Robotic Collaborative Walker with Impedance Control and Augmented Reality for Assisted Walking and User Empowerment

The increasing physical and mental inactivity among vulnerable and aging populations presents significant risks, including higher fall rates and reduced mobility. Traditional rehabilitation programs often fail to engage users effectively, leading to low motivation and suboptimal outcomes. This paper introduces a novel robotic collaborative walker integrated with Impedance Control and Augmented Reality (AR) technologies that considers the psychophysiological status of the user. The system fosters interactive training through immersive AR experiences, promoting motivation and engagement while maintaining a proper level of stress. It enables safe and enriched patient-physician interactions while providing continuous monitoring of patient behaviour and gait recovery. The ultimate goal is to restore user independence through an effective and safe means of rehabilitation

Evaluation of Data-Dependent Transmission Errors in Inter-IC Communication With Isolated I/O PDNs Under IEMI

Intentional electromagnetic interference (IEMI) can induce transient data errors in electronic devices, posing threats to their confidentiality and integrity. This letter experimentally investigates the mechanisms and data dependency of IEMI-induced transmission data errors in parallel inter-IC communication. We focus on the symmetry of the power-distribution networks (PDNs) between the transmitter and receiver and the output states of input/output (I/O) circuits. The experimental results reveal that transmission errors occur only when the PDNs of the transmitter and receiver are physically isolated. Moreover, small voltage drops caused by the output state of the I/O circuits and variations in the return loss of the I/O PDN were found to contribute to the occurrence of errors under IEMI. These findings indicate that both the power configuration between ICs and the transmitted data must be considered when evaluating the IEMI immunity of electronic devices employing inter-IC communication

Understanding the Characteristics of LLM-Generated Property-Based Tests in Exploring Edge Cases

As Large Language Models (LLMs) increasingly generate code in software development, ensuring the quality of LLM-generated code has become important. Traditional testing approaches using Example-based Testing (EBT) often miss edge cases—defects that occur at boundary values, special input patterns, or extreme conditions. This research investigates the characteristics of LLM-generated Property-based Testing (PBT) compared to EBT for exploring edge cases. We analyze 16 HumanEval problems where standard solutions failed on extended test cases, generating both PBT and EBT test codes using Claude-4-sonnet. Our experimental results reveal that while each method individually achieved a 68.75% bug detection rate, combining both approaches improved detection to 81.25%. The analysis demonstrates complementary characteristics: PBT effectively detects performance issues and edge cases through extensive input space exploration, while EBT effectively detects specific boundary conditions and special patterns. These findings suggest that a hybrid approach leveraging both testing methods can improve the reliability of LLM-generated code, providing guidance for test generation strategies in LLM-based code generation

Forecasting Road Traffic Volume Using Temporal-Only Time Series Models

Predicting road traffic in road networks is essential for alleviating congestion and enhancing traffic flow efficiency. This study presents a traffic volume prediction model trained using time-series data collected from loop detectors installed at three junctions in Istanbul, Turkey. The detectors record vehicle counts, providing the historical data used to train a residual LSTM model designed to forecast vehicle volumes 15 minutes into the future. The proposed model achieved an average mean absolute error of 0.26 across multiple loop detectors, demonstrating its effectiveness in accurately predicting short-term traffic volumes

Application of Transitional Mixed Reality Interfaces: A Co-Design Study with Flood-Prone Communities

Flood risk communication in disaster-prone communities often relies on traditional tools (e.g., paper and browser-based hazard/flood maps) that struggle to engage community stakeholders and reflect intuitive flood situations. In this paper, we applied the transitional mixed reality (MR) interface concept from pioneering work and extended it for flood risk communication scenarios through co-design with community stakeholders to help vulnerable residents understand flood risk and facilitate preparedness. Starting with an initial transitional MR prototype, we conducted three iterative workshops - each dedicated to device usability, visualization techniques, and interaction methods. We collaborated with diverse community stakeholders in flood-prone areas, collecting feedback to refine the system according to community needs. Our preliminary evaluation indicates that this co-designed system significantly improves user understanding and engagement compared to traditional tools, though some older residents faced usability challenges. We detailed this iterative co-design process, critical insights and design implications, offering our work as a practical case of mixed reality application in strengthening flood risk communication. We also discuss the system's potential to support community-driven collaboration in flood preparedness

eBPF-Based Ordered Proof of Transit for Trustworthy Service Function Chaining

Service function chaining (SFC) establishes a service path where a sequence of functions is executed according to service requirements. However, SFC lacks a mechanism to ensure proper traversal of relay nodes in the data plane. Misconfigurations and the presence of attackers can lead to forwarding anomalies and path deviation, potentially allowing packets to bypass security network functions in the service path. To mitigate potential security breaches, ordered proof of transit (OPoT) has been proposed as a mechanism to verify whether traffic adheres to the designated path. In this paper, we realize lightweight OPoT-based path verification based on extended Berkeley Packet Filter (eBPF) for trustworthy SFC. Furthermore, by integrating it with the existing SFC proxy, we extend the proposed approach to accommodate both SFC-aware and SFC-unaware virtual network functions (VNFs) in the segment routing over IPv6 data plane (SRv6) domain. Through experiments, we demonstrate the capability of the proposed approach to detect path deviations. Additionally, we reveal the performance limitations of the proposed approach

