Computer: A teaching aid for structural engineering seniors at the University of Riyadh

To cope with modern structural analysis and design using computers seen in the Kingdom of Saudi Arabia, the civil engineering seniors of University of Riyadh have started to select computer oriented-structural final year projects. Nonetheless, seniors have remained reluctant to select such projects, simply because a typical civil engineering student takes as a sophomore a FORTRAN course and can easily complete the B.Sc. in C.E. graduation requirements without going near the computer again. The author, while working in Saudi Arabia, has adopted the computer as a teaching aid for seniors in advanced elective structural course using the Finite Element Method. The selected textbook, and assignments are geared to computer applications on structures. The purpose of the elective course is to reinforce and exploit the material covered in the FORTRAN course, in order to enable the students to accomplish computer oriented-structural final year projects. The paper evaluates the course's achievement and the impact of the course on the performance of seniors in their computer oriented-structural final year projects supervised by the author. The author believes that the time has come for the computer to be adopted as a teaching aid in a series of electives and final year projects in the field of structural engineering at our universities. The paper concludes with the result that such a step is necessary and quite feasible.     

Transmutation of minor actinides discharged from LMFBR spent fuel in a high power density fusion reactor

Significant amounts of nuclear wastes consisting of plutonium, minor actinides and long lived fission products are produced during the operation of commercial nuclear power plants. Therefore, the destruction of these wastes is very important with respect to public health, environment and also the future of nuclear energy. In this study, transmutation of minor actinides (MAs) discharged from LMFBR spent fuel in a high power density fusion reactor has been investigated under a neutron wall load of 10 MW/m² for an operation period of 10 years. Also, the effect of MA percentage on the transmutation has been examined. The fuel zone, containing MAs as spheres cladded with W-5Re, has been located behind the first wall to utilize the high neutron flux for transmutation effectively. Helium at 40 atm has been used as an energy carrier. At the end of the operation period, the total burning and transmutation are greater than the total buildups in all investigated cases, and very high burnups (420–470 GWd/tHM) are reached, depending on the MA content. The total transmutation rate values are 906 and 979 kg/GW_th year at startup and decrease to 140 and 178 kg/GW_th year at the end of the operation for fuel with 10% and 20% MA, respectively. Over an operation period of 10 years, the effective half lives decrease from 2.38, 2.21 and 3.08 years to 1.95, 1.80 and 2.59 years for ²³⁷Np, ²⁴¹Am and ²⁴³Am, respectively. Total atomic densities decrease exponentially during the operation period. The reductions in the total atomic densities with respect to the initial ones are 79%, 81%, 82%, 83%, 85% and 86% for 10%, 12%, 14%, 16%, 18% and 20% MAs, respectively.     

Capillary electrophoresis microchip with a carbon nanotube-modified electrochemical detector

Significant improvements in the performance of a capillary electrophoresis (CE) microchip with an electrochemical detector are observed using a carbon nanotube (CNT)-modified working electrode. The CNT-modified electrode allows CE amperometric detection at significantly lower operating potentials and yields substantially enhanced signal-to-noise characteristics. The electrocatalytic detection is coupled to resistance to surface fouling and hence enhanced stability. Such advantages are illustrated in connection with several classes of hydrazine, phenol, purine, and amino acid compounds. Substantial minimization of surface fouling effects has been demonstrated in connection with the monitoring of phenol and tyrosine. Factors affecting the performance of the new CNT detector were assessed and optimized. CNTs from different sources are evaluated, and the effect of an anodic pretreatment is explored. The broad and significant catalytic activity exhibited by CNT-based CE detectors indicates great promise for a wide range of bioanalytical and environmental applications.     

On the Tritium Breeding Capability of Flibe, Flinabe, and Li20Sn80 in a Fusion-Fission (Hybrid) Reactor

In a commercial (DT) driven fusion reactor, the tritium breeding ratio per incident fusion neutron must be greater than 1.05 to maintain tritium self-sufficiency for the driver. In this study tritium breeding capability of three different coolants, namely Flibe (LiF·BeF₂), Flinabe (LiF·NaF·BeF₂), and Li₂₀Sn₈₀ in a (DT) driven fusion-fission (hybrid) reactor was investigated for different refractory alloys (W-5Re, TZM, T111, and Nb-1Zr) as structural material. Neutron transport calculations were conducted with the help of SCALE 4.3 SYSTEM by solving the Boltzmann transport equation with code XSDRNPM. The contribution of Flibe, Flinabe, and Li₂₀Sn₈₀ with respect to ⁶Li enrichment in their lithium content to overall TBR was investigated. In addition, the effect of structural material type on TBR was examined.     

