241 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Low-cost household water treatment: A techno-behavioural intervention for local sustainable development in Afghanistan

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    Access to safe drinking water is a critical global challenge, in remote rural areas and urban centres alike. A pressing concern within this challenge lies in the sustainability of groundwater and the livelihoods reliant on it. However, a comprehensive study of such a complex issue as water insecurity requires a multidisciplinary approach that can synthesize perspectives from the natural and social sciences. With the overarching aim of studying and developing means to rectify water insecurity in low-income settings, this thesis pursues such an approach and contributes insights to the broader global dialogue through the case of the conflict-affected urban context of Kabul – where groundwater and livelihood challenges are driven especially by the contamination and rapid depletion of the local aquifers. The multidisciplinary study begins with a geo-hydrology perspective that explores the sources of groundwater and the factors contributing to groundwater contamination. Additionally, it explores the potential of using clay disc filters for household water treatment from an earth sciences perspective. Complementing these natural science perspectives, the research also incorporates the COM-B framework, which draws from psychology and behavioural science. By leveraging anthropological techniques with a firm grounding in development research, the thesis further adopts a bottom-up approach to inform survey research. Translating this multidisciplinary approach into the empirical research underlying this thesis, firstly, the groundwater recharge sources and groundwater dynamics in aquifers of Kabul city were explored relying on the analysis of the stable isotopic composition (ή18O and ή2H) of groundwater and surface water from the Upper Kabul River and Logar River. The results showed that precipitation was the primary source of recharge in the Central Kabul sub-basin, while mixed recharge from the river, precipitation, and irrigation return flow governed recharge in the Logar sub-basin. In the Paghman and Lower Kabul, and Upper Kabul sub-basins, increased rainfall input was also observed. The contribution of river water to groundwater recharge decreased from an average of over 60% in 2007 to less than 50% in 2020. Also, substantial groundwater level depletion was documented in the Central Kabul sub-basin and western parts of the city. In addition to examining recharge sources and rates, the bacteriological and chemical characteristics of Kabul’s groundwater were analyzed. In Kabul, 4.1 million people rely on groundwater, making it critical to understand its contamination trends in the face of rapid development and social changes. The results showed an increase in E. coli and NO3-, indicating anthropogenic impacts on shallow groundwater quality. The Water Quality Index revealed that less than 35% of shallow groundwater samples had good quality. To address these issues, the implementation of point-of-use water purification was proposed as a temporary solution for reducing the occurrence of waterborne diseases. Moreover, a qualitative study, based on 68 semi-structured interviews, explored the factors limiting access to clean drinking water in two peri-urban areas in Kabul. These factors included dysfunctional water supply networks, water price inequalities, uneven development, and aid prioritization. In addition, the stressors and dynamic access to water such as droughts, contamination, and electricity disruption were documented. Further, this research examined the nature and underlying factors of inter-household water-sharing practices. Water availability, the costs to the donor, the frequency of requests for water, the period over which they operate, and religious beliefs were all found to play key roles in determining water-sharing practices. The added influence of droughts in limiting water-sharing practices further highlighted the dynamics in performing the behaviour. Furthermore, this research explored the factors that influence household water treatment practices, relied on a comprehensive behaviour change model (i.e., COM-B model). The results of the study showed that reflective and automatic motivation, as well as physical opportunity, had a statistically significant association with the performance of household water treatment behaviour. The findings suggest that socioeconomic, psychosocial, and contextual factors are all important in understanding and promoting household water treatment practices, and should be taken into account to develop interventions that are tailored to the specific needs and obstacles of different communities. Lastly, the potential of using clay disc filters, frequently termed ceramic water filters, made from locally-sourced clay samples, was explored for removing bacteria from water. The clay discs were produced by mixing clay and sorted sawdust in a ratio of 1:2, and the filtration rate was 1 litre per hour. Clay disc filters have the potential to be a low-cost and locally-sourced solution for improving water quality in Afghanistan, but further research and development is needed to optimize their production, particularly by leveraging the skills of local potters in Kabul. Overall, the synergistic combination of disciplinary techniques was thus capable of shedding light on the complex interplay between water resources, technology, and human behaviour (i.e., household water treatment) and provided a comprehensive understanding of the challenges and solutions surrounding access to safe drinking water

    By land, water, and air: an evaluation of the impact of embankment setbacks and two-stage channel design on flood characteristics of the upper River Nith

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    River channelisation is a method of hard engineering that laterally constrains and straightens a river, with the twofold aims of increasing a river’s hydraulic efficiency and minimising land loss due to floodrelated erosion. However, in catchments with low sediment supply, such as Scotland, rivers typically respond to channelisation with channel incision and bed armouring, which further reduce floodplain connectivity and physical habitat diversity. Recently, two-stage channel design and embankment setbacks have been applied as a restoration method to return sediment mobility to channelised rivers with the goal of improving both biodiversity, bank stability and channel-floodplain connectivity. These techniques include carving out small benches to act as a floodplain and pushing back existing embankments to allow for increased river movement. In late-2019, the Scottish Environmental Protection Agency (SEPA) completed restoration works on an upper section of the River Nith near New Cumnock, Scotland by implementing embankment setbacks and two-stage channel design to two sections of the river with the goal of demonstrating natural flood management approaches for this type of channelised, incised river. This thesis investigated the restoration works completed by SEPA and analysed the difference in flood impacts including inundation extent, flow depth, and shear stress, between the pre-restoration and post-restoration topography using a variety of field survey techniques and numerical modelling software. Specifically, data from previous Airborne light detection and ranging (LiDAR) and bathymetry surveys were used in conjunction with unmanned aerial vehicle (UAV) LiDAR, real-time kinematic Global Navigation Satellite System (RTK-GNSS), and echo-sounding via acoustic Doppler current profiler (ADCP) surveys that were conducted as part of this dissertation in 2022. The digital elevation models (DEMs) created from these surveys were then input into Geomorphic Change Detection (GCD) software and the Hydrologic Engineering Center – River Analysis System (HEC-RAS) flood modelling program to quantify topographic change due to channel-floodplain modification and flood impacts by modelling multiple recurrence interval events. Overall, the findings of this thesis suggest that, for particular design flood events, the embankment setbacks and two-stage channel design have increased flood extent and floodplain connectivity while reducing the amount of flow overtopping embankments and decreasing overall water depth and bed shear stress

    Energy-efficient, scalable and modular industrial microwave applicator for high temperature alkaline hydrolysis of PET

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    Microwave-assisted alkaline hydrolysis of PET can be 20 times faster and at lower temperatures. This work presents a novel industrial microwave applicator at 2.45 GHz with homogeneous distribution to support this reaction, which allows an efficient and continuous operation. In addition, an innovative dielectric and calorimetric measurements setup is presented. Furthermore, the modelling of the reaction kinetics based on the measured dielectric parameters is presented

    A review of commercialisation mechanisms for carbon dioxide removal

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    The deployment of carbon dioxide removal (CDR) needs to be scaled up to achieve net zero emission pledges. In this paper we survey the policy mechanisms currently in place globally to incentivise CDR, together with an estimate of what different mechanisms are paying per tonne of CDR, and how those costs are currently distributed. Incentive structures are grouped into three structures, market-based, public procurement, and fiscal mechanisms. We find the majority of mechanisms currently in operation are underresourced and pay too little to enable a portfolio of CDR that could support achievement of net zero. The majority of mechanisms are concentrated in market-based and fiscal structures, specifically carbon markets and subsidies. While not primarily motivated by CDR, mechanisms tend to support established afforestation and soil carbon sequestration methods. Mechanisms for geological CDR remain largely underdeveloped relative to the requirements of modelled net zero scenarios. Commercialisation pathways for CDR require suitable policies and markets throughout the projects development cycle. Discussion and investment in CDR has tended to focus on technology development. Our findings suggest that an equal or greater emphasis on policy innovation may be required if future requirements for CDR are to be met. This study can further support research and policy on the identification of incentive gaps and realistic potential for CDR globally

