Search

Due to numerous computation-intensive and delay-sensitive tasks in the Internet of Vehicles (IoV), Vehicular Edge Computing (VEC) is increasingly playing a crucial role as a key solution in the IoV. However, how to concurrently enhance communication quality and reduce the cost of latency and energy has emerged as a critical challenge in VEC. To tackle the above problem, we propose a Lyapunov-guided offloading based on the Soft Actor-Critic (SAC) algorithm, named LySAC, to minimize the average cost of the Integrated Sensing and Communications (ISAC) technology-aided IoV, where ISAC technology can effectively improve the communication quality by harnessing high-frequency waveforms to seamlessly integrate communication and sensing functionalities. First, we model the offloading process of ISAC-Aided IoV as an optimization problem of the joint cost of delay and energy with long-term energy consumption and queue stability. Then we formulate the optimization problem as a Lyapunov optimization and utilize the SAC method to find the optimal offloading decisions. Finally, we conduct extensive experiments and the results demonstrate the effectiveness and superiority of the proposed LySAC in minimizing total cost while maintaining queue stability and meeting long-term energy requirements compared with other several baseline schemes.

Fetal Heart Rate (FHR) signal is widely used in doppler fetal heart monitors. However, incomplete FHR signals reduce the effectiveness of fetal heart rate monitoring. Filling missing data is a key technique to improve FHR quality, but existing filling algorithms lack consideration the correlation of FHR signals. Therefore, we focus on two correlations related to FHR, and propose a filling algorithm called Diffusion model for Missing Data Imputation in FHR (DMDI-FHR). Firstly, we construct the Dual-dimensional Sample Construction (DDSC) method that finds two FHR signals with maximizing the difference to form correlations between them. Secondly, the Multi-period Decomposition (MPD) method is introduced to obtain the internal correlation of FHR signal. Finally, DMDI-FHR algorithm controls the filling process based on the diffusion model. Experimental results demonstrate the performance of DMDI-FHR algorithm, which provides an effective way to improve the quality of FHR signal.

In solids, the condensation of electron and hole pairs with finite momentum leads to an ordered state known as a charge density wave, in which the charge has spatial modulation. However, lattice symmetry breaking and the accompanying relocation of the ions, which changes the charge distribution, can also occur simultaneously, making it difficult to disentangle the origin of the transition. Here we demonstrate a condensed phase in low-dimensional HfTe2. Angle-resolved photoemission spectroscopy measurements reveal a metal–insulator transition upon lowering the temperature. The observation of an opening gap, the renormalization of the bands and the emergence of replica bands in the low-temperature regime suggests that a charge density wave formed in the ground state. Raman spectroscopy shows no sign of lattice distortion within the detection limit. A small amount of electron doping substantially raises the transition temperature due to a reduced screening effect and a more balanced carrier density for electrons and holes. Our results indicate that the formation mechanism of the charge density wave is consistent with the excitonic insulator phase in low-dimensional HfTe2without any structural modification.

Capturing structural similarity has been a hot topic in the field of network embedding (NE) recently due to its great help in understanding node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks, while the related study on heterogeneous networks is still void. In this article, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we first propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and give two more applicable variants. Then, we devise the HAW embedding (HAWE) and its variants in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods and can be applied on large-scale networks.

Anomaly detection in dynamic networks aims to find network elements (e.g., nodes, edges, subgraphs, change points) with significantly different behaviors from the vast majority, it can also devote to community detection and evolution and prediction tasks. Most existing methods focus on one specific task, that is, only detect anomalies of one type of element isolated, so they lose the ability to model the correlation and driving mechanism between different abnormal behavior. Considering that the anomaly detection of one type of element is helpful to other types of elements, i.e., the temporal evolution hidden the dynamic networks are driven by indivisible behavior patterns. So in this paper, we propose a unified Generation model to analyze the dynamic network for Exploring the Abnormal Behaviors of different Scales (GEABS). It can model the relation and catch different levels (node, community and network) of anomaly with a joint statistical network model and detect the community structure and its evolution. Specifically, we denote the parameters of node popularity, community membership to generate the dynamic network with stochastic block model (SBM), we also describe the varying of node and community by dynamic process. With a well-designed generative mechanism, it can detect the change point on network level, temporal evolution on community level and abnormal behavior on node level synchronously, besides, it also detects the community structure effectively. We also propose an effective optimization algorithm with variational inference. Experimental results show that the GEABS achieves better performance on abnormal behavior and community structure compared with baselines.

