Pilot-scale crossflow-microfiltration and pasteurization to remove spores of Bacillus anthracis (Sterne) from milk

High-temperature, short-time pasteurization of milk is ineffective against spore-forming bacteria such as Bacillus anthracis (BA), but is lethal to its vegetative cells. Crossflow microfiltration (MF) using ceramic membranes with a pore size of 1.4 μm has been shown to reject most microorganisms from skim milk; and, in combination with pasteurization, has been shown to extend its shelf life. The objectives of this study were to evaluate MF for its efficiency in removing spores of the attenuated Sterne strain of BA from milk; to evaluate the combined efficiency of MF using a 0.8-μm ceramic membrane, followed by pasteurization (72°C, 18.6 s); and to monitor any residual BA in the permeates when stored at temperatures of 4, 10, and 25°C for up to 28 d. In each trial, 95 L of raw skim milk was inoculated with about 6.5 log10 BA spores/mL of milk. It was then microfiltered in total recycle mode at 50°C using ceramic membranes with pore sizes of either 0.8 μm or 1.4 μm, at crossflow velocity of 6.2 m/s and transmembrane pressure of 127.6 kPa, conditions selected to exploit the selectivity of the membrane. Microfiltration using the 0.8-μm membrane removed 5.91 ± 0.05 log10 BA spores/mL of milk and the 1.4- μm membrane removed 4.50 ± 0.35 log10 BA spores/ mL of milk. The 0.8-μm membrane showed efficient removal of the native microflora and both membranes showed near complete transmission of the casein proteins. Spore germination was evident in the permeates obtained at 10, 30, and 120 min of MF time (0.8-μm membrane) but when stored at 4 or 10°C, spore levels were decreased to below detection levels (≤0.3 log10 spores/mL) by d 7 or 3 of storage, respectively. Permeates stored at 25°C showed coagulation and were not evaluated further. Pasteurization of the permeate samples immediately after MF resulted in additional spore germination that was related to the length of MF time. Pasteurized permeates obtained at 10 min of MF and stored at 4 or 10°C showed no growth of BA by d 7 and 3, respectively. Pasteurization of permeates obtained at 30 and 120 min of MF resulted in spore germination of up to 2.42 log10 BA spores/mL. Spore levels decreased over the length of the storage period at 4 or 10°C for the samples obtained at 30 min of MF but not for the samples obtained at 120 min of MF. This study confirms that MF using a 0.8-μm membrane before high-temperature, short-time pasteurization may improve the safety and quality of the fluid milk supply; however, the duration of MF should be limited to prevent spore germination following pasteurization

Game Interactive Learning: A New Paradigm towards Intelligent Decision-Making

Decision-making plays an essential role in various real-world systems like automatic driving, traffic dispatching, information system management, and emergency command and control. Recent breakthroughs in computer game scenarios using deep reinforcement learning for intelligent decision-making have paved decision-making intelligence as a burgeoning research direction. In complex practical systems, however, factors like coupled distracting features, long-term interact links, and adversarial environments and opponents, make decision-making in practical applications challenging in modeling, computing, and explaining. This work proposes game interactive learning, a novel paradigm as a new approach towards intelligent decision-making in complex and adversarial environments. This novel paradigm highlights the function and role of a human in the process of intelligent decision-making in complex systems. It formalizes a new learning paradigm for exchanging information and knowledge between humans and the machine system. The proposed paradigm first inherits methods in game theory to model the agents and their preferences in the complex decision-making process. It then optimizes the learning objectives from equilibrium analysis using reformed machine learning algorithms to compute and pursue promising decision results for practice. Human interactions are involved when the learning process needs guidance from additional knowledge and instructions, or the human wants to understand the learning machine better. We perform preliminary experimental verification of the proposed paradigm on two challenging decision-making tasks in tactical-level War-game scenarios. Experimental results demonstrate the effectiveness of the proposed learning paradigm

Prediction of in-flight particle properties and mechanical performances of HVOF-sprayed NiCr-Cr3C2 coatings based on a hierarchical neural network

High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr-Cr3C2 coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future.Natural Science Foundation of Jiangsu Higher Education Institution of ChinaKey Laboratory of Green Fabrication and Surface Technology of Advanced Metal MaterialsHumboldt Fellowshi

Anisotropic hyperelastic strain energy function for carbon fiber woven fabrics

The present paper introduces an innovative strain energy function (SEF) for incompressible anisotropic fiber-reinforced materials. This SEF is specifically designed to understand the mechanical behavior of carbon fiber-woven fabric. The considered model combines polyconvex invariants forming an integrity basisin polynomial form, which is inspired by the application of Noether’s theorem. A single solution can be obtained during the identification because of the relationship between the SEF we have constructed and the material parameters, which are linearly dependent. The six material parameters were precisely determined through a comparison between the closed-form solutions from our model and the corresponding tensile experimental data with different stretching ratios, with determination coefficients consistently reaching a remarkable value of 0.99. When considering only uniaxial tensile tests, our model can be simplified from a quadratic polynomial to a linear polynomial, thereby reducing the number of material parameters required from six to four, while the fidelity of the model’s predictive accuracy remains unaltered. The comparison between the results of numerical calculations and experiments proves the efficiency and accuracy of the method.The People’s Republic of China is acknowledged for its financial support through a grant on the National Natural Science Foundation of China (No. 12302081), the China Postdoctoral Science Foundation (No. 2023M740743), and Natural Science Foundation of Guangdong Province of China (No. 2024A1515012418).National Natural Science Foundation of ChinaChina Postdoctoral Science FoundationNatural Science Foundation of Guangdong Province of Chin

Méthodes éléments finis avancées appliquées à la modélisation de tissus biologiques en biomécanique

This thesis has focused on the construction of strain energy densities for describing the non-linear behavior of anisotropic materials such as biological soft tissues (ligaments, tendons, arterial walls, etc.) or fiber-reinforced rubbers. The densities we have proposed have been developed with the mathematical theory of invariant polynomials, particularly the Noether theorem and the Reynolds operator. Our work involved two types of anisotropic materials, the first with a single fiber family and the second with a four-fiber family. The concept of polyconvexity has also been studied because it is well known that it plays an important role for ensuring the existence of solutions. In the case of a single fiber family, we have demonstrated that it is impossible for a polynomial density of any degree to predict shear tests with a loading parallel and then perpendicular to the direction of the fibers. A linear polynomial density combined with a power-law function allowed to overcome this problem. In the case of a material made of a four-fiber family, a polynomial density allowed to correctly predict bi-axial tensile test data extracted from the literature. The two proposed densities were implemented in C++ language in the university finite element software FER by adopting a total Lagrangian formulation. This implementation has been validated by comparisons with reference analytical solutions exhibited in the case of simple loads leading to homogeneous deformations. More complex three-dimensional examples, involving non-homogeneous deformations, have also been studied.Cette thèse a porté sur la construction de densités d'énergie de déformation permettant de décrire le comportement non linéaire de matériaux anisotropes tels que les tissus biologiques souples (ligaments, tendons, parois artérielles etc.) ou les caoutchoucs renforcés par des fibres. Les densités que nous avons proposées ont été élaborées en se basant sur la théorie mathématique des polynômes invariants et notamment sur le théorème de Noether et l'opérateur de Reynolds. Notre travail a concerné deux types de matériaux anisotropes, le premier avec une seule famille de fibre et le second avec quatre familles. Le concept de polyconvexité a également été étudié car il est notoire qu'il joue un rôle important pour s'assurer de l'existence de solutions. Dans le cas d'un matériau comportant une seule famille de fibre, nous avons démontré qu'il était impossible qu'une densité polynomiale de degré quelconque puisse prédire des essais de cisaillement avec un chargement parallèle puis perpendiculaire à la direction des fibres. Une densité polynomiale linéaire combinée avec une fonction puissance a permis de contourner cet obstacle. Dans le cas d'un matériau comportant quatre familles de fibre, une densité polynomiale a permis de prédire correctement des résultats d'essai en traction bi-axiale extraits de la littérature. Les deux densités proposées ont été implémentées avec la méthode des éléments finis et en langage C++ dans le code de calcul universitaire FER. Pour se faire, une formulation lagrangienne totale a été adoptée. L'implémentation a été validée par des comparaisons avec des solutions analytiques de référence que nous avons exhibée dans le cas de chargements simples conduisant à des déformations homogènes. Des exemples tridimensionnels plus complexes, impliquant des déformations non-homogènes, ont également été étudiés

