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The radioactive chain converts nickel-56 through cobalt-56 into iron-56, releasing energy that can power supernova light curves. Electron capture and, where allowed, beta-plus decay convert protons into neutrons; gamma rays and charged decay products deposit some of the released energy. Ionization and escape of decay radiation affect the observed heating.

Newton–Cartan theory describes Newtonian mechanics geometrically using absolute time, a degenerate spatial inverse metric, and a compatible torsion-free affine connection. It is not a theory with a nondegenerate Lorentzian metric. Gravitationally accelerated particles are geodesics of that connection, whose curvature measures tidal gravity.

Friction is a contact force tangent to the surfaces. Kinetic friction opposes contact slip; static friction maintains contact without contact slip, including rolling without slipping. The perpendicular contact component is the normal force. These two components answer different questions: how strongly the surfaces press together, and how strongly they resist sliding past one another.

The ideal Coulomb dry-contact model combines a bound on static friction with a sliding law for kinetic friction. Its parameters are the coefficient of static friction and the coefficient of kinetic friction; constant coefficients are an empirical approximation, rather than a consequence of Newton's laws of motion. Surface condition matters, and the static coefficient need not universally exceed the kinetic coefficient. ^[ref]

The model's historical setting was practical machinery: Coulomb's Théorie des machines simples investigated friction and the stiffness of ropes. The Académie's bibliography records its 1781 prize and its appearance in volume 10, pages 161–332, of the Recueil des savants étrangers. ^[ref]

Neglecting vertical acceleration and the vertical driving-force component, the radial normal reaction is $-Mg{h}^{\prime }(r)$. A drive parallel to the horizontal velocity cancels when the radial acceleration equation is combined with $r_{\varphi }/r$ times the azimuthal equation. For a prescribed path $r(\varphi )$ this gives the displayed speed constraint. It describes the projected approximation, not arbitrary trajectories with substantial vertical motion.

// Target: classical-mechanics.bigb

A particle constrained to the graph $z=h(x,y)$ has kinetic energy $m[|\dot{\mathbf{r}}{|}^2+(\nabla h\cdot \dot{\mathbf{r}}{)}^2]/2$ and potential energy $mgh$. For a gently varying graph and velocities for which its geometric kinetic correction is higher order, the leading Euler-Lagrange equations are the displayed planar gradient-force equations. Small slope alone at arbitrarily large speed or curvature does not justify discarding the normal inertial acceleration. For a radial height, the leading planar angular momentum is conserved, allowing an effective potential reduction.

Electroencephalography records differences in electric potential using electrodes, commonly at the scalp. Summed activity in neural tissue contributes to the measured signal, providing millisecond-scale information about changes over time.

An event-related potential compares or averages this activity relative to defined events. Volume conduction and overlapping sources limit direct localization, so a scalp waveform cannot simply be equated with one small neural generator.

Magnetoencephalography measures very small magnetic fields generated by electrical activity in neural tissue. It supplies a time-resolved signal that can constrain when activity changes relative to an event.

Inferring its neural sources is an inverse problem: sensor readings combine contributions from multiple locations and depend on their geometry. The technique therefore does not directly display the activity of one isolated neuron.

A sequence of action potential times, represented mathematically as a point process or a sum of Dirac deltas. Its event counts, intervals and correlations summarize different aspects of neural activity.

Representation by a neuron's firing frequency or the expected spike count in a chosen time window. The window length and averaging convention matter; equal counts do not imply equal temporal patterns.

Representation by spike timing, latency, phase or relative timing across neurons, beyond a chosen count summary. Its interpretation requires a timing reference and a decoder capable of resolving the relevant precision.

The region and stimulus features whose variation influences a sensory neuron's response. A visual receptive field may include spatially distinct ON and OFF regions; a linear receptive-field kernel is a signed weighting map, while the full response can be nonlinear and depend on stimulus history.

The neon isotope with ten protons and twelve neutrons.

The neon isotope with ten protons and eleven neutrons.

The neon isotope with ten protons and ten neutrons.

In Escherichia coli, chemotaxis feedback regulation couples receptor activity to modification of the receptors by CheR and CheB. It is a molecular example of negative feedback supporting signaling adaptation, with precision and kinetics assessed by the chemotaxis FRET assay above.

A feedback delay is a time interval between an event and its returning influence. Multiple biochemical steps can introduce an effective lag; a fixed delay is an idealization, not a universal description of signal transduction. To isolate timing, let $x$ be a signed displacement from an equilibrium point and use normalized dimensionless variables:The local removal is $-x$ and the returning influence is negative at every delay. At $d=0$ the ordinary differential equation reduces to the decay already illustrated under signaling feedback. For $d>0$ a delay differential equation requires a history on $[-d,0]$, not just an initial condition at one instant. Negative values of $x$ are displacements, not negative chemical concentrations.