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior. To solve the above-mentioned problem, this article proposes a hierarchical Bayesian dynamic SBM (HB-DSBM) for modeling the node-level and community-level dynamic behavior in a dynamic network synchronously. Based on the SBM, we introduce a hierarchical Dirichlet generative mechanism to associate the global community evolution with the microscopic transition behavior of nodes near-perfectly and generate the observed links across the dynamic networks. Meanwhile, an effective variational inference algorithm is developed and we can easy to infer the communities and dynamic behaviors of the nodes. Furthermore, with the two-level evolution behaviors, it can identify nodes or communities with abnormal behavior. Experiments on simulated and real-world networks demonstrate that HB-DSBM has achieved state-of-the-art performance on community detection and evolution. In addition, abnormal evolutionary behavior and events on dynamic networks can be effectively identified by our model.

Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks. Although some methods for static networks have shown promising results in boosting community detection by integrating community embedding, they are not suitable for temporal networks and unable to capture their dynamics. Furthermore, the dynamic embedding methods only model network varying without considering community structures. Hence, in this article, we propose a novel unsupervised dynamic community detection model, which is based on network embedding and can effectively discover temporal communities and model dynamic networks. More specifically, we propose the community prior by introducing the Gaussian mixture model (GMM) in the variational autoencoder, which can obtain community information and better model the evolutionary characteristics of community structure and node embedding by utilizing the variant of gated recurrent unit (GRU). Extensive experiments conducted in real-world and artificial networks demonstrate that our proposed model has a better effect on improving the accuracy of dynamic community detection.

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the methods and models of NE are based on the local, high-order, or global similarity of the networks, and few studies have focused on the role discovery or structural similarity, which is of great significance in spreading dynamics and network theory. Meanwhile, existing NE models for role discovery suffer from two limitations, that is: 1) they fail to model the varying dependencies between each node and its neighbor nodes and 2) they cannot capture the effective node features which are helpful to role discovery, which makes these methods ineffective when applied to the role discovery task. To solve the above problems of NE for role discovery or structural similarity, we propose a unified deep learning framework, called RDAA, which can effectively represent features of nodes and benefit the Role Discovery-guided NE with a deep autoencoder, while modeling the local links with an Attention mechanism. In addition, we design an elaborately binding technique to combine both parts and optimize the framework in a unified way. We conduct different experiments, including visualization, role classification, role discovery, and running time compared to popular NE methods for both proximity and structural similarity. The RDAA has better performance on all the datasets and achieves good tradeoffs.

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal dependence across network snapshots; 2) the node embedding in the latent space fails to indicate changes in the network topology; and 3) it cannot avoid a lot of redundant computation via parameter inheritance on a series of snapshots. To overcome these problems, we propose a novel TNE method named temporal network embedding method based on the VAE framework (TVAE), which is based on a variational autoencoder (VAE) to capture the evolution of temporal networks for link prediction. It not only generates low-dimensional embedding vectors for nodes but also preserves the dynamic nonlinear features of temporal networks. Through the combination of a self-attention mechanism and recurrent neural networks, TVAE can update node representations and keep the temporal dependence of vectors over time. We utilize parameter inheritance to keep the new embedding close to the previous one, rather than explicitly using regularization, and thus, it is effective for large-scale networks. We evaluate our model and several baselines on synthetic data sets and real-world networks. The experimental results demonstrate that TVAE has superior performance and lower time cost compared with the baselines.

