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This report is about a novel approach to attenuation of fan noise in aerial vehicles, by way of implementing a ducted fan in the chassis of a four meter blended wing body plane. Three different one meter PVC pipes were used and their performances as silencers were tested by measuring the sound power level and calculating the insulation loss compared to a fan by itself. The ducts were either empty or lined with acoustic absorbents and micro perforated panels. Experiments were carried out in the reverberation room at KTH Marcus Wallenberg laboratory for sound and vibration research using the guidelines in ISO 3741 (2010). The results showed that the empty duct lead to a 15.3 dB(A) insulation loss with no decrease in thrust from the fan. The absorbent and micro perforated panel, however, lead to a 22.7 dB(A) insulation loss while giving a major decrease in thrust of more than one order of magnitude. The results show the failure of implementation of the latter two silencers due to choking, but also the success of the empty duct. This shows that there is room for improvement and perhaps even a future possibility of a successful implementation in a real vehicle.

AbstractObjective:Major depressive disorder (MDD) is associated with impaired reward processing and reward learning. The literature is inconclusive regarding whether these impairments persist after remission. The current study examined reward processing during a probabilistic learning task in individuals in remission from MDD (n = 19) and never depressed healthy controls (n = 31) matched for age and sex. The outcome measures were pupil dilation (an indirect index of noradrenergic activity and arousal) and computational modeling parameters.Method:Participants completed two versions (facial/nonfacial feedback) of probabilistic reward learning task with changing contingencies. Pupil dilation was measured with a corneal reflection eye tracker. The hypotheses and analysis plan were preregistered.Result:Healthy controls had larger pupil dilation following losses than gains (p <.001), whereas no significant difference between outcomes was found in individuals with a history of MDD, resulting in an interaction between group and outcome (β = 0.81, SE = 0.34, t = 2.37, p = .018). The rMDD group also achieved lower mean score at the last trial (t[46.77] = 2.12, p = .040) as well as a smaller proportion of correct choices (t[46.70] = 2.09, p = .041) compared with healthy controls.Conclusion:Impaired reward processing may persist after remission from MDD and could constitute a latent risk factor for relapse. Measuring pupil dilation in a reward learning task is a promising method for identifying reward processing abnormalities linked to MDD. The task is simple and noninvasive, which makes it feasible for clinical research.

The Examination Timetabling Problem (ETP) is the problem of scheduling a number of exams during a set time period so that no students are required to sit two exams simultaneously. Despite the complexity of the problem, universities all over the world solve ETPs several times each year. Two known methods for solving ETPs is using either heuristics or Hopfield Neural Networks (HNN). This thesis compares the performance of a heuristic algorithm implemented with Local Search, Simulated Annealing and Tabu Search to the performance of a HNN algorithm. Both algorithms were executed on ten different ETPs reduced to Graph Colouring Problems (GCP). The results show that the heuristic algorithm always generated more satisfactory solutions to the ETPs than the HNN. The HNN was, however, implemented as software in this thesis. It is intended to be implemented as hardware and if this method were to have been used instead the HNN algorithm might have produced other results. At this stage the heuristic algorithm is more suitable than the HNN algorithm for solving ETPs. Schemaläggningsproblem för examinationer (ETP) syftar på problemet att schemalägga ett antal examinationer under ett bestämt tidsintervall så att ingen student behöver närvara på flera examinationer samtidigt. Det är ett komplext problem som universitet världen över behöver lösa flera gånger per år. Två kända metoder för att lösa ETPs är användning av antingen heuristiker eller Hopfield Neural Networks (HNN). Den här uppsatsen jämför prestandan av en heuristik implementerad med Lokal Sökning, Simulerad Härdning och Tabusökning med presentandan av en HNN-algoritm då båda metoderna exekveras på tio ETPs reducerade till Graffärgningsproblem (GCP). Resultaten visar att heuristiken alltid genererade mer tillfredsställande lösningar till schemaläggningsproblemen än HNN-algoritmen gjorde. HNN-algoritmen, som egentligen bör implementeras som hårdvara, implementerades dock som mjukvara i den här avhandlingen. Hade den implementerats som hårdvara istället hade kanske andra resultat producerats. För tillfället lämpar sig heuristiken bättre än HNN-algoritmen för att lösa ETPs.

QC 20210525

Surface and coatings technology 473, 130020 (2023). doi:10.1016/j.surfcoat.2023.130020 Published by Elsevier Science, Amsterdam [u.a.]

Abstract This paper describes a measurement of light-by-light scattering based on Pb+Pb collision data recorded by the ATLAS experiment during Run 2 of the LHC. The study uses 2.2 nb−1 of integrated luminosity collected in 2015 and 2018 at $$ \sqrt{s_{\mathrm{NN}}} $$ s NN = 5.02 TeV. Light-by-light scattering candidates are selected in events with two photons produced exclusively, each with transverse energy $$ {E}_{\mathrm{T}}^{\gamma } $$ E T γ > 2.5 GeV, pseudorapidity |ηγ| < 2.37, diphoton invariant mass mγγ> 5 GeV, and with small diphoton transverse momentum and diphoton acoplanarity. The integrated and differential fiducial cross sections are measured and compared with theoretical predictions. The diphoton invariant mass distribution is used to set limits on the production of axion-like particles. This result provides the most stringent limits to date on axion-like particle production for masses in the range 6–100 GeV. Cross sections above 2 to 70 nb are excluded at the 95% CL in that mass interval.