TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Tracing CO2 emissions in global value chains

This paper integrates two lines of research into a unified conceptual framework: trade in global value chains and embodied emissions. This allows both value added and emissions to be systematically traced at the country, sector, and bilateral levels through various production network routes. By combining value-added and emissions accounting in a consistent way, the potential environmental cost (amount of emissions per unit of value added) along global value chains can be estimated. Using this unified accounting method, we trace CO2 emissions in the global production and trade network among 41 economies in 35 sectors from 1995 to 2009, basing our calculations on the World Input–Output Database, and show how they help us to better understand the impact of cross-country production sharing on the environment.

Unit Root Analysis of Traffic Time Series in Toll Highways

Concession contracts in highways often include some kind of clauses (for example, a minimum traffic guarantee) that allow for better management of the business risks. The value of these clauses may be important and should be added to the total value of the concession. However, in these cases, traditional valuation techniques, like the NPV (net present value) of the project, are insufficient. An alternative methodology for the valuation of highway concession is one based on the real options approach. This methodology is generally built on the assumption of the evolution of traffic volume as a GBM (geometric Brownian motion), which is the hypothesis analyzed in this paper. First, a description of the methodology used for the analysis of the existence of unit roots (i.e., the hypothesis of non-stationarity) is provided. The Dickey-Fuller approach has been used, which is the most common test for this kind of analysis. Then this methodology is applied to perform a statistical analysis of traffic series in Spanish toll highways. For this purpose, data on the AADT (annual average daily traffic) on a set of highways have been used. The period of analysis is around thirty years in most cases. The main outcome of the research is that the hypothesis that traffic volume follows a GBM process in Spanish toll highways cannot be rejected. This result is robust, and therefore it can be used as a starting point for the application of the real options theory to assess toll highway concessions.

Influence of origin of the beans on protein quality and nutritive value of commercial soybean meals.

Chemical composition and correlations between chemical analyses and protein quality of 454 batches of SBM of 3 different origins (USA, n=168; Brazil (BRA), n=139, and Argentine (ARG), n=147) were studied. Samples were collected during a 6-yr period. SBM from USA had more CP, sucrose and stachyose and less NDF (P<0.001) than SBM from ARG and BRA. CP content was negatively related (P<0.001) with sucrose for USA meals and with NDF for ARG and BRA meals. Also, P content was positively related (P<0.01) with CP content of the meals. PDI and KOH solubility were higher (P<0.001) for USA than for ARG or BRA SBM, values that were positively related (P<0.001) with trypsin inhibitor activity of the meals. In addition, USA meals had more lys, met+cys, thr, and trp than BRA and ARG meals (P < 0.001). Per unit of CP, lys content was negatively related (P<0.001) with CP content for USA, positively for BRA, and no relations was found for ARG meals. It is concluded that nutritive values and protein quality of the meals varied widely among soybean origins. Consequently, the origin of the beans should be considered in the evaluation of the nutritive value of commercial SBM for non-ruminant animals.

Power Converter Topologies for a High Performance Transformer Rectifier Unit in Aircraft Applications

This paper presents some power converter architectures and circuit topologies, which can be used to achieve the requirements of the high performance transformer rectifier unit in aircraft applications, mainly as: high power factor with low THD, high efficiency and high power density. The voltage and the power levels demanded for this application are: three-phase line-to-neutral input voltage of 115 or 230V AC rms (360 – 800Hz), output voltage of 28V DC or 270V DC(new grid value) and the output power up to tens of kilowatts.

SOA-Based Model for Value-Added ITS Services Delivery

Integration is currently a key factor in intelligent transportation systems (ITS), especially because of the ever increasing service demands originating from the ITS industry and ITS users. The current ITS landscape is made up of multiple technologies that are tightly coupled, and its interoperability is extremely low, which limits ITS services generation. Given this fact, novel information technologies (IT) based on the service-oriented architecture (SOA) paradigm have begun to introduce new ways to address this problem. The SOA paradigm allows the construction of loosely coupled distributed systems that can help to integrate the heterogeneous systems that are part of ITS. In this paper, we focus on developing an SOA-based model for integrating information technologies (IT) into ITS to achieve ITS service delivery. To develop our model, the ITS technologies and services involved were identified, catalogued, and decoupled. In doing so, we applied our SOA-based model to integrate all of the ITS technologies and services, ranging from the lowest-level technical components, such as roadside unit as a service (RS S), to the most abstract ITS services that will be offered to ITS users (value-added services). To validate our model, a functionality case study that included all of the components of our model was designed.

