Some speculations concerning the selection of clinical psychologists.

The author makes the point that clinical work and scientific research require different intellectual orientations on the part of the psychologist. The ability to be effective and feel comfortable when assuming either attitude is a trait to look for in the selection of clinical psychologists. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Resolution Limits of Analyzers and Oscillatory Systems

This paper considers the resolution limits of those analyzers and oscillatory systems whose performance may be represented by a second-order differential equation. The “signal uncertainty” product Δf·Δt is shown to be controlled by the ability of a system to indicate changes in energy content. The discussion refers the functioning of the system to a signal space whose coordinates are energy, frequency, and time. In this signal space, the product of the resolution limits, U = (ΔE/E₀) (Δf/f₀) (Δt/T₀) is the volume of a region within which no change of state in the system may be observed. Whereas the area element Δf·Δt is freely deformable, no operations upon either Δf or Δt can further the reduction of the energy resolution limit. Thus U is irreducibly fixed by the limiting value of ΔE/E₀. By considering the effects of noise upon ΔE/E₀, and thus upon U, the paper demonstrates the rise of statistical features as signal-to-noise ratios decrease.Functional relationships derived from ΔE/E₀ and U are tabulated. These equations facilitate computation of the limits of observable changes of state in a system, and they provide guidance for the design of experiments to apportion the uncertainties of measurement of transient phenomena as advantageously as possible. A reference bibliography and appendices giving somewhat detailed proofs are included.The basis of this paper is the consideration that the indication of most instruments used in measurement represents either the storage of energy or the flow of power. The least changes that the instruments can indicate, therefore, are controlled by the smallest discernible change in energy storage or power flow.The subject of the resolution limits of measuring instruments in terms of the least amounts of frequency change and the least time interval in which a change may be detected have been treated by several authors, among whom one may cite as examples Gabor, Kharkevich, and Brillouin—and, while this paper was being revised, Pimonow.¹ The present author has also discussed this relationship for scanning analyzers, and indicated that there were circumstances in which limitations were introduced by the presence of a least discernible increment of power or energy.² These papers (ref. 2) are quoted, in addition to the prior work, by Pimonow.Gabor pointed out, by analogy to quantum theory, that there was a “quantum” of information that could be described by the product of differentials representing the least discriminable increment of frequency that could be observed in an increment of time. This relation arose from the application of the Fourier transform to relate an increment of time to its corresponding increment in the frequency domain. The product Δf ? Δt ≈ 1was defined by Gabor as the “Logon.” The fact that, as he says, the product is “of the order of unity” is a consequence of the particular normalization he used in computing the Fourier transform for Gaussian pulses. A similar relation was presented by Brillouin, but as he computed Δt in terms of the half-powers of brief, symmetrical pulses, he found a somewhat different normalization factor, and obtained Δf⋅Δt=14π.Kharkevich adopted a somewhat more general expression for this equation, also in terms of a normalized Fourier transform, by first writing Δf ? Δt = Aand computing A for pulses of various forms. He remarked that A might differ if other criteria were chosen, but related A only to the form of the signal. Further, he pointed out that A is independent of the, damping of the system.The studies by Gabor. Kharkevich, and Brillouin were all carried out for essentially noiseless systems. This paper, on the other hand, does not normalize for unit energy, but considers the energy storage and dissipation in systems whose performance may be described by a linear differential equation of the second order. Thus, by dealing with the energy stored as well as with the time and frequency we are able to study the response of a system to signals other than variously shaped pulses of unit energy, and to signals in noise as well as to noiseless systems.We can also consider, in this way, the case which has been omitted from the previous work: the response of the system which may in itself have a “least count”³ or inherent internal noise.We introduce the means for taking into account the presence of noise with a signal, internal noise in an analyzer, or the least discernible indication of the analyzer (which may be a step limitation—such as a digital step, or a reading limit) by discussing the limits of the analyzer’s performance as being fixed by the least change in energy storage, ΔE, that can be resolved under the circumstances of analysis. Several conditons may combine to fix the value of ΔE. For example, an analyzer having appreciable self-noise may be used to detect a signal in noise. As a rule, the internal and external noise sources in that case would be incoherent, and the sum of the noise energies stored in the analyzer from those sources would fix the value of ΔE.The system for which this discussion is carried out is a system whose working may be described by a linear differential equation of the second order. This behavior is common to many physical systems occurring in nature, and to many instruments used to observe natural phenomena. All of these systems share the same properties, because they are properties inherent in the differential equation that describes them. By virtue of the second-order term, they may be seen to be capable of storing oscillatory energy reversibly. They will respond with a sinusoidal output after excitation by shock or noise. Under sinusoidal excitation, they will respond selectively to excitation of various frequencies.Systems to be discussed in this paper are those in which the storage of energy occurs in the coordinates describing the system: these systems are described by a second-order