Surface-enhanced Raman scattering substrate based on a self-assembled monolayer for use in gene diagnostics

The development of surface-enhanced Raman scattering (SERS)-active substrates for cancer gene detection is described. The detection method uses Raman active dye-labeled DNA gene probes, self-assembled monolayers, and nanostructured metallic substrates as SERS-active platforms. The mercaptohexane-labeled single-stranded DNA (SH-(CH(2))(6)-ssDNA)/6-mercapto-1-hexanol system formed on a silver surface is characterized by atomic force microscopy. The surface-enhanced Raman gene (SERGen) probes developed in this study can be used to detect DNA targets via hybridization to complementary DNA probes. The probes do not require the use of radioactive labels and have a great potential to provide both sensitivity and selectivity. The effectiveness of this approach and its application in cancer gene diagnostics (BRCA1 breast cancer gene) are investigated.     

Volumetric ultrasound imaging using 2-D CMUT arrays

Recently, capacitive micromachined ultrasonic transducers (CMUTs) have emerged as a candidate to overcome the difficulties in the realization of 2-D arrays for real-time 3-D imaging. In this paper, we present the first volumetric images obtained using a 2-D CMUT array. We have fabricated a 128 x 128-element 2-D CMUT array with through-wafer via interconnects and a 420-microm element pitch. As an experimental prototype, a 32 x 64-element portion of the 128 x 128-element array was diced and flip-chip bonded onto a glass fanout chip. This chip provides individual leads from a central 16 x 16-element portion of the array to surrounding bondpads. An 8 x 16-element portion of the array was used in the experiments along with a 128-channel data acquisition system. For imaging phantoms, we used a 2.37-mm diameter steel sphere located 10 mm from the array center and two 12-mm-thick Plexiglas plates located 20 mm and 60 mm from the array. A 4 x 4 group of elements in the middle of the 8 x 16-element array was used in transmit, and the remaining elements were used to receive the echo signals. The echo signal obtained from the spherical target presented a frequency spectrum centered at 4.37 MHz with a 100% fractional bandwidth, whereas the frequency spectrum for the echo signal from the parallel plate phantom was centered at 3.44 MHz with a 91% fractional bandwidth. The images were reconstructed by using RF beamforming and synthetic phased array approaches and visualized by surface rendering and multiplanar slicing techniques. The image of the spherical target has been used to approximate the point spread function of the system and is compared with theoretical expectations. This study experimentally demonstrates that 2-D CMUT arrays can be fabricated with high yield using silicon IC-fabrication processes, individual electrical connections can be provided using through-wafer vias, and flip-chip bonding can be used to integrate these dense 2-D arrays with electronic circuits for practical 3-D imaging applications.     

Fuzzy portfolio selection using genetic algorithm

This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.     

Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease

Abstract: We aimed to examine the diagnostic performances of multilayer perceptron neural networks (MLPNNs) for predicting coronary artery disease and to compare them with different types of artificial neural network methods, namely recurrent neural networks (RNNs) and two statistical methods (quadratic discriminant analysis (QDA) and logistic regression (LR)). MLPNNs were trained with backpropagation, quick propagation, delta-bar-delta and extended delta-bar-delta algorithms as classifiers; the RNN was trained with the Levenberg–Marquardt algorithm; LR and QDA were used for predicting coronary artery disease. Coronary artery disease was classified with accuracy rates varying from 79.9% to 83.9% by MLPNNs. Even though MLPNNs achieved higher accuracy rates than the statistical methods, LR (73.2%) and QDA (58.4%), their performances were lower compared to the RNN (84.7%). Among the four different types of training algorithms that trained MLPNNs, quick propagation achieved the highest accuracy rate; however, it was lower than the RNN trained with the Levenberg–Marquardt algorithm. RNNs, which demonstrated 84.7% accuracy and 86.5% positive predictive rates, may be a helpful tool in medical decision making for diagnosis of coronary artery disease.     

Investigating Neutronic Parameters of a Thorium Fusion Breeder with Recurrent Neural Networks

Artificial neural networks (ANNs) have recently been introduced to the nuclear engineering applications as a fast and flexible vehicle to modeling, simulation and optimization. In this paper, a new approach based on recurrent neural networks (RNNs) was presented for the neutronic parameters of a thorium fusion breeder. The results of the RNNs implemented for the tritium breeding ratio computation, energy multiplication factor and net ²³³U production in a thorium fusion breeder and the results available in the literature obtained by using Scale 4.3 were compared. The drawn conclusions confirmed that the proposed RNNs could provide an accurate computation of the tritium breeding ratio computation, the energy multiplication factor and the net ²³³U production of the thorium fusion breeder.