This study investigated the physicochemical properties of protein-fortified rice flour by mixing rice flour (RF) with untreated and fermented plant proteins. Fermented faba bean protein concentrate (FMFP) and chickpea flour (FMCF) were prepared by solid-state fermentation of faba bean protein concentrate (UTFP) and chickpea flour (UTCF) using Bacillus subtilis. FMFP and FMCF exhibited higher crude protein, reducing sugar and starch contents more than their counterparts. The increased rate of essential and branched-chain amino acids in FMFP and FMCF exceeded that of crude protein. Adding plant proteins to RF decreased swelling power (SP) and increased solubility in RF-UTFP and RF-FMFP mixtures, while SP and solubility increased in RF-UTCF and RF-FMCF mixtures. All RF-plant protein mixtures showed higher gelatinization temperature and lower gelatinization enthalpy than RF. Thermal gelation was found in all RF-plant protein mixtures, but the RF-FMCF mixture may form weak and unstable gel structures. The increase in pasting viscosity was minimal for the RF-UTFP and RF-FMFP mixtures but more pronounced for the RF-UTCF and RF-FMCF mixtures. Overall, FMFP may be a potential protein source to supplement the protein deficiency in RF with minimal changes in RF-based foods’ rheological and textural properties.

Aluminum alloys, known for their high strength-to-weight ratios and impressive electrical and thermal conductivities, are extensively used in numerous engineering sectors, such as aerospace, automotive, and construction. Recently, significant efforts have been made to develop novel aluminum alloys specifically tailored for additive manufacturing. These new alloys aim to provide an optimal balance between mechanical properties and thermal/ electrical conductivities. In this study, nine combinatorial samples with various alloy compositions were fabricated using direct energy deposition (DED) additive manufacturing by adjusting the feeding speeds of Al6061 alloy and Al-12Si alloy powders. The effects of the alloying elements on the microstructure, electrical conductivity, and hardness were investigated. Generally, as the Si and Cu contents decreased, electrical conductivity increased and hardness decreased, exhibiting trade-off characteristics. However, electrical conductivity and hardness showed an optimal combination when the Si content was adjusted to below 4.5 wt%, which can sufficiently suppress the grain boundary segregation of the α- Si precipitates, and the Cu content was controlled to induce the formation of Al2Cu precipitates.

This study investigated the changes in the cyanogenic glycoside (CN-Glc) content of maesil chung (MC) prepared according to its preparation conditions (i.e., maesil part, sugar type, maesil-sugar mixing ratio, liquid separation) and sugaring-ripening period and the quality characteristics of their products finalized through filtration and heat treatment (85oC, 30 min) with the 6-month ripened MC. The CN-Glc content dramatically decreased when the maesil flesh, isomaltooligosaccharide, maesil:sugar ratio of 5:5, and liquid separation after the 4-month sugaring were applied to the MC production. The CN-Glc content decreased with the ripening period. There was no effect of filtration and heat treatment on the CN-Glc reduction of the MC product. The sugar type predominantly affected the soluble solid and total carbohydrate content of the MC products, and their contents increased in the order of high-fructose corn syrup > sucrose > isomaltooligosaccharide. The MC product at a maesil:sugar ratio of 6:4 exhibited the higher organic acid content. There was no direct association between the total polyphenolic compound content and the preparation conditions of the MC product. Overall, the use of maesil flesh as a maesil ingredient and more than 6-month ripening after liquid separation may be a pivotal factor in producing the cyanogenic glycoside-reduced maesil chung.

The objective of this study was to investigate changes in the cyanogenic glycoside (CN-Glc) content of apricot and plum chungs over the sugaring-ripening period and to evaluate their quality characteristics. The whole and flesh parts of the apricot and plum were mixed with sugar to a mixing ratio of 1:1 (w/w) to prepare their chungs, after which the fruit-sugar mixtures were stored for 13 months. The CN-Glc content dramatically increased within 3-4 months, reached the maximum, and gradually decreased over storage by 13 months. The apricot and plum chungs with seeds exhibited much higher CN-Glc contents than those without seeds. All chungs stored for 10 months were filtrated and treated for 30 min at 85oC to measure their quality characteristics. Similar soluble solid contents (53.4- 53.6oBx) were found in all chungs. The apricot and plum chungs without seeds exhibited the higher concentrations of total carbohydrate, organic acid, and total polyphenolic compounds than those with seeds. In addition, the color of the apricot and plum chungs without seeds was darker and deeper yellow than those with seeds. Overall, the apricot and plum flesh may be better for producing the stone fruit chungs with minimal CN-Glc content and better nutrition.

