Function Formulation Research Articles

Probiotic Yoghurt Enriched with Mango Peel Powder: Biotransformation of Phenolics and Modulation of Metabolomic Outputs after In Vitro Digestion and Colonic Fermentation.

This study investigated the health-promoting effects and prebiotic functions of mango peel powder (MPP) both as a plain individual ingredient and when incorporated in yoghurt during simulated digestion and fermentation. The treatments included plain MPP, plain yoghurt (YA), yoghurt fortified with MPP (YB), and yoghurt fortified with MPP and lactic acid bacteria (YC), along with a blank (BL). The identification of polyphenols in the extracts of insoluble digesta and phenolic metabolites after the in vitro colonic fermentation were performed employing LC-ESI-QTOF-MS2. These extracts were also subjected to pH, microbial count, production of SCFA, and 16S rRNA analyses. The characterisation of phenolic profiles identified 62 phenolic compounds. Among these compounds, phenolic acids were the major compounds that underwent biotransformation via catabolic pathways such as ring fission, decarboxylation, and dehydroxylation. Changes in pH indicated that YC and MPP reduced the media pH from 6.27 and 6.33 to 4.50 and 4.53, respectively. This decline in pH was associated with significant increases in the LAB counts of these samples. The Bifidobacteria counts were 8.11 ± 0.89 and 8.02 ± 1.01 log CFU/g in YC and MPP, respectively, after 72 h of colonic fermentation. Results also showed that the presence of MPP imparted significant variations in the contents and profiles of individual short chain fatty acids (SCFA) with more predominant production of most SCFA in the MPP and YC treatments. The 16s rRNA sequencing data indicated a highly distinctive microbial population associated with YC in terms of relative abundance. These findings suggested MPP as a promising ingredient for utilisation in functional food formulations aiming to enhance gut health.

Triaxial compression failure criterion and constitutive relation of concrete containing multiple solid wastes

This study analyzed the mechanical properties and failure criterion of full-solid waste concrete (SCT) made with slag, fly ash, carbide slag, soda residue, and iron tailings aggregate. SCT showed a higher strength growth rate (25.5–57.7%) compared to natural sand and gravel as aggregate (SCN) (20.7–47.5%), but lower than cement + natural sand and gravel (PCN) (32.0–86.1%), due to the latter's susceptibility to microcracks. The study proposed a parabolic pressure meridian failure criterion equation and an improved power function formulation of the Mohr-Coulomb failure criterion for SCT. The findings suggest that SCT can be used in engineering applications.

МОДЕЛЮВАННЯ ДИНАМІКИ ТЕМПЕРАТУРИ СУБСТРАТУ ТА ПОВІТРЯ УГРИБНИЦІ

Economic efficiency of greenhouse vegetable growing depends quite significantly on the cost of energy carriers, which is why the introduction of energy-saving technologies in greenhouse vegetable growing is an urgent issue. One of the ways to save energy resources can be the use of a closed ventilation system of the "plant greenhouse - mushroom greenhouse" type, which is based on the opposite type of respiration of plants and mushrooms. A closed ventilation system includes air exchange between the greenhouse with growing plants and the cultivation room for growing mushrooms. The closed ventilation system allows you to save energy by reducing the heating of the incoming air, as well as increasing the yield of vegetable products due to the increased concentrations of carbon dioxide in the air that flows from the cultivation room for mushrooms to the greenhouse and mushrooms due to the increased concentrations of oxygen in the air that flows into the cultivation room for mushrooms from the greenhouse. Mathematical modeling of the process of heat transfer between greenhouses makes it possible to simulate transitional processes between rooms in order to assess the quality and accuracy of regulation, as well as to evaluate the parameters of the object in transitional modes. Mathematical modeling of dynamic processes is the basis for the formulation of transfer functions for the automatic control system. As a result of the study, mathematical models of the temperature dynamics of the substrate of mushrooms and greenhouse vegetables were obtained due to the analytical solution of the system of differential equations. The adequacy of the solution was verified by the Runge-Kutta method and compared with experimental data. The difference between the theoretical and experimental values is not significant and amounted to -3 % for the substrate temperature and -3.2 % for the air temperature.