differential equation with constant coefficients; i.e., the system parameters are not affected by the energy storage process. By invoking the Boltzmann-Ehrenfest adiabatic principle,⁴ it is also possible to apply many of these equations to systems in which energy is stored by a change of parameters. However, this matter has not been investigated in detail.Although most of the systems to which the second-order differential equation is applicable take part in time-varying phenomena, there is nothing inherent that restricts the equation to functions of time. Certain spatial distributions also may be described by the equation—such as the magnetization on magnetic tape, some types of optical images, and some diffraction effects. Thus, although this paper will deal with application of the equation to time-varying phenomena, its conclusions are also applicable, with a judicious choice of variables, to spatial distributions.For the sake of a coherent structure upon which to base this paper, we choose a mechanical system of inertia, M, dissipation (proportional to velocity) D, and coefficient of restitution, k. This system has a single degree of freedom, along the coordinate x, and its force-free behavior is given by solutions of the homogeneous equation: Mx¨+Dx¨+kx=0.When energy is stored in the system, it is dissipated at a mean rate which bears a constant relationship to the amount of energy stored. This constant is a function of the parameters of the system. In terms of dissipation, the constant is frequently expressed by the relative damping, γ, the ratio of the damping of the system to the critical damping for no oscillation. A reciprocal quantity, the “figure of merit,” Q, is commonly used in communication problems. These two constants are related through the equation: γ=12Q.Because we are more concerned here with storage than with dissipation, the quantity Q will be used in the discussion.The conventional definition for Q applies when the system is driven at its resonance frequency; at other frequencies the ratio of the storage of energy to the rate of dissipation depends upon the driving frequency. When the system is free of excitation, the conventional definition of Q again applies. This value of Q will be denoted as Q₀, to distinguish it from the more general definition of Q to be applied in the appendix. Defining as the natural frequency of the system the quantity f₀, which is the natural frequency of the system in the absence of damping, f0=12πkMand Q0=PeakenergystoredatthenaturalfrequencyEnergydissipatedpercycleatthenaturalperiodIn terms of the parameters of the system, this definition of Q₀ is equivalent to the ratio: Q0=2πf0MD=k2πf0D=kMD.The differential equations for the transient and driven response of the system can be expressed in terms of Q₀, D, and f₀. For the force-free equation: x¨2πf0+x˙Q0+2πf0x=0and, for the driven response of the system to a sinusoidal force of amplitude A and frequency f: x¨2πf0+x˙Q0+2πf0x=AQ0Dej2πft.Letting the ratio of the driving frequency to the natural undamped frequency be represented by ? = f/f₀we can write down the phase relation between the driving force and the resulting motion. The phase angle, θ, when the steady-state condition has been attained is given by θ=tan−1ϕQ0(1−ϕ2)and the energy stored in the steady-state condition is given by: Es=A22D⋅Q02πf0⋅1Q02(1−ϕ2)+ϕ2.The application of the Fourier transform can be considered to be tantamount to referring the behavior of the system to one or the other of two mutually perpendicular planes: The steady-state condition is described by the representation of the state of the system in the energy versus frequency plane and it of necessity deals with the steady state because the variable, time, is not involved. The transient behavior of the system is represented in the energy versus time plane. It is easier to understand that this is orthogonal to the frequency representation if we omit the normalization often used, of choosing the unit of time in terms of the natural period of the system. Nevertheless, it is more convenient to express the behavior of a system in terms relative to its natural parameters, and for the sake of simplicity of equations, much of the discussion will be related to the natural properties of the system.Three properties serve to specify the behavior of an analyzer described by a second-order differential equation: its undamped natural frequency, f₀, its figure of merit, Q₀, and its “least count,” ΔE, the least energy change that can be resolved by the system. The changes in energy content of the system may be thought of as taking place within the signal space bounded by the energy-time and energy-frequency planes, and the representation of the behavior of the system as a function of time or of frequency may be considered as projections upon the principal planes. Since the actual use of the system as an analyzer is never wholly steady-state or completely broadband, the actual process of analysis may be considered as taking place in some plane within the signal space bounded by the principal planes. Depending upon the information sought, the plane of the analyzer will be close to one or the other of the principal planes.Ordinarily, an analyzer indicates a running time average over the energy, E_s, stored in it. For steady-state signals the indication becomes proportional to the input power; for signals of very brief duration the analyzer responds ballistically and thus gives an indirect indication of energy. The limit of resolution is fixed by the least change in energy storage, ΔE, that can be resolved under the circumstances of analysis. In this discussion we shall be dealing with incremental ratios. As the differential will always be considered jointly with the total energy stored over the same time interval, it will be possible in general to discuss ratios of incremental power, ΔW, to input power, W₀, or incremental energy, ΔE, to energy stored, E_s, interchangeably.As a function of time, the building up and decay of the energy stored within the system are exponential processes. Therefore it proves convenient to describe the behavior of the system in terms of an exponential variable. Thus, to express the changes in energy storage, we choose an exponential coefficient, α. The energy resolution limit, ΔE/E₀, may be expressed in terms of α through the