86 research outputs found

    Radial Basis Function (RBF) Neural Network: Effect of Hidden Neuron Number, Training Data Size, and Input Variables on Rainfall Intensity Forecasting

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    Mean daily rainfall of more than 30mm could result in flood hazard. Accurate prediction of rainfall intensity could help in forecasting of flash flood and help to save lives and properties. One of the common machine learning techniques in rainfall prediction is Radial Basis Function (RBF) neural network. Rainfall intensity is classified into four categories, i.e. light (<10mm), medium (11-30mm), heavy (31-50mm)  and very heavy (>50mm) in this study. The rainfall intensity categories is forecasted using the RBF network model utilizing the daily meteorology data for Kuching, Sarawak, Malaysia. The input vectors being considered for the RBF network model are minimum, maximum and mean temperature (°C), mean relative humidity (%), mean wind speed (m/s), mean sea level pressure (hPa) and mean precipitation (mm) for the year 2009 to 2013. The prime focus in this paper is to analyse the ramification of the training data size, number of hidden neurons, and different input variables (i.e. combination of meteorology data) in influencing the performance of the RBF network model. From this study, it could be concluded that, the factor that would influence the performance of the RBF model is only the input variables used, if and only if the network model is equipped with sufficient number of hidden neurons and trained with adequate number of training data. Another interesting observation from this study is that, the RBF network model produced consistent result throughout the testing using a specific hidden neuron number when the RBF network is retrained and tested

    Fluorescent nanoparticles for sensing

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    Nanoparticle-based fluorescent sensors have emerged as a competitive alternative to small molecule sensors, due to their excellent fluorescence-based sensing capabilities. The tailorability of design, architecture, and photophysical properties has attracted the attention of many research groups, resulting in numerous reports related to novel nanosensors applied in sensing a vast variety of biological analytes. Although semiconducting quantum dots have been the best-known representative of fluorescent nanoparticles for a long time, the increasing popularity of new classes of organic nanoparticle-based sensors, such as carbon dots and polymeric nanoparticles, is due to their biocompatibility, ease of synthesis, and biofunctionalization capabilities. For instance, fluorescent gold and silver nanoclusters have emerged as a less cytotoxic replacement for semiconducting quantum dot sensors. This chapter provides an overview of recent developments in nanoparticle-based sensors for chemical and biological sensing and includes a discussion on unique properties of nanoparticles of different composition, along with their basic mechanism of fluorescence, route of synthesis, and their advantages and limitations

    Metabolic health status and fecundability in a Singapore preconception cohort study

