With the expanding applications of biodegradable polymers in absorbable implants and eco-friendly materials, long-term degradation of mechanical performance and prediction of residual service life have emerged as key challenges. Focusing on poly(lactic acid), this paper reviews recent research trends in accelerated degradation test design, mechanical property evaluation, and lifetime prediction modeling from an integrated test–evaluation–prediction perspective. The kinetics of hydrolysis and the transition mechanisms between erosion modes are examined, and the methods used to determine acceleration factors in relevant standards and the conditions required for their validity are critically reviewed. Model choice is discussed in relation to the measured degradation response, the selected mechanical endpoint, and the data available for calibration. The review links these choices to checks on specimen geometry, exposure conditions, sampling intervals, and independent validation.
Multiphase flow transfer pipelines, in which gas, liquid, and solid particles travel together as a combined stream, are widely utilized in oil and gas production facilities, subsea separation systems, and chemical processing plants. Within such pipelines, hazardous gases such as carbon dioxide (CO2) and hydrogen sulfide (H2S) may be present in gas and liquid phases depending on operating conditions, leading to corrosive conditions and adversely affecting process stability. Conventional hazardous gas measurement approaches have largely relied on single-sensor configurations or basic inline measurement methods. However, these techniques are not well suited to multiphase flow environments, where fluctuating flow patterns and liquid droplet ingress introduce considerable variability in concentration readings, thereby undermining measurement reliability. To overcome these limitations, this study presents the design and development of a pretreatment-based sampling architecture integrated with a multi-sensor measurement system for stable and reliable hazardous gas monitoring in multiphase flow transfer pipelines. The proposed system consists of a vent/branch-based sampling structure, a pretreatment (gas conditioning) module, a multi-gas sensor array, and a real-time data acquisition and processing framework. System performance was validated through controlled gas chamber testing, with results demonstrating measurement accuracy of approximately 86
This paper examines the clinical effects and limitations of various types of vibration stimulation applied in the rehabilitation of patients with brain lesions. Sensory recovery outcomes were systematically classified according to both the intensity of vibration stimulation, specifically suprathreshold vibration that can be consciously perceived by patients and imperceptible subthreshold vibration, and the anatomical site of application, including the skin, muscle belly, and tendons. The effects of cutaneous vibration on alleviating decreased tactile sensation, muscle belly vibration on localized recovery, and tendon vibration on proprioceptive recovery were analyzed. Based on these findings, future directions for the research and development of sensory rehabilitation systems utilizing vibration stimulation are proposed.
An inline ultrasonic monitoring system was developed for real-time estimation of the solids content in battery anode slurry. An ultrasonic probe was installed directly in a circulation pipeline to acquire in-pipe ultrasonic signals under flowing slurry conditions. A total of 223,092 ultrasonic data points were collected at seven concentration levels ranging from 48 to 56 wt
Advanced semiconductor packaging technology has become a key enabler of high-performance systems in the post-Moore era. However, thermo-mechanical reliability issues, such as warpage and thermal resistance, have emerged as critical challenges due to increased structural complexity and material heterogeneity. These issues stem from nonlinear interactions among material properties, structural geometries, and process conditions. Although finite element analysis (FEA) provides high predictive accuracy, its application is limited by significant computational cost and time demands, particularly in design exploration and optimization. This review presents a comprehensive analysis of machine learning-based approaches for predicting thermo-mechanical reliability challenges, specifically warpage and thermal resistance, in advanced semiconductor packaging. For each reliability issue, predictive methodologies employing a range of machine learning models are summarized, and recent research trends are examined with emphasis on data acquisition strategies, model architectures, and application cases. In addition, extended approaches encompassing data augmentation, design optimization, and model interpretability are also reviewed. The discussion section addresses key challenges such as model generalization, data scarcity, interpretability, and integration into real-world manufacturing environments, based on the technical background and limitations common to the two reliability issues. Building on this analysis, future research directions are proposed, including physics-informed learning, multi-physics-aware modeling, and hybrid frameworks combining simulation and experimental data. In conclusion, machine learning-based approaches offer a computationally efficient pathway for analyzing thermo-mechanical behavior in advanced semiconductor packaging, enabling design space exploration at speeds infeasible with conventional FEA alone. Looking ahead, these methods are expected to play a pivotal role in design optimization and reliability assessment.
