Epidermal pH is an indication of the skin’s physiological condition. For example, pH of wound can be correlated to angiogenesis, protease activity, bacterial infection, etc. Chronic non-healing wounds are known to have an elevated alkaline environment, while healing process occurs more readily in an acidic environment. Thus, dermal patches capable of continuous monitoring of pH can be used as point-of-care systems for monitoring skin disorder and the wound healing process. Here, we present pH-responsive hydrogel fibers that can be used for long-term monitoring of epidermal wound condition. We load pH-responsive dyes into mesoporous microparticles and incorporate them into hydrogel fibers developed through microfluidic spinning. The fabricated pH-responsive microfibers are flexible and can create conformal contact with skin. The response of pH-sensitive fibers with different compositions and thicknesses are characterized. The suggested technique is scalable and can be used to fabricate hydrogel based wound dressing with a wide range of sizes. Images of the pH-sensing fibers during real-time pH measurement can be captured with a smart phone camera for convenient readout on-site. Through image processing, a quantitative pH map of the hydrogel fibers and the underlying tissue can be extracted. The developed skin dressing can act as a point-of-care device for monitoring the wound healing process.

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With the development of wide-area measurement technology, it is possible to synchronously obtain the traveling waves from different measuring points in a complex power grid. Considering fault-generated traveling wave transmits along the shortest path in the power grid and timestamps of wave fronts can be acquired by phase-mode transformation and wavelets, this paper proposes an optimal deployment scheme of traveling wave recorders (TWR) in the power grid based on the extended double-end fault-location method. Then the fault-location methodology is presented. It is critical to guarantee that the double-end method is applied to the power grid and recognizes the specialized measuring combinations preciously and quickly, and the fault can be located consequently wherever it occurs. At last, fault-location accuracy is improved by the correction approach. All simulations are carried out in PSCAD/EMTDC, and the proposed procedure is applied to the IEEE 30& 57-bus test system to exam its validation. The results show that it can accurately locate a fault with 13 TWRs in IEEE 30-bus test system and 16 TWRs in IEEE 57-bus test system, and absolute error is less than 20 m.

Petroleum-based polymer materials heavily rely on nonrenewable petrochemical resources, and damping materials are an important category of them. As far as green chemistry, recycling, and damping materials are concerned, there is an urgent need for renewable and recyclable biobased materials with high damping performance. Thus, this study designs and synthesizes a series of polylactic acid-based thermoplastic polyurethanes (PLA-based TPUs) composed of modified polylactic acid polyols, 4,4′-diphenylmethane diisocyanate, and 1,4-butanediol. PLA-based TPUs, as prepared, display excellent mechanical properties, damping performance, and biocompatibility. Otherwise, they can be used for three-dimensional printing (3D printing). Under multiple recycling, the overall performance of PLA-based TPUs is still maintained well. Overall, PLA-based TPUs, as designed in this article, show a potential application in damping materials under room temperature and personalized shoes via 3D printing and could realize resource recycling and material reuse.

Through analyzing the factors that influence end-point manganese content during BOF steelmaking process, multiple linear regression model for prediction of end-point manganese content was obtained on the basis of actual production data. Given the advantages of artificial neural network, it was used to predict end-point manganese content during BOF steelmaking process, and BP neural network model was established. By means of combining the characteristics of genetic algorithm and BP neural network completely, a combined GA-BP neural network model was established. The verification and comparison of the above three models show that the combined GA-BP neural network model has the highest prediction accuracy. The hit rate of the combined GA-BP neural network model is 90% and 84% respectively when predictive errors of the model are within ±0.03% and ±0.025%. Compared with two models aboved, the combined GA-BP neural network model could provide the most accurate prediction of end-point manganese content, and thus represents a good reference for real production.

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The high content and regular distribution of hard segment (HS) are necessary to achieve high‐performance thermoplastic polyurethane (TPU), and the HS unit of TPU is generated from the reaction of diisocyanate and chain extender. However, the traditional chain extenders are mainly aliphatic diol that cannot provide rigid groups to improve polyurethane performances. Herein, a novel chain extender bis(hydroxyethyloxycarbonylamino)hexane (BHH) that contains two urethane groups is designed and used to construct high‐performance TPU by increasing content and regular distribution of HS. BHH is synthesized using ethylene carbonate (EC) and hexamethylenediamine (HDA) via a ring‐opening reaction. Subsequently, the effect of BHH on the polyurethane performances is studied with respect to a typical TPU by using polytetramethylene ether glycol (PTMG) and hexamethylene diisocyanate (HDI) as the starting materials. The amount of HDI considerably differs for each polyurethane, aiming to ensure the same total content of urethane groups. When compared with the traditional ethylene glycol‐extended polyurethane, the size and distribution of the HS domains of the BHH‐extended polyurethane are obviously more homogeneous, which proves BHH‐extended polyurethane has better microphase separation. Furthermore, ordered hydrogen bonding, the mechanical properties, and heat‐resistance performance are significantly improved, suggesting a potential method to design TPU delivering outstanding performance.