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This paper addresses the pressing scientific problem of accurately predicting the equivalent stiffness of local resonance metamaterial concrete unit cells. Existing theoretical models often fail to capture the nuanced dynamics of these complex systems, resulting in suboptimal predictions and hindering advancements in engineering applications. To address this deficit, this paper proposes a novel two-dimensional theoretical vibration model that incorporates shear stiffness, a crucial yet often overlooked parameter in previous formulations. Motivated by the need for improved predictive accuracy, this paper rigorously validates a new theoretical model through numerical simulations, considering variations in material parameters and geometric dimensions. The analysis reveals several key findings: firstly, the equivalent stiffness increases with elastic modulus while the error rate decreases, holding geometric parameters and Poisson's ratio constant. Secondly, under fixed geometric parameters and coating elastic modulus, the equivalent stiffness rises with an increasing Poisson's ratio, accompanied by a decrease in error rate. Importantly, this paper demonstrates that the proposed model exhibits the lowest error rate across all parameter conditions, facilitating superior prediction of equivalent stiffness. This advancement holds significant implications for the design and optimization of metamaterial structures in various engineering applications for vibration isolation, with promising enhancements of performance and efficiency. [ABSTRACT FROM AUTHOR]

Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that incorporates a time-aware dual LSTM neural network with similarity graph learning. We further use the stream push technology to enhance the model. We first construct interaction history behavior sequences based on users' resource search history. Then, we establish a category similarity relationship graph structure based on the cosine similarity matrix between remote sensing resource categories. Next, we use LSTM to represent historical sequences and Graph Convolutional Networks (GCN) to represent graph structures. We construct similarity relationship sequences by combining historical sequences to explore exact similarity relationships using LSTM. We embed user IDs to model users' unique characteristics. By implementing three modeling approaches, we can achieve precise recommendations for remote sensing services. Finally, we conduct experiments to evaluate our methods using three datasets, and the experimental results show that our method outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories. [ABSTRACT FROM AUTHOR]

Graph collaborative filtering can efficiently find the hidden interests of users for recommender systems in recent years. This method can learn complex interactions between nodes in the graph, identify user preferences, and provide satisfactory recommendations. However, recommender systems face the challenge of data sparsity. To address this, recent studies have utilized contrastive learning to make use of unlabeled data structures. However, the existing positive and negative example sampling methods are not reasonable. Random-based or data augmentation-based sampling cannot make use of useful latent information. Clustering-based sampling methods ignore the semantics of node features and the relationship between global and local information. To utilize the latent structures in the data, we introduce a novel Community-Enhanced Contrastive Learning method to help the recommendation main task called CECL which uses a community detection algorithm to sample examples with semantic and global information, using both known and hidden community connections in the bipartite interaction graph. Extensive experiments are conducted on two well-known datasets, the results of which show a 12% and 8% performance improvement compared to that of the existing baseline methods. [ABSTRACT FROM AUTHOR]

The incorporation of rubber can enhance concrete's durability and effectively reduce the damage caused by freeze-thaw cycling (FTC). Still, there has been only limited research on the damage mechanism of RC at the fine view level. To gain insight into the expansion process of uniaxial compression damage cracks in rubber concrete (RC) and summarize the internal temperature field distribution law during FTC, a fine RC thermodynamic model containing mortar, aggregate, rubber, water, and interfacial transition zone (ITZ) is established in this paper, and the cohesive element is selected for the ITZ part. The model can be used to study the mechanical properties of concrete before and after FTC. The validity of the calculation method was verified by comparing the calculated results of the compressive strength of concrete before and after FTC with the experimental results. On this basis, this study analyzed the compressive crack extension and internal temperature distribution of RC at 0, 5, 10, and 15% replacement rates before and after 0, 50, 100, and 150 cycles of FTC. The results showed that the fine-scale numerical simulation method can effectively reflect the mechanical properties of RC before and after FTC, and the computational results verify the applicability of the method to rubber concrete. The model can effectively reflect the uniaxial compression cracking pattern of RC before and after FTC. Incorporating rubber can impede temperature transfer and reduce the compressive strength loss caused by FTC in concrete. The FTC damage to RC can be reduced to a greater extent when the rubber incorporation is 10%. [ABSTRACT FROM AUTHOR]

Colorimetric sensors have been widely used to detect numerous analytes due to their cost-effectiveness, high sensitivity and specificity, and clear visibility, even with the naked eye. In recent years, the emergence of advanced nanomaterials has greatly improved the development of colorimetric sensors. This review focuses on the recent (from the years 2015 to 2022) advances in the design, fabrication, and applications of colorimetric sensors. First, the classification and sensing mechanisms of colorimetric sensors are briefly described, and the design of colorimetric sensors based on several typical nanomaterials, including graphene and its derivatives, metal and metal oxide nanoparticles, DNA nanomaterials, quantum dots, and some other materials are discussed. Then the applications, especially for the detection of metallic and non-metallic ions, proteins, small molecules, gas, virus and bacteria, and DNA/RNA are summarized. Finally, the remaining challenges and future trends in the development of colorimetric sensors are also discussed. [ABSTRACT FROM AUTHOR]

