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Item Embargo Technology facilitated intimate partner violence in romantic relationships: Cyber dating abuse(Springer Nature Switzerland, 2026-06-14)This entry explores theoretical perspectives and psychological predictors of cyber dating abuse. It begins with cognitive psychology, which considers distinctions between verbal and physical aggression, the influence of jealousy, and the role of self-esteem. The discussion then turns to evolutionary psychology, which examines mate retention strategies and intrasexual competition, highlighting the importance of mate value in understanding the perpetration of cyber dating abuse. From a social psychological perspective, this behaviour is also explained through social learning theory and demographic characteristics, with particular attention given to monitoring behaviours.Item Open Access Role of plasma membrane ATPase4 in the pathophysiology of pulmonary arterial hypertension(University of Wolverhampton, 2026)Background Pulmonary Arterial Hypertension (PAH) is a progressive vascular disease characterised by pulmonary vascular remodelling, inflammation, and excessive apoptosis of pulmonary arterial endothelial cells (PAECs). Elevated levels of pro- inflammatory cytokines such as TNF-α are a hallmark of PAH and are known to induce endothelial injury. Calcium dysregulation contributes to apoptotic signalling, suggesting that calcium transporters like Plasma Membrane Calcium ATPase 4 (PMCA4) may play an important regulatory role. PMCA4, encoded by ATP2B4, maintains calcium homeostasis and modulates signalling pathways through its scaffolding function at the plasma membrane. This study investigates the contribution of PMCA4 to inflammation-induced endothelial apoptosis and its regulation at the RNA level. Methods and Results To determine the relevance of PMCA4 in PAH, the expression of PAH-associated receptors (BMPR2, ALK1, TGFβR1, Endoglin) was evaluated in lung RNA from PMCA4 wild-type and knockout mice by qPCR, showing no significant differences. In vitro, stimulation of human PAECs and HUVECs with TNF-α and cycloheximide (CHX) significantly reduced PMCA4 mRNA and protein expression. Silencing PMCA4 using siRNA markedly enhanced TNF-α + CHX–induced apoptosis, confirmed by increased TUNEL staining and caspase-3 cleavage. In contrast, BMP9-induced Smad1/5 and TGF-β–induced Smad2 phosphorylation remained unaltered, indicating that PMCA4’s anti-apoptotic role is independent of canonical Smad signaling. A luciferase reporter construct harboring the PMCA4 3′UTR was engineered to assess post-transcriptional regulation. Luciferase assays demonstrated that TNF-α destabilizes PMCA4 transcripts through specific 3′UTR elements. Cloning and validation of these constructs confirmed the integrity of the engineered vector and its recombination efficiency in HEK293T cells, establishing a reliable system for RNA stability studies. Conclusion The downregulation of PMCA4 by TNF-α promotes endothelial apoptosis through caspase-dependent mechanisms without altering BMP9, TGF-β, or MAPK pathways. Post-transcriptional destabilization of PMCA4 mRNA via its 3′UTR represents a novel mechanism of inflammatory regulation in the pulmonary endothelium. Together, these findings highlight PMCA4 as a key protective factor against inflammation-induced endothelial injury and a potential therapeutic target in the pathophysiology of PAH.Item Open Access Big data toward vehicle health monitoring system: an engine health perspective(University of Wolverhampton, 2026)The rapid growth of connected and sensor rich vehicles has produced unprecedented volumes of engine telemetry, posing critical challenges for traditional vehicle health monitoring system (VHMS). Existing systems struggle with high volume, high velocity, and heterogeneous data streams, limiting real-time fault detection, predictive maintenance, and operational reliability. Addressing these limitations requires a cohesive, multi-layered framework that unifies scalable data management, real-time analytics, and intelligent predictive modeling. This thesis leverages a VHMS dataset initially collected by (Rahman et al., 2022) and augmented from 3,003 to 200,000 records with 18 critical features, including vibration, Controller Area Network bus, combustion, lubrication, and thermal signals. Using this high dimensional, sequential, and heterogeneous data, this research developed an integrated end-to-end VHMS paradigm comprising four key contributions: (i) A conceptual big data framework that defined a taxonomy for data collection, preprocessing, storage, and analysis, ensuring modularity, traceability, and scalability across heterogeneous vehicle data sources. (ii) An optimized hybrid big data processing architecture that integrate hadoop for fault tolerant historical storage, Spark for in-memory analytics, and Kafka for real-time streaming. This framework dynamically switches between batch and streaming workloads, achieving 0.030s execution time, 1.67 × 10−7s per-row latency, and a 55.01% reduction in network load compared to standalone approaches (Chukwudi et al., 2025). (iii) A hybrid deep learning diagnostic Model, capable of modeling temporal dependencies and adapting across diverse vehicle types. This model attains 92.18% testing accuracy with a Receiver Operating Characteristic and Area Under the Curve (ROC AUC) of 0.965, outperforming conventional multilayer perception (MLP), long short-term memory (LSTM), and bidirectional gated recurrent units (BiGRU) architectures by 15–20% in both accuracy and generalization (Md Abdur Rahim et al., 2025). (iv) A real-time diagnostic model, combining Support Vector Machines, K-Neighbors, Gradient Boosting, and Decision Trees through a meta-learning layer. The ensemble achieved 94.7% accuracy and Area Under the Curve (AUC) of 0.9702, demonstrating superior robustness to sensor noise and overlapping engine states compared to single model approaches (Chukwudi et al., 2024; Rahman et al., 2022). Collectively, these contributions established a robust, interpretable, and adaptive VHMS paradigm capable of real-time diagnostics, predictive maintenance, and