Acousto-ultrasonic input-output characterization of unidirectional fiber composite plate by SH waves

A unidirectional fiberglass epoxy composite plate specimen is modelled as a homogeneous transversely isotropic continuum plate medium. Acousto-ultrasonic non-contact input-output characterization by tracing SH waves in the continuum is studied theoretically with a transmitting and receiving transducer located on the same face of the plate. It is found that the directional dependence of the phase velocity of the SH waves travelling in the transversely isotropic medium has a significant effect on the delay time as opposed to the phase velocity of the SH wave travelling in an isotropic medium

Comprehensive Detection of Genes Causing a Phenotype using Phenotype Sequencing and Pathway Analysis

Discovering all the genetic causes of a phenotype is an important goal in functional genomics. In this paper we combine an experimental design for multiple independent detections of the genetic causes of a phenotype, with a high-throughput sequencing analysis that maximizes sensitivity for comprehensively identifying them. Testing this approach on a set of 24 mutant strains generated for a metabolic phenotype with many known genetic causes, we show that this pathway-based phenotype sequencing analysis greatly improves sensitivity of detection compared with previous methods, and reveals a wide range of pathways that can cause this phenotype. We demonstrate our approach on a metabolic re-engineering phenotype, the PEP/OAA metabolic node in E. coli, which is crucial to a substantial number of metabolic pathways and under renewed interest for biofuel research. Out of 2157 mutations in these strains, pathway-phenoseq discriminated just five gene groups (12 genes) as statistically significant causes of the phenotype. Experimentally, these five gene groups, and the next two high-scoring pathway-phenoseq groups, either have a clear connection to the PEP metabolite level or offer an alternative path of producing oxaloacetate (OAA), and thus clearly explain the phenotype. These high-scoring gene groups also show strong evidence of positive selection pressure, compared with strictly neutral selection in the rest of the genome

Acousto-ultrasonic input-output characterization of unidirectional fiber composite plate by SV waves

A unidirectional fiberglass epoxy compostie specimen is modelled as a homogeneous transversely isotropic continuum plate medium. Acousto-ultrasonic noncontact input-output characterization is studied theoretically with a transmitting and a receiving transducer located on the same face of the plate. The single reflection problem for an incident SV wave at a plane boundary in transversely isotropic medium is analyzed. An obliquely incident SV wave results in a reflected SV wave and a reflected P wave for an angle of incidence of the incident SV wave less than the critical angle. Otherwise, there exists only an SV wave in the medium as the reflected P wave degenerates into a surface wave travelling parallel to the plane boundary. The amplitude ratio of the reflected SV wave is -1 when the angle of incidence is greater than or = the critical angle. The directional dependence of the phase velocity of the SV wave propagating in the transversely isotropic medium has a significant effect on the delay time, as opposed to the directional independence of the phase velocity of a shear wave propagating in an isotropic medium. The displacements associated with the SV wave in the plate and which may be detected by the noncontact receiving transducer are approximated by an asymptotic solution for an infinite transversely isotropic medium subjected to a harmonic point load

Acousto-ultrasonic input-output characterization of unidirectional fiber composite plate by P waves

The single reflection problem for an incident P wave at a stress free plane boundary in a semi-infinite transversely isotropic medium whose isotropic plane is parallel to the plane boundary is analyzed. It is found that an obliquely incident P wave results in a reflected P wave and a reflected SV wave. The delay time for propagation between the transmitting and the receiving transducers is computed as if the P waves were propagating in an infinite half space. The displacements associated with the P waves in the plate and which may be detected by a noncontact NDE receiving transducer are approximated by an asymptotic solution for an infinite transversely isotropic medium subjected to a harmonic point load

Augmenting the Calvin-Benson-Bassham cycle by a synthetic malyl-CoA-glycerate carbon fixation pathway.

The Calvin-Benson-Bassham (CBB) cycle is presumably evolved for optimal synthesis of C3 sugars, but not for the production of C2 metabolite acetyl-CoA. The carbon loss in producing acetyl-CoA from decarboxylation of C3 sugar limits the maximum carbon yield of photosynthesis. Here we design a synthetic malyl-CoA-glycerate (MCG) pathway to augment the CBB cycle for efficient acetyl-CoA synthesis. This pathway converts a C3 metabolite to two acetyl-CoA by fixation of one additional CO2 equivalent, or assimilates glyoxylate, a photorespiration intermediate, to produce acetyl-CoA without net carbon loss. We first functionally demonstrate the design of the MCG pathway in vitro and in Escherichia coli. We then implement the pathway in a photosynthetic organism Synechococcus elongates PCC7942, and show that it increases the intracellular acetyl-CoA pool and enhances bicarbonate assimilation by roughly 2-fold. This work provides a strategy to improve carbon fixation efficiency in photosynthetic organisms

Integrative genomic mining for enzyme function to enable engineering of a non-natural biosynthetic pathway.

