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Abstract Based on the local resonance theory, the vibration isolation performance of four-component periodic pile barriers consisting of a matrix, an outer pipe, an inner pipe, and a pile core was investigated using the finite element method (FEM). This configuration is contrasted with traditional three-component periodic pile barriers, which are composed of a matrix, a pipe, and a pile core. The four-component systems demonstrate significantly broader bandgaps compared to three-component systems. To validate the efficiency of the FEM, model test on pile barriers were conducted, facilitating a comparison between the FEM and experimental observations of bandgap characteristics. Additionally, the effects of the outer pipe’s density, elastic modulus, and thickness on the complete bandgap characteristics were comprehensively studied within the four-component structure. It is found that the broadest vibration isolation band gaps occur under hexagonal arrangement pattern. In square and hexagonal lattice configurations, the density, elastic modulus, and thickness of the outer pipe pile’s pile have predominantly influenced the upper bound frequency (UBF). There has been a positive correlation between the elastic modulus and the UBF, while the density and thickness have shown inverse relationships. Moreover, there exists a elastic modulus threshold, marking a transition in the displacement mode of the UBF, from the outer irreducible Brillouin zone (M or X) to the inner zone (Γ). Once the threshold is exceeded, both the vibration displacement mode and the width of bandgap remain substantially unchanged. The results obtained in the present paper are very useful for the design and application of periodic pile barriers in ambient vibration reduction.

Abstract Image clustering has received significant attention due to the growing importance of image recognition. Researchers have explored Riemannian manifold clustering, which is capable of capturing the non‐linear shapes found in real‐world datasets. However, the complexity of image data poses substantial challenges for modelling and feature extraction. Traditional methods such as covariance matrices and linear subspace have shown promise in image modelling, and they are still in their early stages and suffer from certain limitations. However, these include the uncertainty of representing data using only one Riemannian manifold, limited feature extraction capacity of single kernel functions, and resulting incomplete data representation and redundancy. To overcome these limitations, the authors propose a novel approach called join multiple Riemannian manifold representation and multi‐kernel non‐redundancy for image clustering (MRMNR‐MKC). It combines covariance matrices with linear subspace to represent data and applies multiple kernel functions to map the non‐linear structural data into a reproducing kernel Hilbert space, enabling linear model analysis for image clustering. Additionally, the authors use matrix‐induced regularisation to improve the clustering kernel selection process by reducing redundancy and assigning lower weights to identical kernels. Finally, the authors also conducted numerous experiments to evaluate the performance of our approach, confirming its superiority to state‐of‐the‐art methods on three benchmark datasets.

Abstract Oxidative stress is an important contributor to gastrointestinal diseases in multiple ways. Taurodeoxycholic acid (TDCA) is a metabolite of bile acids and has anti-inflammatory and protective effects on the intestinal tract. However, whether TDCA can alleviate oxidative stress in the intestine is still unclear. Here, we investigated the effects of TDCA on diquat-induced oxidative stress in the jejunum and its mechanism. The results revealed that TDCA increased the concentrations of antioxidant enzymes in the serum, jejunal tissue and intestinal epithelial cells of the mice, as did the expression of tight junction-associated proteins and the Nrf2 protein in the jejunal epithelial tissue and intestinal epithelial cells. We then explored the mechanism of Nrf2 with ML385 (a specific Nrf2 inhibitor). The results showed that after ML385 treatment, the levels of antioxidant enzymes were significantly decreased in the serum, jejunum, and intestinal epithelial tissues of the mice. The expression of tight junction proteins in jejunum epithelial tissues and intestinal epithelial cells was also decreased. In conclusion, our study suggests that TDCA alleviates oxidative stress to improve intestinal barrier function through the Nrf2-mediated signaling pathway. These findings help elucidate the role of TDCA in protecting the intestinal barrier and its mechanism of action, providing insights for the prevention and treatment of intestinal diseases caused by oxidative stress.

