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The ability to manipulate fluids has always been one of the focuses of scientific research and engineering application. The rapid development of machine learning technology provides a new perspective and method for active flow control. This review presents recent progress in combining reinforcement learning with high-dimensional, non-linear, and time-delay physical information. Compared with model-based closed-loop control methods, deep reinforcement learning (DRL) avoids modeling the complex flow system and effectively provides an intelligent end-to-end policy exploration paradigm. At the same time, there is no denying that obstacles still exist on the way to practical application. We have listed some challenges and corresponding advanced solutions. This review is expected to offer a deeper insight into the current state of DRL-based active flow control within fluid mechanics and inspires more non-traditional thinking for engineering.

In food processing environments, various microorganisms can adhere and aggregate on the surface of equipment, resulting in the formation of multispecies biofilms. Complex interactions among microorganisms may affect the formation of multispecies biofilms and resistance to disinfectants, which are food safety and quality concerns. This article reviews the various interactions among microorganisms in multispecies biofilms, including competitive, cooperative, and neutral interactions. Then, the preliminary mechanisms underlying the formation of multispecies biofilms are discussed in relation to factors, such as quorum-sensing signal molecules, extracellular polymeric substances, and biofilm-regulated genes. Finally, the resistance mechanisms of common contaminating microorganisms to disinfectants in food processing environments are also summarized. This review is expected to facilitate a better understanding of interspecies interactions and provide some implications for the control of multispecies biofilms in food processing. HIGHLIGHTS

Pantothenic acid is an essential metabolite found throughout all branches of life. Although the enzymes responsible for pantothenic biosynthesis have been characterized, those leading to its biodegradation remain poorly understood. In the study described herein, we showed that use of a "genomic enzymology" strategy enabled identification of four biodegradation pathway genes, which were then confirmed by using kinetic analysis of the purified recombinant enzymes encoded in Ochrobactrum anthropi. The reconstituted pathway converts pantothenic acid to β-alanine and (R)-pantoate, and then (R)-pantoate to aldopentoate, which is transformed to (R)-3,3-dimethylmalate and hence to α-ketoisovalerate. The pathway genes are common to Proteobacterial genomes in which they are not colocated.

Salmonella enterica is a prominent foodborne pathogen, including diverse serotypes that are prolific biofilm formers. Its ability to form biofilm can be affected by multiple environmental factors. In this study, the effect of salinity on biofilm formation by S. enterica was evaluated by using two recently isolated strains of Salmonella serotypes Enteritidis and Newport. Although supplementing the growth medium with a low concentration (0.5 to 2%) of sodium chloride (NaCl) slightly enhanced biofilm formation for the strain S. enterica serovar Enteritidis 110, it sharply reduced or abolished biofilm formation by the strain S. enterica serovar Newport 193. This differential effect of salinity on S. enterica strains of different serotypes was poorly correlated with inhibition of planktonic growth but strongly correlated with cell motility. Examining genes known to affect biofilm formation showed that the expression of adrA, csgD, and fliC, which encode proteins required for surface adhesion and cell motility, was significantly downregulated with salinity increase in Salmonella Newport 193 but not in Salmonella Enteritidis 110. Therefore, it is plausible that the differential effect of salinity on biofilm formation by Salmonella Enteritidis 110 and Salmonella Newport 193 resulted from the differential regulation to genes required for cell adherence and motility.

A deep learning framework is proposed for real-time transonic flow prediction. To capture the complex shock discontinuity of transonic flow, we introduce the residual network ResNet and deconvolutional neural networks to learn the nonlinear discontinuity phenomenon in transonic flow, which is affected by the Mach number, angle of attack, Reynolds number, and aerodynamic shape. In our framework, flow field variables on actual grid points are utilized in the neural network training to avoid the interpolation operation and the input of spatial position with a point cloud that is required with traditional convolutional neural networks. To investigate and validate the proposed framework, transonic flows around two-dimensional airfoils and three-dimensional wings are utilized to verify its effectiveness and prediction accuracy. The results prove that the model is able to efficiently learn the transonic flow field under the influence of the Mach number, angle of attack, Reynolds number, and aerodynamic shape. Significantly, some essential physical features, such as shock strength and location, flow separation, and the boundary layer, are accurately captured by this model. Furthermore, it is shown that our framework is able to make accurate predictions of the pressure distribution and aerodynamic coefficients. Thus, the present work provides an efficient and robust surrogate model for computational fluid dynamics simulation that enhances the efficiency of complex aerodynamic shape design optimization tasks and represents a step toward the realization of the digital twin concept.

