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Collective Countermeasures and Regional Cooperation. Strengthening Cybersecurity in Qatar and the Gulf Cooperation Council
Get PDFThis thesis examines the following question: How can the Gulf Cooperation Council (GCC) develop and implement collective countermeasures through regional cooperation in cybersecurity that align with international legal frameworks while addressing unique regional challenges?
This study conducts an in-depth analysis of international cyber law principles, focusing on state responsibility, sovereignty, and the legality of collective countermeasures. It explores the tension between these principles and the need for regional cybersecurity cooperation within the GCC context.
This research project employs a comparative approach, drawing lessons from the Association of Southeast Asian Nations (ASEAN) cybersecurity cooperation model, but primarily focuses on the legal implications for the GCC. It considers the GCC's distinctive features, including its critical energy infrastructure and existing security cooperation mechanisms, within the framework of international law.
Key findings address the compatibility of potential GCC collective countermeasures with international legal norms. The study examines how the principles of sovereignty and state responsibility can be balanced with the need for regional cyber defence. Based on this legal analysis, the thesis proposes recommendations for the GCC, including developing a legally compliant regional cybersecurity strategy and establishing institutional mechanisms for collaboration that respect international law.
This thesis concludes by assessing the GCC's potential to develop a regional cybersecurity cooperation model that enhances its security while adhering to international legal frameworks. It identifies areas for future research, including the evolving norms in international cyber law and their implications for regional cybersecurity efforts
Beam Prediction for mmWave V2I Communication aided by Geolocation and Machine Learning
Get PDFYesMillimetre wave (mmWave) systems require high beamforming gains to overcome the unfavourable impacts of high path losses at mmWave frequencies. Large antenna arrays enable such gains through highly directive narrow beams which then require multiple beams to cover the spatial directions of interest. The required beam management for such systems, particularly for mobile use cases such as the vehicle-to-infrastructure (V2I) scenarios, is challenging. Real-time optimal beam selection from codebooks consumes radio resources and incurs large training overheads. As a result, geolocation side information and machine learning (ML) algorithms are being explored to address beam management challenges. However, prior works have mostly applied their solutions using simulations that are based on synthetic datasets. Recently, real-world datasets based on extensive mmWave measurements have become available. Leveraging the real-world datasets, in this work, we evaluate and compare the performance of three ML (i.e., k-nearest neighbours, support vector machine and decision tree) algorithms on mmWave V2I beam selection aided by global positioning system latitude and longitude coordinates as the only two features for the ML. The results show the impact of codebook sizes on the accuracies of the ML algorithms under ten different scenarios. The results also reveal the limitations of the geolocation-aided beam prediction as average accuracy could go below 30% in some scenarios, and higher than 90% in other scenarios. These performance results point to the need for multi-modal approaches (involving a combination of different sensors' data) for efficient mmWave V2I beam prediction.EPSRC [grant number EP/Z001544/1] through the UKRI-funded MSCA Postdoctoral Fellowship; UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/Y035135/1, and HORIZON-MSCA-2022-SE-01-01-ID: 10113150
The role of human capital and Industry 4.0 in socio-technical dynamic capabilities for freight transport resilience
Get PDFYesThis study examines the role of socio-technical dynamic resources and capabilities in enhancing the resilience of freight transport firms. Focusing on unique characteristics of freight transport sector, this study considers human capital and Industry 4.0 as socio-technical resources whereas network integration and operations planning as socio-technical capabilities. A survey is conducted on freight transport managers in the UK and partial least squares structural equation modelling (PLS-SEM) is employed to analyse the data. Our findings indicate that human capital and Industry 4.0 positively affect network integration and operations planning, which in turn enhance perceived resilience. Among two dynamic capabilities, network integration has a stronger impact on resilience than operations planning. Mediation analysis shows technical resources and capabilities partially enhance the impact of social resources and capabilities in SEM model. Our results suggest that socio resources and capabilities are at least as important as technical ones for building resilience in the freight sector. Practical and policy implications in our study suggest that freight transport firms and policy makers should invest and support skillbuilding initiatives, digital tools, supply chain analytics and collaboration platforms to increase freight transport resilience
