Application of GA-Optimized ANNs to Predict the Water Content,CO2 and H2S Absorption Capacity of Diethanolamine (DEA) in Khangiran Gas Sweetening Plant

Theoretical Foundations of Chemical Engineering - In this work, with the aim of accurate prediction of water content, H2S and CO2 absorption capacity of diethanolamine (DEA) solvent in Khangiran...     

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.

     

A hybrid recommendation technique based on product category attributes

Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.     

Gel-casting of transparent magnesium aluminate spinel ceramics fabricated by spark plasma sintering (SPS)

In this paper, a transparent magnesium aluminate spinel ceramic was fabricated through the newest colloidal gel casting method, using a synthetic powder with the average particle size of 90 nm and Isobutylene-Maleic Anhydride (ISOBAM) additive. ISOBAM served as both a dispersant and a gelation agent to achieve a dense body. Also, the suspension rheological behavior was optimized by the solid loading of 85 wt%, the additive content of 0.7 wt%, and the gelation time of 350 s. This led to a green body with a density equal to 65% of theoretical density and the green strength of 14.48 MPa. The results revealed that the reduction of porosity and the uniform distribution of pores in the green body (smaller than half of the initial powder particle size, 35 nm), as accompanied by spark plasma sintering (SPS), resulted in the final body density of 99.97%, as well as the high in-line transmittance of 86.7% at the wavelength of 1100 nm.     

Characterization of the physicochemical and structural evolution of biomass particles during combined pyrolysis and CO2 gasification

The combination of pyrolysis and CO₂ gasification was studied to synergistically improve the syngas yield and biochar quality. The subsequent 60-min CO₂ gasification at 800 °C after pyrolysis increased the syngas yield from 23.4% to 40.7% while decreasing the yields of biochar and bio-oil from 27.3% to 17.1% and from 49.3% to 42.2%, respectively. The BET area of the biochar obtained by the subsequent 60-min CO₂ gasification at 800 °C was 384.5 m²/g, compared to 6.8 m²/g for the biochar obtained by the 60-min pyrolysis at 800 °C, and 1.4 m²/g for the raw biomass. The biochar obtained above 500 °C was virtually amorphous.     

Effects of active chitosan-flaxseed mucilage-based films on the preservation of minced trout fillets: A comparison among aerobic,vacuum, and modified atmosphere packaging

The objectives of the current study were to examine physical–mechanical, structural, and morphological characteristics of chitosan-flaxseed mucilage films enriched with Ziziphora clinopodioides essential oil (ZEO; 0%, 0.25%, and 0.5%) and sesame oil (SO; 0% and 0.75%) and to provide useful information for the preservation of minced trout fillet's using antimicrobial films under aerobic, vacuum, and modified atmosphere conditions for 16 days. The films showed thickness, tensile strength, puncture force, puncture deformation, water vapor transmission rate, water vapor permeability, swelling index, and oxygen permeability values ranging 0.082–0.86 mm, 33.34–46.83 MPa, 25.69–53.08 N, 11.45–28.45 mm, 17.48–26.73 g/m² h, 8.57–12.49 × 10⁻⁴ g mm/m² h Pa, 12.45–38.43%, and 3.02–13.32 × 10⁻¹² cm³/m² s Pa, respectively. The following order of effect on the microbial spoilage population of treated samples was found in the applied packaging methods: modified atmosphere packaging > vacuum packaging > aerobic packaging. The final microbial population of treated samples was 0.35–4.91 log CFU/g lower than the controls after 16 days of refrigerated storage. At the end of the storage, the total volatile base nitrogen, peroxide value, and thiobarbituric acid reactive substances of untreated samples were 34.02–48.6 mg of N/100 g, 1.43–2.32 meq of peroxide/1000 g, and 3.33–4.24 mg of malondialdehyde/kg, respectively. The lowest corresponding values were recorded for the treated samples with ZEO 0.5% + SO 0.75% films by 14.26–17.73 mg of N/100 g, 0.48–0.86 meq of peroxide/1000 g, and 1.08–1.48 mg of malondialdehyde/kg, respectively.     

Application of Artificial Intelligent Tools to Modeling of Glucosamine Preparation from Exoskeleton of Shrimp

The objective of this study was to forecast and optimize the glucosamine production yield from chitin (obtained from Persian Gulf shrimp) by means of genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural networks (ANNs) as tools of artificial intelligence methods. Three factors (acid concentration, acid solution to chitin ratio, and reaction time) were used as the input parameters of the models investigated. According to the obtained results, the production yield of glucosamine hydrochloride depends linearly on acid concentration, acid solution to solid ratio, and time and also the cross-product of acid concentration and time and the cross-product of solids to acid solution ratio and time. The production yield significantly increased with an increase of acid concentration, acid solution ratio, and reaction time. The production yield is inversely related to the cross-product of acid concentration and time. It means that at high acid concentrations, the longer reaction times give lower production yields. The results revealed that the average percent error (PE) for prediction of production yield by GA, PSO, and ANN are 6.84, 7.11, and 5.49%, respectively. Considering the low PE, it might be concluded that these models have a good predictive power in the studied range of variables and they have the ability of generalization to unknown cases.