1-Deoxy-D-sorbitol, the 1-deoxy analogue of D-sorbitol, has been detected in human urine as well as in natural herbs and spices. Although there are sporadic reports about 1-deoxy-D-sorbitol dehydrogenase, the complete catabolic pathway of 1-deoxy-D-sorbitol remains unsolved. Informed by the promiscuous activities of fructose-6-phosphate aldolase (FSA) which is involved in the sorbitol (glucitol) utilization (gut) operon and guided by the large scale bioinformatics analysis, we predicted and then experimentally verified the gut operon encoded by Bacillus licheniformis ATCC14580 is responsible for the catabolism of both D-sorbitol and 1-deoxy-D-sorbitol by in vitro activity assays of pathway enzymes, in vivo growth phenotypes, and transcriptomic studies. Moreover, the phylogenetic distribution analysis suggests that the D-sorbitol and 1-deoxy-D-sorbitol catabolic gene cluster is mostly conserved in members of Firmicutes phylum.

Deep reinforcement learning (RL) is capable of identifying and modifying strategies for active flow control. However, the classic active formulation of deep RL requires lengthy active exploration. This paper describes the introduction of expert demonstration into a classic off-policy RL algorithm, the soft actor-critic algorithm, for application to vortex-induced vibration problems. This combined online-learning framework is applied to an oscillator wake environment and a Navier–Stokes environment with expert demonstration obtained from the pole-placement method and surrogate model optimization. The results show that the soft actor-critic framework combined with expert demonstration enables rapid learning of active flow control strategies through a combination of prior demonstration data and online experience. This study develops a new data-efficient RL approach for discovering active flow control strategies for vortex-induced vibration, providing a more practical methodology for industrial applications.

In the present work, we propose an optimization framework based on the active learning method, which aims to quickly determine the conditions of tandem flapping wings for optimal performance in terms of thrust or efficiency. Especially, multi-fidelity Gaussian process regression is used to establish the surrogate model correlating the kinematic parameters of tandem flapping wings and their aerodynamic performances. Moreover, the Bayesian optimization algorithm is employed to select new candidate points and update the surrogate model. With this framework, the parameter space can be explored and exploited adaptively. Two optimization tasks of tandem wings are carried out using this surrogate-based framework by optimizing thrust and propulsion efficiency. The response surfaces predicted from the updated surrogate model present the influence of the flapping frequency, phase, and separation distance on thrust and efficiency. It is found that the time-average thrust of the hind flapping wing increases with the frequency. However, the increase in frequency may lead to a decrease in propulsive efficiency in some circumstances.

The definition of a magnetic shuttle is introduced to describe the magnetic space enclosed by two magnetic mirrors with the same field direction and high mirror ratio. Helicon plasma immersed in such a magnetic shuttle (mirror ratio 5) that can provide the confinement of charged particles is modeled using an electromagnetic solver. The perpendicular structure of the wave field along this shuttle is given in terms of stream vector plots, showing a significant change from midplane to ending throats, and the vector field rotates and forms a circular layer that separates the plasma column radially into core and edge regions near the throats. The influences of the driving frequency (f = 6.78 MHz–40.68 MHz), plasma density (nemax = 1016 m−3 to 1019 m−3), and field strength (B0max = 0.017 T–1.7 T) on the wave field structure and power absorption are computed in detail. It is found that the wave energy and power absorption decrease for increased driving frequency and reduced field strength and increase significantly when the plasma density is above a certain value. The axial standing-wave feature always exists, due to the interference between forward and reflected waves from ending magnetic mirrors. Distributions of wave energy density and power absorption density all show a shrinking feature from midplane to ending throats, which is consistent with the nature of the helicon mode that propagates along field lines. Theoretical analysis based on a simple magnetic shuttle and the governing equation of helicon waves shows consistency with computed results and previous studies. This hypothetical work is a valuable to guide the helicon physics prototype experiment, which is designed for the fundamental wave–particle interaction study in helicon plasma, to achieve high plasma density and energy absorption efficiency.

The fourth signal of BDS (BeiDou Navigation System) allows for measurements on frequency S. To take advantage of this new signal, an investigation has been carried out on the models and characteris...

