/BIM standards. Matching these diverse representations against one another can facilitate information flow and eventually lead to a coherent interconnected Digital twin. Consistency measures would be needed to compare one representation against another. For comparison of any kind, a common baseline needs to be established. A tentative approach, as outlined in this paper, is to convert all the model types into a common representation such as ‘volumetric pixels’ or ‘voxels’ but the concept of ‘voxel’ as we know it, is not enough to deal with the rich semantic and organizational structure of modelling representations such as IFC and CityGML. Voxelisation is a complex process and conventional conversion methods concentrate on translating the geometry between representation types but the semantic and class hierarchy information is usually not translated. This paper assesses the need for matching 3D models using a common representation (voxels), discusses the challenges of the voxelisation process and proposes the concept of a ‘RichVoxel’.

A simple unstructured kinetic model for anaerobic treatment of a class of agro‐industrial waste

AbstractBACKGROUNDDynamic modeling of the anaerobic treatment of agro‐industrial waste can help determine optimal operating conditions to maximize the process performance. For this, it is necessary to select an appropriate model and adapt it to the specific conditions of the substrate. In this work, a simple unstructured kinetic model for the anaerobic treatment of different agro‐industrial waste is proposed. The proposed model considers the three most relevant stages of anaerobic digestion (AD) and can be adapted for the treatment of different substrates, modes of operation (batch or continuous), and/or generated products (VFA or methane production), thus requiring a minimum of process macroscopic measurements.RESULTSTo evaluate the scope of the model, two case studies were analyzed: (i) methane production from tequila vinasse and agave hydrolysates, and (ii) volatile fatty acids (VFA) and biogas production from raw cheese whey. For each case, parameter estimations were performed using the Levenberg – Marquardt algorithm, which yielded a simplified model with high determination coefficients (R2 > 0.95). Moreover, the kinetic parameters estimated were consistent physically and statistically.CONCLUSIONNumerical results show that the proposed model can satisfactorily describe the dynamic behavior of the key variables of the three types of agro‐industrial waste evaluated. Therefore, the proposed model might be advantageous to determine the conditions that maximize performance without risking process stability. © 2022 Society of Chemical Industry (SCI).

A Stage-Structured Continuous-/Discrete-Time Population Model: Persistence and Spatial Spread.

Population persistence and spatial propagation and their dependence on demography and dispersal are of great importance in spatial ecology. Many species with highly structured life cycles invade new habitats through the dispersal of organisms in their early life stages (e.g., seeds, larvae, etc.). We develop a stage-structured continuous/discrete-time hybrid model to describe the spatiotemporal dynamics of such species, in which a reaction-diffusion equation describes the random movement of dispersing individuals, while two difference equations describe the demography of sedentary individuals. We obtain a formula for the spreading speed of the population in terms of model parameters. We show that the spreading speed can be characterized as the slowest wave speed of a class of traveling wave solutions. We provide an explicit formula for the critical domain size that separates population persistence from extinction. By comparing our stage-structured model with a physically unstructured model, we find that the structured model reduces to the unstructured one in some special cases. Accordingly, the results about the spreading speed and the critical domain size for the unstructured model represent some special cases of those for the structured one. This highlights the significance of including stage structure in studying the spatial dynamics of species with complex life cycles.

Numerical Weather Prediction at 200 m Local Resolution Based on an Unstructured Grid Global Model

AbstractIt is a great challenge to reach sub‐km resolution in numerical weather prediction models without using domain nesting techniques. The Model for Prediction Across Scales – Atmosphere (MPAS‐A) uses an unstructured grid to achieve smooth transition of resolution in a global model. However, a number of model physical parameterization schemes and methods to enhance computational efficiency assume resolution down to about 3 km only. We extended the MPAS‐A model to support resolution down to 200 m and carried out idealized and hindcast numerical experiments over a domain covering Hong Kong. The Shin‐Hong Planetary Boundary Layer scheme was integrated to parameterize partially resolved turbulence in a scale‐aware sense. The quality of static geographical data and land‐water boundary for a coastal urban locale are found to be vital to the forecast accuracy of ground temperature and humidity forecast. Resolving terrain to 200 m, the model is found to be able to simulate the sheltering effect of mountains as well as high wind speed at hill top comparable to station observation. Our results indicate that global model simulations with an unstructured grid can realistically reproduce local weather conditions, suitable for ultra‐high‐resolution predictions and studies.

Using Forward and Backward Particle Tracking Approaches to Analyze Impacts of a Water Intake on Ichthyoplankton Mortality in the Appomattox River

Municipal intakes of surface water have various uses, and their impacts on the aquatic environment and ecosystem, such as the impingement and entrainment of ichthyoplankton, are a major concern. A robust assessment of the intake impacts on ichthyoplankton in a system generally requires modeling efforts that can simulate the transport and dispersal pathways of the ichthyoplankton. However, it is challenging to simulate hydrodynamics with a high-resolution grid at the scale needed for intake screen sizes in a large system. In this study, a 3D unstructured grid model with a fine resolution grid (<1 m) was developed to investigate potential impacts of an intake on aquatic resources in a tidal freshwater estuary. This approach enables us to directly estimate intake-induced mortality. With the use of the coupled particle-tracking model, we evaluated the total and maximum daily removal rates of particles by the intake that can be used to estimate percent mortality of ichthyoplankton. We further investigated how impacts from the intake vary with spawning locations, flow conditions, and vertical migration velocity of ichthyoplankton. A risk assessment was conducted based on designed flow of water withdrawals. This approach is widely applicable and can address impacts of water intakes in other systems.

