Drugs and the respiratory system

17.1 Drugs for bronchial asthma and Chronic Obstructive Pulmonary Disease (COPD) 17.1.1 Introduction to asthma 17.1.2 Introduction to COPD 17.1.3 Drug delivery by inhalation 17.1.4 Drugs to treat 17.1.4.1 β2-adrenoceptor agonists 17.1.4.2 Muscarinic receptor antagonists 17.1.4.3 Leukotriene receptor antagonists 17.1.4.4 Theophylline 17.1.4.5 Oxygen for COPD 17.1.5 Drugs to prevent asthma 31.5.1 Glucocorticoids 31.5.2 Cromolyn sodium 17.1.6 Combination to treat and prevent asthma 17.1.7 Drug for allergic asthma – omalizumab 17.1.8 Emergency treatment of asthma 17.2. Expectorants, mucolytics, cough and oxygen 17.2.1 Introduction to expectorants and mucolytics 17.2.2 Expectorants 17.2.3 Mucolytics 17.2.4 Cough 17.2.5 Oxygen 17.3. Drugs for rhinitis and rhinorrea 17.3.1 Introduction 17.3.2 Histamine and H1-receptor antagonists 17.3.3 Sympathomimetic 17.3.4 Muscarinic receptor antagonists 17.3.4 Cromolyn sodium 17.3.5 Glucocorticoids

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

Cervical auscultation in the diagnosis of oropharyngeal aspiration in children : a study protocol for a randomised controlled trial

Background Oropharyngeal aspiration (OPA) can lead to recurrent respiratory illnesses and chronic lung disease in children. Current clinical feeding evaluations performed by speech pathologists have poor reliability in detecting OPA when compared to radiological procedures such as the modified barium swallow (MBS). Improved ability to diagnose OPA accurately via clinical evaluation potentially reduces reliance on expensive, less readily available radiological procedures. Our study investigates the utility of adding cervical auscultation (CA), a technique of listening to swallowing sounds, in improving the diagnostic accuracy of a clinical evaluation for the detection of OPA. Methods We plan an open, unblinded, randomised controlled trial at a paediatric tertiary teaching hospital. Two hundred and sixteen children fulfilling the inclusion criteria will be randomised to one of the two clinical assessment techniques for the clinical detection of OPA: (1) clinical feeding evaluation only (CFE) group or (2) clinical feeding evaluation with cervical auscultation (CFE + CA) group. All children will then undergo an MBS to determine radiologically assessed OPA. The primary outcome is the presence or absence of OPA, as determined on MBS using the Penetration-Aspiration Scale. Our main objective is to determine the sensitivity, specificity, negative and positive predictive values of ‘CFE + CA’ versus ‘CFE’ only compared to MBS-identified OPA. Discussion Early detection and appropriate management of OPA is important to prevent chronic pulmonary disease and poor growth in children. As the reliability of CFE to detect OPA is low, a technique that can improve the diagnostic accuracy of the CFE will help minimise consequences to the paediatric respiratory system. Cervical auscultation is a technique that has previously been documented as a clinical adjunct to the CFE; however, no published RCTs addressing the reliability of this technique in children exist. Our study will be the first to establish the utility of CA in assessing and diagnosing OPA risk in young children.

Simulation study for analysis of online dielectric measurement problems on transformer insulation system

Determining the condition as well as the remaining life of an insulation system is essential for the reliable operation of large oil-filled power transformers. Frequency-domain spectroscopy (FDS) is one of the diagnostic techniques used to identify the dielectric status of a transformer. Currently, this technique can only be implemented on a de-energized transformer. This paper presents an initial investigation into a novel online monitoring method based on FDS dielectric measurements for transformers. The proposed technique specifically aims to address the real operational constraints of online testing. This is achieved by designing an online testing model extending the basic “extended Debye” linear dielectric model and taking unique noise issues only experienced during online measurements into account via simulations. Approaches to signal denoising and potential problems expected to be encountered during online measurements will also be discussed. Using fixed-frequency sinusoidal excitation waveforms will result in a long measurement times. The use of alternatives such as a chirp has been investigated using simulations. The results presented in the paper predict that reliable measurements should be possible during online testing.

