What do Experts Know About Ranking Journal Quality? A Comparison with ISI Research Impact in Finance

Experts possess knowledge and information that are not publicly available. The paper is concerned with the ranking of academic journal quality and research impact using a survey of experts from a national project on ranking academic finance journals. A comparison is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Science citations database (hereafter ISI) for the Business - Finance category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to answer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PI-BETA (Papers Ignored - By Even The Authors), 2-year Self-citation Threshold Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are analysed for 21 highly-cited journals in Finance. Harmonic mean rankings of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A simple regression model is used to predict expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative information about a journal.IFI;PI-BETA;STAR;article influence;eigenfactor;h-index;C3PO;impact factor;research assessment measures;C81;C83;C18;expert scores;journal quality

What do Experts Know About Ranking Journal Quality? A Comparison with ISI Research Impact in Finance

Experts possess knowledge and information that are not publicly available. The paper is concerned with the ranking of academic journal quality and research impact using a survey of experts from a national project on ranking academic finance journals. A comparison is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Science citations database (hereafter ISI) for the Business - Finance category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to answer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PIBETA (Papers Ignored - By Even The Authors), 2-year Self-citation Threshold Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are analysed for 21 highly-cited journals in Finance. Harmonic mean rankings of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A simple regression model is used to predict expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative information about a journal.Expert scores; Journal quality; Research assessment measures; Impact factor; IFI; C3PO; PI-BETA; STAR; Eigenfactor; Article Influence; h-index

RAMS-forecasts comparison of typical summer atmospheric conditions over the Western Mediterranean coast

The Regional Atmospheric Modeling System (RAMS) has been used in order to perform a high-resolution numerical simulation of two meteorological events related to the most common atmospheric environments during the summer over the Western Mediterranean coast: mesoscale circulations and western synoptic advections. In this regard, we take advantage of the operational RAMS configuration running within the real-time forecasting system environment already implemented over this Mediterranean area, precisely in the Valencia Region and nearby areas. The attention of this paper is especially focused on identifying the main features of both events and the ability of the model in resolving the associated characteristics as well as in performing a comprehensive evaluation of the model by means of diverse meteorological observations available within the selected periods over the area of study. Additionally, as this paper is centred in RAMS-based forecasts, two simulations are operated applying the most two recent versions of the RAMS model implemented in the above-mentioned system: RAMS 4.4 and RAMS 6.0. Therefore, a comparison among both versions of the model has been performed as well. Finally, it is our intention to contrast the RAMS forecasts for two completely different atmospheric conditions common with the area of study in the summer. A main difference between the simulation of both meteorological situations has been found in the humidity. In this sense, whilst the model underestimates this magnitude considering the mesoscale event, especially at night time, the model reproduces the daily humidity properly under the western synoptic advection

The performance of RAMS in representing the convective boundary layer structure in a very steep valley

Data from a comprehensive field study in the Riviera Valley of Southern Switzerland are used to investigate convective boundary layer structure in a steep valley and to evaluate wind and temperature fields, convective boundary layer height, and surface sensible heat fluxes as predicted by the mesoscale model RAMS. Current parameterizations of surface and boundary layer processes in RAMS, as well as in other mesoscale models, are based on scaling laws strictly valid only for flat topography and uniform land cover. Model evaluation is required to investigate whether this limits the applicability of RAMS in steep, inhomogeneous terrain. One clear-sky day with light synoptic winds is selected from the field study. Observed temperature structure across and along the valley is nearly homogeneous while wind structure is complex with a wind speed maximum on one side of the valley. Upvalley flows are not purely thermally driven and mechanical effects near the valley entrance also affect the wind structure. RAMS captured many of the observed boundary layer characteristics within the steep valley. The wind field, temperature structure, and convective boundary layer height in the valley are qualitatively simulated by RAMS, but the horizontal temperature structure across and along the valley is less homogeneous in the model than in the observations. The model reproduced the observed net radiation, except around sunset and sunrise when RAMS does not take into account the shadows cast by the surrounding topography. The observed sensible heat fluxes fall within the range of simulated values at grid points surrounding the measurement sites. Some of the scatter between observed and simulated turbulent sensible heat fluxes are due to sub-grid scale effects related to local topograph

A reduction in long-term spatial memory persists after discontinuation of peripubertal GnRH agonist treatment in sheep