Identification of Lactobacillus Strains Capable of Fermenting Fructo-Oligosaccharides and Inulin

Novel probiotic strains that can ferment prebiotics are important for functional foods. The utilization of prebiotics is strain specific, so we screened 86 Lactobacillus strains and compared them to Bifidobacterium breve 2141 for the ability to grow and produce SCFA when 1% inulin or fructo-oligosaccharides (FOS) were provided as the carbon source in batch fermentations. When grown anaerobically at 32 °C, ten Lactobacillus strains grew on both prebiotic substrates (OD600 ≥ 1.2); while Lactobacillus coryniformis subsp. torquens B4390 grew only in the presence of inulin. When the growth temperature was increased to 37 °C to simulate the human body temperature, four of these strains were no longer able to grow on either prebiotic. Additionally, L. casei strains 4646 and B441, and L. helveticus strains B1842 and B1929 did not require anaerobic conditions for growth on both prebiotics. Short-chain fatty acid analysis was performed on cell-free supernatants. The concentration of lactic acid produced by the ten Lactobacillus strains in the presence of prebiotics ranged from 73–205 mM. L. helveticus B1929 produced the highest concentration of acetic acid ~19 mM, while L. paraplantarum B23115 and L. paracasei ssp. paracasei B4564 produced the highest concentrations of propionic (1.8–4.0 mM) and butyric (0.9 and 1.1 mM) acids from prebiotic fermentation. L. mali B4563, L. paraplantarum B23115 and L. paracasei ssp. paracasei B4564 were identified as butyrate producers for the first time. These strains hold potential as synbiotics with FOS or inulin in the development of functional foods, including infant formula

A new hyperelastic strain energy function and integrity basis of invariants for modelling transversely isotropic materials

International audienceThe present paper proposes a new Strain Energy Function (SEF) for incompressible transversely isotropic hyperelastic materials, i.e. materials with a single fiber family. This SEF combines polyconvex invariants forming an integrity basis (Ta et al., 2014) in a polynomial and exponential form. Compared to a previous attempt for building a SEF based on the same invariants (Cai et al., 2016), we have reduced the number of material parameters from 23 to 10, without losing any accuracy on the numerical results. The 10 material parameters are identified by comparing the closed form solutions deriving from our model with experimental and numerical data extracted from the literature. These data concern uniaxial tension and shear tests, both parallel and transverse to the fiber direction (Ciarletta et al., 2011; Davis and De Vita, 2014) [3, 4], as well as shear calculations with 9 different fiber angles (Horgan and Murphy, 2017) [5]. Due to the variety of the considered situations, we have developed specific identification strategies based on: 1) the linear or nonlinear nature of the material parameters of the model; 2) the modeling of the free boundary conditions by a spectral approach

A simple polyconvex strain energy density with new invariants for modeling four-fiber family biomaterials

International audienceWe introduce in this paper a new hyperelastic model for the prediction of nonlinear mechanical properties of anisotropic hyperelastic materials under biaxial stretching. The proposed strain energy function (SEF) can be applied for understanding the nature of behavior laws for materials with four-fiber family structures, which has a large potential of applications, particularly in biomechanics, surgical and interventional therapies for peripheral artery disease (PAD). This SEF is built with a recent and new invariant system based on the mathematical theory of invariant polynomials. By recombining them in an appropriate manner, we demonstrate that it is possible to build a polyconvex integrity basis of invariants. Accuracy and reliability of the corresponding numerical model were validated by a comparison with experimental and numerical results extracted from Kamenskiy et al. (2014). 1 1We warmly thank Assistant Professor Kamenskiy to have kindly provided us the numerical data corresponding to the measurements included in These results concerned diseased superficial femoral (SFA), popliteal (PA) and tibial arteries (TA) from one patient under planar biaxial extension. For each kind of arteries tested with 5 combinations of different biaxial stretches, the predicted results of the proposed model and the experimental data are consistent. Our model includes 7 material parameters and their identification result in a single solution because of the linear form we have chosen for the SEF with respect to the material parameters