A prototype system for the digital lodgement of spatial data

This dissertation develops the model of a prototype system for the digital lodgement of spatial data sets with statutory bodies responsible for the registration and approval of land related actions under the Torrens Title system. Spatial data pertain to the location of geographical entities together with their spatial dimensions and are classified as point, line, area or surface. This dissertation deals with a sub-set of spatial data, land boundary data that result from the activities performed by surveying and mapping organisations for the development of land parcels. The prototype system has been developed, utilising an event-driven paradigm for the user-interface, to exploit the potential of digital spatial data being generated from the utilisation of electronic techniques. The system provides for the creation of a digital model of the cadastral network and dependent data sets for an area of interest from hard copy records. This initial model is calibrated on registered control and updated by field survey to produce an amended model. The field-calibrated model then is electronically validated to ensure it complies with standards of format and content. The prototype system was designed specifically to create a database of land boundary data for subsequent retrieval by land professionals for surveying, mapping and related activities. Data extracted from this database are utilised for subsequent field survey operations without the need to create an initial digital model of an area of interest. Statistical reporting of differences resulting when subsequent initial and calibrated models are compared, replaces the traditional checking operations of spatial data performed by a land registry office. Digital lodgement of survey data is fundamental to the creation of the database of accurate land boundary data. This creation of the database is fundamental also to the efficient integration of accurate spatial data about land being generated by modem technology such as global positioning systems, and remote sensing and imaging, with land boundary information and other information held in Government databases. The prototype system developed provides for the delivery of accurate, digital land boundary data for the land registration process to ensure the continued maintenance of the integrity of the cadastre. Such data should meet also the more general and encompassing requirements of, and prove to be of tangible, longer term benefit to the developing, electronic land information industry.

Theoretical analysis of the motion and evaporation of exhaled respiratory droplets of mixed composition

The dynamics of droplets exhaled from the respiratory system during coughing or talking is addressed. A mathematical model is presented accounting for the motion of a droplet in conjunction with its evaporation. Droplet evaporation and motion are accounted for under two scenarios: 1) A well mixed droplet and 2) A droplet with inner composition variation. A multiple shells model was implemented to account for internal mass and heat transfer and for concentration and temperature gradients inside the droplet. The trajectories of the droplets are computed for a range of conditions and the spatial distribution and residence times of such droplets are evaluated.

Damage quantification techniques in acoustic emission monitoring

Acoustic emission (AE) analysis is one of the several diagnostic techniques available nowadays for structural health monitoring (SHM) of engineering structures. Some of its advantages over other techniques include high sensitivity to crack growth and capability of monitoring a structure in real time. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). In AE technique, these stress waves are recorded by means of suitable sensors placed on the surface of a structure. Recorded signals are subsequently analysed to gather information about the nature of the source. By enabling early detection of crack growth, AE technique helps in planning timely retrofitting or other maintenance jobs or even replacement of the structure if required. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. Large amount of data is generated during AE testing, hence effective data analysis is necessary, especially for long term monitoring uses. Appropriate analysis of AE data for quantification of damage level is an area that has received considerable attention. Various approaches available for damage quantification for severity assessment are discussed in this paper, with special focus on civil infrastructure such as bridges. One method called improved b-value analysis is used to analyse data collected from laboratory testing.

Fault symptoms of rolling element bearings under variable operating conditions - a multi domain analysis

Diagnostics of rolling element bearings have been traditionally developed for constant operating conditions, and sophisticated techniques, like Spectral Kurtosis or Envelope Analysis, have proven their effectiveness by means of experimental tests, mainly conducted in small-scale laboratory test-rigs. Algorithms have been developed for the digital signal processing of data collected at constant speed and bearing load, with a few exceptions, allowing only small fluctuations of these quantities. Owing to the spreading of condition based maintenance in many industrial fields, in the last years a need for more flexible algorithms emerged, asking for compatibility with highly variable operating conditions, such as acceleration/deceleration transients. This paper analyzes the problems related with significant speed and load variability, discussing in detail the effect that they have on bearing damage symptoms, and propose solutions to adapt existing algorithms to cope with this new challenge. In particular, the paper will i) discuss the implication of variable speed on the applicability of diagnostic techniques, ii) address quantitatively the effects of load on the characteristic frequencies of damaged bearings and iii) finally present a new approach for bearing diagnostics in variable conditions, based on envelope analysis. The research is based on experimental data obtained by using artificially damaged bearings installed on a full scale test-rig, equipped with actual train traction system and reproducing the operation on a real track, including all the environmental noise, owing to track irregularity and electrical disturbances of such a harsh application.

Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation

Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

A cyclostationary multi-domain analysis of fluid instability in Kaplan turbines

Hydraulic instabilities represent a critical problem for Francis and Kaplan turbines, reducing their useful life due to increase of fatigue on the components and cavitation phenomena. Whereas an exhaustive list of publications on computational fluid-dynamic models of hydraulic instability is available, the possibility of applying diagnostic techniques based on vibration measurements has not been investigated sufficiently, also because the appropriate sensors seldom equip hydro turbine units. The aim of this study is to fill this knowledge gap and to exploit fully, for this purpose, the potentiality of combining cyclostationary analysis tools, able to describe complex dynamics such as those of fluid-structure interactions, with order tracking procedures, allowing domain transformations and consequently the separation of synchronous and non-synchronous components. This paper will focus on experimental data obtained on a full-scale Kaplan turbine unit, operating in a real power plant, tackling the issues of adapting such diagnostic tools for the analysis of hydraulic instabilities and proposing techniques and methodologies for a highly automated condition monitoring system. © 2015 Elsevier Ltd.

Non-alcoholic fatty liver disease : can ultrasound assist in early diagnosis of prediabetes and delay progression to type2 diabetes mellitus

Non Alcoholic Fatty Liver Disease (NAFLD) is a condition that is frequently seen but seldom investigated. Until recently, NAFLD was considered benign, self-limiting and unworthy of further investigation. This opinion is based on retrospective studies with relatively small numbers and scant follow-up of histology data. (1) The prevalence for adults, in the USA is, 30%, and NAFLD is recognized as a common and increasing form of liver disease in the paediatric population (1). Australian data, from New South Wales, suggests the prevalence of NAFLD in “healthy” 15 year olds as being 10%.(2) Non-alcoholic fatty liver disease is a condition where fat progressively invades the liver parenchyma. The degree of infiltration ranges from simple steatosis (fat only) to steatohepatitis (fat and inflammation) steatohepatitis plus fibrosis (fat, inflammation and fibrosis) to cirrhosis (replacement of liver texture by scarred, fibrotic and non functioning tissue).Non-alcoholic fatty liver is diagnosed by exclusion rather than inclusion. None of the currently available diagnostic techniques -liver biopsy, liver function tests (LFT) or Imaging; ultrasound, Computerised tomography (CT) or Magnetic Resonance Imaging (MRI) are specific for non-alcoholic fatty liver. An association exists between NAFLD, Non Alcoholic Steatosis Hepatitis (NASH) and irreversible liver damage, cirrhosis and hepatoma. However, a more pervasive aspect of NAFLD is the association with Metabolic Syndrome. This Syndrome is categorised by increased insulin resistance (IR) and NAFLD is thought to be the hepatic representation. Those with NAFLD have an increased risk of death (3) and it is an independent predictor of atherosclerosis and cardiovascular disease (1). Liver biopsy is considered the gold standard for diagnosis, (4), and grading and staging, of non-alcoholic fatty liver disease. Fatty-liver is diagnosed when there is macrovesicular steatosis with displacement of the nucleus to the edge of the cell and at least 5% of the hepatocytes are seen to contain fat (4).Steatosis represents fat accumulation in liver tissue without inflammation. However, it is only called non-alcoholic fatty liver disease when alcohol - >20gms-30gms per day (5), has been excluded from the diet. Both non-alcoholic and alcoholic fatty liver are identical on histology. (4).LFT’s are indicative, not diagnostic. They indicate that a condition may be present but they are unable to diagnosis what the condition is. When a patient presents with raised fasting blood glucose, low HDL (high density lipoprotein), and elevated fasting triacylglycerols they are likely to have NAFLD. (6) Of the imaging techniques MRI is the least variable and the most reproducible. With CT scanning liver fat content can be semi quantitatively estimated. With increasing hepatic steatosis, liver attenuation values decrease by 1.6 Hounsfield units for every milligram of triglyceride deposited per gram of liver tissue (7). Ultrasound permits early detection of fatty liver, often in the preclinical stages before symptoms are present and serum alterations occur. Earlier, accurate reporting of this condition will allow appropriate intervention resulting in better patient health outcomes. References 1. Chalasami N. Does fat alone cause significant liver disease: It remains unclear whether simple steatosis is truly benign. American Gastroenterological Association Perspectives, February/March 2008 www.gastro.org/wmspage.cfm?parm1=5097 Viewed 20th October, 2008 2. Booth, M. George, J.Denney-Wilson, E: The population prevalence of adverse concentrations with adiposity of liver tests among Australian adolescents. Journal of Paediatrics and Child Health.2008 November 3. Catalano, D, Trovato, GM, Martines, GF, Randazzo, M, Tonzuso, A. Bright liver, body composition and insulin resistance changes with nutritional intervention: a follow-up study .Liver Int.2008; February 1280-9 4. Choudhury, J, Sanysl, A. Clinical aspects of Fatty Liver Disease. Semin in Liver Dis. 2004:24 (4):349-62 5. Dionysus Study Group. Drinking factors as cofactors of risk for alcohol induced liver change. Gut. 1997; 41 845-50 6. Preiss, D, Sattar, N. Non-alcoholic fatty liver disease: an overview of prevalence, diagnosis, pathogenesis and treatment considerations. Clin Sci.2008; 115 141-50 7. American Gastroenterological Association. Technical review on nonalcoholic fatty liver disease. Gastroenterology.2002; 123: 1705-25

