Staffordshire University
  • Stoke-on-Trent, United Kingdom
Recent publications
Building information model (BIM) is primarily a 3D digital representation of a structure and features such as geometry, spatial relationships, and geographic information to support integrated design. BIM in recent years has evolved from simple 3D to interacting with virtual and augmented reality. Such interaction aims to improve work productivity, home comfort, and entertainment, common Internet of things (IoT) goals. The IoT represents a possible evolution of the use of the Internet in which objects are able to communicate data about themselves and with other devices autonomously. One of the main goals of IoT is to build a digital copy of the real world. Therefore, BIM and IoT can integrate through data acquired in a BIM model; this model can be helpful in predictive analysis as needed. This paper aims to describe a methodology that allows the visualization and representation of data from sensors within the BIM environment to support decisions, sometimes complex, requiring interdisciplinary expertise. The study focusses on a real case study: a scale prototype of a single-family house that includes several sensors capable of producing data that feed a database based on the predictive/decision-making phase developed through machine learning techniques. The proposed methodology integrates an IoT-based platform that allows communication between sensors and Dynamo software to access sensor data, automatically updating the information contained in the BIM model.
Footwear has been documented as a significant factor in the aetiology of foot pain in the general population. Assessing footwear in a clinical setting continues to be practitioner specific and there is limited guidance to direct advice. Health professionals must have access to clinically appropriate and reliable footwear assessment tools to educate patients on healthier footwear choices. The primary aim of this study was to critique what elements should be in a footwear assessment tool with a secondary aim of testing the agreed tool for validity. A combined Nominal Group Technique and then a Delphi technique from purposively sampled experts of foot health professions were employed to critique elements of footwear assessment. The agreed tool was then tested by practising podiatrists on 5 different shoes to assess the validity and reliability of the measures. Twelve test evaluation criteria were identified receiving significant ratings to form the final footwear assessment tool consisting of five footwear themes. Application of the tool in a clinical setting validated the themes of footwear characteristics, footwear structure, motion control and wear patterns. However, the assessment of footwear fit was not reliable. The footwear tool was refined based on the collective consensus achieved from the rounds creating a more clinically appropriate tool. The validity of this tool was assessed as high in some of the themes but for those that were lower, a training need was identified.
Although social media-based brand equity has become a vital area of interest for brand managers, insights into its destination-based dynamics and applications remain scarce, specifically in the destination brand context. To address this gap, we develop and test a theoretical model to investigate the role of destination marketing organization-generated and tourist-generated social media communication to determine the brand awareness and brand image of the Gilgit-Baltistan region, which in turn influence customer-based brand equity (CBBE) (i.e. perceived quality), satisfaction, and loyalty. Data come from well-known tourist sites in Gilgit-Baltistan. Using the multi-sequential approach in WarpPLS 7.0, findings shows that the social media communication dimensions show differential impacts on brand awareness as a metric of CBBE. Second, destination awareness demonstrates a differential impact on perceived destination image dimensions. Third, the destination image dimensions exert different effects on the perceived quality of the destination. Fourth, perceived quality positively influences satisfaction, which in turn enhances loyalty. We offer important implications that emerged from the analyzes and also suggest directions for future research.
To identify various pneumonia types, a gap of 15% value is being created every five years. To fill this gap, accurate detection of chest disease is required in the healthcare department to avoid any serious issues in the future. Testing the affected lungs to detect a Coronavirus 2019 (COVID-19) using the same imaging modalities may detect some other chest diseases. This wrong diagnosis strongly needs a multidisciplinary approach to the right diagnosis of chest-related diseases. Only a few works till now are targeting pathological x-ray images. Many studies target only a single chest disease that is not enough to automate chest disease detection. Only a few studies regarding the observation of the COVID-19, but more cases are those where it can be misclassified as detecting techniques not providing any generic solution for all types of chest diseases. However, the existing studies can only detect if the person has COVID-19 or not. The proposed work significantly contributes to detecting COVID-19 and other chest diseases by providing useful analysis of chest-related diseases. One of our testing approaches achieves 90.22% accuracy for 15 types of chest disease with 100% correct classification of COVID-19. Though it analyzes the perfect detection as the accuracy level is high enough, but it would be an excellent decision to consider the proposed study until doctors can visually inspect the input images used by models that lead to its detection.
