The Large Scale Structure in the Universe: From Power-Laws to Acoustic Peaks

The most popular tools for analysing the large scale distribution of galaxies are second-order spatial statistics such as the two-point correlation function or its Fourier transform, the power spectrum. In this review, we explain how our knowledge of cosmic structures, encapsulated by these statistical descriptors, has evolved since their first use when applied on the early galaxy catalogues to the present generation of wide and deep redshift surveys, incorporating the most challenging discovery in the study of the galaxy distribution: the detection of Baryon Acoustic Oscillations.Comment: 20 pages, 12 figures, to appear in "Data Analysis in Cosmology", Lecture Notes in Physics, 2008, eds. V. J. Martinez, E. Saar, E. Martinez-Gonzalez, and M.J. Pons-Borderia, Springer-Verla

Observations of the High Redshift Universe

(Abridged) In these lectures aimed for non-specialists, I review progress in understanding how galaxies form and evolve. Both the star formation history and assembly of stellar mass can be empirically traced from redshifts z~6 to the present, but how the various distant populations inter-relate and how stellar assembly is regulated by feedback and environmental processes remains unclear. I also discuss how these studies are being extended to locate and characterize the earlier sources beyond z~6. Did early star-forming galaxies contribute significantly to the reionization process and over what period did this occur? Neither theory nor observations are well-developed in this frontier topic but the first results presented here provide important guidance on how we will use more powerful future facilities.Comment: To appear in `First Light in Universe', Saas-Fee Advanced Course 36, Swiss Soc. Astrophys. Astron. in press. 115 pages, 64 figures (see http://www.astro.caltech.edu/~rse/saas-fee.pdf for hi-res figs.) For lecture ppt files see http://obswww.unige.ch/saas-fee/preannouncement/course_pres/overview_f.htm

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Predicting UHMWPE wear: decreasing wear rate following a change in sliding direction

The patient demographic for total joint replacement is becoming younger and more active leading to increased performance expectations [1]. To develop new products that meet these demands, computer simulations that predict wear are emerging as potential design tools [2]. The algorithms for these simulations typically assume a linear relationship between wear and sliding distance [3-5] and therefore do not account for the possibility of a variable wear rate along a path (Fig 1). In a previous study we demonstrated that the wear of crosslinked polyethylene did not depend on the sliding distance, rather on the number of crossing events (ie changes in direction) [6], implying that large portions of sliding did not contribute to the total wear. We were unable, however, to identify the transition from high wearing multidirectional sliding to the low wearing unidirectional sliding (Fig 1). The purpose of this study was to further investigate this transition by examining a broader range of sliding distances. We hypothesized that small increases in sliding distance after a direction change would produce additional cumulative wear while no additional wear would be produced at longer sliding distances sufficient to reestablish ‘unidirectional’ sliding

Cross shear, contact pressure and contact area in a simplified TKR wear simulation

Current wear algorithms, which are functions of contact kinematics (ie cross shear (CS)) and contact pressure (CP), predict wear in total knee replacement (TKR) with moderate success. Recent pin-on-disk experiments, however, have demonstrated a dependence of wear on CS and contact area (CA), but not CP. When the CP term is removed from wear algorithms, their predictive power is unaffected. To elucidate the relative contributions of CP, CS, and CA in TKR we performed a wear simulation on flat tibial inserts under two values of maximum load and two levels of IE rotation. In this simplified model, we hypothesized that wear would depend strongly on CA and CS but not C

Predicting implant UHMWPE wear in-silico: A robust, adaptable computational-numerical framework for future theoretical models

Computational methods for the pre-clinical wear prediction for devices such as hip, knee or spinal implants are valuable both to industry and academia. Archard’s wear model laid the basis for the first generation of theoretical wear estimation algorithms, and this has been adapted to account for the importance of multi-directional sliding. These second-generation cross-shear algorithms are useful, but they leave room for improvement.In this paper, we outline a generalised framework for a ‘third generation’ wear model. The essential feature of this proposed approach is that it removes the acausality and scale-independence of current second-generation algorithms. The methodology is presented in such a way that any existing second-generation model could be adapted using this framework. Using this approach, the predictive power against pin-on-disc and implant tests is shown to be improved; however, the model is still essentially a purely adhesive-abrasive wear predictor, accounting for only a limited number of factors as part of the tribological process. Further ongoing work is needed to expand and improve upon the current capabilities of in-silico UHMWPE wear prediction capabilities.<br/

Targeted computational probabilistic corroboration of experimental knee wear simulator: the importance of accounting for variability

Experimental testing is widely used to predict wear of total knee replacement (TKR) devices. Computational models cannot replace this essential in vitro testing, but they do have complementary strengths and capabilities, which make in silico models a valuable support tool for experimental wear investigations. For effective exploitation, these two separate domains should be closely corroborated together; this requires extensive data-sharing and cross-checking at every stage of simulation and testing.However, isolated deterministic corroborations provide only a partial perspective; in vitro testing is inherently variable, and relatively small changes in the environmental and kinematic conditions at the articulating interface can account for considerable variation in the reported wear rates. Understanding these variations will be key to managing uncertainty in the tests, resulting in a ‘cleaner’ investigation environment for further refining current theories of wear.This study demonstrates the value of probabilistic in silico methods by describing a specific, targeted corroboration of the AMTI knee wear simulator, using rigid body dynamics software models. A deterministic model of the simulator under displacement-control was created for investigation. Firstly, a large sample of experimental data (N &gt; 100) was collated, and a probabilistic computational study (N &gt; 1000 trials) was used to compare the kinetic performance envelopes for in vitro and in silico models, to more fully corroborate the mechanical model. Secondly, corresponding theoretical wear-rate predictions were compared to the experimentally reported wear data, to assess the robustness of current wear theories to uncertainty (as distinct from the mechanical variability).The results reveal a good corroboration for the physical mechanics of the wear test rig; however they demonstrate that the distributions for wear are not currently well-predicted. The probabilistic domain is found to be far more sensitive at distinguishing between different wear theories. As such we recommend that in future, researchers move towards probabilistic studies as a preferred framework for investigations into implant wear.<br/