Firm Performance, Worker Commitment and Loyalty

Using matched employer-employee level data drawn from the UK Workplace and Employee Relations Survey, we explore the influence of worker commitment and loyalty on firm level labour productivity and financial performance. Our empirical findings suggest that worker commitment and loyalty enhance both labour productivity and financial performance at the firm level thereby highlighting a hitherto neglected conduit for improved firm performance. Using employee level data, we also explore the determinants of worker commitment and loyalty in order to ascertain how such attachments to the firm may be engendered. In general, our employee level analysis suggests that it is firm level characteristics (such as appraisal schemes, supervision, suspensions and redundancies) that influence attachments to the firm. Such findings suggest that firms may be able to exert some influence over the loyalty and commitment of its workforce, which, in turn, may affect firm performance

A First Comparison of SLOPE and Other LIGO Burst Event Trigger Generators

A number of different methods have been proposed to identify unanticipated burst sources of gravitational waves in data arising from LIGO and other gravitational wave detectors. When confronted with such a wide variety of methods one is moved to ask if they are all necessary, i.e. given detector data that is assumed to have no gravitational wave signals present, do they generally identify the same events with the same efficiency, or do they each 'see' different things in the detector? Here we consider three different methods, which have been used within the LIGO Scientific Collaboration as part of its search for unanticipated gravitational wave bursts. We find that each of these three different methods developed for identifying candidate gravitational wave burst sources are, in fact, attuned to significantly different features in detector data, suggesting that they may provide largely independent lists of candidate gravitational wave burst events.Comment: 10 Pages, 5 Figures, Presented at the 10th Gravitational Wave Data Analysis Workshop (GWDAW-10), 14-17 December 2005 at the University of Texas, Brownsvill

Notes on Fleshy Fungi in Iowa. II

Sixty-four sporocarps of fleshy fungi were collected during the summer and fall of 1963. In the collections were seven species, Cortinarius adustus Pk., Cortinarius albidipes Pk., Cortinarius praepallens Pk., Hygrophorus sordidus Pk., Russula aeruginea Lindb. (non Fr.), Russula pulverulenta Pk., and Russula sororia Fr. not previously reported for the state of Iowa

Notes on Fleshy Fungi in lowa

Eighty-two sporocarps of fleshy fungi were collected during the summer of 1962. In the collections were seven species. Peziza sylvestris (Bond.) Sacc. & Trott., Cortinarius uraceus Fr., Clitopilus subvilis Pk., Inocybe radiata Pk., Inocybe geophylla Fr., Lactarius hysginus Fr., Mycena subcaerulea (Pk.) Sacc., and one genus, Nolanea Fr., not previously reported for the state of Iowa

Multi-Objective Big Data Optimization with jMetal and Spark

Big Data Optimization is the term used to refer to optimization problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster computing system for solving multi-objective Big Data Optimization problems. Our purpose is to study the influence of accessing data stored in the Hadoop File System (HDFS) in each evaluation step of a metaheuristic and to provide a software tool to solve these kinds of problems. This tool combines the jMetal multi-objective optimization framework with Apache Spark. We have carried out experiments to measure the performance of the proposed parallel infrastructure in an environment based on virtual machines in a local cluster comprising up to 100 cores. We obtained interesting results for computational e ort and propose guidelines to face multi-objective Big Data Optimization problems.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Overview of the BlockNormal Event Trigger Generator

In the search for unmodeled gravitational wave bursts, there are a variety of methods that have been proposed to generate candidate events from time series data. Block Normal is a method of identifying candidate events by searching for places in the data stream where the characteristic statistics of the data change. These change-points divide the data into blocks in which the characteristics of the block are stationary. Blocks in which these characteristics are inconsistent with the long term characteristic statistics are marked as Event-Triggers which can then be investigated by a more computationally demanding multi-detector analysis.Comment: GWDAW-8 proceedings, 6 pages, 2 figure

Notes on Fleshy Fungi in Iowa. III

Ninety-nine sporocarps of fleshy fungi were collected during the summer and fall of 1964. In the collections were six species, Boletus piperatus Bull., Clitopilus subplanus Pk., Collybia strictipes Pk., Cortinarius duracinus Fr., Cortinarius imbutus Fr., and Tricholoma acre Pk. not previously reported for the State of Iowa

The acheulean handaxe : More like a bird's song than a beatles' tune?

© 2016 Wiley Periodicals, Inc. KV is supported by the Netherlands Organization for Scientific Research. MC is supported by the Canada Research Chairs Program, the Social Sciences and Humanities Research of Canada, the Canada Foundation for Innovation, the British Columbia Knowledge Development Fund, and Simon Fraser UniversityPeer reviewedPublisher PD

Grasses as Biofactories: Scoping out the Opportunities

Key points 1. Plant biopharming is set to dominate commercial recombinant protein expression for specific proteins. 2. The choice of plant species depends on a multitude of factors and is determined on a caseby- case basis. 3. As a leaf based expression system grasses would have to compete predominantly with tobacco and alfalfa. 4. The grass-endophyte symbiosis offers a number of unique possibilities for biopharming

Grasses as Biofactories: Scoping out the Opportunities

Plant biopharming is set to dominate commercial recombinant protein expression for specific proteins. The choice of plant species depends on a multitude of factors and is determined on a case- by-case basis. As a leaf based expression system grasses would have to compete predominantly with tobacco and alfalfa. The grass-endophyte symbiosis offers a number of unique possibilities for biopharming