Earticle

A wastewater treatment plant is an intricate system with a wealth of information, where the aeration system of the active sludge process is designed to provide oxygen to microorganisms. Owing to the time delay in biochemical reactions, adjustments made by operational staff to the airflow often lead to delayed changes in dissolved oxygen concentration, frequently causing overaeration. The paper introduces a machine learning model that utilizes water quality indicators and air blower indicators to predict current airflow. By leveraging the airflow predicted by this model, the dissolved oxygen concentration for the next hour is successfully maintained within the optimal range of 2 mg/L to 4 mg/L. In the case of airflow prediction, the Transformer model proved more effective than the random forest and long short-term memory models, owing to its self-attention model architecture. In conclusion, the study demonstrates the successful applicability of machine learning models to predict airflow on the promise of maintaining dissolved oxygen stability. These findings present a data-driven approach to guarantee the steady operation of wastewater treatment plants.

The integration of integrated sensing and communication (ISAC) with artificial intelligence (AI)-driven techniques has emerged as a transformative research frontier, attracting significant interest from both academia and industry. As sixth-generation (6G) networks advance to support ultra-reliable, low-latency, and high-capacity applications, machine learning (ML) has become a critical enabler for optimizing ISAC functionalities. Recent advancements in deep learning (DL) and deep reinforcement learning (DRL) have demonstrated immense potential in enhancing ISAC-based systems across diverse domains, including intelligent vehicular networks, autonomous mobility, unmanned aerial vehicles based communications, radar sensing, localization, millimeter wave/terahertz communication, and adaptive beamforming. However, despite these advancements, several challenges persist, such as real-time decision-making under resource constraints, robustness in adversarial environments, and scalability for large-scale deployments. This paper provides a comprehensive review of ML-driven ISAC methodologies, analyzing their impact on system design, computational efficiency, and real-world implementations, while also discussing existing challenges and future research directions to explore how AI can further enhance ISAC’s adaptability, resilience, and performance in next-generation wireless networks. By bridging theoretical advancements with practical implementations, this paper serves as a foundational reference for researchers, engineers, and industry stakeholders, aiming to leverage AI’s full potential in shaping the future of intelligent ISAC systems within the 6G ecosystem.

The need for condition-based maintenance and sustainability management of military assets has become increasingly important due to reductions in military personnel and budget constraints. In this context, research is actively being conducted using sensors that collect environmental data to determine optimal inspection intervals and identify vulnerabilities early. This study proposes an algorithm to dynamically adjust the sampling interval based on the rate of change in measured physical quantities to reduce sensor battery consumption. Various environmental sensing data, such as temperature, humidity, acceleration, and pressure, were collected from a simulation device that mimicked the storage environment of actual guided missiles. The analysis of the collected data revealed that setting the RSME threshold within 5 improved battery lifespans by 11% compared to the original data, while demonstrating that the temperature RMSE was estimated to improve by about 4.76 in adaptive sampling and 5.44 in uniform sampling. This demonstrates that our approach may improve maintenance efficiency by monitoring military assets more effectively and for extended periods.

Advancements in deep learning have enhanced vision-based aggregate analysis. However, further development and studies have encountered challenges, particularly in acquiring large-scale datasets. Data collection is costly and time-consuming, posing a significant challenge in acquiring large datasets required for training neural networks. To address this issue, this study introduces a simulation that efficiently generates the necessary data and labels for training neural networks. We utilized a genetic algorithm (GA) to create optimized lists of aggregates based on the specified values of weight and particle size distribution for the aggregate sample. This enabled sample data collection without conducting sieving tests. Our evaluation of the proposed simulation and GA methodology revealed errors of 1.3% and 2.7 g for aggregate size distribution and weight, respectively. Furthermore, we assessed a segmentation model trained with data from the simulation, achieving a promising preliminary F1 score of 78.18 on the actual aggregate image.

