Solving constrained optimisation problems using the improved imperialist competitive algorithm and Deb’s technique

ABSTRACT

In this paper, an improved imperialist competitive algorithm called I-ICA is proposed for solving constrained optimisation problems. In I-ICA, the use of differential evolution (DE)/rand/1 mutation operator at assimilation step enhances the population diversity. Also, the binomial crossover operator improves the speed of convergence to optimal solution by distributing good information among solutions. Furthermore, Deb’s rules were applied for handling constrains. Examinations were done on 24 well-known functions and 5 engineering design problems. The comparison of I-ICA with basic ICA and 22 state-of-the-art algorithms shows I-ICA’s superiority in terms of the rate of convergence and quality of the obtained solution.     

An experimental investigation into the performance of a domestic thermosyphon solar water heater under varying operating conditions

An experimental investigation has been carried out on a thermosyphon solar water heater. The system consisted of a flat-plate collector of 1.5 m² absorber area with 21 tubes/m width and storage tank of 125 litre capacity. Experiments were carried out for both cloudy and clear weather conditions in winter and summer. The hourly system performance was evaluated for all test conditions. The final mean tank temperature was measured daily which enabled the calculation of the possible contribution of solar energy for domestic hot water supply in Basrah, Iraq (latitude 30.76°N). The system was tested at both no-load and loading conditions. Intermittent and continuous load was imposed, and system performance was evaluated for each condition.     

Solution combustion synthesis of cerium oxide nanoparticles as corrosion inhibitor

In this study, combustion synthesis of cerium oxide nanoparticles was reported using cerium nitrate hexahydrate as starting material as well as urea, glycine, glucose, and citric acid as fuels. The influence of fuel type on structure, microstructure, band gap, and corrosion inhibition was investigated. X-ray diffraction (XRD) patterns and scanning electron microscopy micrographs showed that CeO₂ nanoparticles with different morphologies were obtained depending on the fuel type. Microstructural changes from unreacted gel to sponge-like morphologies were resulted by varying the fuel type from urea, glycine, and glucose to citric acid. In addition to Ce–O bonds, Fourier transform infrared analysis showed carbon bonds of carbonaceous compositions from incomplete combustion which were declined during combustion reaction. Furthermore, corrosion analyses showed that samples synthesized using urea fuel released the most Ce⁺⁴ ions and could have better protection than other samples.     

Alzheimer Disease Detection Empowered with Transfer Learning

Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.     

Synthesis of Bio-Based Polyamide/Acid-Functionalized Multiwalled Carbon Nanotube Nanocomposites Using Vanillin

Synthesis of bio-based polyamide/acid-functionalized multiwalled carbon nanotube nanocomposites (PA/FCNT NCs) is reported in this investigation. New aliphatic–aromatic bio-based polyamide (PA) was synthesized through direct polycondensation reaction between bio-based diacid derived from a renewable resource; vanillin and diamine containing ether linkages. To obtain a homogeneous dispersion of multiwalled carbon nanotubes (MWCNTs) in the PA matrix, acid-functionalized MWCNTs (FCNTs) were used and PA nanocomposites with three different FCNT contents (1, 5 and 7?wt%) were prepared. The resulting NCs were characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and thermogravimetric analysis (TGA).     

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

A biotechnological perspective on the application of iron oxide nanoparticles

In recent decades,magnetic iron nanoparticles (NPs) have attracted much attention due to properties such as superparamagnetism,high surface area,large surface-to-volume ratio,and easy separation under external magnetic fields.Therefore,magnetic iron oxides have potential for use in numerous applications,including magnetic resonance imaging contrast enhancement,tissue repair,immunoassay,detoxification of biological fluids,drug delivery,hyperthermia,and cell separation.This review provides an updated and integrated focus on the fabrication and characterization of suitable magnetic iron NPs for biotechnological applications.The possible perspective and some challenges in the further development of these NPs are also discussed.     