Modifications post-traductionnelles des canaux calciques cardiaques de type L : identification des résidus asparagine qui participent à la glycosylation de la sous-unité auxiliaire CaVα2δ1

Les canaux calciques de type L CaV1.2 sont principalement responsables de l’entrée des ions calcium pendant la phase plateau du potentiel d’action des cardiomyocytes ventriculaires. Cet influx calcique est requis pour initier la contraction du muscle cardiaque. Le canal CaV1.2 est un complexe oligomérique qui est composé de la sous-unité principale CaVα1 et des sous-unités auxiliaires CaVβ et CaVα2δ1. CaVβ joue un rôle déterminant dans l’adressage membranaire de la sous-unité CaVα1. CaVα2δ1 stabilise l’état ouvert du canal mais le mécanisme moléculaire responsable de cette modulation n’a pas été encore identifié. Nous avons récemment montré que cette modulation requiert une expression membranaire significative de CaVα2δ1 (Bourdin et al. 2015). CaVα2δ1 est une glycoprotéine qui possède 16 sites potentiels de glycosylation de type N. Nous avons donc évalué le rôle de la glycosylation de type-N dans l’adressage membranaire et la stabilité de CaVα2δ1. Nous avons d’abord confirmé que la protéine CaVα2δ1 recombinante, telle la protéine endogène, est significativement glycosylée puisque le traitement à la PNGase F se traduit par une diminution de 50 kDa de sa masse moléculaire, ce qui est compatible avec la présence de 16 sites Asn. Il s’est avéré par ailleurs que la mutation simultanée de 6/16 sites (6xNQ) est suffisante pour 1) réduire significativement la densité de surface de! CaVα2δ1 telle que mesurée par cytométrie en flux et par imagerie confocale 2) accélérer les cinétiques de dégradation telle qu’estimée après arrêt de la synthèse protéique et 3) diminuer la modulation fonctionnelle des courants générés par CaV1.2 telle qu’évaluée par la méthode du « patch-clamp ». Les effets les plus importants ont toutefois été obtenus avec les mutants N663Q, et les doubles mutants N348Q/N468Q, N348Q/N812Q, N468Q/N812Q. Ensemble, ces résultats montrent que Asn663 et à un moindre degré Asn348, Asn468 et Asn812 contribuent à la biogenèse et la stabilité de CaVα2δ1 et confirment que la glycosylation de type N de CaVα2δ1 est nécessaire à la fonction du canal calcique cardiaque de type L.

Motor unit number estimation - A Bayesian approach

All muscle contractions are dependent on the functioning of motor units. In diseases such as amyotrophic lateral sclerosis (ALS), progressive loss of motor units leads to gradual paralysis. A major difficulty in the search for a treatment for these diseases has been the lack of a reliable measure of disease progression. One possible measure would be an estimate of the number of surviving motor units. Despite over 30 years of motor unit number estimation (MUNE), all proposed methods have been met with practical and theoretical objections. Our aim is to develop a method of MUNE that overcomes these objections. We record the compound muscle action potential (CMAP) from a selected muscle in response to a graded electrical stimulation applied to the nerve. As the stimulus increases, the threshold of each motor unit is exceeded, and the size of the CMAP increases until a maximum response is obtained. However, the threshold potential required to excite an axon is not a precise value but fluctuates over a small range leading to probabilistic activation of motor units in response to a given stimulus. When the threshold ranges of motor units overlap, there may be alternation where the number of motor units that fire in response to the stimulus is variable. This means that increments in the value of the CMAP correspond to the firing of different combinations of motor units. At a fixed stimulus, variability in the CMAP, measured as variance, can be used to conduct MUNE using the "statistical" or the "Poisson" method. However, this method relies on the assumptions that the numbers of motor units that are firing probabilistically have the Poisson distribution and that all single motor unit action potentials (MUAP) have a fixed and identical size. These assumptions are not necessarily correct. We propose to develop a Bayesian statistical methodology to analyze electrophysiological data to provide an estimate of motor unit numbers. Our method of MUNE incorporates the variability of the threshold, the variability between and within single MUAPs, and baseline variability. Our model not only gives the most probable number of motor units but also provides information about both the population of units and individual units. We use Markov chain Monte Carlo to obtain information about the characteristics of individual motor units and about the population of motor units and the Bayesian information criterion for MUNE. We test our method of MUNE on three subjects. Our method provides a reproducible estimate for a patient with stable but severe ALS. In a serial study, we demonstrate a decline in the number of motor unit numbers with a patient with rapidly advancing disease. Finally, with our last patient, we show that our method has the capacity to estimate a larger number of motor units.

The exact sample complexity of PAC-learning problems with unit VC dimension

The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine learning problems, which may be used to obtain upper and lower bounds on the number of training examples needed to learn to prescribed levels of accuracy. Most of the known bounds apply to the Probably Approximately Correct (PAC) framework, which is the framework within which we work in this paper. For a learning problem with some known VC dimension, much is known about the order of growth of the sample-size requirement of the problem, as a function of the PAC parameters. The exact value of sample-size requirement is however less well-known, and depends heavily on the particular learning algorithm being used. This is a major obstacle to the practical application of the VC dimension. Hence it is important to know exactly how the sample-size requirement depends on VC dimension, and with that in mind, we describe a general algorithm for learning problems having VC dimension 1. Its sample-size requirement is minimal (as a function of the PAC parameters), and turns out to be the same for all non-trivial learning problems having VC dimension 1. While the method used cannot be naively generalised to higher VC dimension, it suggests that optimal algorithm-dependent bounds may improve substantially on current upper bounds.