Dendrobium is a traditional, valuable, and nourishing medicinal herb in China. It benefits the stomach, promotes fluid production, nourishes Yin, and clears heat. Polysaccharides are among the main active ingredients of Dendrobium. By reviewing literature over the past decade, the common structures and biological activities of Dendrobium polysaccharides are systematically summarized. The results illustrate that Dendrobium polysaccharides possess various effects, including antioxidant, antitumor, anti-inflammatory, and immunoregulatory effects, gut microbiota modulation, blood sugar reduction, and anticataract properties. The chemical structures of these polysaccharides are closely related to their biological activities. Previous studies suggest that polysaccharides rich in mannose and glucose exhibit stronger activity, whereas higher acetylation levels enhance immune activity. Higher uronic acid content corresponds to greater antioxidant ability, which may be due to the presence of (1→4)-α-D-Glcp and (1→4)-α-D-Manp on the main chain. Additionally, chemical modifications such as sulfation, selenization, and acetylation can enhance activity, whereas carboxymethylation decreases it. This article summarizes the research progress in recent years on the structure, biological activity, and product development of Dendrobium polysaccharides both home and abroad, elucidating the structure-activity relationship, aiming to provide important references for their in-depth development and application.

Scene-level change detection (SLCD) can provide semantic change information at image level, thus it is of great significance for monitoring land use changes. Supervised SLCD approaches tend to outperform unsupervised ones. However, the existing supervised methods rely on postclassification, which often results in unsatisfactory performance due to classification error accumulation. We therefore formulate SLCD as a similarity learning task, and propose a scene similarity learning network (SSLN) for land use SLCD. To be specific, SSLN is a two-branch network with ResNet as the backbone for feature extraction, where the global feature difference and the multiscale local feature fusion modules are considered in order to better mine the temporal correlation between bitemporal scenes. Then, the trained SSLN is further exploited to obtain the similarity of scene pairs for determining the similarity threshold via threshold traversal algorithm. Finally, the land use scenes are categorized into changed or unchanged by comparing scene similarity with the threshold. Experimental results on the publicly available MtS-WH dataset and our newly released land use scene change detection dataset show that the proposed approach achieves better performance than comparison methods, indicating that our approach is a simple yet effective solution to land use SLCD.

To investigate the impact of the geometric parameters of periodic pile barriers on bandgap characteristics in passive vibration isolation, a two-dimensional, three-component unit cell was developed using the finite element method (FEM). This study analyzed the bandgap properties of periodic pile barriers and validated the effectiveness of the FEM through model testing. The FEM was then methodically applied to evaluate the effects of pipe pile thickness, periodic constant, arrangement pattern, and cross-sectional shape on the bandgap characteristics, culminating in the proposition of a novel H-shaped cross-section for the piles. The results demonstrated that the FEM-calculated bandgap frequency range, featuring steel piles arranged in a square pattern, closely aligned with the attenuation zone in the model tests. The lower band frequency (LBF) was primarily influenced by the pipe pile’s inner radius, while the upper band frequency (UBF) was predominantly affected by its outer radius. As the periodic constant increased, the LBF, UBF, and the width of band gap (WBG) all decreased. Conversely, changing the arrangement pattern from square to hexagonal led to increases in UBF and WBG, while the LBF diminished. Notably, the WBG of the H-section steel piles, possessing the same cross-sectional area, was 1.31 times greater than that of the steel pipe piles, indicating an enhanced vibration isolation performance. Additionally, the impact of transverse and vertical characteristic dimensions of the H-shaped pile on the band gap distribution was assessed, revealing that the transverse characteristic dimensions exerted a more significant influence than the vertical dimensions.

