Experimental Study on a Single Layer Two-Way Grid Shell with Tension Members

Single layer two-way grid shell with in-plane and out-of-plane tension members is a new type of single-layer latticed shell roofs. Compared with traditional single-layer latticed shells,this new type has a unique mesh form and excellent rigidity. In order to further understand the buckling behaviors of single layer two-way grid cylindrical shell roof with tension members,the buckling experiments have been undertaken to investigate the effect of tension members,in either out-of-plane or in-plane placement. A single layer two-way grid cylindrical shell roof with out-of-plane tension members has been tested under symmetric and asymmetric loading. The tension member placement,the introducing initial axial force to tension members and the load patterns are considered to investigate the buckling behavior. Experimental results indicate that four long out-ofplane tension members work well under symmetrical loading,but only two long out-of-plane tension members work under asymmetrical loading. It can be concluded that the PC bar members used as tension members for this study are useful in the construction of a single layer two-way grid cylindrical shell roof with structural members intersecting at small angles.

Domain Decomposition for Wavelet Single Layer on Geometries with Patches

We focus on the single layer formulation which provides an integral equation of the first kind that is very badly conditioned. The condition number of the unpreconditioned system increases exponentially with the multiscale levels. A remedy utilizing overlapping domain decompositions applied to the Boundary Element Method by means of wavelets is examined. The width of the overlapping of the subdomains plays an important role in the estimation of the eigenvalues as well as the condition number of the additive domain decomposition operator. We examine the convergence analysis of the domain decomposition method which depends on the wavelet levels and on the size of the subdomain overlaps. Our theoretical results related to the additive Schwarz method are corroborated by numerical outputs.

Study on electrical defects level in single layer two-dimensional Ta2O5

Two-dimensional atomic-layered material is a recent research focus, and single layer Ta205 used as gate dielectric in field-effect transistors is obtained via assemblies of Ta205 nanosheets. However, the electrical performance is seriously affected by electronic defects existing in Ta2Os. Therefore, spectroscopic ellipsometry is used to calculate the transition energies and corresponding probabilities for two different charged oxygen vacancies, whose existence is revealed by x-ray photoelectron spectroscopy analysis. Spectroscopic ellipsometry fitting also calculates the thickness of single layer Ta2Os, exhibiting good agreement with atomic force microscopy measurement. Nondestructive and noncontact spectroscopic ellipsometry is appropriate for detecting the electrical defects level of single layer Ta2O5.

Steady thermocapillary-buoyant convection in a shallow annular pool. Part 1:Single layer fluid

This paper examines the steady thermocapillarybuoyant convection in a shallow annular pool subjected to a radial temperature gradient. A matched asymptotic theory is used to obtain the asymptotic solutions of the flow and thermal fields in the case of small aspect ratios,which is defined as the ratio of the layer thickness to the gap width. The flow domain is divided into the core region away from the cylinder walls and two end regions near each cylinder wall. Asymptotic solutions are obtained in the core region by solving the core and end flows separately and then joining them through matched asymptotic expansions. For the system of silicon melt,the asymptotic solutions are compared with the results of numerical simulations. It is found that the two kinds of solutions have a good agreement in the core region for a small aspect ratio. With the increase of aspect ratio,the applicability of the present asymptotic solutions decreases gradually.

Calculating single layer production contribution of heavy oil commingled wells by analysis of aromatic parameters in whole-oil GC-MS

Traditional fluid production profile logging is not usually suitable for heavy-viscous crude oil wells.Biodegradation of heavy oil can lead to the loss of n-alkanes,and the use of chromatogram fingerprint techniques in studying the production contributions of single layers in heavy oil commingled wells has limitations.However,aromatic compounds are relatively well preserved.We took the heavy oil commingled wells of small layers NG55 and NG61 in the ninth area of the Gudong oil field as examples.Based on the principle of chromatography,the whole-oil GC-MS was used,and the aromatic parameters which have a strongly linear relationship with the ratio of mixed two end member oils were verified and selected in laboratory.Studies showed that the ratio of （1,4,6-＋ 2,3,6-trimethylnaphthalene） to 1,2,5-trimethylnaphthalene has a strongly linear relationship with the ratio of the mixed two end member oils （R2=0.992）.The oil contributions from single layers NG55 and NG61 in six commingled heavy oil wells were calculated using established charts and this relationship.The calculated results are consistent with the results of long period dynamic monitoring and logging interpretation in the study area and can provide a scientific basis for monitoring production performance and hierarchical management of reservoirs.The study provides a new geochemical method for calculation of the contributions of single layers in heavy oil commingled wells when conventional fluid production profile logging is not suitable.

