Lima, A. I. A.Vasconcelos, M. S.Anselmo, D. H. A. L.
9页查看更多>>摘要：In this work, we study the power laws and fractal properties of the energy spectrum in single-strand DNA stretches composed of pure GAs (Adenines and Guanines) sequences found in human chromosome 7. We have used the transfer matrix method for the tightbinding Hamiltonian to find the electronic energy distribution of this one-dimensional system. We obtain the energy spectra and calculate total energy as a function of site index n in the single-strand chain to characterize the fractality of the energy distribution. To investigate the multifractal behavior of energy bands, we have determined the singularity spectrum f (alpha) using an algorithm based on Shannon, Eggelston, and Billingsley theorems. Our results show that the energy spectra exhibit a double power law. It is revealed a fractal behavior similar to the random Cantor set, with the formation of energy minibands, when n increases.
原文链接:
NSTL
Elsevier
Heidari, HosseinKaramati, Mahdi RezaeiMotavalli, Hossein
10页查看更多>>摘要：In the present investigation, a stochastic tumor growth model is presented based on the Morse potential. The solution of the Fokker-Planck equation is used to study the growth rate and the geometry of breast cancer with and without radiation therapy effects. In the second case, to estimate unknown parameters of the probability density function, breast data from the Surveillance, Epidemiology, and End Results program and machine learning algorithm are used. By considering three groups of women (35-85 years old), the results show that as time goes on, tumor size increases while its growth rate decreases, and the older women have a slower growth rate. Also, the simulation results of breast tumors of mice confirm that our results are consistent with the experimental evidence in both cases of radiotherapy and no treatment. Finally, the finding of this study implies that the present model is accurate than the Gompertz one in predicting tumor size, in the treatment case. (c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Qiu, TianLu, TianChen, GuangZhang, Zi-Ke...
11页查看更多>>摘要：As an effective tool of information filtering, the network-based recommendation algorithms encounter the challenging problem of recommendation bias induced by the object heterogeneity. Previous solutions usually make the improvement based on some specific algorithm, however, are difficult to generalize to different algorithms. In this article, we propose an improved model with a general formula, by inhibiting recommendation bias described by the eigenvalue and eigenvectors of the algorithm similarity matrix, and applied the model into ten different algorithms. Based on four real recommender systems, the experimental results show that nearly all the algorithms are improved in three aspects of recommendation accuracy, diversity and novelty, for all the four datasets. The recommendation accuracy of cold objects is also elevated. Especially, two excellent algorithms are further improved without introducing any other parameter. Our work may shed a new light on developing general recommendation algorithms from the perspective of revealing intrinsic feature in recommender systems. (C) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Liang, BoWang, LinWang, Xiaofan
12页查看更多>>摘要：Network embedding or graph representation learning has recently attracted more researchers' attention and achieved state-of-the-art performance in many areas and tasks. Nevertheless, most of these methods are targeted for monolayer networks and ignore the multiplexity property of nodes which refers to the multifaceted relationships between two elements. Multiplexity provides multiple types of auxiliary information to refine the characteristics of nodes and can be modeled as a multiplex network. In this study, we propose a multiplex network embedding algorithm to learn a unique embedding for each node in each layer or each relation type. A biased path-dependency random walk strategy is adopted to generate node sequences for integrating different types of relations between nodes, which pays more attention to overlapping links and makes neighbor nodes in the sampling sequence more similar to each other. Then the skip-gram model is used to learn an overall embedding over node sequences. To strengthen the expressive power of the embedding in a specific layer, a fine tuning strategy with low time cost is employed to make the embedding comprise information of nodes at this particular layer and preserve their distinctive properties, and the unique embedding is achieved ultimately. To verify the effectiveness of our algorithms, we validate the performance of our algorithm and other baseline methods in the link prediction task. The results demonstrate that the learned embedding can capture the interlayer relationships and preserve the specific characteristics of nodes, and our algorithms can stably obtain better or comparable performance compared with other methods.(C) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Zhou, WenjiaHu, YuanfaCao, JindeLiu, Xiaoyang...
18页查看更多>>摘要：This paper considers the finite-time synchronization (FTS) of coupled neural networks (CNNs) with parameter uncertainties. Based on the adaptive periodically intermittent control method and the finite-time stability theory, some sufficient conditions are derived to achieve synchronization within a finite time. Both the models of CNNs with/without delays are considered and the corresponding upper-bounds of synchronization time are estimated as well. Finally, two illustrative examples are presented to demonstrate the effectiveness and applicability of the theoretical results. (c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Chen, HailiangChen, BinAi, ChuanZhu, Mengna...
