Bringing export performance metrics into Annual Reports: the APEV scale and the PERFEX scorecard

Annual company reports rarely distinguish between domestic and export market performance and even more rarely provide information about annual indicators of a specific export venture's performance. In this study, the authors develop and test a new measure for assessing the annual performance of an export venture (the APEV scale). The new measure comprises five dimensions: (1) annual export venture financial performance, (2) annual export venture strategic performance, (3) annual export venture achievement, (4) contribution of the export venture to annual exporting operations, and (5) satisfaction with annual export venture overall performance. The authors use the APEV scale to generate a scorecard of performance in exporting (the PERFEX scorecard) to assess export performance at the corporate level while comparatively evaluating all export ventures of the firm. Both the scale and the scorecard could help disclose export venture performance and could be useful instruments for annual planning, management, monitoring, and improvement of exporting programs.

Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics-based schemes with 8 years of L1 observations

Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot.

Perceptual cues for orientation in a two finger haptic grasp task

Single point interaction haptic devices do not provide the natural grasp and manipulations found in the real world, as afforded by multi-fingered haptics. The present study investigates a two-fingered grasp manipulation involving rotation with and without force feedback. There were three visual cue conditions: monocular, binocular and projective lighting. Performance metrics of time and positional accuracy were assessed. The results indicate that adding haptics to an object manipulation task increases the positional accuracy but slightly increases the overall time taken.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource

The estimation of the long-term wind resource at a prospective site based on a relatively short on-site measurement campaign is an indispensable task in the development of a commercial wind farm. The typical industry approach is based on the measure-correlate-predict �MCP� method where a relational model between the site wind velocity data and the data obtained from a suitable reference site is built from concurrent records. In a subsequent step, a long-term prediction for the prospective site is obtained from a combination of the relational model and the historic reference data. In the present paper, a systematic study is presented where three new MCP models, together with two published reference models �a simple linear regression and the variance ratio method�, have been evaluated based on concurrent synthetic wind speed time series for two sites, simulating the prospective and the reference site. The synthetic method has the advantage of generating time series with the desired statistical properties, including Weibull scale and shape factors, required to evaluate the five methods under all plausible conditions. In this work, first a systematic discussion of the statistical fundamentals behind MCP methods is provided and three new models, one based on a nonlinear regression and two �termed kernel methods� derived from the use of conditional probability density functions, are proposed. All models are evaluated by using five metrics under a wide range of values of the correlation coefficient, the Weibull scale, and the Weibull shape factor. Only one of all models, a kernel method based on bivariate Weibull probability functions, is capable of accurately predicting all performance metrics studied.

Benchmarking energy use in licensed restaurants and pubs

Commercial kitchens often leave a large carbon footprint. A new dataset of energy performance metrics from a leading industrial partner is presented. Categorising these types of buildings is challenging. Electricity use has been analysed using data from automated meter readings (AMR) for the purpose of benchmarking and discussed in terms of factors such as size and food output. From the analysed results, consumption is found to be almost double previous sector estimates of 6480 million kWh per year. Recommendations are made to further improve the current benchmarks in order to attain robust, reliable and transparent figures, such as the introduction of normalised performance indicators to include kitchen size (m2) and kWh per thousand-pound turnover.

New insights into the structure of exotic nuclei using the RISING active stopper

This conference paper outlines the operation and some of the preliminary physics results using the GSI RISING active stopper. Data are presented from an experiment using combined isomer and beta‐delayed gamma‐ray spectroscopy to study low‐lying spectral and decay properties of heavy‐neutron‐rich nuclei around A∼190 produced following the relativistic projectile fragmentation of 208Pb primary beam. The response of the RISING active stopper detector is demonstrated for both the implantation of heavy secondary fragments and in‐situ decay of beta‐particles. Beta‐delayed gamma‐ray spectroscopy following decays of the neutron‐rich nucleus 194Re is presented to demonstrate the experimental performance of the set‐up. The resulting information inferred from excited states in the W and Os daughter nuclei is compared with results from Skyrme Hartree‐Fock predictions of the evolution of nuclear shape.