Mining for Lags in Updating Critical Security Threats: A Case Study of Log4j Library

The Log4j-Core vulnerability, known as Log4Shell, exposed significant challenges to dependency management in software ecosystems. When a critical vulnerability is disclosed, it is imperative that dependent packages quickly adopt patched versions to mitigate risks. However, delays in applying these updates can leave client systems exposed to exploitation. Previous research has primarily focused on NPM, but there is a need for similar analysis in other ecosystems, such as Maven. Leveraging the 2025 mining challenge dataset of Java dependencies, we identify factors influencing update lags and categorize them based on version classification (major, minor, patch release cycles). Results show that lags exist, but projects with higher release cycle rates tend to address severe security issues more swiftly. In addition, over half of vulnerability fixes are implemented through patch updates, highlighting the critical role of incremental changes in maintaining software security. Our findings confirm that these lags also appear in the Maven ecosystem, even when migrating away from severe threats

Phase change materials particles by Y-shaped polymer composed of polylactide stereocomplex and poly(stearyl methacrylate)

Novel Y-shaped copolymer was designed and synthesized with poly(stearyl methacrylate) as phase change material at foot moiety and poly(L,L-lactide)s (PLLAs) as two arm moieties, in order to prepare the uniform sized particles. After particle preparation poly(D,D-lactide) (PDLA) was added to form stereocomplex on the particle surfaces to increase the thermal stability. The particle size resulted in 87 nm and 101 nm for Y-shaped copolymer and diblock copolymer in dimethylformamide media, respectively. They showed well dispersibility in aqueous media compared under air, implying the phase separation of stearyl group in aqueous media contribute to the particle stability

Prospects for functional genomics of genes involved in coffee-specialized metabolism through cross-species integrative omics

Coffee (Coffea spp.) is one of the most economically important crop species and serves as a rich source of bioactive specialized (secondary) metabolites with various health-promoting properties. Advances in analytical food chemistry and phytochemistry have elucidated an extensive and structurally diverse specialized metabolism in coffee beans, much of which contributes to both organoleptic attributes and adaptive physiological responses in coffee plants. Recent developments in omics-driven methodologies have provided new insights into both coffee metabolism and breeding strategies, particularly those aimed at enhancing both quality traits and environmental resilience. Comparative genomic analyses across Coffea species and cultivars have facilitated the detection of metabolic polymorphisms, enabling inter- and intra-species assessments of biosynthetic pathway variation and the refinement of biosynthetic frameworks for further functional genomics approaches. Such approaches yield critical information regarding the genetic and biochemical determinants underlying specialized metabolite accumulations, which can be directly applied for targeted metabolic engineering and crop improvement. Moreover, cross-species comparative omics and multi-omics integrative analyses, particularly in relation to phylogenetically relevant taxa such as Solanaceae species, exemplified by the model crop tomato (Solanum lycopersicum), provide valuable translational insights into conserved and divergent metabolic architectures

Do Developers Depend on Deprecated Library Versions? A Mining Study of Log4j

Log4j has become a widely adopted logging library for Java programs due to its long history and high reliability. Its widespread use is notable not only because of its maturity but also due to the complexity and depth of its features, which have made it an essential tool for many developers. However, Log4j 1.x, which reached its end of support (deprecated), poses significant security risks and has numerous deprecated features that can be exploited by attackers. Despite this, some clients may still rely on this library. We aim to understand whether clients are still using Log4j 1.x despite its official support ending. We utilized the Mining Software Repositories 2025 challenge dataset, which provides a large and representative sample of open-source software projects. We analyzed over 10,000 log entries from the Mining Software Repositories 2025 challenge dataset using the Goblin framework to identify trends in usage rates for both Log4j 1.x and Log4j-core 2.x. Specifically, our study addressed two key issues: (1) We examined the usage rates and trends for these two libraries, highlighting any notable differences or patterns in their adoption. (2) We demonstrate that projects initiated after a deprecated library has reached the end of its support lifecycle can still maintain significant popularity. These findings highlight how deprecated are still popular, with the next step being to understand the reasoning behind these adoptions