Data hiding is the technique of embedding data into video or audio media. With the development of deep neural networks (DNN), the quality of images generated by novel data hiding methods based on DNN is getting better. However, there is still room for the similarity between the original images and the images generated by the DNN models which were trained based on the existing hiding frameworks to improve, and it is hard for the receiver to distinguish whether the container image is from the real sender. We propose a framework by introducing a key_img for using the over-fitting characteristic of DNN and combined with difference image grafting symmetrically, named difference image grafting deep hiding (DIGDH). The key_img can be used to identify whether the container image is from the real sender easily. The experimental results show that without changing the structures of networks, the models trained based on the proposed framework can generate images with higher similarity to original cover and secret images. According to the analysis results of the steganalysis tool named StegExpose, the container images generated by the hiding model trained based on the proposed framework is closer to the random distribution. [ABSTRACT FROM AUTHOR]

(20S) ginsenoside Rh2 (G-Rh2), a major bioactive metabolite of ginseng, effectively inhibits the survival and proliferation of human liver cancer cells. However, its molecular targets and working mechanism remain largely unknown. Excitingly, we screened out heat shock protein 90 alpha (HSP90A), a key regulatory protein associated with liver cancer, as a potential target of (20S) G-Rh2 by phage display analysis and mass spectrometry. The molecular docking and thermal shift analyses demonstrated that (20S) G-Rh2 directly bound to HSP90A, and this binding was confirmed to inhibit the interaction between HSP90A and its co-chaperone, cell division cycle control protein 37 (Cdc37). It is well-known that the HSP90A-Cdc37 system aids in the folding and maturation of cyclin-dependent kinases (CDKs). As expected, CDK4 and CDK6, the two G0-G1 phase promoting kinases as well as CDK2, a key G1-S phase transition promoting kinase, were significantly downregulated with (20S) G-Rh2 treatment, and these downregulations were mediated by the proteasome pathway. In the same condition, the cell cycle was arrested at the G0-G1 phase and cell growth was inhibited significantly by (20S) G-Rh2 treatment. Taken together, this study for the first time reveals that (20S) G-Rh2 exerts its anti-tumor effect by targeting HSP90A and consequently disturbing the HSP90A-Cdc37 chaperone system. HSP90A is frequently overexpressed in human hepatoma cells and the higher expression is closely correlated to the poor prognosis of liver cancer patients. Thus, (20S) G-Rh2 might become a promising alternative drug for liver cancer therapy. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea virus (PEDV) can cause intestinal infection in neonatal piglets through the nasal cavity. A process in which CD3+ T cells carry PEDV plays a key role. However, the modes through which PEDV bridles CD3+ T cells as a vehicle for migration to the intestinal epithelium have not been clarified. In this study, we first demonstrated that PEDV could survive in blood-derived CD3+ T cells for several hours, depending on the multiplicity of infection. In addition, PEDV preferentially survived in CD4+ T cells over CD8+ T cells. Moreover, viral transmission was mediated by cell-to-cell contact between mesenteric lymph-node-derived CD3+ T cells, but did not occur in blood-derived CD3+ T cells. Following an increase in gut-homing integrin α4β7, blood-derived CD3+ T cells carrying PEDV migrated to the intestines via blood circulation and transferred the virus to intestinal epithelial cells through cell-to-cell contact in neonatal piglets. Our findings have significant implications for understanding PEDV pathogenesis in neonatal piglets, which is essential for developing innovative therapies to prevent PEDV infection. [ABSTRACT FROM AUTHOR]

In this paper, the influence of graphite (Gr) on the dry sliding tribological properties of phenolic resin (PF) composites was studied under different sliding speeds of 3.1–47.1 m/s. The wear mechanism was investigated by the observation of the morphology of the transfer layer during the dry sliding process. It was found that the addition of Gr could decrease the friction coefficient and wear rate effectively, and the friction coefficient and wear rate decreased with the increase of Gr content in the range of 10–30 vol.%. The dominant wear mechanisms of PF-based friction composites changed from adhesive wear to fatigue wear (in the form of peeling-off) in the high sliding speed condition after the addition of Gr. The addition of Gr effectively reduced the sensitivity of PF-based friction materials to sliding speeds, and thus enhanced the stability of the friction coefficient. When the content of Gr was above 20 vol.%, the stability of the friction coefficient was relatively steady. [ABSTRACT FROM AUTHOR]

