How much for your kidney? The rise of the global transplant tourism industry

The term 'Transplant Tourism' is becoming commonly used to describe any form of travel that serves in the attainment of new organs; this practice is utterly condemned by the medical community and the World Health Organisation. Medical Tourism involves tourists travelling to, 'obtain medical, dental and surgical care while simultaneously being holidaymakers' (Connell, 2006, p. 1094). British Medical Journal (2008) highlights that Medical Tourism is a billion dollar industry, where companies advertise health services and attract patients for a fraction of the price they would have paid at home (Turner, 2008a). However, the typically legitimate Medical Tourism industry's reputation is being tarnished by its association with Transplant Tourism. Human organs used in transplantation can be obtained in two ways: live organ donation or cadaveric organ procurement (Lamb, 1990). In general, recipients prefer having living donor transplants over deceased ones, as the former offer them a better chance of survival (Steinberg, 2004). There is a worldwide struggle to meet the demand for organs; the gap between supply and demand has stimulated global organ trade and transplant tourism. Transplant Tourism has been overlooked within tourism literature and hoping to begin a debate, this note investigates the concept of Transplant Tourism, outlining why it cannot, in general, be considered a legitimate part of the Medical Tourism industry

Modeling engagement with multimodal multisensor data: the continuous performance test as an objective tool to track flow

Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leaveone-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential

Keeping your audience : presenting a visitor engagement scale

Understanding visitors’ level of engagement with tourist attractions is vital for successful heritage management and marketing. This paper develops a scale to measure visitors’ level of engagement in tourist attractions. It also establishes a relationship between the drivers of engagement and level of engagement using Partial Least Square, whereby both formative and reflective scales are included. The structural model is tested with a sample of 625 visitors at Kelvingrove Museum in Glasgow, UK. The empirical validation of the conceptual model supports the research hypotheses. Whilst prior knowledge, recreational motivation and omnivore-univore cultural capital positively affect visitors’ level of engagement, there is no significant relationship between reflective motivation and level of engagement. These findings contribute to a better understanding of visitor engagement in tourist attractions. A series of managerial implications are also proposed

Drivers of the Sentenced Population: Probation Analysis

The report examines trends in the number and characteristics of felony probation sentences and caseloads in Illinois, as well as short-term outcomes measures for those discharged from felony probation. The research was performed in collaboration with the Administrative Office of the Illinois Courts and the Illinois Sentencing Policy Advisory Council

Electric-field-induced nematic-cholesteric transition and 3-D director structures in homeotropic cells

We study the phase diagram of director structures in cholesteric liquid crystals of negative dielectric anisotropy in homeotropic cells of thickness d which is smaller than the cholesteric pitch p. The basic control parameters are the frustration ratio d/p and the applied voltage U. Fluorescence Confocal Polarising Microscopy allows us to directly and unambiguously determine the 3-D director structures. The results are of importance for potential applications of the cholesteric structures, such as switchable gratings and eyewear with tunable transparency based.Comment: Will be published in Physical Review

Effect of vitamin C and vitamin E on lung contusion: A randomized clinical trial study

ABSTRACT There is association between lung contusion (lC) and a progressive in fl ammatory response. The protective effect of vitamin C and vitamin E, as strong free radical scavengers on favourite outcome of (LC) in animal models,has been confirmed. Design: to evaluate the effect of vitamins, E and C on arterial blood gas (ABG) and ICU stay, in (LC), with injury severity score (ISS) 18 ± 2, due to blunt chest trauma. Methods: This study was a randomized, double-blind, placebo controlled clinical trial. Patients with (ISS)18 ± 2 blunt chest trauma, who meet criteria, participated in the study. A total of 80 patients from Feb 2015 to Jun2018and were randomly divided into 4 groups. Patients received intravenous vitamin E (1000IU mg), was (group I);intravenous vitamin C (500) (group II). Vitamin C + vitamin E = (group III), and intravenous distilled water = (control group) or (group IV). ABG, serum cortisol, and CRP levels were determined at baseline, 24 h and 48 h after the intervention. Results: a significant decrease in ICU stay in group III compared to other groups (p < 0.001). Co-administration of vitamin C and vitamin E showed significant increases pH (values to reference range from acidemia”), oxygen pressure, and oxygen saturation in group III compared to other groups (p <0.001). A significant decrease in carbon dioxide pressure was also detected after receiving vitamin C and vitamin E in group III, compared to other groups (p < 0.001). There was no significant difference cortisol and CRP levels between groups after the intervention. Conclusion: Co-administration of vitamin C and vitamin E, improve the ABG parameters and reduce I

