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Nano-TiCp/GH3536 composite powders were prepared by mechanical alloying （MA） and thermal plasma spheroidization （TPS） methods. The flowability， bulk density， particle size distribution， sphericity， and microstructure of the composite powders before and after MA-TPS treatment were studied by Hall flow meter，Scott volumeter， XRD， particle size analyzer， scanning electron microscope， and transmission electron microscope. The results show that the preformed powders obtained by MA-5 h treatment are irregular shape powders with non-uniform adhesion of nano-TiC particles on the surface of the GH3536 substrate. Compared with the original GH3536 powders， the number of equiaxed grains in the preformed powders increases and the crystal grains are refined， but the flowability and bulk density decrease.The nano-TiCp/GH3536 composite powders obtained by the second TPS treatment are spherical， and the original irregular nano-TiC particles are spheroidized and uniformly distributed inside the GH3536 substrate. After removing the submicron particles with an average particle size of 183.27 nm， the flowability， bulk density， and sphericity of TPS composite powders are further improved.The TEM images of submicron particles show that the nanoscale GH3536 microspheres are uniformly coated with a layer of TiC， the structure is similar to the core-shell， and spherical TiC particles are found in the powder. The average spherical degree， average particle size， flowability， and bulk density of the nano-TiCp/GH3536 composite powders prepared by the two-step method are 0.965，33.65 μm，17.1 s/50 g，and 4.184 g/cm3， respectively.

Background Primary medical institutions, with the characteristics of comprehensive, continuous, coordinated, convenient and economical, play an important role in the diagnosis and treatment of common and frequently-occurring children's diseases, planned immunization and child health management, and lay a solid foundation for children's health services. It is essential to improve the quality of child health services for children under the management of family doctor contract. There are regional differences in the ability of children's health services at primary institutions in China. Therefore, accurate evaluation of the quality of children's health services at primary institutions is helpful to find problems in time and promote the development of children's health services at a higher level. Objective To assess the quality of community children's health services for contracted children, analyze the quality problems and optimization strategies, and provide international vision and decision-making reference for further improving the quality. Methods Taking a district of Chengdu as a typical case, three community health service centers with stronger child health service ability in the district were selected as the sample institutions, and the parents of children contracted by the sample institutions were surveyed with the online questionnaire on the quality of community child health service by using the Chinese version of primary care assessment tool (PCAT) . Results Totally, 3 631 parents of contracted children were investigated. The total PCAT score of community child health service quality in the sample centers was (58.72 ± 13.43). The dimensions with relatively high PCAT scores of community child health service quality includes "continuity" "community first consultation (service availability and service use) " and "comprehensive service (service provision) ", while the dimensions "children and family-Centered" "comprehensive (available services) " and "coordination (referral) " had low scores. Conclusion It is suggested to strengthen the supply of diagnosis and treatment services for common pediatric diseases in the community, unblock the referral mechanism of Pediatrics, strengthen the awareness of parents of contracted children to be included in the contracted service team to participate in diagnosis and treatment decisions, and pay attention to the health services for children with non local registered residence.

A method based on graph transformation and transfer learning is proposed to further enhance the accuracy of household-transformer relationships and phase identification in low-voltage distribution networks. Firstly， a graph transformation method based on Gramian angular field （GAF） is introduced to convert electricity consumption data into a two-dimensional representation， facilitating the identification of differences in one-dimensional time-series electricity consumption data. Next， to address challenges such as sparse user data in low-voltage distribution networks， limited data acquisition methods， and a scarcity of samples， a deep learning model suitable for household-transformer relationship and phase identification is constructed using transfer learning and leveraging pre-trained parameter weights. Experimental validation demonstrates that the proposed model outperforms mainstream methods in both household-transformer relationship and phase identification， exhibiting improved accuracy and stability.