following definition: If the initial amount of energy stored in the analyzer is E₀, and the minimum change of energy that can be discerned is ΔE, then in terms of the exponential variable α the equivalent statement is that the energy content must decrease to an amount (e^−α) times its original value for the change to be at least equal to the minimum change discernible. Expressed as an equation, this limit is given by ΔE= (1 — e^−α)E₀ for energy, and in the many cases in which we are dealing with energy flow through the system, alternatively as ΔW = (1 — e^−α) W₀ for power. From the definition of α in terms of ΔE and E₀, it is evident that: α=−ln(1−ΔEE0).However, it is not along the time axis alone that α proves such a convenient function. Because of the close relation between exponential and angle functions, it also yields simple equations for the behavior of the system as a function of frequency. For the foregoing reasons, we shall describe the manner in which the independent variable, the energy increment ΔE, influences the resolution limits in frequency and time in terms of the variable α, and we will then return to consideration of what the equations describing these resolution limits mean in terms of ΔE. We will also discuss special types of noise conditions that may give rise to the irreducible increment that ΔE represents.The conditions under which α sets the resolution limit along the time axis are derived from considering the system to contain an amount of energy E₀ at time t = 0, and at that instant and for some time subsequent to that, to be free of any driving force. The resolution limit along the time axis is fixed by the least time interval, Δt, during which the system is capable of changing its energy storage by the factor, e^−α. This corresponds to dissipating at least the discernible energy increment, ΔE, during the time Δt. The resolution limit stated in terms of the natural period of the system, T₀, is thus given by Δt/T₀.When a system of this sort is used as an analyzer, the time interval over which the observation takes place must be of sufficient length for some change to be indicated. Thus the observation interval, Δτ, must equal or exceed the least time interval Δt. (In the previous papers cited in reference 2, the observation interval Δτ was used in place of the least time increment Δt. Use of the least time increment converts several previously found inequalities to equations.)It is a very close approximation (see appendix 1) to consider only the exponential factor in the decay of energy in the system. The oscillatory terms arising from the sinusoidal nature of the dissipation process are always relatively minor. Thus, regardless of the precise initial conditions, EE0|(t=Δt)=e−α≈exp(−2πQ0⋅ΔtT0)and, therefore, the resolution limit along the time axis, expressed in terms of the natural undamped period of the system is ΔtT0≈αQ02π.From this equation one can see that the ratio of α to Δt is twice the real coordinate of the poles of the system on a Nyquist diagram.The system is selective with respect to the sinusoidal frequency of the driving force which acts as a source for the energy it stores. This makes it suitable for the detection of sinusoidal frequencies falling within its range of response, or a group of similar systems with different f₀’s may be used in the analysis of the components of complex signals. For this purpose, the observation takes place in a plane approximating the energy-frequency plane. Along the frequency axis, one may speak of a frequency resolution limit in this sense: If one knows the natural frequency f₀ to which an analyzer is tuned, then a maximum indication of energy storage for a steady-state sinusoidal signal corresponds to a signal in the vicinity of f₀. Until the energy storage has changed by an amount in excess of ΔE or, when expressed as a relative proportion, a factor in excess of ΔE/E₀, the departure from maximum indication is not observable. The change in frequency required to produce this effect, Δf, corresponds to the frequency resolution limit Δf/f₀.As one can see from the equation for the energy stored in the steady-state condition, the response of the system to a sinusoidal frequency other than its natural undamped frequency is diminished to a fraction, F, of the maximum energy that would be stored at the natural frequency. Expressed in terms of the ratio of the frequency of the driving force to the natural undamped frequency of the system (f/f₀=ϕ), the fraction is F=1ϕ2+Q02(1−ϕ2)2.The frequency limits for the region Δf surrounding f₀ are found by solving for the condition e^−α=F. Solving for the upper and lower frequency limits, f_b and f_a, for which the energy stored in the resonating system is a fraction e^−α of the peak response yields the expression: fb2−fa2f02=2Q0(eα−1)+14Q02.And to a very close approximation, the frequency resolution limit comes out as: Δff0=(eα−1)Q0when Δf is defined as (f_b—f_a).The foregoing discussion may now be summarized in geometrical form by reference to a three-dimensional figure in signal space. The limits of resolution of an analyzer may be represented by an irreducible region in a three-dimensional space that must be exceeded before any information about a signal can be found. This space is shown in figure 1. As one can see from the equations, the figure of merit, Q₀, enters into the resolution limits for time and frequency in a complementary way. Thus, it determines the relative proportions between the resolution limits Δf/f₀ and Δt/T₀. One may therefore apportion the relative uncertainties in frequency and time to suit the requirements of an experiment whenever one can control the Q of a system. This process is discussed more fully in “Uniform Transient Error” (see footnote ²). But the product of the resolution limits, the “uncertainty equation” depends in irreducible fashion upon α.Open in a separate windowFigure 1Resolution limits making up the indication limit, U.T₀ is the period corresponding to f₀, the natural undamped frequency of the system. E₀ is the maximum sinusoidal energy stored.Thus the signal uncertainty equation, expressed in terms of α, turns out to be Δf⋅Δt=12π(eα−1)where α is related to ΔE through the definition of the least change ΔE that may be observed in the total stored energy E₀.The basic form of the resolution equations results from