Experimental and Theoretical Research Review of Hybrid Rocket Motor Techniques and Applications

A hybrid rocket motor combines components from both solid fuel and liquid fuel rocket motors. The fuel itself is a solid grain, (often paraffin or hydroxyl-terminated polybutadiene, known as HTPB) while the oxidizing agent is liquid (often hydrogen peroxide or liquid oxygen). These components are combined in the fuel chamber which doubles as the combustion chamber for the hybrid motor. This review looks at the advances in techniques that have taken place in the development of these motors since 1995. Methods of testing the thrust from rocket motors and of measuring the rocket plume spectroscopically for combustion reaction products have been developed. These assessments allow researchers to more completely understand the effects of additives and physical changes in design, in terms of regression rates and thrust developed. Hybrid rocket motors have been used or tested in many areas of rocketry, including tactical rockets and large launch vehicles. Several additives have shown significant improvements in regression rates and thrust, including Guanidinium azotetrazolate (GAT), and various Aluminum alloys. The most recent discoveries have come from research into nano-particle additives. The nano-particles have been shown to provide enhancements to many parameters of hybrid rocket function, and research into specific areas continues in the sub-field of nano-additives for fuel grains.

Applications of Ultrasonic Techniques in Oil and Gas Pipeline Industries: A Review

The diversity of ultrasound techniques used in oil and gas pipeline plants provides us with a wealth of information on how to exploit this technology when combined with other techniques, in order to improve the quality of analysis. The fundamental theory of ultrasonic nondestructive evaluation (NDE) technology is offered, along with practical limitations as related to two factors (wave types and transducers). The focus is limited to the two main techniques used in pipe plants: First, straight beam evaluation and second, angle beam evaluation. The depth of defect (DD) is calculated using straight beam ultrasonic in six different materials according to their relative longitudinal wave (LW) velocities. The materials and respective velocities of LW are: rolled aluminum (6420 m/s), mild steel (5960 m/s), stainless steel-347 (5790 m/s), rolled copper (5010 m/s), annealed copper (4760 m/s), and brass (4700 m/s). In each material eight defects are modeled;the first represents l00% of the material thickness (D), 50.8 mm. The other seven cases represent the DD, as 87.5% of the material thickness, 75%, 62.5%, 50%, 37.5%, 25%, and 12.5%, respectively. Using angle beam evaluation, several parameters are calculated for six different reflection angles (βR) (45°, 50°, 55°, 60°, 65° and 70°). The surface distance (SD), ½skip distance (SKD), full SKD, and 1½SKD,½sound path (SP) length, full SP, and 1½SP are calculated for each βR. The relationship of SKD and SP to the βR is graphed. A chief limitation is noted that ultrasound testing is heavily dependent on the expertise of the operator, and because the reading of the outcome is subjective, precision may be hard to achieve. This review also clarifies and discusses the options used in solving the industrial engineering problem, with a comprehensive historical summary of the information available in the literature. Merging various NDE inspection techniques into the testing of objects is discussed. Eventually, it is hoped to find a suitable technique combined with ultrasonic inspection to deliver highly effective remote testing.

Research on Detection Method of Interest Flooding Attack on Content Centric Network

To improve the attack detection capability of content centric network(CCN),we propose a detection method of interest flooding attack(IFA)making use of the feature of self-similarity of traffic and the information entropy of content name of interest packet.On the one hand,taking advantage of the characteristics of self-similarity is very sensitive to traffic changes,calculating the Hurst index of the traffic,to identify initial IFA attacks.On the other hand,according to the randomness of user requests,calculating the information entropy of content name of the interest packets,to detect the severity of the IFA attack,is.Finally,based on the above two aspects,we use the bilateral detection method based on non-parametric CUSUM algorithm to judge the possible attack behavior in CCN.The experimental results show that flooding attack detection method proposed for CCN can not only detect the attack behavior at the early stage of attack in CCN,but also is more accurate and effective than other methods.