Most of the data warehouse (DW) requirements engineering approaches have not distinguished the early requirements engineering phase from the late requirements engineering phase. There are very few approaches seen in the literature that explicitly model the early & late requirements for a DW. In this paper, we propose an AGDI (Agent-Goal-Decision-Information) model to support the early and late requirements for the development of DWs. Here, the notion of agent refers to the stakeholders of the organization and the dependency among agents refers to the dependencies among stakeholders for fulfilling their organizational goals. The proposed AGDI model also supports three interrelated modeling activities namely, organization modeling, decision modeling and information modeling. Here, early requirements are modeled by performing organization modeling and decision modeling activities, whereas late requirements are modeled by performing information modeling activities. The proposed approach has been illustrated to capture the early and late requirements for the development of a university data warehouse exemplifying our model's ability of supporting its decisional goals by providing decisional information.

This paper proposes how to design and implement data-driven strategies by investigating how a firm can increase its value using data science. Drawing on prior studies on architectural innovation, a behavioral theory of the firm, and the knowledge-based view of the firm as well as the analysis of field observations, the paper shows how data science is abused in dealing with meso-level data while it is underused in using macro-level and alternative data to accomplish machine-human teaming and risk management. The implications help us understand why some firms are better at drawing value from intangibles such as data, data-science capabilities, and routines and how to evaluate such capabilities.

It is crucial to develop effective and efficient big data analytics methods for problem solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

This study evaluates the impact of Korea’s major Free Trade Agreements (FTAs), particularly the Korea–China Free Trade Agreement (KCFTA) and the Korea–United States Free Trade Agreement (KORUS), on Korea’s export performance using econometric and machine learning approaches. Quarterly trade and macroeconomic data were collected from KOSIS, the IMF, the World Bank, and the National Bureau of Statistics of China (NBS). A Difference-in-Differences (DiD) framework was used to estimate relative treatment effects before and after FTA implementation, while Stepwise Regression, Ridge, Lasso, Elastic Net, and Random Forest models were applied for robustness and predictive analysis. The findings do not provide strong evidence that the FTAs generated additional positive export effects during the study period. Instead, GDP and exchange rates emerged as more significant determinants across most models. Among the predictive models, Random Forest demonstrated the strongest predictive performance with the lowest prediction errors. The study highlights the value of combining causal econometric analysis with machine learning techniques to evaluate trade agreement effectiveness from multiple analytical perspectives.

The rapid expansion of online apparel retail has increased the demand for accurate size recommendations that minimize returns and enhance customer satisfaction. This study presents a data-driven analysis of a bioelectrical impedance analysis (BIA)–based size recommendation system implemented on a live e-commerce platform. Using anonymized transaction and feedback data from Boastfit.com, the research compares behavioral and perceptual outcomes between BIA-based recommendations and conventional size guides. The BIA group recorded a return rate of 9.6 percent compared with 17.2 percent in the control group, an average satisfaction score above 8 on a ten-point scale, and a repurchase ratio of 79 percent. These results confirm that physiological data–driven personalization improves predictive accuracy, post-purchase satisfaction, and repurchase intention. The findings contribute to next-generation computing and fashion retail analytics by demonstrating how body-composition data can be integrated into intelligent recommendation systems to enhance user trust and sustainable engagement.

The integration of efficient information-based systems characterizes one of the vital priorities of an Italian regional healthcare environment to meet its clinical, organizational, and managerial needs, especially after the COVID-19 pandemic. The current study proposes that the most likely approach to achieve an Italian regional healthcare system is to use a health level-7-based interoperable communication system implemented by an asynchronous communication infrastructure between healthcare sites. The proposed framework is a comprehensive and integrated management information systems (MIS) centered coordination at an Italian regional level that includes all categories of healthcare levels, including the interoperability concerns; that covers most of the needed elements and can work efficiently and effectively in a vast secure area network to safeguard data privacy and confidentiality of a local the patient. Another essential feature of the proposed framework system solution is that it creates interoperability and compatibility that can be simulated from one healthcare institution to another inside the Italian healthcare environment. In that case, joint interoperability and system-compatible messages can interrelate heterogeneous management-based information systems. In response to the COVID-19 situation in Italy, more than 50 different consortiums have submitted proposals directly to the Italian Government. It is believed that the proposed interoperability framework seems to be widely considered an acceptable solution to enhance information and communication technologies (ICT) developments in the healthcare sector in Italy. Furthermore, this study identifies some flaws inside the Italian healthcare sector with possible elucidations for further research.