Social choice, agency, inclusiveness and capabilities
No full textNoThe capability approach is a versatile framework rooted on issues of justice and multidimensional assessment of quality of life developed in the 1980s as an alternative approach to prevailing mainstream development ideas focused narrowly on economic development. Most closely associated with the work of Amartya Sen, it has become of great interest to development scholars from a variety of different disciplines. Much has already been done exploring the conceptual foundations of the capability approach and discussing Sen's contribution to the field, but few books have explored the links between social choice (another field with rich contributions by Sen) and human development issues. Featuring many of the world's leading experts on social choice theory and capability indicators, Social Choice, Agency, Inclusiveness and Capabilities combines these interrelated themes into one volume and fully explores the relevance of social choice to human development.British Academ
CD-44 targeted nanoparticles for combination therapy in an in vitro model of triple-negative breast cancer: Targeting the tumour inside out
Get PDFYesTriple-negative breast cancer (TNBC) is an aggressive form of breast cancer defined by the lack of three key receptors: estrogen, progesterone, and HER2. This lack of receptors makes TNBC difficult to treat with hormone therapy or drugs, and so it is characterised by a poor prognosis compared to other kinds of breast cancer. This study explores photoactive Poly(lactic-co-glycolic acid) (PLGA) nanoparticles as a potential therapeutic strategy for TNBC. The nanoparticles are functionalised with hyaluronic acid (HA) for targeted delivery to CD-44 receptors overexpressed in TNBC cells, especially under hypoxic conditions. Additionally, we co-loaded the nanoparticles with Doxorubicin (Dox) and Indocyanine Green (ICG) to enable combinatorial chemo-photothermal therapy. After carefully optimising the formulation, we propose an effortless and reproducible preparation of the nanodrugs. We demonstrate that HA-conjugated nanoparticles effectively target TNBC cells and inhibit their proliferation while the treatment efficiency is enhanced during near-infrared light irradiation. We also prove that our treatment is effective in a 3D cell culture model, highlighting the importance of tumour architecture and the metabolic stage of the cells in the tumour microenvironment. This approach is promising for a tumour-targeted theragnostic for TNBC with improved efficacy in hypoxic microenvironments.A.R would like to thank The Royal Society for the Research Grant (RGS\R1\221399) and the MRC Confidence in Concept University of Bradford grant (RM0039). H.R.W acknowledges support from the University of Bradford PhD studentship (DP154). G.G. and A.R would like to thank the University of Bradford (project RIEDA 66007/002GRI) for funding this research. G.R. acknowledges support from EU-NOVA European Union's Horizon Europe Framework Programme: 101058554
A Novel Artificial Intelligence-driven Technique for Enhancing Medical Imaging Techniques to Detect Non-Small Cell Lung Cancer
No full textYesNon-Small Cell Lung Cancer (NSCLC) is a disease wherein malignant cancer cells form in the lung tissue and this accounts for 85% of lung cancers. The five-year survival rate decreases as the NSCLC cancer becomes more advanced, from 40% for stage I to only 1% for stage IV; thus, a vital challenge to overcome is the early and correct detection of NSCLC to achieve a higher chance of survival. Due to radiographers being overworked with many time-consuming duties, new systems that can improve the effectiveness and efficiency of clinical professionals while maintaining appropriate predictive performance levels should be considered. Artificial intelligence (AI)-driven techniques can help provide the tools for radiographers to achieve a more accurate and efficient diagnosis of NSCLC. The deep learning (DL) model presented in this study leverages a novel multi-modal approach of using both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans as inputs to the model. This allows the model to learn from both morphological data from CT scans and also functional, physiological data available from MRI scans. As CT scans are the primary modality used in the structural detection of NSCLC, a higher weight is given to the recommendation made based on the information from the CT scan. One of the most important features analysed by the model is that of the Hounsfield Units (HU) of each pixel within the lung. HU values can be used to pinpoint areas of high density within the lungs that are identified as potential tumours. The model achieved a classification accuracy of 97.1% and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 95.7% on a test dataset of 140 patients
A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease
No full textYesParkinson’s disease (PD) is a complex, progressive neurodegenerative disorder with high heterogeneity, making early diagnosis difficult. Early detection and intervention are crucial for slowing PD progression. Understanding PD’s diverse pathways and mechanisms is key to advancing knowledge. Recent advances in noninvasive imaging and multi-omics technologies have provided valuable insights into PD’s underlying causes and biological processes. However, integrating these diverse data sources remains challenging, especially when deriving meaningful low-level features that can serve as diagnostic indicators. This study developed and validated a novel integrative, multimodal predictive model for detecting PD based on features derived from multimodal data, including hematological information, proteomics, RNA sequencing, metabolomics, and dopamine transporter scan imaging, sourced from the Parkinson’s Progression Markers Initiative. Several model architectures were investigated and evaluated, including support vector machine, eXtreme Gradient Boosting, fully connected neural networks with concatenation and joint modeling (FCNN_C and FCNN_JM), and a multimodal encoder-based model with multi-head cross-attention (MMT_CA). The MMT_CA model demonstrated superior predictive performance, achieving a balanced classification accuracy of 97.7%, thus highlighting its ability to capture and leverage cross-modality inter-dependencies to aid predictive analytics. Furthermore, feature importance analysis using SHapley Additive exPlanations not only identified crucial diagnostic biomarkers to inform the predictive models in this study but also holds potential for future research aimed at integrated functional analyses of PD from a multi-omics perspective, ultimately revealing targets required for precision medicine approaches to aid treatment of PD aimed at slowing down its progression.National Natural Science Foundation of China (32300540) and the Science, Technology and Innovation Commission of Shenzhen Municipality (Grant No. RCBS20221008093338092)
Computer Vision Versus Wearables Assessment of the Up-on-the-toes 30 Second Test
No full textYesThe rising up-on-the-toes (UTT) 30-second test is used clinically to assess ankle muscle strength and endurance. Typically, the test is subjectively evaluated by counting how many UTT movements are completed. We have recently shown that the UTT test can be objectively assessed using signals from small inertial measurement units (IMUs). The current study investigates whether computer vision (CV) analysis of the UTT test gives comparable outcomes to IMU analysis. A CV-based system was applied to video recordings of 29 older adult participants (76.0 ± 4.3 years) performing the UTT test with IMUs attached to their feet. Angular velocity time series signals were generated using both IMU and video object detection of the right foot landmarks, enabling peak plantarflexion velocities during the ascent and descent phases to be extracted. Findings demonstrate that the CV-based approach produces closely aligned output metrics with IMU data, with coefficient of determination (R2) values of ≥0.91
Compact Dual-Band Wearable Antenna for Millimeter-Wave Applications: Designed for Medical and IoT Device Integration
No full textYesThis paper introduces a compact dual-band wearable antenna designed for mmWave applications. The antenna is fabricated on a Rogers 3003 semi-flexible substrate with dimensions of 15 × 15 × 1.52 mm3 and features a circular radiating patch with a full ground plane. Initially designed to resonate at 28 GHz, the antenna incorporates a square split-ring resonator in the ground plane to achieve an additional resonance at 38 GHz. To improve bandwidth and gain, a round necktie configuration is applied by adding two diagonal rectangular patches to the periphery of the radiating patch. The measured impedance bandwidths are 21.4% at 28 GHz and 23.7% at 38 GHz. The antenna achieves gains of 5.91 dBi and 4.57 dBi, with efficiencies of 90% and 78% at the respective operating bands. Simulated SAR values are 0.57 W/kg and 0.31 W/kg for 1 g and 10 g of human tissue at 28 GHz, and 0.18 W/kg and 0.16 W/kg at 38 GHz. These SAR values comply with FCC and ICNIRP safety standards. Additionally, bending tests illustrate that the antenna’s performance was stable under deformation. As a result, the proposed antenna is ideal for fast connectivity 5G and biomedical applications since it efficiently spans fundamental mmWave frequency ranges
The Diverging Impact of Climate Policy Intensity and Climate Adaptation Readiness on U.S. Bank Performance and Risk
No full textYesUsing data from the largest U.S. Banks between 2005 and 2021, we examine how climate policy influence U.S. banks. In particular, the study explores the differential or reverse effects of Environmental Policy Stringency (EPS), Policy Uncertainty (CPU) and Climate Adaptation Readiness (ND Gain Score) on risk and performance of U.S. banks. We examine the hypotheses that the joint effects of Climate Policy Uncertainty and Stringency would increase bank risks whilst climate adaptation initiatives would reduce bank risk and boost banking performance. We further highlight the evolving nature of climate policy landscape and its impact to banking risk and performance by adding tests to explore time varying effects (post Paris agreement). Our research aims to shed more light on the environmental policies-risk-performance nexus, a topic that is gaining traction among stakeholders, regulators and investors. Our findings contribute to understanding the economic impact of climate policies in banking and showcase the crucial role of short-term costs and long-term benefits of adaptation for helping regulators and policymakers in evaluating climate-based turbulence and financial sector regulation