An experimental comparison between rival theories of rapid automatized naming performance and its relationship to reading

Two studies investigated the degree to which the relationship between rapid automatized naming (RAN) performance and reading development is driven by shared phonological processes. Study 1 assessed RAN, phonological awareness, and reading performance in 1010 7- to -10 year-olds. Results showed that RAN deficits occurred in the absence of phonological awareness deficits. These were accompanied by modest reading delays. In structural equation modeling, solutions where RAN was subsumed within a phonological processing factor did not provide a good fit to the data, suggesting that processes outside phonology may drive RAN performance and its association with reading. Study 2 investigated Kail’s proposal that speed of processing underlies this relationship. Children with single RAN deficits showed slower speed of processing than did closely matched controls performing normally on RAN. However, regression analysis revealed that RAN made a unique contribution to reading even after accounting for processing speed. Theoretical implications are discussed.

An experimental comparison between rival theories of rapid automatized naming performance and its relationship to reading

Two studies investigated the degree to which the relationship between Rapid Automatized Naming (RAN) performance and reading development is driven by shared phonological processes. Study 1 assessed RAN, phonological awareness and reading performance in 1010 children aged 7-10 years. Results showed that RAN deficits occurred in the absence of phonological awareness deficits. These were accompanied by modest reading delays. In structural equation modeling, solutions where RAN was subsumed within a phonological processing factor did not provide a good fit to the data, suggesting that processes outside phonology may drive RAN performance and its association with reading. Study 2 investigated Kail's (1991) proposal that speed of processing underlies this relationship. Children with single RAN deficits showed slower speed of processing than closely matched controls performing normally on RAN. However, regression analysis revealed that RAN made a unique contribution to reading even after accounting for processing speed. Theoretical implications are discussed.

The effects of flavonoid and other polyphenol consumption on cognitive performance: A systematic research review of human experimental and epidemiological studies

Literature reviews suggest flavonoids, a sub-class of polyphenols, are beneficial for cognition. This is the first review examining the effect of consumption of all polyphenol groups on cognitive function. Inclusion criteria were polyphenol vs. control interventions and epidemiological studies with an objective measure of cognitive function. Participants were healthy or mildly cognitively impaired adults. Studies were excluded if clinical assessment or diagnosis of Alzheimer’s disease, dementia, or cognitive impairment was the sole measure of cognitive function, or if the polyphenol was present with potentially confounding compounds such as caffeine (e.g. tea studies) or Ginkgo Biloba. 28 studies were identified; 4 berry juice studies, 4 cocoa studies, 13 isoflavone supplement studies, 3 other supplement studies, and 4 epidemiological surveys. Overall, 16 studies reported cognitive benefits following polyphenol consumption. Evidence suggests that consuming additional polyphenols in the diet can lead to cognitive benefits, however, the observed effects were small. Declarative memory and particularly spatial memory appear most sensitive to polyphenol consumption and effects may differ depending on polyphenol source. Polyphenol berry fruit juice consumption was most beneficial for immediate verbal memory, whereas isoflavone based interventions were associated with significant improvements for delayed spatial memory and executive function. Comparison between studies was hampered by methodological inconsistencies. Hence, there was no clear evidence for an association between cognitive outcomes and polyphenol dose response, duration of intervention, or population studied. In conclusion, however, the findings do imply that polyphenol consumption has potential to benefit cognition both acutely and chronically.

Evaluation of two soil carbon models using two Kenyan long term experimental datasets

