• Fisheries and Aquatic Sciences
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• v.25 no.2
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• pp.76-89
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• 2022

The Lophius litulon liver was used as raw material for the extraction of fish oil via various extraction methods. The extraction rate by water extraction, potassium hydroxide (KOH) hydrolysis and protease hydrolysis were compared and the results revealed the protease hydrolysis extraction had a higher extraction rate with good protein-lipid separation as observed by optical microscope. Furthermore, subsequent experiments determined neutrase to be the best hydrolytic enzyme in terms of extraction rate and cost. The extraction conditions of neutrase hydrolysis were optimized by single-factor experiment and response surface analysis, and the optimal extraction rate was 58.40 ± 0.25% with the following conditions: enzyme concentration 2,000 IU/g, extraction time 1.0 h, liquid-solid ratio 1.95:1, extraction temperature 40.5℃ and pH 6.5. The fatty acids composition in fish oil from optimized extraction condition was composed of 19.75% saturated fatty acids and 80.25% unsaturated fatty acids. The content of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) were 8.06% and 1.19%, respectively, with the ratio (6.77:1) surpassed to the recommendation in current researches (5:1). The results in this study suggest protease treatment is an efficient method for high-quality fish oil extraction from Lophius litulon liver with a satisfactory ratio of DHA and EPA.

• Coupled systems mechanics
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• v.11 no.1
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• pp.55-78
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• 2022

The main aim of this paper is the identification of the model parameters for the constitutive model of concrete and concrete-like materials capable of representing full set of 3D failure mechanisms under various stress states. Identification procedure is performed taking into account multi-scale character of concrete as a structural material. In that sense, macro-scale model is used as a model on which the identification procedure is based, while multi-scale model which assume strong coupling between coarse and fine scale is used for numerical simulation of experimental results. Since concrete possess a few clearly distinguished phases in process of deformation until failure, macro-scale model contains practically all important ingredients to include both bulk dissipation and surface dissipation. On the other side, multi-scale model consisted of an assembly micro-scale elements perfectly fitted into macro-scale elements domain describes localized failure through the implementation of embedded strong discontinuity. This corresponds to surface dissipation in macro-scale model which is described by practically the same approach. Identification procedure is divided into three completely separate stages to utilize the fact that all material parameters of macro-scale model have clear physical interpretation. In this way, computational cost is significantly reduced as solving three simpler identification steps in a batch form is much more efficient than the dealing with the full-scale problem. Since complexity of identification procedure primarily depends on the choice of either experimental or numerical setup, several numerical examples capable of representing both homogeneous and heterogeneous stress state are performed to illustrate performance of the proposed methodology.

• Journal of Powder Materials
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• v.30 no.3
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• pp.203-209
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• 2023

Aluminum alloy-based additive manufacturing (AM) has emerged as a popular manufacturing process for the fabrication of complex parts in the automotive and aerospace industries. The addition of an inoculant to aluminum alloy powder has been demonstrated to effectively reduce cracking by promoting the formation of equiaxed grains. However, the optimization of the AM process parameters remains challenging owing to their variability. In this study, the response surface methodology (RSM) was used to predict the crack density of AM-processed Al alloy samples. RSM was performed by setting the process parameters and equiaxed grain ratio, which influence crack propagation, as independent variables and designating crack density as a response variable. The RSM-based quadratic polynomial models for crack-density prediction were found to be highly accurate. The relationship among the process parameters, crack density, and equiaxed grain fraction was also investigated using RSM. The findings of this study highlight the efficacy of RSM as a reliable approach for optimizing the properties of AM-processed parts with limited experimental data. These results can contribute to the development of robust AM processing strategies for the fabrication of high-quality Al alloy components for various applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Steel and Composite Structures
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• v.48 no.2
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• pp.179-190
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• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.540-545
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• 2023

AESA radar is able to instantaneously and adaptively position and control the beam, and this enables to have interleaved mode in modern airborne AESA radar which can maximize situational awareness capability. Interleaved mode provides two or more modes simultaneously, such as Air to Air mode and Sea Surface mode by time sharing technique. In this interleaved mode, performance degradation is inevitable, compared with single mode operation, and effective resource allocation is the key component for the success of interleaved mode. In this paper, we identified performance evaluation items for each mode to analyze interleaved mode performance and proposed effective resource allocation methodology to achieve graceful performance degradation of each mode, focusing on detection range. We also proposed beam scheduling techniques for interleaved mode.