Enhancing Production Efficiency of Oil and Natural Gas Pipes Using Microwave Technology

The research reported in this paper aims at developing means of Non Destructive testing (NDT) to increase the line efficiency of pipe production in oil and natural gas pipe manufacturing plants using the Standard Allowed Minutes (SAM) method. Existing line production stations encounter difficulties in maintaining the recommended testing speed of smaller diameter pipe, due to limitations in the Visual Inspection (VI) station. We propose to implement one additional technique which will prevent the decline of line efficiency in a pipe production factory. The range of diameters identified as a problem in this research is from 254 mm to 762 mm. Microwave techniques are expected to improve the line efficiency by increasing the production of the plant. This happens as a consequence of maintaining the production rates of the identified pipe diameters, so that they equal the production output of the larger pipe diameters. We analyze the velocity traveled by the pipe through Radiographic Testing (RT) according to the VI output (production). The RT velocity is decreased for the diameters identified above, in order to maintain quality control and cover the shortcoming of the VI. The number of pipes produced is computed during shift hours of the factory and pipe lengths of the forming department are determined. We compare the output (production) of a series of NDT line stations with and without the microwave technique for the first of the three pipe cases considered in this study, classified as perfect pipe (PP), repair pipe (RP) and scrap pipe (SP). The velocity of RT stations analyzed in the paper ranges from 50 mm/s for larger diameter pipe, and decline to 16.667 mm/s for the identified diameters. The analytical calculations of line output (production) and line efficiency demonstrate the solution of this velocity problem after the microwave technique is introduced. It demonstrates that an economical and precise methodology to extend the production capability of the pipe plant has been determined.

Quantitative Detection of Inositol Hexakisphosphate (InsP6) in Crop Plants Using Polyacrylamide Gel Electrophoresis (PAGE)

Inositol phosphates are essential for cell development and signaling in all living organisms. Inositol hexakisphosphate (InsP6) is the most abundant phosphoinositol in both plants and animals. While the concentration of inorganic phosphorous (Pi) is often limited in soil, some plants overcome this limitation by creating a phosphate reservoir that serves as a source of Pi during phosphate deficiency. Although this strategy benefits plant development and signaling under adverse environmental conditions, excessive accumulation of Pi in crop plants has raised serious concerns about its toxicity and ill effects on human health. Consumption of crop plants with high InsP6 content or food products made from these crops is found to reduce nutrient intake significantly by way of chelating essential metal cations in human and livestock fed by such plants. Therefore, it is necessary to determine InsP6 contents in crop plants. Several methods have been developed for the screening and detection of InsP6 in plants. These detection methods however, are complex, labor-intensive, and often provide inaccurate results. We have developed a fast, reliable, and cost-effective method for the detection and quantification of InsP6 in plants using polyacrylamide gel electrophoresis (PAGE) with potential applications in industry, quality control labs, and research projects.

Optimizing the Global Digital Elevation Models(GDEMs) and accuracy of derived DEMs from GPS points for Iraq’s mountainous areas

Optimizing the combined horizontal and vertical accuracy of the well-known Global Digital Elevation Models(GDEMs) of various resolutions for each country and region especially in Iraq’s mountainous areas is still questionable. All the three GDEMs, approximately, have the same vertical accuracy with the Root Mean Square(RMSE) of ±7.3 m, ±7.6 m and ±6.5 m via 12 fixed Ground Control Points(GCPs) for the Advanced Land Observation Satellite Phased Array L-band Synthetic Aperture Radar(ALOS PALSAR 12.5 m), the Shuttle Radar Topographic Mission(SRTM 30 m) and the TerraSAR-X(the name of twin satellites) add-on for Digital Elevation Measurement(TanDEM-X 90 m) GDEMs respectively. Moreover, the percentage of outliers that are greater or smaller than ±10 m detection of the height extraction from both the ALOS PALSAR and SRTM Digital Elevation Models(DEMs) contains 16.7% and for TanDEM-X was 25%. In this paper,the special DEM is derived using 2123 handheld GPS points for Sulaymaniyah Governorate, Kurdistan region, Iraq. The height extraction by discarding the outliers of 58% gives the RMSE of ±8.0 m in the case of adding geoid heights(N) to the ellipsoidal heights(h) via the Earth Gravitational Model 2008(EGM2008)and ±5.6 m without adding N. It is expected that the derived DEMs will give more accurate results both horizontally and vertically in the mountainous areas when GPS observations are intensified. The horizontal accuracy is validated through extracting hierarchy stream types of the watershed map from the DEMs for higher than 100 pixels length. The ALOS PALSAR DEM extracted more numbers of stream orders than others.Finally, based on the criteria of RMSE, outlier detection, and the number of extracted stream orders, the ALOS PALSER DEM is regarded as the optimal GDEM in comparison with the close accuracy of both the TanDEM-X and SRTM DEM.

