From triadic closure to conference closure: the role of academic conferences in promoting scientific collaborations

An academic conference is not only a venue for publishing papers but also a nursery room for new scientific encounters. While previous research has investigated scientific collaboration mechanisms based on the triadic closure and focal closure, in this paper, we propose a new collaboration mechanism named conference closure. Conference closure means that scholars involved in a common conference may collaborate with each other in the future. We analyze the extent to which scholars will meet new collaborators from both the individual and community levels by using 22 conferences in the field of data mining extracted from DBLP digital library. Our results demonstrate the existence of conference closure and this phenomenon is more remarkable in conferences with high field rating and large scale attendees. Scholars involved in multiple conferences will encounter more collaborators from the conferences. Another interesting finding is that although most conference attendees are junior scholars with few publications, senior scholars with fruitful publications may gain more collaborations during the conference. Meanwhile, the conference closure still holds if we control the productivity homophily. Our study will shed light on evaluating the impact of a conference from the social function perspective based on the index of conference closure.     

A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The AMKC method first clusters the initialized incomplete data. Then, it constructs a new multiple-kernel-based data space, referred to as K-space, from multiple sources to learn kernel combination coefficients. Finally, it seamlessly integrates an incomplete-kernel-imputation objective, a multiple-kernel-learning objective, and a kernel-clustering objective in order to achieve absent multiple kernel clustering. The three stages in this process are carried out simultaneously until the convergence condition is met. Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is significantly better than state-of-the-art competitors. Meanwhile, the proposed method gains fast convergence speed.     

Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment

In order to have a proper design and analysis for the column of stone in the soft clay soil, it is essential to develop an accurate prediction model for the settlement behavior of the stone column. In the current research, to predict the behavior in the settlement of stone column a support vector machine (SVM) method is developed and examined. In addition, the proposed model has been compared with the existing reference settlement prediction model that using the monitored field data. As SVM mathematical procedure has resilient and robust generalization aptitude and ensures searching for global minima for particular training data as well. Therefore, the potential that support vector regression might perform efficiently to predict the ground soft clay settlement is relatively valuable. As a result, in this study, comparison of two different developed types of SVM method is carried out. Generally, significant reduction in the relative error (RE%) and root mean square error has been achieved. Utilizing nu-SVM-type model through tenfold cross-validation procedure could achieve outstanding performance accuracy level with RE% less than 2% and CR = 0.9987. The study demonstrates high potential for applying SVM in detecting the settlement behavior of SC prediction and ascertains that SVM could be effectively used for settlement stone columns analysis.

     

Lie group method for solving viscous barotropic vorticity equation in ocean climate models

In studying the problem of the nonlinear viscous barotropic non-divergent vorticity equation on $f$- and $\beta$- planes, the method of Lie group has been applied. The method reduces the number of independent variables by one, and consequently, for the case of three independent variables we applied the method successively twice and the nonlinear partial differential equation reduces to ordinary differential equation. Investigation of exact solutions of the viscous barotropic non-divergent vorticity equation on $f$- and $\beta$- planes, via the application of Lie group, provides large classes of new exact solutions which include both Rossby and Rossby–Haurwitz waves as special cases. Also, The Lie symmetries of the viscous barotropic non-divergent vorticity equation with two parameters $F$ and $\beta$, are determined. The possible reductions of the viscous barotropic vorticity equation with two parameters $F$ and $\beta$ have been investigated by means of one- dimensional Lie subalgebras.     

Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey

Multimedia Tools and Applications - Alzheimer’s disease (AD) is a form of brain disorder that causes functions’ loss in a person’s daily activity. Due to the tremendous progress...     

SOLUBILITY OF CARBON DIOXIDE IN AQUEOUS AND ANHYDROUS MIXTURES OF METHYLDIETHANOLAMINE AND TRIETHYLENE GLYCOL MONOMETHYL ETHER

Mixed solvents are a combination of chemical and physical solvents and are used for the removal of acid gases from gas streams. The solubility of CO₂ in a mixed nonaqueous solution of methyldiethanolamine (MDEA) (50 wt.%) and triethylene glycol monomethj'l ether (TEGMME) (50 wt.%) has been measured at 40°C. The data are compared with solubility data of CO₂ in pure TEGMME. The solubility of CO₂ has been measured at 40°C and 100°C in an aqueous mixed solvent consisting of MDEA (40 wt.%) and TEGMME (40 wt. %) and water (20 wt.%) at partial pressures of CO₂ up to 12 MPa. The solubility results were compared with the solubility in the nonaqueous mixed solvent and previously reported data of aqueous MDEA. The solubility model of Deshmukh and Mather was used to correlate the data.     

