Validating Dose Uncertainty Estimates Produced by AUTODIRECT: An Automated Program to Evaluate Deformable Image Registration Accuracy.

Deformable image registration is a powerful tool for mapping information, such as radiation therapy dose calculations, from one computed tomography image to another. However, deformable image registration is susceptible to mapping errors. Recently, an automated deformable image registration evaluation of confidence tool was proposed to predict voxel-specific deformable image registration dose mapping errors on a patient-by-patient basis. The purpose of this work is to conduct an extensive analysis of automated deformable image registration evaluation of confidence tool to show its effectiveness in estimating dose mapping errors. The proposed format of automated deformable image registration evaluation of confidence tool utilizes 4 simulated patient deformations (3 B-spline-based deformations and 1 rigid transformation) to predict the uncertainty in a deformable image registration algorithm's performance. This workflow is validated for 2 DIR algorithms (B-spline multipass from Velocity and Plastimatch) with 1 physical and 11 virtual phantoms, which have known ground-truth deformations, and with 3 pairs of real patient lung images, which have several hundred identified landmarks. The true dose mapping error distributions closely followed the Student t distributions predicted by automated deformable image registration evaluation of confidence tool for the validation tests: on average, the automated deformable image registration evaluation of confidence tool-produced confidence levels of 50%, 68%, and 95% contained 48.8%, 66.3%, and 93.8% and 50.1%, 67.6%, and 93.8% of the actual errors from Velocity and Plastimatch, respectively. Despite the sparsity of landmark points, the observed error distribution from the 3 lung patient data sets also followed the expected error distribution. The dose error distributions from automated deformable image registration evaluation of confidence tool also demonstrate good resemblance to the true dose error distributions. Automated deformable image registration evaluation of confidence tool was also found to produce accurate confidence intervals for the dose-volume histograms of the deformed dose

Seeing coffee tourism through the lens of coffee consumption: A critical review

One of the world’s most popular beverages, coffee is used to satisfy a wide range of consumptions, including tourism. In this article, we examined the existing concepts of coffee consumption and identified additional consumption domains that may contribute to the growing body of knowledge regarding coffee tourism. The paper used a scoping review approach and Critical Media Discourse Analysis (CMDA). The scoping review examined 152 articles on coffee consumption and coffee tourism published up to 2020. Subsequently, CMDA enabled a more in-depth textual and contextual analysis of the literature. In addition, Leximancer was used to illuminate the prominent scopes of the literature. Three prominent scopes of the literature are identified in the textual analysis: consumer behaviour, place consumption,and ethical consumption. In addition, the contextual findings indicate that coffee tourism studies have increased in recent decades. Furthermore, the social context highlights the dynamic nature of the coffee market landscape in the global North and the global South. Future research directions were suggested, and the managerial implications of these findings were discussed

Understanding of Molecular Interactions and Reversibility in Silylamines

Modern Li-ion batteries employ flammable electrolytes that pose safety concerns. Battery safety has become important as several incidents involving Li-ion battery fires have been observed. This issue could be addressed through a switchable battery electrolyte. Reversible ionic liquids (RevILs) could be used in such an electrolyte that would cease operation in the event of rapid temperature elevation because they can switch between states of drastically different properties upon the application of an external stimulus, such as temperature or CO2 addition. Silylamines, in particular, are a class of thermally responsive RevILs that are conducting in its ionic liquid (RevIL) state and non-conducting in its molecular liquid (ML) state. Thus, silylamines could serve as model compounds for the thermally switchable battery electrolyte concept. However, the temperature dynamics and ion-ion interactions within the silylamine system need to be understood before implementation. This thesis explores the viability of the (3-aminopropyl)tripropylsilylamine (TPSA) system based on its thermal responses and salt interactions. Because neat TPSA (RevIL) is viscous and non-conductive, TPSA was also studied as a solvent mixture with DMSO. Variable temperature (VT) FT-IR studies showed that the RevIL to ML switch was maintained in the TPSA/DMSO system. VT conductivity studies showed a conductivity maximum at 90°C. The thermal dependence of conductivity makes TPSA a potential candidate for the thermal switch. HOESY and PGSE NMR experiments were also performed to understand the issue of salt solubility, which correlate the ion-ion interactions within the electrolyte mixture. These experiments revealed that the alkyl groups may participate in interactions upon addition of lithium hexafluorophosphate salt, which results in inhibited diffusion. This signals a possible route for tuning the TPSA structure to improving the conductivity and salt solubility for eventual use as an electrolyte solvent

How can we improve our Northampton Area Psychology (NAP) teachers’ group provision?

