309 resultados para active surveillance
Resumo:
This paper proposes a linear large signal state-space model for a phase controlled CLC (Capacitor Inductor Capacitor) Resonant Dual Active Bridge (RDAB). The proposed model is useful for fast simulation and for the estimation of state variables under large signal variation. The model is also useful for control design because the slow changing dynamics of the dq variables are relatively easy to control. Simulation results of the proposed model are presented and compared to the simulated circuit model to demonstrate the proposed model's accuracy. This proposed model was used for the design of a Proportional-Integral (PI) controller and it has been implemented in the circuit simulation to show the proposed models usefulness in control design.
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In North America and Europe, the binary toxin positive Clostridium difficile strains of the ribotypes 027 and 078 have been associated with death, toxic megacolon and other adverse outcomes. Following an increase in C. difficile infections (CDIs) in Queensland, a prevalence study involving 175 hospitals was undertaken in early 2012, identifying 168 cases of CDI over a 2 month period. Patient demographics and clinical characteristics were recorded, and C. difficile isolates were ribotyped and tested for the presence of binary toxin genes. Most patients (106/168, 63.1%) were aged over 60 years. Overall, 98 (58.3%) developed symptoms after hospitalisation; 89 cases (53.0%) developed symptoms more than 48 hours after admission. Furthermore, 27 of the 62 (67.7%) patients who developed symptoms in the community ad been hospitalised within the last 3 months. Thirteen of the 168 (7.7%) cases identified had severe disease, resulting in admission to the Intensive Care Unit or death within 30 days of the onset of symptoms. The 3 most common ribotypes isolated were UK 002 (22.9%), UK 014 (13.3%) and the binary toxin-positive ribotype UK 244 (8.4%). The only other binary toxin positive ribotype isolated was UK 078 (n = 1). Of concern was the detection of the binary toxin positive ribotype UK 244, which has recently been described in other parts of Australia and New Zealand. No isolates were of the international epidemic clone of ribotype UK 027, although ribotype UK 244 is genetically related to this clone. Further studies are required to track the epidemiology of ribotype UK 244 in Australia and New Zealand. Commun Dis Intell 2014;38(4):E279–E284.
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The creation of a commercially viable and a large-scale purification process for plasmid DNA (pDNA) production requires a whole-systems continuous or semi-continuous purification strategy employing optimised stationary adsorption phase(s) without the use of expensive and toxic chemicals, avian/bovine-derived enzymes and several built-in unit processes, thus affecting overall plasmid recovery, processing time and economics. Continuous stationary phases are known to offer fast separation due to their large pore diameter making large molecule pDNA easily accessible with limited mass transfer resistance even at high flow rates. A monolithic stationary sorbent was synthesised via free radical liquid porogenic polymerisation of ethylene glycol dimethacrylate (EDMA) and glycidyl methacrylate (GMA) with surface and pore characteristics tailored specifically for plasmid binding, retention and elution. The polymer was functionalised with an amine active group for anion-exchange purification of pDNA from cleared lysate obtained from E. coli DH5α-pUC19 pellets in RNase/protease-free process. Characterization of the resin showed a unique porous material with 70% of the pores sizes above 300 nm. The final product isolated from anion-exchange purification in only 5 min was pure and homogenous supercoiled pDNA with no gDNA, RNA and protein contamination as confirmed with DNA electrophoresis, restriction analysis and SDS page. The resin showed a maximum binding capacity of 15.2 mg/mL and this capacity persisted after several applications of the resin. This technique is cGMP compatible and commercially viable for rapid isolation of pDNA.
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We report the electropolymerization of poly(3,4-ethylenedioxythiopene) (PEDOT) from an ionic liquid, butyl-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide (C4mpyrTFSI) onto flexible carbon cloth electrodes. A continuous, homogeneous and well adhered coating of the individual cloth fibres is achieved by employing a sandwich cell arrangement where the carbon cloth which is soaked with electrolyte is placed between two indium tin oxide electrodes isolated from each other by a battery separator. The resultant PEDOT modified carbon cloth electrode demonstrates excellent activity for the oxygen reduction reaction which is due to the doping level, conductivity and morphology of the PEDOT layer and is also tolerant to the presence of methanol in the electrolyte. This simple approach therefore offers a route to fabricate flexible polymer electrodes that could be used in various electronic applications.
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This chapter focuses on the implementation of the TS (Tagaki-Sugino) fuzzy controller for the Doubly Fed Induction Generator (DFIG) based wind generator. The conventional PI control loops for mantaining desired active power and DC capacitor voltage is compared with the TS fuzzy controllers. DFIG system is represented by a third-order model where electromagnetic transients of the stator are neglected. The effectiveness of the TS-fuzzy controller on the rotor speed oscillations and the DC capacitor voltage variations of the DFIG damping controller on converter ratings is also investigated. The results from the time domain simulations are presented to elucidate the effectiveness of the TS-fuzzy controller over the conventional PI controller in the DFIG system. The proposed TS-fuzzy con-troller can improve the fault ride through capability of DFIG compared to the conventional PI controller.