Predictive biophysical models of bivalve larvae dispersal in Scotland

In Scotland, bivalves are widely distributed. However, their larvae dispersion is still largely unknown and difficult to assess in situ. And, while Mytilus spp. dominate shellfish production, it is mostly dependent on natural spat recruitment from wild populations. Understanding the larval distribution pattern would safeguard natural resources while also ensuring sustainable farming practises. The feasibility of a model that simulates biophysical interactions between larval behaviour and ocean motions was investigated. We employed an unstructured tri-dimensional hydrodynamic model (finite volume coastal ocean model) to drive a particle tracking model, where prediction of larval movement and dispersal at defined locations might aid in population monitoring and spat recruitment. Our findings reveal a strong link between larval distribution and meteorological factors such as wind forces and currents velocity. The model, also, depicts a fast and considerable larval movement, resulting in a substantial mix of plankton and bivalve larvae, forming a large connection between the southern and northern regions of Scotland’s West coast. This enables us to forecast the breeding grounds of any area of interest, potentially charting connectivity between cultivated and wild populations. These results have significant implications for the dynamics of ecologically and economically important species, such as population growth and loss, harvesting and agricultural management in the context of climate change, and sustainable shellfish fisheries management. Furthermore, the observations on Scottish water flow suggest that tracking particles with similar behaviour to bivalve larvae, such as other pelagic larval stages of keystone species and potential pathogens such as sea lice, may have policy and farming implications, as well as disease control amid global warming issues.

Two-dimensional Inversion of DC Resistivity Data on Unstructured Grids Using Fuzzy C-means Clustering Model Constraint

Direct current resistivity prospecting is a commonly geophysical method for environmental and engineering applications. In this paper, we propose a fuzzy C-means clustering model constrained inversion algorithm for two-dimensional DC resistivity. To fit arbitrary geological structure and surface of the earth, our inversion algorithm is developed based on unstructured model mesh. To be consistent with the geological structure, the fuzzy C-means clustering model constraint is added to the inversion cost function with the minimum structure model constraint, and the Gauss-Newton optimization method is used to seek solutions of the nonlinear inverse problem. Finally, we verify the performance of our algorithm by synthetic and field data sets. The results show that the resistivity and boundary can be better restored when the correct number and value of priori cluster centers were set. By testing the field data, the inversion algorithm can obtain obvious abnormal boundaries.

Structural variability, implementational irregularities in mathematical modelling of polyhydroxyalkanoates (PHAs) production—A state‐of‐the‐art review

The rate and extent of microbial polyhydroxyalkanoates (PHAs) production rely on the availability of substrates, growth of microbial biomass, and intracellular accumulation of polymer under nitrogen-limited conditions. The dynamics of PHAs production captured through various structured or unstructured models can be extended to design an optimal feeding strategy for process intensification. Large variability in process assumptions, choices of kinetics, and model complexity is expected depending on substrate(s), microbial metabolism, and discretization of the process under consideration. This communication attempts to review the estimation of stoichiometric yield coefficients, metabolic modelling, and choices of unstructured kinetics in microbial PHA production. Implementational irregularities in parameter estimation and quality check in modelling exercises have also been reviewed. It is observed that the scope of the majority of the "modelling" studies is confined to the estimation of stoichiometric parameters with limited utility. In dynamic models, microbial growth is often described using either Monod or logistic variants, while PHAs production adopts a Luedeking-Piret expression with or without substrate inhibition. Though model selection, regression with experimental data, parameter estimation, and model validation are integral parts of the exercise, very few provide sufficient coverage on all those aspects. Application of the model to control or optimize the bioprocess has rarely been attempted.

The problem of closure and questioning attitudes

The problem of closure for the traditional unstructured possible worlds model of attitudinal content is that it treats belief and other cognitive states as closed under entailment, despite apparent counterexamples showing that this is not a necessary property of such states. One solution to this problem, which has been proposed recently by several authors (Schaffer in: Hawthorne and Gendler (eds) Oxford studies in epistemology, Oxford University Press, Oxford, pp 235–271, 2005; Yalcin in Philos Phenomenol Res 97(1):23–47, 2018; Hoek in: Kindermann (ed) Unstructured content, Oxford University Press, Oxford, forthcoming), is to restrict closure in an unstructured setting by treating propositional attitudes as question-sensitive. Here I argue that this line of response is unsatisfying as it stands because the problem of closure is more general than is typically discussed. A version of the problem recurs for attitudes like wondering, entertaining, considering, and so on, which are directed at questions rather than propositions. For such questioning attitudes, the appeal to question-sensitivity is much less convincing as a solution to the problem of closure.

Waves in waterways generated by moving pressure field in Boussinesq equations using unstructured finite element model

A finite element model for depth integrated form of Boussinesq equations is presented. The equations are solved on an unstructured triangular mesh using standard Galerkin method with mixed interpolation scheme. The elemental integrals are calculated analytically and time-stepping is done using Runge–Kutta 4th order method. It is extended to simulate ship-generated waves using moving pressure fields. The unstructured formulation provides the flexibility of mesh refinement as needed, for capturing wave transformation or moving pressure field. The model is verified against experimental and numerical results for wave transformation over the Whalin shoal. The results for moving pressure field are compared against numerical results from FUNWAVE. Further, a simulation of ship navigating a curved path is presented. Finally, a real-life application and validation against field measurements is provided for waves generated by a fast ferry moving along a GPS tracked path in Tallinn Bay, Estonia.