operational decision support. Comparative evaluation confirmed the improvements in accuracy, latency, scalability, and reliability over conventional VHMS. Furthermore, the framework provides a foundation for future studies in federated learning, edge-cloud deployment, and multimodal sensor fusion, offering scalable, low-cost, and inclusive solutions for modern transportation systems. This research advances both practical and scholarly understanding of intelligent VHMS, demonstrating that integrating data engineering, machine learning, and decision-oriented system design is critical for next-generation vehicle diagnostics.Item Open Access The development of novel services for battery storage in domestic applications and communal mini-smart grids(University of Wolverhampton, 2026)Lithium-ion battery technology remains a key factor in energy storage technology due to its advantageous features which include high energy and power density, low self-discharge, and longer lifespan. However, the continuous cycling of the battery over its lifetime results in gradual internal resistance growth due to electrochemical reactions leading to structural and chemical degradation. This study focuses on the utilisation of the Lithium-ion battery within residential applications and relevance in facilitating the decarbonisation of the UK’s energy generation and storage network. Firstly, the comprehensive analysis of various energy storage technologies considering their impact on the environment was carried out. This provides insights into the sustainability of the different renewable energy storage applications. This is followed by investigation of Lithium-ion battery chemistries, highlighting the individual energy and power densities, limitations, and their uses. An analytical model study to investigate internal resistance growth leading to loss of capacity in the battery was carried out through Battery Management System (BMS) design in MATLAB/Simulink software. For this study, State of Charge (SoC) and temperature parameters and their influence on internal resistance growth were investigated, highlighting the role of BMS in optimizing the operation of Lithium-ion batteries. Additionally, for evaluating the performance of the battery, a Decision Tree Energy Throughput prediction model was designed and validated using the Python Software. Moreover, a Solar PV/Battery system sizing case study was carried out with energy consumption data collected in collaboration with the industrial partner to the project. To demonstrate the optimal power flow of the system, an MPPT and DC-DC Boost Converter, highlighting the system performance at different irradiation levels was designed. This thesis contributes to advancing the understanding of Lithium-ion battery technology’s role in sustainable energy systems. The findings and recommendations generated from this study are expected to inform policy-making decisions, shape industry practices and accelerate the transition towards a low-carbon energy future in the UK and beyond.Item Open Access Investigating the expression of plasma membrane calcium ATPase (PMCA) in patient-derived glioblastoma cultures(University of Wolverhampton, 2026)Glioblastoma is the most prevalent and aggressive form of primary brain tumour in adults, characterised by extensive heterogeneity, rapid progression, and resistance to conventional therapies such as surgery, radiotherapy, and chemotherapy. Despite decades of research, the median survival for glioblastoma patients remains approximately 15 months, and recurrence is almost universal. In light of these challenges, this thesis explores the role of Plasma Membrane Calcium ATPases (PMCAs) in the pathology of glioblastoma, with a specific focus on isoforms PMCA1, PMCA2, and PMCA4. Patient-derived glioblastoma cultures and biopsy samples were employed to investigate PMCA expression patterns at both the transcript and protein levels. Quantitative RT-PCR and western blotting revealed that PMCA1 and PMCA4 were significantly overexpressed in several patient-derived cell cultures compared to normal human astrocytes and control cell lines. PMCA2, in contrast, exhibited more variable expression, often low or undetectable in most glioblastoma cultures. Immunofluorescence and flow cytometry analysis supported these findings, demonstrating heterogeneous distribution of PMCA isoforms among samples. A key focus of the study was the effect of tumour hypoxia, a hallmark of glioblastoma, on PMCA expression. Cells cultured under hypoxic conditions (1% O₂) showed altered PMCA isoform expression, particularly a consistent downregulation of PMCA1 and PMCA2, suggesting a role in adaptation to hypoxic stress. This may have implications for calcium- mediated survival mechanisms in the hypoxic tumour core, contributing to treatment resistance and tumour progression. The study also evaluated how clinically relevant drugs modulate PMCA expression. Treatment with the anti-angiogenic agent sunitinib resulted in a dose-dependent upregulation of PMCA1 and downregulation of PMCA2 and PMCA4 at mRNA levels in multiple glioblastoma cell cultures, indicating that PMCA expression may be responsive to targeted therapy. Correlating PMCA expression with patient survival data revealed that elevated PMCA1 expression levels were associated with better overall survival, suggesting their potential as prognostic biomarkers. Furthermore, the characterisation of glioblastoma cultures using markers such as CD44 and Olig2 confirmed the coexistence of mesenchymal and proneural features within individual patient-derived cultures, reinforcing the intratumoral heterogeneity observed in clinical glioblastoma. The results of this study provide evidence that PMCA isoforms, particularly PMCA1 and PMCA4, play a functional role in glioblastoma pathophysiology, potentially contributing to hypoxia tolerance, drug response, and tumour aggressiveness. These findings not only deepen our understanding of calcium signalling in glioblastoma but also propose PMCAs as potential therapeutic targets or biomarkers for stratifying patients and guiding treatment strategies.
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