Using the Gini coefficient with BIOLOG substrate utilisation data to provide an alternative quantitative measure for comparing bacterial soil communities

A measure quantifying unequal use of carbon sources, the Gini coefficient (G), has been developed to allow comparisons of the observed functional diversity of bacterial soil communities. This approach was applied to the analysis of substrate utilisation data obtained from using BIOLOG microtiter plates in a study which compared decomposition processes in two contrasting plant substrates in two different soils. The relevance of applying the Gini coefficient as a measure of observed functional diversity, for soil bacterial communities is evaluated against the Shannon index (H) and average well colour development (AWCD), a measure of the total microbial activity. Correlation analysis and analysis of variance of the experimental data show that the Gini coefficient, the Shannon index and AWCD provided similar information when used in isolation. However, analyses based on the Gini coefficient and the Shannon index, when total activity on the microtiter plates was maintained constant (i.e. AWCD as a covariate), indicate that additional information about the distribution of carbon sources being utilised can be obtained. We demonstrate that the Lorenz curve and its measure of inequality, the Gini coefficient, provides not only comparable information to AWCD and the Shannon index but when used together with AWCD encompasses measures of total microbial activity and absorbance inequality across all the carbon sources. This information is especially relevant for comparing the observed functional diversity of soil microbial communities.

Reliability analysis and economic equipment replacement appraisal for substation and sub-transmission systems with explicit inclusion of non-repairable failures

The modern society has come to expect the electrical energy on demand, while many of the facilities in power systems are aging beyond repair and maintenance. The risk of failure is increasing with the aging equipments and can pose serious consequences for continuity of electricity supply. As the equipments used in high voltage power networks are very expensive, economically it may not be feasible to purchase and store spares in a warehouse for extended periods of time. On the other hand, there is normally a significant time before receiving equipment once it is ordered. This situation has created a considerable interest in the evaluation and application of probability methods for aging plant and provisions of spares in bulk supply networks, and can be of particular importance for substations. Quantitative adequacy assessment of substation and sub-transmission power systems is generally done using a contingency enumeration approach which includes the evaluation of contingencies, classification of the contingencies based on selected failure criteria. The problem is very complex because of the need to include detailed modelling and operation of substation and sub-transmission equipment using network flow evaluation and to consider multiple levels of component failures. In this thesis a new model associated with aging equipment is developed to combine the standard tools of random failures, as well as specific model for aging failures. This technique is applied in this thesis to include and examine the impact of aging equipments on system reliability of bulk supply loads and consumers in distribution network for defined range of planning years. The power system risk indices depend on many factors such as the actual physical network configuration and operation, aging conditions of the equipment, and the relevant constraints. The impact and importance of equipment reliability on power system risk indices in a network with aging facilities contains valuable information for utilities to better understand network performance and the weak links in the system. In this thesis, algorithms are developed to measure the contribution of individual equipment to the power system risk indices, as part of the novel risk analysis tool. A new cost worth approach was developed in this thesis that can make an early decision in planning for replacement activities concerning non-repairable aging components, in order to maintain a system reliability performance which economically is acceptable. The concepts, techniques and procedures developed in this thesis are illustrated numerically using published test systems. It is believed that the methods and approaches presented, substantially improve the accuracy of risk predictions by explicit consideration of the effect of equipment entering a period of increased risk of a non-repairable failure.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Validity and reliability of nutrition screening administered by nurses

Background: Nutrition screening is usually administered by nurses. However, most studies on nutrition screening tools have not used nurses to validate the tools. The 3-Minute Nutrition Screening (3-MinNS) assesses weight loss, dietary intake and muscle wastage, with the composite score of each used to determine risk of malnutrition. The aim of the study was to determine the validity and reliability of 3-MinNS administered by nurses, who are the intended assessors. Methods: In this cross sectional study, three ward-based nurses screened 121 patients aged 21 years and over using 3-MinNS in three wards within 24 hours of admission. A dietitian then assessed the patients’ nutritional status using Subjective Global Assessment within 48 hours of admission, whilst blinded to the results of the screening. To assess the reliability of 3-MinNS, 37 patients screened by the first nurse were re-screened by a second nurse within 24 hours, who was blinded to the results of the first nurse. The sensitivity, specificity and best cutoff score for 3-MinNS were determined using the Receiver Operator Characteristics Curve. Results: The best cutoff score to identify all patients at risk of malnutrition using 3-MinNS was three, with sensitivity of 89% and specificity of 88%. This cutoff point also identified all (100%) severely malnourished patients. There was strong correlation between 3-MinNS and SGA (r=0.78, p<0.001). The agreement between two nurses conducting the 3-MinNS tool was 78.3%. Conclusion: 3-Minute Nutrition Screening is a valid and reliable tool for nurses to identify patients at risk of malnutrition.

Validity and reliability of 3-minute nutrition screening (3-MinNS)performed by nurses on oncology and surgical patients

Background Nutrition screening is usually administered by nurses. However, most studies on nutrition screening tools have not used nurses to validate the tools. The 3-Minute Nutrition Screening (3-MinNS) assesses weight loss, dietary intake and muscle wastage, with the composite score of each used to determine risk of malnutrition. The aim of the study was to determine the validity and reliability of 3-MinNS administered by nurses, who are the intended assessors. Methods In this cross sectional study, three ward-based nurses screened 121 patients aged 21 years and over using 3-MinNS in three wards within 24 hours of admission. A dietitian then assessed the patients’ nutritional status using Subjective Global Assessment within 48 hours of admission, whilst blinded to the results of the screening. To assess the reliability of 3-MinNS, 37 patients screened by the first nurse were re-screened by a second nurse within 24 hours, who was blinded to the results of the first nurse. The sensitivity, specificity and best cutoff score for 3-MinNS were determined using the Receiver Operator Characteristics Curve. Results The best cutoff score to identify all patients at risk of malnutrition using 3-MinNS was three, with sensitivity of 89% and specificity of 88%. This cutoff point also identified all (100%) severely malnourished patients. There was strong correlation between 3-MinNS and SGA (r=0.78, p<0.001). The agreement between two nurses conducting the 3-MinNS tool was 78.3%. Conclusion 3-Minute Nutrition Screening is a valid and reliable tool for nurses to identify patients at risk of malnutrition.