substitution for the exponential coefficient, α, in the equations already derived: ΔtT0=−Q02πln(1−ΔEE0) ΔfF0=1Q0ΔEE0(1−ΔEE0) Δf⋅Δt=1πΔEE0⋅11−ΔEE0ln11−ΔEE0The volume corresponding to the irreducible limits in signal space, a quantity here defined as the “indication limit,” can be computed from the signal uncertainty equation. It is defined by the triple product of the resolution limits along the three axes, U=ΔEE0⋅ΔtT0⋅Δff0,and it is just the volume of the elementary figure shown in figure 1. Its computation might at first glance appear to be somewhat redundant to the signal uncertainty equation. In fact, its functional form permits factoring the expression for U into components that have an interesting connotation in physical measurements.Thus, since: U=ΔEE0⋅Δff0⋅ΔtT0≈ΔWW0⋅Δff0⋅ΔtT0the magnitude of U expressed in terms of the variable, α, is U=αe−α2π(eα−1)3/2and U is given in terms of the energy resolution limit, ΔE/E₀, by: U=12π⋅(ΔEE01−ΔEE0)3/2⋅[−(1−ΔEE0)ln(1−ΔEE0)].It should be noted that the indication limit U and the signal uncertainty relation Δf·Δt depend only upon the energy resolution limit. The energy resolution limit is an independent variable and cannot be reduced by operations altering the function of the system along the f and t axes: for instance, changing the Q₀ of the system.For a system operating at its optimum, the irreducible energy or power increment for the system would be set by the noise energy stored in the system. This noise energy may be due to Brownian motion in the system, for example. Noise of external origin may be present with the driving signal. In the general case the intrinsic and extrinsic noise powers will not be coherent, and the total noise energy stored in the system may usually be considered as the sum of contributions.The prior papers relating the resolution limits to the relative amounts of signal and noise present (see footnote ²) were based upon a derivation subject to the restriction that the signal-to-noise ratio be high. In those papers, the relationship found was Δf⋅Δτ≥(S/N)−3/22πwhere S and N were signal and noise powers, respectively, and where the use of the observation interval Δτ rather than the limiting time increment Δt made the relation an inequality.Such a restriction was, in fact, not required. A tractable and useful expression for the product Δf·Δt can be derived, valid for all values of S/N.In the case where both signal and noise energy are stored, the total energy present in the system is E₀=S+N. An increment in the signal energy stored (or in the input signal power) can be detected only if it equals or exceeds the minimum energy increment the system is capable of indicating. From this definition and the definition of the exponential factor, α: ΔSS+N≥ΔEE0=ΔWW0=(1−e−α).And, where the least energy increment is controlled by the noise energy stored; ΔE→N: or, very nearly ΔEE0=NS+N.Thus, where the limit of detection is set by the noise energy: α=lnN+SSfrom which the signal uncertainty becomes: Δf⋅Δt=12πNS·ln(1+NS).For high values of S/N, this expression approaches the limit (1/2π) (S/N) ^−3/2, a result found previously (see footnote ²).An especially interesting interpretation can be made from the form of the expression for the indication limit, U, when it is written in terms of the signal-to-noise ratio. U=(S/N)−3/22π⋅(−S/N1+S/NlnS/N1+S/N).The first factor can be recognized to be the expression for the signal uncertainty, Δf·Δt, when the signal-to-noise ratio is high. The term in parentheses also has a recognizable functional form, and in fact it is possible to relate it to the limiting probability of information transfer.To facilitate discussion, the factor N may be cleared from the fractions in the term, giving it the form: —S/(S+N) ln S/(S+N). As one can see from the series expansion for the natural logarithm, this product approaches the value N/S for large values of S relative to N, and the indication limit then is merely the trivial product of the reciprocal of the signal-to-noise ratio multiplied by the signal uncertainty function. It is quite another matter as S/N→0. Then the indication limit may be considered to consist of two meaningful terms: one is the same signal uncertainty function that has already been derived for high signal-to-noise ratios (see footnote ²) (i.e., for ΔE small re E₀); the other is a modulating function that we will now proceed to relate to statistical matters.Where one is dealing with the statistical presence of noise and signal, the long-time average signal-tonoise ratio may be described in terms of expected values. The following definition of expected value is taken from a textbook on statistics.⁵ “The expected value of a random variable or any function of a random variable is obtained by finding the average value of the function over all possible values of the variable…. This is the expected value, or mean value of x. It is clear that the same result would have been obtained had we merely multiplied all possible values of x by their probabilities and added the results … we might reasonably expect the average value of x in a great number of trials to be somewhere near the expected value of x.”A special case that is quite common experimentally is one in which the level distributions of signal and noise are precisely the same. If one has either a signal or noise, the probability of the signal being P, then it follows that the probability of the noise is (1–P). If the signal and noise have the same level distribution, G, the modulating term SN+SlnSN+S→P⋅GP⋅G+(1−P)⋅GlnP⋅GP⋅G+(1−P)⋅Gand thus the term that modulates the signal uncertainty function can be seen to reduce to the form —PlnP, where P is the probability of signal occurrence. From very simple considerations, therefore, the limit of detection is shown to be related directly here to a limiting probability of information transfer—a quantity usually derived in information theory by considering signals and noise to be made up of equal-sized unit impulses.It is interesting that this point was arrived at in the reverse direction by Woodward and Davies.⁶ They started with the PlnP term from Shannon’s information function and demonstrated from considering the signals and noise in radar detection that the quantity P was related to the signal-to-noise ratio for radar signals.However, the modulating term in its original form, in terms of S and N, may be seen to represent a generalization of the function defined in information theory as the channel capacity, H. This form is more closely related to the ordinary formulation describing the entropy of a system in terms of the probability distribution of energy states within it.⁷The question of whether a signal is detected in the presence of noise depends upon what the investigator chooses to consider a reasonable limiting probability in deciding whether a signal has been detected. A preponderance of only 1 percent above random distribution would correspond to a much smaller signal-to-noise ratio than would 90 percent. In fact, the common definition that gives the limit of detection as a signal-to-noise ratio of unity corresponds directly to setting the criterion for the limiting probability of detection at 50 percent.For the case N=S, the “—PlnP” term becomes ?½ ln ½ and the indication limit becomes: U1=12π(−12ln12)=ln24πfor which the signal uncertainty function is: Δf⋅Δt=ln24π.These are the limiting forms also where signals and noise are transmitted as “bits.”Thus far we have discussed noise from the standpoint of the noise energy stored in the system. This energy is, so far as the actual storage in the system is concerned, not distinguishable from energy stored that might be derived from purely sinusoidal excitation. If all excitation were withdrawn, and the system left in the force-free state, the energy in it would be dissipated in the usual exponential decay, and the oscillations during the decay would be essentially of frequency f₀, providing the system had moderate energy storage capacity (Q₀>1).Therefore, unless we have some other means for distinguishing among the sources of the energy stored in the system—such as, for example, knowledge of the spectral character of pulsed signals applied to the system—or knowledge of the amount of energy found present in the system when it is considered to be free of any known source, we are left to regard as signal that part of the energy stored in the system that was produced by a sinusoidal signal of power S_i. The remaining driving sources, of more or less broad spectral distribution, would in general be classified as noise.If the system is being driven at its natural frequency, its output signal will be Q₀/2π times its input, for Q₀ is 2π times the ratio of the energy stored to the energy dissipated during the cycle. At any other driving frequency, the energy stored will be weighted by the response, ρ, which relates the energy stored to the power supplied to the system. ρ=Q0/2πf0f2/f02+Q02(1−f2/f02)2.Suppose the noise within the system arises from a source whose spectral distribution is given by the power density function, N_f. The system will store energy with a weighting factor of ρ.The total energy stored in the system due to excitation by noise will then be found from the integral: N=∫0∞Nfρdω.Although we have discussed the behavior of the system in terms of cycles per second, the quantity we have designated as Q₀ is defined directly in terms of the ratio of the energy stored to the energy dissipated per radian. As the parameter of the function we are integrating is Q₀, we must choose the dimensionally similar variable in order to carry out the integration correctly. Thus we must integrate with respect to ω rather than f. This point is given in detail because it is an instance of the dimensionality of angles recently pointed out by C. H. Page.⁸This integral may be evaluated easily for noise of constant energy per unit bandwidth in cycles per second; the result is then⁹ N=πNf2and it is independent of Q₀ because the energy storage capacity of the system and its bandpass for noise are affected in a complementary fashion by changes in the figure of merit.For several other types of noise, an approximate equivalent white noise coefficient, n_f, can be defined, for which the foregoing equations remain applicable. Given a noise whose spectral distribution is v(f), a mean value “equivalent” white noise per unit bandwidth may be computed from the equation: nf=∫fafbv(f)df∫fafbdf.Obviously if v(f) equals a constant, then n_f is the familiar “White Noise” coefficient. However, for several other types of noise the mean-value integration yields an equivalent n_f, which may be treated as a constant, with rather low residual error resulting from this approach. This can be seen from the fact that the system is selective with respect to the frequency components of the power sources from which it stores energy. Thus the restriction is only that the spectral distribution be changing slowly in the frequency region immediately surrounding f₀. For the following noise distributions, the residual terms discarded amount, in the worst case, to no more than 12½ percent of the approximate value:For the noise distributions shown in the left-hand column, the respective mean values are shown in the right-hand column: v(f)=k⋅fnf=k2(fb+fa) v(f)=k/f(constantenergy/octave)nf=klnfb/fa(fb−fa)where, for this purpose, f_b and f_a are taken as the upper and lower half-energy limits of the response weighting function, ρ.One can use the foregoing to predict the ratio of energy stored from the sinusoidal excitation to the energy stored from the source of noise. Thus the effective signal-to-noise ratio in terms of energy stored in the system is given by: S/N=2Siρπnff0.The signal-to-noise ratio will be a maximum if the frequency of the sinusoidal excitation is just equal to the natural undamped frequency of the system. (For this condition, ρ = Q₀/2π.) The maximum ratio is: S0/N=SiQ0π2nff0.The foregoing derivation has meaning also with respect to any system described by a second-order differential equation in which some event of brief duration occurs. A quantity directly analogous to the indication limit may be computed. In this instance, the expressions relate the resolution limits that bound the occurrence of the event, specifying the least increments of energy, frequency and time within which the event can take place.Further, the limiting energy increment, ΔE, need not be formed by noise. For example, in a pulse- height system it would correspond to the smallest step in pulse height that might be indicated. As another example, an atomic system described by a second-order differential equation might have ΔE subject to quantum limitations.     

