The assessment of commercial beef and chicken bouillons in terms of heterocyclic aromatic amines and some of their precursors

Herein, the assessment of commercial beef and chicken bouillons in terms of heterocyclic aromatic amines (HAAs) and some of their precursors was evaluated. Creatine and creatinine levels were ranged between 0.57–0.80 and 0.28–0.94 mg g⁻¹, respectively. Glutamic acid was found to be the most abundant amino acid in both bouillons. 2-amino-3,7,8-trimethylimidazo[4,5-ƒ]quinoxaline (7,8-DiMeIQx, up to 0.03 ng g⁻¹) was the only quantified analyte in beef bouillons, whereas it (up to 0.08 ng g⁻¹) was determined in addition to 2-amino-3-methylimidazo[4,5-ƒ]quinoxaline (IQx, up to 0.08 ng g⁻¹) in chicken bouillons. Creatine, creatinine and free amino acid composition did not have the capacity to initiate the formation of HAAs. Therefore, bouillons do not pose risk in terms of HAAs. However, it should be noted that multiple factors, such as the substrate amount and production conditions, may affect the results. Glutamic acid content is remarkable in commercial bouillons sold in Turkey.     

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.     

Achieving real-time object detection and tracking under extreme conditions

In this survey, we present a brief analysis of single camera object detection and tracking methods. We also give a comparison of their computational complexities. These methods are designed to accurately perform under difficult conditions such as erratic motion, drastic illumination change, and noise contamination.     

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.

     

Corrosion inhibition of stainless steel by polyaniline,poly(2-chloroaniline), and poly(aniline-co-2-chloroaniline) in HCl

Polyaniline (PANi), poly(2-chloroaniline) (PClANi), and poly(aniline-co-2-chloroaniline) (co-PClANi) films were synthesized by electrochemical deposition on 304-stainless steel (SS) from an acetonitrile solution. The structural properties of these polymer films were characterized by spectroscopic (FTIR and UV–vis) and electrochemical (cyclic voltammetry) methods. Open circuit potential–time (E_ocp–time) curves, potentiodynamic polarization, and electrochemical impedance (EIS) measurements showed that these films have significant protective performance against corrosion of SS in 0.5 M HCl solution. It was found that co-PClANi film has acted as a passivator as well as barrier for cathodic reduction reaction in a similar manner as PANi film. However, PClANi film has behaved only as barrier for corrosion protection of SS in 0.5 M HCl.     

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.