Chance constrained optimal power flow (OPF) has been recognized as a promising framework to manage the risk from variable renewable energy (VRE). In presence of VRE uncertainties, this paper discusses a distributionally robust chance constrained approximate AC-OPF. The power flow model employed in the proposed OPF formulation combines an exact AC power flow model at the nominal operation point and an approximate linear power flow model to reflect the system response under uncertainties. The ambiguity set employed in the distributionally robust formulation is the Wasserstein ball centered at the empirical distribution. The proposed OPF model minimizes the expectation of the quadratic cost function w.r.t. the worst-case probability distribution and guarantees the chance constraints satisfied for any distribution in the ambiguity set. The whole method is data-driven in the sense that the ambiguity set is constructed from historical data without any presumption on the type of the probability distribution, and more data leads to smaller ambiguity set and less conservative strategy. Moreover, special problem structures of the proposed problem formulation are exploited to develop an efficient and scalable solution approach. Case studies are carried out on IEEE 14 and 118 bus systems to show the accuracy and necessity of the approximate AC model and the attractive features of the distributionally robust optimization approach compared with other methods to deal with uncertainties.

Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.Index Terms-Aerosol index (AI), artificial neural network (ANN) method, back propagation (BP) network, maximum absolute prediction error, photovoltaic (PV) power forecasting.

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4D printing has attracted tremendous interest because of its potential applications in smart devices, biomedical and tissue engineering. However, conventional shape memory polymers suffer from the single permanent shape and recovery direction, the flexibility of 4D printing is significantly limited. Besides, the cross‐linked networks of photocuring 3D‐printed objects cannot be reprocessed or repaired. To address these issues, the dynamic thiocarbamate bonds are introduced into the photocurable methacrylate to prepare reprocessable and self‐healable 4D printing polythiourethane (4DP‐PTU) with Young's modulus of 1.2 GPa and tensile strength of 61.9 MPa. The printed objects can be easily repaired by reprinting on the damaged surface. The shape memorized 4DP‐PTU features high shape fixity and shape recovery, and reconfigurable permanent shape brought by the solid‐state plasticity. A dual‐mode triggered alarm is obtained by the incorporation of carbon nanotubes to demonstrate the potential application in smart alarms for warning of laser exposure or fire case. Moreover, the surface wettability and cell adhesion performance of 4DP‐PTU with excellent biocompatibility can be facilely adjusted through the exchange reaction with sulfhydryl compounds. Accordingly, 4DP‐PTU may show vast potential applications in the field of robotics, smart alarm, bio‐implants and in solving the environmental challenges of 3D‐printed products.

circRNA CDR1as (CDR1as) has been demonstrated to play important roles in a variety of inflammation-related diseases by acting as miRNA sponges. The present study is aimed at investigating the potential roles of CDR1as in the proliferation of human periodontal ligament stem cells (PDLSCs) under an inflammatory condition induced by Porphyromonas gingivalis-derived lipopolysaccharide (LPS). Human periodontal ligament cells (PDLCs) were isolated from periodontal ligament tissue, and PDLSCs were sorted from PDLCs based on the STRO-1 expression through fluorescence-activated cell sorting. We further found that CDR1as was significantly downregulated in LPS-treated PDLSCs compared to untreated cells, as well as in normal periodontal ligament tissues compared to periodontitis tissues. Knockdown of CDR1as promoted LPS-induced proliferative inhibition of PDLSCs, whereas overexpression of CDR1as alleviated the LPS-induced proliferative ability of PDLSCs. Mechanistically, CDR1as functioned as an miR-7 sponge to activate the ERK signal pathway to mediate the inhibition effect of LPS on cell proliferation. Taken together, our findings revealed the effects of the interacting pair of CDR1as/miR-7 on the proliferation ability of PDLSCs within their surrounding inflammatory microenvironment of periodontitis.