Integrated Diagnosis and Prognosis Model for High Pressure LNG Pump

In condition-based maintenance (CBM), effective diagnostics and prognostics are essential tools for maintenance engineers to identify imminent fault and to predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedules production if necessary. This paper presents a technique for accurate assessment of the remnant life of machines based on historical failure knowledge embedded in the closed loop diagnostic and prognostic system. The technique uses the Support Vector Machine (SVM) classifier for both fault diagnosis and evaluation of health stages of machine degradation. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for multi-class fault diagnosis. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Exposure to particle number, surface area and PM concentrations in pizzerias

The aim of this work was to quantify exposure to particles emitted by wood-fired ovens in pizzerias. Overall, 15 microenvironments were chosen and analyzed in a 14-month experimental campaign. Particle number concentration and distribution were measured simultaneously using a Condensation Particle Counter (CPC), a Scanning Mobility Particle Sizer (SMPS), an Aerodynamic Particle Sizer (APS). The surface area and mass distributions and concentrations, as well as the estimation of lung deposition surface area and PM1 were evaluated using the SMPS-APS system with dosimetric models, by taking into account the presence of aggregates on the basis of the Idealized Aggregate (IA) theory. The fraction of inhaled particles deposited in the respiratory system and different fractions of particulate matter were also measured by means of a Nanoparticle Surface Area Monitor (NSAM) and a photometer (DustTrak DRX), respectively. In this way, supplementary data were obtained during the monitoring of trends inside the pizzerias. We found that surface area and PM1 particle concentrations in pizzerias can be very high, especially when compared to other critical microenvironments, such as the transport hubs. During pizza cooking under normal ventilation conditions, concentrations were found up to 74, 70 and 23 times higher than background levels for number, surface area and PM1, respectively. A key parameter is the oven shape factor, defined as the ratio between the size of the face opening in respect

Study of acoustic emission data analysis tools for structural health monitoring applications

Acoustic emission (AE) technique is one of the popular diagnostic techniques used for structural health monitoring of mechanical, aerospace and civil structures. But several challenges still exist in successful application of AE technique. This paper explores various tools for analysis of recorded AE data to address two primary challenges: discriminating spurious signals from genuine signals and devising ways to quantify damage levels.

Analysis of acoustic emission data for structural health monitoring applications

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. It is one of the several diagnostic techniques currently used for structural health monitoring (SHM) of civil infrastructure such as bridges. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. But several challenges still exist. Due to high sampling rate required for data capture, large amount of data is generated during AE testing. This is further complicated by the presence of a number of spurious sources that can produce AE signals which can then mask desired signals. Hence, an effective data analysis strategy is needed to achieve source discrimination. This also becomes important for long term monitoring applications in order to avoid massive date overload. Analysis of frequency contents of recorded AE signals together with the use of pattern recognition algorithms are some of the advanced and promising data analysis approaches for source discrimination. This paper explores the use of various signal processing tools for analysis of experimental data, with an overall aim of finding an improved method for source identification and discrimination, with particular focus on monitoring of steel bridges.