Principle Component Analysis in Conjuction with Data Driven Methods for Sediment Load Prediction

This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.     

Novel Metrics for Mutation Analysis

A measure of the “goodness” or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault detection ratio of the system. Because it is difficult to identify the system’s potential flaws precisely. In mutation testing, the Fault Detection Ratio (FDR) metric is currently used to express the adequacy of a test suite. However, there are some issues with this metric. If both test suites have the same defect detection rate, the smaller of the two tests is preferred. The test case (TC) is affected by the same issue. The smaller two test cases with identical performance are assumed to have superior performance. Another difficulty involves time. The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic. Our study developed three metrics to address these issues: , , and In this context, most used test generation tools were examined and tested using the developed metrics. Thanks to the metrics we have developed, the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system.     

λτ-space representation of images and generalized edgedetector

An image and surface representation based on regularization theory is introduced in this paper. This representation is based on a hybrid model derived from the physical membrane and plate models. The representation, called the λτ-representation, has two dimensions; one dimension represents smoothness or scale while the other represents the continuity of the image or surface. It contains images/surfaces sampled both in scale space and the weighted Sobolev space of continuous functions. Thus, this new representation can be viewed as an extension of the well-known scale space representation. We have experimentally shown that the proposed hybrid model results in improved results compared to the two extreme constituent models, i.e., the membrane and the plate models. Based on this hybrid model, a generalized edge detector (GED) which encompasses most of the well-known edge detectors under a common framework is developed. The existing edge detectors can be obtained from the generalized edge detector by simply specifying the values of two parameters, one of which controls the shape of the filter (τ) and the other controls the scale of the filter (λ). By sweeping the values of these two parameters continuously, one can generate an edge representation in the λτ space, which is very useful for developing a goal-directed edge detection scheme for a specific task. The proposed representation and the edge detector have been evaluated qualitatively and quantitatively on several different types of image data such as intensity, range, and stereo images     

Electrodeposited Cu2ZnSnSe4 thin film solar cell with 7% power conversion efficiency

High performance Cu₂ZnSnSe₄ (CZTSe) photovoltaic materials were synthesized by electrodeposition of metal stack precursors followed by selenization. A champion solar cell with 7.0% efficiency is demonstrated. This is the highest efficiency among all of the CZTSe solar cells prepared from electrodeposited metallic precursors reported to‐date. Device parameters are discussed from the perspective of material microstructure and composition in order to improve performance. In addition, a high performance electrodeposited CZTS (S only) solar cell was demonstrated and its device characteristics were compared against the CZTSe (Se only) cell. Using secondary ion mass spectrometry for the analysis of the chemical composition of the absorber layer, a higher concentration of oxygen in the electrodeposited absorber is thought to be the root cause of the lower performance of the electrodeposited CZTS or CZTSe solar cells with respect to a solar cell fabricated by evaporation. The grain boundary areas of Sn‐rich composition are thought to be responsible for the lower shunt resistance commonly observed in CZTSe devices. We measured the longest minority carrier lifetime of 18 ns among all reported kesterite devices. This work builds a good baseline for obtaining higher efficiency earth‐abundant solar cells, while it highlights electrodepositon as a low cost and feasible method for earth‐abundant thin film solar cell fabrication. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Usage of bioactivated PCL nanofiber as a fluoxetine capturing matrix in milk

ABSTRACT

In this study, for the first time, polycaprolactone (PCL) nanofiber matrix was bioactivated for the removal of fluoxetine from milk. Bioactivated nanofiber was prepared by immobilizing fluoxetine antibody on PCL nanofiber matrix. The fluoxetine removal efficiency of bioactivated nanofiber in milk was found to be approximately 93.6%. This removal did not significantly change the biochemical composition of milk. In conclusion, as a novel product, bioactivated nanofibrous PCL matrix can be used for the removal of drugs or unwanted chemicals from breast milk or from other fluids.     

Investigating a Suitable Empirical Model and Performing Regional Analysis for the Suspended Sediment Load Prediction in Major Rivers of the Aegean Region,Turkey

This study investigates the appropriateness of four major empirical methods [Lane and Kalinske, Einstein, Brooks, Chang—Simons—Richardson] for predicting suspended sediment loads (SSLs) in three major rivers in the Aegean Region, Turkey. The measured data from 1975 to 2005 were used to test performance of the models. It was found that Brooks method was more appropriate, among the others, for predicting suspended sediment loads from each river. The prediction results of Brooks method were further improved by the use of genetic algorithm (GA_Brooks) optimizing a fitting parameter and showing a comparable performance to those of artificial neural networks (ANNs) and neuro-fuzzy (ANFIS) models for the same rivers. GA_Brooks, ANNs, and ANFIS models can be used for predicting loads at a regional scale. The sensitivity analysis results revealed that suspended and bed material particle diameters affect suspended sediment loads significantly.