Face-to-face or distance training? Two different approaches to motivate SMEs to learn - an update

In the past, too many government-sponsored initiatives have presented learning resources that have been wasted because the target small business audience has failed to make use of them. This paper explores the issue of offering learning materials to small and medium-sized enterprises (SMEs) in a manner that recognizes their working environment, mode of operation and preferred learning methods. It then outlines methods currently being tested in the UK and Ireland, and indicates preliminary findings. The two methodologies are different in that the UK (LSSB - Learning Support for Small Businesses) programme is aimed at distance learning in primarily small businesses, whilst the Irish (University of Limerick and Limerick City Enterprise Board) programme is aimed at face-to-face learning primarily in micro-enterprises. Preliminary findings are presented

Brexit and Irish Consumers. Quarterly Economic Commentary Special Article, Spring 2018.

Concerns about the impact of Brexit on the Irish economy have tended to focus on the challenges to exporting firms. However, as the UK is a significant source of imports into the Irish economy and there is considerable integration of the retail sectors in both countries, the imposition of tariffs or other increases in trading costs could pass through to increased prices for Irish consumers. This paper examines the contribution of UK imports to overall household expenditure in Ireland and their exposure to tariffs and other cost increases from possible restrictions on trade. Our approach generates an estimate of potential increases in the level of CPI of between 2 per cent and 3.1 per cent. In the estimated scenarios, these increases are the equivalent of between €892 (increase in non-tariff trade costs) and €1,360 (tariffs plus other trade cost increases) in the annual cost of its consumption basket for the average household. This assumes that there is no switching or changes in expenditure patterns in response to the cost increases so gives an upper bound to the cost increase effects. We also find that these effects are very unevenly distributed across households. Households with lower income levels would face considerably higher percentage increases as they tend to consume a higher share of products that would be most affected by increases in tariffs and trade costs

Preparation of pure lithium hexafluoroarsenate Final report

Preparation and analysis of high purity lithium hexafluoroarsenat

Exploring the relationship between self-ethnographic research, critical reflection and critical action learning

This paper explores the challenges involved in doing and writing ethnography and the use-value to an employing organisation. It is a joint paper informed by the researcher and the supervisor perspective. Therefore, we also examine the role and value of ethnography in Business School teaching, specifically autoethnography within a Doctorate of Business Administration (DBA). Our paper contributes to this emerging genre of autoethnographic work as we examine the use value of autoethnography within the context of a DBA. The DBA aims to develop researching professionals and in doing so has focused on research which contributes to the development of professional practice and the development of professional practitioners, (Bareham, Bourner et al. 2000) We argue that by using autoethnography we can level the ground between the ‘high ground’ of the academic community and the ‘swampy lowlands’ of practice; producing knowledge and enabling exchange between communities

Predispositions and the Political Behavior of American Economic Elites: Evidence from Technology Entrepreneurs

Economic elites regularly seek to exert political influence. But what policies do they support? Many accounts implicitly assume economic elites are homogeneous and that increases in their political power will increase inequality. We shed new light on heterogeneity in economic elites' political preferences, arguing that economic elites from an industry can share distinctive preferences due in part to sharing distinctive predispositions. Consequently, how increases in economic elites' influence affect inequality depends on which industry's elites are gaining influence and which policy issues are at stake. We demonstrate our argument with four original surveys, including the two largest political surveys of American economic elites to date: one of technology entrepreneurs—whose influence is burgeoning—and another of campaign donors. We show that technology entrepreneurs support liberal redistributive, social, and globalistic policies but conservative regulatory policies—a bundle of preferences rare among other economic elites. These differences appear to arise partly from their distinctive predispositions

Chromospheric Variability in SDSS M Dwarfs. II. Short-Timescale H-alpha Variability