High aspect ratio features in silicon have broad applications in micro-electro-mechanical systems, microfluidic control and advanced packaging. This structure is usually fabricated by Bosch process, and the corresponding scallop should be controlled. Obtaining large scallop size seems easier than that of small scallop, however, it is a big challenge to obtain uniform large scallop. Herein, by using dry etching system, we demonstrate a novel high aspect ratio silicon trench with scallop size uniformly larger than 300 nm in both single crystal silicon and polycrystalline silicon. Additionally, the difference between single crystal silicon etching and polycrystalline silicon etching is compared. This work is beneficial to understanding the silicon etching mechanism in Bosch process and has potential applications in microelectronic and microfluidic devices.

Colocation of the genes encoding ABC, TRAP, and TCT transport systems and catabolic pathways for the transported ligand provides a strategy for discovering novel microbial enzymes and pathways. We screened solute-binding proteins (SBPs) for ABC transport systems and identified three that bind d-apiose, a branched pentose in the cell walls of higher plants. Guided by sequence similarity networks (SSNs) and genome neighborhood networks (GNNs), the identities of the SBPs enabled the discovery of four catabolic pathways for d-apiose with eleven previously unknown reactions. The new enzymes include d-apionate oxidoisomerase, which catalyzes hydroxymethyl group migration, as well as 3-oxo-isoapionate-4-phosphate decarboxylase and 3-oxo-isoapionate-4-phosphate transcarboxylase/hydrolase, which are RuBisCO-like proteins (RLPs). The web tools for generating SSNs and GNNs are publicly accessible ( http://efi.igb.illinois.edu/efi-est/ ), so similar ‘genomic enzymology’ strategies for discovering novel pathways can be used by the community. A bioinformatic strategy beginning with solute-binding proteins involved in sugar transport led to the functional annotation of four previously unknown catabolic pathways of the branched pentose d-apiose.

For film cooling in high-pressure turbines, it is vital to predict the temperature distribution and film cooling effectiveness on the blade surface downstream of the cooling hole. This temperature distribution and film cooling effectiveness depend on the interaction between the hot mainstream and the coolant jet. However, it is difficult to correlate accurately due to the complex mechanism. Based on deep learning techniques, a theoretic model using Deconvolutional Neural Network (Deconv NN) was developed to model the non-linear and high-dimensional mapping between coolant jet parameters and the surface temperature distribution on a flat plate. Computational Fluid Dynamics (CFD) was utilized to provide data for the training models. The input of the model includes blowing ratio, density ratio, hole inclination angle and hole diameters etc. With rigorous testing and validation, it is found that the predicted results are in good agreement with results from CFD. It is compared against the existing semi-empirical correlations and other machine learning techniques, such as support vector machine method. Dataset with different size is tested. The results suggest that the performance and robustness of Deconv NN is much better than other methods.

The resistance of food-borne pathogens to many unfavorable environments after the formation of microbial aggregates is significantly enhanced, which is an important reason for the continuous pathogen contamination. Ralstonia insidiosa can induce the food-borne pathogens to form larger multi-species suspended aggregates, but its formation mechanism is unknown, especially from a transcriptomics point of view. In this study, the condition of forming suspended aggregates by Listeria monocytogenes was investigated, and then, Illumina RNA-seq was used to compare the transcription profiles of L. monocytogenes in floating state and suspended aggregates to identify differentially expressed genes (DEGs): 511 DEGs were identified by expression analysis, including 273 up-regulated genes and 238 down-regulated genes. The functional annotation analysis results of DEGs showed that these genes were mainly involved in two-component system, phosphotransferase system, flagellar assembly, ABC transporter, glycolysis/gluconeogenesis. This study is the first report of suspended aggregates RNA-seq, and our results are of great significance to further exploring the resistance mechanism of L. monocytogenes to adverse environment and the control of pathogenic microorganisms.

Egg spoilage is a microecological phenomenon, involving several specific spoilage microorganisms, that is affected by numerous factors. The specific biological characteristics of egg spoilage microorganisms are particularly significant for their survival in eggs. This research focusses on the specific biological characteristics of 24 spoilage bacteria isolated from eggs. Their protease activities, alkalinity tolerance, egg white and lysozyme tolerance, and survival rates in egg whites were analysed. The results showed that the strains of Bacillus spp. had the strongest protease activity, the strains of Staphylococcus spp. had the strongest alkaline pH tolerance, and most of the spoilage bacteria could tolerate an egg white or lysozyme environment. Notably, the survival rate test of egg whites showed that only the strains of Dermacoccus spp. could survive and grow in egg whites. This study of the specific biological characteristics of spoilage microorganisms in eggs aims to extend eggs’ shelf life and provide theoretical guidance for controlling spoilage microorganisms during storage.

