Power Transformer DGA Data Augmentation Using Conditional Tabular Generative Adversarial Network

Dissolved gas analysis (DGA) serves as an effective method for diagnosing transformer faults, but the sample data obtained from the measurement of equipment failure are often insufficient, low in quality due to the presence of noise and are unevenly distributed. These characteristics of the DGA data, especially the imbalance in the fault classes make it a major challenge significantly impacting the accuracy of fault diagnosis. In this paper, conditional tabular generative adversarial network (CTGAN) is proposed to achieve an even probability distribution of the fault classes through generating realistic synthetic data to augment the minority fault classes. To evaluate the performance of the proposed technique descriptive statistics and evenness indices were used to compare the sample homogeneity of the generated synthetic data to the real DGA data. The results showed fidelity of the synthetic data generated by CTGAN in relation to the original DGA data and an increase in the probability of the minority fault classes

Probing and manipulating the gut microbiome with chemistry and chemical tools

The human gut microbiome represents an extended “second genome” harbouring about 1015 microbes containing >100 times the number of genes as the host. States of health and disease are largely mediated by host-microbial metabolic interplay, and the microbiome composition also underlies the differential responses to chemotherapeutic agents between people. Chemical information will be the key in order to tackle this complexity and discover specific gut microbiome metabolism for creating more personalised interventions. Additionally, rising antibiotic resistance and growing awareness of gut microbiome effects iscreating a need for non-microbicidal therapeutic interventions. We classify chemical interventions for the gut microbiome into categories like molecular decoys, bacterial conjugation inhibitors, colonization resistance-stimulating molecules, “prebiotics” to promote the growth of beneficial microbes and inhibitors of specific gut microbial enzymes. Moreover, small molecule probes including click chemistry probes, artificial substrates for assaying gut bacterial enzymes and receptor agonists/antagonists which engage host receptors interacting with the microbiome, are some other promising developments in the expanding chemical toolkit for probing and modulating the gut microbiome. This review explicitly excludes ‘biologics’ such as probiotics, bacteriophages, and CRISPR to concentrate on chemistry and chemical tools like chemoproteomics in the gut-microbiome context

Transforming Building Energy Management:Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling

The building sector, responsible for 40% of global energy consumption, faces increasing demands for sustainability and energy efficiency. Accurate energy consumption forecasting is essential to optimise performance and reduce environmental impact. This study introduces a hybrid machine learning framework grounded in Sparse, Interpretable, and Transparent (SIT) modelling to enhance building energy management. Leveraging the REFIT Smart Home Dataset, the framework integrates occupancy pattern analysis, appliance-level energy prediction, and probabilistic uncertainty quantification. The framework clusters occupancy-driven energy usage patterns using K-means and Gaussian Mixture Models, identifying three distinct household profiles: high-energy frequent occupancy, moderate-energy variable occupancy, and low-energy irregular occupancy. A Random Forest classifier is employed to pinpoint key appliances influencing occupancy, with a drop-in accuracy analysis verifying their predictive power. Uncertainty analysis quantifies classification confidence, revealing ambiguous periods linked to irregular appliance usage patterns. Additionally, time-series decomposition and appliance-level predictions are contextualised with seasonal and occupancy dynamics, enhancing interpretability. Comparative evaluations demonstrate the framework’s superior predictive accuracy and transparency over traditional single machine learning models, including Support Vector Machines (SVM) and XGBoost in Matlab 2024b and Python 3.10. By capturing occupancy-driven energy behaviours and accounting for inherent uncertainties, this research provides actionable insights for adaptive energy management. The proposed SIT hybrid model can contribute to sustainable and resilient smart energy systems, paving the way for efficient building energy management strategies

Contact-Tracing App Acceptance Decisions are Multifaceted and Entangled

This study aimed to determine whether privacy-related assurances in Contact-Tracing App(CTA) descriptions would positively influence adoption intentions.We modelled the influence of privacy-protective design on CTA adoption during a future pandemic. We used anonline survey to collect data from an English survey panel: (a) in 2021 during the COVID-19 pandemic and (b)in 2024, 17 months after the end of the pandemic.The quantitative analysis showed that privacy assurances in app descriptions, with consequent perceivedprivacy advantages and disadvantages, did not influence the intention to adopt CTAs. Significant adoptionintention predictors were: (a) previous installation of the NHS COVID-19 CTA, (b) perceived self-efficacy, (c)trust in the UK government, and (d) perception of disease spread into the community. Our qualitative analysis,however, demonstrated that privacy preservation and trust in government were important considerationsinforming intention to adopt CTAs, although other factors are also influential.This research makes an original contribution to knowledge by developing and experimentally testing theinfluence of privacy-related assurances on CTA adoption intention within a novel adoption model.Recommendations focus on re-establishing trust and ensuring that adoptersǯ privacy is preserved by the appand that public relations campaigns ensure potential adopters are aware of this

Developing a strategy to guide interprofessional education

Students in health and social care programmes need to develop competence to work and learn together in effective interprofessional teams. Interprofessional education is an approach used to prepare students for the realities of practice – to help them become safe, inclusive and collaborative professionals who deliver person-centred care. However, designing, delivering and evaluating interprofessional education across many student programmes is complex. Having an interprofessional education strategy aids the planning of this type of education, and this article discusses how a collaboration led to the development and implementation of an interprofessional education strategy into a university’s curriculum

