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Summary To avoid false alarms for vibration‐based structural damage detection methods, temperature effects on damage‐sensitive features should be eliminated. In this paper, a novel two‐step damage identification method combining a multilayer neural network and novelty detection is developed to differentiate the changes in natural frequencies (one of the most commonly used damage features that can be obtained reliably and relatively easily) due to damage from those induced by temperature variations. In the first step, a multilayer artificial neural network, which resembles an auto‐associative neural network but uses temperature variables in addition to the frequencies as the inputs, is explored to identify patterns in frequencies of undamaged structures under varying temperatures. Euclidean distance is then utilized as a novelty index to quantify the discordancy between patterns in undamaged cases and candidate cases. Numerical studies using a simply supported beam and finite element models based on an experimental grid structure, which simulate different levels of stiffness reductions under varying temperature conditions, are used to verify the detectability and robustness of the proposed approach. It is shown that the incorporation of the proposed artificial neural network with novelty detection enables one to robustly distinguish damage occurrence and severity regardless of temperature variations and noise perturbations. Using an unsupervised learning scheme, the proposed approach transforms a multivariate analysis using modal frequencies and temperature data into a straightforward univariate discordancy test using the novelty index. Given these competitive advantages, this approach is very attractive for the development of an automated continuous monitoring system in practical applications.

Studies about the role of cytokines on the immunopathogenesis of atopic dermatitis (AD) are generally based on in vitro observations and this role has not been completely clarified yet. Serum levels of total IgE, IL-18, IL-12, IFN-γ and the relationship between these parameters and disease severity, determined using the SCORAD index, in a group of atopic patients were investigated in this study. Serum levels of total IgE were measured by the nephelometric method and serum levels of IL-18, IL-12/p40 and IFN-γ were measured by ELISA method. Serum levels of total IgE and IL-18 were found significantly higher in study group than in controls (P < .001). There was no statistically significant difference between patients and controls in respect of serum levels of IL-12/p40 (P = .227). A statistically significant relationship between SCORAD values and serum levels of total IgE (P < .001), IL-18 (P < .001), and IL-12/p40 (P < .001) was determined. These results show that serum levels of IL-18 can be a sensitive parameter that importantly correlates with clinical severity of AD, can play a role in the immunopathogenesis of AD, and furthermore may be used in the diagnosis and follow-up of the disease in addition to other parameters.

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