Fabrication and characterisation of polymer brushes for the use in area selective deposition

With the constant increasing demand for faster and more efficient electronic devices, the requirement for smaller integrated circuits has grown exponentially. The current method of fabrication for these devices, known as photolithography, employs a ‘top-down’ approach using light and masks for the patterning of substrate surfaces. This method, however, is reaching its size limits and has become extremely costly to carry out. Research into the fabrication of polymer brushes for the use in area selective deposition is vital for the understanding of ‘bottom up’ lithography techniques, such as block copolymer lithography. Such methods rely on the self-assembly of polymers containing active and inactive regions and are being proposed as an alternative to the current ‘top-down’ methods used for the manufacturing of electronic devices. These self-assembled polymer patterns can be exposed to infiltrating materials via a vapour phase process thus allowing for the infiltration of the active regions while blocking deposition in the inactive areas. A major part of these fields is investigating the polymer materials that will either accept or block infiltration by different species such as metals. This work looks at developing fabrication techniques of polymer brushes with a focus on increasing the overall thickness. It then goes on to investigate the infiltration of different polymers as well as looking at the effect that thickness has on a polymers infiltration and blocking mechanisms using hard X-Ray photoelectron spectroscopy as the core analysis method alongside techniques such as ellipsometry, atomic force microscopy and X-Ray reflectivity

Straddlers not spiralists: critical questions for research on fixers, local-foreign news work, and cross-border journalism

This article challenges current trends in the study of fixers and other forms of “localforeign news work” through discussion of questions crucial to future investigations. Responding to Kotisova and Deuze ’s call to complicate the existing “repertoire of concepts, theories, and epistemic categories” now in use in scholarship on fixing (2022: 1172), we provide theoretical frameworks relevant to, but thus far unutilized by, this scholarship. Considering local-foreign news work as a process of straddling political, cultural, and epistemic boundaries allows us to interrogate the conceptual binaries operating in the relevant research, such as west/nonwest, local/foreign, fixer/journalist. By engaging the liminality of local journalistic labor, this article brings into relief dynamics often obscured in current studies, namely, the impact of race and gender identities, and the post-colonial contexts within which much local-foreign news work takes place. Attention to these dynamics challenges the conceptual divisions upon which studies of cross-border journalism often rely, while revealing the consequential – and boundarydefying – positionality of local news workers. Finally, examination of the “cosmopolitanism” of local-foreign news work, and the “situatedness” of the knowledge produced by local news workers, serves to thicken scholarship on the topic in ways that deactivate essentialisms, deepen empirical foundations, and address problematic configurations of power critical to the study of news production today. By diversifying the research queries we pose, and the theoretical perspectives we employ, future research can better account for the dynamism of local-foreign news work in the contemporary global news landscape

Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles

This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets

A systematic review of the use of virtual reality in teaching Chinese as a foreign language

The purpose of this review is to provide a thorough analysis of existing research on the implementation of virtual reality (VR) in teaching Chinese as a foreign language (TCFL). In recent years, there has been a growing interest in the potential benefits of VR for language learning, and multiple studies have explored its application in TCFL. However, to date, no systematic reviews on the specific uses of VR in TCFL have been conducted. This study aims to bridge this gap by conducting a comprehensive review of empirical articles on the topic, identified from the Scopus and Web of Science databases. The search terms include “VR”, “virtual reality”, and “Chinese language”. The findings of this review shed light on the current trends in VR-related publications in the field of TCFL, the research settings where VR is used to support learning, and the potential benefits and effectiveness of VR in enhancing Chinese language learning, such as linguistic growth, communication skills, motivation, and immersive and authentic learning contexts. The review also highlights the challenges and limitations of using this technology in TCFL. The significance of this review lies in its potential to inform educators, researchers, and practitioners interested in using VR in TCFL about the current state of research and its implications for language learning. It can also contribute to the development of best practices for the effective use of VR in TCFL, ultimately leading to improved language learning outcomes for students

Development and psychometric properties of the measure of anxiety in practical examinations

