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The microstructure of Mg-12Y-1Al alloy with solution treatment and aging treatment was studied by optical microscope, X-ray diffraction and scanning electron microscope. The mechanical properties of the alloy before and after heat treatment at room temperature and 200 ℃ were analyzed by tensile test, and the corrosion resistance of the alloy before and after heat treatment was tested by electrochemical methods. The results show that the as-cast microstructure of Mg-12Y-1Al alloy is composed of α-Mg matrix, Mg24Y5 phase and Al2Y phase. Mg-12Y-1Al alloy has excellent high-temperature thermal stability. After solution treatment at 520 ℃ for 16 h(T4), the grain size does not increase, and new long period stacking ordered(LPSO) phase forms; the subsequent aging treatment at 225 ℃ for 30 h(T6) has little effect on the microstructure. Compared with the properties at room temperature, the ultimate tensile strength of T4 alloy at 200 ℃ does not decrease, but the elongation increases from 1.3% to 12.5%. Moreover, the corrosion resistance of Mg-12Y-1Al alloy is improved after solution and aging treatment, and the corrosion current density decreases from 2.799×10-5 A/cm2 to 1.551×10-5 A/cm2.

This paper investigates the multi-layer decision tree optimization algorithm in machine learning and explores its efficiency and accuracy in applying complex data classification. The focus is on the optimization strategy of the algorithm when dealing with large datasets with multiple types of records. The research adopts methods such as data set Discretization, fuzzification processing and hierarchical fuzzy decision tree construction algorithm. Through experimental verification, our constructed multilevel fuzzy decision tree performs well in two application scenarios: rainfall prediction and fingerprint identification. In the rainfall prediction experiment, using 5200 sample data, the algorithm achieves a classification accuracy of 61%. In the fingerprint recognition experiment, from 3,000 fingerprint images, a 100% recognition rate was performed, and the rejection rate was 25.2%. The results show that the multi-layer decision tree optimization algorithm can effectively handle large data sets of multiple types and significantly improve the accuracy and efficiency of classification. The algorithm offers high adaptability and accuracy when dealing with continuous type attributes. This study provides a new perspective and methodology for the application of multi-layer decision trees in machine learning, which is of great significance for the future development of data processing and classification techniques.

In the realm of higher education management, data mining has emerged as a potent tool for elevating the quality of instruction and administrative operations. This study presents the development of a curriculum validation analysis model utilizing association rule clustering. We collected and processed data from design students at a university, addressing missing values and eliminating outliers. Through data mining, we explored the relationships between students’ coursework, skill acquisition, and the demands of potential employers in society. This analysis underpinned a series of reforms in higher education curricula. To assess the impact of these curricular changes, two instructors conducted both process and outcome evaluations of the revised programs. The evaluations revealed that five student groups achieved scores above 80, one group scored between 70 and 80, and three groups scored between 60 and 70. All student groups scored above 60, demonstrating their competence in fundamental job-related tasks and validating their readiness for professional employment. This research offers valuable insights for the revision of professional training programs and curriculum reform in higher education.

The sensitivity and response time of glutamate sensors based on glutamate oxidase immobilized on planar platinum microelectrodes have been improved to near the theoretical performance limits predicted by a detailed mathematical model. Microprobes with an array of electroenzymatic sensing sites have emerged as useful tools for the monitoring of glutamate and other neurotransmitters in vivo; and implemented as such, they can be used to study many complex neurological diseases and disorders including Parkinson's disease and drug addiction. However, less than optimal sensitivity and response time has limited the spatiotemporal resolution of these promising research tools. A mathematical model has guided systematic improvement of an electroenzymatic glutamate microsensor constructed with a 1-2 μm-thick crosslinked glutamate oxidase layer and underlying permselective coating of polyphenylenediamine and Nafion reduced to less than 200 nm thick. These design modifications led to a nearly 6-fold improvement in sensitivity to 320 ± 20 nA μM-1 cm-2 at 37 °C and a ∼10-fold reduction in response time to 80 ± 10 ms. Importantly, the sensitivity and response times were attained while maintaining a low detection limit and excellent selectivity. Direct measurement of the transport properties of the enzyme and polymer layers used to create the biosensors enabled improvement of the mathematical model as well. Subsequent model simulations indicated that the performance characteristics achieved with the optimized biosensors approach the theoretical limits predicted for devices of this construction. Such high-performance glutamate biosensors will be more effective in vivo at a size closer to cellular dimension and will enable better correlation of glutamate signaling events with electrical recordings.