Simple Summary: The gut mucosa of pigs, which contains intestinal epithelium and subepithelial immune cells, forms a barrier against microorganisms. Nonetheless, infectious diseases of the digestive tract remain the most frequent and recurrent conditions in the swine industry. Changes in intestinal morphology and structure primarily occur at birth and during weaning. However, the difference in the intestinal structures between neonatal and weaned piglets remains unclear. In this study, for the first time, we evaluated the differences in the small intestine between neonatal (0-day-old) and weaned piglets (21-day-old) and analyzed the morphology and immunological components of the small intestines of 0- and 21-day-old piglets, thereby providing preliminary data for future mechanistic studies. The gut is one of the body's major immune structures, and the gut mucosa, which contains intestinal epithelium and subepithelial immune cells, is the primary site for eliciting local immune responses to foreign antigens. Intestinal immune system development in pigs is a transitional period during birth and weaning. This study compares the morphological and immunological differences in the small intestine of neonatal and weaned piglets to potentially prevent intestinal infectious diseases in neonatal piglets. Histological analyses of weaned piglet intestines showed increased crypt depth, higher IEL count, and larger ileal Peyer's patches compared with those of neonates. Additionally, the ileal villi of weaned piglets were longer than those of neonatal piglets, and claudin-3 protein expression was significantly higher in weaned than in neonatal piglets. The numbers of CD3+ T, goblet, and secretory cells were also higher in the small intestines of weaned piglets than in those of neonates. No significant differences were observed in the secretory IgA-positive cell number in the jejunum of weaned and neonatal piglets. The mRNA expression of most pattern recognition receptors genes in the duodenum and jejunum was higher in the weaned than neonatal piglets; however, the opposite was true in the ileum. The mRNA levels of IL-1β and TNF-α in the jejunal and ileal mucosa were higher in weaned piglets than in neonatal piglets. There were significantly fewer CD3+, CD4+, and CD8+ T cells from peripheral blood-mononuclear cells in neonatal piglets. Our study provides insights regarding the different immune mechanisms within the small intestines of 0- and 21-day-old piglets. Studies on the additional developmental stages and how differences in the small intestines affect the response of pigs to pathogens remain warranted. [ABSTRACT FROM AUTHOR]

The performance of mobile opportunistic networks mainly relies on collaboration among nodes. Thus far, researchers have ignored the influence of node sociality on the incentive process, leading to poor network performance. Considering the fact that followers always imitate the behavior of superstars, this paper proposes FollowMe, which integrates the social importance of nodes with evolutionary game theory to improve the collaborative behavior of nodes. First, we use the prisoner's dilemma model to establish the matrix of game gains between nodes. Second, we introduce the signal reference as a game rule between nodes. The number of nodes choosing different strategies in a game round is used to calculate the cumulative income of the node in combination with the probability formula. Finally, the Fermi function is used to determine whether the node updates the strategy. The simulation results show that, compared with the random update rule, the proposed strategy is more capable of promoting cooperative behavior between nodes to improve the delivery rate of data packets. [ABSTRACT FROM AUTHOR]

Efficient large-scale and feasible industrial synthesis of the 1-oxacephem core structure from 6-aminopenicillanic acid (6-APA) has been reported for several decades. Via the industrial synthesis route, a byproduct (compound 9) containing a butenolide unit was purified and characterized by NMR and HRMS in this work. It is worth noting that compound 9 is an entirely new compound. Additionally, a plausible mechanism and effects on the formation of 9 by different Lewis acids were proposed. The discovery of compound 9 could improve the purity of this feasible industrial synthesis and provide considerable cost savings. [ABSTRACT FROM AUTHOR]

Candida albicans biofilms contribute to invasive infections and dramatic drug resistance, and anti-biofilm agents are urgently needed in the clinic. Pterostilbene (PTE) is a natural plant product with potentials to be developed as an anti-biofilm agent. In this study, we evaluated the structure-activity relationship (SAR) of PTE analogues against C. albicans biofilms. XTT (Sodium 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2 H -tetrazolium-5-carboxanilide inner salt) reduction assay was used to evaluate the activity of the analogues against C. albicans biofilms. Knowing that hyphal formation is essential for C. albicans biofilms, anti-hyphal assay was further carried out. By comparing a series of compounds tested in this study, we found that compounds with para -hydroxy (-OH) in partition A exhibited better activity than those with other substituents in the para position, and the double bond in partition B and meta -dimethoxy (-OCH₃) in partition C both contributed to the best activity. Consistent results were obtained by anti-hyphal assay. Collectively, para -hydroxy (-OH), double bond and meta -dimethoxy (-OCH₃) are all needed for the best activity of PTE against C. albicans biofilms.