A Novel Fitness Function to Enhance the Performance of Genetic Algorithm Optimization of Micro-strip Patch-antenna Dimensions for 5 GHz

Micro-strip patch antennas (MSPAs) use standard design equations involving multiple design variables. The theoretically obtained design parameters may not conform to the desired requirements in an experimental setup. The trial-and-error method of arriving at the optimum values from the calculated values may not always be fruitful and may be difficult or even impossible when multiple variables need to be optimized. The results may not guarantee the best solution as better solutions exist. Therefore, MSPA dimensions are optimized for better results using a genetic algorithm (GA). Optimization is centered on the formulation of the fitness function. The key to success lies in adequately formulating the fitness function, considering the design variables that need to be optimized to achieve the desired output. A novel fitness function is proposed that uses graded penalties to guide a GA to converge to better results during optimization. Optimization of patch-antenna dimensions using the novel fitness function returned a patch antenna resonating at exactly 5 GHz with a bandwidth of 300 GHz while improving the return loss from - 24.16 dB to -34.79 dB. The role of correctly formed fitness functions in guiding GA towards better results was established.

Nonlinear extension of the Kolosov-Muskhelishvili stress function formalism.

The method of stress function in elasticity theory is a powerful analytical tool with applications to a wide range of physical systems, including defective crystals, fluctuating membranes, and more. A complex coordinates formulation of stress function, known as the Kolosov-Muskhelishvili formalism, enabled the analysis of elastic problems with singular domains, particularly cracks, forming the basis for fracture mechanics. A shortcoming of this method is its limitation to linear elasticity, which assumes Hookean energy and linear strain measure. Under finite loads, the linearized strain fails to describe the deformation field adequately, reflecting the onset of geometric nonlinearity. The latter is common in materials experiencing large rotations, such as regions close to the crack tip or elastic metamaterials. While a nonlinear stress function formalism exists, the Kolosov-Muskhelishvili complex representation had not been generalized and remained limited to linear elasticity. This paper develops a Kolosov-Muskhelishvili formalism for the nonlinear stress function. Our formalism allows us to port methods from complex analysis to nonlinear elasticity and to solve nonlinear problems in singular domains. Upon implementing the method to the crack problem, we discover that nonlinear solutions strongly depend on the applied remote loads, excluding a universal form of the solution close to the crack tip and questioning the validity of previous studies of nonlinear crack analysis.

Fast uncertainty reduction of chemical kinetic models with complex spaces using hybrid response-surface networks

Response-surface (RS) surrogate approaches permit efficient inverse uncertainty quantification (UQ) of combustion kinetic models, wherein the uncertainty of reaction rates is reduced from observed targets. For kinetic models with parameters characterized by large uncertainty factors, strong nonlinearities, and reaction couplings (e.g., reduced mechanisms of real fuels; such models are referred to be “complex” in this work), the global RS is difficult to approximate, precluding conventional surrogate approaches. This paper proposes a framework that is extendable to such systems, termed Hybrid Response Surface Networks followed by a Stochastic Gradient Descent Ensemble (HRSN-SGDE). This technique focuses on mapping the local RS of just the uncertain spaces in the vicinity of the observed target, referred to as the rate target subspace. Two neural network surrogates are considered: a classifier that predicts the probability of data residing in the rate target subspace and a local RS surrogate which maps the RS of this subspace. A hybrid surrogate loss function is then defined using these surrogates to optimize uncertain rates repeatedly to get an ensemble of solutions representing the constrained rate space. HRSN-SGDE is demonstrated on a complex jet fuel model developed using the hybrid chemistry (HyChem) approach with a low temperature chemistry sub-model using a series of ignition delay times as targets. Results show that the method's local RS objective enables efficient and accurate construction of the surrogates through active learning-based sampling. Also, the unique formulation of the surrogate loss function enables optimization that is robust to suboptimal local minima and faster than evolutionary algorithms by several orders of magnitude. It is shown that HRSN-SGDE method is highly efficacious and suitable to conducting inverse UQ on such complex kinetic models.

An enhanced k-[formula omitted] SST model to predict airflows around isolated and urban buildings

The goal of this research is to improve and validate a Reynolds Averaged Navier–Stokes (RANS) turbulence model to perform accurate Computational Fluid Dynamics (CFD) simulations of the urban wind flow. The k-ω SST model is selected for calibration since its blended formulation holds remarkable optimization potential and has increased relevancy in recent studies in the field. A simulation-based optimization approach recalibrates the model closure constants by minimizing the prediction error of wind pressure coefficients on an isolated cubical building because this scenario contains many salient features observed in the flow in actual urban areas. The optimization procedure ensures both the coherence of calibrated model constants involved in the wall function formulations and the relationship between them to satisfy the flow horizontal homogeneity of the atmospheric boundary layer. The tuned closure coefficients increase momentum diffusion in the wake, resulting in shorter and more accurate predictions of the reattachment lengths. Validation case studies with wind tunnel measurement data from various urban scenarios were addressed to comprehensively assess the adaptability of the optimal set of coefficients reached. The results confirm that CFD predictions with the optimized model are consistently in closer agreement with experimental data than the standard version of k-ω SST. The root mean square errors are reduced by about 75% in pressure, 40% in velocity, and 20% in turbulent kinetic energy.