Pitfall of genome-wide association studies: Sources of inconsistency in genotypes and their effects

Personalized medicine will improve heath outcomes and patient satisfaction. However, implementing personalized medicine based on individuals’ biological information is far from simple, requiring genetic biomarkers that are mainly developed and used by the pharmaceutical companies for selecting those patients who benefit more, or have less risk of adverse drug reactions, from a particular drug. Genome-wide Association Studies (GWAS) aim to identify genetic variants across the human genome that might be utilized as genetic biomarkers for diagnosis and prognosis. During the last several years, high-density genotyping SNP arrays have facilitated GWAS that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. The replication studies demonstrated that only a small portion of associated loci in the initial GWAS can be replicated, even within the same populations. Given the complexity of GWAS, multiple sources of Type I (false positive) and Type II (false negative) errors exist. The inconsistency in genotypes that caused either by the genotypeing experiment or by genotype calling process is a major source of the false GWAS findings. Accurate and reproducible genotypes are paramount as inconsistency in genotypes can lead to an inflation of false associations. This article will review the sources of inconsistency in genotypes and discuss its effect in GWAS findings.

Research on the Method of Implementing Named Data Network Interconnection Based on IP Network

In order to extend the application scope of NDN and realize the transmission of different NDNs across IP networks,a method for interconnecting NDN networks distributed in different areas with IP networks is proposed.Firstly,the NDN data resource is located by means of the DNS mechanism,and the gateway IP address of the NDN network where the data resource is located is found.Then,the transmission between different NDNs across the IP network is implemented based on the tunnel technology.In addition,in order to achieve efficient and fast NDN data forwarding,we have added a small number of NDN service nodes in the IP network,and proposed an adaptive probabilistic forwarding strategy and a link cost function-based forwarding strategy to make NDN data obtaining the cache service provided by the NDN service node as much as possible.The results of analysis and simulation experiments show that,the interconnectionmethod of NDN across IP network proposed is generally effective and feasible,and the link cost function forwarding strategy is better than the adaptive probability forwarding strategy.

Four-Order Superconvergent Weak Galerkin Methods for the Biharmonic Equation on Triangular Meshes

A stabilizer-free weak Galerkin(SFWG)finite element method was introduced and analyzed in Ye and Zhang(SIAM J.Numer.Anal.58:2572–2588,2020)for the biharmonic equation,which has an ultra simple finite element formulation.This work is a continuation of our investigation of the SFWG method for the biharmonic equation.The new SFWG method is highly accurate with a convergence rate of four orders higher than the optimal order of convergence in both the energy norm and the L^(2)norm on triangular grids.This new method also keeps the formulation that is symmetric,positive definite,and stabilizer-free.Four-order superconvergence error estimates are proved for the corresponding SFWG finite element solutions in a discrete H^(2)norm.Superconvergence of four orders in the L^(2)norm is also derived for k≥3,where k is the degree of the approximation polynomial.The postprocessing is proved to lift a P_(k)SFWG solution to a P_(k+4)solution elementwise which converges at the optimal order.Numerical examples are tested to verify the theor ies.

