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Satellite remote sensing for change detection on land-sea interaction and its effect on coastal heritage assets.
No full textCoastal infrastructure is an essential part of the built and the natural environment that is prone to natural and anthropogenetic hazards. The coastal regions require continuous monitoring to understand the extent and scale of environmental changes, and their impact can affect coastal heritage, roads and the built environment. Various approaches were implemented to monitor these assets with in-situ monitoring techniques and expert observations. Unprecedented remote sensing data could help with continuous monitoring and terrestrial observations. In this study, Light Detection And Ranging (LiDAR) data and Interferometric Synthetic Aperture Radar (InSAR) observations are used to investigate morphological changes along the coast. The focus is to explore changes along the coastline of the Isles of Scilly, UK. LiDAR data are used and digital terrain models (DTM) from 2007 and 2020 observations are produced. The change in morphology is estimated by applying a raster difference between the two dates. The results show a significant change along the coast that is aligned with the coastal heritage designated at risk by Historic England. Sentinel-1 interferograms from the Looking Inside Continents from Space (LiCSAR) archive are utilised and velocity and time series are estimated for the island. The velocity map and the time series show patches of uplift/subsidence but are limited by the resolution of the data. The results have proven agreement in most locations although they need further validation with other on-site observations. Moreover, for SAR observations, high-resolution data is inevitable to resolve small-scale changes in coastal heritage
Go Gently
No full textTheatre Production at the Drayton Arms Theatre, London, 13th-17th June, 2025
Ruth Rock, an ex-filmstar in her 70s, self-isolated in her country house on the fringes of London, is forced to re-evaluate her life when she takes in her ex's new, and much younger, girlfriend during her convalescence.
Award-winning writer-director Jonathon Crewe teams up with researchers from the University of West London to produce Go Gently; an original and contemporary comedy-drama that aims to challenge how we think about ageing
Automated recognition of gait emotions.
No full textThe physiological state states that the emotions described are generated within our subconscious mind. The responses of emotions to a given event are usually uncontrollable in our bodies. The walking patterns of human individuals differ. The gait conveys information about a person for re-identification, gender classification, and emotion recognition. Traditional emotional recognition is based on facial expressions and speech recognition. The new automated way of recognizing emotions is through gait, with less cooperation from the subject. The advantages of gait are that emotion is recognized from low-illumination images or videos and requires less cooperation from the subject. In this work, gait emotions are recognized and identified using deep learning
Impact of learning management systems and digital skills on TPACK development among pre-service mathematics teachers
Get PDFThe technological era has significantly impacted the quality of mathematics education. In reality, the frequency of technology usage in the classroom is minimal, and teachers continue to face challenges when utilizing learning management systems (LMS). Therefore, the purpose of this study is to identify the impact of LMS on the development of technological pedagogical content knowledge (TPACK) and digital skills of pre-service mathematics teachers. This mixed-methods study, employing an exploratory approach, involved 76 pre-service mathematics teachers (22 males, 54 females). Data was collected through questionnaires and interviews and subsequently compiled using Microsoft Excel and saved in CSV format. The data were then analyzed using Jamovi software version 2.4.8.0. Findings show that digital skills significantly influence TPACK development (β = 0.63, p < 0.001), more so than LMS usage (β = 0.49, p < 0.001), with a combined explanatory power of 65% (R² = 0.65). Higher digital skills improve pre-service teachers' ability to integrate technology into pedagogy and content. Additionally, final-year pre-service teachers show greater competencies. Male pre-service teachers generally exhibit higher digital skills than females (p < 0.001), influencing their capacity to effectively use digital tools in teaching. The strong correlation between digital skills and LMS usage (Spearman’s ρ = 0.86) underscores the need for structured digital competency development in teacher education. These findings emphasize the importance of fostering digital literacy and technology-integrated practicum experiences in mathematics instruction. Universities and school administrators should implement targeted training programs to enhance LMS utilization and help pre-service teachers develop strong TPACK competencies
A Multiple Linear Regression Model for inflation rate in the UK
Get PDFIn this study, the key factors influencing the yearly inflation rate in the United Kingdom (UK) have been investigated using data spanning from 1974 to 2023. A range of economic factors, including interest rates (IR), unemployment rates (UR), exchange rates (EXR), gross domestic product (GDP), consumer price index (CPI), retail price index (RPI), value-added taxes (VAT), producer price index (PPI), and GDP growth (GDPG) has been chosen as predictor variables to analyze the model under consideration. Using these factors, a multiple linear regression without interaction and another model with interaction have been constructed and investigated using least squares methods to estimate the coefficients and identify the most significant determinants of inflation. The interaction model yields better performance, with a high coefficient of determination (R2=0.979), indicating that the most impactful variables are interactions between the Producer Price Index (PPI) and GDP, the Retail Price Index (RPI) and GDP, the RPI and inflation rate (IR), the PPI and IR, as well as GDP itself. These outcomes offer valuable insights into the complex dynamics driving the inflation rate in the UK
Using the conserved hexapeptide in HOX proteins as an antagonist of HOX/PBX interactions
No full textThe HOX and PBX genes encode transcription factors that have key roles in development and cancer, both independently and as a heterodimer within a complex of proteins that recognizes specific sequences in DNA and can both activate or repress transcription of target genes. Due to functional redundancy amongst HOX proteins, knock down or knock out studies of individual genes often do not result in an altered phenotype. An alternative approach is to target the interaction between HOX and PBX proteins, which is dependent on a conserved hexapeptide region within HOX. To this end, several peptides have been developed based on the hexapeptide sequence which act as competitive antagonists of HOX/PBX binding, including HXR9 and HTL001. Here, we review the methodology that has been used in these studies, including peptide syntheses, cell culture, assays, and mouse models
The structure of executive functions in athletes: A latent variable approach.
