Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

This review focuses on deepening the structural understanding of crack-based strain sensors (CBSS) on stretchable and flexible polymeric substrates and promoting sensor performance optimization. CBSS are cutting-edge devices that purposely incorporate cracks into their functional elements, thereby achieving high sensitivity, wide working ranges, and rapid response times. To optimize the performance of CBSS, systematic research on the structural characteristics of cracks is essential. This review comprehensively analyzes the key factors determining CBSS performance such as the crack mechanism, geometrical factors, and functional structures and proposes optimization strategies grounded in these insights. In addition, we explore the potential of numerical analysis and machine learning to offer novel perspectives for sensor optimization. Following this, we introduce various applications of CBSS. Finally, we discuss the current challenges and future prospects in CBSS research, providing a roadmap for next-generation technologies.

A versatile coatable strain (CS) sensor was developed using a wet process, achieving a high sensitivity (GF ≈ 100). It overcame traditional polymer limitations, enabling real-time growth measurements on complex biological and non-planar surfaces.

Wearable electroencephalography (EEG) devices offer a promising solution for continuous brain monitoring outside laboratory settings. However, maintaining stable signal quality over extended periods remains challenging, as skin‐mounted EEG systems are prone to contact‐induced noise caused by skin deformation and body motion. Here, we present a wrinkle‐adaptive kirigami structure and a soft, wearable EEG patch engineered to match subject‐specific forehead wrinkle patterns, thereby stabilizing the electrode‐skin interface. Our two‐step kirigami architecture integrates global conformability with localized strain accommodation, delivering anisotropic deformability and maintaining mechanical stability during facial motion. Leveraging an automated image‐based workflow, we enable scalable, individualized generation of kirigami patterns. When integrated into a soft, wireless wearable system, the personalized patch delivers consistently enhanced signal‐to‐noise ratios across multiple EEG frequency bands, even under diverse motion conditions in at‐home sleep settings. This technology demonstrates broad applicability for sleep EEG monitoring and underscores the potential of morphology‐aware structural design for next‐generation wearable EEG devices.

현재 궤도 좌굴을 예방하기 위하여 고정형 레일 온도 모니터링 시스템을 통해 선로의 온도를 관리하고 있다. 하지만 설치된 레일에 비해 모니터링 시스템의 수가 부족하다. 또한 직접 계측에 사용되는 접촉식 온도계와 비접촉식 온도계는 측정 위치와 다양한 물리량에 따라 값이 달라진다. 본 연구에서는 기존의 레일 온도 계측 시스템의 한계를 개선한 휴대용 레일 온도 측정 시스템을 제안한다. 레일의 형상에 맞추어 설계하여 일정한 위치에서 높은 정확도로 온도를 측정할 수 있다. 또한 박막형 열전대의 사용과 기구부 설계를 통해 빠른 측정 속도와 낮은 측정 오차를 달성했다. 자석 기반의 부착 방식으로 시스템의 설치 및 제거가 용이하며, 스마트 기기에서 데이터의 모니터링 및 저장이 가능하다.

Objective measurement of tremors is important for assessing the severity and preventing misdiagnosis of Parkinson's disease (PD). To address this demand, this study presents a multichannel, wireless, patch‐type tremor monitoring system that integrates a high‐repeatable fibrous crack‐based strain (FCBS) sensor with a flexible wireless communication circuit. The FCBS sensor, incorporating crack structures, random fiber orientation, junction melting, and polymer penetration within a 3D conductive fiber network, achieves high sensitivity (gauge factor up to 476.89), a fast response time (≈ 6.64 ms), a wide working range (>200% strain), low Young's modulus (≈45 kPa), and excellent repeatability (>100 000 cycles). In addition, this study demonstrates the first clinical application of a flexible strain sensor‐based tremor monitoring system in patients with PD and ET for simultaneous multilocation tremor measurement. Through clinical trials, our system measured tremor indicators (peak amplitude and fundamental frequency), and tremor indicators obtained from multilocation showed improved correlation and statistical significance with tremor scoring in UPDRS III under specific conditions. These results demonstrate the applicability and potential of the flexible strain sensor‐based system as a wearable platform for quantitative and objective tremor measurement.