In the present study, an effective optimization framework of aerodynamic shape design is established based on the multi-fidelity deep neural network (MFDNN) model. The objective of the current work is to construct a high-accuracy multi-fidelity surrogate model correlating the configuration parameters of an aircraft and its aerodynamic performance by blending different fidelity information and adaptively learning their linear or nonlinear correlation without any prior assumption. In the optimization framework, the high-fidelity model using a CFD evaluation with fine grid and the low-fidelity model using the same CFD model with coarse grid are applied. Moreover, in each optimization iteration, the high-fidelity infilling strategy by adding the current optimal solution of surrogate model into the high-fidelity database is applied to improve the surrogate accuracy. The low-fidelity infilling strategy which can generate the solutions distributed uniformly in the whole design space is used to update the low-fidelity database for avoiding local optimum. Then, the proposed multi-fidelity optimization framework is validated by two standard synthetic benchmarks. Finally, it is applied to the high-dimensional aerodynamic shape optimization of a RAE2822 airfoil parameterized by 10 design variables and a DLR-F4 wing-body configuration parameterized by 30 design variables. The optimization results demonstrate that the proposed multi-fidelity optimization framework can remarkably improve optimization efficiency and outperform the single-fidelity method.

The broad synthetic utility of labile enol esters demands efficient methods for the stereo- and regioselective synthesis of both Z and E isomers. The available synthetic methods dominated by metal catalysis cannot meet the challenge. We wish to report a metal-free organocatalytic divergent approach to both E and Z isomers of enol esters from the same reactant pools with the same catalytic system. A process involves an amine-catalyzed conjugate addition of carboxylic acids to ynals, which triggers a rearrangement leading to enol esters. The reaction proceeds highly regio- and stereoselectively. Simple manipulation of reaction temperatures enabled us to produce Z isomers at 0 °C (Z:E (15–20):1), whereas E isomers were produced at 30 °C (E:Z (15–20):1). Furthermore, the mild reaction conditions accommodate a broad array of densely functionalized carboxylic acids for the process, including complex biologically relevant structures and ynals. Therefore, synthetically valued, structurally diverse enol esters are...

Inverted pyramids were fabricated through a method combining cesium chloride (CsCl) self-assembly technology and anisotropy corrosion of silicon solar cells. Ti film with nanoporous masks was formed by lift-off the CsCl nanoislands for the inverted pyramids. The pyramids were then formed by anisotropy corrosion of alkaline solution. The average diameter and morphology of the pyramids were controlled by varying the average diameter of CsCl nanoislands from 400 nm to 1.5 mu m and by varying the etching time of alkaline solution from 2 to 8 min. The inverted-pyramid texture could suppress reflection to below 10% at wavelengths from 400 to 1000 nm, which was much lower than that of planar wafer. A solar cell fabricated from the pyramids had higher short-circuit current density (J(sc)) and photovoltaic conversion efficiency (PCE) compared with those of planar solar cells for the good antireflection property. The solar cell showed a PCE of 15.25%, a J(sc) of 38.35 mA/cm(2), and an open-circuit voltage of 555.7 mV. (C) 2015 Elsevier Ltd. All rights reserved.

The genome of Labrenzia aggregata IAM 12614 encodes an uncharacterized member of the muconate lactonizing enzyme (MLE) subgroup of the enolase superfamily (UniProt ID A0NXQ8 ). The gene encoding A0NXQ8 is located between genes that encode members of the proline racemase superfamily, 4R-hydroxyproline 2-epimerase (UniProt ID A0NXQ7 ; 4HypE) and trans-3-hydroxy-l-proline dehydratase (UniProt ID A0NXQ9 ; t3LHypD). A0NXQ8 was screened with a library of proline analogues; two reactions were observed with cis-3-hydroxy-l-proline (c3LHyp), competing 2-epimerization to trans-3-hydroxy-d-proline (1,1-proton transfer) and dehydration to Δ(1)-pyrroline-2-carboxylate (β-elimination; c3LHyp dehydratase), with eventual total dehydration. The genome context encoding A0NXQ8 both (1) confirms its novel c3LHyp dehydratase function and (2) provides evidence for metabolic pathways that allow L. aggregata to utilize several isomeric 3- and 4-hydroxyprolines as sole carbon sources.

