Since a high dropout rate for university students is a significant risk to local communities and countries, a dropout prediction model using machine learning is an active research domain to prevent students from dropping out. However, it is challenging to fulfill the needs of consulting institutes and the office of academic affairs. To the consulting institute, the accuracy in the prediction is of the utmost importance; to the offices of academic affairs and other offices, the reason for dropping out is essential. This paper proposes a Student Dropout Prediction (SDP) system, a hybrid model to predict the students who are about to drop out of the university. The model tries to increase the dropout precision and the dropout recall rate in predicting the dropouts. We then analyzed the reason for dropping out by compressing the feature set with PCA and applying K-means clustering to the compressed feature set. The SDP system showed a precision value of 0.963, which is 0.093 higher than the highest-precision model of the existing works. The dropout recall and F1 scores, 0.766 and 0.808, respectively, were also better than those of gradient boosting by 0.117 and 0.011, making them the highest among the existing works; Then, we classified the reasons for dropping out into four categories: “Employed”, “Did Not Register”, “Personal Issue”, and “Admitted to Other University.” The dropout precision of “Admitted to Other University” was the highest, at 0.672. In post-verification, the SDP system increased counseling efficiency by accurately predicting dropouts with high dropout precision in the “High-Risk” group while including more dropouts in total dropouts. In addition, by predicting the reasons for dropouts and presenting guidelines to each department, the students could receive personalized counseling.

Digital twins can be defined as a virtual representation of a physical object that can replicate its behavior in a virtual environment. This concept involves the integration of three distinct components: the physical space, the virtual space, and the communication between these two spaces. Digital twins are recognized as one of the key driving forces in various industries, and a comprehensive understanding is necessary to utilize them. A thorough review of the advantages and disadvantages of digital twins, including their value, characteristics, applications, and challenges, is essential to analyze their growth potential and investment value. This paper comprehensively reviews the digital twin applications being studied across industries. To this end, we propose three research questions and address them through a structured literature review. First, we examine the core technologies of digital twins and find that they form an integrated framework combining modeling, sensing, and real-time data processing. Second, we analyze their adoption across industries and observe widespread use in aerospace and aeronautics, manufacturing, healthcare, energy, and urban systems with domain-specific objectives. Third, we explore life-cycle applications and identify digital twins as enablers of continuity from design to operation and evolution. These findings offer a broad perspective on the current state and future potential of digital twin applications.

There have been recent advances in multi-core machines with tens and hundreds of cores, and there is an increasing emphasis on the software structure. Many different synchronization mechanism techniques have been developed to improve the performance and the scalability of manycore systems. As the non-blocking algorithms are promising in overcoming performance limits in traditional lock-based blocking synchronization mechanisms, we are observing an increased usage ratio and a number of non-blocking synchronization algorithms. For example, the usage ratio of RCU increased sharply in recent years. Since RCU exhibits low write performance and is difficult to use, the research community introduced RLU and MV-RLU synchronization algorithms to address the issues. RLU and MV-RLU, which are called RCU-style synchronization mechanisms, are promising in terms of providing easy-to-use APIs (Application Programming Interfaces) and better performance in manycore machines. To expand the applicability of RCU-style mechanisms, we need to measure the performance and analyze their measurements in various environments. To meet the goal, we evaluate them at the user and kernel level in sv6 variant, which is a research operating system on a manycore system. In order to enable RCU-style synchronization algorithms in sv6 variant, we implemented and modified some of the libraries and memory allocators in sv6 variant. We use micro-benchmarks that exploit a linked list and hash table to measure the performance while experimenting with parameters of the benchmarks and types of data structures. In most of the experiments, we observed that MV-RLU is scalable. MV-RLU exhibits about thirteen times better throughput than RCU in the case of running 70 threads. In addition, we compare the operation procedures and APIs of each RCU-style synchronization algorithm to analyze the pros and cons of the algorithms.

Airborne health management systems prevent functional failure caused by errors or faults in airborne software. The on-the-fly repairing of atomicity violations in ARINC 653 concurrent software is critical for guaranteeing the correctness of software execution. This paper introduces RAV (Repairing Atomicity Violation), which efficiently treats atomicity violations. RAV diagnoses an error on the fly by utilizing the training results of software and treats to control access to the shared variable of the thread where the error has occurred. The evaluation of RAV measured the time overhead by applying methods found in previous works and RAV to five synthesis programs containing an atomicity violation.

With the rapid growth of the New Space era, the increasing number and diversity of Low Earth Orbit (LEO) satellites demand generic and reusable antenna control software capable of stable and accurate tracking across heterogeneous orbital and antenna environments. Previous works have primarily focused on two-axis antenna systems, relying on step-based tracking or Two-Line Element (TLE)-based orbit prediction. However, such approaches suffer from manual initialization requirements and gimbal lock at high elevation angles, limiting continuous tracking performance. Although three-axis antenna structures have been proposed to address these issues, their lack of software-level generality and experimental validation restricts practical reuse across antenna platforms. This paper presents a generic three-axis parabola antenna control software that integrates TLE-based orbit prediction, a gimbal lock avoidance algorithm, and an antenna specification reflection function. By decoupling antenna-specific parameters from the core tracking logic, the proposed system enables flexible adaptation to diverse antenna configurations. Experimental validation using a real three-axis antenna and simulations demonstrates high tracking accuracy, achieving an average gain error within 0.466 dBm for GEO-KOMPSAT-2A and stable signal strength above 55 dBm for the LEO satellite ARIRANG-5. Furthermore, analysis of 11,469 gimbal-lock-prone satellites confirms robust avoidance performance across diverse orbital conditions.