Platinum nanoparticles coated by graphene layers: A low-metal loading catalyst for methanol oxidation in alkaline media

Platinum catalysts play a major role in the large scale commercialization of direct methanol fuel cells(DMFC).Here,we present a procedure to create a nanostructural graphene-platinum(Gr Pt)composite containing a small amount(5.3 wt%)of platinum nanoparticles coated with at least four layers of graphene.The composite,as Gr Pt ink,was deposited on a glassy carbon electrode and its electrocatalytic activity in a methanol oxidation reaction(MOR)was evaluated in a 1 M CH3OH/1 M NaOH solution.The results indicated an enhanced catalytic performance of GrPt towards MOR in alkaline media compared with the Pt/C material.Electron energy-loss spectroscopy and X-ray photoelectron spectroscopy(recorded before and after the electrochemical assays)were employed to analyze the changes in the chemical composition of the nanomaterial and to explain the transformations that took place at the electrode surface.Our findings suggest that growing of graphene on platinum nanoparticles improve the catalytic performance of platinum-graphene composites towards MOR in alkaline media.

An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks

Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.

Maze Navigation via Genetic Optimization

One of the most interesting applications of genetic algorithms falls into the area of decision support. Decision support problems involve a series of decisions, each of which is influenced by all decisions made prior to that point. This class of problems occurs often in enterprise management, particularly in the area of scheduling or resource allocation. In order to demonstrate the formulation of this class of problems, a series of maze problems will be presented. The complexity of the mazes is intensified as each new maze is introduced. Two solving scenarios are introduced and comparison results are provided. The first scenario incorporated the traditional genetic algorithm procedure for the intended purpose of acquiring a solution based upon a purely evolutionary approach. The second scenario utilized the genetic algorithm in conjunction with embedded domain specific knowledge in the form of decision rules. The implementation of domain specific knowledge is intended to enhance solution convergence time and improve the overall quality of offspring produced which significantly increases the probability of acquiring a more accurate and consistent solution. Results are provided below for all mazes considered. These results include the traditional genetic algorithm final result and the genetic algorithm optimization approach with embedded rules result. Both results were incorporated for comparison purposes. Overall, the incorporation of domain specific knowledge outperformed the traditional genetic algorithm in both performance and computation time. Specifically, the traditional genetic algorithm failed to adequately find an acceptable solution for each example presented and prematurely converged on average within 54% of their specified generations. Additionally, the most complex maze generated an optimal path directional sequence (i.e. N, S, E, W) via a traditional genetic algorithm which possessed only 50% of the required allowable path sequences for maze completion. The incorporation of embedded rules enabled the genetic algorithm to locate the optimum path for all examples considered within 5% of the traditional genetic algorithm computation time.

An Effective of Dye Molecules with Cadmium Sulfide Nanorods in Dye Sensitized Solar Cell (DSSCs)

The aligned hexagonal cadmium sulfide nanorods (CdSNR) have been synthe-sized by hydrothermal technique at 200&#186C on fluorine tin oxide (FTO) sub-strates. Dye sensitized solar cells (DSSCs) based on the photoelectrode core-shell CdSNR array with conductive polymers nanocomposite of polyaniline (PANI) and poly(3,4-ethylenedioxyl-thiophene)/poly(styrene-sulfonate) (PEDOT:PSS) were fabricated and designed with different types of dye molecules. DSSCs were characterized utilizing scanning electron microscopy (SEM), Raman scattering, energy dispersive spectroscopy (EDS), UV-Vis absorption spectroscopy, X-ray diffraction (XRD), and photocurrent-voltage (J-V) characteristic. Results show that under illumination (AM 1.5 G), the high power conversion energy (PCE) was achieved for CdSNR/PANI-PEDOT:PSS device when it sensitized with ruthenium (II) (dye N-719) of 0.91% and a short circuit current density (Jsc) of 4.21 mA/cm2 in comparison with the other devices, which sensitized with natural dyes. The high performance of the CdSNR/PANI-PEDOT:PSS-N719 device attributed to the wide range of absorption and photostability for N719. This work shows that the CdSNR with N719 can be appropriate candidate for photovoltaics device for their low cost fabrication procedure and excellent absorption.

