High output impedance current-mode lowpass,bandpass and highpass filters using current controlled conveyors

Three new current-mode second-order filter configurations which employ one or two current controlled conveyors (CCCIIs) and three passive elements are presented. Each proposed filter can realize one of the bandpass, lowpass or highpass responses all at high impedance outputs. The proposed filters offer current-control of pole angular frequency ω_o without disturbing the parameter ω_o /Q. No component matching is required and all the passive and active sensitivities are low. PSPICE simulation results are given to confirm the theoretical analysis.     

Modifying of Cotton Fabric Surface with Nano-ZnO Multilayer Films by Layer-by-Layer Deposition Method

ZnO nanoparticle–based multilayer nanocomposite films were fabricated on cationized woven cotton fabrics via layer-by-layer molecular self-assembly technique. For cationic surface charge, cotton fabrics were pretreated with 2,3-epoxypropyltrimethylammonium chloride (EP3MAC) by pad-batch method. XPS and SEM were used to examine the deposited nano-ZnO multilayer films on the cotton fabrics. The nano-ZnO films deposited on cotton fabrics exhibited excellent antimicrobial activity against Staphylococcus aureus bacteria. The results also showed that the coated fabrics with nano-ZnO multilayer films enhanced the protection of cotton fabrics from UV radiation. Physical tests (tensile strength of weft and warp yarns, air permeability and whiteness values) were performed on the fabrics before and after the treatment with ZnO nanoparticles to evaluate the effect of layer-by-layer (LbL) process on cotton fabrics properties.     

Forecasting of entropy production due to buoyant convection using support vector machines (SVM) in a partially cooled square cross-sectional room

Forecasting of entropy generation of laminar natural convection in a partially cooled square cross-sectional room has been performed using support vector machines (SVM). The two-dimensional room was modeled as floor heating story with a window. Values of temperature and velocities were obtained by solving governing equations of natural convection with finite difference technique and using these values entropy generation was calculated with its definition. Forecasting of entropy generation due to fluid friction irreversibility (FFI) and heat transfer irreversibility (HTI) were made with known values for unknown parameters using SVM. Thus, calculation time was extremely reduced and values were obtained even for non-convergence cases. It was observed that the SVM was a strong method to predict the entropy generation without computational fluid dynamic analysis for all cases.     

Effect of Boron and Heat Treatment on Mechanical Properties of White Cast Iron for Mining Application

 Heat treatment methods were applied to white cast iron for improving the impact and wear resistance. Additionally, chemical composition optimization was made. Furthermore, the effect of boron addition on such applications was investigated. Samples were investigated by using optical and electron microscope methods. Hardness, wear and impact tests were conducted. The results showed that the secondary carbides in the standard alloy were iron-enriched, needle-like carbides M3C when the boron-added alloy contained Fe23(C,B)6 type, globular secondary carbides. It was concluded that heat treatment B provided higher wear and hardness properties, compared to the standard heat treatment. Optimum mechanical properties were obtained by lower destabilisation temperatures and increasing temperature reduced the wear resistance and hardness.     

A decision support system for locating weapon and radar positions in stationary point air defence

In this study, a decision support system (DSS) based on the interactive use of location models and geographical information systems (GIS) was developed to determine the optimal positions for air defence weapons and radars. In the location model, the fire units are considered as the facilities to be located and the possible approach routes of air vehicles are treated as demand points. Considering the probability that fire by the units will miss the targets, the objective of the problem is to determine the positions that provide coverage of the approach routes of the maximum number of weapons while considering the military principles regarding the tactical use and deployment of units. In comparison with the conventional method, the proposed methodology presents a more reliable, faster, and more efficient solution. On the other hand, owing to the DSS, a battery commander who is responsible for air defence becomes capable of determining the optimal weapon and radar positions, among the alternative ones he has identified, that cover the possible approach routes maximally. Additionally, he attains the capability of making such decisions in a very short time without going to the field over which he will perform the defence and hence without being subject to enemy threats. In the decision support system, the digital elevation model is analysed using Map Objects 2.0, the mathematical model is solved using LINGO 4.0 optimization software, and the user interface and data transfer are supported by Visual Basic 6.0.     

