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The current extraction speed of artificial intelligence interactive elements is low, with the low effect, resulting in the poor effect of artificial intelligence interaction. Therefore, a new artificial intelligence interaction method has been developed for digital multimedia technology, performing analysis based on the current background of artificial intelligence, providing a good environment foundation for the interactive place, so that it can integrate artificial intelligence technology after interaction. Aiming at the current problems of digital multimedia technology in the use of artificial intelligence interaction design, innovative exploration of artificial intelligence technology will be carried out based on multimedia technology and innovative thinking. Based on the in-depth analysis of digital multimedia technology, the relationship between artificial intelligence technology and digital multimedia technology is analyzed, and an artificial intelligence interactive design system based on digital multimedia technology is proposed. Finally, the digital multimedia technology is verified on the artificial intelligence interactive design through case analysis.

If Kaluza–Klein ideas were correct as an explanation of Yang–Mills and General Relativity on spacetime, the extra fibre geometry would have to be a sphere of constant size of the order of 10 Planck lengths, hence subject to quantum gravity corrections. Conversely, it was shown in previous work that modelling such corrections by noncommutative fibre coordinates indeed forces the Kaluza–Klein cylinder ansatz form of the metric, and we now propose that the remaining restrictions needed come from quantum gravity on the fibre. Working with a fuzzy sphere fibre, we find that the expected value of the metric is indeed spherical and we propose that it can be taken as of constant size due to freedom in the renormalisation of divergences. In this way, we outline a mechanism whereby the observed structure of gravity plus Yang–Mills can emerge at low energies as a consequence of quantum gravity effects.

A bstract Using the framework of quantum Riemannian geometry, we show that gravity on the product of spacetime and a fuzzy sphere is equivalent under minimal assumptions to gravity on spacetime, an su 2 -valued Yang-Mills field A μi and a real-symmetric-matrix valued Liouville-sigma model field h ij for gravity on the fuzzy sphere. Moreover, a massless real scalar field on the product appears as a tower of scalar fields on spacetime, with one for each internal integer spin l representation of SU(2), minimally coupled to A μi and with mass depending on l and the fuzzy sphere size. For discrete values of the deformation parameter, the fuzzy spheres can be reduced to matrix algebras M 2 j +1 (ℂ) for j any non-negative half-integer, and in this case only integer spins 0 ≤ l ≤ 2 j appear in the multiplet. Thus, for j = 1 a massless field on the product appears as a massless SU(2) internal spin 0 field, a massive internal spin 1 field and a massive internal spin 2 field, in mass ratio 0 , 1 , 3 respectively, which we conjecture could arise in connection with an approximate SU(2) flavour symmetry.

Strengthening the synergistic development of science and technology finance in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is of great significance in further promoting financial liberalization and innovation, and deepening the financial co-operation and development between the Mainland, Hong Kong and Macao. In view of the positioning and mission of the GBA, there are still a lot of areas that need to be strengthened in the synergistic development of science and technology finance among the three places. For example, there is no synergy in regional science and technology co-operation and innovation, such as differences in institutional systems, lack of science and technology and financial talents, lack of mechanisms for circulation and sharing of innovative resources, and uneven distribution of innovative advantages. Based on this, GBA needs to build a unified and efficient platform for collaborative innovation and vigorously promote the circulation of resources; strengthen innovation cooperation and cultivate a strategic engine to lead industrial development; and focus on industrial upgrading and promote institutional innovation to form a new pattern of science and technology financial cooperation.

Radiotherapy (RT) is a cancer treatment that uses high doses of radiation to kill cancer cells and shrink tumors. Although great success has been achieved on radiotherapy, there is still an intractable challenge to enhance radiation damage to tumor tissue and reduce side effects to healthy tissue. Radiosensitizers are chemicals or pharmaceutical agents that can enhance the killing effect on tumor cells by accelerating DNA damage and producing free radicals indirectly. In most cases, radiosensitizers have less effect on normal tissues. In recent years, several strategies have been exploited to develop radiosensitizers that are highly effective and have low toxicity. In this review, we first summarized the applications of radiosensitizers including small molecules, macromolecules, and nanomaterials, especially those that have been used in clinical trials. Second, the development states of radiosensitizers and the possible mechanisms to improve radiosensitizers sensibility are reviewed. Third, the challenges and prospects for clinical translation of radiosensitizers in oncotherapy are presented.

