The local meridional eddy buoyancy flux is the covariance of disturbance meridional velocity with disturbance buoyancy. In a Boussinesq approximation, $b^{\prime }=-g{\rho }^{\prime }/{\rho }_0$. This is a local transport density, distinct from a plume's cross-section-integrated buoyancy flux. In the transformed Eulerian mean, division by the background buoyancy frequency squared gives an eddy-induced meridional overturning streamfunction.

A statistic is invariant if $A(gy)=A(y)$; it is maximal when it also separates different group orbits. Consequently every invariant procedure factors through the maximal invariant, subject to the usual measurability conditions on the orbit space. Standardizing a sample by an equivariant estimator of location and positive scale gives a maximal invariant for the positive affine group.

An estimator respects the group action if the displayed identity holds. If it takes values in the transformation group itself, normalization $T(y{)}^{-1}y$ is a maximal invariant: equivariance proves invariance, and equality of normalized samples reconstructs the transformation between them. For estimates in a homogeneous parameter space with a nontrivial stabilizer, normalization is defined only modulo that stabilizer; quotienting the remaining action is essential.

Hubbard-Stratonovich decoupling of an exponentially coupled Ising chain gives a scalar-field action with a nearest-neighbor gradient term and $U(m)=\tanh (\kappa /2)m^2/J-\log [2\cosh (2m+h)]$. Its continuum partition function is the Euclidean path integral of the displayed quantum particle. At zero field the potential can have two wells, but a finite barrier still permits quantum tunnelling and gives a unique symmetric ground state. The excitation gap produces a finite classical correlation length, so there is no finite-temperature spontaneous magnetization for fixed positive $\kappa$. One classical spatial coordinate becomes imaginary time; a $d$-dimensional classical model corresponds to a quantum model with $d-1$ spatial dimensions.

The hypothesis makes $P$ abelian and makes any two conjugate elements of $P$ equal. For the latter assertion, Sylow conjugacy inside the centralizer of the target element adjusts a conjugating element into $N_G(P)$, where it centralizes $P$. Evaluating group transfer to $P$ on an element $u\in P$, each coset cycle contributes a conjugate of $u$ raised to the cycle length, hence that same power of $u$. The restriction is consequently $u\mapsto u^{[G:P]}$, an automorphism of the abelian $p$-group. Transfer is surjective, and its kernel is the required normal p-complement.

For a specified transcription unit, the coding strand is the DNA strand matching the initial RNA product in the $5^{\prime }\to 3^{\prime }$ direction, with T in place of U. The opposite DNA strand is the template. The name does not imply that every transcribed gene encodes a protein. See Clark, Douglas and Choi: coding and template strands.

An RNA polymerase used in transcription is an enzyme that synthesizes RNA from a DNA template. Unlike a replicating DNA polymerase, it can start a new strand without a pre-existing paired strand end; initiation still requires the appropriate DNA signals and associated machinery. See Alberts et al.: RNA polymerase and transcription initiation.

For distinct algebraic numbers ${\alpha }_1,\dots ,{\alpha }_s$, the numbers $e^{{\alpha }_1},\dots ,e^{{\alpha }_s}$ are linearly independent over $\overline{\mathbb{Q}}$. Equivalently, exponentials of numbers linearly independent over $\mathbb{Q}$ are algebraically independent over $\overline{\mathbb{Q}}$. The special case of a nonzero algebraic exponent gives a transcendental exponential. In particular $e^{i\pi }=-1$ forces $\pi$ to be transcendental.

In the modern strict sense an E-function satisfies a nonzero linear differential equation over $\overline{\mathbb{Q}}(z)$, has algebraic coefficients $a_n$ with all conjugates bounded by $C^{n+1}$, and has common positive denominators $d_n\le C^{n+1}$ making $d_n{a}_0,\dots ,d_n{a}_n$ algebraic integers. Siegel's original arithmetic growth condition permits, for each $ϵ>0$, bounds of size $O_{ϵ}(n^{ϵn})$ instead. The strict and original growth formulations should be distinguished rather than asserted equivalent. Exponentials with algebraic exponent satisfy the stronger conditions and a first-order differential equation.

Mahler's method uses functional equations relating a function at $z$ and at $z^l$ to transport an auxiliary polynomial to points rapidly approaching zero. High vanishing order gives an upper bound which conflicts with an algebraic norm or height lower bound under assumed algebraicity of the values. Functional transcendence ensures that the auxiliary expression is not identically zero.

For algebraic $a\ne 0,1$ and algebraic irrational $b$, every value $\exp (b\lambda )$ with $e^{\lambda }=a$ is transcendental. Consequently any quotient of chosen nonzero logarithms of algebraic numbers is either rational or transcendental.

