A hybrid influence method based on information entropy to identify the key nodes

Identifying the key nodes in complicated networks is an essential topic. A number of methods have been developed in recent years to solve this issue more effectively. Multi-attribute ranking is a widely used and efficient method to increase the accuracy of identifying the key nodes. Using k-shell iteration information and propagation threshold differences, we thoroughly analyze the node’s position attribute and the propagation attribute to offer a hybrid influence method based on information entropy. The two attributes will be weighted using the information entropy weighting method, and then the nodes’ influence ranking will be calculated. Correlation experiments in nine different networks were carried out based on the Susceptible–Infected–Recovered (SIR) model. Among these, we use the imprecision function, Kendall’s correlation coefficient, and the complementary cumulative distribution function to validate the suggested method. The experimental results demonstrate that our suggested method outperforms previous node ranking methods in terms of monotonicity, relevance, and accuracy and performs well to achieve a more accurate ranking of nodes in the network

A DNA aptamer for binding and inhibition of DNA methyltransferase 1.

DNA methyltransferases (DNMTs) are enzymes responsible for establishing and maintaining DNA methylation in cells. DNMT inhibition is actively pursued in cancer treatment, dominantly through the formation of irreversible covalent complexes between small molecular compounds and DNMTs that suffers from low efficacy and high cytotoxicity, as well as no selectivity towards different DNMTs. Herein, we discover aptamers against the maintenance DNA methyltransferase, DNMT1, by coupling Asymmetrical Flow Field-Flow Fractionation (AF4) with Systematic Evolution of Ligands by EXponential enrichment (SELEX). One of the identified aptamers, Apt. #9, contains a stem-loop structure, and can displace the hemi-methylated DNA duplex, the native substrate of DNMT1, off the protein on sub-micromolar scale, leading for effective enzymatic inhibition. Apt. #9 shows no inhibition nor binding activity towards two de novo DNMTs, DNMT3A and DNMT3B. Intriguingly, it can enter cancer cells with over-expression of DNMT1, colocalize with DNMT1 inside the nuclei, and inhibit the activity of DNMT1 in cells. This study opens the possibility of exploring the aptameric DNMT inhibitors being a new cancer therapeutic approach, by modulating DNMT activity selectively through reversible interaction. The aptamers could also be valuable tools for study of the functions of DNMTs and the related epigenetic mechanisms

Machine Learning-based Malicious Application Detection of Android

In this paper, we propose a machine learning based approach to detect malicious mobile malware Android applications. Our work is able to capture instantaneous attacks that cannot be effectively detected in past work. Based on the proposed approach, we implemented a malicious app detection tool, named Androidetect. First, we analyze the relationship between system functions, sensitive permissions and sensitive APIs. The combination of system functions has been used to describe the application behaviors and construct eigenvectors. Subsequently, based on the eigenvectors, we compare the methodologies of naive Bayesian, J48 decision tree and application functions decision algorithm (AFDA) regarding effective detection of malicious Android applications. Androidetect is then applied to test sample programs and real world applications. The experimental results prove that Androidetect can better detect malicious applications of Android by using a combination of system functions compared with previous work.Peer reviewe

Information Entropy Based on Propagation Feature of Node for Identifying the Influential Nodes

For understanding and controlling spreading in complex networks, identifying the most influential nodes, which can be applied to disease control, viral marketing, air traffic control, and many other fields, is of great importance. By taking the effect of the spreading rate on information entropy into account, we proposed an improved information entropy (IIE) method. Compared to the benchmark methods in the six different empirical networks, the IIE method has been found with a better performance on Kendall’s Tau and imprecision function under the Susceptible Infected Recovered (SIR) model. Especially in the Facebook network, Kendall’s Tau can grow by 120% as compared with the original IE method. And, there is also an equally good performance in the comparative analysis of imprecise functions. The imprecise functions’ value of the IIE method is smaller than the benchmark methods in six networks