Porosity is a key parameter in shale gas reservoir evaluation and reserve calculation and its accurate test is the basis for calculating geological reserves of shale gas and determining development plans. In order to clarify the differences between different porosity test methods and their influences on the calculation results of shale gas reserves, we collected 65 shale samples of Lower Silurian Longmaxi Formation from six shale gas wells in the Zhaotong National Shale Gas Demonstration Area of the southern Sichuan Basin for comparative experiments using three porosity test method, including gas injection porosimetry (GIP) method, water immersion porosimetry (WIP) method and nuclear magnetic resonance (NMR) method. Then, these three methods were comparatively analyzed based on the test results. Finally, it was proposed to optimize the key parameters of these three shale porosity test methods. And the following research results were obtained. First, in terms of the GIP method, the particle size of shale sample shall be in the range of 20–60 mesh and the helium saturation equilibrium time shall be over 1800 s. Second, in terms of the WIP method, the sample shall be dried for at least 48 h under 110 °C and saturated for 24 h under the confining pressure of 15 MPa. Third, in terms of the NMR method, NMR porosity calculation shall not be conducted until the NMR signal of the dried sample is deducted, on the basis of echo time and waiting time optimization. Fourth, porosity average and median value obtained by these three shale porosity test methods follow the relationship of WIP porosity > NMR porosity > particle GIP porosity > plunger GIP porosity. Fifth, different shale porosity test methods have greater influences on the calculation results of shale gas geological reserves, whose difference can reach 20%. In conclusion, during the application of NMR method and WIP method, fluid is introduced for saturation, which may damage the shale pores. However, the particle GIP porosity can reflect the entire space of shale more comprehensively and is not influenced by the properties of the applied fluid. Therefore, it is suggested to adopt the particle GIP method to calculate shale gas geological reserves.

In our pervious study, a dual-functional peptide R7 was developed to form a complex with paclitaxel (PTX) for enhancement of PTX translocation. However, because of the unstable noncovalent bond between R7 and PTX, PTX redistributed after the introduction of heparin, leading to R7-PTX complex dissociation, further causing less PTX penetration than expected. Thus, a novel positive CPP carrier of P9 was developed to improve CPP-PTX affinity via a double-proline (Pro, P) hairpin tail and enhance PTX translocation through the reduction of translocation energy barrier, confirmed by the MM-PBSA analysis and umbrella sampling simulation. Cellular uptake study reveals that P9 can quickly translocate into the HeLa cells within 1 min and exhibits no noticeable cytotoxicity. Compared to R7, P9 is able to help PTX translocation, leading to a remarkable increase in the intracellular concentration of PTX, eventually resulting in a significant loss in tumor cell viability. In vivoexperiments demonstrate that a vein injection of P9-PTX complex dramatically inhibits tumor growth. Our study provides a novel perspective for designing CPP-facilitated drug carrier to enhance antitumor efficiency.

1,5-Pentanediamine carbonate (PDAC) can be used as the raw material to prepare pentamethylene diisocyanate (PDI), a new biobased aliphatic isocyanate. The preparation of high-quality PDAC by crystallization is taken to be valuable for the preparation of PDI, and it can be used as an effective approach for the separation and purification of 1,5-pentanediamine from the biocatalytic system as well. In this work, the solubility of PDAC from 283.15 to 323.15 K in water and binary solvent mixtures was measured by the polythermal method, and the experimental solubility data were correlated and analyzed by the modified Apelblat equation and van’t Hoff equation. In addition, three crystallization methods were investigated, and PDAC crystals were finally prepared with high bulk density, good flowability, and stability. It was found that the PDAC has two crystal forms, both existing as a spherical aggregate. They were characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, powder X-ray diffraction, thermogravimetry and differential scanning calorimetry, and hot stage microscopy. Besides, the sensitivity of the two crystal forms to humidity and their transformation behavior were studied. Compared with form I, form II was proven to be the thermodynamic stable polymorph in the temperature range from 298.15 to 338.15 K.

•We propose a new pretext task between the original view and the augmented view.•We measure the node-level accurate difference between the two views.•We combine various strategies to generate multiple augmented views.

