122 research outputs found

    Intelligent robotic disassembly optimisation for sustainability using the bees algorithm

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    Robotic disassembly plays a pivotal role in achieving efficient and sustainable product lifecycle management, with a focus on resource conservation and waste reduction. This thesis discusses robotic disassembly sequence planning (RDSP) and robotic disassembly line balancing (RDLB), with a specific emphasis on optimising sustainability models. The overarching goal was to enhance the efficiency and effectiveness of disassembly processes through intelligent robotic disassembly optimisation techniques. At the heart of this research lies the application of the Bees Algorithm (BA), a metaheuristic optimisation algorithm inspired by the foraging behaviour of honeybees. By harnessing the power of the BA, this research aims to address the challenges associated with RDSP and RDLB, ultimately facilitating sustainable disassembly practices. The thesis gives an extensive literature review of RDSP and RDLB to gain deeper insight into the current research landscape. The challenges of the RDSP problem were addressed in this work by introducing a sustainability model and various scenarios to enhance disassembly processes. The sustainability model considers three objectives: profit, energy savings, and environmental impact reduction. The four explored scenarios were recovery (REC), remanufacture (REM), reuse (REU), and an automatic recovery scenario (ARS). Two novel tools were developed for assessing algorithm performance: the statistical performance metric (SPM) and the performance evaluation index (PEI). To validate the proposed approach, a case study involving the disassembly of gear pumps was used. To optimise the RDSP, single-objective (SO), multiobjective (MO) aggregate, and multiobjective nondominated (MO-ND) approaches were adopted. Three optimisation algorithms were employed — Multiobjective Nondominated Bees Algorithm (MOBA), Nondominated Sorting Genetic Algorithm - II (NSGA-II), and Pareto Envelope-based Selection Algorithm - II (PESA-II), and their results were compared using SPM and PEI. The findings indicate that MO-ND is more suitable for this problem, highlighting the importance of considering conflicting objectives in RDSP. It was shown that recycling should be considered the last-resort recovery option, advocating for the exploration of alternative recovery strategies prior to recycling. Moreover, MOBA outperformed other algorithms, demonstrating its effectiveness in achieving a more efficient and sustainable RDSP. The problem of sequence-dependent robotic disassembly line balancing (RDLBSD) was next investigated by considering the interconnection between disassembly sequence planning and line balancing. Both aspects were optimised simultaneously, leading to a balanced and optimal disassembly process considering profitability, energy savings, environmental impact, and line balance using the MO-ND approach. The findings further support the notion that recycling should be considered the last option for recovery. Again, MOBA outperformed other algorithms, showcasing its capability to handle more complex problems. The final part of the thesis explains the mechanism of a new enhanced BA, named the Fibonacci Bees Algorithm (BAF). BAF draws inspiration from the Fibonacci sequence observed in the drone ancestry. This adoption of the Fibonacci-sequence-based pattern reduces the number of algorithm parameters to four, streamlining parameter setting and simplifying the algorithm’s steps. The study conducted on the RDSP problem demonstrates BAF’s performance over the basic BA, particularly in handling more complex problems. The thesis concludes by summarising the key contributions of the work, including the enhancements made to the BA and the introduction of novel evaluation tools, and the implications of the research, especially the importance of exploring alternative recovery strategies for end-of-life (EoL) products to align with Circular Economy principles

    Converging Human Intelligence With AI Systems to Advance Flood Evacuation Decision Making

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    The powers that artificial intelligence (AI) has developed are astounding, with recent success in integrating into a human cognitive workflow. AI will attain its full potential only if, as part of its intelligence, it also actively teams up with humans to co-create solutions. Combining AI simulation with human understanding and strategic abilities through data convergence may optimize the process and provide a capacity akin to teaming intelligence. This thesis will introduce the concepts of Human AI Convergence (HAC) capabilities for flood evacuation decision-making. The concept introduced in this thesis is the first step toward the HAC concept in weather disaster applications. This research demonstrates a synergy between humans and AI by integrating the data produced by humans through social media with an AI system to enhance a flood evacuation decision-making problem. The prediction from Long short-term memory (LSTM) and a river hydraulic model, i.e., Height Above Nearest Drainage (HAND), is integrated with human data from X (previously Twitter) to visualize flood inundation areas, which acts as a 3rd party agent for a HAC system. The goal is to synthesize and analyze HAC competence in flood evacuation emergency management and harness the full potential of AI as a partner in real-time planning and decision-making. This thesis has explored why HAC intelligence is essential to emergency planning and decision-making, providing a general structure for researchers to use HAC to devise effective systems that cooperate well and evaluate state-of-the-art, and, in doing so, providing a research agenda and a roadmap for future flood evacuation emergency management, rescue, and decision making. This state-of-the-art flood evacuation product stands to advance the frontier of human-AI collaborative research significantly