The ability to biosynthetically produce chemicals beyond what is commonly found in Nature requires the discovery of novel enzyme function. Here we utilize two approaches to discover enzymes that enable specific production of longer-chain (C5-C8) alcohols from sugar. The first approach combines bioinformatics and molecular modelling to mine sequence databases, resulting in a diverse panel of enzymes capable of catalysing the targeted reaction. The median catalytic efficiency of the computationally selected enzymes is 75-fold greater than a panel of naively selected homologues. This integrative genomic mining approach establishes a unique avenue for enzyme function discovery in the rapidly expanding sequence databases. The second approach uses computational enzyme design to reprogramme specificity. Both approaches result in enzymes with >100-fold increase in specificity for the targeted reaction. When enzymes from either approach are integrated in vivo, longer-chain alcohol production increases over 10-fold and represents >95% of the total alcohol products

Application of Artificial Neural Networks to Power System State Estimation

State estimation function is essential for effective and timely execution of power system automation and control systems, especially in modern active distribution systems where more intermittent renewable energy systems are integrated into the grid. Distribution system state estimation faces a lot of challenges including lack of monitoring devices and possible incorrect topology information. Developing efficient state estimation for distribution systems is thus of great interest. This paper presents results on utilizing artificial neural networks for this purpose. Artificial neural networks have been used in power distribution system state estimation. However, there is a lack of systematic analysis and study of which types of ANNs and what structures including parameters are most suitable for state estimation applications. When designing an ANN for a state estimator, trial and error approach has been common and there is no systematic method available to guide the process. The ultimate goal of the research is to examine the performance of various types of ANNs (e.g., Multi-Layer Perceptron (MLPs), Convolutional Neural Networks (CNNs) and Long-Short- Term-Memory Networks (LSTMs)) with different structures and also provide possible guidance on how to choose the different parameters, including model parameters such as number of hidden layers and number neurons in a layer, and algorithm parameters such as adjustable learning rate, for desired performance metrics. The paper presents preliminary results based on MLPs. IEEE standard 34-bus test system is used to illustrate the proposed methods and their effectiveness. The paper seeks to contribute to a more systematic approach to neural network and deep learning applied to power system state estimation, thus enhancing situational awareness, system resiliency and real-time monitoring and control of power distribution systems. Successful state estimation function will increase the ability of distribution systems to integrate more renewable energy based generations

Fish Swimming in a Kármán Vortex Street:Kinematics, Sensory Biology and Energetics

Fishes often live in environments characterized by complex flows. To study the mechanisms of how fishes interact with unsteady flows, the periodic shedding of vortices behind cylinders has been employed to great effect. In particular, fishes that hold station in a vortex street (i.e., K?rm?n gaiting) show swimming kinematics that are distinct from their patterns of motion during freestream swimming in uniform flows, although both behaviors can be modeled as an undulatory body wave. K?rm?n gait kinematics are largely preserved across flow velocities. Larger fish have a shorter body wavelength and slower body wave speed than smaller fish, in contrast to freestream swimming where body wavelength and wave speed increases with size. The opportunity for K?rm?n gaiting only occurs under specific conditions of flow velocity and depends on the length of the fish; this is reflected in the highest probability of K?rm?n gaiting at intermediate flow velocities. Fish typically K?rm?n gait in a region of the cylinder wake where the velocity deficit is about 40% of the nominal flow. The lateral line plays a role in tuning the kinematics of the K?rm?n gait, since blocking it leads to aberrant kinematics. Vision allows fish to maintain a consistent position relative to the cylinder. In the dark, fish do not show the same preference to hold station behind a cylinder though K?rm?n gait kinematics are the same. When oxygen consumption level is measured, it reveals that K?rm?n gaiting represents about half of the cost of swimming in the freestreamauthorsversionPeer reviewe

Application of Deep Neural Networks to Distribution System State Estimation and Forecasting

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks (LSTMs) to mitigate the aforementioned challenges in power distribution systems. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the correlation of the dataset being predicted. The performance of MLPS, CNNs, and LSTMs to perform state estimation and state forecasting will be presented in terms of average root-mean square error (RMSE) and training execution time. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting