Contributions to the chemistry of human hair: III. Protein chemical aspects of permanent waving treatments

The influence of permanent waving on hair proteins was studied in order to obtain additional information about the chemistry of this cosmetic treatment. It was shown by amino acid analysis that with increasing reduction time during treatment fewer disulphide bonds were reformed in hair during subsequent reoxidation. Simultaneously, an increasing amount of sulphur-containing material is liberated from the hair, as demonstrated by the sulphur balance calculated from the sulphur-containing amino acids. The amount liberated is increased when an extensive soaking of the samples in water between the reduction and reoxidation step is performed. Comparing treatments with the use of reducing solutions of pH values between 7.5 and 10.0, it was found that the largest amount of cystine cleavage occurs at pH 9.0. All hair samples reduced at pH values above pH 8.5 showed incomplete reformation of the disulphide bonds during subsequent reoxidation. This was indicated by the content of free SH-groups and cysteic acid, as quantified by amino acid analysis. The damage to the hair proteins due to permanent waving was further confirmed by the determination of the pronase solubility. The reductive treatment of hair at pH 7.5 led to a relatively low degree of reduction, however all sulphur bonds were reformed during subsequent reoxidation.     

A Model for Adapting Explanations to the User's Likely Inferences

In order to generate natural, high quality textual presentations in technical domains, good explanations must not only be adapted to the knowledge attributed to the intended audience, but they must also take into account the inferential capabilities of the addressees. In this paper, we present a model for anticipating contextually-motivated inferences addressees are likely to draw. This model is used to motivate choices in presenting or omitting individual pieces of information; it takes into account the addressees' domain expertise and expectations about logical consequences of purposefully presented information. Several kinds of empirical evidence are incorporated into a text planning process that aims at exploiting conversational implicature, so that a most suitable portion of the plan can be selected for being uttered explicitly. This way, our method adds to discourse planners based on Rhetorical Structure Theory (RST) the ability to omit easily inferable information. Thus, it overcomes one of the main shortcomings of RST. In the course of this process, rules anticipating user inferences are invoked to determine contextually justified derivability of information. In this manner, text variants can be composed on the basis of a text plan entailing annotations about the inferability of pieces of information. Moreover, pragmatically-motivated preference criteria can be used to choose among several plausible variants. The model is formulated in a reasonably domain-independent way, so that the rules expressing aspects of conversational implicature can be incorporated into typical RST-based text planners.     

Comparison of unit root tests for time series with level shifts

Unit root tests are considered for time series which have a level shift at a known point in time. The shift can have a very general nonlinear form, and additional deterministic mean and trend terms are allowed for. Prior to the tests, the deterministic parts and other nuisance parameters of the data generation process are estimated in a first step. Then, the series are adjusted for these terms and unit root tests of the Dickey–Fuller type are applied to the adjusted series. The properties of previously suggested tests of this sort are analysed and modifications are proposed which take into account estimation errors in the nuisance parameters. An important result is that estimation under the null hypothesis is preferable to estimation under local alternatives. This contrasts with results obtained by other authors for time series without level shifts.     

ANALYSIS AND CHARACTERISTICS OF THE DISK-ROTOR INDUCTION MOTOR

ABSTRACT

The induction motor with an axial air gap and a flat homogeneous disk-rotor shows some performance characteristics that are superior to conventional induction machines. Large rotational speeds and small moments of inertia promise high power densities and small mechanical time constants. This paper presents an analytical model which includes the two-dimensional current distribution in the secondary, thus extending a previous simplified treatment. The model also accounts for fringing phenomena and can handle a secondary with a radius-dependent thickness and conductivity. Numerical results for the local distributions of field, current density, and forces; a typical torque-speed characteristic; a current locus; and an equivalent circuit are presented and serve as a basis for discussion and comparison.     

The Effect of Striae on the Strength of Glass

Severe striations caused by compositional inhomogeneity had no significant effect on the strength of either annealed glass disks or those strengthened by an ion-exchange surface compression technique. Electron microprobe and interferometric measurements revealed that the striae were caused by variations in the SiO₂/Al₂O₃ ratio rather than by variations in alkali concentration and therefore did not adversely influence the ion-exchange process.     

Recognition of thin, flat microelectromechanical structures for automation of the assembly process

Visual recognition of microelectromechanical parts is necessary for automation of the assembly process. The visual recognition system that we have developed is based partly on neural networks and partly on digital image-processing techniques. The system takes grey-level microscope images and produces recognition code as the output as well as information about micropart position. The recognition procedure is not sensitive to micropart position. This is ensured by preprocessing based on calculation of the image moment properties. For the recognition, a supervised feedforward neural network is utilized. A combination of standard backpropagation and resilient propagation is chosen for learning the network. The performance of the system is tested on recognition of the parts of a microvalve system. The results are satisfactory with respect to recognition accuracy and recognition time.     

EXPERIMENTAL INVESTIGATION OF A WET DUST SCRUBBER: DUST COLLECTION BASED ON TURBULENT DIFFUSION

ABSTRACT

The “nozzle scrubber” is a wet scrubber in which the scrubbing water is dispersed in the dust laden gas stream by means of one or more pneumatic nozzles. This scrubber is distinguished by an excellent collection efficiency for submicron dust at an unusually low energy and water consumption. No well-defined theory exists for this process. The collection efficiency in the “nozzle scrubber” depends primarily on turbulent diffusion respectively on the interaction of particles and droplets induced by turbulence, and not on inertial separation as in the case of the venturi scrubber. A light scattering device was used to measure the particle distributions. The experimental set-up was built up in a technical scale. The influence of operation parameters, especially water consumption, residence time, and pressurized air, on the grade efficiency has been demonstrated by their systematic variation. The contribution of turbulent diffusion to the collection efficiency has been confirmed.