16页查看更多>>摘要：With the development of network models, the importance of community structure had caught much attention. How can the community size and the community distance affect the network structure remains unexplored. Therefore, in this paper, the MoncSid-N and the MoncSid-E are proposed in response to the issue The community size and distance preferences are introduced in these two models. The networks generated by the MoncSid-N show a better similarity to the real-world networks. The network metrics, including average degree, distribution of node degree, and distribution of community size, are used to analyze the performance of the MoncSid-N. Meanwhile, the MoncSid-E solves the problems of the evolution of large-scale networks. A parallel implementation by Pregel of the MoncSid-E is proposed. It is shown that the network with millions of nodes can be generated by the MoncSid-E efficiently. Based on the plenty of simulations and the comparison of real-world networks, the performances of the MoncSid-N and the MoncSid-E are testified. (C) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Martinez-Herrera, J. G.Rodriguez-Lopez, O. A.Solis, M. A.
11页查看更多>>摘要：We present a generalized expression for the transfer matrix of finite and infinite onedimensional spin chains within a magnetic field with spin pair interaction J/rp, where r & ISIN; {1, 2, 3, ... , n(v)} is the distance between two spins, nv is the number of nearest neighbors reached by the interaction, and 1 < p < 2. Using this transfer matrix, we calculate the partition function, the Helmholtz free energy, and the specific heat for both finite and infinite ferromagnetic 1D Ising models within a zero external magnetic field. We focus on the temperature T-max where the specific heat reaches its maximum, needed to compute J/(k(B)T(max)) numerically for every value of nv & ISIN; {1, 2, 3, ... , 30}, which we interpolate and then extrapolate up to the critical temperature as a function of p, by using a novel inter-extrapolation function. We use two different procedures to reach the infinite spin chain with an infinite interaction range: increasing the chain size as well as the interaction range by the same amount and increasing the interaction range for the infinite chain. As we expected, both extrapolations lead to the same critical temperature within their uncertainties by two different concurrent curves. Critical temperatures fall within the upper and lower bounds reported in the literature, showing a better coincidence with many existing approximations for p close to 1 than the p values near 2. It is worth mentioning that the well-known cases for nearest (original Ising model) and next-nearest neighbor interactions are recovered doing n(v)= 1 and n(v) = 2, respectively. (c) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Macias Torres, M.Naranjo Mayorga, F.
10页查看更多>>摘要：In this work, the resilience study of the Aedes aegypti mosquito network built in urban areas of Colombia is presented. We define the network based on the Skeeter-Buster model, where each node is represented by a mosquito habitat in each zone. The state that defines the population of each node depends on the gonotrophic cycle of the species and the environmental conditions. Interactions between nodes are defined by the probability that mosquitoes migrate from one node to another (P (d(ij))). The topology of the network is evaluated and the dynamic equation of the system is defined, through which the universal resilience function is obtained in the A. aegypti mosquito network. We found that the more heterogeneous networks are more likely to be resilient, so a strategy could be sought to manipulate this property in A. aegypti networks. The phase transitions have been located for each constructed network and the fixed points in the phase space were characterized. One of the most important contributions is the migration probability of the vector P (d(ij)), which offers a good approximation to the migratory behavior of the vector as a function of the mean flight distance and the distance between habitats. Finally, it is observed in the dynamics of the network that the population growth presents different values of effective mean degree (beta(eff)), with values between 1.6 and 5.57, highlighting the case of Villavicencio with a value of 1.6309. (C) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Cacciapaglia, GiacomoCot, Corentinde Hoffer, AdeleHohenegger, Stefan...
26页查看更多>>摘要：We propose a physics-inspired mathematical model underlying the temporal evolution of competing virus variants that relies on the existence of (quasi) fixed points capturing the large time scale invariance of the dynamics. To motivate our result we first modify the time-honoured compartmental models of the SIR type to account for the existence of competing variants and then show how their evolution can be naturally re-phrased in terms of flow equations ending at quasi fixed points. As the natural next step we employ (near) scale invariance to organise the time evolution of the competing variants within the effective description of the epidemic Renormalisation Group framework. We test the resulting theory against the time evolution of COVID-19 virus variants that validate the theory empirically.
原文链接:- NSTL
- Elsevier
