Spatial dynamics of picosecond CO sub 2 laser pulses produced by optical switching in Ge

The design, test and optimization of a picosecond CO{sub 2} pulse-forming system are presented. The system switches a semiconductor's optical characteristics at 10 {mu}m under the control of a synchronized 1.06-{mu}m Nd:YAG picosecond laser pulse. An energy-efficient version of such a system using collimated beams is described. A simple, semi-empirical approach is used to simulate the switching process, specifically including the spatial distributions of the laser energy and phase, which are relevant for experiments in laser-driven electron acceleration. 11 refs., 7 figs

MARKAL-MACRO: A linked model for energy-economy analysis

MARKAL-MACRO is an experiment in model linkage for energy and economy analysis. This new tool is intended as an improvement over existing methods for energy strategy assessment. It is designed specifically for estimating the costs and analyzing the technologies proposed for reducing environmental risks such as global climate change or regional air pollution. The greenhouse gas debate illustrates the usefulness of linked energy-economy models. A central issue is the coupling between economic growth, the level of energy demands, and the development of an energy system to supply these demands. The debate is often connected with alternative modeling approaches. The competing philosophies may be labeled top-down macroeconomic'' and bottom-up engineering'' perspectives. MARKAL is a systems engineering (physical process) analysis built on the concept of a Reference Energy System (RES). MARKAL is solved by means of dynamic linear programming. In most applications, the end use demands are fixed, and an economically efficient solution is obtained by minimizing the present value of energy system's costs throughout the planning horizon. MACRO is a macroeconomic model with an aggregated view of long-term economic growth. The basis input factors of production are capital, labor and individual forms of energy. MACRO is solved by nonlinear optimization

MARKAL-MACRO: An overview

MARKAL-MACRO is an experiment in model linkage. This new tool is intended as an improvement over existing methods for energy policy assessment. It is designed specifically for estimating the costs and analyzing alternative technologies and policies proposed for reducing environmental risks such as global climate change or regional air pollution. The greenhouse gas debate illustrates the usefulness of linked energy-economy models. A central issue is the coupling between economic growth, the level of energy demands, and the evolution of an energy system to supply these demands. The debate is often connected with alternative modeling approaches. The competing philosophies may be labeled [open quotes]top-down macroeconomic[close quotes] and [open quotes]bottom-up engineering[close quotes] perspectives. Do macroeconomic models, with their descriptions of effects within the total economy but few technical details on the energy system, tend to overestimate future energy demands Conversely, do engineering models, ignoring feedbacks to the general economy and non-technical market factors but containing rich descriptions of technology options, tend to take too optimistic a view of conservation and the use of renewable energy sources Or is the principal difference that the engineering models ignore new sources of energy demands, and that the macroeconomic models ignore saturation effects for old categories of demands An efficient modeling tool must have the scope and detail to match the width and depth of the policy problem being analyzed. In order to respond to major environmental risks (e.g., the possibility of global climate changes), there must be long-range, fundamental changes in the energy system. For an analysis of these changes and an understanding of their nature, the modeling tool must be able to capture the complex network of relations within the energy system, as well as the opportunities of new or improved technologies

Orexin-A activates locus coeruleus cell firing and increases arousal in the rat

Original article can be found at: http://www.pnas.org/ Copyright by The National Academy of Sciences [Full text of this article is not available in the UHRA]Peer reviewe

The search for novelty continues for rewilding

We agree wholeheartedly with Derham et al. that the term rewilding requires explicit explanation, and that the refinement of new terms is fundamental to scientific advancement – hence our determined, but ultimately unsuccessful, attempt to identify the unique elements of rewilding that distinguish it from restoration (Hayward et al., 2019). We fail to understand why Derham et al. claim that scientific progress would grind to a halt if all definitions were concrete, complete and universally accepted. There are many definitions of scientific terms that similarly require refinement, and these improve our understanding of processes and theories, rather than hinder scientific progress through confusion. Indeed, we highlighted the problems associated with poorly defined language that led to the creation of clearly defined terms in the reintroduction and statistical fields (Hayward et al., 2019). Yet Derham et al.' reference two more definitions of rewilding (in Jepson's (2019) optimistic narrative and Corlett's (2016) proposal to ignore historical states) that, coupled with the Australian version of rewilding that emphasises small mammals in fenced, urban areas (Sweeney et al., in press), just increase the degree of confusion about what is unique about rewilding compared to restoration. This is particularly true when these versions reference existing definitions that are explicitly linked to restoration. For example, Dietl et al. (2015) use rewilding, under the umbrella of restoration, for reconstructing current ecosystems using the fossil record and extinct species replacements, potentially leading to the phrase Pleistocene rewilding restoration, where restoration would suffice.http://www.elsevier.com/locate/biocon2020-08-01hj2019Mammal Research InstituteZoology and Entomolog

Modeling approaches to the indirect estimation of migration flows: From entropy to EM

The paper presents probability models to recover information on migration flows from incomplete data. Models are used to predict migration and to combine data from different sources. The parameters of the model are estimated from the data by the maximum likelihood method. If data are incomplete, an extension of the maximum likelihood method, the EM algorithm, may be applied. Two models are considered: the binomial (multinomial) model, which underlies the logit model and the logistic regression, and the Poisson model, which underlies the loglinear model, the log-rate model and the Poisson regression. The binomial model is viewed in relation to the Poisson model. By way of illustration, the probabilistic approach and the EM algorithm are applied to two different missing data problems. The first problem is the prediction of migration flows using spatial interaction models. The probabilistic approach is compared to conventional methods, such as the gravity model and entropy maximization. In fact, spatial interaction models are particular variants of log-linear models. The second problem is one of unobserved heterogeneity. A mixture model is applied to determine the relative sizes of different migrant categories.Migration, Missing data, Probability models, Entropy, Maximum likelihood, EM,