Removal of dimethylsulfide, n-hexane and toluene from waste air in a flat membrane bioreactor under continuous conditions

Dimethylsulfide (DMS), n-hexane and toluene removal from a waste air was carried out by using a flat composite membrane bioreactor under continuous feeding conditions. The composite membrane consisted of a dense polydimethylsiloxane top layer with an average thickness of 1.5 μm supported with a porous polyacrylonitrile layer of 50 μm. The membrane bioreactor (MBR) was operated during 9 months in which several operational conditions were applied. The inlet load of each compound ranged from 0 to 350 g m-3 h-1 and removal efficiencies of 80, 70 and 0 to 30 % were reached for DMS, toluene and hexane respectively. Two different empty bed residence time (EBRT) were applied on the MBR in order to check the influence of the residence time on the reactor performance. In this case, DMS and toluene removal increased with an increasing EBRT, while the removal of hexane remained constant. By increasing the flow rate of the recirculated liquid from 22 l min-1 to 45 l min-1, the total performance of the biofilter decreased. To increase the mass transfer of hexane in order to get a higher removal, an emulsion of water/silicone oil 80/20 V% was used as recirculated medium at the liquid side of the reactor. This caused a decrease in DMS removal while the removal of toluene remained constant. The variation on the hexane removal decreased significantly, so the reactor became more reliable for degrading hexane

In-depth comparative evaluation of supervised machine learning approaches for detection of cybersecurity threats

This paper describes the process and results of analyzing CICIDS2017, a modern, labeled data set for testing intrusion detection systems. The data set is divided into several days, each pertaining to different attack classes (Dos, DDoS, infiltration, botnet, etc.). A pipeline has been created that includes nine supervised learning algorithms. The goal was binary classification of benign versus attack traffic. Cross-validated parameter optimization, using a voting mechanism that includes five classification metrics, was employed to select optimal parameters. These results were interpreted to discover whether certain parameter choices were dominant for most (or all) of the attack classes. Ultimately, every algorithm was retested with optimal parameters to obtain the final classification scores. During the review of these results, execution time, both on consumerand corporate-grade equipment, was taken into account as an additional requirement. The work detailed in this paper establishes a novel supervised machine learning performance baseline for CICIDS2017

Hyperthyroidism in cats, part I : anatomy, physiology, pathophysiology, diagnosis and imaging

In the first part of this review article, thyroid anatomy, physiology and pathophysiology are reviewed to continue more specifically on hyperthyroidism, the most common thyroid disorder in cats. The diagnostic work-up of this disorder is discussed with emphasis on thyroid gland imaging. Scintigraphy is most commonly used and best suited to assess thyroid function, which will be discussed extensively in the second part of this review article. All other available imaging modalities do not offer a functional assessment and are therefore of limited use in the diagnosis and evaluation of hyperthyroidism

Hyperthyroidism in cats, part II : scintigraphic diagnosis and radioiodine treatment

In the second part of this review article, the diagnostic aspects of thyroid scintigraphy are discussed, with major emphasis on hyperthyroidism, followed by an overview of radioiodine treatment