Emotion and performance

The study of emotions in organizational settings has attained considerable prominence in recent years, but I critical issue remains unresolved. This is the relationship between emotion and performance. in this special issue, 5 articles address this topic from a variety of viewpoints. Two are theoretical essays that deal, respectively, with emotion and creativity and the relationships between individual and team performance. Three are empirical studies that canvass the emotion-performance nexus across levels of analysis: within person, between persons, and in groups. Between them, the 5 articles present a strong case for the nexus of emotions and performance, but, more important, they provide a platform for potentially fruitful future research in this burgeoning area.

Emotional Intelligence as a Moderator of Emotional and Behavioral Reactions to Job Insecurity

We present a model linking perceptions of job insecurity to emotional reactions and negative coping behaviors. Our model is based on the idea that emotional variables explain, in part, discrepant findings reported in previous research. In particular, we propose that emotional intelligence moderates employees' emotional reactions to job insecurity and their ability to cope with associated stress. In this respect, low emotional intelligence employees are more likely than high emotional intelligence employees to experience negative emotional reactions to job insecurity and to adopt negative coping strategies.

Emotional awareness and emotional intelligence in leadership teaching

Recent research has highlighted the importance of emotional awareness and emotional intelligence in organizations, and these topics are attracting increasing attention. In this article, the authors present the results of a preliminary classroom study in which emotion concepts were incorporated into an undergraduate leadership course. In the study, students completed self report and ability tests of emotional intelligence. The test results were compared with students' interest in emotions and their performance in the course assessment. Results showed that interest in and knowledge of emotional intelligence predicted team performance, whereas individual performance was related to emotional intelligence.

Innovation as a Meta-Attribute for Graduate Engineers

This paper reviews the attitudes, skills and knowledge that engineering innovators should possess. It critically analyses and compares sets of graduate attributes from the USA, Australia and Malaysia in terms of which of these relate to the ability to innovate. Innovation can be described as an integrative, meta attribute that overarches most of the other graduate attributes. Due to the “graduate attribute paradox”, it is shown how meeting the stated attributes of graduates by industry does not necessarily satisfy the requirements of industry. It is argued that the culture of the engineering school is an important influence on fostering innovation in engineers.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Appropriate surveying methods in the Philippines: Can modern GPS units help?

An investigation was undertaken to test the effectiveness of two procedures for recording boundaries and plot positions for scientific studies on farms on Leyte Island, the Philippines. The accuracy of a Garmin 76 Global Positioning System (GPS) unit and a compass and chain was checked under the same conditions. Tree canopies interfered with the ability of the satellite signal to reach the GPS and therefore the GPS survey was less accurate than the compass and chain survey. Where a high degree of accuracy is required, a compass and chain survey remains the most effective method of surveying land underneath tree canopies, providing operator error is minimised. For a large number of surveys and thus large amounts of data, a GPS is more appropriate than a compass and chain survey because data are easily up-loaded into a Geographic Information System (GIS). However, under dense canopies where satellite signals cannot reach the GPS, it may be necessary to revert to a compass survey or a combination of both methods.

Gypsum solubility in seawater, and its application to bauxite residue amelioration

The solubilities and dissolution rates of three gypsum sources (analytical grade (AG), phosphogypsum (PG) and mined gypsum (MG)) with six MG size fractions ((mm) > 2.0, 1.0-2.0, 0.5-1.0, 0.25-0.5, 0.125-0.25, and < 0.125) were investigated in triple deionised water (TDI) and seawater to examine their suitability for bauxite residue amelioration. Gypsum solubility was greater in seawater (3.8 g L 1) than TDI (2.9 g L 1) due to the ionic strength effect, with dissolution in both TDI and seawater following first order kinetics. Dissolution rate constants varied with gypsum source (AR > PG > MG) due to reactivity and surface area differences, with 1:20 gypsum:solution suspensions reaching saturation within 15 s (AR) to 30 min (MG > 2.0). The ability of bauxite residue to adsorb Ca from solution was also examined. The quantity of the total solution Ca adsorbed was found to be small (5 %). These low rates of solution Ca adsorption combined with the comparatively rapid dissolution rates preclude the application of gypsum to the residue sand/seawater slurry as a method for residue amelioration. Instead, direct field application to the residue would ensure more efficient gypsum use. In addition, the formation of a sparingly soluble CaCO3 coating around the gypsum particles after mixing in a highly alkaline seawater/supernatant liquor (SNL) solution greatly reduced the rate of gypsum dissolution.