Identification of lactic acid bacteria isolated from oat sourdoughs and investigation into their potential for the improvement of oat bread quality

The use of sourdough in wheat and rye breads has been extensively studied; however, little is known about its potential effect when baking oat bread. Consequently, the impact of sourdough on oat bread quality was investigated. Two different sourdoughs were prepared from wholegrain oat flour without the addition of starter cultures, by continuous propagation at 28 (SD 28) or 37 °C (SD 37) until the composition of the lactic acid bacteria remained stable. The dominant LAB were identified by sequence analysis of the 16S rDNA isolated from pure cultures. LAB from SD 28 belonged to the species Leuconostoc argentinum, Pedicoccus pentosaceus and Weissella cibaria, while Lactobacillus coryniformis dominated SD 37. The isolated LAB were further used as starter cultures for the production of oat sourdoughs. Fundamental rheology revealed softening of the sourdoughs compared to non-acidified and chemically acidified controls, which could not be attributed to proteolytic activity. Incorporation of oat sourdough into an oat bread recipe resulted in significantly increased loaf-specific volume as well as improved texture, independent of addition level or sourdough type. Overall, the results of this study show that sourdoughs containing lactic acid bacteria isolated from oats have the potential to enhance oat bread quality.     

IR spectroscopy: Suitable method for determination of curing degree and crosslinking type in melamine–formaldehyde resins

