Risk-based model aids selection of pipeline inspection, maintenance strategies

The existing method of pipeline monitoring, which requires an entire pipeline to be inspected periodically, wastes time and is expensive. A risk-based model that reduces the amount of time spent on inspection has been developed. This model not only reduces the cost of maintaining petroleum pipelines, but also suggests an efficient design and operation philosophy, construction method and logical insurance plans.The risk-based model uses analytic hierarchy process, a multiple attribute decision-making technique, to identify factors that influence failure on specific segments and analyze their effects by determining the probabilities of risk factors. The severity of failure is determined through consequence analysis, which establishes the effect of a failure in terms of cost caused by each risk factor and determines the cumulative effect of failure through probability analysis.

Selection of power market structure using the analytic hierarchy process

Selection of a power market structure from the available alternatives is an important activity within an overall power sector reform programme. The evaluation criteria for selection are both subjective as well as objective in nature and the selection of alternatives is characterised by their conflicting nature. This study demonstrates a methodology for power market structure selection using the analytic hierarchy process (AHP), a multiple attribute decision-making technique, to model the selection methodology with the active participation of relevant stakeholders in a workshop environment. The methodology is applied to a hypothetical case of a State Electricity Board reform in India.

Tracking of non-stationary time-series using resource allocating RBF networks

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Using electricity load data and training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise and forgetting factors for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. We also find that a recently-proposed alternative novelty criterion, found to be more robust in stationary environments, does not fare so well in the non-stationary case due to the need for filter adaptability during training.

Gaussian regression and optimal finite dimensional linear models

The problem of regression under Gaussian assumptions is treated generally. The relationship between Bayesian prediction, regularization and smoothing is elucidated. The ideal regression is the posterior mean and its computation scales as O(n³), where n is the sample size. We show that the optimal m-dimensional linear model under a given prior is spanned by the first m eigenfunctions of a covariance operator, which is a trace-class operator. This is an infinite dimensional analogue of principal component analysis. The importance of Hilbert space methods to practical statistics is also discussed.

Is subclinical thyroid dysfunction in the elderly associated with depression or cognitive dysfunction?

Background: Widespread use of automated sensitive assays for thyroid hormones and thyroid-stimulating hormone (TSH) has increased identification of mild thyroid dysfunction, especially in elderly patients. The clinical significance of this dysfunction, however, remains uncertain, and associations with cognitive impairment, depression, and anxiety are unconfirmed. Objective: To determine the association between mild thyroid dysfunction and cognition, depression, and anxiety in elderly persons. Design: Cross-sectional study. Associations were explored through mixed-model analyses. Setting: Primary care practices in central England. Patients: 5865 patients 65 years of age or older with no known thyroid disease who were recruited from primary care registers. Measurements: Serum TSH and free thyroxine (T4) were measured. Depression and anxiety were assessed by using the Hospital Anxiety and Depression Scale (HADS), and cognitive functioning was established by using the Middlesex Elderly Assessment of Mental State and the Folstein Mini-Mental State Examination. Comorbid conditions, medication use, and sociodemographic profiles were recorded. Results: 295 patients met the criteria for subclinical thyroid dysfunction (127 were hyperthyroid, and 168 were hypothyroid). After confounding variables were controlled for, statistically significant associations were seen between anxiety (HADS score) and TSH level (P = 0.013) and between cognition and both TSH and free T4 levels. The magnitude of these associations lacked clinical relevance: A 50-mIU/L increase in the TSH level was associated with a 1-point reduction in the HADS anxiety score, and a 1-point increase in the Mini-Mental State Examination score was associated with an increase of 50 mIU/L in the TSH level or 25 pmol/L in the free T4 level. Limitations: Because of the low participation rate, low prevalence of subclinical thyroid dysfunction, and other unidentified recruitment biases, participants may not be representative of the elderly population. Conclusions: After the confounding effects of comorbid conditions and use of medication were controlled for, subclinical thyroid dysfunction was not associated with depression, anxiety, or cognition. © 2006 American College of Physicians.

