Exact(1)
To provide inequality (7), it is sufficient to choose ψ ( t ) = t 10.
Similar(59)
Specifically, we provide inequalities for the dispersion of a function f defined on a measure space ((Omega, Sigma, mu)), with respect to a positive weight ω on Ω with (int_{Omega}omega(t),dmu(t)=1), that is, biggl( int_{Omega}omega f^{2},dmu- biggl( int_{Omega}omega f,dmubiggr) ^{2} biggr) ^{frac{1}{2}}.
Also, our approach provides inequalities that allow new approximations of the functions operatorname{arcsin} x - frac{3x}{2+sqrt{1-x^{2}}} quadmbox{and}quad operatorname{arcsin} x - frac{pi x}{2+sqrt{1-x^{2}}} quadmbox{for all } x in[0,1].
Because Lemma (2.1) provides inequalities between vertex probabilities for each vertex in a graph, the main idea for inferring entropy bounds is to add up the obtained inequalities.
Using Theorem 1, we provide some inequalities for the gamma function.
In the next section, we provide further inequalities of this type.
This additional information is obtained here by using majorization techniques in order to provide new inequalities for the HL-index of bipartite and non-bipartite graphs.
The next theorem aims to provide coefficient inequalities for functions (f z)) belonging to the class (mathcal{S}_{(lambda_{p}), mu _{q}),b}^{s,a,lambda,*} alpha)).
Our approach will, under certain conditions, provide dissipation inequalities which remain satisfied for all input-output pairs that the system can produce, though only having been derived from finitely many of them.
Here we give the proof of this inequality and provide other two inequalities.
These measures of inequality provide a broad descriptive analysis of inequalities in MNCH.
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