Threshold-based Naïve Bayes classifier

The Threshold-based Naive Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original Naive Bayes classifier. Tb-NB extracts the sentiment from a Natural Language text corpus and allows the user not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. It is based on the estimation of a single threshold value that concurs to define a decision rule that classifies a text into a positive (negative) opinion based on its content. One of the main advantage deriving from Tb-NB is the possibility to utilize its results as the input of post-hoc analysis aimed at observing how the quality associated to the different dimensions of a product or a service or, in a mirrored fashion, the different dimensions of customer satisfaction evolve in time or change with respect to different locations. The effectiveness of Tb-NB is evaluated analyzing data concerning the tourism industry and, specifically, hotel guests' reviews from all hotels located in the Sardinian region and available on Booking.com. Moreover, Tb-NB is compared with other popular classifiers used in sentiment analysis in terms of model accuracy, resistance to noise and computational efficiency

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Complex Response Function of Magnetic Resonance Spectrometers

A vectorial analysis of magnetic resonance spectrometers, based on traveling wave resonators and including the reference arm and the automatic control of frequency, has been developed. The proposed model, valid also for stationary wave resonators, gives the response function of the spectrometer for any working condition, including scalar detectors with arbitrary response law and arbitrary excitation frequency. The purely dispersive and purely absorptive linear responses are discussed in detail for different scalar detectors. The developed approach allows for optimizing the performances of the spectrometer and for obtaining the intrinsic lineshape of the sample in a very broad range of working conditions. More complex setups can be modeled following the proposed scheme.Comment: PDF Acrobat 4.0 file, 15 pages, 2 figures, revised version with added reference and corrected typo

Advances in geometrical parametrization and reduced order models and methods for computational fluid dynamics problems in applied sciences and engineering: Overview and perspectives

Several problems in applied sciences and engineering require reduction techniques in order to allow computational tools to be employed in the daily practice, especially in iterative procedures such as optimization or sensitivity analysis. Reduced order methods need to face increasingly complex problems in computational mechanics, especially into a multiphysics setting. Several issues should be faced: stability of the approximation, efficient treatment of nonlinearities, uniqueness or possible bifurcations of the state solutions, proper coupling between fields, as well as offline-online computing, computational savings and certification of errors as measure of accuracy. Moreover, efficient geometrical parametrization techniques should be devised to efficiently face shape optimization problems, as well as shape reconstruction and shape assimilation problems. A related aspect deals with the management of parametrized interfaces in multiphysics problems, such as fluid-structure interaction problems, and also a domain decomposition based approach for complex parametrized networks. We present some illustrative industrial and biomedical problems as examples of recent advances on methodological developments

Hybrid UWB-Inertial TDoA-based Target Tracking with Concentrated Anchors

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively

Evidence for Anisotropic Vortex Dynamics and Pauli Limitation in the Upper Critical Field of FeSe1-xTex

We have determined HC2(T) for FeSe1-xTex (x=0.52) single crystals using resistivity measurements at high static and pulsed magnetic field, as well as specific heat measurements up to 9T. We find that the significant anisotropy of the initial slope of HC2(T) determined from resistivity measurements, is not present when HC2 is determined from the specific heat results. This suggests that the thermodynamic upper critical field is almost isotropic, and that anisotropic vortex dynamics play a role. Further evidence of anisotropic vortex dynamics is found in the behaviour in pulsed field. We also find that Pauli limiting must be included in order to fit the temperature dependence of HC2, indicating probably higher effective mass in FeSe1-xTex than in other Fe superconductors

dDAVP Downregulates the AQP3-Mediated Glycerol Transport via V1aR in Human Colon HCT8 Cells

Vasopressin (AVP) plays a key function in controlling body water and salt balance through the activation of the vasopressin receptors V1aR and V2R. Abnormal secretion of AVP can cause the syndrome of inappropriate antidiuresis that leads to hyponatremia, which is an electrolyte disorder often observed in the elderly hospitalized and oncologic patients. Beyond kidneys, the colonic epithelium modulates water and salt homeostasis. The water channel AQP3, expressed in villus epithelial cells is implicated in water absorption across human colonic surface cells. Here, the action of dDAVP, a stable vasopressin analog, was evaluated on the AQP3 expression and function using human colon HCT8 cells as an experimental model. Confocal and Western Blotting analysis revealed that HCT8 cells express both V1aR and V2R. Long-term (72 h) treatment with dDAVP reduced glycerol uptake and cell viability. These effects were prevented by SR49059, a synthetic antagonist of V1aR, but not by tolvaptan, a specific V2R antagonist. Of note, the SR49059 action was impaired by DFP00173, a selective inhibitor of AQP3. Interestingly, compared to the normal colonic mucosa, in the colon of patients with adenocarcinoma, the expression of V1aR was significantly decreased. These findings were confirmed by gene expression analysis with RNA-Seq data. Overall, data suggest that dDAVP, through the V1aR dependent pathway, reduces AQP3 mediated glycerol uptake, a process that is reversed in adenocarcinoma, suggesting that the AVP-dependent AQP3 pathway may represent a novel target in colon diseases associated with abnormal cell growth

Midwifery capacity building in Papua New Guinea: Key achievements and ways forward

© 2015 Australian College of Midwives. Background: Papua New Guinea has some of the poorest health outcomes in the Asia-Pacific region. Maternal mortality is unacceptably high and there is a severe midwifery shortage requiring a quadrupling of the workforce. Aim: This paper outlines the findings of an evaluation of the Maternal Child Health Initiative (MCHI) (2012-2013) to determine key factors contributing to maternal health workforce strengthening. Method: A descriptive mixed methods study was undertaken. Data were gathered through interviews, focus group discussions and surveys with clinicians, midwifery students and staff from nursing and midwifery schools and National Department of Health staff. Documentation from stakeholder meetings and regular site reports were reviewed. Each data set was analysed separately and meta-inferences were drawn across all data. Findings: Learning opportunities were found to have increased for midwifery educators and improvements were described in midwifery educators teaching capacity and student clinical education experience. There was an increase in the number of midwifery graduates and improvements were noted in the working environment and skills of clinical staff. Education challenges were described including the lack of clinical preceptoring and limited continuing education for clinical educators. Participants recommended increasing clinical education hours and extending the length of the midwifery program. Ongoing efforts to accredit the midwifery curricula and regulate midwifery graduates were noted. Conclusion: The MCHI has contributed to strengthening the midwifery workforce nationally. However, scaling-up and sustaining these achievements requires leadership and funding commitments from the midwifery schools and government alongside the accreditation of midwifery curricula and regulation of new graduates