Vertical two-dimensional non-hydrostatic pressure model with single layer

The vertical two-dimensional non-hydrostatic pressure models with multiple layers can make prediction more accurate than those obtained by the hydrostatic pres- sure assumption. However, they are time-consuming and unstable, which makes them unsuitable for wider application. In this study, an efficient model with a single layer is developed. Decomposing the pressure into the hydrostatic and dynamic components and integrating the x-momentum equation from the bottom to the free surface can yield a horizontal momentum equation, in which the terms relevant to the dynamic pressure are discretized semi-implicitly. The convective terms in the vertical momentum equation are ignored, and the rest of the equation is approximated with the Keller-box scheme. The velocities expressed as the unknown dynamic pressure are substituted into the continuity equation, resulting in a tri-diagonal linear system solved by the Thomas algorithm. The validation of solitary and sinusoidal waves indicates that the present model can provide comparable results to the models with multiple layers but at much lower computation cost.

Compressed Sensing Based on the Single Layer Wavelet Transform for Image Fusion

In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.

Single Layer Centrifugation with Androcoll-E^(TM) improved progressive motility and percentage of live spermatozoa with intact acrosome of chilled stallion semen but did not have an effect on DNA integrity

A significant number of stallions produce low quality ejaculates with high sensibility to chilling. Single Layer Centrifugation (SLC) with Andro-coll-ETM has been presented as an efficient method of selecting good quality spermatozoa. In the current study, changes in sperm quality (motility, viability, acrosome integrity and DNA damage) occurring during storage at 5?C for a maximum of 72 h, were investigated. For that, one ejaculate from 12 stallions was split in two aliquots: control and SLC-selected. Both aliquots were chilled and stored at 5?C and spermatozoa were evaluated for motility, viability and acrosome integrity at 24, 48 and 72 h post collection. DNA damage was evaluated at 48 h post collection using the comet assay. In the SLC-selected aliquots, there was a significant improvement in terms of progressive motility (0 h: P = 0.005;24 h: P 0.05). SLC with Androcoll-ETM improved semen quality prolonging sperm longevity of chilled semen (P = 0.012). This positive effect was more evident in ejaculates most sensitive to chilling that had a sharp decrease in motility in the first 24 h of refrigeration and for all ejaculates at 72 h post-chilling. Therefore, this method reveals to be a useful technique for selecting spermatozoa and maintain sperm quality during storage.

Study on Preparation and Ns-Laser Damage of HfO₂Single Layers

The effects of the main parameters of argon flux, oxygen flux and beam voltage on the surface morphology, transmittance spectrum and laser damage of the HfO2 single layers prepared by ion beam sputtering are studied. The HfO2 amorphous single layers have porous surface morphologies. Different processes will cause differences in coatings absorption and surface morphology, which in turn will cause changes in the spectral transmittance curve. The ion beam sputtering HfO2 single layers have high content of argon (4.5% - 8%). The laser damage of HfO2 single layers is related to argon inclusions and non-stoichiometric defects. The changes of argon flux and beam voltage have a greater impact on argon content and O/Hf ratio. When the argon content in the coatings is lower and the O/Hf ratio is higher, the laser damage thresholds of the HfO2 single layers are higher.