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    Background: Obesity compromises metabolic health and female fertility, yet not all obese women are similar in metabolic status. The extent to which fecundability is influenced by the metabolic health status of women who are overweight or obese before conception is unknown. Objective: This study aimed to: (1) determine the metabolic health status, and (2) examine the association between metabolic health status and fecundability of overweight and obese women trying to conceive in the Singapore PREconception Study of long-Term maternal and child Outcomes cohort study. Study Design: We conducted a prospective preconception cohort study of Asian women (Chinese, Malay, and Indian) aged 18 to 45 years trying to conceive who were treated from 2015 to 2017 in KK Women's and Children's Hospital in Singapore (n=834). We defined women to have metabolically unhealthy status if they: (1) met 3 or more modified Joint Interim Statement metabolic syndrome criteria; or (2) had homeostasis model assessment-insulin resistance index ≥2.5. Body mass index was categorized as normal (18.5–22.9 kg/m2), overweight (23–27.4 kg/m2), or obese (≥27.5 kg/m2) on the basis of cutoff points for Asian populations. Fecundability was measured by time to pregnancy in menstrual cycles within a year of enrolment. Discrete-time proportional hazards models were used to estimate fecundability odds ratios, with adjustment for confounders and accounting for left truncation and right censoring. Results: Of 232 overweight women, 28 (12.1%) and 25 (10.8%) were metabolically unhealthy by metabolic syndrome ≥3 criteria and homeostasis model assessment-insulin resistance ≥2.5, respectively. Of 175 obese women, 54 (30.9%) and 93 (53.1%) were metabolically unhealthy by metabolic syndrome ≥3 criteria and homeostasis model assessment-insulin resistance ≥2.5, respectively. Compared with metabolically healthy normal-weight women, lower fecundability was observed in metabolically unhealthy overweight women on the basis of metabolic syndrome criteria (fecundability odds ratios, 0.38 [95% confidence interval, 0.15–0.92]) and homeostasis model assessment-insulin resistance (fecundability odds ratios, 0.68 [95% confidence interval, 0.33–1.39]), with metabolic syndrome criteria showing a stronger association. Metabolically unhealthy obese women showed lower fecundability than the healthy normal-weight reference group by both metabolic syndrome (fecundability odds ratios, 0.35; 95% confidence interval, 0.17–0.72) and homeostasis model assessment-insulin resistance criteria (fecundability odds ratios, 0.43; 95% confidence interval, 0.26–0.71). Reduced fecundability was not observed in overweight or obese women who showed healthy metabolic profiles by either definition. Conclusion: Overweight or obesity was not synonymous with having metabolic syndrome or insulin resistance. In our preconception cohort, metabolically unhealthy overweight and obese women showed reduced fecundability, unlike their counterparts who were metabolically healthy. These findings suggest that metabolic health status, rather than simply being overweight and obese per se, plays an important role in fecundability.acceptedVersionPeer reviewe

    Towards a global partnership model in interprofessional education for cross-sector problem-solving

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    Objectives A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. Methods This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students’ data. Results We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest–posttest differences in students’ readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students’ social interaction anxiety after the IPE simulation. Conclusions The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education

    Asian Pacific Society of Cardiology Consensus Recommendations on the Use of MitraClip for Mitral Regurgitation

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    Transcatheter mitral valve repair with the MitraClip, a catheter-based percutaneous edge-to-edge repair technique to correct mitral regurgitation (MR), has been demonstrated in Western studies to be an effective and safe MR treatment strategy. However, randomised clinical trial data on its use in Asian-Pacific patients is limited. Hence, the Asian Pacific Society of Cardiology convened an expert panel to review the available literature on MitraClip and to develop consensus recommendations to guide clinicians in the region. The panel developed statements on the use of MitraClip for the management of degenerative MR, functional MR, and other less common indications, such as acute MR, dynamic MR, hypertrophic obstructive cardiomyopathy, and MR after failed surgical repair. Each statement was voted on by each panel member and consensus was reached when 80% of experts voted ‘agree’ or ‘neutral’. This consensus-building process resulted in 10 consensus recommendations to guide general cardiologists in the evaluation and management of patients in whom MitraClip treatment is being contemplated

    Molecular imprinting science and technology: a survey of the literature for the years 2004-2011

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    An analysis of earnings and dividend forecasts.

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    This paper examines the accuracy of earnings forecast made by management of Singapore IPO firms during 1989-1998 and also investigates the factors influencing the accuracy. Furthermore the paper compares the accuracy of earnings forecast with dividend forecast and explore the factors that may conttibute to accuracy of dividend forecasts

    Charge-induced conductance modulation of carbon nanotube field effect transistor memory devices

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    We effectively tailored the charge trapping and transport behavior of a carbon nanotube field effect transistor memory device using charge interaction with underlying Ge nanoparticles in a HfO2 high-κ dielectric. We also suggest a new route for modulating the Schottky barrier at the nanotube–electrode interface with localized charge trapping in discrete nanoparticles. This modification leads to an effective increase in the read-out conductance ratio of two to three orders magnitude under low voltage operation, associated with a large memory window of ∼5.3 V. Furthermore, we achieved a more controllable and reliable memory effect due to stable charge storage in deep nanoparticle traps, as compared to shallow HfO2 defect states
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