Effect of ultra-fine composite Mineral Admixtures on the Mechanical Properties and Slump of Concrete

With the technology of the Mineral Admixtures’ application in Cement-based materials becoming mature, the superfinezied of mineral admixtures has also become a trend . After superfinezied, the hydration activity of the mineral admixtures is greatly improved, while increasing the degree of structural compactness of Cement-based materials ,improving the workability of Cement-based materials. The mineral admixtures applying to concrete materials after superfinezied, which has a certain improvement effect on the performance of concrete. In this paper, the effect of superfinezied mineral admixture on the workability of dry hard concrete and the high performance concrete was investigated. The proportion of ultra-fine composite mineral admixture replacing cement was 0, 20% and 30% respectively, and the workability of concrete was characterized by compressive strength, flexural strength and slump. Meanwhile,the effect of ultra-fine mineral admixture on the hydration products of concrete was characterized by Quantitative XRD. According to the experimental results, the ultra-fine mineral admixture can significantly increase the mechanical strength of concrete, reduce the water requirement of concrete and improve the flowability of concrete materials. The clinker content in the hydration products was significantly consumed, accelerating the hydration of C2S and C3S in the cement

A Study on the Influencing Factors of Social Media in the Communication of Cultural Heritage Education: A Systematic Literature Review

This study examined the impact of social media on disseminating cultural heritage education. After reviewing two databases, 29 articles met our inclusion criteria. This study found that social media can expand the educational scope of cultural heritage and increase public awareness and interest in cultural heritage tourism sites and museums. However, social media is only a publicity channel. It is necessary to consider five influencing factors in social media: the subject of information distribution, the motivation of distribution, the purpose of distribution, the content of distribution, and the method of distribution, and to analyze the specific practices of social media in disseminating cultural heritage education. Therefore, more research is needed to explore the influence of social media on cultural heritage education dissemination, to explore the educational nature of social media in cultural heritage education communication, and to provide a theoretical basis for social media to promote cultural heritage education dissemination

Taming Diffusion Models for Music-driven Conducting Motion Generation

Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion. Prior works have applied Generative Adversarial Networks (GAN) to this task, but the promising diffusion model, which recently showed its advantages in terms of both training stability and output quality, has not been exploited in this context. This paper presents Diffusion-Conductor, a novel DDIM-based approach for music-driven conducting motion generation, which integrates the diffusion model to a two-stage learning framework. We further propose a random masking strategy to improve the feature robustness, and use a pair of geometric loss functions to impose additional regularizations and increase motion diversity. We also design several novel metrics, including Frechet Gesture Distance (FGD) and Beat Consistency Score (BC) for a more comprehensive evaluation of the generated motion. Experimental results demonstrate the advantages of our model.Comment: Accepted by AAAI 2023 Summer Symposiu

The effect of SPOC hybrid model on deep learning effectiveness : A systematic literature review

The hybrid model of SPOC (Small Private Online Course) may have an impact on the effectiveness of deep learning. Nevertheless, few studies have validated the relationship between SPOC and deep learning effectiveness. This systematic review focuses on exploring the impact of the blended model of SPOC on deep learning effectiveness. The article delves into the three major SPOC categories in deep learning and the effects of deep learning in SPOC mode. In addition, the article explores various factors that influence the effectiveness of SPOC on deep learning. To accomplish this, an exhaustive review of the relevant literature was conducted to reveal potential connections and interactions between the SPOC blended model and deep learning effectiveness. This study provides educators and researchers with insights on how to more effectively combine SPOC and deep learning to optimize teaching and learning experiences

Thermodynamic properties of higher-dimensional dS black holes in dRGT massive gravity

On the basis of the state parameter of de Sitter space-time satisfying the first law of thermodynamics,we can derive some effective thermodynamic quantities.When the temperature of the black hole horizon is equal to that of the cosmological horizon, we think that the effective temperature of the space-time should have the same value. Using this condition, we obtain a differential equation of the entropy of the de Sitter black hole in the higherdimensional de Rham, Gabadadze and Tolley (dRGT) massive gravity. Solving the differential equation, we obtain the corrected entropy and effective thermodynamic quantities of the de Sitter black hole. The results show that for multiparameter black holes, the entropy satisfied differential equation is invariable with different independent state parameters. Therefore, the entropy of higher-dimensional dS black holes in dRGT massive gravity is only a function of the position of the black hole horizon, and is independent of other state parameters. It is consistent with the corresponding entropy of the black hole horizon and the cosmological horizon. The thermodynamic quantities of self-consistent de Sitter spacetime are given theoretically, and the equivalent thermodynamic quantities have the second-order phase transformation similar to AdS black hole, but unlike AdS black hole, the equivalent temperature of de Sitter space-time has a maximum value. By satisfying the requirement of thermodynamic equilibrium and stability of space-time, the conditions for the existence of dS black holes in the universe are obtained.Comment: 11 pages, 6 figure