A randomized controlled trial of the effect of thyroxine replacement on cognitive function in community-living elderly subjects with subclinical hypothyroidism: the Birmingham elderly thyroid study

Context: Subclinical hypothyroidism (SCH) and cognitive dysfunction are both common in the elderly and have been linked. It is important to determine whether T4 replacement therapy in SCH confers cognitive benefit. Objective: Our objective was to determine whether administration of T4 replacement to achieve biochemical euthyroidism in subjects with SCH improves cognitive function. Design and Setting: We conducted a double-blind placebo-controlled randomized controlled trial in the context of United Kingdom primary care. Patients: Ninety-four subjects aged 65 yr and over (57 females, 37 males) with SCH were recruited from a population of 147 identified by screening. Intervention: T4 or placebo was given at an initial dosage of one tablet of either placebo or 25 µg T4 per day for 12 months. Thyroid function tests were performed at 8-weekly intervals with dosage adjusted in one-tablet increments to achieve TSH within the reference range for subjects in treatment arm. Fifty-two subjects received T4 (31 females, 21 males; mean age 73.5 yr, range 65–94 yr); 42 subjects received placebo (26 females, 16 males; mean age 74.2 yr, 66–84 yr). Main Outcome Measures: Mini-Mental State Examination, Middlesex Elderly Assessment of Mental State (covering orientation, learning, memory, numeracy, perception, attention, and language skills), and Trail-Making A and B were administered. Results: Eighty-two percent and 84% in the T4 group achieved euthyroidism at 6- and 12-month intervals, respectively. Cognitive function scores at baseline and 6 and 12 months were as follows: Mini-Mental State Examination T4 group, 28.26, 28.9, and 28.28, and placebo group, 28.17, 27.82, and 28.25 [not significant (NS)]; Middlesex Elderly Assessment of Mental State T4 group, 11.72, 11.67, and 11.78, and placebo group, 11.21, 11.47, and 11.44 (NS); Trail-Making A T4 group, 45.72, 47.65, and 44.52, and placebo group, 50.29, 49.00, and 46.97 (NS); and Trail-Making B T4 group, 110.57, 106.61, and 96.67, and placebo group, 131.46, 119.13, and 108.38 (NS). Linear mixed-model analysis demonstrated no significant changes in any of the measures of cognitive function over time and no between-group difference in cognitive scores at 6 and 12 months. Conclusions: This RCT provides no evidence for treating elderly subjects with SCH with T4 replacement therapy to improve cognitive function.

A study of the flow performance of a sodium silicate furnace

The flow characteristics of neutral sodium silicate glass in an open hearth regenerative furnace have been studied using a one tenth scale physical model. The constraints of similarity have been investigated and discussed, and the use of sodium liquor as a cold modelling solution has been developed. Methylene Blue and Sulphacid Brill Pink are used as delineators, and a technique for analysing the concentration of each even in a mixture has been developed. The residence/time distributions from the model have been simulated using a mixed model computer program which identifies the nature and size of the most significant flow streams within the furnace. The results clearly show that the model gives a true representation of the furnace and illustrates a number of alternatives for operating or design changes which will lead to improved production efficiency.

Design methodology for the servo control of high speed multi-axis machinery (BL): decoupling and synchronisation of a generic machine by blockwise decentralised MIMO control

Traditional machinery for manufacturing processes are characterised by actuators powered and co-ordinated by mechanical linkages driven from a central drive. Increasingly, these linkages are replaced by independent electrical drives, each performs a different task and follows a different motion profile, co-ordinated by computers. A design methodology for the servo control of high speed multi-axis machinery is proposed, based on the concept of a highly adaptable generic machine model. In addition to the dynamics of the drives and the loads, the model includes the inherent interactions between the motion axes and thus provides a Multi-Input Multi-Output (MIMO) description. In general, inherent interactions such as structural couplings between groups of motion axes are undesirable and needed to be compensated. On the other hand, imposed interactions such as the synchronisation of different groups of axes are often required. It is recognised that a suitable MIMO controller can simultaneously achieve these objectives and reconciles their potential conflicts. Both analytical and numerical methods for the design of MIMO controllers are investigated. At present, it is not possible to implement high order MIMO controllers for practical reasons. Based on simulations of the generic machine model under full MIMO control, however, it is possible to determine a suitable topology for a blockwise decentralised control scheme. The Block Relative Gain array (BRG) is used to compare the relative strength of closed loop interactions between sub-systems. A number of approaches to the design of the smaller decentralised MIMO controllers for these sub-systems has been investigated. For the purpose of illustration, a benchmark problem based on a 3 axes test rig has been carried through the design cycle to demonstrate the working of the design methodology.