Effect of Mn deficiency and legume inoculation on rhizosphere pH in highly alkaline soils

Although plant growth is often limited at high pH, little is known about root-induced changes in the rhizospheres of plants growing in alkaline soils. The effect of Mn deficiency in Rhodes grass (Chloris gayana cv. Pioneer) and of legume inoculation in lucerne (Medicago sativa L. cv. Hunter River), on the rhizosphere pH of plants grown in highly alkaline bauxite residue was investigated. Rhizosphere pH was measured quantitatively, with a micro pH electrode, and qualitatively, with an agar/pH indicator solution. Manganese deficiency in Rhodes grass increased root-induced acidification of the rhizosphere in a soil profile in which N was supplied entirely as NO3-. Rhizosphere pH in the Mn deficient plants was up to 1.22 pH units lower than that of the bulk soil, while only 0.90 to 0.62 pH units lower in plants supplied with adequate Mn. When soil N was supplied entirely as NO3-, rhizosphere acidification was more efficient in inoculated lucerne (1.75 pH unit decrease) than in non-inoculated lucerne (1.16 pH unit decrease). This difference in capacity to lower rhizosphere pH is attributable to the ability of the inoculated lucerne to fix atmospheric N2 rather than relying on the soil N (NO3 ) reserves as the non-inoculated plants. Rhizosphere acidification in both Rhodes grass and lucerne was greatest in the meristematic root zone and least in the maturation root zone.

Working memory in schizophrenia and mania: Acute and subacute phases

Baekground: Patients with schizophrenia tend to have impaired performance on tests of working memory (WM). Neurocognitive models have linked WM to certain symptoms of schizophrenia. This study aimed to assess WM in schizophrenia and mania in the acute and subacute phases of the illness and explore correlations between WM and symptom clusters. Methods: A visuo-spatial delayed response task was used to assess WM in schizophrenia (n=20), mania (n= 14) and well controls (n=20). Patients were tested during the first week of an acute admission, and subjects were retested after four weeks. WM, symptoms (PANSS, TLC) and executive ability (COWAT, Stroop, Trail Making) were assessed at both time points. Results: When assessed for overall WM errors (both sensory and memory), there was a significant group difference (F- 11.53, df 2, 40; p

Improved methods for predicting individual leaf area and leaf senescence in maize (Zea mays)

The ability to predict leaf area and leaf area index is crucial in crop simulation models that predict crop growth and yield. Previous studies have shown existing methods of predicting leaf area to be inadequate when applied to a broad range of cultivars with different numbers of leaves. The objectives of the study were to (i) develop generalised methods of modelling individual and total plant leaf area, and leaf senescence, that do not require constants that are specific to environments and/or genotypes, (ii) re-examine the base, optimum, and maximum temperatures for calculation of thermal time for leaf senescence, and (iii) assess the method of calculation of individual leaf area from leaf length and leaf width in experimental work. Five cultivars of maize differing widely in maturity and adaptation were planted in October 1994 in south-eastern Queensland, and grown under non-limiting conditions of water and plant nutrient supplies. Additional data for maize plants with low total leaf number (12-17) grown at Katumani Research Centre, Kenya, were included to extend the range in the total leaf number per plant. The equation for the modified (slightly skewed) bell curve could be generalised for modelling individual leaf area, as all coefficients in it were related to total leaf number. Use of coefficients for individual genotypes can be avoided, and individual and total plant leaf area can be calculated from total leaf number. A single, logistic equation, relying on maximum plant leaf area and thermal time from emergence, was developed to predict leaf senescence. The base, optimum, and maximum temperatures for calculation of thermal time for leaf senescence were 8, 34, and 40 degrees C, and apply for the whole crop-cycle when used in modelling of leaf senescence. Thus, the modelling of leaf production and senescence is simplified, improved, and generalised. Consequently, the modelling of leaf area index (LAI) and variables that rely on LAI will be improved. For experimental purposes, we found that the calculation of leaf area from leaf length and leaf width remains appropriate, though the relationship differed slightly from previously published equations.