Chronic gonadotropin-releasing hormone agonist (GnRHa) administration is used where suppression of hypothalamic-pituitary-gonadal axis activity is beneficial, such as steroid-dependent cancers, early onset gender dysphoria, central precocious puberty and as a reversible contraceptive in veterinary medicine. GnRH receptors, however, are expressed outside the reproductive axis, e.g. brain areas such as the hippocampus which is crucial for learning and memory processes. Previous work, using an ovine model, has demonstrated that long-term spatial memory is reduced in adult rams (45 weeks of age), following peripubertal blockade of GnRH signaling (GnRHa: goserelin acetate), and this was independent of the associated loss of gonadal steroid signaling. The current study investigated whether this effect is reversed after discontinuation of GnRHa-treatment. The results demonstrate that peripubertal GnRHa-treatment suppressed reproductive function in rams, which was restored after cessation of GnRHa-treatment at 44 weeks of age, as indicated by similar testes size (relative to body weight) in both GnRHa-Recovery and Control rams at 81 weeks of age. Rams in which GnRHa-treatment was discontinued (GnRHa-Recovery) had comparable spatial maze traverse times to Controls, during spatial orientation and learning assessments at 85 and 99 weeks of age. Former GnRHa-treatment altered how quickly the rams progressed beyond a specific point in the spatial maze at 83 and 99 weeks of age, and the direction of this effect depended on gonadal steroid exposure, i.e. GnRHa-Recovery rams progressed quicker during breeding season and slower during non-breeding season, compared to Controls. The long-term spatial memory performance of GnRHa-Recovery rams remained reduced (P < 0.05, 1.5-fold slower) after discontinuation of GnRHa, compared to Controls. This result suggests that the time at which puberty normally occurs may represent a critical period of hippocampal plasticity. Perturbing normal hippocampal formation in this peripubertal period may also have long lasting effects on other brain areas and aspects of cognitive function

Mass conservation above slopes in the regional atmospheric modelling system (RAMS)

This paper examines the mass balance in calculations with the Regional Atmospheric Modelling System (RAMS). An error is pointed out that concerns the calculation of the surface fluxes on slopes. This error affects all the prognostic variables in RAMS when sloping terrain is involved. Here we explain how the error can be corrected. To study the impact of the error, we compared simulations with the uncorrected and corrected model. The model contains C

The Cost of Address Translation

Modern computers are not random access machines (RAMs). They have a memory hierarchy, multiple cores, and virtual memory. In this paper, we address the computational cost of address translation in virtual memory. Starting point for our work is the observation that the analysis of some simple algorithms (random scan of an array, binary search, heapsort) in either the RAM model or the EM model (external memory model) does not correctly predict growth rates of actual running times. We propose the VAT model (virtual address translation) to account for the cost of address translations and analyze the algorithms mentioned above and others in the model. The predictions agree with the measurements. We also analyze the VAT-cost of cache-oblivious algorithms.Comment: A extended abstract of this paper was published in the proceedings of ALENEX13, New Orleans, US

Development of Ambient PM 2.5 Management Strategies

INE/AUTC 11.2

Smart railroad maintenance engineering with stochastic model checking

RAMS (reliability, availability, maintenance and safety) requirements are of utmost important for safety-critical systems like railroad infrastructure and signaling systems. Fault tree analysis (FTA) is a widely applied industry standard for RAMS analysis and is often one of the techniques preferred by railways organizations. FTA yields system availability and reliability, and can be used for critical path analysis. It can however not yet deal with a pressing aspect of railroad engineering: maintenance. While railroad infrastructure providers are focusing more and more on managing cost/performance ratios, RAMS can be considered as the performance specification, and maintenance the main cost driver. Methods facilitating the management of this ratio are still very uncommon. This paper presents a powerful, flexible and transparent technique to incorporate maintenance aspects in fault tree analysis, based on stochastic model checking. The analysis and comparison of different maintenance strategies (such as age-based, clockbased and condition-dependent maintenance) and their impact on reliability and availability metrics are thus enabled. Thus, the trade off between cost and RAMS performance is facilitated. To keep the underlying state space small, two aggressive state space reduction techniques are employed namely: compositional aggregation and smart semantics. The approach presented is illustrated using several existing, large fault tree models in a case study from Movares, a major RAMS consultancy firm in the Netherlands