The traditional preparation process of superhydrophobic surfaces(SHS) is complicated and the mechanical stability of SHS is less than satisfactory in most cases, which seriously restricts the practical application. The "binder+nanoparticles" strategy was used to prepare a nonfluorinated, durable and stable superhydrophobic epoxy composite coating on magnesium alloy. The contact angle test results show that the maximum contact angle of the composite coating is 160.2°, and the contact angle is still as high as 103° even after 30 days of soaking in 3.5%(mass fraction) NaCl solution; EIS results indicate that the |Z|0.01 Hz of the composite coating is still above 109 Ω·cm2 even after five accelerated aging cycles, demonstrating excellent resistance to salt fog and anticorrosion performance; Friction and wear test results reveal that the |Z|0.01 Hz of the composite coating is as high as 1.84×109 Ω·cm2 after mechanical friction under 19.6 N load for 8 h. Due to the excellent blocking barrier of "air cushion", the composite coating can provide efficient and durable corrosion protection for magnesium alloy and the "adhesive+nanoparticles" strategy provides a new direction for the preparation of superhydrophobic coating.

The |$k$| th min threshold is to judge whether the |$k$| th smallest element of an attribute set along with a confidential file is greater than a predefined threshold, which is a fundamental, primitive operation in data evaluation, such as risk evaluation in business investment. However, it will compromise the privacy of the confidential files when proceeding with such a data evaluation because there is often a large amount of sensitive information involved in them, which the organizations/individuals are reluctant to expose due to the risk of losing a competitive advantage. Motivated by the issue how to preserve the privacy of the confidential files during data evaluation, in this research, we first present a new encryption notion called |$k$| th min threshold encryption (KTE) for serving privacy-preserving data evaluation. In this notion, the confidential file will be encrypted under an attribute set for its privacy protection prior to being sent to a receiver, and a decryption key is generated from a threshold |$d$| and a rank |$k$| of element, both selected by the receiver. The decryption will be successful if and only if the |$k$| th smallest element of the attribute set is greater than |$d$|⁠. We then describe a concrete construction of KTE in the public-key setting. In particular, our construction features optimally short private keys, which only consists of one group element. By virtue of this advantage, it is quite practical because of only two pairing operations for decryption computation. [ABSTRACT FROM AUTHOR]

In this research, the diffusion behaviors within the Ti-Fe-Cr ternary system were examined at the temperatures of 1273 K and 1373 K through the diffusion couple technique. This study led to the determination of both ternary inter-diffusion and impurity diffusion coefficients in the body-centered cubic (bcc) phase for the Ti-Fe-Cr alloy, utilizing the Whittle–Green and Hall methods. The statistics show that the average diffusion coefficients D ˜ F e F e T i and D ˜ C r C r T i measured at 1273 K were 1.34 × 10−12 and 3.66 × 10−13, respectively. At 1373 K, the average values of D ˜ F e F e T i and D ˜ C r C r T i were 4.89 × 10−12 and 1.43 × 10−12. By adopting the CALPHAD method, a self-consistent database for atomic mobility in the bcc phase of the Ti-Fe-Cr system was established. This database underwent refinement by comparing the newly acquired diffusion coefficients with data from the existing literature. Diffusion simulations for the diffusion couples were performed, drawing on the established database. The error between the simulated diffusion coefficient and the experimental measurement data is within 15%, and the simulated data of the component distance distribution and diffusion path are in good agreement with the experimental data. The simulations generated results that aligned well with the observed experimental diffusion characteristics, thereby affirming the reliability and accuracy of the database. [ABSTRACT FROM AUTHOR]