6,671 research outputs found

    Optimising water quality outcomes for complex water resource systems and water grids

    Get PDF
    As the world progresses, water resources are likely to be subjected to much greater pressures than in the past. Even though the principal water problem revolves around inadequate and uncertain water supplies, water quality management plays an equally important role. Availability of good quality water is paramount to sustainability of human population as well as the environment. Achieving water quality and quantity objectives can be conflicting and becomes more complicated with challenges like, climate change, growing populations and changed land uses. Managing adequate water quality in a reservoir gets complicated by multiple inflows with different water quality levels often resulting in poor water quality. Hence, it is fundamental to approach this issue in a more systematic, comprehensive, and coordinated fashion. Most previous studies related to water resources management focused on water quantity and considered water quality separately. However, this research study focused on considering water quantity and quality objectives simultaneously in a single model to explore and understand the relationship between them in a reservoir system. A case study area was identified in Western Victoria, Australia with water quantity and quality challenges. Taylors Lake of Grampians System in Victoria, Australia receives water from multiple sources of differing quality and quantity and has the abovesaid problems. A combined simulation and optimisation approach was adopted to carry out the analysis. A multi-objective optimisation approach was applied to achieve optimal water availability and quality in the storage. The multi-objective optimisation model included three objective functions which were: water volume and two water quality parameters: salinity and turbidity. Results showed competing nature of water quantity and quality objectives and established the trade-offs. It further showed that it was possible to generate a range of optimal solutions to effectively manage those trade-offs. The trade-off analysis explored and informed that selective harvesting of inflows is effective to improve water quality in storage. However, with strict water quality restriction there is a considerable loss in water volume. The robustness of the optimisation approach used in this study was confirmed through sensitivity and uncertainty analysis. The research work also incorporated various spatio-temporal scenario analyses to systematically articulate long-term and short-term operational planning strategies. Operational decisions around possible harvesting regimes while achieving optimal water quantity and quality and meeting all water demands were established. The climate change analysis revealed that optimal management of water quantity and quality in storage became extremely challenging under future climate projections. The high reduction in storage volume in the future will lead to several challenges such as water supply shortfall and inability to undertake selective harvesting due to reduced water quality levels. In this context, selective harvesting of inflows based on water quality will no longer be an option to manage water quantity and quality optimally in storage. Some significant conclusions of this research work included the establishment of trade-offs between water quality and quantity objectives particular to this configuration of water supply system. The work demonstrated that selective harvesting of inflows will improve the stored water quality, and this finding along with the approach used is a significant contribution to decision makers working within the water sector. The simulation-optimisation approach is very effective in providing a range of optimal solutions, which can be used to make more informed decisions around achieving optimal water quality and quantity in storage. It was further demonstrated that there are range of planning periods, both long-term (>10 years) and short-term (<1 year), all of which offer distinct advantages and provides useful insights, making this an additional key contribution of the work. Importantly, climate change was also considered where it was found that diminishing water resources, particularly to this geographic location, makes it increasingly difficult to optimise both quality and quantity in storage providing further useful insights from this work.Doctor of Philosoph

    A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks

    Full text link
    Cyber threat attribution is the process of identifying the actor of an attack incident in cyberspace. An accurate and timely threat attribution plays an important role in deterring future attacks by applying appropriate and timely defense mechanisms. Manual analysis of attack patterns gathered by honeypot deployments, intrusion detection systems, firewalls, and via trace-back procedures is still the preferred method of security analysts for cyber threat attribution. Such attack patterns are low-level Indicators of Compromise (IOC). They represent Tactics, Techniques, Procedures (TTP), and software tools used by the adversaries in their campaigns. The adversaries rarely re-use them. They can also be manipulated, resulting in false and unfair attribution. To empirically evaluate and compare the effectiveness of both kinds of IOC, there are two problems that need to be addressed. The first problem is that in recent research works, the ineffectiveness of low-level IOC for cyber threat attribution has been discussed intuitively. An empirical evaluation for the measure of the effectiveness of low-level IOC based on a real-world dataset is missing. The second problem is that the available dataset for high-level IOC has a single instance for each predictive class label that cannot be used directly for training machine learning models. To address these problems in this research work, we empirically evaluate the effectiveness of low-level IOC based on a real-world dataset that is specifically built for comparative analysis with high-level IOC. The experimental results show that the high-level IOC trained models effectively attribute cyberattacks with an accuracy of 95% as compared to the low-level IOC trained models where accuracy is 40%.Comment: 20 page