Suppose nonzero algebraic numbers ${\beta }_j$ form a Galois-stable set, with Galois-invariant integer weights $b_j$, and $k\ne 0$ is an integer. Choose $P\in \mathbb{Z}[X]$ vanishing at all ${\beta }_j$ with $P(0)\ne 0$, and a positive integer $c$ making all $c{\beta }_j$ algebraic integers. For a large prime $p$, put $D=(1+\mathrm{deg}P)p-1$ and $f_p(X)=c^D{X}^{p-1}P(X{)}^p/(p-1)!$, and let $F_p$ be the sum of its derivatives. The weighted sum $k{F}_p(0)+\sum b_j{F}_p({\beta }_j)$ is an integer, nonzero modulo $p$ when $p\nmid kcP(0)$. If the displayed exponential expression vanished, integrating $(e^{-z}{F}_p(z){)}^{\prime }=-e^{-z}{f}_p(z)$ along the finitely many segments to ${\beta }_j$ would bound that nonzero integer by $C^p/(p-1)!\to 0$. This contradiction proves the obstruction and supplies direct proofs for $e$ and $\pi$.

Let $f_1,\dots ,f_s$ be meromorphic functions with $f_j^{\prime }\in K[f_1,\dots ,f_s]$ for a fixed number field $K$. Suppose $f_1,f_2$ are algebraically independent over $\mathbb{C}$ and have finite orders ${\rho }_1,{\rho }_2$. The set $S$ of points where every $f_j$ is finite and belongs to $K$ satisfies the displayed bound. The orders are growth orders for meromorphic functions. For $f_1=z$, $f_2=e^z$, the orders are zero and one and the functions are algebraically independent; an algebraic value of $e^{\alpha }$ at nonzero algebraic $\alpha$ would produce infinitely many such points $n\alpha$.

The steady-state tracking error iswhen this finite limit exists. A finite steady-state tracking error does not require the reference signal, the plant output, or the control input to become constant: all can grow while their difference settles. If the tracking error grows without bound or keeps oscillating, report that behavior rather than assigning a finite steady-state tracking error. Eventual accuracy and asymptotic stability are different questions.

For the first-order low-pass filter $\tau \dot{y}+y=Ku+d$ introduced in reference signal, assume exact measurement, constant control disturbance $d$, and no actuator saturation or feedback delay. Under proportional control $u=k_p(r-y)$ with $k_p>0$, a constant reference signal $r=R$ givesThe decaying homogeneous solution has rate $(1+K{k}_p)/\tau >0$, so this equilibrium point is approached from any finite initial condition. A nonzero tracking error is needed to maintain the control input that balances the model's tendency to relax toward $y=d$. This differs from the accumulating controlled plant $\dot{x}=u+w$, for which proportional control already tracks a constant reference signal exactly when $w=0$. The presence or absence of a step offset depends on both the controlled plant and the controller.

For a ramp reference signal $r=vt$, the same proportional control givesIts long-time particular solution grows with slope $v/(1+K{k}_p)$ when $v\ne 0$. Thus the controlled plant and homogeneous closed-loop response are stable, but the tracking error has no finite steady-state tracking error. A changing goal requires a different performance test from a fixed destination.

Now use proportional-integral control $u=k_{p}e+k_{i}z$, $\dot{z}=e$, with $k_i>0$. For the same controlled plant, differentiate the error equation on intervals where the inputs are smooth:The characteristic polynomial $\tau {\lambda }^2+(1+K{k}_p)\lambda +K{k}_i$ has both roots of a polynomial in the open left half-plane: real roots have negative sum and positive product, and a nonreal complex conjugate pair has negative real part. This proves decay of the homogeneous error for this model. It does not prove stability of an arbitrary controlled plant controlled by positive gains. At a reference jump, use the original ordinary differential equations to set the new initial conditions, rather than treating the reference as smooth at that jump.

With a constant reference signal and constant control disturbance, proportional-integral control therefore gives $e_{\mathrm{ss}}=0$. At its equilibrium point, $y_{\ast }=R$, $u_{\ast }=(R-d)/K$, and $z_{\ast }=(R-d)/(K{k}_i)$. The stored integral maintains the required command while the current tracking error vanishes. For a ramp reference signal, however, the forcing above is $v$, andThe tracking error settles even though the accumulated integral and the required control input grow. This finite offset is the error needed to keep increasing that command. For $r=a{t}^2/2$ with $a\ne 0$, the forcing is $\tau a+at$ and even this proportional-integral control leaves a tracking error growing with slope $a/(K{k}_i)$. Integral action is therefore a precise mechanism for selected input classes, not a promise of perfect tracking for every reference signal. ^[ref]

Here are completely explicit synthetic examples with $y(0)=z(0)=0$, $d=0$, and no sensor bias. Choose $\tau =2\mathrm{s}$, $K=1{}^{\circ }\mathrm{C}$ per percentage point, $k_p=1$ percentage point per degree, and $k_i=0.5$ percentage points per degree-second. Thus $K{k}_p=1$ is dimensionless, and $K{k}_i=0.5{\mathrm{s}}^{-1}$. In the following formulas, $t$ denotes the numerical time in seconds and $y,e,r$ denote numerical temperature rises in degrees. For the unit-step reference signal $r=1$, proportional control and proportional-integral control respectively giveFor the ramp reference signal $r=0.2t$, the corresponding solutions areThe control inputs here are numerical heater-command percentage points. Substitution into $2\dot{y}+y=u$ and into the controller equations checks these solutions without invoking an unverified limiting rule. The proportional control step offset is $0.5$ degrees; the proportional-integral control step offset is zero. The proportional control ramp error grows; the proportional-integral control ramp offset is $0.4$ degrees, exactly $v/(K{k}_i)$.