Abstract The development of the Internet and big data has led to the emergence of graphs as an important data representation structure in various real-world scenarios. However, as data size increases, computational complexity and memory requirements pose significant challenges for graph embedding. To address this challenge, this paper proposes a multilevel embedding refinement framework (MERIT) based on large-scale graphs, using spectral distance-constrained graph coarsening algorithms and an improved graph convolutional neural network model that addresses the over-smoothing problem by incorporating initial values and identity mapping. Experimental results on large-scale datasets demonstrate the effectiveness of MERIT, with an average AUROC score 8% higher than other baseline methods. Moreover, in a node classification task on a large-scale graph with 126,825 nodes and 22,412,658 edges, the framework improves embedding quality while enhancing the runtime by 25 times. The experimental findings highlight the superior efficiency and accuracy of the proposed approach compared to other graph embedding methods.

To study the impact of cutting blasting on the surface, a vibration waveform prediction function was constructed, and a method of dividing the affected area was proposed. Based on the equivalent spherical charge theory, it is possible to establish a connection between the fitting coefficient and the engineering parameters in the equivalent source intensity function. Furthermore, a blasting vibration waveform function suitable for engineering can be constructed. Secondly, the reliability of the method introduced is verified through the data monitored on-site. Finally, the affected partitions of blasting vibration are divided based on the peak particle velocity and vibration displacement as standards. The results show that the vibration waveform prediction system introduced can restore the vibration waveform corresponding to cutting blasting. In addition, the zoning method can reasonably divide the scope of the affected area.

Abstract Significant progress has been made in circular RNA (circRNA) research in recent years. Increasing evidence suggests that circRNAs play important roles in many cellular processes, and their dysregulation is implicated in the pathogenesis of various diseases. CircRNAs are highly stable and usually expressed in a tissue- or cell type-specific manner. Therefore, they are currently being explored as potential therapeutic targets. Gain-of-function and loss-of-function approaches are typically performed using circRNA expression plasmids and RNA interference-based strategies, respectively. These strategies have limitations that can be mitigated using nanoparticle and exosome delivery systems. Furthermore, recent developments show that the cre-lox system can be used to knockdown circRNAs in a cell-specific manner. While still in the early stages of development, the CRISPR/Cas13 system has shown promise in knocking down circRNAs with high specificity and efficiency. In this review, we describe circRNA properties and functions and highlight their significance in disease. We summarize strategies that can be used to overexpress or knockdown circRNAs as a therapeutic approach. Lastly, we discuss major challenges and propose future directions for the development of circRNA-based therapeutics.

How to deal with data sets in high-dimensional space is the focus of image processing. At present, subspace clustering method is one of the most commonly used methods for processing high-dimensional data sets. Traditional subspace clustering assumes that the data comes from different linear subspaces, and the different subspace regions do not overlap. However, the real data often does not satisfy these two constraints, which affects the effect of subspace clustering. In order to deal with these two problems, we first introduce the consensus Hilbert space to solve the nonlinear problem of subspace data, that is, a method that can learn the best consensus Hilbert space from a given set of kernel space; The second-order neighbors of the subspace coefficient matrix are used to deal with overlapping subspace problems. The second-order neighbors can effectively optimize the sparseness and connectivity of the self-representation. Subsequently, in order to effectively handle the above complex iterative optimization steps, an alternating direction multiplier method was developed. Finally, the designed multiple kernel subspace clustering algorithm is tested on a total of sixteen real data sets of four types, including face, handwritten, UCI and biomedical data sets. It can be seen from the experimental results that our proposed method has improved clustering accuracy, normalized mutual information and adjusted rand index compared to the latest algorithms.

Clustering, which is a commonly used tool, has been applied in machine learning, data mining and so on, and has received extensive research. However, there are usually noise and outliers in the data, which will bring about significant errors in the clustering results. In this paper, a robust clustering model with adaptive graph regularization (RCAG) is proposed, on which, sparse error matrix is introduced to express sparse noise, such as impulse noise, dead line, stripes, and ℓ1 norm is introduced to alleviate the sparse noise. In addition, the ℓ2,1 norm is also proposed mitigating the effects of outliers, and it has rotation invariance property. Therefore, our RCAG is insensitive to data noise and outliers. More importantly, the adaptive graph regularization is introduced into the RCAG to improve the clustering performance. Aiming at the optimization objective, we propose an iterative updating algorithm, named the Augmented Lagrangian Method (ALM), to update each optimization variable respectively. The convergence and time complexity of RCAG is also proved in theory. Finally, experimental results on fourteen datasets of four application scenarios, such as face image, handwriting recognition and UCI, elaborate the superiority of proposed method over seven existing classical clustering methods. The experimental results demonstrate that our approach achieves better clustering performance in ACC and Purity, which is a little less impressive in other ways.

Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks. Unlike approaches using the Nyström method, which randomly samples the training examples, we make use of random Fourier features, whose basis functions (i.e., cosine and sine ) are sampled from a distribution independent from the training sample set, to cluster preference data which appears extensively in recommender systems. Firstly, we propose a two-stage preference clustering framework. In this framework, we make use of random Fourier features to map the preference matrix into the feature matrix, soon afterwards, utilize the traditional k-means approach to cluster preference data in the transformed feature space. Compared with traditional preference clustering, our method solves the problem of insufficient memory and greatly improves the efficiency of the operation. Experiments on movie data sets containing 100 000 ratings, show that the proposed method is more effective in clustering accuracy than the Nyström and k-means, while also achieving better performance than these clustering approaches.

Abstract Background Tomato fruit (Lycopersicon esculentum Mill.) has been suggested to be useful for the prevention of diabetes. Esculeoside A is the main saponin compounds in tomatoes. This study investigated the hypoglycemic effects and the underlying mechanism of esculeoside A in C57BLKS/Leprdb (db/db) mice. Methods Wild-type C57BLKS (db/dm) mice were used in the db/dm mouse group and db/db mice were randomly divided into 2 groups: untreated and treated db/db mouse groups. Esculeoside A (100 mg/kg) was administered by gavage for 56 days to the treated db/db mouse group. Distilled water was administered to the db/dm mouse group and the untreated db/db mouse group. The blood and liver biochemical parameters and the expression of liver insulin signaling-related proteins were examined. Results The results showed that esculeoside A reduced the fasting blood glucose (FBG) levels and improved the glucose tolerance. Further investigation revealed that hepatic protein expressions of total AMP-activated protein kinase (T-AMPK), phosphorylated AMP-activated protein kinase (p-AMPK), insulin receptor substrate-1 (IRS-1), and glucokinase (GCK) were significantly upregulated after esculeoside A treatment. In contrast, the hepatic protein expression of phosphoenolpyruvate carboxykinase (PEPCK) was significantly downregulated by esculeoside A treatment. Conclusion These findings suggested that esculeoside A has a potential of alleviating the metabolic abnormalities in db/db mice via regulation of AMPK/IRS-1 pathway. Our findings supported a possible application of esculeoside A as a functional supplement for diabetes treatment.

As a newly discovered type of RNA, circular RNAs (circRNAs) are widespread throughout the eukaryotic genome. The expression of circRNAs is regulated by both cis-elements and trans-factors, and the expression pattern of circRNAs is cell type- and disease-specific. Similar to other types of non-coding RNAs, functions of circRNAs are also versatile. CircRNAs have been reported previously to function as microRNA (miRNA) sponges, protein sponges, coding RNAs or scaffolds for protein complexes. Recently, several circRNAs have been reported to play important roles in human malignancies, including glioma. Here, we reviewed several reports related to circRNAs and glioma, as well as the potential diagnostic and therapeutic applications of circRNAs in brain cancer. In general, some circRNAs, such as circSMARCA5 and circCFH, are found to be expressed in a glioma-specific pattern, these circRNAs may be used as tumor biomarkers. In addition, some circRNAs have been found to play oncogenic roles in glioma (e.g., circNFIX and circNT5E), whereas others have been reported to function as tumor suppressors (e.g., circFBXW7 and circSHPRH). Furthermore, circRNA is a good tool for protein expression because of its higher stability compared to linear RNAs. Thus, circRNAs may also be an ideal choice for gene/protein delivery in future brain cancer therapies. There are some challenges in circRNA research in glioma and other diseases. Research related to circRNAs in glioma is comparatively new and many mysteries remain to be solved.

