Oscillation Theorems for Second Order Nonlinear Differential Equations with Damping

Some oscillation criteria for solutions of a general ordinary differential equation of second order of the form (r(t)ψ(x(t))ẋ(t)) . + h(t)ẋ(t) + q(t)ϕ (g(x(t)), r(t)ψ(x(t))ẋ(t)) = H(t, x(t),ẋ(t)) with alternating coefficients are discussed. Our results improve and extend some existing results in the literature. Some illustrative examples are given with its numerical solutions which are computed using Runge Kutta method of fourth order

Excitation of electronic states in tetrahydrofuran by electron impact

We report on differential and integral cross section measurements for the electron impact excitation of the three lowest lying Rydberg bands of electronic states in tetrahydrofuran. The energy range of the present experiments was 15–50 eV with the angular range of the differential cross section measurements being 15°–90°. The important effects of the long-range target dipole moment and the target dipole polarizability, on the scattering dynamics of this system, are evident from the present results. To the best of our knowledge, there are no other theoretical or experimental data against which we can compare the cross section results from this study

Optimal control for a linear quadratic neuro Takagi--Sugeno fuzzy singular system using genetic programming

Optimal control for a linear neuro Takag--Sugeno fuzzy singular system with quadratic performance is obtained using genetic programming (gp). To obtain the optimal control, the solution of a matrix Riccati differential equation is computed by solving a differential algebraic equation using the gp approach. The obtained solution is equivalent or very close to the exact solution of the problem. The accuracy of the solution computed by the gp approach is qualitatively better than the traditional Runge--Kutta method. An illustrative numerical example is presented for the proposed method. References P. Balasubramaniam, J. Abdul Samath, N. Kumaresan and A. Vincent Antony Kumar, Solution of matrix Riccati differential equation for the linear quadratic singular system using neural networks, Appl. Math. Comput. 182(2):1832–1839, 2006. doi:10.1016/j.amc.2006.06.020 G. Da Prato and A. Ichikawa, Quadratic control for linear periodic systems, Appl. Math. Opt. 18:39–66, 1988. doi:10.1007%2FBF01443614 D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addision Wesley, 1989. http://dl.acm.org/citation.cfm?id=534133 J. Jang, ANFIS: adaptive-network-based fuzzy inference systems, IEEE T. Syst. Man. Cyb. 23(3):665–685, 1993. doi:10.1109/21.256541 J. R. Koza, Genetic programming: on the programming of computers by means of natural selection. MIT Press, 1992. http://mitpress.mit.edu/books/genetic-programming M. O'Neill and C. Ryan, Evolutionary automatic programming in an arbitrary language, Genetic Programming, Vol. 4, Kluwer Academic Publishers, 2003. http://www.springer.com/computer/ai/book/978-1-4020-7444-8 N. Kumaresan, Optimal control for stochastic linear quadratic singular periodic neuro Takagi–Sugeno fuzzy system with singular cost using ant colony programming, Appl. Math. Model., 35:3797–3808, 2011. doi:10.1016/j.apm.2011.02.017 T. Takagi and M. Sugeno, Derivation of fuzzy control rules from human operator's actions, IFAC-IFIP-IFORS Symp., Fuzzy information, knowledge representation and decision analysis, 55–60, 1983. http://dl.acm.org/citation.cfm?id=577582 I. G.Tsoulos and I. E. Lagaris, Solving differential equations with genetic programming, Genet. Program. Evolv. M., 7:33–54, 2006. doi:10.1007/s10710-006-7009-y S.-J. Wu, H.-H. Chiang, H.-T. Lin and T.-T. Lee, Neural-nerwork-based optimal fuzzy controller design for nonlinear systems, Fuzzy Set. Syst., 154:182–207, 2005. doi:10.1016/j.fss.2005.03.01

Perceptions of Scholars in the Field of Economics on Co-Authorship Associations: Evidence from an International Survey.

Scholars (n = 580) from 69 countries who had contributed articles in the field of Economics during the year 2015 participated in a survey that gauged their perceptions of various aspects of co-authorship, including its benefits, motivations, working relationships, order of authorship and association preferences. Among the main findings, significant differences emerged in the proportion of co-authored papers based on age, gender and number of years the researchers had spent in their present institution. Female scholars had a greater proportion of co-authored papers than male scholars. Respondents considered improved quality of paper, contribution of mutual expertise, and division of labor as the biggest benefits of and motivation for co-authorship. Contrary to common perceptions that Economics researchers used a predominantly alphabetical order of authorship, our study found that a considerable percentage of respondents (34.5%) had practiced an order of authorship based on the significance of the authors' contribution to the work. The relative importance of tasks differed significantly according to whether researchers co-authored as mentors or co-authored as colleagues. Lastly, researchers were found to associate, to varying degrees, with other researchers based on socio-academic parameters, such as nationality, ethnicity, gender, professional position and friendship. The study indicates that Economics authors perceive co-authorship as a rewarding endeavor. Nonetheless, the level of contribution and even the choice of association itself as a co-author depends to a great extent on the type of working relationship and socio-academic factors

Detecting Community Structure by Using a Constrained Label Propagation Algorithm.

Community structure is considered one of the most interesting features in complex networks. Many real-world complex systems exhibit community structure, where individuals with similar properties form a community. The identification of communities in a network is important for understanding the structure of said network, in a specific perspective. Thus, community detection in complex networks gained immense interest over the last decade. A lot of community detection methods were proposed, and one of them is the label propagation algorithm (LPA). The simplicity and time efficiency of the LPA make it a popular community detection method. However, the LPA suffers from instability detection due to randomness that is induced in the algorithm. The focus of this paper is to improve the stability and accuracy of the LPA, while retaining its simplicity. Our proposed algorithm will first detect the main communities in a network by using the number of mutual neighbouring nodes. Subsequently, nodes are added into communities by using a constrained LPA. Those constraints are then gradually relaxed until all nodes are assigned into groups. In order to refine the quality of the detected communities, nodes in communities can be switched to another community or removed from their current communities at various stages of the algorithm. We evaluated our algorithm on three types of benchmark networks, namely the Lancichinetti-Fortunato-Radicchi (LFR), Relaxed Caveman (RC) and Girvan-Newman (GN) benchmarks. We also apply the present algorithm to some real-world networks of various sizes. The current results show some promising potential, of the proposed algorithm, in terms of detecting communities accurately. Furthermore, our constrained LPA has a robustness and stability that are significantly better than the simple LPA as it is able to yield deterministic results

Statistical test to determine significant difference in the proportion of co-authored papers based on demographic profile.

<p>Statistical test to determine significant difference in the proportion of co-authored papers based on demographic profile.</p

Characteristics of respondents.

<p>Characteristics of respondents.</p

Preference to co-author with other researchers based on socio-academic parameters.

<p>Preference to co-author with other researchers based on socio-academic parameters.</p