Feature Extraction Method Based on Social Network Analysis

Due to rapid development of Internet technology and electronic business, fraudulent activities have increased. One of the ways to cope with damages of them is fraud detection. In this field, there is a need for methods accurate and fast. Therefore, a novel and efficient feature extraction method based on social network analysis called FEMBSNA is proposed for fraud detection in banking accounts. In this method, in order to increase accuracy and control runtime in the first step, features based on network level are considered using social network analysis and extracted feature is combined with other features based on user level in the next phase. To evaluate our feature extraction method, we use PCK-means method as a basic method to learn. The results show using the proposed feature extraction as a pre-processing step in fraud detection improves the accuracy remarkably while it controls runtime in comparison with other methods.     

A new method for detecting various variants of GoF design patterns using conceptual signatures

Software design patterns are well-known solutions for solving commonly occurring problems in software design. Detecting design patterns used in the code can help to understand the structure and behavior of the software, evaluate the quality of the software, and trace important design decisions. To develop and maintain a software system, we need sufficient knowledge of design decisions and software implementation processes. However, the acquisition of knowledge related to design patterns used in complex software systems is a challenging, time-consuming, and costly task. Therefore, using a suitable method to detect the design patterns used in the code reduces software development and maintenance costs. In this paper, we proposed a new method based on conceptual signatures to improve the accuracy of design pattern detection. So we used the conceptual signatures based on the purpose of patterns to detect the patterns’ instances that conform to the standard structure of patterns, and cover more instances of patterns’ variants and implementation versions of the patterns and improve the accuracy of pattern detection. The proposed method is a specific process in two main phases. In the first phase, the conceptual signature and detection formula for each pattern is determined manually. Then in the second phase, each pattern in the code is detected in a semi-automatic process using the conceptual signature and pattern detection formula. To implement the proposed method, we focused on GoF design patterns and their variants. We evaluated the accuracy of our proposed method on five open-source projects, namely, Junit v3.7, JHotDraw v5.1, QuickUML 2001, JRefactory v2.6.24, and MapperXML v1.9.7. Also, we performed our experiments on a set of source codes containing the instances of GoF design patterns’ variants for a comprehensive and fair evaluation. The evaluation results indicate that the proposed method has improved the accuracy of design pattern detection in the code.

     

Formulation and Characterization of Human Milk Fat Substitutes Made from Blends of Refined Palm Olein,and Soybean,Olive, Fish,and Virgin Coconut Oils

The simplest and the most cost-effective way of human milk fat substitute (HMFS) production is formulating of suitable vegetable oils at proper ratios. To do this, the D-optimal mixture design was used to optimize the HMFS formulation. The design included 25 formulations made from refined palm olein (35–55%), soybean oil (5–25%), olive oil (5–20%), virgin coconut oil (5–15%), and fish oil (0–10%). Samples were produced in laboratory and characterized in terms of fatty acid and triacylglycerol (TAG) compositions, free fatty acid content, peroxide value, iodine value, and oxidative stability index (OSI). HMFS samples were also compared with Codex Alimentarius (CA) and Iran National Standards Organization (INSO) standards. Each characteristic of HMFS samples was then expressed as a function of ingredient ratio using regression models. Finally, using numerical optimization, four optimized blends (PB₁-PB₄) were selected, made in the laboratory (HMFS₁-HMFS₄), characterized, and compared with CA and INSO standards. The properties of all the optimized blends (except the palmitic acid content of HMFS₂ and the monounsaturated fatty acid [MUFA] content of HMFS₃) met the standards. HMFS₄ showed the highest OSI in Rancimat and the lowest oxidation rate in Schaal oven tests. POL (19.53–21.73%), PPO (20.77–21.73%), OOO (9.11–11.16%), and OPO (8.84–9.46%) were the main (totally about 60%) TAG species found in HMFS samples. In conclusion, the HMFS₄ formula (55% palm olein, 13.5% soybean oil, 16% refined olive oil, 15% virgin coconut oil, and 0.5% fish oil) was suggested as the best formula for HMFS production.