본 연구는 객관적인 데이터 기반 방법을 통해 인간 중심 디자인 과정을 효과적으로 보완하는 디자인 프로세스를 제시한다. 즉, 주관적 방법에 의한 인간 중심 디자인 프로세스에서 결여되는 객관성이 데이터 기반 접근에 의해 보완되 어 숨겨진 사용자의 니즈를 효과적으로 발견하는 프로세스로 발전될 수 있다. 이에 본 연구에서는 설문조사 데이터 마이닝 분석 과정과 공동 디자인 프로세스가 접목된 인간 중심 디자인 프로세스를 제시하며, 스마트 난방복 사례연구를 통해 이를 검증한다. 설문조사 데이터 마이닝 분석 과정에서는 클러스터링과 의사결정 나무의 두 가지 분석 방법이 사용된다. 클러스터링은 타겟 그룹을 선정하는 기준이 되는 페르소나의 초안을 제시하며, 의사결정 나무는 제품 구매에 중요한 사용자 인식 속성 파악과 사용자 가치 체계를 일차적으로 제안한다. 이후 데이터 분석을 통해 얻어진 광범위한 관점에 대하여 타겟 그룹을 대표하는 사용자가 직접 참여하는 공동 디자인 프로세스가 수행되며 맞춤형 워크북을 이용 하여 신제품에 대한 사용자의 여정맵, 니즈, 아이디어, 가치 체계 등을 체계적으로 도출한다. 본 논문에서 수행한 스마 트 난방복 사례 연구는 제안된 방법론의 적용성을 보여주고 있다.

This research suggests a design process that effectively complements the human-centered design through an objective data-driven approach. The subjective human-centered design process can often lack objectivity and can be supplemented by the data-driven approaches to effectively discover hidden user needs. This research combines the data mining analysis with co-design process and verifies its applicability through the case study on the smart heating jacket. In the data mining process, the clustering can group the users which is the basis for selecting the target groups and the decision tree analysis primarily identifies the important user perception attributes and values. The broad point of view based on the data analysis is modified through the co-design process which is the deeper human-centered design process by using the developed workbook. In the co-design process, the journey maps, needs and pain points, ideas, values for the target user groups are identified and finalized. They can become the basis for starting new product development.

데이터 기반 학습(Data-driven learning; DDL) 연구는 최근 중등교육 맥락으로 관심을 확장하고 있으나, 이 맥락에서 멀티모달 입력이 학습을 어떻게 지원할 수 있는지는 아직 실증적으로 검토되지 않았다. 본 연구는 멀티미디어 학습의 인지 이론(CTML)에 기반하여 중등 학습자를 위한 멀티모달 DDL을 적용한 최초의 시도 중 하나이다. 연구의 탐색적 성격을 고려하여, 학습 결과와 학습자 인식을 살펴보기 위해 단일집단 설계를 사용하였다. 참여자는 CEFR A2 수준의 한국 중학생 43명이었다. 멀티모달 DDL 수업은 40분씩 총 3차시로 진행되었으며, 각 차시에는 상대적 지위, 사회적 거리, 요청 부담이 낮은 상황과 높은 상황에 각각 적절한 요청 표현을 한 가지씩 다루었다. 학습자들은 차시마다 멀티모달 플랫폼에서 영상을 시청하고 안내 질문에 응답한 뒤, 협력 학습지를 활용하여 짝토의와 요청 표현 말하기 연습을 수행하였다. 사전ㆍ사후ㆍ지연 사후 검사 결과, 사회화용론 점수는 즉시 사후 검사에서 상승했으나 화용언어론에서는 뚜렷한 변화가 나타나지 않았고, 두 지식 영역 모두에서 장기 유지의 증거는 제한적이었다. 플랫폼의 안내 질문에 대한 응답, 인식 설문, 그리고 인터뷰 자료는 참여자들이 영상 이해와 구두 담화 완성 시험 수행에 어려움을 겪었음에도, 멀티모달 단서와 시각적 강화에 주목하여 대화 상황의 사회화용론적 특성을 추론했음을 보여주었다. 본 연구 결과는 중등학습자를 위한 멀티모달 DDL이 사회화용론 발달을 지원할 가능성을 시사하며, 화용언어론적 발달과 장기 학습을 지원하기 위한 향후 연구 및 교수 설계의 방향을 제시한다.