Characterisation of multi-layered structures using a vector-based gradient descent algorithm at terahertz frequencies.

Material characterisation and imaging applications using terahertz radiation have gained interest in the past few years due to their enormous potential for industrial applications. The availability of fast terahertz spectrometers or multi-pixel terahertz cameras has accelerated research in this domain. In this work, we present a novel vector-based implementation of the gradient descent algorithm to fit the measured transmission and reflection coefficients of multilayered objects to a scattering parameter-based model, without requiring any analytical formulation of the error function. We thereby extract thicknesses and refractive indices of the layers within a maximum 2% error margin. Using the precise thickness estimates, we further image a 50 nm-thick Siemens star deposited on a silicon substrate using wavelengths larger than 300 µm. The vector-based algorithm heuristically finds the error minimum where the optimisation problem cannot be analytically formulated, which can be utilised also for applications outside the terahertz domain.

Seismic fragility matrices for large scale probabilistic structural safety assessment

Structural safety assessment of urbanised areas exposed to seismic risk is a complex task and can be approached at different scales. Fragility functions are the most effective tool to perform an estimation of the structural risk, since they describe the exceeding probability of the limit state of interest given a level of ground shaking. The formulation of a fragility function is based on the selection of an Intensity Measure of the ground shaking, commonly represented by peak-ground or spectral parameters or by macroseismic intensity grades. In the present paper, a novel approach to estimate the seismic vulnerability at a large scale of urbanised areas is presented. The scenario earthquakes are selected according to the current formulation of the seismic hazard disaggregation provided by National and International codes, i.e. through the disaggregation in terms of magnitude (M), epicentral distance (R) and number of standard deviation from the median ground motion (ε). To this aim, a vectorial seismic Intensity Measure is assumed to compute structural safety at large scale, and a change of variables is made to turn the traditional fragility curve into a matrix. Typological fragility matrices are processed and combined to define the so-called Urban Fragility Matrices, representative of the vulnerability of selected urban sectors. The procedure is then implemented and validated referring to the city of Pozzuoli (Southern Italy) and its effectiveness validated with reference to the damage survey relative to the 4th October 1983 seismic event which struck the city, resulting a good matching between predicted and observed scenarios.

Identification of multiple nonlinear lap joints using instantaneous power flow balance

PurposeThe purpose of this paper is to apply the novel instantaneous power flow balance (IPFB)-based identification strategy to a specific practical situation like nonlinear lap joints having single and double bolts. The paper also investigates the identification performance of the proposed power flow method over conventional acceleration-matching (AM) methods and other methods in the literature for nonlinear identification.Design/methodology/approachA parametric model of the joint assembly formulated using generic beam element is used for numerically simulating the experimental response under sinusoidal excitations. The proposed method uses the concept of substructure IPFB criteria, whereby the algebraic sum of power flow components within a substructure is equal to zero, for the formulation of an objective function. The joint parameter identification problem was treated as an inverse formulation by minimizing the objective function using the Particle Swarm Optimization (PSO) algorithm, with the unknown parameters as the optimization variables.FindingsThe errors associated with identified numerical results through the instantaneous power flow approach have been compared with the conventional AM method using the same model and are found to be more accurate. The outcome of the proposed method is also compared with other nonlinear time-domain structural identification (SI) methods from the literature to show the acceptability of the results.Originality/valueIn this paper, the concept of IPFB-based identification method was extended to a more specific practical application of nonlinear joints which is not reported in the literature. Identification studies were carried out for both single-bolted and double-bolted lap joints with noise-free and noise-contamination cases. In the current study, only the zone of interest (substructure) needs to be modelled, thus reducing computational complexity, and only interface sensors are required in this method. If the force application point is outside the substructure, there is no need to measure the forcing response also.

