DEVELOPMENT OF THE UTILIZATION OF PASTORAL LAND IN THE EU 25 AFTER 2003

Farming systems based on products of roughage feeders are a vital part of the EU agricultural production. Up to the CAP reform in 2003 the EU promoted these systems via a wide variety of measures. This paper highlights the different impacts European, national and regional support measures have on the utilization of pastoral resources across the EU-25. Based on an expert survey and a literature review the paper summarizes the expected developments of pastoral farming systems across the EU.Pastoral Systems, EU, 2003 CAP Reform, Enlargement, Land Economics/Use,

GCN-based reinforcement learning approach for scheduling DAG applications

Applications in various fields such as embedded systems or High-Performance-Computing are often represented as Directed Acyclic Graphs (DAG), also known as taskgraphs. DAGs represent the data flow between tasks in an application and can be used for scheduling. When scheduling taskgraphs, a scheduler needs to decide when and on which core each task is executed, while minimising the runtime of the schedule.This paper explores offline scheduling of dependent tasks using a Reinforcement Learning (RL) approach. We propose two RL schedulers, one using a Fully Connected Network (FCN) and another one using a Graph Convolutional Network (GCN). First, we detail the different components of our two RL schedulers and illustrate how they schedule a task. Then, we compare our RL schedulers to a Forward List Scheduling (FLS) approach based on two different datasets. We demonstrate that our GCN-based scheduler produces schedules that are as good or better than the schedules produced by the FLS approach in over 85% of the cases for a dataset with small taskgraphs. The same scheduler performs very similar to the FLS scheduler (at most 5% degradation) in almost 76% of the cases for a more challenging dataset

The Proposed 1972 Constitution for the State of Montana

This paid advertising supplement includes the text of the proposed 1972 constitution for the State of Montana as well as introduction and an article-by-article analysis. The supplement was distributed in several Montana newspapers.https://scholarworks.umt.edu/montanaconstitution/1047/thumbnail.jp

Condition-specific outcome measures for low back pain: Part I: Validation

A literature review of the nine most widely used, condition-specific, self-administered assessment questionnaires for low back pain has been undertaken. General and historic aspects, reliability, responsiveness and minimum clinically important difference, external validity, floor and ceiling effects and available languages were analysed for the nine most-used outcome tools. When considering which condition-specific measure to employ, the present overview on assessment tools should provide the necessary information to define the technical aspects of the nine questionnaires. These criteria, however, are only part of the consideration. In part II the construction of these scales in relationship to the measurement domains will be evaluate

Condition-specific outcome measures for low back pain: Part II: Scale construction

A literature review of the most widely used, condition-specific, self-administered assessment questionnaires for low back pain has been undertaken. In part I, technical issues such as validity, reliability, availability and comparability were analyzed for the nine most widely used outcome tools. This second part focuses on the content and wording of questions and answers in each of the nine questionnaires, and an analysis of the different score results is performed. The issue of score bias is discussed and suggestions are given in order to increase the construct validity in the practical use of the individual questionnaire

Global Positioning System (GPS) Precipitable Water in Forecasting Lightning at Spaceport Canaveral

This paper evaluates the use of precipitable water (PW) from Global Positioning System (GPS) in lightning prediction. Additional independent verification of an earlier model is performed. This earlier model used binary logistic regression with the following four predictor variables optimally selected from a candidate list of 23 candidate predictors: the current precipitable water value for a given time of the day, the change in GPS-PW over the past 9 hours, the KIndex, and the electric field mill value. This earlier model was not optimized for any specific forecast interval, but showed promise for 6 hour and 1.5 hour forecasts. Two new models were developed and verified. These new models were optimized for two operationally significant forecast intervals. The first model was optimized for the 0.5 hour lightning advisories issued by the 45th Weather Squadron. An additional 1.5 hours was allowed for sensor dwell, communication, calculation, analysis, and advisory decision by the forecaster. Therefore the 0.5 hour advisory model became a 2 hour forecast model for lightning within the 45th Weather Squadron advisory areas. The second model was optimized for major ground processing operations supported by the 45th Weather Squadron, which can require lightning forecasts with a lead-time of up to 7.5 hours. Using the same 1.5 lag as in the other new model, this became a 9 hour forecast model for lightning within 37 km (20 NM)) of the 45th Weather Squadron advisory areas. The two new models were built using binary logistic regression from a list of 26 candidate predictor variables: the current GPS-PW value, the change of GPS-PW over 0.5 hour increments from 0.5 to 12 hours, and the K-index. The new 2 hour model found the following for predictors to be statistically significant, listed in decreasing order of contribution to the forecast: the 0.5 hour change in GPS-PW, the 7.5 hour change in GPS-PW, the current GPS-PW value, and the KIndex. The new 9 hour forecast model found the following five independent variables to be statistically significant, listed in decreasing order of contribution to the forecast: the current GPSPW value, the 8.5 hour change in GPS-PW, the 3.5 hour change in GPS-PW, the 12 hour change in GPS-PW, and the K-Index. In both models, the GPS-PW parameters had better correlation to the lightning forecast than the K-Index, a widely used thunderstorm index. Possible future improvements to this study are discussed