General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Abstract-This paper presents a flexible 2.45-GHz wireless power harvesting wristband that generates a net dc output from a −24.3-dBm RF input. This is the lowest reported system sensitivity for systems comprising a rectenna and impedancematching power management. A complete system has been implemented comprising: a fabric antenna, a rectifier on rigid substrate, a contactless electrical connection between rigid and flexible subsystems, and power electronics impedance matching. Various fabric and flexible materials are electrically characterized at 2.45 GHz using the two-line and the T-resonator methods. Selected materials are used to design an all-textile antenna, which demonstrates a radiation efficiency above 62% on a phantom irrespective of location, and a stable radiation pattern. The rectifier, designed on a rigid substrate, shows a best-inclass efficiency of 33.6% at −20 dBm. A reliable, efficient, and wideband contactless connection between the fabric antenna and the rectifier is created using broadside-coupled microstrip lines, with an insertion loss below 1 dB from 1.8 to over 10 GHz. A self-powered boost converter with a quiescent current of 150 nA matches the rectenna output with a matching efficiency above 95%. The maximum end-to-end efficiency is 28.7% at −7 dBm. The wristband harvester demonstrates net positive energy harvesting from −24.3 dBm, a 7.3-dB improvement on the state of the art.

Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

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Novel stereoregular helical poly­(phenylacetylene) derivatives (PPA-Phe and PPA-Phg) with an amide linkage bearing l-phenylalanine and l-phenylglycine ethyl ester pendants were synthesized for use as chiral stationary phases (CSPs) in HPLC. The polymers showed different chiral recognition abilities depending on the coating solvents. Both PPA-Phe and PPA-Phg exhibited higher chiral recognitions when coated with CHCl3 by having preferable conformations. Their chiral recognition abilities depended on their molecular weight and optical rotations which were influenced by the polymerization solvents and monomer concentration. PPA-Phe and PPA-Phg showed rather different chiral recognitions, indicating that the benzyl group of the former and the phenyl group of the latter also play important roles in the chiral recognition. A few racemates were completely separated on PPA-Phe or PPA-Phg with separation factors comparable or higher than those obtained on the popular polysaccharide-based CSPs.

The living ring-opening polymerization of δ-valerolactone (VL) initiated from 6-azide-1-hexanol using 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) and 1-[3,5-bis(trifluoromethyl)phenyl]-3-cyclohexylthiourea (BCT) was carried out to prepare the poly(δ-valerolactone)s (N 3 -PVL-OH) bearing azide groups at the R-chain ends with M n,NMR s (PDIs) of 2600 (1.08), 4700 (1.11), and 9900 (1.09). The acetylene functionality was introduced at the ω-end of N 3 -PVL-OH using 5-hexynoyl chloride to afford the telechelic poly(δ-valerolactone) with the azide group at the R-end and acetylene group at the ω-end (N 3 -PVL-CtCH). The click reaction between the R-azide and the ω-acetylene of N 3 -PVL-CtCH in DMF was carried out under the highly diluted condition as [N 3 -PVL-CtCH] = 0.18 mM, which was monitored by IR and 1 H NMR measurements. The SEC peak of the cyclic-PVL shifted to the lower molecular weight region than that of N 3 -PVL-CtCH, and the intrinsic viscosity of the cyclic-PVL significantly decreased. In addition, there was no change in the molecular weight of the resulting polymer through the click cyclization, which was confirmed on the basis of the MALDI-TOF MS measurement. Finally, we succeeded in the synthesis of a well-defined cyclic-PVL having a narrow polydispersity (M w /M n = 1.09-1.15) and the predicted molecular weight (M n,NMR = 2800-9500) in reasonable yield (60-80%) using the click cyclization.