The Impact of Late-Career Health and Employment Shocks on Social Security and Other Wealth

About one-quarter of workers age 51 to 55 in 1992 developed health-related work limitations and about one-fifth were laid off from their jobs before age 62. Although late-career health and employment shocks often derail retirement savings plans, Social Security's disability insurance, spouse and survivor benefits, and progressive benefit formula provide important protections. In fact, health shocks increase Social Security's lifetime value, primarily because the system's disability insurance allows some disabled workers to collect benefits before age 62. However, if the system's disability insurance program did not exist, the onset of health-related work limitations would substantially reduce Social Security wealth

Demonstration of the Transition from the Particle Approach to the Wave Approach

This article describes a demonstration, suitable for elementary physics, to show the transition from the particle to the wave approach. Standing waves in a continuous string are approximated by particles fastened to a much lower density or massless string. The agreement between the continuous and discrete systems is good, and the wave approach is shown to be only another way of describing systems

Will Employers Want Aging Boomers?

Explores the status quo of older workers; why baby boomers are likely to work longer; and how changes in needed skills, the characteristics of older workers, and labor force growth will affect demand for older workers. Includes policy recommendations

Expression and purification of an adenylation domain from a eukaryotic nonribosomal peptide synthetase: Using structural genomics tools for a challenging target

Nonribosomal peptide synthetases (NRPSs) are large multimodular and multidomain enzymes that are involved in synthesising an array of molecules that are important in human and animal health. NRPSs are found in both bacteria and fungi but most of the research to date has focused on the bacterial enzymes. This is largely due to the technical challenges in producing active fungal NRPSs, which stem from their large size and multidomain nature. In order to target fungal NRPS domains for biochemical and structural characterisation, we tackled this challenge by using the cloning and expression tools of structural genomics to screen the many variables that can influence the expression and purification of proteins. Using these tools we have screened 32 constructs containing 16 different fungal NRPS domains or domain combinations for expression and solubility. Two of these yielded soluble protein with one, the third adenylation domain of the SidN NRPS (SidNA3) from the grass endophyte Neotyphodium lolii, being tractable for purification using Ni-affinity resin. The initial purified protein exhibited poor solution behaviour but optimisation of the expression construct and the buffer conditions used for purification, resulted in stable recombinant protein suitable for biochemical characterisation, crystallisation and structure determination

Statistical Models of Reconstructed Phase Spaces for Signal Classification

This paper introduces a novel approach to the analysis and classification of time series signals using statistical models of reconstructed phase spaces. With sufficient dimension, such reconstructed phase spaces are, with probability one, guaranteed to be topologically equivalent to the state dynamics of the generating system, and, therefore, may contain information that is absent in analysis and classification methods rooted in linear assumptions. Parametric and nonparametric distributions are introduced as statistical representations over the multidimensional reconstructed phase space, with classification accomplished through methods such as Bayes maximum likelihood and artificial neural networks (ANNs). The technique is demonstrated on heart arrhythmia classification and speech recognition. This new approach is shown to be a viable and effective alternative to traditional signal classification approaches, particularly for signals with strong nonlinear characteristics

Generalized Phase Space Projection for Nonlinear Noise Reduction

Improved phase space projection methods, adapted from related work in the linear signal processing field based on subspace decomposition, are presented for application to the problem of additive noise reduction in the context of phase space analysis. These methods improve upon existing methods such as Broomhead–King singular spectrum analysis projection by minimizing overall signal distortion subject to constraints on the residual error, rather than using a direct least-squares fit. This results in a range of weighted projections which estimate and compensate for the portion of the principal component\u27s singular values corresponding to noise rather than signal energy, and which include least-squares (LS) and least minimum mean square error (LMMSE) as subcases. The nature of phase space covariance, the key element in construction of the projection matrix, is examined across global phase spaces as well as within local neighborhood regions. The resulting algorithm, illustrated on a noisy Henon map as well as on the task of speech enhancement, is applicable to a wide variety of nonlinear noise reduction tasks

Time-Domain Isolated Phoneme Classification Using Reconstructed Phase Spaces

This paper introduces a novel time-domain approach to modeling and classifying speech phoneme waveforms. The approach is based on statistical models of reconstructed phase spaces, which offer significant theoretical benefits as representations that are known to be topologically equivalent to the state dynamics of the underlying production system. The lag and dimension parameters of the reconstruction process for speech are examined in detail, comparing common estimation heuristics for these parameters with corresponding maximum likelihood recognition accuracy over the TIMIT data set. Overall accuracies are compared with a Mel-frequency cepstral baseline system across five different phonetic classes within TIMIT, and a composite classifier using both cepstral and phase space features is developed. Results indicate that although the accuracy of the phase space approach by itself is still currently below that of baseline cepstral methods, a combined approach is capable of increasing speaker independent phoneme accuracy