Use of otoliths and eye lenses for measuring trace-metal incorporation in fishes: a biogeographic study

The otoliths and lenses of the temperate damselfish Parma microlepis (Gunther) (Pomacentridae) showed similar differences in trace-metal profile for selected locations along the coast of New South Wales, Australia. Otoliths and lenses displayed a differential ability to accumulate metals. Metal concentrations were ranked differently in the two structures (e.g. Sr > Ba > Pb > Rb > Hg in otoliths, and Hg > Sr similar or equal to Rb > Pb > Ba in lenses), and where similar metals were accumulated, they were accumulated at vastly different concentrations (e.g. Ba concentrations in otoliths are a thousand-fold greater than in lenses). Analyses of the otoliths and lenses of P. microlepis from locations close to Sydney and up to 100 kill from the city were able to distinguish amongst these locations with respect to a number of metals, namely Ba, Mn and Hg. Multivariate analyses of otolith and lens data gave similar results among locations (agreement was obtained for Ii out of 15 pair-wise comparisons), and differences were attributable to the differential ability of the two structures to accumulate metals such as Mn and Hg. Trace-metal differences between locations were found to coincide with the proximity of sewage (including industrial waste) and petroleum storage facilities to the different locations.

Improving wheat simulation capabilities in Australia from a cropping systems perspective - III. The integrated wheat model (I_WHEAT)

Previous work has identified several short-comings in the ability of four spring wheat and one barley model to simulate crop processes and resource utilization. This can have important implications when such models are used within systems models where final soil water and nitrogen conditions of one crop define the starting conditions of the following crop. In an attempt to overcome these limitations and to reconcile a range of modelling approaches, existing model components that worked demonstrably well were combined with new components for aspects where existing capabilities were inadequate. This resulted in the Integrated Wheat Model (I_WHEAT), which was developed as a module of the cropping systems model APSIM. To increase predictive capability of the model, process detail was reduced, where possible, by replacing groups of processes with conservative, biologically meaningful parameters. I_WHEAT does not contain a soil water or soil nitrogen balance. These are present as other modules of APSIM. In I_WHEAT, yield is simulated using a linear increase in harvest index whereby nitrogen or water limitations can lead to early termination of grainfilling and hence cessation of harvest index increase. Dry matter increase is calculated either from the amount of intercepted radiation and radiation conversion efficiency or from the amount of water transpired and transpiration efficiency, depending on the most limiting resource. Leaf area and tiller formation are calculated from thermal time and a cultivar specific phyllochron interval. Nitrogen limitation first reduces leaf area and then affects radiation conversion efficiency as it becomes more severe. Water or nitrogen limitations result in reduced leaf expansion, accelerated leaf senescence or tiller death. This reduces the radiation load on the crop canopy (i.e. demand for water) and can make nitrogen available for translocation to other organs. Sensitive feedbacks between light interception and dry matter accumulation are avoided by having environmental effects acting directly on leaf area development, rather than via biomass production. This makes the model more stable across environments without losing the interactions between the different external influences. When comparing model output with models tested previously using data from a wide range of agro-climatic conditions, yield and biomass predictions were equal to the best of those models, but improvements could be demonstrated for simulating leaf area dynamics in response to water and nitrogen supply, kernel nitrogen content, and total water and nitrogen use. I_WHEAT does not require calibration for any of the environments tested. Further model improvement should concentrate on improving phenology simulations, a more thorough derivation of coefficients to describe leaf area development and a better quantification of some processes related to nitrogen dynamics. (C) 1998 Elsevier Science B.V.