Influence of monovalent cations and CuO nanoparticles on X-nanozeolite in uranium anionic species separation from contaminated drinking water

The main purpose of this work was the modification of NaX nanozeolite using copper oxide nanoparticles and various monovalent cations such as K~+, Cs~+, and Ag~+in order to make the negatively charged zeolite surface accessible for anionic forms of uranium which are the most dominant species of uranium in the contaminated radioactive waters at natural p H. Various methods such as the X-ray fluorescence(XRF), X-ray powder diffraction(XRD), field emission scanning electron microscopy(FE-SEM), transmission electron microscopy(TEM), Fourier transform infrared spectroscopy(FT-IR), and atomic absorption spectroscopy(AAS) were used to characterize the final synthesized absorbents. Batch technique was used to study the adsorption behavior of uranium ions from polluted drinking water by Na X nanozeolite and its modified forms. In order to better understand the performance of them, the results were compared with those that were obtained for synthesizing bulk NaX zeolite and Na-form of clinoptilolite natural zeolite. Preliminary results indicated that uranium sorption increased as the loading level of CuO nanoparticles on NaX nanozeolite increased from 2.1 wt% to 11.2 wt%. In addition,from the obtained data, an increase in uranium removal efficiency resulted as charge/ionic radius ratio of exchanged cation decreased. Also, the effect of contact time, solid–liquid ratio, initial concentration and temperature on the adsorption process was studied. It is worth mentioning that, in this study, the sorption of uranium was performed under natural conditions of pH and the presence of competing cations and anions which are available in drinking waters.     

Performance Analysis and Optimization Based on Adaptive Modulation and Chaotic Interleaving for Helicopter-Satellite Communications

Overcoming periodic blockage of the received signal and the carrier frequency fluctuations caused by rotor blades and Doppler shift, respectively, are considered the most challenging issues in helicopter-satellite communication systems. In this study, we propose an automatic frequency control method based on an adaptive modulation scheme. We employ a hybrid modulation not only depending on quadrature phase shift keying, but also based on binary phase shift keying for accurate cancellation of periodic blockage. On the other hand, we apply a chaotic interleaving scheme with a hybrid modulation scheme in order to achieve a better Bit Error Rate (BER) performance in LOS and NLOS environments. Finally, we validate the mathematical analysis of the proposed scheme through simulations. We evaluated the performance of the proposed scheme and performed a comparison with conventional schemes. Our results show that the proposed scheme is significantly capable of reducing the acquisition time and working with various velocities of helicopter blades in addition to providing a better BER performance in shadow fading environments.     

CEO观点：在新兴市场中，微型蜂窝基站制造业者须保持弹性!

无线营运业者的目标，是提供充分的移动能力及无缝式的漫游，以为使用者确保能永久存取宽带连接功能。目前这个目标可透过移动电话网络达成．但其涵盖率于室内和偏远地区具有局限性，这也意味着并非所有使用者都能得到想要的无缝式涵盖率。     

Modeling managerial behavior in real options valuation for project-based environments

Project valuation, as a decision-making tool for initiating investments in projects, should be able to value project flexibilities and incorporate reasonable risk preferences of relevant decision makers. Real options valuation methods are the available approaches for valuing project flexibilities, whereas they have shortcomings in considering managers’ reasonable risk preferences in project decisions. Therefore, researchers have suggested approximating the perspective on risk of real options methods and practitioners in project management. This study proposes a fair real options valuation for project-based environments by a behavioral economic approach, which adopts binomial lattice method, Monte-Carlo simulation, and cumulative prospect theory. The results show that behavioral factors such as ‘risk attitude’ and ‘loss aversion’ should be accepted in project investment decisions while limited to an acceptable amount depending on the project conditions (e.g. uniqueness of decision-making scenarios). This research contributes to the project management domain by enhancing project investment decisions that include project flexibilities.     

