Welcome to WIRE
(Wolverhampton Intellectual Repository and E-Theses)
WIRE is an open access repository for the research publications and other outputs from postgraduate students and staff at the University of Wolverhampton.
Wolverhampton staff: to deposit your publication to WIRE, go to: https://www.wlv.ac.uk/lib/research/wire/
Use the search box above or the browse function on the left to discover publications from the research community at the University of Wolverhampton.
University students and staff can also search WIRE using LibrarySearch
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Metamaterials for energy harvestingMetamaterials offer significant potentials for numerous applications due to their unique acoustics, electromagnetic, optical, and mechanical properties. The increasing interest in the development of metamaterials is also driven by the inability of traditional architecture to offer novel functionalities offered by metamaterials. Recently it has been shown that the metamaterial phenomenon can be exploited for the development of energy harvesting devices especially in the field of energy scavenging at low intensity. Approaches include algorithmically arranged building blocks at the sub-micron level to achieve the desired order of response against incident energy. Furthermore, the ease of customisation with regards to metamaterials in alignment with energy sources such as acoustic, mechanical, optical and microwave offer numerous avenues for energy harvesting. For the development and selection of suitable energy harvesting metamaterial a critical understanding of their classifications, fabrication, and opportunities for customisation with respect to size, shape and lattice spacing is required, which this paper aims to provide. Furthermore, various concepts and experiments implemented to demonstrate and assess energy using metamaterials from sources such as sound waves, solar waves and mechanical vibrations are also covered.
Interpreting in international sign: decisions of Deaf and non-Deaf interpretersThe professional use of Deaf Interpreters (DIs) is increasing in several countries and across several contexts. However, there have been few studies that have explored the nature of the work when it involves a Deaf and nondeaf interpreting team. The current study examined the work of two teams of Deaf/non-deaf interpreters providing service in a conference setting. The participants were videotaped while providing service in order to examine the linguistic decisions made by non-deaf interpreters acting as a natural signed language feed, the linguistic decisions made by Deaf interpreters working into International Sign (IS), as well as the meta-communication strategies the team used while constructing the interpretation. The data suggest that interpreting teams that are more familiar with each other rely on different strategies when chunking information, asking for feeds, and for making accommodations. There also appear to be significant differences in the work when the two interpreters share a common natural signed language. All of the data analyzed thus far offer insight into the nature of the relationship and may provide guidance to those arranging interpreting services for international events.
Handling cross and out-of-domain samples in Thai word segmentationWhile word segmentation is a solved problem in many languages, it is still a challenge in continuous-script or low-resource languages. Like other NLP tasks, word segmentation is domain-dependent, which can be a challenge in low-resource languages like Thai and Urdu since there can be domains with insufficient data. This investigation proposes a new solution to adapt an existing domaingeneric model to a target domain, as well as a data augmentation technique to combat the low-resource problems. In addition to domain adaptation, we also propose a framework to handle out-of-domain inputs using an ensemble of domain-specific models called MultiDomain Ensemble (MDE). To assess the effectiveness of the proposed solutions, we conducted extensive experiments on domain adaptation and out-of-domain scenarios. Moreover, we also proposed a multiple task dataset for Thai text processing, including word segmentation. For domain adaptation, we compared our solution to the state-of-the-art Thai word segmentation (TWS) method and obtained improvements from 93.47% to 98.48% at the character level and 84.03% to 96.75% at the word level. For out-of-domain scenarios, our MDE method significantly outperformed the state-of-the-art TWS and multi-criteria methods. Furthermore, to demonstrate our method’s generalizability, we also applied our MDE framework to other languages, namely Chinese, Japanese, and Urdu, and obtained improvements similar to Thai’s.
The association between training load indices and upper respiratory tract infections (URTIs) in elite soccer playersThis study aimed to investigate the association between training load indices and Upper Respiratory Tract Infection (URTI) across different lag periods in elite soccer players. Internal training load was collected from 15 elite soccer players over one full season (40 weeks). Acute, chronic, Acute:Chronic Workload Ratio (ACWR), Exponentially Weighted Moving Averages (EWMA) ACWR, 2, 3 and 4-week cumulative load, training strain and training monotony were calculated on a rolling weekly basis. Players completed a daily illness log, documenting any signs and symptoms, to help determine an URTI. Multilevel logistic regression was used to analyze the associations between training load indices and URTIs across different lag periods (1 to 7-days). The results found a significant association between 2-week cumulative load and an increased likelihood of a player contracting an URTI 3 days later (Odds Ratio, 95% Confidence Interval: OR = 2.07, 95% CI = 0.026-1.431). Additionally, a significant association was found between 3-week cumulative load and a players’ increased risk of contracting an URTI 4 days later (OR = 1.66, 95% CI = 0.013–1.006). These results indicate that accumulated periods of high training load (2- and 3-week) associated with an increased risk of a player contracting an URTI, which may lead to performance decrements, missed training sessions or even competitions.
Sentiment analysis for Urdu online reviews using deep learning modelsMost existing studies are focused on popular languages like English, Spanish, Chinese, Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. In this paper, we develop a deep learning model for the sentiments expressed in this under-resourced language. We develop an open-source corpus of 10,008 reviews from 566 online threads on the topics of sports, food, software, politics, and entertainment. The objectives of this work are bi-fold (1) the creation of a human-annotated corpus for the research of sentiment analysis in Urdu; and (2) measurement of up-to-date model performance using a corpus. For their assessment, we performed binary and ternary classification studies utilizing another model, namely LSTM, RCNN Rule-Based, N-gram, SVM, CNN, and LSTM. The RCNN model surpasses standard models with 84.98 % accuracy for binary classification and 68.56 % accuracy for ternary classification. To facilitate other researchers working in the same domain, we have open-sourced the corpus and code developed for this research.