As a graphical model, Conditional Preference Networks (CP-nets) are used to describe the qualitative conditional preferences of attributes, and the structure learning plays an important role in the research of CP-nets. Different from traditional CP-nets structure learning methods, a Maximum Relevance Minimum Common Redundancy (mRMCR) algorithm based on the information theory and feature selection is proposed and discussed detailedly. Firstly, a mutual information solution formula on the preference database is established, it regards mutual information as mutual relation between one attribute and its feasible father set, which also avoids the calculation of conditional mutual information. Secondly, in order to make our graphical model include relevant, exclude irrelevant and control the use of redundant features, a formula for calculating mRMCR is designed. The mRMCR algorithm can measure the dependent relationship effectively and determine the causal relationship between variables, and can get the structure of CP-nets. Finally, the effectiveness of the algorithm is verified on the movie recommendation datasets. The experimental results show that the proposed mRMCR algorithm can not only obtain the causal relationship between variables quickly and effectively but also extract the feasible father set of each attribute and then obtain the topological structure of CP-nets.

To study the influence of annular trenches on a vibration isolation area, the depth, width, vibration source distance, and central angle of the trench are analysed as research variables, and a contour diagram of the amplitude reduction ratio is drawn based on an outdoor test of the trench. Taking an area with an amplitude reduction ratio less than 0.40 as the evaluation index of the effective vibration isolation area, the effects of the above geometric parameters on the vibration isolation area are analysed. Limited to the test conditions of this paper, the results show that the depth, vibration source distance, and deep width ratio are the important factors affecting the effective vibration isolation area; with the increase of the above parameters, the effective vibration isolation area increases significantly, but the area increase rate decreases gradually. The width has a relatively little effect on the effective vibration isolation area. When the ratio of depth to width is from 7.05 to 9.15, and the width reaches 0.23 times the Rayleigh wavelength, the annular trench can have a good effective vibration isolation area. When the central angle of the trench is less than 90°, a discontinuous effective vibration isolation area will form in the vibration isolation region. The selection of the central angle of the trench is related to the frequency. With the same trench size, the effective vibration isolation area decreases as frequency decreases. In addition, the effect of distance depth ratio on the effective vibration isolation area presents a fluctuation. When the ratio of distance to depth is from 1.21 to 2.05, a good effective vibration isolation area can be obtained and it is reasonable.

Functional peptides can be encoded by short open reading frames in non-coding RNA. Here, the authors identify a 87aa peptide encoded by the circular form of the long intergenic non-protein-coding RNA p53-induced transcript (LINC-PINT) that can reduce glioblastoma proliferation via interaction with PAF1 which sequentially inhibits the transcriptional elongation of some oncogenes.

With the sharp increase of digital data emerging at present, the data in new applications are generated fast. Continuous cumulative data have gradually become massive and difficult to be handled due to limited workspace and limited amount of time. The conventional learning conditional preference networks' algorithm cannot successfully process the data streams. In this paper, we introduce the model of learning CP-nets from preference data streams and formalize the question. Then, an incremental approach is presented through which we can learn the CP-nets with gradually increasing data streams. The proposed method is verified on simulated data and real data, and it is also compared with other works.