    A review on modelling methods, tools and service of integrated energy systems in China

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    An integrated energy system (IES) is responsible for aggregating various energy carriers, such as electricity, gas, heating, and cooling, with a focus on integrating these components to provide an efficient, low-carbon, and reliable energy supply. This paper aims to review the modeling methods, tools, and service modes of IES in China to evaluate opportunities for improving current practices. The models reviewed in this paper are classified as demand forecasting or energy system optimization models based on their modeling progress. Additionally, the main components involved in the IES modeling process are presented, and typical domestic tools utilized in the modeling processes are discussed. Finally, based on a review of several demonstration projects of IES, future development directions of IES are summarized as the integration of data-driven and engineering models, improvements in policies and mechanisms, the establishment of regional energy management centers, and the promotion of new energy equipment

    Sine Cosine Algorithm for Optimization

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    This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA

    A Step Toward Improving Healthcare Information Integration & Decision Support: Ontology, Sustainability and Resilience

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    The healthcare industry is a complex system with numerous stakeholders, including patients, providers, insurers, and government agencies. To improve healthcare quality and population well-being, there is a growing need to leverage data and IT (Information Technology) to support better decision-making. Healthcare information systems (HIS) are developed to store, process, and disseminate healthcare data. One of the main challenges with HIS is effectively managing the large amounts of data to support decision-making. This requires integrating data from disparate sources, such as electronic health records, clinical trials, and research databases. Ontology is one approach to address this challenge. However, understanding ontology in the healthcare domain is complex and difficult. Another challenge is to use HIS on scheduling and resource allocation in a sustainable and resilient way that meets multiple conflicting objectives. This is especially important in times of crisis when demand for resources may be high, and supply may be limited. This research thesis aims to explore ontology theory and develop a methodology for constructing HIS that can effectively support better decision-making in terms of scheduling and resource allocation while considering system resiliency and social sustainability. The objectives of the thesis are: (1) studying the theory of ontology in healthcare data and developing a deep model for constructing HIS; (2) advancing our understanding of healthcare system resiliency and social sustainability; (3) developing a methodology for scheduling with multi-objectives; and (4) developing a methodology for resource allocation with multi-objectives. The following conclusions can be drawn from the research results: (1) A data model for rich semantics and easy data integration can be created with a clearer definition of the scope and applicability of ontology; (2) A healthcare system's resilience and sustainability can be significantly increased by the suggested design principles; (3) Through careful consideration of both efficiency and patients' experiences and a novel optimization algorithm, a scheduling problem can be made more patient-accessible; (4) A systematic approach to evaluating efficiency, sustainability, and resilience enables the simultaneous optimization of all three criteria at the system design stage, leading to more efficient distributions of resources and locations for healthcare facilities. The contributions of the thesis can be summarized as follows. Scientifically, this thesis work has expanded our knowledge of ontology and data modelling, as well as our comprehension of the healthcare system's resilience and sustainability. Technologically or methodologically, the work has advanced the state of knowledge for system modelling and decision-making. Overall, this thesis examines the characteristics of healthcare systems from a system viewpoint. Three ideas in this thesis—the ontology-based data modelling approach, multi-objective optimization models, and the algorithms for solving the models—can be adapted and used to affect different aspects of disparate systems

    Proceedings of the 2nd Energy Security and Chemical Engineering Congress

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    Mechanical engineering is a field that is continuously evolving as a profession to provide sustainable design, products and technologies for society. Mechanical engineering products, in conjunction with technological advances in other sectors, contribute to noise, water and air pollution, and the degradation of land and landscape. The rate of production, both energy and products, is increasing at such a rapid rate that natural regeneration can no longer sustain. Emission control is a fast-growing topic for mechanical engineers and others, encompassing the development of machines and processes that produce fewer pollutants as well as new materials and processes that can decrease or eliminate pollution that has already been generated. And, in an increasingly environmentally conscious world, the concept of sustainability is also intrinsically important to the success or failure of any engineering product or processes. Mechanical engineers thus play a central role in applying a truly modern approach for enabling the global transition to green energy and sustainable prac-tices. To address climate change, researchers are progressively looking into a wide range of novel solutions for energy conversion, engine efficiency, alternative fuels, nature-inspired materials, enhanced manufacturing processes and so on. In this context, this book presents part of the proceedings of the Mechanical and Materials track of the 2nd Energy Security and Chemical Engineering Congress (ESChE 2021) as presented by the academics, researchers and postgraduate students. The book provides insights into different aspects of mechanical processes, nanoma-terials and alternate fuels that set the stage for development of sustainable techno-logical solutions. The content of this book will be useful for students, researchers and professionals working in the areas of mechanical engineering, materials, energy technologies, optimization and allied fields

