Electrochemical mass transfer between an impinging jet and a rotating disk in a confined system

This paper presents the mass transfer results from an impinging liquid jet to a rotating disk. The mass transfer coefficients were measured using the electrochemical limiting diffusion current technique (ELDCT). Rotational Reynolds number (Re_r) in the range of 3.4 × 10⁴–1.2 × 10⁵, jet Reynolds number (Re_j) 1.7 × 10⁴–5.3 × 10⁴ and non-dimensional jet-to-disk spacing (H/d) 2–8 were taken into consideration as parameters. It was found that the jet impingement resulted in a substantial enhancement in the mass transfer compared to the case of the rotating disk without jet.     

Space-domain method of moments solution for a strip on a dielectricslab

This paper presents a space-domain method of moments (MoM) solution to the problem of a strip dipole on a dielectric slab. The solution involves the use of a special junction basis function which models the nearly singular polarization currents in the vicinity of the strip/dielectric junction     

Locational analysis for regionalization of Turkish Red Crescent blood services

After a series of earthquakes in 1999, Turkish Red Crescent (TRC) has engaged in a restructuring for all of its activities, including the blood services. Our study on the blood management system had been started as part of this initiative to restructure the blood services and improve both their effectiveness and efficiency. In the current system of TRC, not much consideration has been given to how the locational decisions affect the performance of blood centers, stations and mobile units. In recent years, however, there has been much discussion regarding the regionalization of the blood management system in Turkey. In this study, we develop several mathematical models to solve the location–allocation decision problems in regionalization of blood services. We report our computational results, obtained by using real data, for TRC blood services.     

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

Trans FA and solid fat contents of margarines marketed in Turkey

Trans FA (TFA), solid fat contents (SFC), and slip melting points of 12 different tub and stick margarines marketed in Turkey were examined in this study. No trans isomers were found in four margarines, which suggests they were formulated from interesterified or blended fats and oils. The products with no TFA generally had more short-chain saturated FA, which suggests coconut oil-based oil components. TFA content of the other 10 products varied from 7.7 to 37.8%. Compared to the products formulated in North America, Turkish margarines contain more TFA and have higher SFC.     

AHP and multichoice goal programming integration for course planning

Course planning is one of the important problems in the education systems of universities. The processes cannot continue efficiently without planning, and various interruptions can appear in the system. This way decisions concerning which courses, when, how, and for what purposes should be answered by considering the available resources and stakeholders’ preferences. Besides, universities aiming to be in the European Higher Education Area (EHEA) have to adapt their systems to the Bologna process in order to create a lifelong student‐centered, learning‐oriented area based on quality assurance. In this study, an integrated approach based on the analytic hierarchy process (AHP) and multichoice goal programming (MCGP) model was proposed to construct an efficient course plan following the Bologna process. The proposed approach was applied in an industrial engineering department.     

Fusion of smartphone sensor data for classification of daily user activities

Multimedia Tools and Applications - New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users...     

Noise analysis of robot manipulator using neural networks

Due to a lot of robot manipulators application in industry, low noise degree is very important criteria for robot manipulator's joints. In this paper, joint noise problem of a robot manipulator with five joints is investigated both theoretically and experimentally. The investigation is consisted of two steps. First step is to analyze the noise of joints using a hardware and software. The hardware is a part of noise sensors. The second step; according to experimental results, some neural networks are employed for finding robust neural noise analyzer. Five types of neural networks are used to compare each other. From the results, it is noted that the proposed RBFNN gives the best results for analyzing joint noise of the robot manipulator.