A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

One major cost of improving the automotive fuel economy while simultaneously reducing tailpipe emissions is increased powertrain complexity. This complexity has consequently increased the resources (both time and money) needed to develop such powertrains. Powertrain performance is heavily influenced by the quality of the controller/calibration. Since traditional control development processes are becoming resource-intensive, better alternate methods are worth pursuing. Recently, reinforcement learning (RL), a machine learning technique, has proven capable of creating optimal controllers for complex systems. The model-free nature of RL has the potential to streamline the control development process, possibly reducing the time and money required. This article reviews the impact of choices in two areas on the performance of RL-based powertrain controllers to provide a better awareness of their benefits and consequences. First, we examine how RL algorithm action continuities and control–actuator continuities are matched, via native operation or conversion. Secondly, we discuss the formulation of the reward function. RL is able to optimize control policies defined by a wide spectrum of reward functions, including some functions that are difficult to implement with other techniques. RL action and control–actuator continuity matching affects the ability of the RL-based controller to understand and operate the powertrain while the reward function defines optimal behavior. Finally, opportunities for future RL-based powertrain control development are identified and discussed.

A meshless generalized finite difference scheme for the stream function formulation of the Naiver-Stokes equations

In this study, a meshless numerical scheme, which is the combination of the generalized finite difference method, the fictitious-nodes technique, and the two-step Newton-Raphson method, was proposed to solve the stream function formulation of the Navier-Stokes equations. To reduce the numbers of unknowns and partial differential equations for the two-dimensional flow field of a viscous incompressible fluid, the stream function formulation, which is a fourth-order nonlinear partial differential equation with only one unknown variable, can be derived by introducing the definition of vorticity and stream function. By applying the generalized finite difference method, the derivatives in the stream function formulation can be simply expressed as a linear combination of functional data and weighting values at several nearest nodes. To deal with the two boundary conditions for the fourth-order partial differential equations, the fictitious-nodes technique was utilized to set up the fictitious nodes outside the computational domain, and cooperate with the generalized finite difference method to form a system of nonlinear algebraic equations. The aforementioned numerical procedure can yield a square Jacobian matrix, so the Newton-Raphson method can be adopted to efficiently acquire the numerical solutions. Several numerical examples were tested to verify the practicability of the proposed meshless numerical scheme.

Linguistic Diplomacy: Roman Jakobson between East and West, 1956-68.

Roman Jakobson remains a crucial figure in the history of linguistics and literary criticism. This paper explores how a mid-twentieth-century intellectual curated his own legacy across Cold War divides. Thinking with Jakobson's own formulation of communication functions, this paper argues for a connection between the success of particular structuralist ideas in academic contexts and the tactical efforts of individual scholars embedded in scholarly institutions. Considered in this light, investigating Roman Jakobson's late career during the period known as the Thaw can move beyond "vulgar biographism" to a better understanding of the different fates of formalism between East and West.

Economic optimization and predictive control for nonlinear systems using Lyapunov based nonlinear cost function design

Optimal control of physical processes depends on the formulation of objective functions and contemplation of appropriate predictive control. In this article, we propose a new framework, to blend the concepts of the Lyapunov-based economic model predictive control (Ly-EMPC) scheme with the novel cost-efficient profit function design. Furthermore, Lyapunov’s stability criteria is employed to verify the existence of duality and optimality for the designed economic stage costs. This method differs from the existing EMPC techniques by implementing Lagrange multipliers in the rotated cost function as a measure of stability. The closed-loop performance using the regularized Ly-EMPC is validated against a standard tracking objective-based nonlinear MPC for three benchmark examples through an econometrics gauge factor. Interestingly, in contrast to tracking NMPC, the proposed Ly-EMPC scheme excels in economic performance, while the formation of nonlinear economic stage cost is conceptually simpler, novel, and practically profitable.

Antidiabetic potential of Chlorella pyrenoidosa functional formulations in streptozocin-induced type 2 diabetic mice

Antidiabetic potential of Chlorella pyrenoidosa functional formulations in streptozocin-induced type 2 diabetic mice

Manufacturing an Exact Solution for 2D Thermochemical Mantle Convection Models

AbstractIn this study, we manufacture an exact solution for a set of 2D thermochemical mantle convection problems. The derivation begins with the specification of a stream function corresponding to a non‐stationary velocity field. The method of characteristics is then applied to determine an expression for composition consistent with the velocity field. The stream function formulation of the Stokes equation is then applied to solve for temperature. The derivation concludes with the application of the advection‐diffusion equation for temperature to solve for the internal heating rate consistent with the velocity, composition, and temperature solutions. Due to the large number of terms, the internal heating rate is computed using Maple™, and code is also made available in Fortran and Python. Using the method of characteristics allows the compositional transport equation to be solved without the addition of diffusion or source terms. As a result, compositional interfaces remain sharp throughout time and space in the exact solution. The exact solution presented allows for precision testing of thermochemical convection codes for correctness and accuracy.

