The magic carpet: an arbitrary spectrum wave maker for internal waves

Abstract: We present a novel apparatus for generating internal waves of arbitrary size and shape, including both phase-locked and propagating waves. It is an actively driven, flexible “magic carpet” in the base of a tank. Our wave maker is computer-controlled to enable easy configuration. The actuation of a smooth, flexible surface produces clean waveforms with a predictable spectrum, for which we derive a theoretical model. We demonstrate the versatility of our wave maker through an experimental study of linear and nonlinear, isolated, and combined internal waves, including some that are sufficiently nonlinear to break remote from their source. Graphic abstract

New strategies for sustainable fisheries management: A case study of Atlantic salmon

This briefing paper considers the alarming declines in fish stocks in recent years, and how holistic, integrated approaches can help manage fish stocks within biologically sustainable limits. Using Atlantic salmon as a case study, the authors highlight the challenges facing fisheries management and conservation, and the implications for policy and management

Systematic variation in food web body-size structure linked to external subsidies.

The relationship between body mass (M) and size class abundance (N) depicts patterns of community structure and energy flow through food webs. While the general assumption is that M and N scale linearly (on log-log axes), nonlinearity is regularly observed in natural systems, and is theorized to be driven by nonlinear scaling of trophic level (TL) with M resulting in the rapid transfer of energy to consumers in certain size classes. We tested this hypothesis with data from 31 stream food webs. We predicted that allochthonous subsidies higher in the web results in nonlinear M-TL relationships and systematic abundance peaks in macroinvertebrate and fish size classes (latter containing salmonids), that exploit terrestrial plant material and terrestrial invertebrates, respectively. Indeed, both M-N and M-TL significantly deviated from linear relationships and the observed curvature in M-TL scaling was inversely related to that observed in M-N relationships. Systemic peaks in M-N, and troughs in M-TL occurred in size classes dominated by generalist invertebrates, and brown trout. Our study reveals how allochthonous resources entering high in the web systematically shape community size structure and demonstrates the relevance of a generalized metabolic scaling model for understanding patterns of energy transfer in energetically 'open' food webs

Going the extra mile: why clinical research in cystic fibrosis must include children

This is an exciting time for research and novel drug development in cystic fibrosis. However, rarely has the adage, “Children are not just little adults” been more relevant. This article is divided into two main sections. In the first, we explore why it is important to involve children in research. We discuss the potential benefits of understanding a disease and its treatment in children, and we highlight that children have the same legal and ethical right to evidence-based therapy as adults. Additionally, we discuss why extrapolation from adults may be inappropriate, for example, medication pharmacokinetics may be different in children, and there may be unpredictable adverse effects. In the second part, we discuss how to involve children and their families in research. We outline the importance and the complexities of selecting appropriate outcome measures, and we discuss the role co-design may have in improving the involvement of children. We highlight the importance of appropriate staffing and resourcing, and we outline some of the common challenges and possible solutions, including practical tips on obtaining consent/assent in children and adolescents. We conclude that it is unethical to simply rely on extrapolation from adult studies because research in young children is challenging and that research should be seen as a normal part of the paediatric therapeutic journey

Parametric Polyhedra with at least kk Lattice Points: Their Semigroup Structure and the k-Frobenius Problem

Given an integral $d \times n$ matrix $A$, the well-studied affine semigroup \mbox{ Sg} (A)=\{ b : Ax=b, \ x \in {\mathbb Z}^n, x \geq 0\} can be stratified by the number of lattice points inside the parametric polyhedra $P_A(b)=\{x: Ax=b, x\geq0\}$. Such families of parametric polyhedra appear in many areas of combinatorics, convex geometry, algebra and number theory. The key themes of this paper are: (1) A structure theory that characterizes precisely the subset \mbox{ Sg}_{\geq k}(A) of all vectors b \in \mbox{ Sg}(A) such that $P_A(b) \cap {\mathbb Z}^n$ has at least $k$ solutions. We demonstrate that this set is finitely generated, it is a union of translated copies of a semigroup which can be computed explicitly via Hilbert bases computations. Related results can be derived for those right-hand-side vectors $b$ for which $P_A(b) \cap {\mathbb Z}^n$ has exactly $k$ solutions or fewer than $k$ solutions. (2) A computational complexity theory. We show that, when $n$, $k$ are fixed natural numbers, one can compute in polynomial time an encoding of \mbox{ Sg}_{\geq k}(A) as a multivariate generating function, using a short sum of rational functions. As a consequence, one can identify all right-hand-side vectors of bounded norm that have at least $k$ solutions. (3) Applications and computation for the $k$-Frobenius numbers. Using Generating functions we prove that for fixed $n,k$ the $k$-Frobenius number can be computed in polynomial time. This generalizes a well-known result for $k=1$ by R. Kannan. Using some adaptation of dynamic programming we show some practical computations of $k$-Frobenius numbers and their relatives

A Regression Tree Approach using Mathematical Programming

Regression analysis is a machine learning approach that aims to accurately predict the value of continuous output variables from certain independent input variables, via automatic estimation of their latent relationship from data. Tree-based regression models are popular in literature due to their flexibility to model higher order non-linearity and great interpretability. Conventionally, regression tree models are trained in a two-stage procedure, i.e. recursive binary partitioning is employed to produce a tree structure, followed by a pruning process of removing insignificant leaves, with the possibility of assigning multivariate functions to terminal leaves to improve generalisation. This work introduces a novel methodology of node partitioning which, in a single optimisation model, simultaneously performs the two tasks of identifying the break-point of a binary split and assignment of multivariate functions to either leaf, thus leading to an efficient regression tree model. Using six real world benchmark problems, we demonstrate that the proposed method consistently outperforms a number of state-of-the-art regression tree models and methods based on other techniques, with an average improvement of 7–60% on the mean absolute errors (MAE) of the predictions

Effects of dependence in high-dimensional multiple testing problems

<p>Abstract</p> <p>Background</p> <p>We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices.</p> <p>Results</p> <p>We study the robustness against dependence of several FDR procedures that are popular in microarray studies, such as Benjamin-Hochberg FDR, Storey's q-value, SAM and resampling based FDR procedures. False Non-discovery Rates and estimates of the number of null hypotheses are computed from those methods and compared. Our simulation study shows that methods such as SAM and the q-value do not adequately control the FDR to the level claimed under dependence conditions. On the other hand, the adaptive Benjamini-Hochberg procedure seems to be most robust while remaining conservative. Finally, the estimates of the number of true null hypotheses under various dependence conditions are variable.</p> <p>Conclusion</p> <p>We discuss a new method for efficient guided simulation of dependent data, which satisfy imposed network constraints as conditional independence structures. Our simulation set-up allows for a structural study of the effect of dependencies on multiple testing criterions and is useful for testing a potentially new method on <it>π</it>₀or FDR estimation in a dependency context.</p