Melamine–formaldehyde (MF) resins are widely used as adhesives and finishing materials in the wood industry. During resin cure, either methylene ether or methylene bridges are formed, leading to the formation of a three-dimensional resin network. Not only the curing degree, but also the chemical species present in the cured resin determine the quality of the final product. Analytical methods allowing a detailed investigation of network formation are of great benefit to manufacturers. In the present work, resin cure of an MF precondensate is studied at different temperatures (100–200 °C) without considering the initial pH as a factor. Isoconversional kinetic analysis based on exothermal curing enthalpies enables calculation of the crosslinking degree at a given time/temperature regime. A semiquantitative determination of the chemical groups present is performed based on solid-state nuclear magnetic resonance data. Fourier transform infrared spectroscopy has shown to be a fast and reliable analytical tool with high sensitivity toward functional groups and with great potential for at-line process control. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47691.     

Isolation and partial characterization of Mexican taro (Colocasia esculenta L.) starch

Taro starch was isolated from Mexican variety and its morphological, physicochemical, and molecular characteristics were evaluated. Yield starch (in dry basis) was 81%, and this starch had low AM content (2.5%). Taro starch granules showed a mixture of shapes with sizes between 1 and 5 µm. Taro starch presented an A‐type XRD pattern with a crystallinity level of 38.26%. Solubility and water retention capacity did not change in the temperature range of 50–70°C and thereafter they increased as temperature increased too. Taro starch showed high peak viscosity due to its high AP content. The peak temperature of gelatinization of taro starch was 80.6°C with an enthalpy value of 10.6 J/g, with low retrogradation rate due to its low AM content. Weight‐average molar mass (Mw) and gyration radius (Rz) of taro starch were 1.21 ± 0.8 × 10⁹ g/mol and 424 ± 70 nm, respectively. Taro tuber could be an alternative for starch isolation with functional and physicochemical characteristics for food and non‐food applications.     

Preparation of spherical aggregates of taro starch granules

Taro starch spherical aggregates were prepared by including a spray drying step in a conventional pilot plant-scale starch isolation procedure. The aim of this study was to characterize these spherical aggregates. The procedure was repeated three times to verify the spherical aggregates formation, which was shown by scanning electron microscopy. Amylose was not be detected in the aggregates, and a protein content of 4.5 g/100 g was recorded. Aggregation of taro starch granules did not affect the X-ray diffraction pattern. The maximum peak viscosity of the aggregate preparation was obtained at a relatively high temperature (i.e. start of the temperature holding step). The phase transition of taro starch aggregates in excess water showed high temperatures, with low molecular reorganization during storage. During water retention capacity (WRC) and solubility tests, taro starch aggregates were stable until 70 °C. The spray drying conditions used produced spherical aggregates of taro starch that presented physicochemical and functional characteristics with potential for encapsulation of substances.     

