A multimode optical fiber supports several guided normal modes at the operating wavelength. Different mode populations can change its output cone, so launch conditions matter when measuring numerical aperture or interpreting focal-ratio degradation.

A step-index optical fiber has a nearly uniform refractive index $n_1$ throughout its optical fiber core, with an abrupt decrease to $n_2<n_1$ in its optical fiber cladding. In a straight, ideal guide, meridional rays stay in a plane containing the optical axis and trace a zigzag through repeated total internal reflection. This is a geometrical optics approximation; it is particularly useful for a multimode optical fiber whose core diameter is large compared with the wavelength.

The upper panel shows an accepted meridional ray; the lower panel enlarges the limiting geometry. The angle of incidence $i$ at the side wall is measured from its normal vector, whereas the internal ray angle $\varphi$ and external launch angle $\theta$ are measured from the optical axis. Thus $i+\varphi =90^{\circ }$. The limiting ray is a boundary of the ideal model, rather than a complete normal mode solution.

A numerical algorithm implements a numerical method through specified finite computation steps, including arithmetic operations and any required function evaluations.

For any finite set of quadrature nodes $x_1,\dots ,x_m$ in $[a,b]$, $a<b$, consider the polynomialEvery sample is zero, so every quadrature rule at these quadrature nodes gives $Q(p)=0$. Yet $p$ is nonnegative and strictly positive away from its finitely many polynomial roots; its continuity makes the integral positive. Consequently the smooth functions $0$ and $g(x)=p(x)/A$ have identical samples but definite integrals $0$ and $1$. Multiplying $g$ by any positive constant makes the difference arbitrarily large. This is an information limit, even with exact arithmetic; merely assuming smoothness without quantitative bounds does not remove it. The related degree ceiling for quadrature exactness uses the same squared nodal polynomial to limit polynomial exactness.

For example, the endpoint quadrature nodes $0,1$ give $p(x)=x^2(1-x{)}^2$, $A=1/30$, and $g(x)=30x^2(1-x{)}^2$. Both the zero function and $g$ produce zero under the one-panel trapezoidal rule, although $I(g)=1$. More generally, any estimate based only on their shared samples has absolute error at least $1/2$ for one of these two integrands, by the triangle inequality.

The obstruction also applies to a deterministic adaptive procedure that terminates after finitely many evaluations on the zero function: form $p$ using every quadrature node it visited. On $g$, the same zero responses lead to the same choices and answer. Certified quadrature error control therefore needs additional information, such as a known bound on relevant derivatives, rather than agreement among sampled approximations alone.

If every interspike interval is at least $t_{\mathrm{abs}}>0$, a sustained spike train has mean firing rate at most $1/t_{\mathrm{abs}}$. Across a window of duration $T$, the event count satisfies $N(T)\le 1+T/t_{\mathrm{abs}}$; dividing by $T$ and taking the long-time limit gives the bound. The extra one accounts for a possible first event. The bound permits much slower firing and says nothing about whether stronger drive can maintain the limiting rate in a real neuron.

For the chosen phenomenological paired-pulse recovery, the hold alone gives $500\mathrm{Hz}$. Add the further assumption that every successful event resets precisely the same recovery curve, with no accumulation from earlier events. Then $0.35\mathrm{nA}$ onset-timed pulses can sustain at most $1000/4.942488\approx 202.327\mathrm{Hz}$ in that model. This repeatable reset is an extra assumption: a two-pulse result alone cannot establish it. A real neuron's sustained rate also depends on neural adaptation, input delivery and evolving ion-channel inactivation. The existing leaky integrate-and-fire model similarly adds integration time to its imposed hold.

A homogeneous Poisson process of rate $\lambda$ has interspike intervals with an exponential distribution, soWith the purely illustrative choices $\lambda =100\mathrm{Hz}$ and $t_{\mathrm{abs}}=0.002\mathrm{s}$, this is $1-e^{-0.2}\approx 0.181269$. Thus about $18.13\%$ of intervals predicted by that model violate this chosen hold. Matching a mean spike train count does not make a stimulus-only Poisson process respect recent firing.