Geometrical Dimensional Effect on Natural Frequency of Single Layer Graphene in Armchair Configuration

Graphene has remarkable strength,such as yield strength and elastic constant.The dynamic behaviour of graphene sheet is affected by geometrical variation in atomic arrangement.This paper introduced graphene with armchair atomic structure for estimating fundamental natural frequencies.The presented analysis can be useful for the possible high frequency nanomechanical resonator systems.The analytical formulation,based on classical plate theory and continuum solid modelling based finite element method have been performed for estimation of fundamental natural frequencies of single layer graphene sheet(SGLS)with different boundary conditions.The free edge and clamped edge boundary conditions have been considered.For simplifying analytical formulations,Blevins approach for dynamic solution has been adopted and for validating analytical results.The finite element analysis of SLGS has been performed using ANSYS software.The effect of variation in geometrical parameters in terms of width and length of SLGS has been analysed for realization of ultra-high frequency based nanomechanical resonator systems.

SnO_2 hollow nanospheres assembled by single layer nanocrystals as anode material for high performance Li ion batteries

SnO2 hollow nanospheres were successfully synthesized via a facile one-step solvothermal method.Characterizations show that the as-prepared SnO2 spheres are of hollow structure with a diameter at around 50 nm,and especially,the shell of the spheres is assembled by single layer SnO2 nanocrystals.The surface area of the material reaches up to 202.5 m^2/g.As an anode material for Li ion batteries,the sample exhibited improved electrochemical performance compared with commercial SnO2 particles.After cycled at high current rate of 0.5 C,1 C and 0.5 C for 20 cycles,respectively,the electrode can maintain a capacity of 509 mAh/g.The suitable shell thickness/diameter ratio endows the good structural stability of the material during cycling,which promises the excellent cycling performance of the electrode.The large surface area and the ultra thin shell ensure the high rate performance of the material.

A single layer zero skew clock routing in X architecture

With its advantages in wirelength reduction and routing flexibility compared with conventional Manhattan routing, X architecture has been proposed and applied to modern IC design. As a critical part in high-performance integrated circuits, clock network design meets great challenges due to feature size decrease and clock frequency increase. In order to eliminate the delay and attenuation of clock signal introduced by the vias, and to make it more tolerant to process variations, in this paper, we propose an algorithm of a single layer zero skew clock routing in X architecture （called Pianar-CRX）. Our Planar- CRX method integrates the extended deferred-merge embedding algorithm （DME-X, which extends the DME algorithm to X architecture） with modified Ohtsuki＇s line-search algorithm to minimize the total wirelength and the bends. Compared with planar clock routing in the Manhattan plane, our method achieves a reduction of 6.81% in total wirelength on average and gets the resultant clock tree with fewer bends. Experimental results also indicate that our solution can be comparable with previous non-planar zero skew clock routing algorithm.

Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better reliability.This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics.There are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation etc.There are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction etc.To overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved BAT optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral features.In HDIB,we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)image.Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology.Then,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of classification.Finally,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively.

A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks

This paper aims to propose a framework for manifold regularization(MR) based distributed semi-supervised learning(DSSL) using single layer feed-forward neural network(SLFNN). The proposed framework, denoted as DSSL-SLFNN is based on the SLFNN, MR framework, and distributed optimization strategy. Then, a series of algorithms are derived to solve DSSL problems. In DSSL problems, data consisting of labeled and unlabeled samples are distributed over a communication network, where each node has only access to its own data and can only communicate with its neighbors. In some scenarios, DSSL problems cannot be solved by centralized algorithms. According to the DSSL-SLFNN framework, each node over the communication network exchanges the initial parameters of the SLFNN with the same basis functions for semi-supervised learning(SSL). All nodes calculate the global optimal coefficients of the SLFNN by using distributed datasets and local updates. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that DSSL-SLFNN based algorithms work in a fully distributed fashion and are privacy preserving methods. Finally, several simulations are presented to show the efficiency of the proposed framework and the derived algorithms.

