Teacher retention is a significant international problem. Up to half of current teachers suggest they may leave the profession in the near future, and half of newly qualified teachers leave within the first five years of joining the profession. This unsustainable retention problem threatens the stability of students' education worldwide. There are a variety of reasons that teachers state for influencing their departure from the profession. However, the most common, particularly for newly qualified teachers, is the feeling of burnout or being overworked. Artificial Intelligence (AI) presents opportunities to support teachers and to automate some of their work, thereby reducing their workload. However, such techniques use opaque methods, which often produce content that is not correct or credible. In this paper, we propose AskEFO, a chatbot that supports newly qualified teachers and answers queries based solely on input from approved qualified professionals. Evaluated by a cohort of 14 newly qualified English teachers, 79% stated they would use AskEFO instead of Google in the classroom, 93% stated that AskEFO would enhance their confidence when teaching and 100% indicated that it would positively impact their students.

As an alternative biological matrix, fingernails act as a non-invasive window into systemic diseases such as cardiovascular diseases (CVDs) or diabetes mellitus (DM). The presence of such diseases manifests physically, altering the fingernail plate's topography, as well as its elemental composition. Scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometry (EDX) and scanning transmission electron microscopy detector (STEM), allows for detailed composition analysis, elemental mapping and spectroscopy. As complementary detectors, the presence of key elements such as calcium, oxygen, potassium and silicon can be quantified and investigated in relation to the presence of disease. Furthermore, topographical STEM images provide insight into the prevalence of tissue damage to the fingernail, which is often attributed to hyperglycemia or poor circulation. The EDX weight% values of identified elements within the fingernails were exported into Matlab R2024a, where a machine learning algorithm, principal component analysis (PCA), was applied to identify patterns between the elemental composition of healthy and diseased fingernails. Analysis of variance (ANOVA) results demonstrated relationships between confounding variables such as biological sex and diet and elemental composition. Female participants demonstrated a significantly higher weight% value of titanium than males due to the exposure of titanium in personal-care products and sun creams. The weight% value of calcium was also significantly different in fingernails of individuals who practiced extremely healthy, healthy and unhealthy diets. In the presence of disease, calcium and sodium showed significantly different weight% values in healthy versus diseased fingernails. STEM images further confirmed the presence of disease, with CVD and/or diabetic fingernail clippings showing rough, topographical textures. In contrast, fingernails taken from healthy participants displayed smooth areas, with small amounts of rough texture. Therefore, demonstrating the ability of SEM to detect the presence and severity of disease.

This study explores the application of deep learning techniques, specifically Convolutional Neural Networks, for the classification of dog breeds from images. By employing varying input image resolutions, the research evaluates the impact of resolution on the accuracy and efficiency of the model. Five experiments were conducted using resolutions of 64, 128, 224, 256, and 512 pixels to assess model performance. The results indicate that an input resolution of 256x256 pixels yields the highest accuracy, achieving 94.74% with an optimal balance between detail and processing complexity. However, certain breeds, such as the American Foxhound and Anatolian Shepherd Dog, exhibited lower classification performance, highlighting the importance of considering breed-specific characteristics in model development. The findings emphasize the critical role of image resolution in training deep learning models and suggest that a 256x256 resolution offers the best trade-off between accuracy and computational efficiency for dog breed classification.

Covid-19 is a novel coronavirus that emerged in 2019 and spread across the globe, establishing a worldwide pandemic. Vaccination was presented as the most effective solution against the virulence of Covid-19. Accelerated vaccination programmes pushed several nucleic acid-based vaccines into production. Global desperation and limited vaccine supply allowed substandard and falsified (SF) Covid-19 vaccines to enter the supply chain. Conventional analytical methods can be cumbersome, costly and sophisticated to operate. Thus, this study presented a comparison of handheld surface-enhanced Raman spectroscopy (SERS) and Raman microscopy with machine learning algorithms (MLAs) for the rapid authentication of Covid-19 vaccines. Measurements were taken using the Metrohm MIRA XTR DS handheld Raman spectrometer and the Horiba XploRA Plus Raman microscope. Raman spectroscopy showed strong potential as a vaccine authentication method, allowing identification of nucleic acid-specific bands in spectra. SERS showed enhancement of up to $498 \%$ when applied to vaccines of sufficient concentration. Clustering based on principal component analysis (PCA) showed some accuracy but indicated poor repeatability for SERS, although, multiple classification models obtained $100 \%$ accuracy and area under the curve (AUC) for vaccine brand prediction based on spectral characteristics. Raman microscopy produced variable results with improved spectral quality over Raman spectroscopy for a number of samples. However, significant fluorescence was observed in numerous vaccine spectra, limiting the identification potential of the method. Clustering based on PCA showed accuracy in distinguishing between vaccine samples, but showed limited performance in vaccine brand identification. Therefore, this paper presents proof of concept for the use of both handheld Raman spectroscopy and confocal Raman microscopy alongside MLAs for the rapid, on-site authentication of Covid-19 vaccines, with further method optimisation required to combat fluorescence interference in vaccine spectra and expansion of sample size to address the potential of overfitting in the MLAs.