Cytidine 5′-monophosphate (CMP) and guanosine 5′-monophosphate (GMP) were separated based on hyper-cross-linked resins. The adsorption performances of different resins to the two 5′-ribonucleotides were compared. The adsorption equilibria, kinetics, and dynamic separation process of CMP and GMP with pH gradient elution were studied experimentally and theoretically. The nonpolar resin XDA-1 was selected as the best adsorbent for separating CMP and GMP. A pH-dependent Langmuir isotherm model was developed. The model predicted the adsorption isotherms of CMP and GMP at different pH levels satisfactorily. The surface diffusion model can describe the adsorption kinetics curves of CMP and GMP better than the pseudo-first-order rate model and the pseudo-second-order rate model. CMP and GMP could be both separated with a high separation resolution when the loading amount is 0.031 or 0.093 bed volume (BV). A novel pH-dependent film-surface diffusion model was developed and the model predicted the dynamic separation process of CMP and GMP with pH gradient elution satisfactorily.

Nanoparticulate antitumor photodynamic therapy (PDT) is suffering from a very short lifetime, limited diffusion distance of reactive oxygen species (ROS). Herein, a hypoxia/ROS/pH triple‐responsive metal–organic framework (MOF) is designed to facilitate the on‐demand release of photosensitizers and hence enhanced PDT efficacy. Tailored azo‐containing imidazole ligand is coordinated with zinc to form MOF where photosensitizer (Chlorin e6/Ce6) is encapsulated. Azo can be reduced by overexpressed azoreductase in hypoxic tumor cells, resulting in depletion of glutathione (GSH) and thioredoxin (Trx) which are major antioxidants against ROS oxidative damage in PDT, resulting in rapid cargo release and additional efficacy amplification. The imidazole ionization causes a proton sponge effect to ensure the disintegration of the nanocarriers in acidic organelles, allowing the rapid release of Ce6 through lysosome escape. Under light irradiation, ROS produced by Ce6 may oxidize imidazole to urea, resulting in rapid cargo release. All of the triggers are expected to show interactive synergism. The pH‐ and hypoxia‐responsiveness can improve the release rate of Ce6 for enhanced PDT therapy, whereas the consumption of oxygen by PDT may induce elevated hypoxia and hence in turn enhanced cargo release. This work highlights the role of triple‐responsive nanocarriers for triggered photosensitizer release and improved antitumor PDT efficacy. To address the compromised photodynamic therapy (PDT) efficacy caused by the very short lifetime and limited diffusion distance of reactive oxygen species (ROS), a hypoxia/pH/ROS triple‐responsive metal–organic framework (MOF) nanoplatform which contains imidazole and azo groups is designed in the current work to facilitate on‐demand release of photosensitizers and hence enhance PDT efficacy.

L-phenylalanine, one of the nine essential amino acids for the human body, is extensively used as an ingredient in food, pharmaceutical and nutrition industries. A suitable equilibrium model is required for purification of l-phenylalanine based on ion-exchange chromatography. In this work, the equilibrium uptake of l-phenylalanine on a strong acid-cation exchanger SH11 was investigated experimentally and theoretically. A modified Donnan ion-exchange (DIX) model, which takes the activity into account, was established to predict the uptake of l-phenylalanine at various solution pH values. The model parameters including selectivity and mean activity coefficient in the resin phase are presented. The modified DIX model is in good agreement with the experimental data. The optimum operating pH value of 2.0, with the highest l-phenylalanine uptake on the resin, is predicted by the model. This basic information combined with the general mass transfer model will lay the foundation for the prediction of dynamic behavior of fixed bed separation process.

Under endoplasmic reticulum (ER) stress, tumor plays multifaceted roles in endothelial cell dysfunction through secreting exosomal miRNAs. However, for the head and neck squamous cell carcinoma (HNSCC), it is still unclear about the impact of ER-stressed HNSCC cell derived exosomes on vascular endothelial cells. To address this gap, herein, systemic research was conducted including isolation and characterization of ER-stressed HNSCC cell (HN4 cell line as an in vitromodel) derived exosomes, identification of regulatory exosomal miRNAs, target exploration and downstream signaling pathway investigation of exosomal miRNAs in human umbilical vein endothelial cell (HUVEC). ER-stressed HN4 cell-derived exosomes inhibited angiogenesis and migration of HUVEC cells in vitro. Furthermore, RNA-seq analysis demonstrated that miR-424-5p was highly upregulated in ER-stressed HN4 cell-derived exosomes. Through matrigel tube formation and transwell assays of HUVEC cells, miR-424-5p displayed great capabilities on inhibiting angiogenesis and migration. Finally, based on western blot and luciferase reporter, it was demonstrated that LAMC1 is the target of miR-424-5p which could inhibit the angiogenesis and migration of HUVEC cells by repressing the LAMC1-mediated Wnt/β-catenin signaling pathway. ER-stressed HNSCC cell-induced exosomal miR-424-5p inhibits angiogenesis and migration of HUVEC cells through LAMC1-mediated Wnt/β-catenin signaling pathway. This study offers a new insight for understanding the complicated mechanism behind ER-stress induced anti-angiogenesis of HNSCC.

