This paper explores a convergent approach that combines advanced informatics and computational science to develop road-paving materials. It also analyzes research trends that apply artificial-intelligence technologies to propose research directions for developing new materials and optimizing them for road pavements. This paper reviews various research trends in material design and development, including studies on materials and substances, quantitative structure–activity/property relationship (QSAR/QSPR) research, molecular data, and descriptors, and their applications in the fields of biomedicine, composite materials, and road-construction materials. Data representation is crucial for applying deep learning to construction-material data. Moreover, selecting significant variables for training is important, and the importance of these variables can be evaluated using Pearson’s correlation coefficients or ensemble techniques. In selecting training data and applying appropriate prediction models, the author intends to conduct future research on property prediction and apply string-based representations and generative adversarial networks (GANs). The convergence of artificial intelligence and computational science has enabled transformative changes in the field of material development, contributing significantly to enhancing the performance of road-paving materials. The future impacts of discovering new materials and optimizing research outcomes are highly anticipated.

Semiconductors, optimized for artificial intelligence (AI) applications, are efficiently handling large-scale data processing and complex computations with high speed and low power consumption. They accelerate AI model training and inference in data centers, cloud services, autonomous vehicles, and mobile devices. As demand for high-speed data transmission and extensive data processing grows, global companies are developing proprietary AI semiconductors, and subsequently, high-density packaging technologies are needed to interconnect multiple processor chips. To achieve this, an interposer is required. An interposer is a layer used in packaging technology for combining multiple chips, which includes wiring that is inserted to electrically connect a semiconductor chip with a substrate that has a significant pitch difference. Among the materials employed as substrates or interposers, organic, silicon and glass are being considered. While silicon interposers are usually used to connect the main substrate and multiple chips, producing very thin silicon wafers and controlling warpage is challenging, and so they suffer from poor yield and integration. Also, organic substrates have difficulty achieving fine pitch because of their uneven surface and warpage. On the other hand, glass substrates and interposers have good electrical and thermal properties. For this reason, this study investigated AI semiconductor packaging trends and through glass via (TGV) technology, emphasizing the importance of suitable glass material selection, reliable glass-metal bonding and application to solder bumping on TGV. Advances in AI and TGV technologies are expected to drive next-generation AI semiconductor packaging development.

We introduce the technology required todevelop a bracket process for installing and verifying FRT bumper sensors for passenger cars. Establish and demonstrate process automation through actual design and manufaturing. We conduct quality inspection of the production process using artificial intelligence and develop technology to automatically detect good and defective products and increase the reliability of the process

This study explores the ways in which sociocultural perspectives on English language education can contribute to teacher education the era of artificial intelligence (AI). Three key words that represent the relationship between sociocultural perspectives and English teacher education—context, interaction, and social practice—can each be linked to the key concepts of criticality, multimodality, and action research. Teachers of English need to be ready for the forthcoming changes in the AI era, for which they must be equipped with a critical ability to focus on issues and needs in the Korean context. This ability can be applied in teaching students various types of interactions, especially those involving the use of computers, and will create opportunities for teachers to conduct research of their own and cultivate a professional teacher identity. This study concludes by recommending substantial changes in the current pre-service and in-service English teacher education programs in accordance with these key concepts.

본 연구는 반도체 제품과 설비기술을 대상으로 제품-공정기술 공진화를 교차영향분석(cross impact analysis)을 통해 구체화하고, 공진화 관계를 기술 인텔리전스의 대표적 도구 중 하나인 기술레이더에 통합하는 방법을 제시한다. 교차영향분석을 통해 공정기술 발전과 제품특성 개선이 반복되는 공진화 경로와 축이 되는 세부기술들을 파악했다. 또한 공진화 관계를 기술레이더의 기술가치평가 프로세스에 반영 해 가치평가와 연구개발 포트폴리오의 신뢰성을 제고했다. 학술적 측면에서 기술간 공진화를 세부기술 단위에서 구체화했으며, 기술 공진화 이론과 기술 인텔리전스의 접점을 제시했다는 의미가 있다. 실무적 측면에서는 반도체 관통전극-하이브리드 패키지 제품과 주요 후공정 기술간 공진화 및 기술레이더 분석 실례를 제시하고, 이를 통해 기술간 공진화 관계를 기존 기술전략 및 기획도구에 반영 해 기업의 미래준비역량과 전략기획의 신뢰성을 제고하는 방법을 구체화했다는 점에서 가치가 있다.

In order to monitor the long-term condition of structures in nuclear waste disposal system and evaluate the degree of damage, it is necessary to secure quantitative monitoring, diagnosis, and prediction technology. However, at present, only simple monitoring or deterioration evaluation of the structure is being performed. Recently, there is a trend to develop monitoring systems using artificial intelligence algorithms, such as to introduce artificial intelligence-based failure diagnosis technology in nuclear power plant facilities. An artificial intelligence algorithm was applied to distinguish the noise signal and the destructive signal collected in the field. This can minimize false alarms in the monitoring system. However, it is difficult to apply artificial intelligence to industrial sites only by learning through laboratory data. Therefore, a database of noise signals and destructive signals was constructed through laboratory data, and signals effective for quantitative soundness determination of structures were separated and learned. In addition, an adaptive artificial intelligence algorithm was developed to enable additional learning and adaptive learning using field data, and its performance was verified through experiments.

The performance of a teacher has an important role in the success of education in general. This study aims to determine the factors that affect the decline in teacher performance in one of the junior secondary schools in Indonesia. Based on the literature review, four exogenous variables were identified, namely, training, utilization of information technology, intellectual intelligence, and emotional intelligence. This study uses primary data, collected from a questionnaire distributed to respondents. The questionnaire items are measured using a Likert scale. The sample in this study were all teachers at MTS Darul Falah Sirahan, totaling 32 people. The analysis technique used in testing the hypothesis of this study is multiple regression analysis. Statistical product and service solutions are used as analysis tools. The results of this study indicate that only the variable ‘utilization of information technology’ has a positive and significant effect. However, the variables ‘training,’ ‘intellectual intelligence,’ and ‘emotional intelligence’ did not have a significant effect. This finding contradicts the literature in general. Therefore, this study recommends improving training methods, both those carried out internally by schools and by related agencies, and schools still need to optimize guidance and potential for teacher’s intelligence in improving performance.