RothC and Century are two of the most widely used soil organic matter (SOM) models. However there are few examples of specific parameterisation of these models for environmental conditions in East Africa. The aim of this study was therefore, to evaluate the ability of RothC and the Century to estimate changes in soil organic carbon (SOC) resulting from varying land use/management practices for the climate and soil conditions found in Kenya. The study used climate, soils and crop data from a long term experiment (1976-2001) carried out at The Kabete site at The Kenya National Agricultural Research Laboratories (NARL, located in a semi-humid region) and data from a 13 year experiment carried out in Machang'a (Embu District, located in a semi-arid region). The NARL experiment included various fertiliser (0, 60 and 120 kg of N and P2O5 ha(-1)), farmyard manure (FYM - 5 and 10 t ha(-1)) and plant residue treatments, in a variety of combinations. The Machang'a experiment involved a fertiliser (51 kg N ha(-1)) and a FYM (0, 5 and 10 t ha(-1)) treatment with both monocropping and intercropping. At Kabete both models showed a fair to good fit to measured data, although Century simulations for treatments with high levels of FYM were better than those without. At the Machang'a site with monocrops, both models showed a fair to good fit to measured data for all treatments. However, the fit of both models (especially RothC) to measured data for intercropping treatments at Machang'a was much poorer. Further model development for intercrop systems is recommended. Both models can be useful tools in soil C Predictions, provided time series of measured soil C and crop production data are available for validating model performance against local or regional agricultural crops. (C) 2007 Elsevier B.V. All rights reserved.

Negative correlations between control performance and verbalizable knowledge: indicators for implicit learning in process control tasks?

Negative correlations between task performance in dynamic control tasks and verbalizable knowledge, as assessed by a post-task questionnaire, have been interpreted as dissociations that indicate two antagonistic modes of learning, one being “explicit”, the other “implicit”. This paper views the control tasks as finite-state automata and offers an alternative interpretation of these negative correlations. It is argued that “good controllers” observe fewer different state transitions and, consequently, can answer fewer post-task questions about system transitions than can “bad controllers”. Two experiments demonstrate the validity of the argument by showing the predicted negative relationship between control performance and the number of explored state transitions, and the predicted positive relationship between the number of explored state transitions and questionnaire scores. However, the experiments also elucidate important boundary conditions for the critical effects. We discuss the implications of these findings, and of other problems arising from the process control paradigm, for conclusions about implicit versus explicit learning processes.

The effect of supplementing maize stover with cowpea (Vigna unguiculata) haulms on the intake and growth performance of Ethiopian sheep

This study compared the effect of supplementing maize stover (MS) with cowpea (Vigna unguiculata) haulms or commercial concentrate (CC) on feed intake, nutrient digestibility, live weight gain and carcass yield of male Ethiopian Highland sheep. Two cowpea genotypes, 12688 (forage) and IT96D-774 (dual-purpose), were used. A randomised block design was applied with groups of eight sheep, blocked by weight, allocated to one of six treatments; MS ad libitum either unsupplemented or supplemented daily with 150 or 300g dry matter (DM) of either cowpea or CC. MS contained more neutral detergent fibre (NDF), acid detergent fibre (ADF) and lignin than either cowpeas or CC Crude protein (CP) content of the forage-type cowpeas was higher than either dual-purpose or CC, while MS had the lowest CP content Relative to the negative control group, cowpea at either level significantly (P < 0.01) increased both MS intake and total NDF and lignin. Supplementation significantly (P < 0.01) increased nitrogen (N) intakes relative to the negative control, with N intake for CC and dual-purpose cowpea (high level) being similar to the intakes for cowpeas at 150g. N intake with the forage-type cowpea offered at higher levels was significantly (P < 0.01) greater than the other groups. No significant differences (P > 0.01) in MS intake were identified between cowpeas at either level or CC and, although intake level of CC increased, it did not differ significantly from the negative control group. Supplementation significantly (P < 0.01) improved average daily gain, with the negative control group losing weight over the experimental period, and increased final live weight, carcass cold weight and dressing percentage. Supplementation significantly improved the apparent digestibility of DM, organic matter and NDF, with no significant difference found between cowpeas at either level. N retention was negative for sheep offered only MS, but positive with all supplements, with cowpeas improving N retention to a greater extent than CC. Interestingly, N retention/N intake was higher with cowpeas offered at the lower level suggesting an improvement in utilisation efficiency. The results indicate that the supplementation of MS with cowpea enhanced ruminant production through improvements in digestibility and intake. Further, as production improvements associated with the two levels of supplementation did not differ significantly, it is suggested that where limited quantities of cowpea are available, it may be of greater nutritional benefit to offer smaller quantities over an increased number of animal days.