The conclusions have specific limits. With constant sensor bias $b$, stable proportional-integral control for a feasible constant reference signal drives the measured tracking error to zero but leaves the physical discrepancy ${\epsilon }_{\mathrm{ss}}=b$. Under proportional control, the measured offset instead becomes $(R-b-d)/(1+K{k}_p)$. If actuator saturation prevents the required control input, zero step offset is not achievable by these formulas. A changing control disturbance contributes $-\dot{d}$ to the error equation, while feedback delay can change stability; neither is covered by the constant-disturbance result. These restrictions are what make steady-state tracking error useful: it identifies the input, measurement convention, and model for which an accuracy claim is actually justified.

For two normalized pure states, the difference of their projectors has two eigenvalues $\pm \sqrt{1-|⟨u|v⟩{|}^2}$, giving that trace distance. Write a mixed density operator as $\rho ={\sum }_j{q}_j|u_j⟩⟨u_j|$. Convexity of the trace norm and weighted Cauchy-Schwarz inequality give $D(\rho ,|v⟩⟨v|)\le {\sum }_j{q}_j\sqrt{1-|⟨u_j|v⟩{|}^2}\le \sqrt{1-⟨v|\rho |v⟩}$. Thus a small probability of failing the pure-state projector test forces trace-distance proximity, including for correlated multipartite outputs.

For the pure qubit $\alpha |0⟩+\beta |1⟩$, subtracting the output of dephasing channel leaves a Hermitian matrix with zero diagonal and off-diagonal modulus $|\alpha \beta ||1-c|$. Its two eigenvalues are that number and its negative, giving the displayed trace distance. For real $c=1-2p$, this is $2p|\alpha \beta |$.

By the variational characterization of trace distance, maximize $\mathrm{Tr}[P\mathcal{E}(\rho -\sigma )]$ over $0\le P\le I$. The adjoint of a quantum channel is positive and unital, so $0\le {\mathcal{E}}^{\ast }(P)\le I$. Moving the channel onto $P$ therefore restricts, rather than enlarges, the effects available in the input variational problem. This proves the displayed bound for every pair of density operators.

For qubit density operators, the trace distance has a particularly simple geometry. Write their Bloch vectors as $\mathbf{r},\mathbf{s}$ in the Bloch ball:where $I$ is the identity operator and $\mathbf{\sigma }=({\sigma }_x,{\sigma }_y,{\sigma }_z)$ denotes the Pauli matrices. This Bloch vector representation includes both pure states on the Bloch sphere and mixed states in the interior of the Bloch ball. ^[ref]

The Pauli matrix multiplication law gives $[(\mathbf{r}-\mathbf{s})\cdot \mathbf{\sigma }{]}^2=|\mathbf{r}-\mathbf{s}{|}^{2}I$. Hence the two eigenvalues of $\Delta$ are $\pm |\mathbf{r}-\mathbf{s}|/2$, and their absolute sum givesThe trace distance is half the straight-line Euclidean distance between the Bloch vectors, not the angular or surface distance along the Bloch sphere. Opposite Bloch vectors have trace distance one; the centre $I/2$ is at trace distance $1/2$ from every pure state.

For the pure states $|0⟩$ and $|+⟩=(|0⟩+|1⟩)/\sqrt{2}$, the Bloch vectors are $\mathbf{r}=(0,0,1)$ and $\mathbf{s}=(1,0,0)$. Their straight-line separation is $\sqrt{2}$, soThese Bloch vectors are perpendicular, but the pure states are not orthogonal: $|⟨0|+⟩|=1/\sqrt{2}$. The Helstrom measurement for the zero and plus states measures along the difference direction $\mathbf{n}=(\mathbf{r}-\mathbf{s})/\sqrt{2}=(-1,0,1)/\sqrt{2}$, rather than along either input Bloch vector.

Count the ancestor and the next $n-1$ generations of a Galton-Watson process. Conditioning on the ancestor's offspring, their independent descendant trees each have the preceding finite-horizon probability generating function, proving the displayed recursion. If the count instead excludes the ancestor, the recursion is ${\hat{H}}_{n+1}(s)=G(s{\hat{H}}_n(s))$ with ${\hat{H}}_0=1$. The indexing convention must be specified.

The abstract of Information Theoretical Analysis of Multivariate Correlation by Satosi Watanabe (1960, printed p. 66) names the sum of individual information entropies minus joint entropy "total correlation." It presents this as the finest grouping of the variables and identifies applications to geometric pattern recognition and shuffling cards marked zero or one. The name therefore connects the uncertainty of a system with the structure linking its parts; the worked integer parity and noisy-integer parity models here are separate examples calculated from their stated laws.