The Northampton Area Psychology (NAP) Teachers Group is a historical collaboration initiative led by Psychology lecturers from the University of Northampton working with local Psychology A level teachers. NAP meetings are a resource for both parties to facilitate exchange of ideas, a community of staff, communication and alignment of curriculum updates and new research in the field of Psychology. The meetings also offer networking opportunities, resources share, alignment of Further / Higher Education curriculum specifications and continuing professional development. These meetings previously have taken place bi-annually. In addition to this NAP events have been part of the collaboration and are arranged and ran by Psychology staff and students. These events are aimed at inspiring local students by giving them the opportunity to take part in their own research. Events can increase students’ subject knowledge, develop skills for their current assessments and offer a unique discussion point in their UCAS statement demonstrating commitment to the subject and readiness to participate in extracurricular activities. It also gives the students the opportunity to experience and gain an insight into studying at the University of Northampton. The aim of this project was to understand and gain perspectives from Psychology Teachers on what is important to them in terms of a collaboration with the University of Northampton in the field of Psychology. Findings to be presented will illustrate best practice for how NAP meetings are carried out, frequency of meetings, as well as benefits for those engaging in them, including for teacher practice and student university transitions

Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization

The uncertainty quantification of prediction models (e.g., neural networks) is crucial for their adoption in many robotics applications. This is arguably as important as making accurate predictions, especially for safety-critical applications such as self-driving cars. This paper proposes our approach to uncertainty quantification in the context of visual localization for autonomous driving, where we predict locations from images. Our proposed framework estimates probabilistic uncertainty by creating a sensor error model that maps an internal output of the prediction model to the uncertainty. The sensor error model is created using multiple image databases of visual localization, each with ground-truth location. We demonstrate the accuracy of our uncertainty prediction framework using the Ithaca365 dataset, which includes variations in lighting, weather (sunny, snowy, night), and alignment errors between databases. We analyze both the predicted uncertainty and its incorporation into a Kalman-based localization filter. Our results show that prediction error variations increase with poor weather and lighting condition, leading to greater uncertainty and outliers, which can be predicted by our proposed uncertainty model. Additionally, our probabilistic error model enables the filter to remove ad hoc sensor gating, as the uncertainty automatically adjusts the model to the input dataComment: Extended version of our ICRA2023 pape

Moderating Social Media Discourse for a Healthy Democracy

The prevalence of hate speech and misinformation on the internet, heightened by the COVID-19 pandemic, directly harms minority groups that are the target of vitriol, as well as our society at large (Müller & Scwarz, 2020). In addition, the intersection between the two only exacerbates their harmful effects leading to an increase in intolerance and polarization (Kim & Kesari 2021). Current platform moderation techniques, as well as Section 230 under the Communications Decency Act, have been insufficient in addressing this problem, resulting in a lack of transparency from internet service providers, clear boundaries on user-platforms relations, and sufficient tools to handle a rapidly expanding internet. To address this problem space, we advocate for the following solutions: 1. Algorithmic governance & transparency: Internet Service Providers should be more transparent with users about content moderation policies and algorithms, and clarify users’ basic rights on the platform. 2. Flagging recommendations: We advocate a more effective, efficient and comprehensive flagging system through a combined strategy of content- and user-based approaches. 3. Multiplatform collaboration: Fighting harmful online content requires a collaborative effort among policy makers, civil society groups, researchers, and different platforms. 4. Long-term considerations: Building a regular and prolonged tracking system is essential to make anti-misinformation efforts more efficient and effective, especially in complex scenarios.Journalism and Medi

Evaluating the outcomes of intergenerational shared experiences in learning environments: Perspectives from HE (Higher Education) students

This study evaluated the barriers, enablers, and outcomes of intergenerational activities, by interviewing students in higher education who engaged in intergenerational activities. This student-led and co-designed URB@N project focused on current HE students’ experience of activities involving older adults aged 65+ and younger adults, for those who had taken part in at least one intergenerational activity. This poster will illustrate how such research enabled an understanding of HE student involvement and experiences in these activities, including what activities are typically undertaken in HE settings; self-reported outcomes, barriers and facilitators for students and for the success of activity engagement, including how this impacts the student experience