Good results for early treatment of clinically isolated syndrome prior to multiple sclerosis with interferon beta-1b and glatiramer

Background: The first sign of developing multiple sclerosis is a clinically isolated syndrome that resembles a multiple sclerosis relapse. Objective/methods: The objective was to review the clinical trials of two medicines in clinically isolated syndromes (interferon β and glatiramer acetate) to determine whether they prevent progression to definite multiple sclerosis. Results: In the BENEFIT trial, after 2 years, 45% of subjects in the placebo group developed clinically definite multiple sclerosis, and the rate was lower in the interferon β-1b group. Then all subjects were offered interferon β-1b, and the original interferon β-1b group became the early treatment group, and the placebo group became the delayed treatment group. After 5 years, the number of subjects with clinical definite multiple sclerosis remained lower in the early treatment than late treatment group. In the PreCISe trial, after 2 years, the time for 25% of the subjects to convert to definite multiple sclerosis was prolonged in the glatiramer group. Conclusions: Interferon β-1b and glatiramer acetate slow the progression of clinically isolated syndromes to definite multiple sclerosis. However, it is not known whether this early treatment slows the progression to the physical disabilities experienced in multiple sclerosis.

Using decision trees in economizer repair decision making

The economiser is a critical component for efficient operation of coal-fired power stations. It consists of a large system of water-filled tubes which extract heat from the exhaust gases. When it fails, usually due to erosion causing a leak, the entire power station must be shut down to effect repairs. Not only are such repairs highly expensive, but the overall repair costs are significantly affected by fluctuations in electricity market prices, due to revenue lost during the outage. As a result, decisions about when to repair an economiser can alter the repair costs by millions of dollars. Therefore, economiser repair decisions are critical and must be optimised. However, making optimal repair decisions is difficult because economiser leaks are a type of interactive failure. If left unfixed, a leak in a tube can cause additional leaks in adjacent tubes which will need more time to repair. In addition, when choosing repair times, one also needs to consider a number of other uncertain inputs such as future electricity market prices and demands. Although many different decision models and methodologies have been developed, an effective decision-making method specifically for economiser repairs has yet to be defined. In this paper, we describe a Decision Tree based method to meet this need. An industrial case study is presented to demonstrate the application of our method.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations

Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed-form expression for the transitional probability density function of the process is not available. As a result, a large number of competing estimation procedures have been proposed. This article provides a critical evaluation of the various estimation techniques. Special attention is given to the ease of implementation and comparative performance of the procedures when estimating the parameters of the Cox–Ingersoll–Ross and Ornstein–Uhlenbeck equations respectively.

Phenomenological modeling of cell-to-cell and beat-to-beat variability in isolated Guinea Pig ventricular myocytes

Experimental action potential (AP) recordings in isolated ventricular myoctes display significant temporal beat-to-beat variability in morphology and duration. Furthermore, significant cell-to-cell differences in AP also exist even for isolated cells originating from the same region of the same heart. However, current mathematical models of ventricular AP fail to replicate the temporal and cell-to-cell variability in AP observed experimentally. In this study, we propose a novel mathematical framework for the development of phenomenological AP models capable of capturing cell-to-cell and temporal variabilty in cardiac APs. A novel stochastic phenomenological model of the AP is developed, based on the deterministic Bueno-Orovio/Fentonmodel. Experimental recordings of AP are fit to the model to produce AP models of individual cells from the apex and the base of the guinea-pig ventricles. Our results show that the phenomenological model is able to capture the considerable differences in AP recorded from isolated cells originating from the location. We demonstrate the closeness of fit to the available experimental data which may be achieved using a phenomenological model, and also demonstrate the ability of the stochastic form of the model to capture the observed beat-to-beat variablity in action potential duration.

M-ary trees for combinatorial asset management decision problems

A novel m-ary tree based approach is presented to solve asset management decisions which are combinatorial in nature. The approach introduces a new dynamic constraint based control mechanism which is capable of excluding infeasible solutions from the solution space. The approach also provides a solution to the challenges with ordering of assets decisions.

Compact multiport antenna with isolated ports

The small element spacing of compact multiport arrays introduces strong mutual coupling between the antenna ports. Due to this coupling, the input impedance of the array changes when elements excitations are varied, and consequently, the array cannot be matched for an arbitrary excitation. Decoupling networks have in the past been used to provide an additional connection between antenna ports in order to cancel the coupling between elements. An alternative approach is to design the antenna so that each port does not excite a single element, but all elements simultaneously instead. The geometry of the antenna is optimized so that this direct excitation of elements counteracts the mutual coupling, thus yielding decoupled ports. This paper describes the design of such a 4-port antenna.

CO2 emissions from the combustion of native Australian trees

Carbon dioxide (CO2), as a primary product of combustion, is a known factor affecting climate change and global warming. In Australia, CO2 emissions from biomass burning are a significant contributor to total carbon in the atmosphere and therefore, it is important to quantify the CO2 emission factors from biomass burning in order to estimate their magnitude and impact on the Australian atmosphere. This paper presents the quantification of CO2 emission factors for five common tree species found in South East Queensland forests, as well as several grasses taken from savannah lands in the Northern Territory of Australia, under controlled ‘fast burning’ and ‘slow burning’ laboratory conditions. The results showed that CO2 emission factors varied according to the type of vegetation and burning conditions, with emission factors for fast burning being 2574 ± 254 g/kg for wood, 394 ± 40 g/kg for branches and leaves, and 2181 ± 120 g/kg for grass. Under slow burning conditions, the CO2 emission factors were 218 ± 20 g/kg for wood, 392± 80 g/kg for branches and leaves, and 2027 ± 809 g/kg for grass.