Re-employment hazard of displaced German workers: evidence from the GSOEP

This study investigates the re-employment hazard of displaced German workers using the first fourteen sweeps of the German Socio-Economic Panel (GSOEP) data. As well as parametric and non-parametric discrete-time specifications for the baseline hazard, the study employs alternative mixing distributions to account for unobserved heterogeneity. Findings of the study suggest negative duration dependence, even after accounting for unobserved heterogeneity. In terms of covariate effects, those at the lower end of the skills ladder, those who had been working in manufacturing and those with previous experience of non-employment are found to have lower hazard of exit via reemployment.

Pilot study of the utility and acceptability of tampon sampling for the diagnosis of Neisseria gonorrhoeae and Chlamydia trachomatis infections by duplex realtime polymerase chain reaction in United Kingdom sex workers

We aimed to evaluate the acceptability of self-collected tampon samples for the screening of female sex workers for sexually transmitted infections. We recruited 65 sex workers, and 63 agreed to provide tampon samples. The tampon samples were processed by realtime polymerase chain reaction (PCR) targeting Neisseria gonorrhoeae and Chlamydia trachomatis. Urethral and endocervical swabs were also obtained from 61 of 63 participants and tested using culture (N. gonorrhoeae) and the BD ProbeTec strand displacement amplification (SDA) (C. trachomatis) assay. Tampon sampling was preferred by 95% of the women and all favoured being tested away from genitourinary medicine clinics; the most common reasons cited were avoidance of embarrassment (40%) and convenience (30%). Besides near-universal acceptability of tampon sampling, the tampon sampling-PCR approach described in this study appeared to have enhanced sensitivity compared with conventional testing, suggesting the possibility of a residual hidden burden of N. gonorrhoeae and/or C. trachomatis genital infections in UK female sex workers.

Identification of meat spoilage by FTIR spectroscopy and neural networks

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.

Against divisiveness: Digital workers of the world unite! A rejoinder to César Bolaño and Eloy Vieira

This piece is a short rejoinder to César Bolaño’s paper The Political Economy of the Internet and related articles (e.g., Comor, Foley, Huws, Reveley, Rigi and Prey, Robinson) that center around the relevance of Marx’s labor theory of value for understanding social media. I argue that Dallas Smythe’s assessment of advertising was made to distinguish his approach from the one by Baran and Sweezy. Smythe developed the idea of capital’s exploitation of the audience at a time when both feminist and anti-imperialist Marxists challenged the orthodox idea that only white factory workers are exploited. The crucial question is how to conceptualize productive labor. This is a theoretical, normative, and political question. A mathematical example shows the importance of the “crowdsourcing” of value-production on Facebook. I also point out parallels of the contemporary debate to the Soviet question of who is a productive or unproductive worker in the Material Product System.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage