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Align two complete strings using matching letters or a letter opposite a gap, with no unequal-letter columns and no double-gap columns. Give each match score one and each gap penalty $g\ge 0$. The dynamic programming table for prefixes starts with $D_{i,0}=-gi$ and $D_{0,j}=-gj$. Its last-column cases give the displayed recurrence, where the diagonal candidate is omitted on a mismatch. Construction takes $O(mn)$ time for positive lengths and $O(mn)$ space with full alignment traceback.

An alignment with $\ell$ matches has $m+n-2\ell$ gaps and score $(1+2g)\ell -g(m+n)$. Therefore a longest common subsequence determines its optimum. A finite mismatch penalty would solve a different problem.

Choose one contiguous substring from each string and align them under the same matching and gap rules as global sequence alignment without mismatches. Let $L_{i,j}$ maximize the score over aligned suffixes of the two prefixes. Zero permits a fresh empty alignment; the other cases extend one ending at adjacent prefix indices. The answer is the largest table entry, which can occur before either input ends. Starting all boundaries at zero yields $O(mn)$ time for positive lengths. Alignment traceback stops on reaching zero.

Store a maximizing predecessor at each dynamic programming table entry. A diagonal predecessor emits a pair of letters; a vertical or horizontal predecessor emits a letter opposite a gap. Construct the columns in reverse and then reverse the output. For global sequence alignment without mismatches, start at the full-prefix entry and stop at the origin. For local sequence alignment without mismatches, start at any largest entry and stop at a zero entry. Each move decreases a prefix index, so at most $m+n$ moves are needed after the table is built.

An afferent pathway in the vagus nerve, including pathways that carry information from the gastrointestinal tract into brainstem circuits.

An antidune is a bedform associated with supercritical free-surface flow, in which bed and surface undulations are approximately in phase. In the elementary erodible-bed model with suspended sediment, an unstable branch can migrate upstream. Its growth and migration depend on the hydraulic and sediment-response parameters; the high-wavenumber antidune dispersion calculation makes the required supercritical regime explicit.

A dune is a sediment bedform. In the elementary subcritical flow description of river dunes, bed undulations and free-surface undulations are out of phase. This phase relation identifies a hydraulic response; instability additionally requires a positive real part of the relevant dispersion relation. Contrast the in-phase surface of an antidune.

Suspended sediment is particulate material carried within a fluid rather than solely along the bed. Its concentration balance includes advection, erosion into suspension and deposition from suspension. For water depth $h$ and suspended mass concentration $c$, the conservative balance $(hc{)}_t+(huc{)}_x={\rho }_s(v_E-v_D)$ reduces using water continuity to $h(c_t+u{c}_x)={\rho }_s(v_E-v_D)$.

Bedload transport is motion of sediment grains along or close to the bed, represented in a depth-averaged model by a solid-volume flux per unit width. The Exner equation balances this flux divergence and changes in porous-bed solid storage against exchange with suspended sediment. Consistent mass units are necessary when adding this equation to a suspended mass-concentration balance.

In a reduced sediment-bed model with normalized discharge one, water momentum gives $s=1-u^{-1}+F^2(1-u^2)/2$, while concentration adaptation and bed exchange give $c_x=E(u)-c=-s_t$. Linearizing about $u=c=1,s=0$ yields $\tilde{s}=(1-F^2)\tilde{u}$ and $(1+ik)\tilde{c}=E^{\prime }(1)\tilde{u}$. Elimination proves the displayed dispersion relation.

For $E^{\prime }(1)>0$, the growth rate is $E^{\prime }(1)k^2/[(F^2-1)(1+k^2)]$, positive exactly on the supercritical side $F>1$ for nonzero $k$. The phase speed is $-E^{\prime }(1)/[(F^2-1)(1+k^2)]$, so unstable bedforms migrate upstream and are antidunes. At high wavenumber growth approaches a finite plateau rather than becoming unbounded. This closure selects no finite fastest wavelength; short-scale selection needs additional physics. The critical limit $F=1$ and a vanishing entrainment derivative are degenerate cases outside the displayed conclusion.

For a holomorphic function on the unit disk fixing zero, $g(z)=f(z)/z$ has a removable singularity at zero and a bounded holomorphic extension. A bounded holomorphic function evaluated along planar Brownian motion is a bounded stopped martingale before domain exit. Stop at the first exit from a concentric radius-$r$ disk with $|z|<r<1$. Optional stopping gives $g(z)={\mathbb{E}}_{z}g(Z_{T_r})$, hence $|g(z)|\le 1/r$. Letting $r$ increase to one proves the displayed bound.

If $|g(z_0)|=1$ at a nonzero point, put $c=g(z_0)$. The same expectation identity forces $\mathrm{Re}(\overline{c}g(Z_{T_r}))=1$ almost surely. The exit density is strictly positive on every boundary arc by the Poisson kernel for the unit disk, and $g$ is continuous there, so $g=c$ on that circle. The expectation identity for every starting point inside it then gives $g=c$ throughout that smaller disk. Letting $r$ increase to one shows $f(z)=cz$ on the full disk, with $|c|=1$. This derives both the inequality and its rigidity using Brownian harmonic measure.

A process scheduling policy that can replace a running, still-runnable task with another task. Time-sliced round-robin scheduling uses a hardware timer interrupt to regain control when a scheduling quantum expires.

The time at which all work in a scheduling instance has finished, equivalently the largest completion time measured from a common start. For divisible job sizes $t_i\ge 0$ and positive machine speeds $s_j$, when each machine's completion time is its total assigned size divided by its speed, the minimum makespan is ${\sum }_i{t}_i/{\sum }_j{s}_j$. The lower bound follows from total processing capacity; assigning the fraction $s_j/{\sum }_h{s}_h$ of each job to machine $j$ attains it.

For single-machine jobs all available at time zero, order by nondecreasing deadline. Keep a tentative on-time list. Whenever appending a job makes its completion exceed its deadline, delete a job of largest processing time from that list and place it on a rejected list. Append all rejected jobs at the end. This minimizes the number of late jobs: at the first missed deadline at least one job of that prefix must be rejected, and rejecting a longest one frees at least as much processing time as any alternative. The exchange argument applies recursively to the remaining prefixes. The algorithm needs no ordering of the rejected jobs to determine the minimum count.