    DEMIX Method Ranks COPDEM and FABDEM as Top 1'' Global DEMs

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    We present a practical approach to inter-compare a range of candidate digital elevation models (DEMs) based on pre-defined criteria and statistically sound ranking approach. The presented approach integrates the randomized complete block design (RCBD) into a novel framework which has been named the DEMIX wine contest. Ranking a collection of wines or a set of DEMs from a given set of candidates leads to a mathematically similar problem. The method presented provides a flexible, statistically sound and customizable tool for evaluating the quality of any raster - in this case a DEM - by means of a ranking approach, which takes into account a confidence level, and can use both quantitative and qualitative criteria. The users can design their own criteria for the quality evaluation in relation to their specific needs. The application of the wine contest to six 1'' global DEMs, considering a wide set of study sites, covering different morphological and landcover settings, highlights the potentialities of the approach. We used a suite of criteria relating to the differences in the elevation, slope, and roughness distributions compared to reference DEMs aggregated from 1-5 m lidar-derived DEMs to 1 second DEM. Results confirmed significant superiority of COPDEM and its derivative FABDEM as the overall best 1'' global DEMs. They are slightly better than ALOS, and clearly outperform NASADEM and SRTM, which are in turn much better than ASTER

    Investigation on Design and Development Methods for Internet of Things

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    The thesis work majorly focuses on the development methodologies of the Internet of Things (IoT). A detailed literature survey is presented for the discussion of various challenges in the development of software and design and deployment of hardware. The thesis work deals with the efficient development methodologies for the deployment of IoT system. Efficient hardware and software development reduces the risk of the system bugs and faults. The optimal placement of the IoT devices is the major challenge for the monitoring application. A Qualitative Spatial Reasoning (QSR) and Qualitative Temporal Reasoning (QTR) methodologies are proposed to build software systems. The proposed hybrid methodology includes the features of QSR, QTR, and traditional databased methodologies. The hybrid methodology is proposed to build the software systems and direct them to the specific goal of obtaining outputs inherent to the process. The hybrid methodology includes the support of tools and is detailed, integrated, and fits the general proposal. This methodology repeats the structure of Spatio-temporal reasoning goals. The object-oriented IoT device placement is the major goal of the proposed work. Segmentation and object detection is used for the division of the region into sub-regions. The coverage and connectivity are maintained by the optimal placement of the IoT devices using RCC8 and TPCC algorithms. Over the years, IoT has offered different solutions in all kinds of areas and contexts. The diversity of these challenges makes it hard to grasp the underlying principles of the different solutions and to design an appropriate custom implementation on the IoT space. One of the major objective of the proposed thesis work is to study numerous production-ready IoT offerings, extract recurring proven solution principles, and classify them into spatial patterns. The method of refinement of the goals is employed so that complex challenges are solved by breaking them down into simple and achievable sub-goals. The work deals with the major sub-goals e.g. efficient coverage of the field, connectivity of the IoT devices, Spatio-temporal aggregation of the data, and estimation of spatially connected regions of event detection. We have proposed methods to achieve each sub-goal for all different types of spatial patterns. The spatial patterns developed can be used in ongoing and future research on the IoT to understand the principles of the IoT, which will, in turn, promote the better development of existing and new IoT devices. The next objective is to utilize the IoT network for enterprise architecture (EA) based IoT application. EA defines the structure and operation of an organization to determine the most effective way for it to achieve its objectives. Digital transformation of EA is achieved through analysis, planning, design, and implementation, which interprets enterprise goals into an IoT-enabled enterprise design. A blueprint is necessary for the readying of IT resources that support business services and processes. A systematic approach is proposed for the planning and development of EA for IoT-Applications. The Enterprise Interface (EI) layer is proposed to efficiently categorize the data. The data is categorized based on local and global factors. The clustered data is then utilized by the end-users. A novel four-tier structure is proposed for Enterprise Applications. We analyzed the challenges, contextualized them, and offered solutions and recommendations. The last objective of the thesis work is to develop energy-efficient data consistency method. The data consistency is a challenge for designing energy-efficient medium access control protocol used in IoT. The energy-efficient data consistency method makes the protocol suitable for low, medium, and high data rate applications. The idea of energyefficient data consistency protocol is proposed with data aggregation. The proposed protocol efficiently utilizes the data rate as well as saves energy. The optimal sampling rate selection method is introduced for maintaining the data consistency of continuous and periodic monitoring node in an energy-efficient manner. In the starting phase, the nodes will be classified into event and continuous monitoring nodes. The machine learning based logistic classification method is used for the classification of nodes. The sampling rate of continuous monitoring nodes is optimized during the setup phase by using optimized sampling rate data aggregation algorithm. Furthermore, an energy-efficient time division multiple access (EETDMA) protocol is used for the continuous monitoring on IoT devices, and an energy-efficient bit map assisted (EEBMA) protocol is proposed for the event driven nodes