A high-resolution (1 km × 1 km) monthly gridded rainfall data product during 1901–2018, named Bangladesh Gridded Rainfall (BDGR), was developed in this study. In-situ rainfall observations retrieved from a number of sources, including national organizations and undigitized data from the colonial era, were used. Leave-one-out cross-validation was used to assess product’s ability to capture spatial and temporal variability. The results revealed spatial variability of the percentage bias (PBIAS) in the range of −2 to 2%, normalized root mean square error (NRMSE) <20%, and correlation coefficient (R ² ) >0.88 at most of the locations. The temporal variability in mean PBIAS for 1901–2018 was in the range of −4.5 to 4.3%, NRMSE between 9 and 19% and R ² in the range of 0.87 to 0.95. The BDGR also showed its capability in replicating temporal patterns and trends of observed rainfall with greater accuracy. The product can provide reliable insights regarding various hydrometeorological issues, including historical floods, droughts, and groundwater recharge for a well-recognized global climate hotspot, Bangladesh.
Spirituality is recognised as a fundamental aspect of health and nursing care. Yet, there are few studies exploring how this concept may be understood outside of Western culture. This scoping review seeks to address this omission by focusing specifically on research conducted with Chinese populations. This is important because people from Chinese backgrounds (PBC) are now residing all over the world, and their spirituality and spiritual needs should be considered when providing healthcare. Adopting a purely generalist understanding and application of spirituality may not capture the cultural difference that exists between the East and West. This scoping review adopted Arksey and O’Malley’s method to focus on spirituality and spiritual care among PBC in health and nursing. The systematic strategy was adopted and used to search the main databases in health and nursing. Eighteen (n = 18) empirical studies were included in the review: 11 qualitative studies and seven quantitative involving 1870 participants. The scoping review revealed that in the Chinese understanding of spirituality is an abstract and personal concept which can refer to an internal vital force, experiences of suffering, and traditional Chinese cultural and religious values. As the multidimensional understanding of spirituality and spiritual care may cause confusion, these findings may provide a direction for the researchers emphasising the need for cultural and religious sensitivity when understanding of spirituality.
Aims To describe how people of African descent perceive and understand type 2 diabetes, and to examine the impact of their perceptions and beliefs on the uptake of diet, exercise, weight control and adherence to medication recommendations. Design Systematic literature review of quantitative and qualitative studies. Data sources We searched MEDLINE, CINAHL Complete, Psych INFO, Academic Search Premier, Education Research Complete, Web of Science and Scopus, World Health Organization (WHO), Diabetes UK and American Diabetes Association for articles published from January 1999 to December 2019. Review methods Informed by the PRISMA guidelines, we independently reviewed titles and abstracts, identified articles for full‐text review that met inclusion criteria, conducted a quality assessment and extracted data. Findings were synthesized using a thematic approach. Results Twenty‐six studies met the inclusion criteria. Knowledge and understanding of diabetes were poor. Beliefs and behaviours about diet, exercise, weight and health care were erroneous. Most diabetic participants could not recognize diabetes symptoms, failed to take their diagnosis seriously and did not adhere to medication recommendations. The resultant effect was an increased risk of complications with undesirable outcomes. Conclusion Poor diabetes perceptions are linked to negative consequences and may be responsible for poorer outcomes among people of African descent. This review highlights the need to consider this population's beliefs and practices in structuring culturally sensitive programmes for diabetes management. Impact This systematic literature review is the first to exclusively explore perceptions of people of African descent in relation to diabetes. It is important to consider people of African descents' diabetes perceptions and practices before formulating interventions for their diabetes management.
There is a considerable body of literature that outlines the dangers of mobile phone use by drivers. However, there is very little research that explores the role and effectiveness of attempts to tackle this specific road user problem. Generally, normative motives are more likely to generate compliance with traffic law, and are more likely to be developed through approaches which focus on engagement and education. There would seem to be little potential for them to be developed through the use of penalty points and fines, which rely on more instrumental logic. Nonetheless, the decision was made in the UK in recent years to cease offering ‘courses’ (inputs to detected phone-using drivers offered as an alternative to prosecution) for mobile phone offences. This decision was made despite a lack of evidence one way or another about their effectiveness in tackling both handheld mobile phone use and handsfree mobile phone distraction – a form of distraction not explicitly covered in law. This research project aimed to explore driver education as an alternative to prosecution for mobile phone use while driving offences, focusing on perceptions and experiences of one particular educational intervention. This paper draws on 46 semi-structured interviews with those involved in delivering a specific intervention aimed at reducing handheld mobile phone use by drivers that was previously offered as an alternative to prosecution in the UK; the police officers identifying offenders for referral to such courses, those delivering the intervention, drivers attending the course as an alternative to prosecution and members of the public attending the course as general education. Four key themes, with underpinning subthemes, emerged; 1) Police officer discretion and control over entry into the criminal justice system 2) Police-public interactions, 3) Course experiences, and 4) Post-course considerations. Firstly, police officer discretion is an important determinant of criminal justice system outcome, based on subjective rather than legal decisions about whether or not to report drivers for an offence. Secondly, police officers negotiate encounters with road users using the avoidance of prosecution as a way of diffusing difficult conversations, sometimes by offering a course as a preferable alternative to prosecution, sometimes by encouraging handsfree phone use. Thirdly, course attendance provides an opportunity to develop both normative alignment through increased understanding of police work, and to appreciate a range of instrumental consequences associated with mobile phone use. Both self-reportedly impacted upon mobile phone use while driving. Finally, post-course considerations emphasised a focus on who should be offered courses as an alternative to prosecution, focusing upon desires for both punitive and rehabilitative responses to mobile phone using drivers.