Introduction: High levels of exam anxiety are evident in healthcare students. Practical exams are an integral part of healthcare profession programs. However, no standardised reliable and valid instrument exists to measure practical exam anxiety in healthcare students. Objective: This study aimed to modify a valid and reliable measure used to examine anxiety in job interviews, for use in practical examinations. We then aimed to examine the psychometric properties of the new modified instrument, now characterized as the Measure of Anxiety in Practical Examinations (MAPE) and determine if any differences in gender, personal history of Generalised Anxiety Disorder (GAD) or family history of GAD impacted MAPE scores. Methods: Exploratory factor analysis using principal component analysis was conducted and Cronbach’s alpha examined internal consistency of the instrument. Results: Most A five factor structure was supported (Performance, Appearance, Behaviour, Communication, and Preparedness) which accounted for 60.6% of the variance in responses. The 25 item modified instrument demonstrated sufficient internal consistency (Cronbach’s alpha = 0.93). Females (p = 0.01) and those with a personal history of GAD (0.002) presented with higher MAPE scores. Conclusion: This The MAPE is an acceptable measure of identifying students who present with practical exam anxiety and can help support healthcare profession students to alleviate practical exam anxiety and ensure students’ grades more accurately reflect their skill acquisition. Gender and personal history of GAD can also impact practical exam anxiety and should be considered when addressing practical exam anxiety in healthcare profession students

Motion aware self-supervision for generic event boundary detection

The task of Generic Event Boundary Detection (GEBD) aims to detect moments in videos that are naturally perceived by humans as generic and taxonomy-free event boundaries. Modeling the dynamically evolving temporal and spatial changes in a video makes GEBD a difficult problem to solve. Existing approaches involve very complex and sophisticated pipelines in terms of architectural design choices, hence creating a need for more straightforward and simplified approaches. In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task. We perform extensive experiments on the challenging Kinetics-GEBD and TAPOS datasets to demonstrate the efficacy of the proposed approach compared to the other self-supervised state-of-the-art methods. We also show that this simple self-supervised approach learns motion features without any explicit motion-specific pretext task

Use and uptake of technology by people with dementia and their supporters during the COVID-19 pandemic

Objective: This rapid review aims to identify the types of technologies used by people with dementia and their supporters during the COVID-19 pandemic, and the issues which influenced technology adoption within their usual care routines. Methods: PubMed, PsychInfo, Scopus, and Cochrane COVID reviews were searched to identify peer-review studies published since 2020. A total of 18 studies were included and synthesised thematically. Results: Of these, most were conducted in the community (n=15) with people with dementia only (n=11) and involved qualitative methods (n=11). The majority (n=12) focused on digital off-the-shelf and low-cost solutions, such as free video conferencing platforms, to access care, socialise or take part in interventions. Whilst often well-accepted and associated with positive outcomes (such as improved social connectedness), lack of digital literacy or support to use technologies, limited access to appropriate technology, individuals’ physical, cognitive, or sensory difficulties, were highlighted and likely to threaten the adoption of these solutions. The quality of the evidence was mixed, neither very robust nor easily generalisable which may be attributed to the challenges of conducting research during the pandemic or the need to rapidly adapt to a new reality. Conclusion: While COVID-19 has fast-tracked the adoption of technology, its use is likely to continue beyond the pandemic. We need to ensure this technology can leverage dementia support and care and that people with dementia are enabled and empowered to use it

Student engagement with technology-enhanced resources in mathematics in higher education: a review

The effectiveness of technology-enhanced resources in mathematics in higher education is far from clear, nor is student engagement with such resources. In this review article, we investigate the existing literature in three interrelated areas: student engagement with technology in higher education and mathematics; what works and what does not in technology in education and in mathematics in higher education; evaluating the use of technology in higher education and mathematics; and the use of frameworks and models. Over 300 research articles were identified for this purpose and the results are reported in this review. We found a dearth of studies in undergraduate mathematics education that specifically focus on student engagement with technology. In addition, there is no overarching framework that describes both the pedagogical aspects and the educational context of technology integration in mathematic

Modern designs of electrochemical sensor platforms for environmental analyses: principles, nanofabrication opportunities, and challenges

In recent decades, much attention has been paid to using nanomaterials in the development of highly-sensitive sensors for environmental monitoring. This review describes how nanomaterials are being used to develop electrochemical sensing platforms for environmental analysis (air pollution, water quality, soil nutrients, and soil pathogens). In particular, we discuss the use of nanofabrication techniques (e.g., monolayer self-assembly, dropcasting, molecular imprinting, electrodeposition, in situ polymerization, hydrogenation, and 3D printing) in the fabrication of high-sensitive electrodes is addressed. The potential use of carbon, organic, inorganic, and hybrid nanomaterials in electrochemical sensing platforms and to enable automation, real-time detection, and multiplexed test development are also addressed. Recent applications of mobile, disposable, wearable, implantable, and self-powered electrochemical sensors for monitoring ions, particles, compounds, nutrients, microorganisms, and contaminants in real environmental samples are covered. Finally, the opportunities and challenges in nanofabrication high-performance electrochemical sensors and optimizing their performance in testing real samples are highlighted