For the closed deterministic SIR model with $S(0)=S_0$, $R(0)=0$, infection rate $\beta SI$ and recovery rate $\gamma I$, division of the two flow equations gives $S(t)=S_0{e}^{-\beta R(t)/\gamma }$. Population conservation then expresses $I$ in terms of $R$.

The deterministic SIR model with initial susceptible count $S_0$ grows initially exactly when $\beta S_0>\gamma$. Its basic reproduction number is $\beta S_0/\gamma$ under unnormalized mass-action infection. If that ratio is at most one, decreasing $S$ prevents later infectious growth.

A triangulation of a topological space is a homeomorphism from the realization of a simplicial complex to that space. In three-manifold topology, thickening its one-skeleton and the dual one-skeleton constructs a Heegaard splitting.

In a feasible simplex basis, increasing a nonbasic variable by $\theta$ changes basic coordinates to $x-\theta d$. Nonnegativity limits $\theta$ by the smallest ratio $x_i/d_i$ with $d_i>0$; a minimizing coordinate leaves the basis. Ties can leave zero-valued basic variables. This pivot test is distinct from the analytic ratio test for series convergence.

A simplex dictionary expresses the basic variables and the objective as affine functions of the nonbasic variables in a linear program. Setting the nonbasic variables to zero gives a basic feasible solution when all basic values are nonnegative. A simplex algorithm pivot exchanges one basic and one nonbasic variable. For maximization, a feasible dictionary with all nonbasic objective coefficients nonpositive certifies optimality.

The dual simplex algorithm keeps the objective row dual feasible while repairing negative basic values. In a maximization dictionary $x_B=b+{\sum }_j{a}_j{x}_j$ with $b<0$ and nonbasic objective coefficients $d_j\le 0$, eligible entering columns have $a_j>0$; choose one minimizing $(-d_j)/a_j$. The pivot restores progress toward primal feasibility without losing the objective bound.

For the simple symmetric random walk on $\mathbb{Z}$, the renewal equation relates first-return and return probability generating functions. The central-binomial return series is $U(s)=(1-s^2{)}^{-1/2}$, giving $F(s)=1-U(s{)}^{-1}$. Its value tends to one at $s=1$, but its derivative diverges there: the walk has null recurrent states.

For a simple symmetric random walk, the estimate $\mathbb{E}{e}^{\theta S_n}\le e^{n{\theta }^2/2}$ and the exponential maximal inequality give $\mathbb{P}({\max }_{k\le n}{S}_k\ge t\sqrt{n})\le e^{-t^2/2}$ for $t\ge 0$.

A $z$-rough integer has no prime factor below $z$; $1$ is included. Some authors instead exclude prime factors at most $z$, so the endpoint convention should be stated.

The continuous Buchstab function satisfies $w(u)=1/u$ for $1\le u\le 2$ and $\frac{d}{du}(uw(u))=w(u-1)$ for $u>2$. It describes the density of rough numbers.

The large sieve bounds how much an exponential sum can concentrate at separated points of the circle group. Its variance form of the large sieve also bounds simultaneous concentration in residue classes modulo many primes.

A positive integer in this set has no prime factor equal to two or congruent to three modulo four. Its prime factors all lie in the residue class one modulo four. For example $5\cdot 13=65$ belongs, while $3^2=9$ does not, even though $9$ itself is congruent to one modulo four. A half-dimensional interval sieve bounds the count in an interval of length $z$ by $O(z/\sqrt{\log z})$.

On a complex manifold, this sheaf consists of smooth differential forms of type (p, q). The type decomposition of the complexified tangent bundle gives ${\mathcal{A}}^k={⨁}_{p+q=k}{\mathcal{A}}^{p,q}$.

A Reynolds number based on particle diameter and relative speed in the carrier fluid. Small values justify Stokes drag law; a high-Reynolds-number regime with nearly constant drag coefficient instead gives quadratic drag. The drag law must be evaluated in the appropriate regime for every species.

For spheres with the same density contrast, balance submerged weight, proportional to $r^3$, against Stokes drag law, proportional to $r{W}_s$, to obtain $W_s\propto r^2$. With constant drag coefficient, quadratic drag is proportional to $r^2{W}_s^2$, giving $W_s\propto r^{1/2}$. The two radius laws require their corresponding particle Reynolds number limits.

A mapping $S:X\rightrightarrows Y$ assigns a subset $S(u)\subset Y$ to each $u$. Empty and multiple values are allowed. The graph of a set-valued mapping records the relation as a subset of $X\times Y$.

An elementary union-intersection compression of a finite multiset of subsets replaces occurrences of $A,B$ by $A\cup B,A\cap B$. A compression is a finite sequence of these moves. The move preserves every coordinate multiplicity. On incomparable sets it increases ${\sum }_S|S{|}^2$ by $2|A\setminus B||B\setminus A|$, so repeated compression reaches a chain under inclusion. For any submodular set function, the sum of its values cannot increase.

A set family is laminar if any two members are disjoint or one contains the other. Its distinct inclusion-maximal members are therefore disjoint. Applied to the true sets in a hierarchical family of intersection hypotheses, this gives the disjoint-block bound used for familywise error control for a laminar hypothesis family.

A self-dual set family on $[n]$ contains exactly one of $A,A^c$ for every subset $A$. Its relative proportions $a_j$ in complementary uniform levels satisfy $a_{n-j}=1-a_j$. In particular its measure under a uniform random subset is $1/2$.

The biased measure of a set family is its probability when each ground-set element is independently included with probability $p$. If $a_j$ is the proportion of the $j$th level occupied by the family, then ${\mu }_p(\mathcal{F})={\sum }_j\left(\genfrac{}{}{0ex}{}{n}{j}\right)a_j{p}^j(1-p{)}^{n-j}$. This translates a combinatorial level bound into a probability inequality for independent Bernoulli random variables.