Although data-driven learning (DDL) research has begun to extend to pre-tertiary settings, the potential of multimodal input for supporting learners in these settings has not yet been empirically examined. This study presents one of the first implementations of multimodal DDL for pre-tertiary learners, drawing on the Cognitive Theory of Multimedia Learning (CTML). Given the exploratory nature of the study, a single-group design was used to examine learning outcomes and learner perceptions. Participants were 43 Korean middle school learners at the CEFR A2 level. The main intervention consisted of three 40-minute sessions, each targeting one request expression appropriate for low-PDR situations and another appropriate for high-PDR situations (six total). In each session, participants first used a multimodal DDL platform to view videos and answer guiding questions and then completed pair discussions and speaking practice with a collaborative worksheet. Pre-test, post-test, and delayed post-test scores indicated an immediate increase in sociopragmatic scores, but not pragmalinguistic scores, with little evidence of long-term retention in either domain. Participants’ answers to the guiding questions, questionnaire responses, and interviews revealed that, despite difficulties in video comprehension and spoken discourse completion tests (DCTs), they drew on multimodal cues and visual enhancements to infer sociopragmatic features of communicative situations.

본 연구는 주요 미국 통화정책 변화가 암호화폐 시장의 가치 재평가의 연관성을 찾기 위해 데이터 기반 융합 금융 분석 체계를 제시한다. 특히, 연구는 총 암호화폐 시가총액(TMC)과 비트코인 시가총액(BTC), 미국의 정책금리 월별 자료를 이용하여, 단기 변동성 충격과 중기 가치 조정 효과를 구분해 분석한다. 정책의 금리 변동은 완화, 긴축 사건으로 분류되며, 충격 이후 반응은 사건 구간 통계 및 국소 예측과 능형 정규 분포 시차 진단을 통해 평가한다. 분석 결과, 충격 후 주반응은 변동성보다 가치평가 수준에서 관찰된다. TMC와 BTC는 완화 국면에서 중기 구간의 긍정적 가치 재평가가 나타난다. 반면에, 긴축 국면은 추정 정밀도가 낮게 관찰된다. 따라서 두시가총액의 비교는 시장 전체 시가총액이 비트코인보다 더 디지털자산 시장의 조정 양상을 모니터링하는 데 유용한 기준임을 시사한다.

This study develops a data-driven convergence-finance framework for examining how major U.S. monetary-policy movements are associated with cryptocurrency market revaluation. Using monthly data on total cryptocurrency market capitalisation (TMC), Bitcoin market capitalisation (BTC), and the U.S. policy-rate series, the analysis separates short-run volatility disturbance from medium-run valuation adjustment. Policy-rate changes are classified into easing and tightening episodes, and post-shock responses are evaluated using event-window statistics, local projections, and ridge-regularised distributed-lag diagnostics. The results indicate that the main post-shock signal is observed in valuation levels rather than volatility. Easing episodes are followed by positive medium-run revaluation in both TMC and BTC, while tightening responses are weaker and less precisely estimated. The comparison between TMC and BTC suggests that market-wide capitalisation provides a useful benchmark for monitoring digital-asset adjustment beyond Bitcoin-specific movements.

Understanding features of self-repairs is critical to the exploration of interpreters’ underlying self-monitoring mechanism. As most previous taxonomies just made slight amendments on Levelt’s tripartite taxonomy model — which is designed for monolingual speech production, they failed to match features of interpreting output. In order to resolve the defects of previous classifications, the present paper established an interpreting-tailored taxonomy of self-repairs, which categorized repairs into five major types with altogether nine sub-types. Rather than directly referring to Levelt’s model, each category in the new taxonomy is derived from the descriptive study of data collected from ten interpreting trainees, including their interpreting recordings, retrospection and notes. The qualitative analysis on distribution of each repair category also shows that trainees’ adoption of repairs is mainly triggered by competence deficiency rather than the attempt to facilitate communication.