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

    Get PDF
    This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

    Essays on nonsampling errors in household panel surveys

    Get PDF
    Household surveys represent the predominant form of data collection in low- and middle-income countries and function as crucial substitutes to constrained administrative data. In recent years, following an increasing demand for data, researchers and policymakers alike have addressed the continued issue of low-quality data. While much progress has been made, many sources of data, including household surveys, have been identified as being insufficiently accurate and reliable, thus constraining informed decision-making on behalf of policymakers. Indeed, the importance of obtaining high-quality outputs has been recognised in the Sustainable Development Goals, which emphasise that to date, data is key to informing policy, monitoring progress, and ultimately achieving formulated goals. This thesis aims to provide a better understanding of survey methodological issues in low- and middle-income countries and provide an outlook on the future of panel survey applications. Thereby, the first two essays deal with identification of nonsampling errors in household survey datasets, factors influencing their prevalence, and their impact. Conversely, the third essay examines the continued role of agriculture in rural development. The first essay investigates the prevalence of nonsampling errors in the seventh survey wave of a long-term household panel survey conducted in Thailand and Vietnam, which encompasses 3,812 households. An analysis of the distribution of nonsampling errors is undertaken in order to ascertain which type of error is most prevalent in the underlying computerised survey instrument. These findings are then compared with those of an earlier study, which examined the prevalence of nonsampling errors in a paper-based survey instrument. Thereafter, a negative binomial model is applied to analyse factors influencing nonsampling errors, which simultaneously assesses the influence of the interviewer, respondent, and interview and survey environment. The second essay utilises data from the same panel, albeit making use of the longitudinal nature of data. Using seven waves of panel survey data from Thailand, which were collected between 2007 and 2019, interviews of 1,542 identical households were examined with a focus on the consistency of reported employments. A three-stage approach is developed to identify inconsistent reporting thereof between pairs of consecutive survey waves. Additionally, a two-stage multilevel logistic model is applied in order to analyse interviewer and employment characteristics that influence inconsistent reporting. Further, the impact of inconsistent reporting on policy pertaining to household welfare is examined. The third essay utilises three waves of household survey data from Thailand, which were conducted in 2007, 2013, and 2019, and considers 1,160 identical households. A descriptive analysis is undertaken in which changes in livelihoods of rural households in Northeast Thailand are examined. Further, a logit regression is applied to identify factors influencing poverty incidence, which differentiates by the typology of household based on the importance of agriculture. The first essay finds that computerised survey instruments have a substantially lower count of missing data, whereas measurement errors remain a pressing issue. The findings of the negative binomial regression model highlight the importance of interviewer training and indicate that more outgoing and sympathetic interviewers produce interviews of higher quality. Additionally, conditions of the interview and survey are shown to influence the prevalence of nonsampling errors. Notably, the results suggest that measurement errors are most likely to occur in initial survey weeks, whereas the likelihood of refusal increases as the survey progresses. In Vietnam, incongruence of ethnicity between interviewers and respondents indicated a substantial increase in nonsampling errors. Further, survey providers in endeavours to collect high-quality data must account for differences in survey implementation. The second essay identifies substantial cases of underreporting of employments throughout pairs of consecutive survey waves. Notably, informal employments are less likely to be consistently reported and more complex household compositions are positively correlated with inconsistency. The impact of omitted employments on welfare indicators is demonstrated to be substantial with poverty headcounts being overestimated by, on average 6.7 percentage points at the provincial level. The third essay highlights that while income has been observed to increase over a 12-year period, which has coincided with an increasing proportion of agriculture-based households being classified as non-poor, little has changed in rural livelihoods in rural Northeast Thailand. Despite substantial out-migration of working-aged household members, most households remain engaged in agriculture and can be described as part-time, small-scale farmers. Further, those households mainly engaged in agriculture are observed to become increasingly dependent on government interventions due to the region’s propensity to droughts. In conclusion, the essays examining data quality of household surveys in Thailand and Vietnam provide new perspectives regarding factors that survey providers must consider in conducting surveys. Further, shortcomings of labour modules that are typically used in household surveys in developing countries are identified and provide an entry point to a debate on possible approaches to more accurately collecting employment data. The third essay highlights that rural populations remain highly reliant on agriculture and that the role of agriculture in development cannot be understated