The prevalence of cardiovascular diseases (CVDs) and diabetes mellitus (DM) has become a global concern with figures as high as $\mathbf{1 7. 9}$ and $\mathbf{1. 5}$ million lives lost annually [1, 2]. Global figures also suggested that the majority of CVDs and DM are present within low- and middle-income countries (LMICs), where medical equipment, staff and training is limited. As a result, many patients go underdiagnosed or undertreated and instead are left to manifest into further complications such as heart failure or diabetic ketoacidosis, respectively. Therefore, this study aimed to investigate the use of nearinfrared (NIR) spectroscopy paired with machine learning algorithms (MLAs) for detection of CVDs and DM in fingernails. The findings showed key NIR bands related to the glycation of proteins within the fingernails and indicated the presence of disease. Furthermore, binary and multi-class classification models were explored for the classification of healthy, unhealthy, CVD and diabetic fingernails.

COVID-19 is a novel coronavirus first emerging in Wuhan, China in December 2019 and has since spread rapidly across the globe escalating into a worldwide pandemic causing millions of fatalities. Emergency response to the pandemic included social distancing and isolation measures as well as the escalation of vaccination programmes. The most popular COVID-19 vaccines are nucleic acid-based. The vast spread and struggles in containment of the virus has allowed a gap in the market to emerge for counterfeit vaccines. This study investigates the use of handheld Raman spectroscopy as a method for nucleic acid-based vaccine authentication and utilises machine learning analytics to assess the efficacy of the method. Conventional Raman spectroscopy requires a large workspace, is cumbersome and energy consuming, and handheld Raman systems show limitations with regards to sensitivity and sample detection. Surface Enhanced Raman spectroscopy (SERS) however, shows potential as an authentication technique for vaccines, allowing identification of characteristic nucleic acid bands in spectra. SERS showed strong identification potential through Correlation in Wavelength Space (CWS) with all vaccine samples obtaining an r value of approximately 1 when plotted against themselves. Variance was observed between some excipients and a selected number of DNA-based vaccines, possibly attributed to the stability of the SERS colloid where the colloid-vaccine complex had been measured over different time intervals. Further development of the technique would include optimisation of the SERS method, stability studies and more comprehensive analysis and interpretation of a greater sample size.

Near infrared (NIR) spectroscopy offers portable and rapid analysis of endogenous constituents and drugs within fingernails. Fingernails are a useful alternative biological matrix to blood and urine specimen as they provide the advantage of being non-invasive and require minimal sample size (1–3 mm). This work utilised NIR spectroscopy for the detection of (1) drugs in fingernails including benzocaine, calcium carbonate, cocaine hydrochloride (HCl), levamisole HCl, lidocaine HCl and procaine HCl; and (2) endogenous constituents such as carbohydrates, lipids, proteins and water. Fingernails were analysed initially ‘as received’ to identify the aforementioned endogenous constituents. Seven sets of fingernails were then spiked with one the identified drugs and measured over a six-week period. Spectra were exported into Matlab 2019a for spectral interpretation and machine learning analytics (MLAs). MLAs included correlation wavenumber space (CWS), principal component analysis (PCA) and Artificial Neural Networks Self-Organising Maps (SOM). The results showed that NIR spectra of spiked nails showed key characteristic features at specific wavelengths that corresponded to their spiked drug (1). When combined with CWS and PCA, NIR spectroscopy was able to differentiate between spiked and un-spiked nails and distinguish between the drugs that did not share similar chemical structures. CWS values (r values) and PCA loading scores highlighted spectra/spectral features that were significant. In addition, SOM showed further classes beyond PCA that corresponded to changes in physical properties of the fingernails. Thus, finding confirmed that NIR spectroscopy combined with MLAs possessed the ability to characterise fingernails based on their endogenous constituents and to detect the presence of drugs within fingernails.

The use of handheld Raman spectroscopy has increased in popularity based on its ability to provide portable and rapid analysis of endogenous compounds and diagnostic biomarkers in alternative biological matrices, such as fingernails. The application of fingernails as a diagnostic matrix allows for non-invasive, non-intrusive sampling, which can be carried out in the comfort of the patient’s home. This study aimed to identify diagnostic biomarkers in fingernails related to cardiovascular diseases (CVDs) and diabetes mellitus (DM) using Raman spectroscopy and machine learning algorithms (MLAs). The findings showed that Raman spectroscopy successfully identified the presence of disease specific biomarkers in CVD and diabetic fingernails. Furthermore, when used in combination with MLAs, Raman spectroscopy was able to differentiate between healthy, CVD and diabetic fingernails. Further investigation will look at applying additional MLAs for determining the prognosis of disease.