Spectroscopic investigation of defects mediated oxidization of single-layer MoS_(2)

Due to the extremely large surface to bulk ratio,the properties of single layer(SL)MoS_(2)are largely determined by its interaction with environment.One critical interaction process that has been intensively investigated is the oxidation process of MoS_(2).Despite numerous previous explorations,the detailed mechanism regarding how MoS_(2)reacts with oxygen is still not well understood.In this work,we systematically investigate the impact of intrinsic and pre-created defects on the oxidation process of SL MoS_(2).For pristine SL MoS_(2),the oxidation is found to initiate near point defects and grain boundaries,leading to the formation of triangle pits in the basal plane and cracks near the grain boundaries.The pre-created defects introduced by ion irradiation are found to serve as the oxidation center,resulting in a more uniform oxidation process.The oxidation is found to introduce p-type doping in the SL MoS_(2),leading to the blue shift of Raman and photoluminescence(PL)spectra.The shift is found to be more for the region near the grain boundary and for the samples with more pre-created defects.Our results suggest that the presence of defects can strongly promote the oxidation reaction of SL MoS_(2)in ambient condition,which significantly affects the stability and functionality of materials.

Recent Development of Reluctance Machines with Different Winding Configurations,Excitation Methods,and Machine Structures

This paper reviews the performances of some newly developed reluctance machines with different winding configurations,excitation methods,stator and rotor structures,and slot/pole number combinations.Both the double layer conventional(DLC-),double layer mutually-coupled(DLMC),single layer conventional(SLC-),and single layer mutually-coupled(SLMC-),as well as fully-pitched(FP)winding configurations have been considered for both rectangular wave and sinewave excitations.Different conduction angles such as unipolar􀫚120°elec.,unipolar/bipolar􀫚180°elec.,bipolar􀫛240°elec.and bipolar􀫜360°elec.have been adopted and the most appropriate conduction angles have been obtained for the SRMs with different winding configurations.In addition,with appropriate conduction angles,the 12-slot/14-pole SRMs with modular stator structure is found to produce similar average torque,but lower torque ripple and iron loss when compared to non-modular 12-slot/8-pole SRMs.With sinewave excitation,the doubly salient synchronous reluctance machines with the DLMC winding can produce the highest average torque at high currents and achieve the highest peak efficiency as well.In order to compare with the conventional synchronous reluctance machines(SynRMs)having flux barriers inside the rotor,the appropriate rotor topologies to obtain the maximum average torque have been investigated for different winding configurations and slot/pole number combinations.Furthermore,some prototypes have been built with different winding configurations,stator structures,and slot/pole combinations to validate the predictions.

Extreme learning with chemical reaction optimization for stock volatility prediction

Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.

Crystallographic orientation effect on cutting-based single atomic layer removal

The ever-increasing requirements for the scalable manufacturing of atomic-scale devices emphasize the significance of developing atomic-scale manufacturing technology. The mechanism of a single atomic layer removal in cutting is the key basic theoretical foundation for atomic-scale mechanical cutting. Material anisotropy is among the key decisive factors that could not be neglected in cutting at such a scale. In the present study, the crystallographic orientation effect on the cutting-based single atomic layer removal of monocrystalline copper is investigated by molecular dynamics simulation. When undeformed chip thickness is in the atomic scale, two kinds of single atomic layer removal mechanisms exist in cutting-based single atomic layer removal, namely, dislocation motion and extrusion, due to the differing atomic structures on different crystallographic planes. On close-packed crystallographic plane, the material removal is dominated by the shear stress-driven dislocation motion, whereas on non-close packed crystallographic planes, extrusion-dominated material removal dominates. To obtain an atomic, defect-free processed surface, the cutting needs to be conducted on the close-packed crystallographic planes of monocrystalline copper.

Free vibration of functionally graded sandwich plates in thermal environments

This paper presents an analytical solution for the free vibration of functionally graded material(FGM)sandwich plates in a thermal environment.An equivalentsingle‐layer(ESL)plate theory with four variables is used to obtain the solution.Two types of sandwich plates are examined in this study:one with FGM face sheets and a homogeneous core and the other with an FGM core and homogeneous face sheets.The governing equations of motion are derived based on Hamilton's principle and then solved using the Navier method.The results of natural frequencies of simply supported FGM sandwich plates are compared with the available solutions in the literature.The effects of volume fraction distribution,geometrical parameters,and temperature increments on the free vibration characteristics are discussed in detail.

Fuzzy adaptive tracking control within the full envelope for an unmanned aerial vehicle

Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.