A minimal alternative is a renewal process with independent and identically distributed intervals $S=t_{\mathrm{abs}}+X$, where $X$ has an exponential distribution of rate $\lambda$. Its hazard function is zero during the hold and $\lambda$ afterwards. Its mean interval is $t_{\mathrm{abs}}+1/\lambda$ and long-run firing rate isThe same $100{\mathrm{s}}^{-1}$ rate after a $2\mathrm{ms}$ hold gives $83.333\mathrm{Hz}$, not $100\mathrm{Hz}$. This separate stochastic example has no gradual relative recovery; it illustrates why an available-state rate differs from an observed mean firing rate. Smooth dependence on time since the last event can extend the hazard function, while longer histories require more than this simple renewal process. Time-rescaled spike-train diagnostics can test the resulting history-dependent predictions against recorded spike trains.

Neuronal paired-pulse recovery probes the effect of an earlier action potential with a later test stimulus. First establish a conditioning action potential; then vary the delay $\Delta$ to a test pulse of specified amplitude, duration and delivery site. Record whether a second action potential occurs, its timing and, where relevant, its waveform. Repeating the test across amplitudes distinguishes recovery of responsiveness to one pulse from recovery of the minimum effective pulse. Direct current clamp stimulation can probe the receiving neuron's excitability; stimulation through a synapse additionally allows synaptic plasticity to alter the input.

A documented whole-nerve teaching experiment keeps a bundle of axons in physiological solution, with its proximal end drawn into a stimulating tube and its distal end into a recording tube. The suction electrodes use short plastic tubes and $1\mathrm{ml}$ syringe-plunger terminals, held by O-rings. Longitudinal electric currents create a voltage drop in the thin solution layer between bundle and tube, read by a cathode-ray oscilloscope. Electrode fit affects amplitude. An immediate stimulation artifact remains even without the nerve, whereas the neural signal arrives after a delay.^[ref][ref]

The paired-stimulus exercise sets $0.1\mathrm{ms}$ pulses to obtain a maximal undistorted A wave of the compound action potential, then increases the spacing. The described first response remains constant while the second grows with spacing. Different axons recover at different times; the summed response therefore grows as more contribute. A smaller second compound action potential does not mean every individual action potential has shrunk. The source contrasts this with a single axon's response or failure. Its response-amplitude recovery criterion is not a universal single-cell threshold criterion.^[ref]

Useful additional controls would include single-pulse baselines interleaved with paired trials, a verified conditioning action potential, enough rest between trials, and fixed temperature, electrode geometry, pulse width and initial membrane potential. Confirm stimulus delivery and amplifier recovery, separate the stimulus artifact from a second neural event, and check whether waveforms overlap before measuring amplitudes. These are proposed checks, not additional results claimed for that teaching experiment. Even a clean compound action potential does not uniquely identify sodium availability: recruitment, propagation and synchronization of different axons influence the summed signal. Nor does failure at a distant recording site alone distinguish failed initiation from failed propagation.

During a relative neuronal refractory period, another action potential is possible, but incomplete recovery changes the excitation needed to obtain it. Remaining sodium ion-channel inactivation and elevated potassium electrical conductance both matter. A hyperpolarization after an action potential can increase the voltage excursion needed for firing, while extra electrical conductance can drain an injected pulse faster. Thus a single fixed neuronal firing threshold measured from the instantaneous voltage misses relevant state variables.^[ref]

Recovery can depend on earlier spikes as well as the last one; the Hodgkin-Huxley model and neural adaptation provide richer histories than an imposed reset. A fixed hold in a leaky integrate-and-fire model is a useful simplification with a specified domain, rather than a complete explanation of a relative neuronal refractory period.^[ref]

During an absolute neuronal refractory period, the excitable region cannot initiate a second distinct regenerative action potential under the physiological stimulation conditions being considered. Extensive sodium ion-channel inactivation is central for a fast sodium-dependent action potential. “Cannot fire” concerns regeneration; an imposed pulse can still move membrane potential or leave a recording artifact. Failure of just one fixed-amplitude test pulse does not establish an absolute neuronal refractory period: the same failure can occur in the relative neuronal refractory period.