Matching of DC motors to photovoltaic generators for maximum dailygross mechanical energy

The matching to solar-cell generators of both separately excited and series DC motors driving pumping loads is addressed. It is shown that the maximum gross mechanical power can be obtained at slightly higher voltages and slightly lower currents compared to the maximum electrical-power points on the solar-cell generator characteristics at different insolation levels. Guidelines for constructing the loci of the motor voltage-current points for maximum mechanical power and for determining the optimal motor parameters to match the solar generator are derived in terms of the mechanical load characteristics and the solar array parameters. Results of applying these design criteria to practical case studies enabled the assessment and comparison of both kinds of motors, especially as far as the daily utilizable output mechanical energy is concerned. The superiority of the separately excited motors in such systems is quantitatively indicated. The results are also compared to corresponding information available in the literature     

The Emotional Wardrobe

Since the industrial revolution, fashion and technology have been linked through the textile and manufacturing industries, a relationship that has propelled technical innovation and aesthetic and social change. Today, a new alliance is emerging through the integration of electronic technology and smart materials on the body. This study addresses the integration of technology with clothing from a fashion perspective, and examines its expressive and interactive potential. It proposes the concept of The Emotional Wardrobe: clothing that represents and stimulates emotional response through the interface of technology. It asks if fashion can offer a more personal and provocative definition of self, which actively involves the wearer in a mutable aesthetic identity. A multi-disciplinary framework combines fashion, material science and the real-time, affective computing platform, called AffectiveWare. By merging technology and fashion, The Emotional Wardrobe becomes a poetic interface, shifting emphasis from human–computer interaction to computer-aided, human–human communication.     

Parameter identification of deep-bar induction motors using genetic algorithm

In recent years, the use of three-phase deep-bar induction motors in power systems has increased. Proper modeling and precise parameter identification of the model are essential for motors’ operating analysis. In this paper, among the proposed models of deep-bar induction motors, a model based on two-axis theory is discussed and developed to improve precision. A real coded genetic algorithm estimates the parameters of the model. The accuracy and validity of the model and its identified parameters are verified with the help of a 5.5 kW, 380 V, 50 Hz, 1,450 rpm deep-bar induction motor.     

Orthogonal experimental design applied for wear characterization of aluminum/C<Subscript>sf</Subscript> metal composite fabricated by the thixomixing method

High silicon content aluminum alloy (hypereutectic) possess good tribological characteristics with low coefficients of friction, when embedded with short carbon fiber (C_sf), making this composite a good material choice where good wear and high strength properties are required in light weight components. There is no previously published information available, to the knowledge of the authors, regarding the influence of wear parameters and their interactions on the tribological behavior of C_sf reinforced metal matrix composites. In this study a Taguchi design of experiment (DoE) was conducted to optimize and analyze the effects of the wear parameters on the tribological properties of Al/C_sf metal matrix composite. A novel thixomixing method which was used to process the metal within the semisolid state was employed to embed short carbon fibers homogenously into the metal matrix. The influences of the sliding speed, applied load and volume fraction, of C_sf on the specific wear rate and coefficient of friction were examined, with each of these input parameters tested at three levels(0, 4.2, 8.1%vol.). The results were indicated that Al/C_sf composite had better tribological properties than Al alloy due to which contains carbon as solid lubricant. According to the statistical analysis, the influence of volume fraction of carbon fiber on wear parameters was ranked first; so the load and sliding speed are at the following rankings. The contribution percentage for each parameter was determined by the analysis of variance. The relatively good interfacial adherence of carbon fiber and matrix alloy were demonstrated. The coherent and adherent graphite-rich layer on the worn surface was characterized using scanning electron microscopy (SEM).     

Comparison of spectral and spatial windows for local anomaly detection in hyperspectral imagery

A new way of implementing two local anomaly detectors in a hyperspectral image is presented in this study. Generally, most local anomaly detector implementations are carried out on the spatial windows of images, because the local area of the image scene is more suitable for a single statistical model than for global data. These detectors are applied by using linear projections. However, these detectors are quite improper if the hyperspectral dataset is adopted as the nonlinear manifolds in spectral space. As multivariate data, the hyperspectral image datasets can be considered to be low-dimensional manifolds embedded in the high-dimensional spectral space. In real environments, the nonlinear spectral mixture occurs more frequently, and these manifolds could be nonlinear. In this case, traditional local anomaly detectors are based on linear projections and cannot distinguish weak anomalies from background data. In this article, local linear manifold learning concepts have been adopted, and anomaly detection algorithms have used spectral space windows with respect to the linear projection. Output performance is determined by comparison between the proposed detectors and the classic spatial local detectors accompanied by the hyperspectral remote-sensing images. The result demonstrates that the effectiveness of the proposed algorithms is promising to improve detection of weak anomalies and to decrease false alarms.