Everyday humans use cars to move faster, and the world is a chaotic place, and a little distraction or a mistake could be the reason for an accident and bring people great pain. An assistance system that can distinguish and detect signs on the roads and brings the driver's attention to road signs and make them aware of their meaning could be beneficial. The most important part of the Traffic Sign Recognition System is the algorithm. In this paper, a new way toward Traffic Sign Recognition algorithm taking the advantages of Color Segmentation, support vector machines, and histograms of oriented gradients on the GTSRB dataset is proposed. The unsupervised shuffled frog-leaping algorithm is employed for segmenting the images. The results show remarkable improvements by using meta-heuristic algorithms.
Trust is an essential factor in online and offline transactions. However, the role of customer trust has received limited attention in the home-sharing economy. Drawing on the revised stimulus organism response model and trust transfer theory, this paper examines how customer trust in home-sharing hosts and platforms affects customer relationships, manifested in customer engagement and loyalty. As artificial intelligence (AI) is extensively utilized within home-sharing platforms to facilitate business operations and enhance the customer experience, this study also examines the influence of AI on customer trust and other related outcomes. The research was undertaken in China, with respondents who had used home-sharing platforms. Results from structural equation modeling show that customer trust had a significant positive relationship with customer engagement and loyalty. Customer engagement mediates the relationship between trust and loyalty, while AI may have a negative moderating effect between host trust and customer engagement and customer engagement and loyalty. The paper contributes to marketing, sharing economy and AI research. The work has implications for practitioners offering suggestions to develop marketing strategies for business growth and sustainability.
Background COVID-19 was named a global pandemic by the World Health Organization in March 2020. Governments across the world issued various restrictions such as staying at home. These restrictions significantly influenced mental health worldwide. This study aims to document the prevalence of mental health problems and their relationship with the quality and quantity of social relationships affected by the pandemic during the United States national lockdown. Methods Sample data was employed from the COVID-19 Impact Survey on April 20–26, 2020, May 4–10, 2020, and May 30–June 8, 2020 from United States Dataset. A total number of 8790, 8975, and 7506 adults participated in this study for April, May and June, respectively. Participants’ mental health evaluations were compared clinically by looking at the quantity and quality of their social ties before and during the pandemic using machine learning techniques. To predict relationships between COVID-19 mental health and demographic and social factors, we employed random forest, support vector machine, Naive Bayes, and logistic regression. Results The result for each contributing feature has been analyzed separately in detail. On the other hand, the influence of each feature was studied to evaluate the impact of COVID-19 on mental health. The overall result of our research indicates that people who had previously been diagnosed with any type of mental illness were most affected by the new constraints during the pandemic. These people were among the most vulnerable due to the imposed changes in lifestyle. Conclusion This study estimates the occurrence of mental illness among adults with and without a history of mental disease during the COVID-19 preventative limitations. With the persistence of quarantine limitations, the prevalence of psychiatric issues grew. In the third survey, which was done under quarantine or house restrictions, mental health problems and acute stress reactions were substantially greater than in the prior two surveys. The findings of the study reveal that more focused messaging and support are needed for those with a history of mental illness throughout the implementation of restrictions.
Mapping potential changes in bioclimatic characteristics are critical for planning mitigation goals and climate change adaptation. Assessment of such changes is particularly important for Southeast Asia (SEA) — home to global largest ecological diversity. Twenty-three global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were used in this study to evaluate changes in 11 thermal bioclimatic indicators over SEA for two shared socioeconomic pathways (SSPs), 2–4.5 and 5–8.5. Spatial changes in the ensemble mean, 5th, and 95th percentile of each indicator for near (2020–2059) and far (2060–2099) periods were examined in order to understand temporal changes and associated uncertainty. The results indicated large spatial heterogeneity and temporal variability in projected changes of bioclimatic indicators. A higher change was projected for mainland SEA in the far future and less in maritime region during the near future. At the same time, uncertainty in the projected bioclimatic indices was higher for mainland than maritime SEA. Analysis of mean multi-model ensemble revealed a change in mean temperature ranged from − 0.71 to 3.23 °C in near and from 0.00 to 4.07 °C in far futures. The diurnal temperature range was projected to reduce over most of SEA (ranging from − 1.1 to − 2.0 °C), while isothermality is likely to decrease from − 1.1 to − 4.6%. A decrease in isothermality along with narrowing of seasonality indicated a possible shift in climate, particularly in the north of mainland SEA. Maximum temperature in the warmest month/quarter was projected to increase a little more than the coldest month/quarter and the mean temperature in the driest month to increase more than the wettest month. This would cause an increase in the annual temperature range in the future.
Researchers are beginning to explore the antecedents to anxiety symptomology. Such antecedents to anxiety symptomology may be that of irrational beliefs and motivation regulation. It has been intimated that both irrational beliefs and motivation regulation can be risk factors for increased anxiety in athletes. Research is yet to explore the association between these two antecedents, and how and whether they interact in predicting anxiety symptomology. The present paper investigates such associations within two phases. In phase one, we identify the predictive capacity of irrational beliefs and motivation regulation on anxiety symptomology in 61 elite ultra-marathon runners. Results support intimated associations between irrational beliefs and motivation regulation, evidencing that irrational performance beliefs negatively associated with relative autonomous motivation. In addition, it was found that irrational performance beliefs positively associated with anxiety symptomology, whilst autonomous motivation negatively associated with anxiety symptomology in elite ultra-marathon runners. In phase two, we use a narrative approach to understand seven elite athletes’ stories surrounding their performance beliefs, motivation, and anxiety symptomology. Phase two supports findings in phase one, evidencing that the co-existence of both irrational performance beliefs and controlled motivation is an antecedent to anxiety symptomology and dysfunctional behaviours in ultra-marathon runners. The findings of both phase one and phase two are discussed in relation to the theoretical and practical implications for elite athletes.
The recent development of the Internet of Things (IoT) devices offers an intelligent concept for integrating massive number of interconnected wireless devices. However, supporting such massive number of IoT connections (hundreds of billions) requires an efficient and dynamic spectrum access strategy. Accordingly, the integration of non‐orthogonal multiple access (NOMA) in the cognitive radio (CR) systems, referred to as a multi‐carrier NOMA CR‐enabled system, is envisioned as a potential spectrum‐efficient networking paradigm that can support massive energy‐efficient IoT connectivity in B5G networks while maintaining the quality of services (QoS) of the IoT devices. However, the power consumption issue of the multi‐carrier NOMA CR‐based system becomes as one of key challenges, especially when considering the expected massive connectivity. Accordingly, several power‐aware optimization frameworks have been considered to address this issue. While most of the existing CR‐based multi‐carrier NOMA power allocation mechanisms only optimize the allocated per‐user power, this article proposes a novel joint bandwidth and power allocation problem over each available idle channel with the aim of minimizing the overall transmission power under a set of QoS constrains. Specifically, the bandwidth of each channel is adaptively subdivided into two variable‐width sub‐channels, each is capable to serve a group of CR users using power‐domain NOMA. This novel design is formulated as a joint optimization problem that is shown to be a non‐convex. Therefore, an iterative algorithm with a second‐order cone programming is deployed to handle the non‐convexity of the problem, and thus, determine the solution of it. Simulation results reveal that the developed variable bandwidth adaptive power allocation mechanism outperforms the conventional equal sub‐channel allocation in terms of the required transmission power, which significantly improves the energy efficiency in the system.
The COVID-19 pandemic created a challenge for providing assistive technology (AT) and rehabilitation services, with many service providers implementing telehealth service provision for the first time. The objective of this study was to explore the experiences of people accessing and providing AT and rehabilitation services during the pandemic and to assess the implementation of telehealth service delivery at an assistive technology and rehabilitation center in India. A mixed-methods design, combining analysis of clinical data and semi-structured interviews, was utilized. A descriptive analysis of demographics and clinical characteristics of service users accessing services through telehealth, or in-person mode was completed. In addition, service users were interviewed to explore their experiences of accessing services during the pandemic. Service providers were also interviewed to gather their opinions on telehealth service delivery during the pandemic. Findings showed that telehealth was an alternative tool in the pandemic for continuing to deliver services in a low-resource setting. However, not all types of services could be successfully delivered via telehealth. There are barriers to the delivery of telehealth services that need to be considered and addressed to allow successful implementation, and it is important to consider that telehealth consultations are not suitable for all service users.
Fingerprint has been widely used in biometric applications. Numerous established researches on image enhancement techniques have been done to improve the quality of fingerprint images. However, the production of low-quality images due to the presence of scars remains a challenge in biometrics. The scars damage the fingerprint minutiae information due to broken ridges and they reduce the accuracy of identification. This research developed an image enhancement approach to improve the quality of scarred fingerprint images to generate accurate minutiae extraction. To achieve the aim, the scarred image was improved by removing noise using a new filter, Median Sigmoid (MS), and the corrected ridges were reconstructed using ridges structure enhancement algorithm. This was done to enhance the broken ridges structure. MS filter is a combination of median filter and modified sigmoid function that improves the image contrast and simultaneously removes noise in the fingerprint image. Following that, the filtered image was used in the ridges structure enhancement process. To identify true minutiae, the broken ridges structure in the filtered image needed to be accurately verified. In the ridges structure reconstruction process, an algorithm was enhanced to identify the best value of Sigma parameter (σ) used in the Gaussian Low-pass filter to generate a better orientation image. The image is important to reconstruct the corrupted fingerprint ridges structure. The evaluation for the proposed approach used the National Institute of Standards and Technology Special Database 14, and the results showed a 37% improvement of the quality index in comparison to approaches found in related research. The findings of the evaluation showed that the proposed enhancement approach produced a better minutiae extraction result and this is very significant in the field of fingerprint image enhancement.
Background Extracorporeal Membrane Oxygenation (ECMO) therapy for respiratory failure is an increasingly popular modality of support. Patient selection is an important aspect of outcome success. This review assesses the efficacy of the popular prognostic tools Respiratory ECMO Survival Prediction Score (RESP) and Predicting Death for Severe ARDS on VV-ECMO score (PRESERVE) for ECMO patient selection. Methods A literature search was performed. Six publications were found to match the specified selection criteria. These publications were assessed and compared using the area under the receiver operating characteristic (AUROC) curve statistical method to ascertain the discriminatory ability of the models to predict treatment outcome. Results Six articles were included in this review from 306 screened, of which all were retrospective cohort studies. Data was generated over a period of 3–9 years from 13 referring hospitals. Studies consisted of 467 male and 221 female (30 unknown) participants in total with a high heterogeneity. The PRESERVE prognostic model was found to have a higher AUROC score than the RESP model, however both models were found to be sub-optimal in their discriminatory ability. A high chance of bias was seen across all included studies. Conclusion It was the findings of this review, indicated by analysis using the AUROC measures, that the prognostic model PRESERVE performed better than RESP for predicting post ECMO therapy outcomes, for patients presenting with Acute Respiratory Distress Syndrome within their respective validated time frames, i.e., RESP at Intensive care unit (ICU) discharge and PRESERVE at 6 months post ICU discharge. However, It was recognized that comparator groups were small thereby introducing bias into the study. Further prospective, randomized studies would be necessary to effectively assess the utility of these predictive survival scores.
Plastics are ubiquitous. It has been used in human activities, from agriculture to packaging, infrastructure, and health. The wide range of usage makes plastics an omnipresent pollutant in the environment. This study investigated the abundance and type of plastics in agricultural soil in the Adana/Karataş region in Turkey, where disposable low-tunnel greenhouse plastic films and irrigation pipes were in use. For this purpose, 1 kg of soil samples from the top 5 cm (from the surface) was taken from 10 different sampling locations. An average of 16.5 ± 2.4 pcs/kg was found in the soil samples. The highest amount of plastics was seen at the Bahçe-4 location with 39.7 ± 12 pcs/kg and the lowest amount of plastics at the Karataş-1 location with 0.7 ± 0.3 pcs/kg. The average size of plastics was found to be 18.2 ± 1.3 mm. The average size of plastics originating from greenhouse cover was 18.9 ± 1.4 mm, and from disposable irrigation pipes was 12.5 ± 3.5 mm. It was determined that 41.9% of extracted plastics were microplastics, 36.3% were mesoplastics, 16.3% were macroplastics, and 5.6% were megaplastics. Results indicated that residual plastics decreased in the soil where used plastics were removed after usage. As a result, it is worth noting that a significant amount of plastics remain in soil due to plastics being used in agricultural areas. Graphical abstract
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6,924 members
Helen Branthwaite
  • Faculty of Health Sciences
Nachiappan Chockalingam
  • Faculty of Health Sciences
Naomi J Ellis
  • Centre for Health and Development
Christopher Gidlow
  • Centre for Health and Development (CHAD)
Janice Mooney
  • School of Health and Social Care
College Road, ST4 2DE , Stoke-on-Trent, United Kingdom
Head of institution
Professor Liz Barnes
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