Efficacy of memantine for agitation in Alzheimer's dementia:a randomised double-blind placebo controlled trial

Background - Agitation in Alzheimer’s disease (AD) is common and associated with poor patient life-quality and carer distress. The best evidence-based pharmacological treatments are antipsychotics which have limited benefits with increased morbidity and mortality. There are no memantine trials in clinically significant agitation but post-hoc analyses in other populations found reduced agitation. We tested the primary hypothesis, memantine is superior to placebo for clinically significant agitation, in patients with moderate-to-severe AD. Methods and Findings - We recruited 153 participants with AD and clinically significant agitation from care-homes or hospitals for a double-blind randomised-controlled trial and 149 people started the trial of memantine versus placebo. The primary outcome was 6 weeks mixed model autoregressive analysis of Cohen-Mansfield Agitation Inventory (CMAI). Secondary outcomes were: 12 weeks CMAI; 6 and 12 weeks Neuropsychiatric symptoms (NPI), Clinical Global Impression Change (CGI-C), Standardised Mini Mental State Examination, Severe Impairment Battery. Using a mixed effects model we found no significant differences in the primary outcome, 6 weeks CMAI, between memantine and placebo (memantine lower -3.0; -8.3 to 2.2, p = 0.26); or 12 weeks CMAI; or CGI-C or adverse events at 6 or 12 weeks. NPI mean difference favoured memantine at weeks 6 (-6.9; -12.2 to -1.6; p = 0.012) and 12 (-9.6; -15.0 to -4.3 p = 0.0005). Memantine was significantly better than placebo for cognition. The main study limitation is that it still remains to be determined whether memantine has a role in milder agitation in AD. Conclusions - Memantine did not improve significant agitation in people with in moderate-to-severe AD. Future studies are urgently needed to test other pharmacological candidates in this group and memantine for neuropsychiatric symptoms.

Practical methods of tracking of nonstationary time series applied to real-world data

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.

Fuzzy assessment of performance of a decision making units using DEA:a non-radical approach

Data Envelopment Analysis (DEA) is recognized as a modern approach to the assessment of performance of a set of homogeneous Decision Making Units (DMUs) that use similar sources to produce similar outputs. While DEA commonly is used with precise data, recently several approaches are introduced for evaluating DMUs with uncertain data. In the existing approaches many information on uncertainties are lost. For example in the defuzzification, the a-level and fuzzy ranking approaches are not considered. In the tolerance approach the inequality or equality signs are fuzzified but the fuzzy coefficients (inputs and outputs) are not treated directly. The purpose of this paper is to develop a new model to evaluate DMUs under uncertainty using Fuzzy DEA and to include a-level to the model under fuzzy environment. An example is given to illustrate this method in details.

The dynamics of a genetic algorithm under stabilizing selection

A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest than the test cases they consider. In this paper, the technique is applied to the subset sum problem, which is a combinatorial optimization problem with a strongly non-linear energy (fitness) function and many local minima under single spin flip dynamics. It is a problem which exhibits an interesting dynamics, reminiscent of stabilizing selection in population biology. The dynamics are solved under certain simplifying assumptions and are reduced to a set of difference equations for a small number of relevant quantities. The quantities used are the population's cumulants, which describe its shape, and the mean correlation within the population, which measures the microscopic similarity of population members. Including the mean correlation allows a better description of the population than the cumulants alone would provide and represents a new and important extension of the technique. The formalism includes finite population effects and describes problems of realistic size. The theory is shown to agree closely to simulations of a real genetic algorithm and the mean best energy is accurately predicted.