Experimental Study on the Effect of Tabs with Asymmetric Projections on the Mixing Characteristics of Subsonic Jets

This study experimentally explores the effect of tabs with asymmetric projections on the mixing effectiveness of jets at different nozzle exit Mach numbers with subsonic ranges of 0.4, 0.6, and 0.8. The results obtained with the tab-controlled jet are compared with those of uncontrolled jets. In this experimental investigation, a pair of identical tabs is deployed along a diameter of a convergent nozzle with inlet and exit cross sections of a circle, where each tab has two triangular projections configured at locations offset to each other at a distance of 1 mm on a plain rectangular stem. The geometrical blockage due to the presence of both tabs is maintained at 5.09% to minimize the thrust loss incurred due to tabs. The counter-rotating vortices generated at different locations of the tabs, caused instability or shear distortions at the nozzle exit, promoting jet mixing and eventually leading to rapid velocity decay along the jet axis and accentuating the reduction of the potential core. Compared to plain jet, reductions in core length of about 70%, 76%, and 81% at Mach 0.4, 0.6, and 0.8, respectively, are observed with the tab-controlled jets. The total pressure decay characteristics in the radial profile along the tab and normal-to-tab orientations have shown significant distortion in the jet structure, making it asymmetrical again owing to the asymmetrical positioning of projections on the tabs. Besides, in comparison with the plain nozzle, the total pressure decay characteristics in the radial profiles of tab-controlled jets are significantly different along the axial locations in the downstream direction due to the same reason of the asymmetrical positioning of triangular projections on the tabs. The primary research goal of this experimental investigation with asymmetrical tabs is to promote jet mixing asymmetrically to achieve thrust vectoring of jets

Carbon nanotube-rich domain effects on bulk electrical properties of nanocomposites

Carbon nanotube (CNT)/epoxy composites are intriguing materials that enable materials scientists and engineers to tailor structural and electrical properties for applications in the automotive and aerospace industries. Recent insights into CNT-rich domain formation and its influence on electrical properties raise questions about which processing variables can be used to tune the overall electrical conductivity. Here, we investigate how mass fraction and curing temperature influence these electrical properties. CNT nanocomposites were fabricated varying the mass fraction of CNT and the epoxy curing temperature. First, scanning lithium ion microscopy coupled with transmission electron microscopy were employed to investigate the morphology of CNT-rich domains that formed more readily at elevated curing temperatures than during room temperature curing. Then, oscillatory shear rheology measurements of the unfilled curing epoxy informed a simple kinetic argument to explain the CNT-rich domain formation. Finally, the electrical conductivity (both alternating and direct current) was characterized with a novel microwave cavity perturbation spectroscopy technique (alternating current conductivity) and a standard four-point probe station (direct current conductivity). The overarching conclusion of the work was that the CNT-rich domains formed a secondary percolated network surrounded by an almost completely unfilled epoxy matrix that allowed for higher conductivities at lower loadings. This work demonstrates that perfect dispersion of the nanoparticulate is, at least in this instance, not necessarily the preferred morphology

Effectiveness and safety of Levofloxacin containing regimen in the treatment of Isoniazid mono-resistant pulmonary Tuberculosis: a systematic review

BackgroundWe aimed to determine the effectiveness and safety of the Levofloxacin-containing regimen that the World Health Organization is currently recommending for the treatment of Isoniazid mono-resistant pulmonary Tuberculosis.MethodsOur eligible criteria for the studies to be included were; randomized controlled trials or cohort studies that focused on adults with Isoniazid mono-resistant tuberculosis (HrTB) and treated with a Levofloxacin-containing regimen along with first-line anti-tubercular drugs; they should have had a control group treated with first-line without Levofloxacin; should have reported treatment success rate, mortality, recurrence, progression to multidrug-resistant Tuberculosis. We performed the search in MEDLINE, EMBASE, Epistemonikos, Google Scholar, and Clinical trials registry. Two authors independently screened the titles/abstracts and full texts that were retained after the initial screening, and a third author resolved disagreements.ResultsOur search found 4,813 records after excluding duplicates. We excluded 4,768 records after screening the titles and abstracts, retaining 44 records. Subsequently, 36 articles were excluded after the full-text screening, and eight appeared to have partially fulfilled the inclusion criteria. We contacted the respective authors, and none responded positively. Hence, no articles were included in the meta-analysis.ConclusionWe found no “quality” evidence currently on the effectiveness and safety of Levofloxacin in treating HrTB.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022290333, identifier: CRD42022290333

Reports of cases argued and determined in the Circuit Court of United States for the First Circuit /

Includes index."July 1870-June 1875."Vol. 1; no more published.Title on spine: Holmes' reports.Mode of access: Internet

Fair Outlier Detection

An outlier detection method may be considered fair over specified sensitive attributes if the results of outlier detection are not skewed towards particular groups defined on such sensitive attributes. In this task, we consider, for the first time to our best knowledge, the task of fair outlier detection. In this work, we consider the task of fair outlier detection over multiple multi-valued sensitive attributes (e.g., gender, race, religion, nationality, marital status etc.). We propose a fair outlier detection method, FairLOF, that is inspired by the popular LOF formulation for neighborhood-based outlier detection. We outline ways in which unfairness could be induced within LOF and develop three heuristic principles to enhance fairness, which form the basis of the FairLOF method. Being a novel task, we develop an evaluation framework for fair outlier detection, and use that to benchmark FairLOF on quality and fairness of results. Through an extensive empirical evaluation over real-world datasets, we illustrate that FairLOF is able to achieve significant improvements in fairness at sometimes marginal degradations on result quality as measured against the fairness-agnostic LOF method.Comment: In Proceedings of The 21th International Conference on Web Information Systems Engineering (WISE 2020), Amsterdam and Leiden, The Netherland

A Holistic Approach for Detecting DDoS Attacks by Using Ensemble Unsupervised Machine Learning

Distributed Denial of Service (DDoS) has been the most prominent attack in cyber-physical system over the last decade. Defending against DDoS attack is not only challenging but also strategic. Tons of new strategies and approaches have been proposed to defend against different types of DDoS attacks. The ongoing battle between the attackers and defenders is full-fledged due to its newest strategies and techniques. Machine learning (ML) has promising outcomes in different research fields including cybersecurity. In this paper, ensemble unsupervised ML approach is used to implement an intrusion detection system which has the noteworthy accuracy to detect DDoS attacks. The goal of this research is to increase the DDoS attack detection accuracy while decreasing the false positive rate. The NSL-KDD dataset and twelve feature sets from existing research are used for experimentation to compare our ensemble results with those of our individual and other existing models