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.     

Interface feature prioritization for web services: Case of online flight reservations

Acceptance, utility, and usability of system designs have become a focal interest in human–computer interaction (HCI) research, yet at present there is a lack detailed understandings of which system design features influence them. The purpose of the study is to examine the effects of five product design features; customization, adaptive behavior, memory load, content density, and speed on user preference through an experimental study by using conjoint analysis. In experimental study, instead of classical conjoint cards, prototypes were generated for products. Besides, desirability and market segments of product prototypes were identified. In line with the results, among the five product design features, speed is the most and customization is the least important features that affect user preference. Contrary to the expectations, customization has a relatively small importance value in this research. Subsequent design features that influence user preference after speed are minimal memory load, adaptive behavior, and content density, respectively. According to findings, interfaces that have high-speed, minimal memory load, adaptive behavior, low content density, and customization features are more preferable than those that do not.     

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.     

DNA immobilization and SAW response in ZnO nanotips grown on LiNbO3 substrates.

DNA immobilization enhancement is demonstrated in a structure consisting of ZnO nanotips on 128 degrees Y-cut LiNbO3. The ZnO nanotips are grown by metalorganic chemical vapor deposition (MOCVD) on the top of a SiO2 layer that is deposited and patterned on the LiNbO3 SAW delay path. The effects of ZnO nanotips on the SAW response are investigated. X-ray diffraction and scanning electron microscopy are used to analyze the ZnO nanotips, which are of single crystalline quality, and they are uniformly aligned with their c-axis perpendicular to the substrate surface. The photoluminescence (PL) spectrum of the ZnO nanotips shows strong near bandedge transition with insignificant deep level emission, confirming their good optical property. DNA immobilization enhancement is experimentally validated by radioactive labeling tests and SAW response changes. The ZnO nanotips enhance the DNA immobilization by a factor of 200 compared to ZnO film with flat surface. DNA hybridization with complementary and noncomplementary second strand DNA oligonucleotides is used to study the selective binding of the structure. This device structure possesses the advantages of both traditional SAW sensors and ZnO nanostructures.     

Modeling and Optimization of SE and SI of Copper Flotation via Hybrid GA–ANN

In the present paper, a new attitude has been proposed for optimization of the separation efficiency (SE) and the Gaudin’s selectivity index (SI) in a flotation process by Hybrid artificial neural network (ANN) and genetic algorithm (GA). The chemical reagent’s dosage (collector, frother and fuel oil), pH, solid percentage, feed rate, Cu, Mo, and Fe grades in the flotation feed were selected as input variables and the SE-Cu and SI-Mo and SI-Fe were selected as output ones. Multilayer NN with back propagation (BP) algorithm was trained by the standard Bayesian regulation algorithm in which the validation data set did not required to be apart from its training. This algorithm with four-layer was used to relate output and input variables. Employment of Hybrid GA–ANN method resulted in significant improvement on GA fitnesses, as SE-Cu = 88, SI-Mo = 4.47 and SI of Fe = 12.85 were achieved. The input parameters corresponding to the fitnesses were as follows: pH = 12.25; the grade of Cu = 0.55%, Mo = 0.04% and Fe = 5.53%; the collector, frother and fuel–oil concentrations being 16.55, 15.54 and 2.71 (g/ton), respectively; the solid percentage was 25.84% and feed rate was 38,380 ton/day. The best fitness of GA was obtained after 10 generations by MSE value of 2.23.     

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.     

Production of activated carbon coated polystyrene resins under hydrothermal conditions and their use for gold cyanide adsorption

Polystyrene beads were coated with activated carbon under hydrothermal conditions above the glass transition temperature of polystyrene. By precisely controlling the temperature between 140 and 145 °C under vigorous mixing conditions in the presence of an excess of activated carbon, the surface of polystyrene beads were made sufficiently tacky so that finely sized activated carbon particles adhered to their surfaces, producing polystyrene/activated carbon, PS-AC, beads. The usefulness of these beads, as an alternative to pure activated carbon particles, for adsorption of gold cyanide ions under industrial conditions, was also demonstrated experimentally.     

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.     

Grouting a tunnel cave-in from the surface: a case study on Kurtkula ı irrigation tunnel, Turkey

This paper describes the treatment of tunneling problems at Kurtkula ı irrigation tunnel, particularly focusing on the Kurtkula ı tunnel which is 1255 m in length and 3.50 m in diameter, is an important of the Yumurtalık Plain Irrigation Project. The tunnel was excavated using conventional dull and blast, but during the tunneling problems occurred. Within a 70 m long section of the tunnel, cave-in occurred at two locations both along the fault zone in tunnel alignment. Slope stability problems were experienced too at inlet and outlet section of the tunnel. A sinkhole was formed at the surface as a result of the cave-in. There are an Allocthonous Cretaceous complex sequence and Miocene sandstone–claystone units, which have a faulty contact in the tunnel alignment area. The complex sequence consists of mainly weathered andesite, spilitic agglomerate, radiolarite, serpentinite and limestone blocks. The claystone to sandstone is thin-medium bedded and has medium-weak rock substance properties. The fault zone with an approximate width of 150 m and consisting of extremely weak crushed rocks crosses the tunnel alignment at mid-section. Some treatment methods were applied to remedy this collapse. Cut and cover method was applied for the first 955 m of the tunnel inlet due to the shallow overburden and very weak rock properties. However, cave-ins delayed the tunnel construction for 1 year and increased the cost by 9%.     

Potential Anomaly Separation and Archeological Site Localization Using Genetically Trained Multi‐level Cellular Neural Networks

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi‐level cellular neural network (ML‐CNN) is introduced, developed, and applied to real data. ML‐CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas‐Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.