An exponential function $x=e^{\lambda t}$ gives the characteristic equation of a delay differential equationPutting $\lambda =i\omega$ and equating real and imaginary parts gives $\cos (\omega d)=-1/2$ and $\omega =2\sin (\omega d)$. The first crossing with $\omega >0$ therefore occurs atFor this particular mathematical model, the equilibrium point is asymptotically stable for $0\le d<d_c$. No nonnegative real delayed feedback characteristic root can satisfy the equation; the first loss of asymptotic stability is the indicated pair of imaginary delayed feedback characteristic roots. Differentiating the characteristic equation of a delay differential equation along a root yieldsThus the pair moves into growth as the delay increases through $d_c$. The neutral mode at $d_c$ is a periodic function, but its amplitude depends on history: this linear mathematical model does not select an attracting limit cycle.

The figure plots $x(t)=e^{at}\cos (bt)$ using a delayed feedback characteristic root $\lambda =a+ib$ and that same formula on the history interval. At $d=0.7$ the displayed mode decays; at $d=1.5$ it grows. These are exact modes up to numerical root accuracy, not step responses with identical histories. Molecular bounds and a changed return law would be needed to interpret what happens after an unstable biological response becomes large.

For a Newtonian fluid and a flat wall with $0<b<\infty$, the Navier slip boundary condition is equivalent to a tangential wall-on-fluid tractionThe sign expresses resisting friction: ${\partial }_{n}u$ uses the inward unit normal, whereas the wall-on-fluid traction uses the outward unit normal of the fluid domain. The irreversible loss of mechanical energy per unit wall area and time isFor two tangential directions replace $u_{\mathrm{rel}}^2$ by the squared Euclidean norm of relative tangential velocity. At a moving wall this relative-velocity loss must be distinguished from the power supplied by the wall's motion. Bulk viscous dissipation also remains present. A finite slip length models reduced interfacial resistance, without declaring every boundary ideal or removing dynamic viscosity.

The limiting loss depends on how the flow is driven. In Couette flow with a slipping lower wall,It tends to zero for both $b\to 0$ and $b\to \infty$ at fixed $H$ and $U$. At $b=0$ impose the no-slip boundary condition directly, rather than dividing by zero. In planar Poiseuille flow with equal wall slip at fixed $G$, each wall instead dissipates $G^{2}b{H}^2/(4\mu )$, which grows with $b$ because relative velocity grows. The bulk loss per unit width is $G^2{H}^3/(12\mu )$; adding both wall losses gives $Gq$, the pressure work per unit channel length and width. This energy balance shows why small friction coefficients do not guarantee small losses under every driving constraint.

For an ideal two-layer natural ventilation transient following a heating change $Q\mapsto \lambda Q$, let $V_0$ and ${\theta }_0$ be the initial flow and temperature excess, $s=V_{0}t/(A_{r}H)$, $\eta =h/H$, and $x=\eta (1-\theta /{\theta }_0)$. The integrated buoyancy pressure gives $V/V_0=\sqrt{1-x}$, while heat balance gives $x_s=\sqrt{1-x}-\lambda$. A material interface satisfies ${\eta }_s=\sqrt{1-x}$, so $\eta =x+\lambda s$. For $0<\lambda <1$, $x$ increases toward $1-{\lambda }^2$ and ${\eta }_s\ge \lambda$, ensuring finite-time filling of the room. After filling, $y=\theta /{\theta }_0$ obeys $y_s=\lambda -y^{3/2}$ and tends to ${\lambda }^{2/3}$. This model assumes well-mixed individual layers, negligible wall heat capacity and a sharp advected interface.

Planck units use fundamental constants to set natural units of mass, length and time. The displayed convention sets the ordinary Planck mass to one. The related reduced Planck units convention instead normalizes the reduced Planck mass.

Include the positive integers in $[1,2),[4,8),[16,32),[64,128),\dots$, and exclude the intervening integer intervals and zero. Equivalently, include $[2^{2k},2^{2k+1})\cap \mathbb{Z}$ for $k\ge 0$. Each included block is large enough to raise the running proportion; the following excluded block lowers it again.

With $C_A(N)=|A\cap \{0,\dots ,N-1\}|$, the exact boundary counts follow from a geometric series:The first sum is empty at $k=0$. Dividing by the corresponding boundary, rather than using one denominator for both, givesFor example, $C_A(16)=1+4=5$, whereas $C_A(32)=1+4+16=21$, yielding proportions $5/16$ and $21/32$. For $4^k\le N\le 2\cdot 4^k$, the exact count is $(4^k-1)/3+N-4^k$; for $2\cdot 4^k\le N\le 4^{k+1}$ it stays $(4^{k+1}-1)/3$. Hence the proportion increases through each included block and decreases through each excluded block. These boundary subsequences give the actual limit inferior and limit superior:Since these differ, $A$ has no natural density. Its upper asymptotic density still records its largest limiting proportion.