Solubilities of Carbon Dioxide in Polyethylene Glycol Ethers

New extensive data are reported for the solubility of carbon dioxide in fourteen physical solvents, and compared to two other solvents widely used in industry (selexol® and sulfolane). The solubility data are expressed by Henry's law constants and have been measured at 25 °C, 40 °C and 60 °C, using an Autoclave cell. The study concludes that polyethylene glycol dimethyl ethers, and mixtures of these solvents are the best solvents for CO₂ removal.     

VAE/MPM/GA Technique for DoA Estimation Using Optimized Antenna Arrays

Many algorithms have been proposed to estimate the direction of arrival for the targets, but through using a large number of snapshots. In real time applications such as automotive radar, this is unacceptable as it causes delay and heavy processing. Instead, if only a small number of snapshots or, optimally, a single snapshot is available for DoA estimation, it will be fast and efficient. Single snapshot algorithms have a drawback as they require a large number of antenna elements, which considered a limiting factor. In this paper, a single snapshot DoA estimation technique is introduced by using optimized antenna arrays. The proposed algorithm is based on utilization of virtual array extension, matrix pencil method, and the genetic algorithm. The use of virtual array extension greatly improves the MPM performance. Furthermore, it exhibits a high DoA estimation accuracy by using a reduced number of antenna elements. The genetic algorithm is employed to determine the minimum number of antenna elements, which are required to estimate the DoAs with minimal root mean square error.

     

A smart multi-user massive MIMO system for next G Wireless communications using evolutionary optimized antenna selection

Recently multiserver queues with setup times have been extensively studied because they have applications in power-saving data centers. A challenging model is the M/M/c/Setup queue where a server is turned off when it is idle and is turned on if there are some waiting jobs. Recently, Gandhi et al. (in: Proceedings of the ACM SIGMETRICS, pp. 153–166, ACM, 2013; Queueing Syst. 77(2):177–209, 2014) obtain the generating function of the number of jobs in the system, as well as the Laplace transform of the response time using the recursive renewal reward approach and the distributional Little’s law (Keilson and Servi in Oper Res Lett 7(5):223– 227, 1988). In this paper, we derive exact solutions for the joint stationary queue length distribution of the same model using two alternative methodologies: generating function approach and matrix analytic method. The generating function approach yields exact closed form expressions for the joint stationary queue length distribution and the conditional decomposition formula. On the other hand, the matrix analytic approach leads to an exact recursive algorithm to calculate the joint stationary distribution and performance measures so as to provide some application insights.     

Diffusing-CRN k-means: an improved k-means clustering algorithm applied in cognitive radio ad hoc networks

With increasing demand of new wireless applications and increasing number of wireless user’s, problem of spectrum scarcity arises. In this context, cognitive radio supports dynamic spectrum access to address spectrum scarcity problem. Cognitive radio defined the cognitive radio nodes by their ability to intelligently adapt the environment to achieve specific objectives through advanced techniques. The variance of channel availability for cognitive radio nodes degrades connectivity and robustness of this type of network; in this case the use of clustering is an effective approach to meet this challenge. Indeed, the geographical areas are homogeneous in terms of type of radio spectrum, radio resources are better allocated by grouping cognitive radio nodes per cluster. Clustering is interesting to effectively manage the spectrum or routing in cognitive radio ad hoc networks. In this paper, we aim to improve connectivity and cooperativeness of cognitive radio nodes based on the improvement of the k-means algorithm. Our proposed algorithm is applied in cognitive radio ad hoc networks. The obtained results in terms of exchange messages and execution time show the feasibility of our algorithm to form clusters in order to improve connectivity and cooperativeness of cognitive radio nodes in the context of cognitive radio ad hoc networks.