Currently, only a few 18 F-radiolabeling methods were conducted in aqueous media, with non-macroelement fluoride acceptors and stringent conditions required. Herein, we describe a one-step non-solvent-biased, room-temperature-driven 18 F-radiolabeling methodology based on organophosphine fluoride acceptors. The high water tolerance for this isotope-exchange-based 18 F-labeling method is attributed to the kinetic and thermodynamic preference of F/F over the OH/F substitution based on computational calculations and experimental validation. Compact [ 18/19 F]di- tert -butyl-organofluorophosphine and its derivatives used as 18 F-labeling synthons exhibit excellent stability in vivo. The synthons are further conjugated to several biomolecular ligands such as c(RGDyk) and human serum albumin. The one-step labeled biomolecular tracers demonstrate intrinsic target imaging ability and negligible defluorination in vivo. The current method thus offers a facile and efficient 18 F-radiolabeling pathway, enabling further widespread application of 18 F.

Although photoacoustic imaging (PAI) in the second near-infrared (NIR-II) region (1.0–1.7 μm) is admired for deeper penetration and higher contrast, few organic NIR-II absorbers are available as exogenous contrast agents in vivo. A1094 belongs to the very few ∼1.1 μm absorbing croconaine dyes that have superior extinction coefficient and tend to form irregular aggregation. In this study, shape-controlled A1094@DSPE-PEG2000 micelles with a J-aggregate core with remarkable 1.2–1.3 μm absorption are fabricated as biocompatible organic agents. Excellent capabilities in photothermal conversion, photostability, and PAI are found in in vitro studies. In vivo PAI of inguinal lymph nodes and in situ glioma pre- and post-resection, all demonstrate high lymph/tumor-targeting efficiency. An ∼4.54 mm deep brain lesion is imaged at 1200 nm with minimized background and increased contrast compared to 970 nm. Overall, we achieved significant bathochromic shift of organic absorbers and expanded their PAI application to the long-wavelength end of the NIR-IIa region.

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With the expansion of China’s power grid construction scale, the transmission line span are gradually improved, which also increases the risk of BL stroke on the transmission line. However, the traditional passive BL protection has many problems, such as weak pertinence and high investment cost, which can not meet the needs of social development. KNN can well describe the similarity measure between the two, which can effectively reduce the training samples. SVM can find the best compromise between model complexity and learning ability in small samples, which is a good sample training method. Through KNN - in-depth learning of the historical data of BL activities accumulated in the power grid, a supervised BL early warning model (hereinafter referred to as EWM) of transmission line can be trained. At the same time, the BL strike of transmission line tower (hereinafter referred to as TLT) has complex meteorological conditions, which requires comprehensive confirmation of various monitoring point parameters. Therefore, it is of great significance to study the BL EWM of TLT based on KNN-SVM algorithm. Firstly, this paper analyzes the KNN-SVM algorithm. Then, this paper establishes an EWM. Finally, this paper is verified.

With the further acceleration of urbanization in China, the proportions of both urban residents’ energy consumption and energy-consuming terminal electricity are showing an increasing trend at the same time. In view of the dynamic and time-varying complex system characteristics of power system, it is of great significance to study the impact mechanism of urbanization residents’ electricity consumption on the realization of demand-side management (DSM) and environmental protection. Based on the one-year follow-up survey data obtained from household meter reading, this paper studies the impact mechanism of urban residents’ electricity consumption in different seasons (summer, winter, and the whole year) and terminals (with and without air-conditioning and full samples) by descriptive analysis and multiple linear regression model. The results show that, on the whole, electricity is a necessity for urban households and does not change significantly with changes in income. At the turn of summer and autumn and the turn of winter and spring, high-income families tend to use higher levels of energy in pursuit of comfort, while low- and middle-income families do not have luxury consumption. In different seasons, the influence mechanism of household electricity consumption at different terminals is different.