Get PDFThe role of executive function (EF) in expert sport performance has become a popular topic in sport and exercise psychology research. Research in this area often adopts the unity/diversity framework of EF (i.e., inhibition, shifting, and updating). However, recent investigations into the suitability of this unity/diversity model, and other competing models (e.g., the nested model of EF), has questioned whether this model is suitable for across all populations (e.g., athletes). The aim of the present study was to use confirmatory factor analysis to outline the most suitable EF model in a sample of athletes. In total, 131 participants with varying levels of athletic expertise completed two inhibition, shifting, and updating tasks. All analyses were performed in RStudio. The results revealed the nested model of EF provided the best fit to the data indicating its suitability for athletes. Acceptable fit was also found for the unity/diversity mode of EF. Overall, the results suggest that, despite recent criticism of the nested model and unity/diversity framework of EF, such structures appear to be suitable for use with athletic populations
Evaluation of security and performance impact of cryptographic and hashing algorithms in site-to-site virtual private networks
Get PDFThe secure and efficient operation of Site-to-Site Virtual Private Networks (VPNs) is critical for modern data transmission, yet the current literature lacks a comprehensive analysis of the trade-offs between security and performance. This paper addresses this gap by evaluating the impact of various cryptographic algorithms and hashing functions on VPN performance. Evaluating the impact of cryptographic algorithms on network performance in a Site-to-Site VPN is essential for determining data transmission efficiency. Several factors, including encryption methods, hashing, bandwidth limitations and others could, influence VPN performance. Further, cyberattacks such as Denial of Service (DoS), Media Access Control (MAC) flooding, and synchronize (SYN) flooding can target VPN infrastructures. Therefore, it is crucial to assess cryptographic algorithms to identify the most suitable ones for different network characteristics and find balance that the end user needs. The paper's contributions are threefold. Firstly, it explores the complex relationship between cryptographic and hashing algorithms and their implications for security and network performance, aiming to enhance Site-to-Site VPN security without compromising efficiency. Secondly, we implement a dynamic VPN configuration tool and conduct performance tests in a virtual environment using Graphical Network Simulator-3 (GNS3) and File Transfer Protocol (FTP), Datafile transfers to measure the impact of various encryption pairs, including Advance Encryption Algorithm (AES), Data Encryption Standard (DES), Triple Data Encryption Standard (3DES), and hashing functions such as Secure Hash Algorithm 2 (SHA2) and Message Digest (MD5). Finally, we assess the resilience of VPNs to specific cyberattacks and evaluate the trade-offs between security and transmission efficiency. The findings show that 3DES with SHA2 offers an acceptable balance between speed and security, making it a solid choice when both are important
Targeting neurodegeneration: three machine learning methods for G9a inhibitors discovery using PubChem and scikit-learn
No full textIn light of the increasing interest in G9a’s role in neuroscience, three machine learning (ML) models, that are time efficient and cost effective, were developed to support researchers in this area. The models are based on data provided by PubChem and performed by algorithms interpreted by the scikit-learn Python-based ML library. The first ML model aimed to predict the efficacy magnitude of active G9a inhibitors. The ML models were trained with 3112 and tested with 778 samples. The Gradient Boosting Regressor perform the best, achieving 17.81% means relative error, 21.48% mean absolute error, 27.39% root mean squared error and 0.02 coefficient of determination (R2) error. The goal of the second ML model, called a CID_SID ML model, utilised PubChem identifiers to predict the G9a inhibition of a small biomolecule that has been primarily designed for different purposes. The ML models were trained with 58,552 samples and tested with 14,000. The most suitable classifier for this case study was the Extreme Gradient Boosting Classifier, which obtained 79.7% accuracy, 83.2% precision,67.7% recall, 74.7% F1-score and 78.4% ROC. Up to date, this methodology has been used in seven studies, achieving a mean accuracy of 82.75%, precision of 90.71%, Recall of 73.01%, F1-score of 80.79% and ROC of 80.63% across all case studies. The third ML model utilised IUPAC names. It was based on the Random Forest Classifier algorithm, trained with 19,455 samples and tested with 14,100. The probability of this prediction was 68.2% accuracy. Its feature importance list was reordered by the relative proportion of active cases in which they participate. Thus, “iodide” was identified as the one with the highest relative proportion of the active cases to all cases where this fragment participated. In addition, ‘iodo’ was identified as the most desirable fragment, and “phenylcarbamate” as the least desirable based on their participation only in active or inactive cases, respectively. The computational approach has been initially developed and demonstrated using a case study on Tyrosyl-DNA phosphodiesterase 1(TDP 1) inhibition