Development of efficient data management and analytics tools for Intelligent sanitation network design.

Williams, Leon - Associate SupervisorAccording to the World Health Organisation, billions of people lack access to basic sanitation facilities and services, resulting in estimated 2.9 million cases of diseases and 95,000 deaths each year. This is because poor planning, design, maintenance, and access in traditional sanitation networks. Nowadays, intelligent sanitation systems leveraging the Internet of Things (IoT) technology can provide efficient and sustainable services, incorporating sensors, hardware, software, and wireless communication. Furthermore, advanced data analytics tools combined with the intelligent sanitation systems can provide a deeper insight into operations, make informed decisions, and enhance user experience, thereby improving sanitation services. The thesis provides a comprehensive review of literature on intelligent sanitation systems from both academic and industrial perspectives, with the objective of identifying recent advances, research gaps, opportunities, and challenges. Existing solutions for intelligent sanitation are fragmented and immature due to a lack of a unified framework and tool. To address these issues, the thesis introduces a generalised Sanitation-IoT (San-IoT) framework to manage sanitation facilities and a standardised Sanitation-IoT-Data Analytics (San-IoT-DA) tool to analyse sanitation data. The framework and tool can serve as a foundation for future research and development in intelligent sanitation systems. The San-IoT framework can enhance the connectivity, operability, and management of IoT-based sanitation networks. The San-IoT-DA tool is designed to standardise the collection, analysis, and management of sanitation data for providing efficient data processing and improving decision making. The feasibility of the proposed framework and tool was evaluated on a case study of the Cranfield intelligent toilet. The San-IoT framework has the potential to enable system monitoring and control, user health monitoring, user behaviour analysis, improve water usage efficiency, reduce energy consumption, and facilitate decision-making among global stakeholders. The San-IoT-DA tool can detect patterns, identify trends, predict outcomes, and detect anomalies. The thesis offers valuable insights to practitioners, academics, engineers, policymakers, and other stakeholders on leveraging IoT and data analytics to improve the efficiency, accessibility, and sustainability of the sanitation industry.PhD in Desig

Applications of Mixed Reality for Smart Aviation Industry: Opportunities and Challenges

Nowadays, mixed reality has improved operational efficiency and enhanced passenger experience in the aviation industry. Integrated with advanced machine learning and artificial intelligence techniques, mixed reality can easily deal with tons of aviation data to support decision-making processes in this industry. The chapter presents the state-of-the-art applications of mixed reality in smart aviation industry. Opportunities and challenges of integrating mixed reality with advanced machine learning and artificial intelligence techniques into the aviation industry are introduced. This chapter focuses on how the integrated mixed reality can improve the quality and reliability of maintenance, operation, piloting, training, and product design in smart aerospace engineering. It also describes autonomous, self-service, and data visualization systems in smart airports to enhance passenger experience. Finally, this chapter discusses airline’s digital-based responses to the COVID-19 crisis

Advanced visual slam and image segmentation techniques for augmented reality

Augmented reality can enhance human perception to experience a virtual-reality intertwined world by computer vision techniques. However, the basic techniques cannot handle complex large-scale scenes, tackle real-time occlusion, and render virtual objects in augmented reality. Therefore, this paper studies potential solutions, such as visual SLAM and image segmentation, that can address these challenges in the augmented reality visualizations. This paper provides a review of advanced visual SLAM and image segmentation techniques for augmented reality. In addition, applications of machine learning techniques for improving augmented reality are presented

Machine learning and mixed reality for smart aviation: applications and challenges

The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency

A hybrid algorithm for large-scale non-separable nonlinear multicommodity flow problems

We propose an approach for large-scale non-separable nonlinear multicommodity flow problems by solving a sequence of subproblems which can be addressed by commercial solvers. Using a combination of solution methods such as modified gradient projection, shortest path algorithm and golden section search, the approach can handle general problem instances, including those with (i) non-separable cost, (ii) objective function not available analytically as polynomial but are evaluated using black-boxes, and (iii) additional side constraints not of network flow types. Implemented as a toolbox in commercial solvers, it allows researchers and practitioners, currently conversant with linear instances, to easily manage large-scale convex instances as well. In this article, we compared the proposed algorithm with alternative approaches in the literature, covering both theory and large test cases. New test cases with non-separable convex costs and non-network flow side constraints are also presented and evaluated. The toolbox is available free for academic use upon request

Feasibility and safety of a self-developed sleeve for the endoscopic removal of refractory foreign body incarceration

ObjectiveThis study aimed to assess the feasibility and safety of a novel self-designed sleeve for the endoscopic removal of a refractory incarcerated foreign body in the upper gastrointestinal tract (UGIT).MethodsAn interventional study was conducted between June and December 2022. A total of 60 patients who underwent an endoscopic removal of a refractory incarcerated foreign body from the UGIT were randomly allocated to the self-developed sleeve and the conventional transparent cap. The study evaluated and compared the operation time, successful removal rate, new injury length at the entrance of the esophagus, new injury length at the impaction site, visual field clarity, and postoperative complications between the two groups.ResultsThe success rates of the two cohorts in the foreign body removal display no significant discrepancy (100% vs. 93%, P = 0.529). Nevertheless, the methodology of the novel overtube-assisted endoscopic foreign body removal has culminated in a significant reduction in the removal duration [40 (10, 50) min vs. 80 (10, 90) min, P = 0.01], reduction in esophageal entrance traumas [0 (0, 0) mm vs. 4.0 (0, 6) mm, P < 0.001], mitigation of injuries at the location of the foreign body incarceration [0 (0, 2) mm vs. 6.0 (3, 8) mm, P < 0.001], an enhanced visual field (P < 0.001), and a decrement in postoperative mucosal bleeding (23% vs. 67%, P < 0.001). The self-developed sleeve effectively negated the advantages of incarceration exclusion during removal.ConclusionThe study findings support the feasibility and safety of the self-developed sleeve for the endoscopic removal of a refractory incarcerated foreign body in the UGIT, with advantages over the conventional transparent cap

Classification related to immunogenic cell death predicts prognosis, immune microenvironment characteristics, and response to immunotherapy in lower-grade gliomas

BackgroundImmunogenic cell death (ICD) is a form of cell death that elicits immune responses against the antigens found in dead or dying tumor cells. Growing evidence implies that ICD plays a significant role in triggering antitumor immunity. The prognosis for glioma remains poor despite many biomarkers being reported, and identifying ICD-related biomarkers is imminent for better-personalized management in patients with lower-grade glioma (LGG).Materials and methodsWe identified ICD-related differentially expressed genes (DEGs) by comparing gene expression profiles obtained across Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) cohorts. On the foundation of ICD-related DEGs, two ICD-related clusters were identified through consensus clustering. Then, survival analysis, functional enrichment analysis, somatic mutation analysis, and immune characteristics analysis were performed in the two ICD-related subtypes. Additionally, we developed and validated a risk assessment signature for LGG patients. Finally, we selected one gene (EIF2AK3) from the above risk model for experimental validation.Results32 ICD-related DEGs were screened, dividing the LGG samples from the TCGA database into two distinct subtypes. The ICD-high subgroup showed worse overall survival (OS), greater immune infiltration, more active immune response process, and higher expression levels of HLA genes than the ICD-low subgroup. Additionally, nine ICD-related DEGs were identified to build the prognostic signature, which was highly correlated with the tumor-immune microenvironment and could unambiguously be taken as an independent prognostic factor and further verified in an external dataset. The experimental results indicated that EIF2AK3 expression was higher in tumors than paracancerous tissues, and high-expression EIF2AK3 was enriched in WHO III and IV gliomas by qPCR and IHC, and Knockdown of EIF2AK3 suppressed cell viability and mobility in glioma cells.ConclusionWe established novel ICD-related subtypes and risk signature for LGG, which may be beneficial to improving clinical outcome prediction and guiding individualized immunotherapy

A prognostic signature based on snoRNA predicts the overall survival of lower-grade glioma patients

IntroductionSmall nucleolar RNAs (snoRNAs) are a group of non-coding RNAs enriched in the nucleus which direct post-transcriptional modifications of rRNAs, snRNAs and other molecules. Recent studies have suggested that snoRNAs have a significant role in tumor oncogenesis and can be served as prognostic markers for predicting the overall survival of tumor patients. MethodsWe screened 122 survival-related snoRNAs from public databases and eventually selected 7 snoRNAs that were most relevant to the prognosis of lower-grade glioma (LGG) patients for the establishment of the 7-snoRNA prognostic signature. Further, we combined clinical characteristics related to the prognosis of glioma patients and the 7-snoRNA prognostic signature to construct a nomogram.ResultsThe prognostic model displayed greater predictive power in both validation set and stratification analysis. Results of enrichment analysis revealed that these snoRNAs mainly participated in the post-transcriptional process such as RNA splicing, metabolism and modifications. In addition, 7-snoRNA prognostic signature were positively correlated with immune scores and expression levels of multiple immune checkpoint molecules, which can be used as potential biomarkers for immunotherapy prediction. From the results of bioinformatics analysis, we inferred that SNORD88C has a major role in the development of glioma, and then performed in vitro experiments to validate it. The results revealed that SNORD88C could promote the proliferation, invasion and migration of glioma cells. DiscussionWe established a 7-snoRNA prognostic signature and nomogram that can be applied to evaluate the survival of LGG patients with good sensitivity and specificity. In addition, SNORD88C could promote the proliferation, migration and invasion of glioma cells and is involved in a variety of biological processes related to DNA and RNA

Toward baggage-free airport terminals: a case study of London City Airport

Nowadays, the aviation industry pays more attention to emission reduction toward the net-zero carbon goals. However, the volume of global passengers and baggage is exponentially increasing, which leads to challenges for sustainable airports. A baggage-free airport terminal is considered a potential solution in solving this issue. Removing the baggage operation away from the passenger terminals will reduce workload for airport operators and promote passengers to use public transport to airport terminals. As a result, it will bring a significant impact on energy and the environment, leading to a reduction of fuel consumption and mitigation of carbon emission. This paper studies a baggage collection network design problem using vehicle routing strategies and augmented reality for baggage-free airport terminals. We use a spreadsheet solver tool, based on the integration of the modified Clark and Wright savings heuristic and density-based clustering algorithm, for optimizing the location of logistic hubs and planning the vehicle routes for baggage collection. This tool is applied for the case study at London City Airport to analyze the impacts of the strategies on carbon emission quantitatively. The result indicates that the proposed baggage collection network can significantly reduce 290.10 tonnes of carbon emissions annually

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data