Flight-vehicle structures education in the united states—Assessment and recommendations

An assessment is made of the technical contents of flight-vehicle structures curricula at 41 U.S. universities with accredited aerospace engineering programs. The assessment is based on the technical needs for the new and projected aeronautical and space systems as well as on the likely characteristics of the aerospace engineering work environment. A number of deficiencies and areas of concern are identified and recommendations are presented for enhancing the effectiveness of flight-vehicle structures education. A number of government supported programs that can help aerospace engineering education are listed in the appendix.     

Estimation of the Substrate Concentration Profiles in Petroleum Pipelines Containing Biofilms

In industrial systems, the formation of biofilms can cause many problems, such as an increase in the flow resistance of pipelines, energy losses in fluid transport and heat exchangers, product contamination, materials deterioration, and biocorrosion. As a result, biofilms contribute substantially to economic losses in the industry. Corrosion is particularly an issue in the petroleum industry and its implications range from down-hole completion through petroleum processing units. Much of this corrosion is attributed to microbial activities. This paper proposes a mathematical model for predicting substrate concentration for such microbial growth. Substrate concentrations in the system and near the biofilm surface are one of the parameters that has a great effect in determining the extent of the problems associated with biofilms. In this study, a convective-diffusion model under various flow conditions (stagnant, laminar, and turbulent) has been solved using the finite difference technique, employing the alternating direction implicit (ADI) method. The model assumes that a liquid containing substrate and bacteria is flowing in a pipeline with known concentrations at the inlet and then predicts the variation of the transient (as a function of time) substrate concentration along the pipeline and as a function of the pipe radius. The model is then used to predict and estimate the substrate concentration profiles on the biofilm surface under different environmental conditions. A parametric study was also conducted to study the effect of the different parameters influencing the substrate concentration profiles in the system and on the biofilm surface.     

Direct conversion receiver for radio communication systems

Direct conversion receiver (DCR) architecture, which has introduced the zero intermediate frequency (IF) approach, supports efficient wireless handset designs with a high level of integration. Transmission over wireless channels is subject to time dispersion due to multipath propagation and frequency dispersion due to Doppler effect, the design of wireless receivers are tremendously important in supporting reliable communication links. With wireless technology growing, the choice of optimal wireless receiver architecture that supports monolithic integration without performance degradation becomes an important dimension in modern handset design. The increasing demand for mobile terminals with smaller physical dimensions has led to the investigation of DCR, which supports single chip and multinode designs. Since DCR is a promising architecture for fourth generation mobile terminals, it is important to note that implementation of a certain dc offset technique does not compromise the benefits of low complexity receiver design.     

Lipid peroxidation and lysosomal integrity in different inflammatory models in rats: the effects of indomethacin and naftazone

The authors tried to establish quantitative and qualitative acoustic parameters of a good voice, suitable for future voice professionals. In their work they used long-time average spectrum analysis (LTAS) and three-dimensional analysis of periodicity (3D-PAN). They consider the regression straight line of formant regions and the parameters offered by 3D-PAN--jitter first of all--as the main acoustic parameters for the evaluation of voice quality and draw attention to the fact that acoustic parameters represent only one part of the evaluation of voice quality.     

Applications,databases and open computer vision research from drone videos and images: a survey

Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this paper, based on the type of analyzing videos and images captured by drones in computer vision, we have reviewed these applications by categorizing them into three groups. The first group is related to remote sensing with challenges such as camera calibration, image matching, and aerial triangulation. The second group is related to drone-autonomous navigation, in which computer vision methods are designed to explore challenges such as flight control, visual localization and mapping, and target tracking and obstacle detection. The third group is dedicated to using images and videos captured by drones in various applications, such as surveillance, agriculture and forestry, animal detection, disaster detection, and face recognition. Since most of the computer vision methods related to the three categories have been designed for real-world conditions, providing real conditions based on drones is impossible. We aim to explore papers that provide a database for these purposes. In the first two groups, some survey papers presented are current. However, the surveys have not been aimed at exploring any databases. This paper presents a complete review of databases in the first two groups and works that used the databases to apply their methods. Vision-based intelligent applications and their databases are explored in the third group, and we discuss open problems and avenues for future research.

     

3D Simulation Research on Urea-SCR DeNO<em>x Catalyst for Diesel Engine

In order to reduce oxides of nitrogen (NOx) emanated from a diesel eng ine, a comprehensive urea selective catalyst reduction (SCR) DeNOx catalys t was modeled in which numerical simulations were used as a complementary tool f or the experimental investigations to make the design decisions, and hence short en the development process. In this approach, relevant conversion reactions were studied in 1D model, and the parameters obtained in this way were transferred t o 3D simulations. According to the results ...     

直觉线性μ-演算中的合成推理

讨论了以基于前缀封闭集合的Heyting代数的直觉解释的线性μ-演算(IμTL)作为描述“假设-保证”的逻辑基础的问题,提出了一个基于IμTL的“假设-保证”规则.该规则比往常应用线性时序逻辑(LTL)作为规范语言的那些规则具有更好的表达能力,扩展了对形如“always ?”等安全性质的“假设-保证”的范围,具备更一般的“假设-保证”推理能力及对循环推理的支持.     

Theoretical Study of Electronic,Magnetic, and Optical Response of Fe-doped ZnS: First-Principle Approach

In this study, we demonstrate Zn_1?x Fe _x S (x = 0.0, 0.25, 0.50, 0.75, and 1.0) device applications by reporting electronic, magnetic, and optical properties, computed with Wien2k software, using density functional theory (DFT). The modified Becke and Johnson (mBJ) potential has been applied to accurately determine the material band gap. The presence of half-metallic ferromagnetism (HMF) is demonstrated. Moreover, the observed ferromagnetism is justified in terms of various splitting energies and the exchange constants. The Fe magnetic moment decreases from 4.0 μ _B due to the strong p ? d hybridization. A complete set of various optical parameters is also presented. The variation in the calculated static dielectric constant, due to Fe doping, is inversely related to the band gap that verifies Penn’s model. Moreover, the band gap of ZnS is tunable by the Fe doping, from ultraviolet to visible regions, depicting that the materials are appropriate for optoelectronic devices.     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.     

Iterative methods for solving extended general mixed variational inequalities

In this paper, we introduce and consider a new class of mixed variational inequalities involving four operators, which are called extended general mixed variational inequalities. Using the resolvent operator technique, we establish the equivalence between the extended general mixed variational inequalities and fixed point problems as well as resolvent equations. We use this alternative equivalent formulation to suggest and analyze some iterative methods for solving general mixed variational inequalities. We study the convergence criteria for the suggested iterative methods under suitable conditions. Our methods of proof are very simple as compared with other techniques. The results proved in this paper may be viewed as refinements and important generalizations of the previous known results.