Chemotherapy results in increased free radical formation and depletion of tissue antioxidants. Moreover, patients receiving chemotherapy are under emotional stress, which is also accompanied by low antioxidant levels. In the present study, we measured cortisol, the main stress hormone, and total antioxidant capacity (TAC) in serum of 51 cancer patients during chemotherapy. Antioxidant activity was estimated by measuring the influence of serum in oxidation of ABTS (2,2΄-azino-bis (3-ethylbenz-thiazoline-6-sulfonic acid to ABTS+ by methmyoglobin (Antioxidant kit of Cayman). Serum cortisol was measured using an ELISA colorimetric assay. Serum TAC was significantly decreased (75% decrease compared to normal levels, p = 0.001) in all patients during chemotherapy, while blood cortisol concentration was increased by 10%, (p = 0.044). Lower antioxidant levels and higher cortisol concentration were detected in patients receiving chemotherapeutic drugs daily, compared to the ones receiving chemotherapy once a week. A difference between sexes was observed with male patients presenting lower antioxidant status and higher cortisol levels than females. A significant and persistent decrease in antioxidant capacity accompanied by increased cortisol concentration was observed in all patients during chemotherapy. This fact, which is probably generated by biological and emotional stress, increases the probability of harmful side effects and organism weakening and needs to be considered during patients' treatment.

Fatigue damage of turbine components is typically computed by running a rain-flow counting algorithm on the load signals of the components. This process is not linear and time consuming, thus, it is non-trivial for an application of wind farm control design and optimisation. To compensate this limitation, this paper will develop and compare different types of surrogate models that can predict the short term damage equivalent loads and electrical power of wind turbines, with respect to various wind conditions and down regulation set-points, in a wind farm. More specifically, Linear Regression, Artificial Neural Network and Gaussian Process Regression are the types of the developed surrogate models in this work. The results showed that Gaussian Process Regression outperforms the other types of surrogate models and can effectively estimate the aforementioned target variables.

Modern data-centric flows in the telecommunications industry require real time analytical processing over a rapidly changing and large dataset. The traditional approach of separating OLTP and OLAP workloads cannot satisfy this requirement. Instead, a new class of integrated solutions for handling hybrid workloads is needed. This paper presents an industrial use case and a novel architecture that integrates key-value-based event processing and SQL-based analytical processing on the same distributed store while minimizing the total cost of ownership. Our approach combines several well-known techniques such as shared scans, delta processing, a PAX-fashioned storage layout, and an interleaving of scanning and delta merging in a completely new way. Performance experiments show that our system scales out linearly with the number of servers. For instance, our system sustains event streams of 100,000 events per second while simultaneously processing 100 ad-hoc analytical queries per second, using a cluster of 12 commodity servers. In doing so, our system meets all response time goals of our telecommunication customers; that is, 10 milliseconds per event and 100 milliseconds for an ad-hoc analytical query. Moreover, our system beats commercial competitors by a factor of 2.5 in analytical and two orders of magnitude in update performance.

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