[Abridged] We present the first comprehensive study of short-timescale chromospheric H-alpha variability in M dwarfs using the individual 15 min spectroscopic exposures for 52,392 objects from the Sloan Digital Sky Survey. Our sample contains about 10^3-10^4 objects per spectral type bin in the range M0-M9, with a total of about 206,000 spectra and a typical number of 3 exposures per object (ranging up to a maximum of 30 exposures). Using this extensive data set we find that about 16% of the sources exhibit H-alpha emission in at least one exposure, and of those about 45% exhibit H-alpha emission in all of the available exposures. Within the sample of objects with H-alpha emission, only 26% are consistent with non-variable emission, independent of spectral type. The H-alpha variability, quantified in terms of the ratio of maximum to minimum H-alpha equivalent width (R_EW), and the ratio of the standard deviation to the mean (sigma_EW/), exhibits a rapid rise from M0 to M5, followed by a plateau and a possible decline in M9 objects. In particular, R_EW increases from a median value of about 1.8 for M0-M3 to about 2.5 for M7-M9, and variability with R_EW>10 is only observed in objects later than M5. For the combined sample we find that the R_EW values follow an exponential distribution with N(R_EW) exp[-(R_EW-1)/2]; for M5-M9 objects the characteristic scale is R_EW-1\approx 2.7, indicative of stronger variability. In addition, we find that objects with persistent H-alpha emission exhibit smaller values of R_EW than those with intermittent H-alpha emission. Based on these results we conclude that H-alpha variability in M dwarfs on timescales of 15 min to 1 hr increases with later spectral type, and that the variability is larger for intermittent sources.Comment: Submitted to ApJ; 20 pages, 15 figure

Why Local Party Leaders Don't Support Nominating Centrists

Would giving party leaders more influence in primary elections in the United States decrease elite polarization? Some scholars have argued that political party leaders tend to support centrist candidates in the hopes of winning general elections. In contrast, the authors argue that many local party leaders - especially Republicans - may not believe that centrists perform better in elections and therefore may not support nominating them. They test this argument using data from an original survey of 1,118 county-level party leaders. In experiments, they find that local party leaders most prefer nominating candidates who are similar to typical co-partisans, not centrists. Moreover, given the choice between a more centrist and more extreme candidate, they strongly prefer extremists: Democrats do so by about 2 to 1 and Republicans by 10 to 1. Likewise, in open-ended questions, Democratic Party leaders are twice as likely to say they look for extreme candidates relative to centrists; Republican Party leaders are five times as likely. Potentially driving these partisan differences, Republican leaders are especially likely to believe that extremists can win general elections and overestimate the electorate's conservatism by double digits

Research support of the WETNET Program

This study examines various aspects of the Microwave Vegetation Index (MVI). MVI is a derived signal created by differencing the spectral response of the 37 GHz horizontally and vertically polarized passive microwave signals. The microwave signal employed to derive this index is thought to be primarily influenced by vegetation structure, vegetation growth, standing water, and precipitation. The state of California is the study site for this research. Imagery from the Special Sensor Microwave/Imager (SSM/I) is used for the creation of MVI datasets analyzed in this research. The object of this research is to determine whether MVI corresponds with some quantifiable vegetation parameter (such as vegetation density) or whether the index is more affected by known biogeophysical parameters such antecedent precipitation. A secondary question associated with the above is whether the vegetation attributes that MVI is employed to determine can be more easily and accurately evaluated by other remote sensing means. An important associated question to be addressed in the study is the effect of different multi-temporal composting techniques on the derived MVI dataset. This work advances our understanding of the fundamental nature of MVI by studying vegetation as a mixture of structural types, such as forest and grassland. The study further advances our understanding by creating multitemporal precipitation datasets to compare the affects of precipitation upon MVI. This work will help to lay the groundwork for the use of passive microwave spectral information either as an adjunct to visible and near infrared imagery in areas where that is feasible or for the use of passive microwave alone in areas of moderate cloud coverage. In this research, an MVI dataset, spanning the period February 15, 1989 through April 25, 1990, has been created using National Aeronautic and Space Administration (NASA) supplied brightness temperature data. Information from the DMSP satellite 37 GHz wavelength SSM/I sensor in both horizontal and vertical polarization has been processed using the MVI algorithm. In conjunction with the MVI algorithm a multitemporal compositing technique was used to create datasets that correspond to 14 day periods. In this technical report, Section Two contains background information on the State of California and the three MVI study sites. Section Three describes the methods used to create the MVI and independent variables datasets. Section Four presents the results of the experiment. Section Five summarizes and concludes the work