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For two independent standard normal random variables, the maximum of their absolute values has distribution function $(2\Phi (x)-1{)}^2$ for $x\ge 0$. Differentiation and integration by parts, using ${\phi }^{\prime }(x)=-x\phi (x)$, give its expectation as $8{\int }_0^{\infty }\phi (x{)}^{2}dx=2/\sqrt{\pi }$. This exact value can detect missing factors in two-sided Gaussian-maximum estimates.

For a real symmetric positive-definite matrix $A=D+L+L^T$, let $e_k$ be the Gauss-Seidel method error and $d_k=e_k-e_{k+1}$. Its recurrence gives $A{e}_k=(D+L)d_k$. Expanding the two squared energy norms yields $2d_k^{T}A{e}_k-d_k^{T}A{d}_k=d_k^{T}D{d}_k$. Every diagonal entry of $D$ is positive, so the error energy strictly decreases unless $d_k=0$. In that case $A{e}_k=0$ and the exact solution has already been reached. This identity exhibits the method as a sequence of energy-reducing coordinate corrections.

For the rate-parameterized Gamma distribution with shape $r>0$ and rate $\lambda >0$, integration of its probability density giveswhen $t<\lambda$, by the gamma function integral and a change of variables. The integral is infinite for $t\ge \lambda$. In particular the moment-generating function is finite on an open interval containing zero, so the uniqueness theorem for moment-generating functions applies. Multiplication of these moment-generating functions proves additivity of independent gamma distributions with a common rate.

For a divergence-free drift $\mathbf{b}$ and a function $v$ with zero boundary Sobolev trace, integration by parts gives ${\int }_{\Omega }v\mathbf{b}\cdot \nabla v=\frac{1}{2}{\int }_{\partial \Omega }{v}^2\mathbf{b}\cdot \mathbf{n}-\frac{1}{2}{\int }_{\Omega }(\nabla \cdot \mathbf{b})v^2=0$. Consequently the advection matrix in a conforming Galerkin method has zero quadratic form; its symmetric part vanishes. Adding a diffusion stiffness matrix gives a nonpositive spatial generator and a nonincreasing L2 norm in the semidiscrete advection-diffusion equation. The same identity holds with a suitable zero normal-flux boundary term instead of zero trace.

Suppose a first-order language has logical implication as its only connective and has either or both of the quantifiers, but no primitive falsity. In a singleton structure with every atomic predicate true, all atomic equalities are also true, and structural induction makes every formula true. No formula of this language can therefore express a negated equality. Quantifiers cannot repair this failure of expressive completeness, even though they add syntactic operations beyond propositional connectives.

Among sixteen binary Boolean truth functions, eight become functionally complete together with negation. The eight affine functions cannot express conjunction because composition preserves affinity. Each of the other eight truth tables has exactly one or three true rows. Negating the output if needed and selected inputs transforms its unique true row to the conjunction table. Thus conjunction and negation become definable and disjunctive normal form expresses every Boolean function.

A constant symbol is a zero-ary function symbol. Its interpretation in a first-order structure names one fixed domain element, and the symbol is a closed term. A language can contain no constant symbols unless a construction explicitly adds them.

The supraorbital nerve is a terminal branch of the frontal nerve supplying forehead and anterior scalp sensation. Its exit near the upper orbital margin provides an anatomical landmark connecting a surface sensory field to a deeper nerve course.

The supratrochlear nerve is a terminal branch of the frontal nerve supplying sensation near the medial forehead and upper eyelid. Its smaller medial territory complements the more lateral distribution of the supraorbital nerve.

For a formal power series over a field of characteristic zero, the displayed coefficientwise operation chooses constant coefficient zero. Its formal derivative is the original formal power series: differentiating the term with exponent $n+1$ cancels its denominator. Conversely, formally integrating a formal derivative removes the original constant term. These are algebraic identities; analytic convergence is not required.

For a symmetric gap $h(x)=\sqrt{1+x^2}$ on $[-1,1]$, unit lower-wall speed and equal zero gauge pressures at the ends, the flux average for a translating lubrication gap gives $Q=\pi /(4\sqrt{2})$. Integrating $p_x=6(h-2Q)/h^3$ gives the displayed odd pressure. It has a positive maximum at $x=-\sqrt{{\pi }^2/8-1}$ and a negative minimum at the opposite point. The converging and diverging portions of a symmetric gap therefore have opposite pressure lobes even though the geometry itself is even: the directed wall motion breaks reflection symmetry of the flow.