Information Spreading on Activity-Driven Temporal Networks with Two-Step Memory

Information spreading dynamics on the temporal network is a hot topic in the field of network science. In this paper, we propose an information spreading model on an activity-driven temporal network, in which a node is accepting the information dependents on the cumulatively received pieces of information in its recent two steps. With a generalized Markovian approach, we analyzed the information spreading size, and revealed that network temporality might suppress or promote the information spreading, which is determined by the information transmission probability. Besides, the system exists a critical mass, below which the information cannot globally outbreak, and above which the information outbreak size does not change with the initial seed size. Our theory can qualitatively well predict the numerical simulations

The Impact of Accessibility of Urban Central Parks on Housing Prices of Fuzhou

The paper analysed the impact of accessibility of urban parks within the third ring on housing prices of Fuzhou through network analysis and SPSS correlation analysis based on data of sources like remote-sensing image, web crawling and urban road network, discovering that: (1)The density of residence within the third ring of Fuzhou decreases from the centre to the edges; (2) The housing price, ranging between 18000 and 28000 RMB/ m2, peaks in the city centre; (3) For houses within the third ring of Fuzhou, Gulou District enjoys the greatest access to urban parks while Cangshan District is the poorest in this regard. (4) The residence within the third ring of Fuzhou could be rated as A or A- in terms of access to urban parks, with an overall excellence performance; (5) The walking distance to the parks is significantly correlated with the housing price. The shorter the distance, the higher the price. Regarding this, the paper proposed the following suggestions: (1) Revise the routes to Gaogai Mountain Park by increasing entrances and exists to improve its accessibility; (2) Improve the transportation network and increase footpaths to the park, thus shortening the distance between the park and the surrounding residences

Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach

Acknowledgements: N.Y. acknowledges the funding from the National Science Foundation of China (Grant No. 62106139). Q.G. is grateful for the support from the Shanghai Artificial Intelligence Laboratory and the National Key R&D Program of China (Grant NO.2022ZD0160100). G.-Z.Y. acknowledges the funding from the Science and Technology Commission of Shanghai Municipality (Grant No. 20DZ2220400).AbstractAnalog deep neural networks (DNNs) provide a promising solution, especially for deployment on resource-limited platforms, for example in mobile settings. However, the practicability of analog DNNs has been limited by their instability due to multi-factor reasons from manufacturing, thermal noise, etc. Here, we present a theoretically guaranteed noise injection approach to improve the robustness of analog DNNs without any hardware modifications or sacrifice of accuracy, which proves that within a certain range of parameter perturbations, the prediction results would not change. Experimental results demonstrate that our algorithmic framework can outperform state-of-the-art methods on tasks including image classification, object detection, and large-scale point cloud object detection in autonomous driving by a factor of 10 to 100. Together, our results may serve as a way to ensure the robustness of analog deep neural network systems, especially for safety-critical applications.</jats:p

High-Performance Amorphous Zinc&#x2013;Tin&#x2013;Oxide Thin-Film Transistors With Low Tin Concentration

In this paper, we present thin-film transistors (TFTs) with a zinc&#x2013;tin&#x2013;oxide (ZTO) layer achieved through magnetron co-sputtering. Amorphous ZTO TFTs with an Sn concentration of 2.49&#x0025;, 6.95&#x0025;, 7.11&#x0025;, 11.95&#x0025;, and 16.47&#x0025; were fabricated, to investigate the effect of low-doped Sn. With a doping of 2.49&#x0025; Sn, the electrical characteristics of TFTs can be obviously improved. After annealing at 440 &#x00B0;C, the optimal TFTs displayed a field-effect mobility of 8.71 cm2/ $\text{V}\cdot \text{s}$ , a high $\text{I}_{\mathrm{ on/off}}$ ratio of over $10^{8}$ , a subthreshold swing of 0.17 V/decade, and a turn-on voltage of &#x2212;0.4 V, even with an Sn concentration of only 11.95&#x0025;. Meanwhile, the shift of turn-on voltage under negative gate bias stress was only &#x2212;0.4 V