The martensite start temperature (M ) plays a pivotal role in formulating heat treatment regimes for steel. This paper, through the compilation of experimental data from literature and the incorporation of expert knowledge to construct features, employs machine learning algorithms to predict the M of steel. The study highlights that the ETR algorithm attains optimal prediction accuracy, and the inclusion of atomic features enhances the model's performance. Feature selection is accomplished by evaluating linear and nonlinear relationships between data using the Pearson correlation coefficient (PCC), variance inflation factor (VIF), and maximum information coefficient (MIC). Subsequently, the performance of machine learning models on unknown data is compared to validate the model's generalization ability. The introduction of SHAP values for model interpretability analysis unveils the influencing mechanisms between features and the target variable. Finally, utilizing a specific steel type as an illustration, the paper underscores the practical value of the model.

Abstract Following steps analogous to classical Kaluza-Klein theory, we solve for the quantum Riemannian geometry on C ∞ (M) ⊗ M 2(ℂ) in terms of classical Riemannian geometry on a smooth manifold M , a finite quantum geometry on the algebra M 2(ℂ) of 2 × 2 matrices, and a quantum metric cross term. Fixing a standard form of quantum metric on M 2(ℂ), we show that this cross term data amounts in the simplest case to a 1-form A μ on M, which we regard as like a gauge-fixed background field. We show in this case that a real scalar field on the product algebra with its noncommutative Laplacian decomposes on M into two real neutral fields and one complex charged field minimally coupled to A μ . We show further that the quantum Ricci scalar on the product decomposes into a classical Ricci scalar on M, the Ricci scalar on M 2(ℂ), the Maxwell action ||F||2 of A and a higher order ||A.F||2 term. Another solution of the QRG on the product has A = 0 and a dynamical real scalar field ϕ on M which imparts mass-splitting to some of the components of a scalar field on the product as in previous work.

Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

Neurodegenerative diseases are a group of disorders characterized by progressive loss of certain populations of neurons, which eventually lead to dysfunction. These diseases include Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Immune pathway dysregulation is one of the common features of neurodegeneration. Recently, there is growing interest in the specific role of T helper Th 17 cells and Interleukin-17A (IL-17A), the most important cytokine of Th 17 cells, in the pathogenesis of the central nervous system (CNS) of neurodegenerative diseases. In the present study, we summarized current knowledge about the function of Th17/IL-17A, the physiology of Th17/IL-17A in diseases, and the contribution of Th17/IL-17A in AD, PD, and ALS. We also update the findings on IL-17A-targeting drugs as potentially immunomodulatory therapeutic agents for neurodegenerative diseases. Although the specific mechanism of Th17/IL-17A in this group of diseases is still controversial, uncovering the molecular pathways of Th17/IL-17A in neurodegeneration allows the identification of suitable targets to modulate these cellular processes. Therapeutics targeting IL-17A might represent potentially novel anti-neurodegeneration drugs.

Metallosalen-covalent organic frameworks have recently gained attention in photocatalysis. However, their use in CO 2 photoreduction is yet to be reported. Moreover, facile preparation of metallosalen-covalent organic frameworks with good crystallinity remains considerably challenging. Herein, we report a series of metallosalen-covalent organic frameworks produced via a one-step synthesis strategy that does not require vacuum evacuation. Metallosalen-covalent organic frameworks possessing controllable coordination environments of mononuclear and binuclear metal sites are obtained and act as photocatalysts for tunable syngas production from CO 2 . Metallosalen-covalent organic frameworks obtained via one-step synthesis exhibit higher crystallinity and catalytic activities than those obtained from two-step synthesis. The optimal framework material containing cobalt and triazine achieves a syngas production rate of 19.7 mmol g −1 h −1 (11:8 H 2 /CO), outperforming previously reported porous crystalline materials. This study provides a facile strategy for producing metallosalen-covalent organic frameworks of high quality and can accelerate their exploration in various applications. Here, the authors report a gram-scale, one-step synthesis of metallosalen-covalent organic frameworks (M(salen)-COFs) without vacuum evacuation. M(salen)-COFs exhibit good crystallinity and adjustable activities in CO2 photoreduction to syngas.