Surgical resection for non-small cell lung cancer : clinical features and outcomes for a consecutive series at an Irish tertiary referral centre

Background: Few patients diagnosed with lung cancer are still alive 5 years after diagnosis. The aim of the current study was to conduct a 10-year review of a consecutive series of patients undergoing curative-intent surgical resection at the largest tertiary referral centre to identify prognostic factors. Methods: Case records of all patients operated on for lung cancer between 1998 and 2008 were reviewed. The clinical features and outcomes of all patients with non-small cell lung cancer (NSCLC) stage I-IV were recorded. Results: A total of 654 patients underwent surgical resection with curative intent during the study period. Median overall survival for the entire cohort was 37 months. The median age at operation was 66 years, with males accounting for 62.7 %. Squamous cell type was the most common histological subtype, and lobectomies were performed in 76.5 % of surgical resections. Pneumonectomy rates decreased significantly in the latter half of the study (25 vs. 16.3 %), while sub-anatomical resection more than doubled (2 vs. 5 %) (p < 0.005). Clinico-pathological characteristics associated with improved survival by univariate analysis include younger age, female sex, smaller tumour size, smoking status, lobectomy, lower T and N status and less advanced pathological stage. Age, gender, smoking status and tumour size, as well as T and N descriptors have emerged as independent prognostic factors by multivariate analysis. Conclusion: We identified several factors that predicted outcome for NSCLC patients undergoing curative-intent surgical resection. Survival rates in our series are comparable to those reported from other thoracic surgery centres. © 2012 Royal Academy of Medicine in Ireland.

A single institution experience of streptozocin/fluorouracil combination chemotherapy : a case series

Background The combination chemotherapy regimen of streptozocin and 5-fluorouracil (FU/STZ) has been used for the treatment of metastatic neuroendocrine tumours. Aim The aim of this study was to analyse the use of this regimen in a tertiary oncology referral centre over a 10-year period. Method We retrospectively analysed nine cases from February 2000 to May 2010. Patient demographics, chemotherapy schedule, toxicities, progression-free and overall survival were tabulated for each patient. Result The median progression-free survival was 17 months (range 3-48+ months), and overall survival 31 months (range 12-53+ months) with no toxicity related deaths. Conclusion FU/STZ was a well-tolerated regimen that produced significant benefit in the setting of metastatic and progressive disease. Our case series demonstrated comparable progression-free survival and overall survival in relation to randomized controlled studies and previous case series. © Royal Academy of Medicine in Ireland 2011.

Exact series solutions of reactive transport models with general initial conditions

Exact solutions of partial differential equation models describing the transport and decay of single and coupled multispecies problems can provide insight into the fate and transport of solutes in saturated aquifers. Most previous analytical solutions are based on integral transform techniques, meaning that the initial condition is restricted in the sense that the choice of initial condition has an important impact on whether or not the inverse transform can be calculated exactly. In this work we describe and implement a technique that produces exact solutions for single and multispecies reactive transport problems with more general, smooth initial conditions. We achieve this by using a different method to invert a Laplace transform which produces a power series solution. To demonstrate the utility of this technique, we apply it to two example problems with initial conditions that cannot be solved exactly using traditional transform techniques.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Polarization of forecast densities : a new approach to time series classification

Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.

A novel dc-link voltage regulation method for single source hybrid multilevel inverters

This paper presents a novel dc-link voltage regulation technique for a hybrid inverter system formed by cascading two 3-level inverters. The two inverters are named as “bulk inverter” and “conditioning inverter”. For the hybrid system to act as a nine level inverter, conditioning inverter dc link voltage should be maintained at one third of the bulk inverter dc link voltage. Since the conditioning inverter is energized by two series connected capacitors, dc-link voltage regulation should be carried out by controlling the capacitor charging/discharging times. A detailed analysis of conditioning inverter capacitor charging/discharging process and a simplified general rule, derived from the analysis, are presented in this paper. Time domain simulations were carried out to demonstrate efficacy of the proposed method on regulating the conditioning inverter dc-link voltage under various operating conditions.

