Investigation of the stability of tablets prepared from sucrose and citric acid anhydride utilizing response surface methodology

Inversion of sucrose is a stability problem particularly of candies with acidic taste that contain sucrose and small amounts of organic acids such as citric acid, since the free d-fructose produced by hydrolysis is hygroscopic. The following possibilities were investigated for preventing the hydrolysis of sucrose in tablets containing sucrose and citric acid: Adding various amounts of tri-sodium citrate to the formulation to neutralize the citric acid, (Hot) melt coating of citric acid and tri-sodium citrate with a vegetable fat at different coating ratios, variation of the ratio of coated citric acid and tri-sodium citrate in formulations, and compressing the formulations with different compression forces. After tablet processing and storage of tablets, the concentration of d-fructose was determined on the basis of enzymatic reactions. A response surface central composite design was used. The above-mentioned variations were chosen as independent variables and the amount of d-fructose was chosen as response variable. The lowest rates of inversion could be achieved by increasing the content of tri-sodium citrate and the ratio of coating material and decreasing the ratio of coated citric acid and tri-sodium citrate in the tablet formulations. The compression force had no significant effect on the inversion of sucrose.     

Synthesis and characterization of novel polyacrylate-clay sol-gel materials

Organo-functional silanes which were able to form chemical bonds with kaolinite and could also have an affinity to the materials of concern here, were studied by the sol-gel process. Polymethacrylate with trialkoxy silyl functional groups were prepared, hydrolysed and co-condensed with kaolinite. The progress of the hydrolysis, which proceeded very slowly, was followed by Karl-Fischer titration. Thermal behavior was investigated by differential thermal analysis. The extent of the reaction leading to network formation was qualitatively followed by Fourier transform-infrared spectroscopy and X-ray diffraction. Free-radical polymerization was carried out ultrasonically in the presence of a catalyst. Trimethoxy silane end-capped silane was found to be covalently bonded to kaolinite. The copolymers, with various amounts of kaolinite, were then hydrolysed and co-condensed in the presence of a catalyst to yield sol-gel materials which have a controllable combination of properties of both the polymer and kaolinite.     

The effect of elevated temperature and silicon addition on a cobalt-based wear resistant superalloy

The effects of an elevated temperature and a 5 wt% silicon addition on the resultant microstructure and inherent phases of Stellite 6 were investigated by using room and high temperature optical microscopy, X-ray diffraction, scanning electron microscopy (SEM) and also bulk hardness and microhardness measurements. It has been observed that exposing Stellite 6 to heat treatments at 1000°C results in a characteristic textured structure and coarsening of interdendritic regions due to bulk diffusion. In addition, both dendritic and interdendritic hardness values increase due to texture formation and increased amounts of carbide and intermetallic phases, respectively. On the other hand, silicon addition to Stellite 6 causes the transformation of the original spongy dendritic microstructure in as-cast Stellite 6 to a eutectic dendritic and skeleton interdendritic structure. Also, when silicon added Stellite 6 was heat treated at 1000°C, particulates emanating from the interdendritic skeleton become irregularly dispersed in the dendritic region. In addition, similarly to Stellite 6; a high temperature heat treatment results in an increase in hardness values of silicon added Stellite 6 due to the presence of an Co₂ Si intermetallic phase.     

General solution of one-dimensional,time dependent coupled heat flow systems

A new, powerful method of analysis, involving the combined use of finite integral transform and finite element techniques, is presented for the solution of time dependent heat flow systems composed of many one-dimensional elements connected through the nodes. This method leads to an eigenvalue problem which is not of the conventional Sturm-Liouville type. A procedure for the determination of the eigenvalues is described. The solution obtained is in the form of an infinite series and contains quasi-steady and transient terms. The general solution obtained can be applied in the mathematical modelling of many engineering applications such as the determination of the penetration of the daily temperature cycle into buildings, the analysis of heat transfer in array of extended surfaces in compact heat exchangers, and many others.     

Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics

Ultra-high-performance concrete (UHPC) is a recent class of concrete with improved durability, rheological and mechanical and durability properties compared to traditional concrete. The production cost of UHPC is considerably high due to a large amount of cement used, and also the high price of other required constituents such as quartz powder, silica fume, fibres and superplasticisers. To achieve specific requirements such as desired production cost, strength and flowability, the proportions of UHPC’s constituents must be well adjusted. The traditional mixture design of concrete requires cumbersome, costly and extensive experimental program. Therefore, mathematical optimisation, design of experiments (DOE) and statistical mixture design (SMD) methods have been used in recent years, particularly for meeting multiple objectives. In traditional methods, simple regression models such as multiple linear regression models are used as objective functions according to the requirements. Once the model is constructed, mathematical programming and simplex algorithms are usually used to find optimal solutions. However, a more flexible procedure enabling the use of high accuracy nonlinear models and defining different scenarios for multi-objective mixture design is required, particularly when it comes to data which are not well structured to fit simple regression models such as multiple linear regression. This paper aims to demonstrate a procedure integrating machine learning (ML) algorithms such as Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR) to develop high-accuracy models, and a metaheuristic optimisation algorithm called Particle Swarm Optimisation (PSO) algorithm for multi-objective mixture design and optimisation of UHPC reinforced with steel fibers. A reliable experimental dataset is used to develop the models and to justify the final results. The comparison of the obtained results with the experimental results validates the capability of the proposed procedure for multi-objective mixture design and optimisation of steel fiber reinforced UHPC. The proposed procedure not only reduces the efforts in the experimental design of UHPC but also leads to the optimal mixtures when the designer faces strength-flowability-cost paradoxes.

     

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.

     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

Performance Evaluation of Range Queries in Key Value Stores

Recently there has been a considerable increase in the number of different Key-Value stores, for supporting data storage and applications on the cloud environment. While all these solutions try to offer highly available and scalable services on the cloud, they are significantly different with each other in terms of the architecture and types of the applications, they try to support. Considering three widely-used such systems: Cassandra, HBase and Voldemort; in this paper we compare them in terms of their support for different types of query workloads. We are mainly focused on the range queries. Unlike HBase and Cassandra that have built-in support for range queries, Voldemort does not support this type of queries via its available API. For this matter, practical techniques are presented on top of Voldemort to support range queries. Our performance evaluation is based on mixed query workloads, in the sense that they contain a combination of short and long range queries, beside other types of typical queries on key-value stores such as lookup and update. We show that there are trade-offs in the performance of the selected system and scheme, and the types of the query workloads that can be processed efficiently.