Understanding high-value manufacturing in Scottish SMEs

Purpose: The purpose of this paper is to investigate the use of high-value manufacturing (HVM) concepts in Scottish SMEs and define how they are being used to gain competitive advantage. Design/methodology/approach: Cross-sectional research carried out using a large-scale survey of 435 SMEs and semi-structured interviews of a subset of 50 SMEs. Findings: Findings indicate that HVM is not a homogeneous state but an umbrella term for a number of operational models adopted by manufacturers that are progressively moving from simple price-based production; companies must, as a foundation, be operationally excellent in all lifecycle phases before extending their capability by offering a more comprehensive service; HVM is not a static state but a journey that differs in nature for each manufacturer depending on the nature of its market and customer. Research limitations/implications: The approach to theory must be more integrated combining aspects of marketing, strategic and operational theory. Research must be carried out using the supply chain, rather than the firm, as the unit of analysis. Practical implications: Manufacturing efficiency has now become an order qualifier and competitive advantage should now be sought through the integration of design, production and service activities from strategic levels down to operational levels across all the functions of a business which link seamlessly to customer and supplier activities. Originality/value: This paper contains insights into Scottish SMEs and their practice of HVM; defines the activity that makes up HVM at an operational as opposed to an economic or strategic level; proposes a model that characterises the stages of HVM that SMEs transition through. © Emerald Group Publishing Limited.

Novel method for the determination of water content and higher heating value of pyrolysis oil

This research provides a novel approach for the determination of water content and higher heating value of pyrolysis oil. Pyrolysis oil from Napier grass was used in this study. Water content was determined with pH adjustment using a Karl Fischer titration unit. An equation for actual water in the oil was developed and used, and the results were compared with the traditional Karl Fischer method. The oil was found to have between 42 and 64% moisture under the same pyrolysis condition depending on the properties of the Napier grass prior to the pyrolysis. The higher heating value of the pyrolysis oil was determined using an oil-diesel mixture, and 20 to 25 wt% of the oil in the mixture gave optimum and stable results. A new model was developed for evaluation of higher heating value of dry pyrolysis oil. The dry oil has higher heating values in the range between 19 and 26 MJ/kg. The developed protocols and equations may serve as a reliable alternative means for establishing the actual water content and the higher heating value of pyrolysis oil.

Numerical solution of the Dirichlet initial boundary value problem for the heat equation in exterior 3-dimensional domains using integral equations

A numerical method for the Dirichlet initial boundary value problem for the heat equation in the exterior and unbounded region of a smooth closed simply connected 3-dimensional domain is proposed and investigated. This method is based on a combination of a Laguerre transformation with respect to the time variable and an integral equation approach in the spatial variables. Using the Laguerre transformation in time reduces the parabolic problem to a sequence of stationary elliptic problems which are solved by a boundary layer approach giving a sequence of boundary integral equations of the first kind to solve. Under the assumption that the boundary surface of the solution domain has a one-to-one mapping onto the unit sphere, these integral equations are transformed and rewritten over this sphere. The numerical discretisation and solution are obtained by a discrete projection method involving spherical harmonic functions. Numerical results are included.

Strategic Human Resources Management Performance Metrics for Unit-Level Managers: An Exploratory Study of U.S Casual Restaurants

Many restaurant organizations have committed a substantial amount of effort to studying the relationship between a firm’s performance and its effort to develop an effective human resources management reward-and-retention system. These studies have produced various metrics for determining the efficacy of restaurant management and human resources management systems. This paper explores the best metrics to use when calculating the overall unit performance of casual restaurant managers. These metrics were identified through an exploratory qualitative case study method that included interviews with executives and a Delphi study. Experts proposed several diverse metrics for measuring management value and performance. These factors seem to represent all stakeholders’interest.

The development of an online neonatal intensive care unit family education

Background: British Columbia’s Fraser Health Authority (FHA) neonatal intensive care units (NICUs) value family centered care (FCC). Nevertheless, there is limited evidence that FCC is actually incorporated into practice, as well as some concern that FHA NICU education is inaccessible, inconsistent, or disorganized. Purpose: The mission of this project is to support the principles of FCC throughout the development of an FHA online NICU family education guide by reflecting upon the needs of families throughout their NICU journey. Methods: A needs assessment was initially completed and included literature reviews, consultations, and an environmental scan. This data informed development of an online NICU family education guide which plots current education materials along key stages of the NICU journey: prenatal, admission, early days, growing and developing, discharge and at home. For the purposes of this practicum, only the prenatal stage was fully developed and will serve as a template for other stages following a formative evaluation. A pamphlet and revised FHA Neonatal Checkpoint will also be developed to augment teaching by health care professionals. Implementation and evaluation plans were adapted from the Center for Disease Control Framework for Program Evaluation in Public Health. Results: The needs assessment validates and directs the development, implementation, and evaluation of the online guide illustrating an FCC approach. The online guide centralizes and organizes education by selecting education topics that relate to each stage of the NICU journey. This family-directed design enables families’ access to consistent and reliable information and offers them an opportunity to learn at their own pace. Conclusion: The process of creating, implementing, and evaluating an online family education program for FHA NICUs elucidates the intricacies and the advantages of integrating FCC into NICU practice.