A LEAST SQUARE BASED WEAK GALERKIN FINITE ELEMENT METHOD FOR SECOND ORDER ELLIPTIC EQUATIONS IN NON-DIVERGENCE FORM

This article is devoted to establishing a least square based weak Galerkin method for second order elliptic equations in non-divergence form using a discrete weak Hessian operator.Naturally,the resulting linear system is symmetric and positive definite,and thus the algorithm is easy to implement and analyze.Convergence analysis in the H2 equivalent norm is established on an arbitrary shape regular polygonal mesh.A superconvergence result is proved when the coefficient matrix is constant or piecewise constant.Numerical examples are performed which not only verify the theoretical results but also reveal some unexpected superconvergence phenomena.

Automatic seizure detection with different time delays using SDFT and time-domain feature extraction

Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram(EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform(SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response(IIR)structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.

A Prediction Method of Fracture Toughness of Nickel-Based Superalloys

Fracture toughness plays a vital role in damage tolerance design of materials and assessment of structural integrity.To solve these problems of com-plexity,time-consuming,and low accuracy in obtaining the fracture toughness value of nickel-based superalloys through experiments.A combination prediction model is proposed based on the principle of materials genome engineering,the fracture toughness values of nickel-based superalloys at different temperatures,and different compositions can be predicted based on the existing experimental data.First,to solve the problem of insufficient feature extraction based on manual experience,the Deep Belief Network(DBN)is used to extract features,and an attention mechanism module is introduced.To achieve the purpose of strengthen-ing the important features,an attention weight is assigned to each feature accord-ing to the importance of the feature.Then,the feature vectors obtained by the DBN module based on the Attention mechanism(A-DBN)are spliced with the original features.Thus,the prediction accuracy of the model is improved by extracting high-order combined features and low-order linear features between input and output data.Finally,the spliced feature vectors are put into the Support Vector Regression(SVR)model to further improve the regression prediction abil-ity of the model.The results of the contrast experiment show that the model can effectively improve the prediction accuracy of the fracture toughness value of nickel-based superalloys.

Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization

Agent based simulation has successfully been applied to model complex organizational behavior and to improve or optimize aspects of organizational performance. Agents, with intelligence supported through the application of a genetic algorithm are proposed as a means of optimizing the performance of the system being modeled. Local decisions made by agents and other system variables are placed in the genetic encoding. This allows local agents to positively impact high level system performance. A simple, but non trivial, peg game is utilized to introduce the concept. A multiple objective bin packing problem is then solved to demonstrate the potential of the approach in meeting a number of high level goals. The methodology allows not only for a systems level optimization, but also provides data which can be analyzed to determine what constitutes effective agent behavior.

Using distance education in teaching orthotic fabrication to occupational therapy students

Occupational therapy (OT) education requires educators to provide students with entry level skills in orthotic fabrication. These skills have been taught in a traditional face-to-face classroom and labs. The concept of distance education (DE) has evolved over the years with the advancement of the technology. DE started with print media, radio broadcasting, and progressed to utilizing video conferencing. Currently DE relies heavily on using the internet to deliver quality, cost-effective and convenient education. DE Literature focused on teaching basic knowledge, however, no research was found that compared DE to traditional education in teaching skills or the application of knowledge. This pilot study investigated the effectiveness of using DE pedagogy in teaching OT students the skills of splint making, and to compare effectiveness of DE to that of traditional classroom. Forty OT students participated in the study, they self-selected to be in the experimental, or control group. The experimental group received training on splint making via the Blackboard?, while the control group received similar training the traditional way. The quality of splints was evaluated;Mann-Whitney concluded that the difference between the means of the two groups was no significant, indicating that the quality of the splints were equivalent.