The boundary is an operational question about distinguishable action potentials and the range of stimulation tested. A drawn vertical line is useful in the phenomenological paired-pulse recovery model, where it is imposed explicitly; it should not be mistaken for a universal sharp biochemical switch.

The spindle checkpoint can delay anaphase while kinetochore attachment is incomplete. This couples progression to the state of the spindle apparatus, rather than requiring every biological cell to separate its sister chromatids after a fixed waiting time. Alignment in a picture alone is not a demonstration that this control is satisfied.^[ref]

Count the same hypothetical diploid animal biological cell in three ways: chromosomes, double-stranded DNA molecules, and nuclear DNA amount. Assume one continuous double-stranded DNA molecule per unreplicated chromosome or replicated chromatid, complete S phase, and no losses or mutations. Exclude mitochondrion DNA. Here $n=2$ is the size of one haploid set, and $C$ is its unreplicated nuclear DNA amount, as defined in cell-cycle DNA content. A double-stranded DNA molecule counts as one, despite containing two strands.

The allocation can be checked copy by copy. DNA replication turns $A$ into $A_1,A_2$, $a$ into $a_1,a_2$, and similarly for $B,b$. One daughter receives $A_1,a_1,B_1,b_1$ and the other $A_2,a_2,B_2,b_2$. Each therefore inherits the same four chromosome identities and the same gene and allele content as the unreplicated parent, under the stated assumptions. Across both daughters there are $4+4=8$ DNA molecules and $2C+2C=4C$: partition conserves the material present after S phase.

The transient whole-cell count of eight does not mean that each daughter's ploidy has doubled. Each completed daughter is diploid, containing both homologues of each homologous chromosome pair. This differs from separating homologous chromosomes during meiosis I. Faithful mitosis preserves a nuclear genome through growth and renewal; equal inherited genes need not imply equal gene expression or equal cell fate. Mutations or chromosome segregation errors can break the ideal inheritance pattern.

In open mitosis, telophase includes reassembly of nuclear envelopes around the separated chromosome groups and relaxation of their condensation.^[ref]

In metaphase, replicated chromosomes align near the spindle apparatus equator, with their two sister chromatids connected through kinetochores toward opposite poles.

In open mitosis, nuclear envelope breakdown permits spindle apparatus microtubules to reach kinetochores at centromere regions.

In prophase, replicated chromosomes condense and the spindle apparatus begins to organize.

Condensation helps organize replicated chromosomes for later separation, while spindle microtubules begin to establish the division apparatus. Prophase precedes the stage at which all chromosomes are aligned; condensation alone is therefore not evidence of correct spindle attachment. ^[ref]

Consider the diagonal matrix $D=2I_2$ and a Jordan blockBoth have characteristic polynomial $(t-2{)}^2$, trace $4$, determinant $4$, and eigenvalue $2$ with algebraic multiplicity two. But $\ker (D-2I)$ is the whole plane, whereas $(J-2I)(a,b{)}^T=(b,0{)}^T$ vanishes only when $b=0$. Their eigenspaces have different dimensions, so eigenvectors under similarity rules out matrix similarity. There is an even shorter proof: $S^{-1}(2I)S=2I$ for every invertible matrix $S$.

The missing information is how the linear operator acts beyond its eigenvectors. Here $D-2I=0$, while $J-2I\ne 0$ but $(J-2I{)}^2=0$. Their minimal polynomials are respectively $t-2$ and $(t-2{)}^2$. Over a field where the characteristic polynomial splits, the full sizes of the Jordan blocks, encoded by the Jordan normal form, determine the matrix similarity class.