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Enriching and validating geographic information on the web

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    The continuous growth of available data on the World Wide Web has led to an unprecedented amount of available information. However, the enormous variance in data quality and trustworthiness of information sources impairs the great potential of the large amount of vacant information. This observation especially applies to geographic information on the Web, i.e., information describing entities that are located on the Earth’s surface. With the advent of mobile devices, the impact of geographic Web information on our everyday life has substantially grown. The mobile devices have also enabled the creation of novel data sources such as OpenStreetMap (OSM), a collaborative crowd-sourced map providing open cartographic information. Today, we use geographic information in many applications, including routing, location recommendation, or geographic question answering. The processing of geographic Web information yields unique challenges. First, the descriptions of geographic entities on the Web are typically not validated. Since not all Web information sources are trustworthy, the correctness of some geographic Web entities is questionable. Second, geographic information sources on the Web are typically isolated from each other. The missing integration of information sources hinders the efficient use of geographic Web information for many applications. Third, the description of geographic entities is typically incomplete. Depending on the application, missing information is a decisive criterion for (not) using a particular data source. Due to the large scale of the Web, the manual correction of these problems is usually not feasible such that automated approaches are required. In this thesis, we tackle these challenges from three different angles. (i) Validation of geographic Web information: We validate geographic Web information by detecting vandalism in OpenStreetMap, for instance, the replacement of a street name with advertisement. To this end, we present the OVID model for automated vandalism detection in OpenStreetMap. (ii) Enrichment of geographic Web information through integration: We integrate OpenStreetMap with other geographic Web information sources, namely knowledge graphs, by identifying entries corresponding to the same world real-world entities in both data sources. We present the OSM2KG model for automated identity link discovery between OSM and knowledge graphs. (iii) Enrichment of missing information in geographic Web information: We consider semantic annotations of geographic entities on Web pages as an additional data source. We exploit existing annotations of categorical properties of Web entities as training data to enrich missing categorical properties in geographic Web information. For all of the proposed models, we conduct extensive evaluations on real-world datasets. Our experimental results confirm that the proposed solutions reliably outperform existing baselines. Furthermore, we demonstrate the utility of geographic Web Information in two application scenarios. (i) Corpus of geographic entity embeddings: We introduce the GeoVectors corpus, a linked open dataset of ready-to-use embeddings of geographic entities. With GeoVectors, we substantially lower the burden to use geographic data in machine learning applications. (ii) Application to event impact prediction: We employ several geographic Web information sources to predict the impact of public events on road traffic. To this end, we use cartographic, event, and event venue information from the Web.Durch die kontinuierliche Zunahme verfĂŒgbarer Daten im World