Sentence examples for connectivity variables from inspiring English sources

Not including maritime connectivity variables may generate an overestimated impact of the latter.

Column (7) of Table 11 reports the results obtained without including any of our maritime connectivity variables.

We note that in the above optimization problem, scheduling and connectivity variables ( {x}_{r,{n}_h}^{j_t},{overset{sim }{x}}_{n_h}^{j_t} ) are treated as constants.

This framework was used to establish a detailed assessment of the influence of each landscape connectivity variable on the pathogen population dynamics.

In other words, we are interested in the evolution of the connectivity variable, which evolves on a slow time-scale, under the influence of the external input and some noise added on the fast variable.

As defined in Table 1., the scheduling variable ( {x}_{r,{n}_h}^{m_t} ) denotes number of type r VMs on the server assigned to serve job n h and connectivity variable ( {overset{sim }{x}}_{n_h}^{m_t} ) denotes total number of VMs assigned to job n h on the m th type t server assigned to serve job n h, where, ( {overset{sim }{x}}_{n_h}^{m_t}=sum limits_{r=1}^R{x}_{r,{n}_h}^{m_t} ).

In other words, standard gravity results may suffer from some omitted variable bias if connectivity related variables are not included.

Complex problems are a potential source of stress and arousal because they are characterized by informational and analytical complexity, uncertainty, a lack of transparency, a high degree of connectivity of variables, time pressure, and unclearly specified problem-solving operators (Dörner and Wearing 1995; Funke 1991, 2012; Mayer and Wittrock 2006).

36 The presence of mediation signifies that instead of having a direct effect between the independent variable (functional hub connectivity) and dependent variable (seizure frequency/duration); the mediator (anatomical hub connectivity) plays an important role in the association between these two variables.

The elasticity of changes in access time and changes in connectivity and facilities variables was translated into access time reductions at the train stations.

Only two variables, connectivity (CON) and percent forest (PCF), of the seven variables examined to predict R. aurora occurrence had at least a relatively strong effect using the ODA algorithm; these results were further supported by a LOO analysis (Table 2).