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The steel industry is an important consumer of electrical energy having a significant impact on the electricity network and accounting to a significant part of production costs. Thus, there is the opportunity of closer cooperation between grid operators and steel industry to improve the power consumption prediction and actively contribute to a secure network operation. This paper aims to describe an overall dynamical approach for electricity demand monitoring and timely reactions to the grid situation, to avoid non flexible equipment disconnection, financial fines when deviating from energy contingent and contributing to the grid stability. Energy management, simulation, decision support procedures and process control tools will be integrated in an agent based system able to predict and manage power consumption. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is the most lethal of all gynecological cancers. Due to vague symptoms, OC is mostly detected at advanced stages, with a 5-year survival rate (SR) of only 30%; diagnosis at stage I increases the 5-year SR to 90%, suggesting that early diagnosis is essential to cure OC. Currently, the clinical need for an early, reliable diagnostic test for OC screening remains unmet; indeed, screening is not even recommended for healthy women with no familial history of OC for fear of post-screening adverse events. Salivary diagnostics is considered a major resource for diagnostics of the future. In this work, we searched for OC biomarkers (BMs) by comparing saliva samples of patients with various stages of OC, breast cancer (BC) patients, and healthy subjects using an unbiased, high-throughput proteomics approach. We analyzed the results using both logistic regression (LR) and machine learning (ML) for pattern analysis and variable selection to highlight molecular signatures for OC and BC diagnosis and possibly re-classification. Here, we show that saliva is an informative test fluid for an unbiased proteomic search of candidate BMs for identifying OC patients. Although we were not able to fully exploit the potential of ML methods due to the small sample size of our study, LR and ML provided patterns of candidate BMs that are now available for further validation analysis in the relevant population and for biochemical identification.

Non destructive test systems are increasingly applied in the industrial context for their strong potentialities in improving and standardizing quality control. Especially in the intermediate manufacturing stages, early detection of defects on semi-finished products allow their direction towards later production processes according to their quality, with consequent considerable savings in time, energy, materials and work. However, the raw data coming from non destructive test systems are not always immediately suitable for sophisticated defect detection algorithms, due to noise and disturbances which are unavoidable, especially in harsh operating conditions, such as the ones which are typical of the steelmaking cycle. The paper describes some pre-processing operations which are required in order to exploit the data coming from a non destructive test system. Such a system is based on the joint exploitation of Laser and Electro-Magnetic Acoustic Transducer technologies and is applied to the detection of surface and sub-surface cracks in cold and hot steel slabs., (Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.)