Wide Web, besteht heute eine noch nie da gewesene Menge verfĂŒgbarer Informationen. Das große Potential dieser Daten wird jedoch durch hohe Schwankungen in der DatenqualitĂ€t und in der VertrauenswĂŒrdigkeit der Datenquellen geschmĂ€lert. Dies kann vor allem am Beispiel von geografischen Web-Informationen beobachtet werden. Geografische Web-Informationen sind Informationen ĂŒber EntitĂ€ten, die ĂŒber Koordinaten auf der ErdoberflĂ€che verfĂŒgen. Die Relevanz von geografischen Web-Informationen fĂŒr den Alltag ist durch die Verbreitung von internetfĂ€higen, mobilen EndgerĂ€ten, zum Beispiel Smartphones, extrem gestiegen. Weiterhin hat die VerfĂŒgbarkeit der mobilen EndgerĂ€te auch zur Erstellung neuartiger Datenquellen wie OpenStreetMap (OSM) gefĂŒhrt. OSM ist eine offene, kollaborative Webkarte, die von Freiwilligen dezentral erstellt wird. Mittlerweile ist die Nutzung geografischer Informationen die Grundlage fĂŒr eine Vielzahl von Anwendungen, wie zum Beispiel Navigation, Reiseempfehlungen oder geografische Frage-Antwort-Systeme. Bei der Verarbeitung geografischer Web-Informationen mĂŒssen einzigartige Herausforderungen berĂŒcksichtigt werden. Erstens werden die Beschreibungen geografischer Web-EntitĂ€ten typischerweise nicht validiert. Da nicht alle Informationsquellen im Web vertrauenswĂŒrdig sind, ist die Korrektheit der Darstellung mancher Web-EntitĂ€ten fragwĂŒrdig. Zweitens sind Informationsquellen im Web oft voneinander isoliert. Die fehlende Integration von Informationsquellen erschwert die effektive Nutzung von geografischen Web-Information in vielen AnwendungsfĂ€llen. Drittens sind die Beschreibungen von geografischen EntitĂ€ten typischerweise unvollstĂ€ndig. Je nach Anwendung kann das Fehlen von bestimmten Informationen ein entscheidendes Kriterium fĂŒr die Nutzung einer Datenquelle sein. Da die GrĂ¶ĂŸe des Webs eine manuelle Behebung dieser Probleme nicht zulĂ€sst, sind automatisierte Verfahren notwendig. In dieser Arbeit nĂ€hern wir uns diesen Herausforderungen von drei verschiedenen Richtungen. (i) Validierung von geografischen Web-Informationen: Wir validieren geografische Web-Informationen, indem wir Vandalismus in OpenStreetMap identifizieren, zum Beispiel das Ersetzen von Straßennamen mit Werbetexten. (ii) Anreicherung von geografischen Web-Information durch Integration: Wir integrieren OpenStreetMap mit anderen Informationsquellen im Web (Wissensgraphen), indem wir EintrĂ€ge in beiden Informationsquellen identifizieren, die den gleichen Echtwelt-EntitĂ€ten entsprechen. (iii) Anreicherung von fehlenden geografischen Informationen: Wir nutzen semantische Annotationen von geografischen EntitĂ€ten auf Webseiten als weitere Datenquelle. Wir nutzen existierende Annotationen kategorischer Attribute von Web-EntitĂ€ten als Trainingsdaten, um fehlende kategorische Attribute in geografischen Web-Informationen zu ergĂ€nzen. Wir fĂŒhren ausfĂŒhrliche Evaluationen fĂŒr alle beschriebenen Modelle durch. Die vorgestellten LösungsansĂ€tze erzielen verlĂ€sslich bessere Ergebnisse als existierende AnsĂ€tze. Weiterhin demonstrieren wir den Nutzen von geografischen Web-Informationen in zwei Anwendungsszenarien. (i) Korpus mit Embeddings von geografischen EntitĂ€ten: Wir stellen den GeoVectors-Korpus vor, einen verlinkten, offenen Datensatz mit direkt nutzbaren Embeddings von geografischen Web-EntitĂ€ten. Der GeoVectors-Korpus erleichtert die Nutzung von geografischen Daten in Anwendungen von maschinellen Lernen erheblich. (ii) Anwendung zur Prognose von Veranstaltungsauswirkungen: Wir nutzen Karten-, Veranstaltungs- und VeranstaltungsstĂ€tten-Daten aus dem Web, um die Auswirkungen von Veranstaltungen auf den Straßenverkehr zu prognostizieren
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