Hydroxypropyl cellulose (HPC) hydrogels exhibit thermal-responsive transparency change due to their temperature-sensitive miscible–immiscible transitions, making them promising thermochromic materials for fabricating energy-saving smart windows. However, their transition temperatures, named lower critical solution temperature (LCST), are too high for building window applications, and it is also challenging to reduce LCST to comfortable room temperature range (e.g., 26–28 °C) in hot seasons. In this work, we report smart windows prepared using poly­(acrylic acid) (PAA)-modified HPC hydrogels and demonstrate that the LCST of the resulting HPC/PAA hybrid hydrogels can be effectively tuned by solution pH, from 44 to 10 °C with decreasing pH from 6.0 to 1.0. At pH 2.5, an optimized LCST at 26.5 °C has been achieved. The sandwich-structured smart window, composed of two glass panes and an optimized HPC/PAA hydrogel in between, shows a high visible-light transmittance (T lum = 90.1%), excellent solar energy modulation (ΔT sol = 47.5%), outstanding heat-shielding performance, and excellent stability after 100 heating and cooling cycles. These optical properties outperform the reported thermosensitive cellulose-based materials, vanadium oxide based smart windows, and other thermosensitive hydrogel-based smart windows. Furthermore, HPC/PAA hydrogels are easy to prepare, nontoxic, biocompatible, low-cost, and environmentally friendly, making them very promising materials for energy-saving and climate-adaptable smart windows.

Cilia loss is common in cancer, and its roles remain unknown. Deng et al. show that cilia loss sensitizes cells to transformation by activating a mevalonate pathway through β-catenin–TCF signaling. The mevalonate pathway inhibitor statin blocks the progression of pancreatic cancer.

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This paper develops a hybrid algorithm of adaptive network-based fuzzy inference system (ANFIS) and regions with convolutional neural network (R-CNN) for stereo vision-based object recognition and manipulation. The stereo camera at an eye-to-hand configuration firstly captures the image of the target object. Then, the shape, features, and centroid of the object are estimated. Similar pixels are segmented by the image segmentation method, and similar regions are merged through selective search. The eye-to-hand calibration is based on ANFIS to reduce computing burden. A six-degree-of-freedom (6-DOF) robot arm with a gripper will conduct experiments to demonstrate the effectiveness of the proposed system.

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