Simulation-based density estimation for time series using covariate data

This paper proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

Wonder and method

In this paper, my aim is to address the twin concerns raised in this session - models of practice and geographies or spaces of practice - through regarding a selection of works and processes that have arisen from my recent research. Setting up this discussion, I first present a short critique of the idea of models of creative practice, recognising possible problems with the attempt to generalise or abstract its complexities. Working through a series of portraits of my working environment, I will draw from Lefebvre’s Rhythmanalysis as a way of understanding an art practice both spatially and temporally, suggesting that changes and adjustments can occur through attending to both intuitions and observations of the complex of rhythmic layers constantly at play in any event. Reflecting on my recent studio practice I explore these rhythms through the evocation of a twin axis: the horizontal and the vertical and the arcs of difference or change that occur between them, in both spatial and temporal senses. What this analysis suggests is the idea that understanding does not only emerge from the construction of general principles, derived from observation of the particular, but that the study of rhythms allows us to maintain the primacy of the particular. This makes it well suited to a study of creative methods and objects, since it is to the encounter with and expression of the particular that art practices, most certainly my own, are frequently directed.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.

A simple method for estimating growth parameters from multiple length-frequency data in presence of continuous recruitment

The extended recruitment season for short-lived species such as prawns biases the estimation of growth parameters from length-frequency data when conventional methods are used. We propose a simple method for overcoming this bias given a time series of length-frequency data. The difficulties arising from extended recruitment are eliminated by predicting the growth of the succeeding samples and the length increments of the recruits in previous samples. This method requires that some maximum size at recruitment can be specified. The advantages of this multiple length-frequency method are: it is simple to use; it requires only three parameters; no specific distributions need to be assumed; and the actual seasonal recruitment pattern does not have to be specified. We illustrate the new method with length-frequency data on the tiger prawn Penaeus esculentus from the north-western Gulf of Carpentaria, Australia.

Prediction of masonry compressive behaviour using a damage mechanics inspired modelling method

Masonry under compression is affected by the properties of its constituents and their interfaces. In spite of extensive investigations of the behaviour of masonry under compression, the information in the literature cannot be regarded as comprehensive due to ongoing inventions of new generation products – for example, polymer modified thin layer mortared masonry and drystack masonry. As comprehensive experimental studies are very expensive, an analytical model inspired by damage mechanics is developed and applied to the prediction of the compressive behaviour of masonry in this paper. The model incorporates a parabolic progressively softening stress-strain curve for the units and a progressively stiffening stress-strain curve until a threshold strain for the combined mortar and the unit-mortar interfaces is reached. The model simulates the mutual constraints imposed by each of these constituents through their respective tensile and compressive behaviour and volumetric changes. The advantage of the model is that it requires only the properties of the constituents and considers masonry as a continuum and computes the average properties of the composite masonry prisms/wallettes; it does not require discretisation of prism or wallette similar to the finite element methods. The capability of the model in capturing the phenomenological behaviour of masonry with appropriate elastic response, stiffness degradation and post peak softening is presented through numerical examples. The fitting of the experimental data to the model parameters is demonstrated through calibration of some selected test data on units and mortar from the literature; the calibrated model is shown to predict the responses of the experimentally determined masonry built using the corresponding units and mortar quite well. Through a series of sensitivity studies, the model is also shown to predict the masonry strength appropriately for changes to the properties of the units and mortar, the mortar joint thickness and the ratio of the height of unit to mortar joint thickness. The unit strength is shown to affect the masonry strength significantly. Although the mortar strength has only a marginal effect, reduction in mortar joint thickness is shown to have a profound effect on the masonry strength. The results obtained from the model are compared with the various provisions in the Australian Masonry Structures Standard AS3700 (2011) and Eurocode 6.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.