무선센서 네트워크는 센서 데이터베이스 관리 시스템을 통해 보다 효율적으로 개발 및 운용될 수 있다. 센서 데이터베이스 관리 시스템은 무선센서 입력에 대해 선언된 사용자 정의 질의를 처리하기 위해 사용자들에게 익숙한 SQL 유형의 사용자 접속을 지원한다. 무선센서 네트워크상의 전형적 질의 유형은 임의의 스냅 샷 값 검색 이나 오래도록 지속되는 연속 질의 형태를 갖는다. 무선센서 네트워크상에서 질의 처리는 베이스스테이션으로부터 여러 노드들로 질의를 보내는 과정과 여러 노드에서 얻어지는 질의 결과를 베이스스테이션으로 회수하는 과정이 있는데 이러한 질의의 파급이나 베이스스테이션으로의 결과 전송은 많은 에너지 소모를 요구한다. 이 논문은 무선 센서 네트워크상에서 영역 질의를 처리함에 있어 질의 및 결과를 파급시키는데 소모되는 에너지를 절약시켜 주기 위한 분산정보수집(DIG: Distributed Information Gathering)이라고 이름붙인 효율적 색인 방법을 제안한다.

Wireless sensor networks (WSN) can be more reliable and easier to program and use with the help of sensor database management systems (SDMS). SDMS establish a user-friendly SQL-based interface to process declarative user-defined queries over sensor readings from WSN. Typical queries in SDMS are ad-hoc snapshot queries and long-running, continuous queries. In SDMSs queries are flooded to all nodes in the sensor net, and query results are sent back from nodes that have qualified results to a base station. For query flooding to all nodes, and result flooding to the base station, a lot of communication energy consuming is required. This paper suggests an efficient in-network index solution, named Distributed Information Gathering (DIG) to process range queries in a sensor net environment that can save energy by reducing query and result flooding.

With the advent of the Newspace era and the importance of systematic safety management for the unique hazards of the space environment has been highlighted. Conventional safety management approaches have limitations in effectively responding to complex and real-time evolving space risks. This study proposes the necessity of establishing a data-driven and systematic space risk safety management system to protect human lives and assets and ensure the sustainability of space activities. By analyzing the definition and current status of space risks and examining the limitations of existing safety management methods, this research emphasizes the significance of a data-driven risk management system specifically tailored for the space sector. Such a system plays a crucial role in various aspects, including enhancing mission success rates, protecting lives and assets, preventing accidents and providing early warnings, enabling integrated risk analysis across systems, and establishing a robust safety culture. Ultimately, this study aims to contribute to the formulation and implementation of comprehensive space safety management policies for safe and sustainable space development and activities in the Newspace era.

Artificial intelligence algorithms and quantum computing have become core computational methodologies for the applications of artificial intelligence techniques in smart and sustainable cities. In development and sustainability. Artificial intelligence technologies have faced numerous challenges in natural, social, and cultural aspects. The scientific dialogue on artificial intelligence has been significantly extended to understand and cover sustainability as the practice of living enclosed by resources into the distant future. Quantum computing can improve environmental management, applications of artificial intelligence technologies, big data analytics, and utilization of blockchain in smart and sustainable cities. Policy decision-makers may need to consider the introduction of quantum computing technologies for applications of artificial intelligence in their smart cities for urban growth in order to lower barriers to utilizing quantum artificial intelligence.

With the popularization of social media, companies are using it as a new advertising channel to replace traditional advertising channels. As a result, influencer marketing, which utilizes influencers who are highly influential on social media for advertising, is gaining attention. Views are a representative marketing metric, and in this study, we propose a model to predict the number of views of influencer marketing videos using YouTube. To this end, we collected 16,348 influencer marketing videos in the food industry and built a deep learning-based view prediction model using multimodal data such as thumbnails and title text. We also verified the effectiveness of the proposed methodology by performing the same modeling on influencer marketing videos in the tech sector. The results of this study can provide implications for companies and video producers who utilize influencer marketing.