Influence maximization based on threshold models in hypergraphs.

Influence maximization problem has received significant attention in recent years due to its application in various domains, such as product recommendation, public opinion dissemination, and disease propagation. This paper proposes a theoretical analysis framework for collective influence in hypergraphs, focusing on identifying a set of seeds that maximize influence in threshold models. First, we extend the message passing method from pairwise networks to hypergraphs to accurately describe the activation process in threshold models. Then, we introduce the concept of hypergraph collective influence (HCI) to measure the influence of nodes. Subsequently, we design an algorithm, HCI-TM, to select the influence maximization set, taking into account both node and hyperedge activation. Numerical simulations demonstrate that HCI-TM outperforms several competing algorithms in synthetic and real-world hypergraphs. Furthermore, we find that HCI can be used as a tool to predict the occurrence of cascading phenomena. Notably, we find that the HCI-TM algorithm works better for larger average hyperdegrees in Erdös-Rényi hypergraphs and smaller power-law exponents in scale-free hypergraphs.

GFNet: Automatic segmentation of COVID-19 lung infection regions using CT images based on boundary features.

In early 2020, the global spread of the COVID-19 has presented the world with a serious health crisis. Due to the large number of infected patients, automatic segmentation of lung infections using computed tomography (CT) images has great potential to enhance traditional medical strategies. However, the segmentation of infected regions in CT slices still faces many challenges. Specially, the most core problem is the high variability of infection characteristics and the low contrast between the infected and the normal regions. This problem leads to fuzzy regions in lung CT segmentation. To address this problem, we have designed a novel global feature network(GFNet) for COVID-19 lung infections: VGG16 as backbone, we design a Edge-guidance module(Eg) that fuses the features of each layer. First, features are extracted by reverse attention module and Eg is combined with it. This series of steps enables each layer to fully extract boundary details that are difficult to be noticed by previous models, thus solving the fuzzy problem of infected regions. The multi-layer output features are fused into the final output to finally achieve automatic and accurate segmentation of infected areas. We compared the traditional medical segmentation networks, UNet, UNet++, the latest model Inf-Net, and methods of few shot learning field. Experiments show that our model is superior to the above models in Dice, Sensitivity, Specificity and other evaluation metrics, and our segmentation results are clear and accurate from the visual effect, which proves the effectiveness of GFNet. In addition, we verify the generalization ability of GFNet on another "never seen" dataset, and the results prove that our model still has better generalization ability than the above model. Our code has been shared at https://github.com/zengzhenhuan/GFNet.

Impact of Factors of Online Deceptive Reviews on Customer Purchase Decision Based on Machine Learning.

Online deceptive reviews widely exist in the online shopping environment. Numerous studies have investigated the impact of online product reviews on customer behaviour and sales. However, the existing literature is mainly based on real product reviews; only a few studies have investigated deceptive reviews. Based on the results of deceptive reviews, this article explores the factors that affect customer purchase decision in online review systems, which is flooded by deceptive reviews. Therefore, a deceptive review influence model is proposed based on three influential factors of online review system, sentiment characteristics, review length, and online seller characteristics. Based on them, text mining is used to quantify the indicators of the three influential factors. Through principal component analysis and linear regression, the experimental results of electronic appliances on Tmall show that the three influential factors are positively related to customers' purchase intention and decision making.

A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization.

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein-protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein-protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein-protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein-protein interaction networks.

Design and Analysis of a Novel Integral Recurrent Neural Network for Solving Time-Varying Sylvester Equation.

To solve a general time-varying Sylvester equation, a novel integral recurrent neural network (IRNN) is designed and analyzed. This kind of recurrent neural networks is based on an error-integral design equation and does not need training in advance. The IRNN can achieve global convergence performance and strong robustness if odd-monotonically increasing activation functions [i.e., the linear, bipolar-sigmoid, power, or sigmoid-power activation functions (SP-AFs)] are applied. Specifically, if linear or bipolar-sigmoid activation functions are applied, the IRNN possess exponential convergence performance. The IRNN has finite-time convergence property by using power activation function. To obtain faster convergence performance and finite-time convergence property, an SP-AF is designed. Furthermore, by using the discretization method, the discrete IRNN model and its convergence analysis are also presented. Practical application to robot manipulator and computer simulation results with using different activation functions and design parameters have verified the effectiveness, stability, and reliability of the proposed IRNN.

Excellent thermoelectric properties of monolayer RbAgM (M = Se and Te): first-principles calculations.

Based on the atomic substitution method, the RbAgM monolayers (M = Se and Te), a class of derivative compounds of KAgSe, have been successfully predicted, which exhibit ultra-high mobility and poor heat transport ability, indicating their broad application potential in thermoelectric (TE) technology. Using density functional theory (DFT) and the Boltzmann transport equation (BTE), we carry out systematic studies on their electronic band structures, heat transport abilities and TE properties. Our calculated results show that the RbAgTe monolayer possesses ultra-low lattice thermal conductivity (0.90 W m-1 K-1) at room temperature and a high Seebeck coefficient (2320 µV K-1). Additionally, we also focus on the analysis of phonon velocity and Grüneisen parameter to further explain their ultra-low thermal conductivity. By combining these calculated parameters, the predicted maximum ZT values of RbAgSe and RbAgTe are as high as 2.2 and 4.1 at 700 K with optimum n-type doping, respectively, which are comparable to that of the famous TE material SnSe (ZT = 2.6 at 923 K). Our research results provide a strong theoretical basis for the experimental exploration of the TE properties of RbAgM, and help to promote further experimental verification.