Online Job Search Application with Automatic Recommendation and Notification System:Leveraging AI and ML for Enhanced User Experience

With the continuous advancement of technology and the need to keep pace with the digital era, the implementation of robust automated job recommendation systems has become essential to address the limitations of traditional methods and manual processes. This dissertation focuses on developing an online job search website application that integrates an automatic recommendation, alert and notification system, facilitating a more efficient connection between employers and job applicants using ML. Employers will have the ability to post job openings, review applicant profiles, and select the most qualified candidates. The user profile will be utilized to recommend jobs to candidates through a Semantic-Based Search System, with the Cosine Similarity technique serving as the key factor for automated job recommendations and comparing alongside the behavior of Jaccard similarity and the Jaccard similarity with subset matching. In other to understand what the best scenario for use for each is. The development of this job portal application aims to address the challenges faced by companies in filling vacancies and by job seekers in finding suitable employment opportunities

Agri Sage:A Mobile Application for Agricultural Disease Detection, E-Commerce, and Real-Time Information Systems

This paper introduces Agri Sage, a mobile application aimed at addressing critical challenges in modern agriculture. Agri Sage integrates real-time plant disease detection using machine learning, an e-commerce platform for agricultural products, and weather updates alongside government pricing and subsidy information. Leveraging TensorFlow Lite for image analysis, the app diagnoses plant diseases even under suboptimal image conditions. The e-commerce platform allows farmers to connect with buyers and manage bulk transactions, while real-time weather and government data help farmers make informed decisions. This holistic approach enhances farm management, boosts productivity, and makes technology accessible to farmers in diverse environments. Extensive testing of Agri Sage demonstrated its efficacy in real-world applications, particularly in improving crop health and market access

Experimental Study of Premixed Hydrogen-Enriched Flame Quenching and Emission Characteristics for Industrial Burner Applications

As a promising renewable energy source, hydrogen has garnered substantial attention and has been widely adoptedin various industries. However, the safety concerns associated with its usage have consistently posed a significanttechnical challenge, hindering the rapid development of hydrogen-based technologies in the energy sector. Toaddress these safety concerns and develop effective inhibitory strategies for hydrogen combustion, this studyexperimentally investigates the impact of wire mesh and porous materials on the quenching behaviour of pre-mixed hydrogen-enriched flames for industrial burner applications. A Bunsen burner is employed to generate thehydrogen-enriched flame, and mass flow controllers are utilised to precisely control the hydrogen enrichment.Additionally, emission characteristics are analysed for different hydrogen-enriched fuel mixtures, such as propaneLPG plus hydrogen. Key parameters, including thickness, aperture, porosity, melting temperature, and thermalconductivity, are determined to select meshes and porous materials suitable for industrial burner applications. Aninfrared camera with a spectral range of 1-14 µm is utilised for flame detection and verification. The study’sfindings demonstrate promising results in achieving carbon-free emissions by increasing the hydrogen enrichmentpercentage for all studied cases. This research serves as a valuable reference for designing hydrogen flame arrestorsthat enhance the safety of hydrogen combustion in burner applications and hydrogen transportation engineering

Replicator-mutator dynamics for public goods games with institutional incentives

Understanding the emergence and stability of cooperation in public goods games is important due to its applications in fields such as biology, economics, and social science. However, a gap remains in comprehending how mutations, both additive and multiplicative, as well as institutional incentives, influence these dynamics. In this paper, we study the replicator-mutator dynamics, with combined additive and multiplicative mutations, for public goods games both in the absence or presence of institutional incentives. For each model, we identify the possible number of (stable) equilibria, demonstrate their attainability, as well as analyse their stability properties. We also characterise the dependence of these equilibria on the model's parameters via bifurcation analysis and asymptotic behaviour. Our results offer rigorous and quantitative insights into the role of institutional incentives and the effect of combined additive and multiplicative mutations on the evolution of cooperation in the context of public goods games

Alpha-synuclein aggregation induces prominent cellular lipid changes as revealed by Raman spectroscopy and machine learning analysis

The aggregation of α-synuclein is a central neuropathological hallmark in neurodegenerative disorders known as Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. In the aggregation process, α-synuclein transitions from its native disordered/α-helical form to a β-sheet-rich structure, forming oligomers and protofibrils that accumulate into Lewy bodies, in a process that is thought to underlie neurodegeneration. Lipids are thought to play a critical role in this process by facilitating α-synuclein aggregation and contributing to cell toxicity, possibly through ceramide production. This study aimed to investigate biochemical changes associated with α-synuclein aggregation, focusing on lipid changes, using Raman spectroscopy coupled with machine learning. HEK293, Neuro2a and SH-SY5Y expressing increased levels of α-synuclein were treated with sonicated α-synuclein pre-formed fibrils, to model seeded aggregation. Raman spectroscopy, complemented by an in-house lipid spectral library, was used to monitor the aggregation process and its effects on cellular viability over 14 days. We detected α-synuclein aggregation by assessing β-sheet peaks at 1045 cm⁻1, in cells treated with α-synuclein pre-formed fibrils, using machine learning (principal component analysis and uniform manifold approximation and projection) analysis based on Raman spectral features. Changes in lipid profiles, and especially sphingolipids, including a decrease in sphingomyelin and increase in ceramides, were observed, consistent with oxidative stress and apoptosis. Altogether, our study informs on biochemical alterations that can be considered for the design of therapeutic strategies for Parkinson's disease and related synucleinopathies