• Advances in aircraft and spacecraft science
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• v.10 no.3
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• pp.223-243
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• 2023

There is a burgeoning demand for minimizing the mass of satellites because of its direct impact on reducing launch-to-orbit cost. This must be done without compromising the structure's efficiency. The present paper introduces a relatively low-cost and easily implementable approach for optimizing structural mass to a maximum natural frequency. The natural frequencies of the satellite are of utmost pertinence to the application requirements, as the sensitive electronic instrumentation and onboard computers should not be affected by the vibrations of the satellite structure. This methodology is applied to a realistic model of Al-Azhar University micro-satellite in partnership with the Egyptian Space Agency. The procedure used in structural design can be summarized in two steps. The first step is to select the most favorable primary structural configuration among several different candidate variants. The nominated variant is selected as the one scoring maximum relative dynamic stiffness. The second step is to use perforation patterns reduce the overall mass of structural elements in the selected variant without changing the weight. The results of the presented procedure demonstrate that the mass reduction percentage was found to be 39% when compared to the unperforated configuration that had the same plate thickness. The findings of this study challenge the commonly accepted notion that isogrid perforations are the most effective means of achieving the goal of reducing mass while maintaining stiffness. Rather, the study highlights the potential benefits of exploring a wider range of perforation unit cells during the design process. The study revealed that rectangular perforation patterns had the lowest efficiency in terms of modal stiffness, while triangular patterns resulted in the highest efficiency. These results suggest that there may be significant gains to be made by considering a broader range of perforation shapes and configurations in the design of lightweight structures.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.4
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• pp.233-242
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• 2024

This study evaluates the potential of various coagulants to enhance the efficiency of total phosphorus removal facilities in a sewage treatment plant. After analyzing the existing water quality conditions of the sewage treatment plant, the coagulant of poly aluminium chloride was experimentally applied to measure its effectiveness. In this process, the use of poly aluminium chloride and polymers in various ratios was explored to identify the optimal combination of coagulants. The experimental results showed that the a coagulants combination demonstrated higher treatment efficiency compared to exclusive use of large amounts of poly aluminium chloride methods. Particularly, the appropriate combination of poly aluminium chloride and polymers played a significant role. The optimal coagulant combination derived from the experiments was applied in a micro flotation method of real sewage treatment plant to evaluate its effectiveness. This study presents a new methodology that can contribute to enhancing the efficiency of sewage treatment processes and reducing environmental pollution. This research is expected to make an important contribution to improving to phosphorus remove efficiency of similar wastewater treatment plant and reducing the ecological impact from using coagulants in the future.

• Journal of Microbiology and Biotechnology
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• v.34 no.9
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• pp.1898-1911
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• 2024

Phylloporia lonicerae is an annual fungus that specifically parasitizes living Lonicera plants, offering significant potential for developing new resource food and medicine. However, wild resources and mycelium production of this fungus is limited, and its anti-tumor active ingredients and mechanisms remain unclear, hampering the development of this fungus. Thus, we optimized the fermentation medium of P. lonicerae and studied the anti-tumor activity of its mycelium. The results indicated that the optimum fermentation medium consisted of 2% sucrose, 0.2% peptone, 0.1% KH₂PO₄, 0.05% MgSO₄·7H₂O, 0.16% Lonicera japonica petals, 0.18% P fungal elicitor, and 0.21% L. japonica stem. The biomass reached 7.82 ± 0.41 g/l after 15 days of cultivation in the optimized medium, a 142% increase compared with the potato dextrose broth medium, with a 64% reduction in cultivation time. The intracellular alcohol extract had a higher inhibitory effect on A549 and Eca-109 cells than the intracellular water extract, with half-maximal inhibitory concentration values of 2.42 and 2.92 mg/ml, respectively. Graded extraction of the alcohol extract yielded petroleum ether phase, chloroform phase, ethyl acetate phase, and n-butanol phase. Among them, the petroleum ether phase exhibited a better effect than the positive control, with a half-maximal inhibitory concentration of 113.3 ㎍/ml. Flow cytometry analysis indicated that petroleum ether components could induce apoptosis of Eca-109 cells, suggesting that this extracted component can be utilized as an anticancer agent in functional foods. This study offers valuable technical support and a theoretical foundation for promoting the comprehensive development and efficient utilization of P. lonicerae.