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Background Internet-based surveillance systems provide a novel approach to monitoring infectious diseases. Surveillance systems built on internet data are economically, logistically and epidemiologically appealing and have shown significant promise. The potential for these systems has increased with increased internet availability and shifts in health-related information seeking behaviour. This approach to monitoring infectious diseases has, however, only been applied to single or small groups of select diseases. This study aims to systematically investigate the potential for developing surveillance and early warning systems using internet search data, for a wide range of infectious diseases. Methods Official notifications for 64 infectious diseases in Australia were downloaded and correlated with frequencies for 164 internet search terms for the period 2009–13 using Spearman’s rank correlations. Time series cross correlations were performed to assess the potential for search terms to be used in construction of early warning systems. Results Notifications for 17 infectious diseases (26.6%) were found to be significantly correlated with a selected search term. The use of internet metrics as a means of surveillance has not previously been described for 12 (70.6%) of these diseases. The majority of diseases identified were vaccine-preventable, vector-borne or sexually transmissible; cross correlations, however, indicated that vector-borne and vaccine preventable diseases are best suited for development of early warning systems. Conclusions The findings of this study suggest that internet-based surveillance systems have broader applicability to monitoring infectious diseases than has previously been recognised. Furthermore, internet-based surveillance systems have a potential role in forecasting emerging infectious disease events, especially for vaccine-preventable and vector-borne diseases
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Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.
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Over 800 cities globally now offer bikeshare programs. One of their purported benefits is increased physical activity. Implicit in this claim is that bikeshare replaces sedentary modes of transport, particularly car use. This paper estimates the median changes in physical activity levels as a result of bikeshare in the cities of Melbourne, Brisbane, Washington, D.C., London, and Minneapolis/St. Paul. This study is the first known multi-city evaluation of the active travel impacts of bikeshare programs. To perform the analysis, data on mode substitution (i.e. the modes that bikeshare replaces) were used to determine the extent of shift from sedentary to active transport modes (e.g. when a car trip is replaced by bikeshare). Potentially offsetting these gains, reductions in physical activity when walking trips are replaced by bikeshare was also estimated. Finally a Markov Chain Monte Carlo analysis was conducted to estimate confidence bounds on estimated impacts on active travel given uncertainties in data sources. The results indicate that on average 60% of bikeshare trips replace sedentary modes of transport (from 42% in Minneapolis/St. Paul to 67% in Brisbane). When bikeshare replaces a walking trip, there is a reduction in active travel time because walking a given distance takes longer than cycling. Considering the active travel balance sheet for the cities included in this analysis, bikeshare activity in 2012 has an overall positive impact on active travel time. This impact ranges from an additional 1.4 million minutes of active travel for the Minneapolis/St. Paul bikeshare program, to just over 74 million minutes of active travel for the London program The analytical approach adopted to estimate bikeshare’s impact on active travel may act as the basis for future bikeshare evaluations or feasibility studies.
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This review provides details on the role of Geographical Information Systems (GIS) in current dengue surveillance systems and focuses on the application of open access GIS technology to emphasize its importance in developing countries, where the dengue burden is greatest. It also advocates for increased international collaboration in transboundary disease surveillance to confront the emerging global challenge of dengue.
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Influenza is associated with substantial disease burden [ 1]. Development of a climate-based early warning system for in fluenza epidemics has been recommended given the signi fi - cant association between climate variability and influenza activity [2]. Brisbane is a subtropical city in Australia and offers free in fluenza vaccines to residents aged ≥65 years considering their high risks in developing life-threatening complications, especially for in fluenza A predominant seasons. Hong Kong is an international subtropical city in Eastern Asia and plays a crucial role in global infectious diseases transmission dynamics via the international air transportation network [3, 4]. We hypothesized that Hong Kong in fluenza surveillance data could provide a signal for in fluenza epidemics in Brisbane [ 4]. This study aims to develop an epidemic forecasting model for influenza A in Brisbane elders, by combining climate variability and Hong Kong in fluenza A surveillance data. Weekly numbers of laboratoryconfirmed influenza A positive isolates for people aged ≥65 years from 2004 to 2009 were obtained for Brisbane from Queensland Health, Australia, and for Hong Kong from Queen Mary Hospital (QMH). QMH is the largest public hospital located in Hong Kong Island, and in fluenza surveillance data from this hospital have been demonstrated to be representative for influenza circulation in the entirety of Hong Kong [ 5]. The Brisbane in fluenza A epidemics occurred during July –September, whereas the Hong Kong in fluenza A epidemics occurred during February –March and May –August.