Nonlinear transformation of term frequencies for term weighting in text categorization

In automatic text categorization, the influence of features on the decision is set by the term weights which are conventionally computed as the product of term frequency and collection frequency factors. The raw form of term frequencies or their logarithmic forms are generally used as the term frequency factor whereas the leading collection frequency factors take into account the document frequency of each term. In this study, it is firstly shown that the best-fitting form of the term frequency factor depends on the distribution of term frequency values in the dataset under concern. Taking this observation into account, a novel collection frequency factor is proposed which considers term frequencies. Five datasets are firstly tested to show that the distribution of term frequency values is task dependent. The proposed method is then proven to provide better F₁ scores compared to two recent approaches on majority of the datasets considered. It is confirmed that the use of term frequencies in the collection frequency factor is beneficial on tasks which does not involve highly repeated terms. It is also shown that the best F₁ scores are achieved on majority of the datasets when smaller number of features are considered.     

Cooperative communications with multilevel/AES-SD4-CPFSK in wireless sensor networks

In this paper, a new joint multilevel data encryption and channel coding mechanism is proposed, which is called ??multilevel/advanced encryption standard?Csystematic distance 4?Ccontinuous phase frequency shift keying?? (ML/AES-SD4-CPFSK). In the proposed scheme, we have not only taken advantage of spatial diversity gains but also optimally allocated energy and bandwidth resources among sensor nodes as well as providing high level of security and error protection for cooperative communications in wireless sensor networks. Relay protocols of cooperative communications, such as amplify-and-forward and decode-and-forward with/without adversary nodes, have been studied for 4CPFSK, 8CPFSK, and 16CPFSK of ML/AES-SD4-CPFSK. We have evaluated the error performances of multilevel AES for data encryption, multilevel SD-4 for channel coding, and various CPFSK types for modulation utilizing cooperative communications in wireless sensor networks. According to computer simulation results, significant diversity gain and coding gain have been achieved. As an example, bit error rate (BER) performance of 10^?5 value has been obtained at a signal-to-noise ratio (SNR) of ?6?dB for SD-4-CPFSK scheme in a compared related journal paper, whereas in our proposed system, we have reached the same BER value at a SNR of ?23?dB with amplify-and-forward with direct path signal protocol in 16-level AES, two-level SD-4 coded 16CPFSK, and at the same time, we have reached the same BER value at a SNR of ?22?dB with amplify-and-forward without direct path signal protocol in 16-level AES, two-level SD-4 coded 16CPFSK.     

Investigation of the effects of geometrical parameters on heat transfer from buried finned pipes

Heat transfer from finned pipes buried in the soil has been investigated using experimental, analytical and numerical techniques. The experimental setup consists of a ground source heat pump system and two separate ground heat exchangers. Constant surface temperature approach based on Carslaw and Jaeger’s theory study was used for calculating the heat transfer from the pipes analytically. The problem was modeled and solved using a CFD program numerically. Effects of the fin number, fin length and simulation time on the heat transfer rate have been investigated and compared with bare tube. A modified analytical formula was also proposed for finned pipes in this study.     

Numerical Study on Heat Transfer Between Turbulent Flow and Porous Rectangular Cylinders with Uniform Injection or Suction

The heat transfer characteristics of the flow around rectangular cylinders having various aspect ratios with uniform injection or suction through the rear, top, bottom, and all surfaces have been studied numerically. The effects of injection or suction on heat transfer have been linked to the analysis via wall functions of the velocity and temperature sublayers. The computational code employed in the present study was verified with a typical benchmark problem of the flow around a square cylinder. The results show that the heat transfer between the main flow and rectangular cylinders is substantially influenced by injection or suction—that is, suction enhances the heat transfer, while injection through the rear surface provides some thermal protection.     

Impact of non-normal random effects on inference by multiple imputation: A simulation assessment

Multivariate extensions of well-known linear mixed-effects models have been increasingly utilized in inference by multiple imputation in the analysis of multilevel incomplete data. The normality assumption for the underlying error terms and random effects plays a crucial role in simulating the posterior predictive distribution from which the multiple imputations are drawn. The plausibility of this normality assumption on the subject-specific random effects is assessed. Specifically, the performance of multiple imputation created under a multivariate linear mixed-effects model is investigated on a diverse set of incomplete data sets simulated under varying distributional characteristics. Under moderate amounts of missing data, the simulation study confirms that the underlying model leads to a well-calibrated procedure with negligible biases and actual coverage rates close to nominal rates in estimates of the regression coefficients. Estimation quality of the random-effect variance and association measures, however, are negatively affected from both the misspecification of the random-effect distribution and number of incompletely-observed variables. Some of the adverse impacts include lower coverage rates and increased biases.