Development of flexible strain sensors that can be attached directly onto the skin, such as skin-mountable or wearable electronic devices, has recently attracted attention. However, such flexible sensors are generally exposed to various harsh environments, such as sweat, humidity, or dust, which cause noise and shorten the sensor lifetimes. This study reports the development of a nano-crack-based flexible sensor with mechanically, thermally, and chemically stable electrical characteristics in external environments using a novel one-step laser encapsulation (OLE) method optimized for thin films. The OLE process allows simultaneous patterning, cutting, and encapsulating of a device using laser cutting and thermoplastic polymers. The processes are simplified for economical and rapid production (one sensor in 8 s). Unlike other encapsulation methods, OLE does not degrade the performance of the sensor because the sensing layers remain unaffected. Sensors protected with OLE exhibit mechanical, thermal, and chemical stability under water-, heat-, dust-, and detergent-exposed conditions. Finally, a waterproof, flexible strain sensor is developed to detect motions around the eye, where oil and sweat are generated. OLE-based sensors can be used in several applications that are exposed to a large amount of foreign matter, such as humid or sweaty environments.

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.

Monitoring rail temperature is very important for determining the safe running speed of trains and to prevent buckling. In general, the maximum variation of the internal rail temperature can be >7 ℃ depending on the point of measurement. However, there is as yet no sufficient information about how to predict the measurement point to represent the thermal deformation due to temperature distribution. In this study, the authors report a new point, called the representative measurement point, at which the rail temperature can be measured. This point considers the average deformation of the rail through structural analysis by adopting experimental and actual rail temperature data. The authors designed and installed a measurement system similar to an actual rail environment. Using the system, various data were acquired (internal/surface rail temperature and weather data) for 10 months. On the basis of these data, an analysis was done to calculate the average deformation point through thermal analysis. Finally, the representative measurement point was proposed as the position at which the average deformation point converges regardless of weather or seasons. The authors believe that the method described herein is advantageous in that it could be used in a high-accuracy temperature-monitoring system and for predicting thermal deformation and buckling.

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We introduce a new type of nanofibrous sunscreen solution that is applied via a novel portable electrospinning system. This UV skin protection is highly waterproof, dust-resistant, and biocompatible while being highly breathable and skin-conformable.

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PICO question In cats with feline infectious peritonitis (FIP), does treatment with the nucleoside analogue GS-441524 or the protease inhibitor GC376, compared to supportive measures alone, lead to longer survival times?   Clinical bottom line Category of research question Treatment The number and type of study designs reviewed Five studies, including four uncontrolled interventional studies and one case-series were critically reviewed Strength of evidence Moderate Outcomes reported The reviewed studies collectively provide moderate evidence in support of the application of GS-441524 or GC376 to extend the survival time of cats suffering from feline infectious peritonitis Conclusion While these antiviral drugs are considered the most likely options for FIP treatment, more robust evidence should be obtained through well-designed randomised controlled trials to verify the observed positive effects in treating various forms of the disease and the potential long-term side effects. However, the ethical dilemmas of conducting double blinded placebo-controlled trials, which by necessity include untreated cats with an invariably fatal disease are recognised   How to apply this evidence in practice The application of evidence into practice should take into account multiple factors, not limited to: individual clinical expertise, patient’s circumstances and owners’ values, country, location or clinic where you work, the individual case in front of you, the availability of therapies and resources. Knowledge Summaries are a resource to help reinforce or inform decision making. They do not override the responsibility or judgement of the practitioner to do what is best for the animal in their care.  

Correction for ‘Nanofibrous sunscreen’ by Byeongjun Lee et al., J. Mater. Chem. C, 2022, https://doi.org/10.1039/d2tc00933a.