Development of a pedestrian bridge with GFRP profiles and fibre reinforced self-compacting concrete deck

In recent years, the number of pedestrian bridges built from composites materials has notably increased. The combination of fiber reinforced polymers (FRP) profiles with fiber reinforced concrete (FRC) elements is being adopted in this type of structures, since the ductility, high post-cracking tensile strength, compressive stiffness and strength of FRC can be combined with the benefits derived from the use of FRP’s profiles to obtain high performance structural systems. In the context of the present work a 12 m long single span pedestrian bridge with two composite Iprofiles was designed. In terms of deflection requirements imposed by serviceability limit states, the influence of the height and thickness of GFRP (Glass Fiber Reinforced Polymer) profiles, as well as the addition of a thin layer of prestressed carbon fiber sheet in the bottom flange of the GFRP profile was evaluated. Using software based on the finite element method, the structural behavior of the developed structural systems was analyzed. Furthermore, two prototypes of this structural system were built and monitored in order to assess their long-term deformational behavior when subjected to a loading configuration correspondent to the load combination for the deflection serviceability limit states. The main results obtained are presented and discussed.This work is part of the research project QREN number 3456, PONTALUMIS- Development of a prototype of a pedestrian bridge in GFRP-ECC concept, involving the Company ALTO - Perfis Pultrudidos, Lda., the ISISE/University of Minho and the ICIST/Technical University of Lisbon. The first and fourth authors wish to acknowledge the research grants under this project. The authors also wish to acknowledge the Civitest Company for the conception and development of the steel fiber reinforced self-compacting concrete used in this work, and to Secil, S&P Clever Reinforcement Iberica Lda. and Hilti Portugal - Productos e Servicos Lda. for the supplied materials and technical support

Multi-modalities in classroom learning environments

This paper will present initial findings from the second phase of a Horizon 2020 funded project, Managing Affective-learning Through Intelligent Atoms and Smart Interactions (MaTHiSiS). The project focusses on the use of different multi-modalities used as part of the project in classrooms across Europe. The MaTHiSiS learning vision is to develop an integrated learning platform, with re-usable learning components which will respond to the needs of future education in primary, secondary, special education schools, vocational environments and learning beyond the classroom. The system comprises learning graphs which attach individual learning goals to the system. Each learning graph is developed from a set of smart learning atoms designed to support learners to achieve progression. Cutting edge technologies are being used to identify the affect state of learners and ultimately improve engagement of learners. Much research identifies how learners engage with learning platforms (c.f. [1], [2], [3]). Not only do e-learning platforms have the capability to engage learners, they provide a vehicle for authentic classroom and informal learning [4] enabling ubiquitous and seamless learning [5] within a non-linear environment. When experiencing more enjoyable interaction learners become more confident and motivated to learn and become less anxious, especially those with learning disabilities or at risk of social exclusion [6], [13]. [7] identified the importance of understanding the affect state of learners who may experience emotions such as 'confusion, frustration, irritation, anger, rage, or even despair' resulting in disengaging with learning. The MaTHiSiS system will use a range of platform agents such as NAO robots and Kinects to measure multi-modalities that support the affect state: facial expression analysis and gaze estimation [8], mobile device-based emotion recognition [9], skeleton motion using depth sensors and speech recognition. Data has been collected using multimodal learning analytics developed for the project, including annotated multimodal recordings of learners interacting with the system, facial expression data and position of the learner. In addition, interviews with teachers and learners, from mainstream education as well as learners with profound multiple learning difficulties and autism, have been carried out to measure engagement and achievement of learners. Findings from schools based in the United Kingdom, mainstream and special schools will be presented and challenges shared