To study the propagation characteristics of Rayleigh waves and the isolation mechanism of a single-row of piles by isolation effects, in this paper we draw a two-dimensional contour map of ζ (normalized acceleration amplitude relative to a measure close at the vibration source) using a vibration test carried out on a sand foundation. In this experiment, we study, in addition to the free field and the single pile cases, settings with two and three piles. The result shows that the vibration caused by the point source in the free field excites Rayleigh waves in a radial direction along the surface of the foundation. Meanwhile, the vibrations of the points along the propagation path on the surface of the foundation are gradually weakened. There is a steady transition when the ζ drops to 0.6 and a placid decline when ζ decreases to less than 0.25. The vibration-shielded region, the strengthened region, and the strengthened strips will appear on the surface of the foundation. The vibration-shielded region is located behind the piles, and the region presents a trumpet-shaped area that takes the pile as the vertex. Increasing the quantity of piles contributes to increasing the vibration isolation effect, not only that involving the degree of isolation but also for the area of the shielded area. The vibration-strengthened regions include the diffraction regions at the pile corners on both sides of the single-row of piles and the scattering region at the gaps of the piles. In addition, the composite regions are located among the vibration source and the scattering and diffraction-strengthened regions. Increasing the number of piles has little influence on the scattering and diffraction-strengthened regions but can significantly enhance the vibrations of the composite regions. In general, the vibration-strengthened strips are connected with the scattering-strengthened regions. However, in the test of a single pile, the pile is connected to the diffraction-strengthened regions near its two anterior angles.

K -means plays an important role in different fields of data mining. However, k -means often becomes sensitive due to its random seeds selecting. Motivated by this, this article proposes an optimized k -means clustering method, named k* -means, along with three optimization principles. First, we propose a hierarchical optimization principle initialized by k * seeds ( k * > k ) to reduce the risk of random seeds selecting, and then use the proposed “top- n nearest clusters merging” to merge the nearest clusters in each round until the number of clusters reaches at k . Second, we propose an “optimized update principle” that leverages moved points updating incrementally instead of recalculating mean and SSE of cluster in k -means iteration to minimize computation cost. Third, we propose a strategy named “cluster pruning strategy” to improve efficiency of k -means. This strategy omits the farther clusters to shrink the adjustable space in each iteration. Experiments performed on real UCI and synthetic datasets verify the efficiency and effectiveness of our proposed algorithm.

Mogroside IIE is a bitter triterpenoid saponin which is the main component of unripe Luo Han Guo fruit and a precursor of the commercially available sweetener mogroside V. In this study, we developed an enzymatic glycosyl transfer method, by which bitter mogroside IIE could be converted into a sweet triterpenoid saponin mixture. The reactant concentration, temperature, pH and buffer system were studied. New saponins with the α-glucose group were isolated from the resulting mixtures, and the structures of three components of the extract were determined. The structure-taste relationships of these derivatives were also studied together with those of the natural mogrosides. The number and stereoconfiguration of glucose groups present in the mogroside molecules were found to be the main factor to determine the sweet or bitter taste of a compound. The antioxidant and food safety properties were initially evaluated by their radical scavenging ability and via 7 day mice survival tests, respectively. The results showed that the sweet triterpenoid saponin mixture has the same favorable physiological and safety characteristics as the natural mogrosides.

Conditional preference networks (CP-nets) are recently an emerging topic as a graphical model for compactly representing ordinal conditional preference relations on multi-attribute domains. As we know, the treewidth, which can decrease the solving complexity for many intractability problems, is exactly a fundamental property of a graph. Therefore, we can utilize treewidth to solve some reasoning tasks on induced graphs, such as the dominance queries on the CP-nets in the future. In this paper, we present an efficient algorithm for computing the treewidth of induced graphs of CP-nets; what we need is to make an assumption that the induced graph of a CP-net has been given. Then, we can leverage the Bucket Elimination technique to solve treewidth within polynomial time. At last, it is revealed that by our experiment, the treewidth of induced graphs of CP-nets is much smaller with regard to the number of vertices. For example, for an induced graph of CP-net with 1024 vertices, its treewidth is only 10. As far as we know, this is the first time, using the Bucket Elimination, to compute the treewidth of an induced graph of a CP-net. This approach for solving the treewidth may lay a good foundation for efficiently solving dominance queries on CP-nets in the future.