The rate at which genome sequencing data is accruing demands enhanced methods for functional annotation and metabolism discovery. Solute binding proteins (SBPs) facilitate the transport of the first reactant in a metabolic pathway, thereby constraining the regions of chemical space and the chemistries that must be considered for pathway reconstruction. We describe high-throughput protein production and differential scanning fluorimetry platforms, which enabled the screening of 158 SBPs against a 189 component library specifically tailored for this class of proteins. Like all screening efforts, this approach is limited by the practical constraints imposed by construction of the library, i.e., we can study only those metabolites that are known to exist and which can be made in sufficient quantities for experimentation. To move beyond these inherent limitations, we illustrate the promise of crystallographic- and mass spectrometric-based approaches for the unbiased use of entire metabolomes as screening libraries. Together, our approaches identified 40 new SBP ligands, generated experiment-based annotations for 2084 SBPs in 71 isofunctional clusters, and defined numerous metabolic pathways, including novel catabolic pathways for the utilization of ethanolamine as sole nitrogen source and the use of d-Ala-d-Ala as sole carbon source. These efforts begin to define an integrated strategy for realizing the full value of amassing genome sequence data.

A simple fabrication technology for both nanoscrews and nanoholes by Cesium Chloride (CsCl) self-assembly lithography and dry etching on silicon substrates is demonstrated. The porous Al template ranging from 400 nm to 2 mu M average diameter is formed by lift-off the CsCl nanoislands in DI water as the ICP etching masks for nanoholes. Nanoscrews and nanoholes of desired height/depth from 1.2 to 4 mu m are obtained by this method. The reflectance of nanoscrew and nanohole structures can achieve below 5% from wavelength of 400 to 1000 am which is much lower than that of pyramid but the performance of pyramid solar cells is the best. Results show that the performance of nanoscrew and nanohole solar cells is strongly affected by some other factors such as surface passivation and electrode-contact property. Therefore, not only the antireflection advantage but also the surface passivation and improving the electrode-contact property should be considered together to improve nanostructure solar cells photovoltaic performance.

With the rapid development and widespread adoption of wireless sensor networks (WSNs), security has become an increasingly prominent problem. How to establish a session key in node communication is a challenging task for WSNs. Considering the limitations in WSNs, such as low computing capacity, small memory, power supply limitations and price, we propose an efficient identity-based key management (IBKM) scheme, which exploits the Bloom filter to authenticate the communication sensor node with storage efficiency. The security analysis shows that IBKM can prevent several attacks effectively with acceptable computation and communication overhead.

The silicon solar cell with selective pillars is fabricated by Cesium Chloride (CsCI) self-assembly lithography and UV-lithography. This solar cell has pillars selectively in the active area (i.e. emitter exposed to light) while keeping electrode area plain to overcome the problem of metal contacts on pillars' surface. CsCI self-assembly and dry etching are used to make silicon pillars only on the active silicon surface as antireflection layer for the solar cell. UV-lithography is used to provide the selective area for the silicon pillars fabrication and the pattern for electrode. Ti/Ag layer is deposited on the electrode pattern surface with the alignment shadow mask by thermal evaporation. The solar cell with selective pillars has the lower reflectivity below 5% in the spectral ranging from 400 to 1000 nm compared to the planar cell without antireflection coating, and it also has the better electrode contacts compared to the cell with pillars all over the entire front surface. The photovoltaic conversion efficiency (PCE) improves from 12.18% to 15.37% at the experiment condition. (C) 2014 Elsevier Ltd. All rights reserved.

A simple technology with cesium chloride (CsCl) self-assembly lithography and wet electrochemical etching is introduced to fabricate the wafer scale, disordered, well-aligned, and high aspect ratio silicon nanopillars. The original nano structures of CsCl islands with diameters of 500–2000 nm are formed by self-assembly and used as template of lift-off for the nanoporous gold film for wet electrochemical etching as the catalyst in etching solution of HF and H2O2. The average diameter of silicon nanopillars is determined by the CsCl nanoislands with 500–2000 nm, and the height of silicon nanopillars is mainly determined by the etching time in etching solution with 3–12 μm. The aspect ratio can achieve to 60. The solar cells with different height nanopillars are made for the research of photovoltaic conversion efficiency (PCE). The reflectance of the nanopillars with different height is measured from the wavelength of 400 to 1000 nm and the 9 μm height silicon nanopillars has the lowest one which is below 3%. The PCE shows the highest value of 14.19% at the condition of 3 μm height nanopillars and 12.18% of planar one with the same fabrication process.