Phonon transport and thermal conductivity of diamond superlattice nanowires: a comparative study with SiGe superlattice nanowires.

Due to the coupling of a superlattice's longitudinal periodicity to a nanowire's radial confinement, the phonon transport properties of superlattice nanowires (SLNWs) are expected to be radically different from those of pristine nanowires. In this work, we present the comparative investigation of phonon transport and thermal conductivity between diamond SLNWs and SiGe SLNWs by using molecular dynamics simulations. In the case of period length ∼ 25 Å, the thermal conductivities of diamond SLNWs and SiGe SLNWs both increase linearly with increasing the period number, which implies the wave-like coherent phonons dominate the heat transport of SLNWs. In the case of period length ∼ 103 Å, the thermal conductivity of SiGe SLNWs is length-independent with increasing the period number, indicating that the particle-like incoherent phonons in SiGe SLNWs control the heat transport, because the phonon-phonon scattering causes phonons to not retain their phases and the coherence is destroyed before the reflection at interfaces. However in diamond SLNWs the coherent phonons still dominate heat conduction and the thermal conductivity is length-dependent, because the mean free path of phonon-phonon scattering in diamond SLNWs is much longer. The spatial distribution of phonon localized modes further supports these opinions. These results are helpful not only to understand the coherent and incoherent phonon transport, but also to modulate the thermal conductivity of SLNWs.

Controlled growth, intense upconversion emissions and concentration induced luminescence switching of bifunctional Tm(3+) doped hexagonal NaYb0.55Gd0.45F4 nanorods.

Bifunctional hexagonal Tm(3+) doped NaYb0.55Gd0.45F4 nanorods with tunable size are prepared via in situ cation-exchange reaction using hydrothermal method. The measured field dependence of magnetization of the NaYb0.55Gd0.45F4 nanorods shows typical paramagnetic characteristics that can be ascribed to the non-interacting localized nature of the magnetic moment of rare-earth ions. When excited by a 980nm laser, these nanorods exhibit intense multi-color up-conversion (UC) emissions in infrared, red, blue and especially ultraviolet. In addition, luminescent switching between different UC emission wavelengths of 480nm and 450nm is observed by adjusting Tm(3+) doping concentration. Based on power-dependent spectral analyses, it is found that with the increase of Tm(3+) doping concentration, due to the suppressed saturation effect, the dominative UC process redistribute the populations at (1)G4 and (1)D2(Tm(3+)) states of Tm(3+) ion resulting in the above luminescent switching. Our results indicate that bifunctional hexagonal NaYb1-xGdxF4 nanocrystals have potential applications in miniaturized solid-state light sources, optical processing sensors and fluorescent biolabels.

The simultaneous separation and determination of six flavonoids and troxerutin in rat urine and chicken plasma by reversed-phase high-performance liquid chromatography with ultraviolet-visible detection.

The method of high-performance liquid chromatography (HPLC) with UV-vis detection was used and validated for the simultaneous determination of six flavonoids (puerarin, rutin, morin, luteolin, quercetin, kaempferol) and troxerutin in rat urine and chicken plasma. Chromatographic separation was performed using a VP-ODS column (150 mm x 4.6 mm, 5.0 microm) maintained at 35.0 degrees C. The mobile phase was a mixture of water, methanol and acetic acid (57:43:1, v/v/v, pH 3.0) at the flow rate of 0.8 mL/min. Six flavonoids and troxerutin were analyzed simultaneously with good separation. On optimum conditions, calibration curves were found to be linear with the ranges of 0.10-70.00 microg/mL (puerarin, rutin, morin, luteolin, quercetin, kaempferol) and 0.50-350.00 microg/mL (troxerutin). The detection limits were 0.010-0.050 microg/mL. The method was validated for accuracy and precision, and it was successfully applied to determine drug concentrations in rat urine and chicken plasma samples from rat and chicken that had been orally administered with six flavonoids and troxerutin.

Trace measurement of phenothiazine drugs in tablets by micellar-enhanced fluorophotometric method.

It was found that in buffer solution of pH 7.0, the addition of sodium dodecyl sulfate (SDS) to the solution of phenothiazine drugs, such as chlorpromazine, promethazine and trifluoperazine, showed a remarkable enhancement of their fluorescence intensity. A further study proved that the phenothiazine drugs can be determined by fluorophotometric method in micellar system. Under optimal conditions, there was a good linear relationship between fluorescence intensity and phenothiazine compounds concentration, and the detection limit of 3.0 x 10(-8) M chlorpromazine, 3.0 x 10(-8) M promethazine and 1.5 x 10(-8) M trifluoperazine (S/N=3) were also obtained. This method has been used to determine phenothiazine drugs in tablets with satisfactory results.