If a normal ordinal function defined on all ordinals begins at a countable value and preserves countability from $\Phi (\xi )$ to $\Phi (\xi +1)$, transfinite induction shows it maps all countable ordinals to countable ordinals. At countable limits this uses countable unions of countable sets, hence axiom of countable choice in the usual proof. Iteration from zero either stabilizes at a countable fixed point or has a countable limit fixed by continuity. Applying this to ${\omega }^{\alpha }$ gives the countable epsilon number ${ϵ}_0$.

Precision comparisons using Fisher information must specify what one observation records. A Bernoulli distribution with success probability $\theta$ gives information $I_{\mathrm{Bern}}=1/[\theta (1-\theta )]$ per trial. A geometric distribution on $1,2,\dots$ records the number of trials until the first success. Its log-likelihood for one waiting time $X$ is $\log \theta +(X-1)\log (1-\theta )$, soAt equal numbers of observations, geometric waiting times therefore provide $1/\theta$ times as much information, and the asymptotic variance of their maximum-likelihood estimator is $\theta$ times the Bernoulli variance. But each waiting time uses an expected $1/\theta$ trials. Information per expected underlying trial is consequently $\theta I_{\mathrm{geom}}=I_{\mathrm{Bern}}$. This separates an observation count from the cost of collecting the observations.

If $X$ has an exponential distribution with rate $\lambda >0$, a full observation has Fisher information $1/{\lambda }^2$. Recording only $Y=1_{\{X>T\}}$ for a fixed $T>0$ instead gives a Bernoulli distribution with parameter $r=e^{-\lambda T}$. The chain rule for its score function givesTo see the strict inequality, write $e^s-1=s+s^2/2+s^3/6+\cdots$. For $0<s\le 1$ the first two terms already exceed $s^2$. For $s\ge 1$, the function $e^s-1-s^2$ is positive at one, its derivative $e^s-2s$ is positive there, and that derivative is increasing for $s\ge 1$. Thus every positive threshold discards information. For independent observations, both information quantities multiply by the sample size. The Cramér-Rao bound and asymptotic normality of a maximum likelihood estimator explain the corresponding loss of attainable precision.

For $n$ independent exponential lifetimes with rate $\lambda$, recording all lifetimes gives Fisher information $n/{\lambda }^2$. Recording only the count alive at a fixed time $T>0$ gives a binomial distribution with success probability $s=e^{-\lambda T}$. Its information in $s$ is $n/[s(1-s)]$, and the change of parameter $ds/d\lambda =-Ts$ gives the displayed information.

The retained fraction is $R(z)=z^2/(e^z-1)$, $z=\lambda T$. It is strictly below one, so every single snapshot loses information. The inequality follows, for instance, from $h(z)=e^z-1-z^2$: its derivative is $e^z-2z>0$ for positive $z$, because that derivative has its minimum at $z=\log 2$ with value $2-2\log 2>0$. Also $R(z)$ tends to zero as $z\downarrow 0$ and as $z\to \infty$.

Differentiating $R$ shows that the best snapshot time solves $2(1-e^{-z})=z$. The difference of the two sides is strictly concave, starts at zero with positive derivative, and eventually becomes negative, so it has exactly one positive root, approximately $1.59362$. The largest retained fraction is about $0.64761$. Thus even an optimally timed snapshot keeps less than two thirds of the full lifetime information. This is a local design result: choosing that time requires a nominal rate or a preceding estimate.

A first-order term containing no variables. Constant symbols are ground terms; applying a function symbol to ground terms produces another ground term.

A closed term contains no variables, so its value in a first-order structure does not depend on an assignment. In a language containing only relations and the constant zero, zero is the only closed term. A term-generated model has every element denoted by such a term.

After replacing functions by graph predicates and constants by singleton predicates, $E_t(z)$ says that $z$ is the value of a term $t$. For a unary function graph $P$ and constant predicate $Q$, use $E_x(z)=(z=x)$, $E_c(z)=Q(z)$ and $E_{f(t)}(z)=\exists y(E_t(y)\wedge P(y,z))$. Fresh witnesses avoid variable capture. Totality and uniqueness axioms make each value formula have exactly one witness under every assignment.