Enhanced fuel ethanol production from rice straw hydrolysate by an inhibitor-tolerant mutant strain of Scheffersomyces stipitis

The aim of the present study was to develop an inhibitor-tolerant strain of Scheffersomyces stipitis and establish an efficient ethanol fermentation process for cost-effective ethanol production from lignocellulosic biomass. By a strategy of three successive rounds of UV mutagenesis following adaptation, we isolated a S. stipitis mutant with improved tolerance against ethanol and inhibitors in the form of acetic acid, furfural and vanillin. The mutant strain exhibited excellent ethanol fermentation performance; both the xylose and glucose consumption rate and ethanol productivity were almost two times higher than the parental strain in batch fermentation. To overcome the issue of product inhibition and carbon catabolite repression (CCR) effect, the membrane integrated continuous fermentation system was employed. The maximum ethanol titer of 43.2 g l−1 and productivity of 2.16 g l−1 h−1 was achieved at a dilution rate of 0.05 h−1, higher than the relevant studies ever reported. These results suggested the novel process of cell recycling continuous fermentation using S. stipitis mutant has great potential for commercial ethanol production from lignocelluloses-based biomass

Large AI Model Empowered Multimodal Semantic Communications

Multimodal signals, including text, audio, image and video, can be integrated into Semantic Communication (SC) for providing an immersive experience with low latency and high quality at the semantic level. However, the multimodal SC has several challenges, including data heterogeneity, semantic ambiguity, and signal fading. Recent advancements in large AI models, particularly in Multimodal Language Model (MLM) and Large Language Model (LLM), offer potential solutions for these issues. To this end, we propose a Large AI Model-based Multimodal SC (LAM-MSC) framework, in which we first present the MLM-based Multimodal Alignment (MMA) that utilizes the MLM to enable the transformation between multimodal and unimodal data while preserving semantic consistency. Then, a personalized LLM-based Knowledge Base (LKB) is proposed, which allows users to perform personalized semantic extraction or recovery through the LLM. This effectively addresses the semantic ambiguity. Finally, we apply the Conditional Generative adversarial networks-based channel Estimation (CGE) to obtain Channel State Information (CSI). This approach effectively mitigates the impact of fading channels in SC. Finally, we conduct simulations that demonstrate the superior performance of the LAM-MSC framework.Comment: To be submitted for journal publicatio

LAMBO: Large Language Model Empowered Edge Intelligence

Next-generation edge intelligence is anticipated to bring huge benefits to various applications, e.g., offloading systems. However, traditional deep offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. In this context, the integration of offloading with large language models (LLMs) presents numerous advantages. Therefore, we propose an LLM-Based Offloading (LAMBO) framework for mobile edge computing (MEC), which comprises four components: (i) Input embedding (IE), which is used to represent the information of the offloading system with constraints and prompts through learnable vectors with high quality; (ii) Asymmetric encoderdecoder (AED) model, which is a decision-making module with a deep encoder and a shallow decoder. It can achieve high performance based on multi-head self-attention schemes; (iii) Actor-critic reinforcement learning (ACRL) module, which is employed to pre-train the whole AED for different optimization tasks under corresponding prompts; and (iv) Active learning from expert feedback (ALEF), which can be used to finetune the decoder part of the AED while adapting to dynamic environmental changes. Our simulation results corroborate the advantages of the proposed LAMBO framework.Comment: To be submitted for possible journal publicatio

Large AI Model-Based Semantic Communications

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model provides new solutions to overcome above issues. Here, we propose a large AI model-based SC framework (LAM-SC) specifically designed for image data, where we first design the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semantic-aware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the significance of the large AI model-based KB development in future SC paradigms.Comment: Plan to submit it to journal for possible publicatio

Phase transition and entropic force of de Sitter black hole in massive gravity

It is well known that de Sitter(dS) black holes generally have a black hole horizon and a cosmological horizon, both of which have Hawking radiation. But the radiation temperature of the two horizons is generally different, so dS black holes do not meet the requirements of thermal equilibrium stability, which brings certain difficulties to the study of the thermodynamic characteristics of black holes. In this paper, dS black hole is regarded as a thermodynamic system, and the effective thermodynamic quantities of the system are obtained. The influence of various state parameters on the effective thermodynamic quantities in the massive gravity space-time is discussed. The condition of the phase transition of the de Sitter black hole in massive gravity space-time is given. We consider that the total entropy of the dS black hole is the sum of the corresponding entropy of the two horizons plus an extra term from the correlation of the two horizons. By comparing the entropic force of interaction between black hole horizon and the cosmological horizon with Lennard-Jones force between two particles, we find that the change rule of entropic force between the two system is surprisingly the same. The research will help us to explore the real reason of accelerating expansion of the universe.Comment: 14 pages,11 figure