Oscillatory Marangoni flow in a deep layer of a Carreau fluid

We study the spreading dynamics of an insoluble and non-diffusive surfactant on the free surface of a deep layer of a Carreau fluid. When a non-uniform distribution of surfactant is imposed on the free surface of an initially motionless fluid, variations in surface tension are induced, causing the fluid motion from regions of lower surface tension to those of higher tension. Such movement redistributes the surfactant concentration until a uniform condition is reached. The two-dimensional momentum and convection–diffusion equations are employed to determine the hydrodynamics in the deep fluid layer and the evolution of the surfactant on the fluid surface. The modified vorticity-stream function formulation is used to solve numerically the hydrodynamic field, where the non-Newtonian dependent variables of the problem are decomposed into a Newtonian part and a non-Newtonian contribution. One of the most critical variables in practical applications regarding this phenomenon concerns the time required for the surfactant to reach a uniform distribution over the interface. The results show that the dimensionless parameters that control the decay of the variations in surfactant concentration in time are the Reynolds number Re, the fluid behavior index n, and ε, reflecting the influence of the inertia of the fluid. The results show that the temporal decay of the surfactant concentration on the fluid surface increases significantly for high values of the Reynolds number, it is more attenuated in pseudoplastic fluids than in Newtonian fluids, and the surfactant concentration decay exhibits asymmetric oscillations when inertial effects increase.

Potential differentiation of successive SARS-CoV-2 mutations by RNA: DNA hybrid analyses

The constant mutation of SARS-CoV-2 has triggered a new round of public health crises and has had a huge impact on existing vaccines and diagnostic tools. It is essential to develop a new flexible method to distinguish mutations to prevent the spread of the virus. In this work, we used the combination of density functional theory (DFT) and non-equilibrium Green's function formulation with decoherence, to theoretically study the effect of viral mutation on charge transport properties of viral nucleic acid molecules. We found that all mutation of SARS-CoV-2 on spike protein was accompanied by the change of gene sequence conductance, this is attributed to the change of nucleic acid molecular energy level caused by mutation. Among them, the mutations L18F, P26S, and T1027I caused the largest conductance change after mutation. This provides a theoretical possibility for detecting virus mutation based on the change of molecular conductance of virus nucleic acid.

A Mathematical Model of Optimal Manufacturer-Retailer Trade Credit Policy with End-User Credit Support

The formulation and incorporation of credit function into trade credit models is a much recent development. In addition extension of this innovation to the end-user has not been achieved. This work models trade credit interaction extension from a manufacturer to a consumer through the retailer. It considers a Stackelberg game-theoretic model setting in which the manufacturer provides channel trade credit through the retailer to the end-user, while the retailer engages in promotion of the product. The paper uses backward induction to obtain the Stackelberg equilibrium for the promotion effort, the retailer’s credit period and the manufacturer’s credit period. It also obtains the long-run Stackelberg equilibria for the decision variables. The paper shows that the retailer is more liberal with allowable credit period than the manufacturer. In general, the players are credit period-liberal with retail margin, and ungenerous with credit period with manufacturer’s margin. It further shows that the manufacturer’s credit period increases more rapidly than the retailer’s credit period with retailer’s margin. On the other hand the manufacturer’s credit period decreases more rapidly than the retailer’s credit period with the manufacturer’s margin.

A Theoretical Framework to Investigate Interdependency in the Assessment of Fire Resilience

Communities and ecosystems may be particularly vulnerable to fire hazard. In addition, modern societies are connected with interdependent infrastructures, and the assessment of their resilience to fire may be extremely challenging. In this regard, fire resilience may be described as the ability to maintain the functionality of infrastructures to deliver services during and after hazard events. This paper considers several typologies of interdependency in order to propose several models that may quantify fire resilience. These models are based on the previous literature and the applications recently proposed for earthquakes. Fire resilience is herein calculated by considering a multi-dimensional formulation of the repair function that depends on time and the different components of the systems. The formulations that are described may be applied for preliminary studies aimed at pre- and post-fire assessments. Many stakeholders may take advantages of such formulations to consider the interconnections between the different infrastructures, their components, and subcomponents subjected to fire hazard.