科帕卡格拉纳建筑 奥地利维亚纳

正科帕卡格拉纳位于新多瑙河12.5公里处,毗邻Reichsbrücke大桥。出于同旧有设计保持协调性考虑,力求能让大楼融入当地环境,迎合自身功能性需要,GERNER GERNER PLUS团队充分考虑现有周边环境,对大楼重新规划设计。新的设计突显建筑的特殊性和重要性,面向河流的立面是科帕卡格拉纳大楼最重要的部分,所以设计的重点也放在了这里。现有的城市中轴线是一个参照物,为了视觉上和氛围上形成一体化城市的感     

Multivariate process trajectories for molecular description of MF thermal curing and correlation with hydrolytic stability

During curing of thermosetting resins the technologically relevant properties of binders and coatings develop. However, curing is difficult to monitor due to the multitude of chemical and physical processes taking place. Precise prediction of specific technological properties based on molecular properties is very difficult. In this study, the potential of principal component analysis (PCA) and principal component regression (PCR) in the analysis of Fourier transform infrared (FTIR) spectra is demonstrated using the example of melamine-formaldehyde (MF) resin curing in solid state. FTIR/PCA-based reaction trajectories are used to visualize the influence of temperature on isothermal cure. An FTIR/PCR model for predicting the hydrolysis resistance of cured MF resin from their spectral fingerprints is presented which illustrates the advantages of FTIR/PCR compared to the combination differential scanning calorimetry/isoconversional kinetic analysis. The presented methodology is transferable to the curing reactions of any thermosetting resin and can be applied to model other technologically relevant final properties as well.     

Fourier transform ion cyclotron resonance mass spectrometry at the true cyclotron frequency

Ion cyclotron resonance (ICR) cells provide stability and coherence of ion oscillations in crossed electric and magnetic fields over extended periods of time. Using the Fourier transform enables precise measurements of ion oscillation frequencies. These precisely measured frequencies are converted into highly accurate mass-to-charge ratios of the analyte ions by calibration procedures. In terms of resolution and mass accuracy, Fourier transform ICR mass spectrometry (FT-ICR MS) offers the highest performance of any MS technology. This is reflected in its wide range of applications. However, in the most challenging MS application, for example, imaging, enhancements in the mass accuracy of fluctuating ion fluxes are required to continue advancing the field. One approach is to shift the ion signal power into the peak corresponding to the true cyclotron frequency instead of the reduced cyclotron frequency peak. The benefits of measuring the true cyclotron frequency include increased tolerance to electric fields within the ICR cell, which enhances frequency measurement precision. As a result, many attempts to implement this mode of FT-ICR MS operation have occurred. Examples of true cyclotron frequency measurements include detection of magnetron inter-harmonics of the reduced cyclotron frequency (i.e., the sidebands), trapping field-free (i.e., screened) ICR cells, and hyperbolic ICR cells with quadrupolar ion detection. More recently, ICR cells with spatially distributed ion clouds have demonstrated attractive performance characteristics for true cyclotron frequency ion detection. Here, we review the corresponding developments in FT-ICR MS over the past 40 years.     

New light-sensitive nucleosides for caged DNA strand breaks

Phototriggered bod cleavage has found wide application in chemistry as well as in biology. Nevertheless, there are only a few methods available for site-specific photochemical induction of DNA strand scission despite numerous potential applications. In this study we report the development of new photocleavable nucleotides based on the photochemistry of o-nitrobenzyl esters. The light-sensitive moieties were generated through introduction of o-nitrophenyl groups at the 5'C position of the nucleoside sugar backbone. The newly synthesized, modified nucleosides were incorporated in oligonucleotides and are able to build stable DNA duplexes. In such a way modified oligonucleotides ca cleaved site-specifically upon irradiation with > 360 nm light with high efficiency. Furthermore, we show that these modifications can be bypassed in DNA synthesis promoted by Thermus aquaticus DNA polymerase.