Aluminum alloys are widely utilized in diverse industries, such as automobiles, aerospace, and architecture, owing to their high specific strength and resistance to oxidation. However, to meet the increasing demands of the industry, it is necessary to design new aluminum alloys with excellent properties. Thus, a new method is required to efficiently test additively manufactured aluminum alloys with various compositions within a short period during the alloy design process. In this study, a combinatory approach using a direct energy deposition system for metal 3D printing process with a dual feeder was employed. Two types of aluminum alloy powders, namely Al6061 and Al-12Cu, were utilized for the combinatory test conducted through 3D printing. Twelve types of Al-Si-Cu-Mg alloys were manufactured during this combinatory test, and the relationship between their microstructures and properties was investigated.

Metal additive manufacturing (AM) has transformed conventional manufacturing processes by offering unprecedented opportunities for design innovation, reduced lead times, and cost-effective production. Aluminum alloy, a material used in metal 3D printing, is a representative lightweight structural material known for its high specific strength and corrosion resistance. Consequently, there is an increasing demand for 3D printed aluminum alloy components across industries, including aerospace, transportation, and consumer goods. To meet this demand, research on alloys and process conditions that satisfy the specific requirement of each industry is necessary. However, 3D printing processes exhibit different behaviors of alloy elements owing to rapid thermal dynamics, making it challenging to predict the microstructure and properties. In this study, we gathered published data on the relationship between alloy composition, processing conditions, and properties. Furthermore, we conducted a sensitivity analysis on the effects of the process variables on the density and hardness of aluminum alloys used in additive manufacturing.

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

Aluminum alloys are extensively employed in several industries, such as automobile, aerospace, and architecture, owing to their high specific strength and electrical and thermal conductivities. However, to meet the rising industrial demands, aluminum alloys must be designed with both excellent mechanical and thermal properties. Computer-aided alloy design is emerging as a technique for developing novel alloys to overcome these trade-off properties. Thus, the development of a new experimental method for designing alloys with high-throughput confirmation is gaining focus. A new approach that rapidly manufactures aluminum alloys with different compositions is required in the alloy design process. This study proposes a combined approach to rapidly investigate the relationship between the microstructure and properties of aluminum alloys using a direct energy deposition system with a dual-nozzle metal 3D printing process. Two types of aluminum alloy powders (Al-4.99Si-1.05Cu-0.47Mg and Al-7Mg) are employed for the 3D printing-based combined method. Nine types of Al-Si-Cu-Mg alloys are manufactured using the combined method, and the relationship between their microstructures and properties is examined.

본 연구에서는 예비교사를 대상으로 이루어지는 뇌관련 교육의 현황을 확인하고 이러한 교육과 뇌가소성 지식 및 믿음 간 관계를 확인하고자 하였다. 이를 위해 955명(초등 349명, 중등 606명)의 예비교사가 뇌 관련 경험에 대해 응답하였으며 이들 중 514명(초등 266명, 중등 248명)은 추가적으로 뇌가소성 지식과 믿음 수준에 대해 응답하였다. 연구 결과 30%(955명 중 288명)의 예비교사들이 뇌관련 교육 경험을 가지고 있었다. 뇌가소성 지식 4문항 중 한 문항("학습은 신경연결망의 변화와 관련이 있다")이 초등 예비교사와 전체에서 뇌관련 교육 경험이 있을 때 더 높은 정답률을 보이는 것으로 나타났다. 뇌가소성에 대한 믿음은 뇌지식이 교수에 도움이 된다고 믿는 정도와 중요하다고 생각하는 정도와 정적 상관을 보였으며 이러한 관계는 초등 및 중등 예비교사에서 모두 나타났다. 본 연구의 결과는 현재 이루어지고 있는 뇌관련 교육 경험이 예비교사들의 뇌가소성 지식 및 믿음과 낮은 연관을 가짐을 보여준다. 예비교사를 대상으로 한 뇌가소성 지식과 믿음을 높일 수 있는 프로그램이 개발되어야 할 것이다.