Human mouthfeel panel investigating the acceptability of electrospun and solvent cast orodispersible films

A human panel study was performed to investigate the acceptability of orodispersible electrospun and solvent cast films. 50 healthy volunteers took two drug-free samples of polyvinyl alcohol films prepared by the two methods. On a 5-point hedonic scale, the volunteers assessed the films’ perceived size, stickiness, thickness, disintegration time, thickening effect on saliva, and handling. The films manufactured by both methods were similar in their end-user acceptability. The modal values of perceived size, thickness, disintegration time, saliva thickening effect, and handling were high (4 or 5). However, for both, the stickiness mode was 2 (strongly sticky) and the only negative attribute. Both films were reported to take approximately 30 s to disintegrate completely in the mouth. Electrospun films scored similarly high to solvent cast orodispersible films in most attributes of end-user acceptability. Electrospun films were marginally preferred, with 27 out of 50 participants picking electrospinning when presented with a forced choice test of both fabrication methods. This is the first study to show that electrospinning enables the fabrication of orodispersible films that are acceptable to adult human participants in terms of handling and mouthfeel and suggests that the potential for clinical translation of such formulations is high

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

© 2015 IEEE. An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05)

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

© 2017 IEEE. This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system

Improving EEG-based driver fatigue classification using sparse-deep belief networks

© 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively

Vision and Foraging in Cormorants: More like Herons than Hawks?

Background Great cormorants (Phalacrocorax carbo L.) show the highest known foraging yield for a marine predator and they are often perceived to be in conflict with human economic interests. They are generally regarded as visually-guided, pursuit-dive foragers, so it would be expected that cormorants have excellent vision much like aerial predators, such as hawks which detect and pursue prey from a distance. Indeed cormorant eyes appear to show some specific adaptations to the amphibious life style. They are reported to have a highly pliable lens and powerful intraocular muscles which are thought to accommodate for the loss of corneal refractive power that accompanies immersion and ensures a well focussed image on the retina. However, nothing is known of the visual performance of these birds and how this might influence their prey capture technique. Methodology/Principal Findings We measured the aquatic visual acuity of great cormorants under a range of viewing conditions (illuminance, target contrast, viewing distance) and found it to be unexpectedly poor. Cormorant visual acuity under a range of viewing conditions is in fact comparable to unaided humans under water, and very inferior to that of aerial predators. We present a prey detectability model based upon the known acuity of cormorants at different illuminances, target contrasts and viewing distances. This shows that cormorants are able to detect individual prey only at close range (less than 1 m). Conclusions/Significance We conclude that cormorants are not the aquatic equivalent of hawks. Their efficient hunting involves the use of specialised foraging techniques which employ brief short-distance pursuit and/or rapid neck extension to capture prey that is visually detected or flushed only at short range. This technique appears to be driven proximately by the cormorant's limited visual capacities, and is analogous to the foraging techniques employed by herons

Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in acute lung injury to reduce pulmonary dysfunction (HARP-2) trial : study protocol for a randomized controlled trial

Acute lung injury (ALI) is a common devastating clinical syndrome characterized by life-threatening respiratory failure requiring mechanical ventilation and multiple organ failure. There are in vitro, animal studies and pre-clinical data suggesting that statins may be beneficial in ALI. The Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in Acute lung injury to Reduce Pulmonary dysfunction (HARP-2) trial is a multicenter, prospective, randomized, allocation concealed, double-blind, placebo-controlled clinical trial which aims to test the hypothesis that treatment with simvastatin will improve clinical outcomes in patients with ALI

DEM of triaxial tests on crushable sand

This paper presents simulations of high-pressure triaxial shear tests on a crushable sand. The discrete element method is used, featuring a large number of particles and avoiding the use of agglomerates. The triaxial model features a flexible membrane, therefore allowing realistic deformation, and a simple breakage mechanism is implemented using the octahedral shear stress induced in the particles. The simulations show that particle crushing is essential to replicate the realistic behaviour of sand (in particular the volumetric contraction) in high-pressure shear tests. The general effects of crushing during shear are explored, including its effects on critical states, and the influence of particle strength and confining pressure on the degree of crushing are discussed

Process evaluation of appreciative inquiry to translate pain management evidence into pediatric nursing practice

Background Appreciative inquiry (AI) is an innovative knowledge translation (KT) intervention that is compatible with the Promoting Action on Research in Health Services (PARiHS) framework. This study explored the innovative use of AI as a theoretically based KT intervention applied to a clinical issue in an inpatient pediatric care setting. The implementation of AI was explored in terms of its acceptability, fidelity, and feasibility as a KT intervention in pain management. Methods A mixed-methods case study design was used. The case was a surgical unit in a pediatric academic-affiliated hospital. The sample consisted of nurses in leadership positions and staff nurses interested in the study. Data on the AI intervention implementation were collected by digitally recording the AI sessions, maintaining logs, and conducting individual semistructured interviews. Data were analysed using qualitative and quantitative content analyses and descriptive statistics. Findings were triangulated in the discussion. Results Three nurse leaders and nine staff members participated in the study. Participants were generally satisfied with the intervention, which consisted of four 3-hour, interactive AI sessions delivered over two weeks to promote change based on positive examples of pain management in the unit and staff implementation of an action plan. The AI sessions were delivered with high fidelity and 11 of 12 participants attended all four sessions, where they developed an action plan to enhance evidence-based pain assessment documentation. Participants labeled AI a 'refreshing approach to change' because it was positive, democratic, and built on existing practices. Several barriers affected their implementation of the action plan, including a context of change overload, logistics, busyness, and a lack of organised follow-up. Conclusions Results of this case study supported the acceptability, fidelity, and feasibility of AI as a KT intervention in pain management. The AI intervention requires minor refinements (e.g., incorporating continued follow-up meetings) to enhance its clinical utility and sustainability. The implementation process and effectiveness of the modified AI intervention require evaluation in a larger multisite study

Unraveling the Complexity of Tourist Experience with NFC Technology and Mobile Wallets

By considering the tourist experience as a complex dynamic system, in this paper we depict the traveler as a kybernetes (\u3ba\u3c5\u3b2\u3b5\u3c1\u3bd\u3ae\u3c4\u3b7\u3c2 is the ancient Greek word for \u2018sea captain\u2019, \u2018steersman\u2019, or \u2018governor\u2019) in search of powerful tools to help him or her to obtain directions in the mare magnum of complexity, overcoming the fear of action and taking decisions. We focus our attention on the key role of Near Field Communication technology and mobile wallet as \u2018attenuators of complexity\u2019 in the travel and tourism industr

Inflation, moduli (de)stabilization and supersymmetry breaking

We study the cosmological inflation from the viewpoint of the moduli stabilization. We study the scenario that the superpotential has a large value during the inflation era enough to stabilize moduli, but it is small in the true vacuum. This scenario is discussed by using a simple model, one type of hybrid models.Comment: 17 pages, 7 figure