    Modeling and Simulation in Engineering

    Get PDF
    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

    Circular supply chain management: a bibliometric analysis-based literature review

    Get PDF
    Purpose Supply chain management (SCM) research has contributed to the transition to a circular economy (CE). Still, confusions exist on the related terms, and no review has mapped out the development trends in the domain. This research clarifies the boundaries of the relevant concepts. Then, it conducts a comprehensive review of the circular SCM (CSCM) literature and identifies opportunities for future research. Design/methodology/approach Using relevant keywords, 1,130 journal articles published in December 31, 2021 were identified. Unlike the published reviews, which mainly relied on content analysis, this review uses bibliometric analysis tools, including citation analysis, co-citation analysis and cluster analysis. The review identifies general trends, influential researchers, high-impact publications, citation patterns and established and emergent research themes. Findings The extant CSCM literature includes five prominent clusters: (1) reverse channel optimization; (2) CSCM review and empirical studies; (3) closed-loop supply chain (CLSC) and consumers; (4) CLSC and inventory management and (5) CLSC and reverse logistics (RL). Significant research gaps exist in the use of secondary and longitudinal data, a wider range of theories, mixed-methods, multi-method, action research and behavioral experiment. The least researched topics include zero waste, industrial symbiosis, circular product design, sourcing and supply management and reuse. Originality/value This is the first bibliometric analysis-based literature review on CSCM. It clarifies the interrelated supply chain sustainability terms and thus reduces related confusion. It offers insights into the patterns in the CSCM literature and suggests important research directions

    Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

    Get PDF
    Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity.&nbsp; Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput

    Utilizing artificial intelligence in perioperative patient flow:systematic literature review

    Get PDF
    Abstract. The purpose of this thesis was to map the existing landscape of artificial intelligence (AI) applications used in secondary healthcare, with a focus on perioperative care. The goal was to find out what systems have been developed, and how capable they are at controlling perioperative patient flow. The review was guided by the following research question: How is AI currently utilized in patient flow management in the context of perioperative care? This systematic literature review examined the current evidence regarding the use of AI in perioperative patient flow. A comprehensive search was conducted in four databases, resulting in 33 articles meeting the inclusion criteria. Findings demonstrated that AI technologies, such as machine learning (ML) algorithms and predictive analytics tools, have shown somewhat promising outcomes in optimizing perioperative patient flow. Specifically, AI systems have proven effective in predicting surgical case durations, assessing risks, planning treatments, supporting diagnosis, improving bed utilization, reducing cancellations and delays, and enhancing communication and collaboration among healthcare providers. However, several challenges were identified, including the need for accurate and reliable data sources, ethical considerations, and the potential for biased algorithms. Further research is needed to validate and optimize the application of AI in perioperative patient flow. The contribution of this thesis is summarizing the current state of the characteristics of AI application in perioperative patient flow. This systematic literature review provides information about the features of perioperative patient flow and the clinical tasks of AI applications previously identified

    Atlas

    Get PDF
    Public libraries want to contribute to an inclusive and innovative society and aim to enable their patrons to acquire the necessary 21st century skills. Dutch public libraries are therefore gradually adding more and more activities to their curriculum, teaching these different types of skills, such as ‘invention literacy’. They also often provide a ‘performative space’ (i.e. a makerspace) for their patrons. This means library spaces are no longer dominated by books, but rather reflect the current development in libraries’ core business, moving from collections to connections in order to serve their local communities. The KB, the National Library of The Netherlands, participated in the KIEM1 project Performative Spaces in Dutch Public Libraries. Stepping Stones of Inclusive Innovation, researching the development of performative spaces in libraries. This project, a collaboration with the Faculty of Architecture and the Built Environment at the Delft University of Technology, fits the KBs strategic interests in providing an innovative and socially aware library system
    corecore