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Alumina is a common mineralizer in silica-based ceramic cores, but its promotion or inhibition effects on the crystallization of fused-silica are in contrary conclusions. To explore the effects of different alumina mineralizers on the property and crystallization behavior of silica-based ceramic cores, silica/alumina ceramic cores were fabricated using high-pure alumina and common commercial alumina powders as mineralizers, respectively. The high-pure alumina powders hinder the crystallization of cristobalite, but the common alumina powders promote the crystallization behavior. The common alumina powders have larger amounts of impurities than that of high-pure alumina powders, including alkali oxides and alkaline-earth oxides, which highly decrease the crystallization activation energy and promote the crystallization of cristobalite. There is significant difference in the effects of two alumina mineralizers on the linear shrinkage rate, densification, and mechanical properties of ceramic cores. Due to the common alumina powders promoting the crystallization and sintering densification of silica glass, the resistance of creep deformation is improved. When the content of common alumina powders is 5% (mass fraction), the ceramic cores exhibit superior performance, including linear shrinkage of 0.63%, room-temperature flexture strength of 20.2 MPa, and deflection of 0.23 mm via double cantilever beam method at 1540 ℃/0.5 h, meeting the requirement for precision casting.

Recently, the plant extracts used to synthesize nanoparticles (NPs) have been considered an excellent alternative to physical and chemical ways. The applications of NPs in the fields of agriculture, industry, and medicine are so many and diverse that they cannot be counted. In recent years, silver nanoparticles (AgNPs) have attracted the consideration of several scientists because of their special characteristics and many applications in various fields, including optoelectronic catalysts, biological markers, and pharmaceutical and medical applications. In the current experiment, the cytotoxic potential of the properties of AgNPs green formulation using green tea on human colorectal cancer cells were determined. The NPs characterization was done by field emission-scanning electron microscopes, Fourier transform infrared spectroscopy, and X-ray diffraction analysis. The average diameter of the particles was about 35 nm. The presence of (111), (200), (220), and (311) peaks at the positions of 38°, 44°, 63°, and 77° indicate the presence of AgNPs, which confirms the correct synthesis of AgNPs. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay was used to measure anti-colorectal carcinoma (on HCT-8, HT-29, MDST8, HCA-7 Colony 29, HCT 116, and Ramos.2G6.4C10 cells) properties of AgNPs. The findings indicate that in 3 days, the cancer cell survival percentage in various dilations reduced as much as the NPs concentration increased. The best anticancer effect was reported at 1,000 μg/mL dilation. The IC50 was 141, 46, 149, 125, 125, and 44 µg/mL against HCT-8, HT-29, MDST8, HCA-7 Colony 29, HCT 116, and Ramos.2G6.4C10 colorectal cancer cells, respectively. The results indicated that these NPs could inhibit colorectal cancer cells more strongly than normal cells. After doing the clinical trial studies, the recent AgNPs are a suitable option for colorectal cancer treatment.

In order to improve the recognition accuracy of symphonic music contour, this paper constructs an intelligent music main melody recognition system based on artificial intelligence technology to make melody recognition with certain search adaptation capabilities. Based on the traditional melody recognition system, the fundamental tone sequence of symphony fragments is obtained by using the fundamental tone extraction and short-time autocorrelation function in the melody contour feature extraction algorithm, which is transformed into the melody contour sequence after regularization and merging to determine the similarity of the music melody signal itself. The wavelet transform method and radial basis function algorithm are used to improve the defects of monophonic discrimination in the traditional recognition model so that the artificial intelligence technique can effectively fit with the symphony recognition model of music melody contour. The experiments show that: The average recognition accuracy of the AI-based music melody recognition system is 90.5%, which is significantly better than 69.5% of Sound Hunter software and 76.5% of Shazam software. For the five monophonic chords, the system’s recognition accuracy is as high as 98.3%, especially in the field of hanging chords with significant recognition effects. It can be seen that the artificial intelligence-based music main melody recognition system provides a scientific and authoritative recognition means for the dissemination and development of symphonic music and is conducive to improving the recognition accuracy of symphonic melodies.

Focusing on virtual chorus, this paper analyzes the training process of VR technology and music choral conductor talents. After the construction of the online data platform, the image splicing, image alignment and image fusion algorithms of VR technology are integrated with the Harris corner detection improvement algorithm to realize the virtual chorus in the context of deep learning. The influence of family support and school aesthetic education on various dimensions of psychological needs for talent cultivation was modeled using structural equation modeling. Based on the data of music teachers using VR technology, mediated effect analysis and total effect analysis were conducted to analyze the factors influencing the use of imagery with VR technology. ANOVA was utilized to compare the data on the four dimensions of rhythmic memory, pitch, melodic memory, and listening ability to verify the effectiveness of VR technology on music teachers’ talent cultivation. The total effect analysis showed that the order of the influence of each influencing factor on the intention to use was 0.361>social influence 0.263>perceived usefulness 0.263>perceived ease of use 0.251>computer self-efficacy 0.169>convenience conditions 0.162>job relevance 0.134>simulation 0.075.