The aim of this study was to investigate claudin-7 (CLDN7) expression in salivary adenoid cystic carcinoma (SACC) and its function in SACC cells. We determined CLDN7 expression in SACC tumors via immunohistochemistry and western blotting and evaluated the association between CLDN7 expression and clinicopathologic variables. Besides this, we constructed a stably transfected CLDN7 knockdown SACC-LM cell line via RNAi and assessed its biological behavior changes (cell viability, migration, and invasion). The correlation between CLDN7 and epithelial-mesenchymal transition (EMT) was analyzed. Additionally, a subcutaneous tumor formation model was used to assess SACC-LM cells tumorigenicity after the CLDN7 knockdown. In the present study, we found the CLDN7 expression of tumor group was lower than that in normal salivary glands and was significantly correlated with lymph node metastasis, recurrence, and gender. CLDN7 knockdown could add the proliferation and metastasis ability of SACC by regulating EMT through Wnt/β-catenin signaling pathway. In addition, CLDN7 knockdown in SACC promoted tumor growth in nude mice. CLDN7 inhibits cell proliferation and metastasis by inactivating the Wnt/β-catenin signaling in SACC. Thus, CLDN7 expression might be a useful marker to identify the potential for progression in SACC.

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide. Long noncoding RNAs were proved to be associated with the development and progression in HNSCC. However, the mechanism of LINC00460 in HNSCC needs to be further investigated. The study used quantitative real-time polymerase chain reaction assay to detect the expression of LINC00460 in cancer tissues and cell lines. Gain and loss of function experiments were conducted to analyze the effects of LINC00460 and miR-4443 on cell proliferation, invasion, and apoptosis of HNSCC cells in vitro. The interactions among miR-4443 and LINC00460 were detected by dual-luciferase reporter assay. Here, the study showed that LINC00460 was highly expressed in HNSCC tissues and cell lines. Functionally, knockdown of LINC00460 inhibited HNSCC cell proliferation and migration in vitro. Besides, LINC00460 promoted cell progression by sponging miR-4443, and miR-4443 inhibitor could reverse the effects of si-LINC00460 on cell proliferation and migration. In summary, LINC00460 could potentially promote cell progression and epithelial mesenchymal transition by sponging miR-4443 in HNSCC. LINC00460 could be used as a potential therapeutic target for HNSCCs.

In the aspect of stability analysis of tunneling engineering, geomechanical model test is an important research method. A similar material is the prerequisite for the success of geomechanical model test. In the field of major engineering applications, a variety of similar materials are prepared for different geological conditions of surrounding rock and applied in some major engineering. With the use of standard sand, fine sand, and silt clay as materials, similar materials for weak surrounding rock were developed. Based on the orthogonal design method, through the direct shear test, the range analysis and variance analysis of various factors affecting the physical and mechanical parameters of weak surrounding rock are carried out. The results show similar material can meet the requirements in weak surrounding rock. Standard sand is the key factor that influences the internal friction angle of similar materials, and silt clay is the key factor affecting the cohesion of similar materials. Similar materials can meet the elastic modulus and severe requirements of the weak surrounding rock and can be used for the weak surrounding rock engineering. The new type of similar material configuration is widely used in shallow buried tunnel entrance section and urban shallow buried excavation engineering, in addition to tunnel engineering in loess stratum, and the problems of engineering design and construction are solved through geomechanical model test.