    Small Object Detection and Tracking: A Comprehensive Review

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    Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities. However, detecting and tracking small objects introduce significant challenges within computer vision due to their subtle appearance and limited distinguishing features, which results in a scarcity of crucial information. This deficit complicates the tracking process, often leading to diminished efficiency and accuracy. To shed light on the intricacies of small object detection and tracking, we undertook a comprehensive review of the existing methods in this area, categorizing them from various perspectives. We also presented an overview of available datasets specifically curated for small object detection and tracking, aiming to inform and benefit future research in this domain. We further delineated the most widely used evaluation metrics for assessing the performance of small object detection and tracking techniques. Finally, we examined the present challenges within this field and discussed prospective future trends. By tackling these issues and leveraging upcoming trends, we aim to push forward the boundaries in small object detection and tracking, thereby augmenting the functionality of surveillance systems and broadening their real-world applicability

    Development and characterization of pore-blocking small molecules against cholesterol-dependent cytolysins as anti-virulence strategy

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    Cholesterol-dependent cytolysins (CDC) are acknowledged virulence factors of a wide range of pathogenic gram-positive bacteria like Streptococci, Clostridia, and Listeria etc. CDC selectively target membranes that contain cholesterol and induce pathogenesis in the host organisms by versatile mechanisms. Bacteria release CDC during an infection, which infuriate the infection and the treatment of infection becomes challenging with the contemporary regime of anti-infectives. The noxious responses of CDC include direct toxicity via pore formation, manipulation of cellular signaling to promote bacterial survival and evasion of the immune system. Worldwide, millions of deaths are associated with CDC like PLY, PFO, LLO etc. Therefore, CDC have been considered as a potential target for drugs. In the research of the last few decades, multiple natural and synthetic small molecules have been proposed as anti-toxins, however, due to lack of specificity, none of them were contemplated for clinical testing. Thus, to date there is no specific treatment available to counter CDC manifestations and the goal of this project is to develop and validate specific small molecules against CDC. PLY was selected as a prototype CDC for testing of small molecules. In our earlier work, a model of PLY-pore was developed to virtually screen half a million molecules from online databases that could block the oligomerization and ultimately pore formation of PLY. Pore-Blocker-1 (PB-1), was the only molecule that actively inhibited PLY in a hemolysis assay and, thereon, its optimization led to the discovery of PB-2 with ten times improved activity. In this dissertation, PB-1 and 2 were validated in the BLI assay. Cryo-TEM analysis showcased that PB-2 prevented the PLY penetration in membranes of cholesterol-liposomes. By analyzing several closely related derivatives of PB-2 the pharmacophore of inhibitors was identified, which enabled the effective alteration of scaffolds to produce PB-3, a more chemically stable and potent inhibitor of PLY. PB-3 exhibited an IC50 of 3 µM in the hemolysis assay and a KD value of 256 nM against PLY in the BLI assay. Analogous potency of PB-3 was observed in the LDH assay and cellular microscopy. PB-3 was generated and quantified through the protein-catalyzed ligation of precursor fragments. The actual mechanism of inhibitors was unveiled by evaluating the activity of inhibitors against multiple PLY-mutants and in contrast to the virtually proposed mechanism of binding, a cysteine mutant of PLY suggested that PB-3 might be binding to a cysteine in the membrane-binding domain of PLY. Then, BLI and MS investigations of cysteine mutant PLY and wild type confirmed the cysteine-mediated reversible covalent interaction between PB-3 and PLY. Afterwards, PB-3 was observed barely active against other cysteine-proteases (PTP-1B and