High fidelity numerical simulations of ship and sub-marine hydrodynamics

This paper discusses the use of wall-modeled LES and hybrid RANS-LES models for the prediction of ship and submarine flows. Results from applied cases are discussed to il-lustrate the use of these methods for practical problems as well as the differences between methods. The paper then discusses the underlying theories and assumptions of wall-modeled LES and hybrid RANS-LES models. The focus of this presentation is on wall-modeled LES as these methods are theoretically more well-founded than hybrid RANS-LES models. Re-sults from both canonical and building block flows are then presented and discussed in order to provide a more firm and practical foundation for the recommendations for applied use that are provided in the final concluding remarks section

"Of course I will ..." : The combined effect of certainty and level of expectancies on persistence and performance

The importance of performance expectancies for the prediction of regulation of behavior and actual performance has long been established. Building on theories from the field of social cognition, we suggest that the level of performance expectancies, as well as the certainty of the expectancy, have a joint influence on an individual’s beliefs and behavior. In two studies (one cross sectional using a sample of secondary school students and one longitudinal using a sample of university students) we found that expectancies more strongly predicted persistence, and subsequent performance, the more certain the expectancy was. This pattern was found even if prior performance was controlled, as in Study 2. The data give an indication that it may be useful to include certainty as an additional variable in expectancy models

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria

Axion-like Particles from String Compactifications

We review how axion-like particles (ALPs) naturally emerge in the low-energy effective field theory of string compactifications. We focus on the study of their mass spectrum and couplings, stressing that they depend on the mechanism used to fix the moduli. We present concrete examples where either open or closed string modes behave as QCD axions which do not overproduce cold dark matter. Relativistic ALPs can also be produced by the decay of the lightest modulus which drives reheating. These ALPs contribute to dark radiation and could be detected via axion-photon conversion in astrophysical magnetic fields.Comment: 8 pages; Contributed to the 9th Patras Workshop on Axions, WIMPs and WISPs, Mainz, June 24-28, 201

Rare Flavor Processes in Maximally Natural Supersymmetry

We study CP-conserving rare flavor violating processes in the recently proposed theory of Maximally Natural Supersymmetry (MNSUSY). MNSUSY is an unusual supersymmetric (SUSY) extension of the Standard Model (SM) which, remarkably, is un-tuned at present LHC limits. It employs Scherk-Schwarz breaking of SUSY by boundary conditions upon compactifying an underlying 5-dimensional (5D) theory down to 4D, and is not well-described by softly-broken $\mathcal{N}=1$ SUSY, with much different phenomenology than the Minimal Supersymmetric Standard Model (MSSM) and its variants. The usual CP-conserving SUSY-flavor problem is automatically solved in MNSUSY due to a residual almost exact $U(1)_R$ symmetry, naturally heavy and highly degenerate 1st- and 2nd-generation sfermions, and heavy gauginos and Higgsinos. Depending on the exact implementation of MNSUSY there exist important new sources of flavor violation involving gauge boson Kaluza-Klein (KK) excitations. The spatial localization properties of the matter multiplets, in particular the brane localization of the 3rd generation states, imply KK-parity is broken and {\it tree-level} contributions to flavor changing neutral currents are present in general. Nevertheless, we show that simple variants of the basic MNSUSY model are safe from present flavor constraints arising from kaon and $B$-meson oscillations, the rare decays $B_{s,d} \to \mu^+ \mu^-$, $\mu \to {\bar e}ee$ and $\mu$-$e$ conversion in nuclei. We also briefly discuss some special features of the radiative decays $\mu \to e \gamma$ and ${\bar B}\to X_s \gamma$. Future experiments, especially those concerned with lepton flavor violation, should see deviations from SM predictions unless one of the MNSUSY variants with enhanced flavor symmetries is realized.Comment: 28 pages, 19 figures; references added, typos correcte

Design thinking support: information systems versus reasoning

Numerous attempts have been made to conceive and implement appropriate information systems to support architectural designers in their creative design thinking processes. These information systems aim at providing support in very diverse ways: enabling designers to make diverse kinds of visual representations of a design, enabling them to make complex calculations and simulations which take into account numerous relevant parameters in the design context, providing them with loads of information and knowledge from all over the world, and so forth. Notwithstanding the continued efforts to develop these information systems, they still fail to provide essential support in the core creative activities of architectural designers. In order to understand why an appropriately effective support from information systems is so hard to realize, we started to look into the nature of design thinking and on how reasoning processes are at play in this design thinking. This investigation suggests that creative designing rests on a cyclic combination of abductive, deductive and inductive reasoning processes. Because traditional information systems typically target only one of these reasoning processes at a time, this could explain the limited applicability and usefulness of these systems. As research in information technology is increasingly targeting the combination of these reasoning modes, improvements may be within reach for design thinking support by information systems

Bayesian Learning Models of Pain: A Call to Action

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain