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Developing conflict resolution strategies and building resilient midwifery students: A mixed methods research protocol
No full textYesThis study will undertake a preparatory phase summarising the body of literature on midwifery students’ knowledge, understanding and experiences of workplace bullying, and violence
High-security image encryption based on a novel simple fractional-order memristive chaotic system with a single unstable equilibrium point
Get PDFYesFractional-order chaotic systems have more complex dynamics than integer-order chaotic systems. Thus, investigating fractional chaotic systems for the creation of image cryptosystems has been popular recently. In this article, a fractional-order memristor has been developed, tested, numerically analyzed, electronically realized, and digitally implemented. Consequently, a novel simple three-dimensional (3D) fractional-order memristive chaotic system with a single unstable equilibrium point is proposed based on this memristor. This fractional-order memristor is connected in parallel with a parallel capacitor and inductor for constructing the novel fractional-order memristive chaotic system. The system's nonlinear dynamic characteristics have been studied both analytically and numerically. To demonstrate the chaos behavior in this new system, various methods such as equilibrium points, phase portraits of chaotic attractor, bifurcation diagrams, and Lyapunov exponent are investigated. Furthermore, the proposed fractional-order memristive chaotic system was implemented using a microcontroller (Arduino Due) to demonstrate its digital applicability in real-world applications. Then, in the application field of these systems, based on the chaotic behavior of the memristive model, an encryption approach is applied for grayscale original image encryption. To increase the encryption algorithm pirate anti-attack robustness, every pixel value is included in the secret key. The state variable's initial conditions, the parameters, and the fractional-order derivative values of the memristive chaotic system are used for contracting the keyspace of that applied cryptosystem. In order to prove the security strength of the employed encryption approach, the cryptanalysis metric tests are shown in detail through histogram analysis, keyspace analysis, key sensitivity, correlation coefficients, entropy analysis, time efficiency analysis, and comparisons with the same fieldwork. Finally, images with different sizes have been encrypted and decrypted, in order to verify the capability of the employed encryption approach for encrypting different sizes of images. The common cryptanalysis metrics values are obtained as keyspace = 2648, NPCR = 0.99866, UACI = 0.49963, H(s) = 7.9993, and time efficiency = 0.3 s. The obtained numerical simulation results and the security metrics investigations demonstrate the accuracy, high-level security, and time efficiency of the used cryptosystem which exhibits high robustness against different types of pirate attacks
Barriers and facilitators to the uptake of new medicines into clinical practice: a systematic review
Get PDFYesImplementation and uptake of novel and cost-effective medicines can improve patient health outcomes and healthcare efficiency. However, the uptake of new medicines into practice faces a wide range of obstacles. Earlier reviews provided insights into determinants for new medicine uptake (such as medicine, prescriber, patient, organization, and external environment factors). However, the methodological approaches used had limitations (e.g., single author, narrative review, narrow search, no quality assessment of reviewed evidence). This systematic review aims to identify barriers and facilitators affecting the uptake of new medicines into clinical practice and identify areas for future research.
A systematic search of literature was undertaken within seven databases: Medline, EMBASE, Web of Science, CINAHL, Cochrane Library, SCOPUS, and PsychINFO. Included in the review were qualitative, quantitative, and mixed-methods studies focused on adult participants (18 years and older) requiring or taking new medicine(s) for any condition, in the context of healthcare organizations and which identified factors affecting the uptake of new medicines. The methodological quality was assessed using QATSDD tool. A narrative synthesis of reported factors was conducted using framework analysis and a conceptual framework was utilised to group them.
A total of 66 studies were included. Most studies (n = 62) were quantitative and used secondary data (n = 46) from various databases, e.g., insurance databases. The identified factors had a varied impact on the uptake of the different studied new medicines. Differently from earlier reviews, patient factors (patient education, engagement with treatment, therapy preferences), cost of new medicine, reimbursement and formulary conditions, and guidelines were suggested to influence the uptake. Also, the review highlighted that health economics, wider organizational factors, and underlying behaviours of adopters were not or under explored.
This systematic review has identified a broad range of factors affecting the uptake of new medicines within healthcare organizations, which were grouped into patient, prescriber, medicine, organizational, and external environment factors. This systematic review also identifies additional factors affecting new medicine use not reported in earlier reviews, which included patient influence and education level, cost of new medicines, formulary and reimbursement restrictions, and guidelines.
PROSPERO database (CRD42018108536).This work presents research funded by the Pharmacy Research UK (grant reference: PRUK-2018-GA-1-KM) and Leeds Teaching Hospitals NHS Trust
Fifty Years of Information Management Research: A Conceptual Structure Analysis using Structural Topic Modeling
Get PDFYesInformation management is the management of organizational processes, technologies, and people which collectively create, acquire, integrate, organize, process, store, disseminate, access, and dispose of the information. Information management is a vast, multi-disciplinary domain that syndicates various subdomains and perfectly intermingles with other domains. This study aims to provide a comprehensive overview of the information management domain from 1970 to 2019. Drawing upon the methodology from statistical text analysis research, this study summarizes the evolution of knowledge in this domain by examining the publication trends as per authors, institutions, countries, etc. Further, this study proposes a probabilistic generative model based on structural topic modeling to understand and extract the latent themes from the research articles related to information management. Furthermore, this study graphically visualizes the variations in the topic prevalences over the period of 1970 to 2019. The results highlight that the most common themes are data management, knowledge management, environmental management, project management, service management, and mobile and web management. The findings also identify themes such as knowledge management, environmental management, project management, and social communication as academic hotspots for future research
Computational Studies of Lipid-Wrapped Gold Nanoparticle Transport Through Model Lung Surfactant Monolayers
Get PDFYesColloidal nanoparticles, such as gold nanoparticles (AuNPs), are promising materials for the delivery of hydrophilic drugs via the pulmonary route. The inhaled nanoparticle drug carriers primarily deposit in lung alveoli and interact with the alveolar surface known as lung surfactants. Therefore, it is vital to understand the interactions of nanocarriers with the surfactant layer. To understand the interactions at the molecular level, here we simulated model lung surfactant monolayers with phospholipid (PL)-wrapped AuNPs at the vacuum-water interface using coarse-grained molecular dynamics simulations. The PL-wrapped AuNPs quickly adsorbed into the surfactant layer, altered the structural properties of the monolayer, and at high concentrations initiated the compressed monolayer to collapse/buckle. Among the surfactant monolayer lipid components, cholesterol adsorbed to the AuNPs preferentially over PL species. The position of the adsorbed PL-AuNPs within the monolayer, and subsequent monolayer perturbation, vary depending on the monolayer phase, monolayer composition, and species of PL used as a ligand. Information provided by these molecular dynamic simulations helps to rationalize why some colloidal nanoparticles work better as nanocarriers than others and aid the design of new ones, to avoid biological toxicity and improve efficacy for pulmonary drug delivery
Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems
Get PDFYesCarbon capture and storage (CCS) has attracted renewed interest in the re-evaluation of the equations of state (EoS) for the prediction of thermodynamic properties. This study also evaluates EoS for Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) and their capability to predict the thermodynamic properties of CO2-rich mixtures. The investigation was carried out using machine learning such as an artificial neural network (ANN) and a classified learner. A lower average absolute relative deviation (AARD) of 7.46% was obtained for the PR in comparison with SRK (AARD = 15.0%) for three components system of CO2 with N2 and CH4. Moreover, it was found to be 13.5% for PR and 19.50% for SRK in the five components’ (CO2 with N2, CH4, Ar, and O2) case. In addition, applying machine learning provided promise and valuable insight to deal with engineering problems. The implementation of machine learning in conjunction with EoS led to getting lower predictive AARD in contrast to EoS. An of AARD 2.81% was achieved for the three components and 12.2% for the respective five components mixture
Scales of relevance and the importance of ambiguity
Get PDFYesAHRC, University of Bradford and the Higher Education Innovation Fund
Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson’s disease and COVID-19
Get PDFYesThis study introduces a quantum-inspired computational paradigm to address the unresolved problem of Convolutional Neural Networks (CNNs) using the Rectified Linear Unit (ReLU) activation function (AF), i.e., the ‘dying ReLU’. This problem impacts the accuracy and the reliability in image classification tasks for critical applications, such as in healthcare. The proposed approach builds on the classical ReLU and Leaky ReLU, applying the quantum principles of entanglement and superposition at a computational level to derive two novel AFs, respectively the ‘Quantum ReLU’ (QReLU) and the ‘modified-QReLU’ (m-QReLU). The proposed AFs were validated when coupled with a CNN using seven image datasets on classification tasks involving the detection of COVID-19 and Parkinson’s Disease (PD). The out-of-sample/test classification accuracy and reliability (precision, recall and F1-score) of the CNN were compared against those of the same classifier when using nine classical AFs, including ReLU-based variations. Findings indicate higher accuracy and reliability for the CNN when using either QReLU or m-QReLU on five of the seven datasets evaluated. Whilst retaining the best classification accuracy and reliability for handwritten digits recognition on the MNIST dataset (ACC = 99%, F1-score = 99%), avoiding the ‘dying ReLU’ problem via the proposed quantum AFs improved recognition of PD-related patterns from spiral drawings with the QReLU especially, which achieved the highest classification accuracy and reliability (ACC = 92%, F1-score = 93%). Therefore, with these increased accuracy and reliability, QReLU and m-QReLU can aid critical image classification tasks, such as diagnoses of COVID-19 and PD.The authors declare that this was the result of a HEIF 2020 University of Bradford COVID-19 response-funded project ‘Quantum ReLU-based COVID-19 Detector: A Quantum Activation Function for Deep Learning to Improve Diagnostics and Prognostics of COVID-19 from Non-ionising Medical Imaging’. However, the funding source was not involved in conducting the study and/or preparing the article
Investigating logistics-related food loss drivers: A study on fresh fruit and vegetable supply chain
Get PDFYesFood loss is one of the significant threats to sustainable development. Although various studies investigating food loss drivers disclosed that logistics is a significant reason for food loss, logistics-related food loss drivers have not been thoroughly studied. Thus, this paper aims to identify, classify and rank the logistics-related food loss drivers, having more influence on the amount of food loss in the fruit and vegetable supply chain, with the help of a mixed-method approach. First, a literature review was conducted to identify potential logistics-related food loss drivers. A total of 49 articles were analyzed. Second, 21 semi-structured interviews were conducted with industry experts to finalize the drivers. Third, the analytical hierarchy process was employed to rank the drivers having more influence on the amount of food loss. Five main drivers and nineteen sub-drivers were identified at the end of the literature review and interview process. Categorization of main drivers is based on the logistics activities. According to results, warehousing-related drivers and transportation-related drivers are the two most influential drivers on the amount of food loss, while lack of coordination and improper packaging material are the two most influential sub-drivers. Following the ranking of drivers and sub-drivers, mitigation strategies to diminish food loss are also discussed. The findings of this study are intended to guide fruit and vegetable supply chain actors in tackling food loss
Leveraging customer engagement to improve the operational efficiency of social commerce start-ups
Get PDFYesDespite the surge of literature on customer engagement (CE) in social media, few studies shed light on how to leverage CE to improve firms’ operational efficiency. This research proposes a fresh framework using social media data to improve demand forecasting accuracy, resulting in a cost-efficient inventory control strategy. Drawing upon the resource mobilization perspective in particular, this research quantifies the construct of CE from the view of input–output efficiency evaluation using the Data Envelopment Analysis (DEA) model, and then leverages CE to forecast consumer online demand and reconfigure inventory management strategy. Using a 71-week data set from a social commerce start-up in China, this research shows that this new framework dramatically increases demand forecasting accuracy and reduces operational costs in inventory management. This study contributes to the literature by demonstrating the value of social media data in improving operational efficiency, particularly regarding inventory management.National Natural Science Foundation of China (Project No. 71671152