SARS-CoV2), confirming the increased affinity of PB-3 toward PLY. PB-3 blocked PLY in a bacterial infection-model assay, this experiment further affirms selectively of PB-3 to PLY because the cell culture medium contained 13000-fold more free-cysteine than PLY. Finally, PB-3 was examined against two further CDC, PFO and ILY. PFO is analogous to PLY and possesses a cysteine residue in the undecapeptide and when PB-3 was tested, it was nearly 10 times more potent against PFO than PLY. Conversely, ILY is devoid of cysteine and PB-3 was expectedly inactive against ILY. In the light of these results, we presumably consider PB-3 as an inhibitor of cysteine-containing CDC.Cholesterin-abhängige Cytolysine (CDC) sind anerkannte Virulenzfaktoren einer breiten Palette pathogener grampositiver Bakterien wie Streptokokken, Clostridien und Listerien usw. CDC zielen selektiv auf Membranen ab, die Cholesterin enthalten, und induzieren die Pathogenese in den Wirtsorganismen durch vielseitige Mechanismen. Bakterien setzen während einer Infektion CDCs frei, die die Invasivität der Infektion erhöhen, und die Behandlung von Infektionen wird mit der derzeitigen Therapie mit Antiinfektiva zu einer Herausforderung. Zu den schädlichen Reaktionen von CDC gehören die direkte Toxizität über die Porenbildung, die Manipulation der zellulären Signalübertragung zur Förderung des bakteriellen Überlebens und die Umgehung des Immunsystems. Millionen von Todesfällen weltweit sind mit CDC wie PLY, PFO, LLO usw. verbunden. Daher wurden CDC als potenzielles Arzneimittelziel angesehen. Während der Forschung der letzten Jahrzehnte wurden mehrere natürliche und synthetische kleine Moleküle als Antitoxine vorgeschlagen, aufgrund mangelnder Spezifität wurde jedoch keines von ihnen für klinische Tests in Betracht gezogen. In unserer früheren Forschung wurde ein Modell von PLY-Poren entwickelt, um virtuell eine halbe Million Moleküle aus Online-Datenbanken zu Screening, die die Oligomerisierung und letztendlich die Porenbildung von PLY blockieren könnten. Pore Blocker-1 (PB-1) war das einzige Molekül, das PLY in einem Hämolyse-Assay aktiv hemmte, und seine Optimierung führte anschließend zur Entdeckung von PB-2 mit zehnfach verbesserter Aktivität. In dieser Dissertation wurden PB-1 und 2 mit dem BLI-Test validiert. Die Cryo-TEM-Analyse zeigte, dass PB-2 das Eindringen von PLY in die Membranen von Cholesterin-Liposomen verhinderte. Das Pharmakophor der Inhibitoren wurde durch die Analyse mehrerer eng verwandter Derivate von PB-2 identifiziert, was eine effektive Veränderung der Gerüste ermöglichte, um PB-3, einen chemisch stabileren und stärkeren Inhibitor von PLY, zu entwickeln. PB-3 hatte eine IC50 von 3 µM im Hämolyse-Assay und einen KD-Wert von 256 nM für PLY im BLI-Assay. Eine ähnliche Wirksamkeit von PB-3 wurde im LDH-Assay und in der Zellmikroskopie nachgewiesen. PB-3 wurde durch die protein-katalysierte Ligation von Precursor-Fragmenten erzeugt und quantifiziert. PB-3 wurde durch die Proteintemplat-unterstützte Ligation von Precursor-Fragmenten erzeugt und quantifiziert. Der wahre Mechanismus der Inhibitoren wurde durch die Auswertung der Aktivität der Inhibitoren gegen mehrere PLY-Mutanten entdeckt. Im Kontrast zu dem virtuell vorgeschlagenen Bindungsmechanismus deutete eine Cystein-Mutante von PLY darauf hin, dass PB-3 an ein Cystein in der Membran-Bindungsdomäne von PLY binden könnte. Anschließend bestätigten BLI und MS Untersuchungen der Cystein-Mutante PLY und des Wildtyps die Cystein-vermittelte reversible kovalente Interaktion zwischen PB-3 und PLY. Im Anschluss daran wurde PB-3 als kaum aktiv gegen andere Cystein-Proteasen (PTP-1B und SARS-CoV2) identifiziert, was die erhöhte Affinität von PB-3 gegenüber PLY bestätigt. PB-3 blockierte PLY in einem bakteriellen Infektionsmodell-Assay. Dieses Experiment bestätigt außerdem die Selektivität von PB-3 für PLY, da das Zellkulturmedium 13000-mal mehr freies Cystein als PLY enthielt. Zum Schluss wurde PB-3 gegen zwei weitere CDC, PFO und ILY, getestet. PFO ist analog zu PLY und enthält Cystein, und bei der Prüfung von PB-3 wurde festgestellt, dass es fast zehnmal stärker gegen PFO wirkt als PLY. Umgekehrt ist ILY frei von Cystein und PB-3 war erwartungsgemäß inaktiv gegenüber ILY. In Anbetracht dieser Ergebnisse halten wir PB-3 vermutlich für einen Hemmstoff gegen Cystein-haltiges CDC
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