Under $B=S^{-1}AS$, an eigenvector in old coordinates becomes $S^{-1}v$ in new coordinates. Indeed,Since $S^{-1}$ is an invertible matrix, the new coordinate vector is nonzero. The inverse conversion $S$ proves that the two eigenspaces have the same dimension, hence the same geometric multiplicity.

For $A=\mathrm{diag}(2,5)$, the eigenvector $e_2=(0,1{)}^T$ has eigenvalue $5$. In the oblique basis of the opening example, its coordinate vector isThe tuple changes from $(0,1)$ to $(-1,1)$, but reconstruction gives $-b_1+b_2=e_2$: the physical vertical direction is unchanged. Calling the eigenvectors unchanged is accurate only for the underlying vectors, not for their coordinate vectors.

Matrix similarity invariants are quantities unchanged by replacing $A$ with $S^{-1}AS$. Important examples are the characteristic polynomial, trace, determinant, matrix rank, and eigenvalues with their algebraic multiplicities. The characteristic polynomial convention here is ${\chi }_A(t)=\det (tI-A)$, where $I$ is the identity matrix.^[ref]

The short determinant proof works over any field: because $tI-B=S^{-1}(tI-A)S$, multiplicativity of the determinant givesThus its roots, the eigenvalues, retain their algebraic multiplicities in any field extension where the polynomial splits. Also $\det B=\det A$. The cyclic property of the trace gives $\mathrm{tr}(S^{-1}AS)=\mathrm{tr}(AS{S}^{-1})=\mathrm{tr}A$; equivalently, the trace is determined by the coefficient of $t^{n-1}$ in the characteristic polynomial for an $n$-by-$n$ matrix.^[ref]

For the two matrices in the opening example,Their entries differ, yet these quantities agree. Agreement is a useful necessary test for matrix similarity; it is not sufficient in general, as same eigenvalues do not imply similarity illustrates.

The relation also preserves repeated application. Adjacent factors $S{S}^{-1}$ cancel to giveFor the opening example, matrix powers therefore have the explicit formAt $k=2$, this is $\left(\begin{array}{cc}4& -21\\ 0& 25\end{array}\right)$. Acting on $y=(1,1{)}^T$ gives $(-17,25{)}^T$, which reconstructs as $S(-17,25{)}^T=(8,25{)}^T=A^{2}x$. More generally $q(B)=S^{-1}q(A)S$ for every polynomial $q$, so the minimal polynomial is also unchanged. Multiplication by invertible matrices preserves matrix rank, giving another invariant without finding eigenvalues.

Assign each edge $e$ a real benefit $w_e$. A maximum-weight matching maximizes ${\sum }_{e\in M}{w}_e$ over all matchings in a graph $M$; the empty matching in a graph is allowed. Setting every $w_e=1$ recovers a maximum cardinality matching, but otherwise the objectives can disagree.

On the same path graph $P_4$, give the middle edge benefit $3$ and each outer edge benefit $1$. The matching in a graph $\{(2,3)\}$ has total benefit $3$, while the two-edge matching in a graph $\{(1,2),(3,4)\}$ has total benefit $2$. Thus the smaller choice is the unique maximum-weight matching. If instead the requirement is to find a maximum cardinality matching and then maximize benefit among those, the outer edges must be chosen. Requiring a perfect matching before optimizing cost is another distinct problem, covered by minimum-weight perfect matching. With negative benefits a maximum-weight matching can even be empty, so it need not be a maximal matching.

A maximum cardinality matching is a matching in a graph with as many edges as any matching in a graph of the same graph. It has cardinality $\nu (G)$, the matching number, and need not be unique or be a perfect matching. For example, any single edge of a triangle in a graph is a maximum cardinality matching, but leaves one vertex unmatched. Berge's theorem characterizes these optima by the absence of an augmenting path in a matching.