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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "INTELLIGENT MODELLING"

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Green, H. S., and T. Triffet. "Modelling Intelligent Behavior." Journal of Intelligent Material Systems and Structures 4, no. 1 (January 1993): 35–42. http://dx.doi.org/10.1177/1045389x9300400104.

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2

Chin, Cheng Siong, and Rongxin Cui. "Intelligent Marine Robotics Modelling, Simulation and Applications." Journal of Marine Science and Engineering 8, no. 6 (May 27, 2020): 383. http://dx.doi.org/10.3390/jmse8060383.

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Анотація:
Creating this inaugural Special Issue on Intelligent Marine Robotics Modelling, Simulation, and Applications is important due to the rapid technological advancement and the aim to reduce human involvement via artificial intelligence [...]
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3

Hurrion, R. D. "Intelligent Visual Interactive Modelling." European Journal of Operational Research 54, no. 3 (October 1991): 349–56. http://dx.doi.org/10.1016/0377-2217(91)90110-h.

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4

Njeri, Ndung’u Rachael. "Data Preparation For Machine Learning Modelling." International Journal of Computer Applications Technology and Research 11, no. 06 (June 2022): 231–35. http://dx.doi.org/10.7753/ijcatr1106.1008.

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Анотація:
The world today is on revolution 4.0 which is data-driven. The majority of organizations and systems are using data to solve problems through use of digitized systems. Data lets intelligent systems and their applications learn and adapt to mined insights without been programmed. Data mining and analysis requires smart tools, techniques and methods with capability of extracting useful patterns, trends and knowledge, which can be used as business intelligence by organizations as they map their strategic plans. Predictive intelligent systems can be very useful in various fields as solutions to many existential issues. Accurate output from such predictive intelligent systems can only be ascertained by having well prepared data that suits the predictive machine learning function. Machine learning models learns from data input using the ‘garbage-in-garbage-out’ concept. Cleaned, pre-processed and consistent data would produce accurate output as compared to inconsistent, noisy and erroneous data.
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Verlan, A. A., and Jo Sterten. "Intelligent Object-Oriented Approach to Dynamic Energy Systems’ Modelling." Mathematical and computer modelling. Series: Technical sciences, no. 21 (November 2, 2020): 43–51. http://dx.doi.org/10.32626/2308-5916.2020-21.43-51.

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6

G, Manju. "Rule-based Cognitive Modelling for Multimodal Intelligent Tutoring Systems." International Journal of Psychosocial Rehabilitation 24, no. 1 (January 20, 2020): 1754–60. http://dx.doi.org/10.37200/ijpr/v24i1/pr200275.

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Griffin, S., W. Bauwens, and K. Ahmad. "Urban Drainage Modelling Intelligent Assistant." Water Science and Technology 29, no. 1-2 (January 1, 1994): 427–36. http://dx.doi.org/10.2166/wst.1994.0691.

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Анотація:
The work reported here focuses on research being conducted within the Artificial Intelligence Group at the University of Surrey and the Laboratory of Hydrology at Vrije Universiteit Brussel under the COMETT programme. The paper describes the “Urban Drainage Modelling Intelligent Assistant,” a computer-based tool kit which provides guidance, instruction and support for training on aspects of network modelling in urban drainage design and simulation models commonly used in Europe. The tool kit comprises four interrelated, interactive components: an expert system, a data preparation and model execution tool, a document browsing facility, and a term bank. The results of the work are illustrated with the aid of snapshots of the system in use. The specific emphasis here is on the role played by each component in a) the transfer of knowledge within both an inter-disciplinary field such as urban drainage modelling (hydrology, hydraulics, mathematics, computing), and a multilingual community (of particular importance in Europe at present) and b) the training of novices in urban drainage, enabling them to grasp the domain primitives, their interrelationships and meanings.
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8

Riviere, J.-M., M. Bayart, J.-M. Thiriet, A. Boras, and M. Robert. "Intelligent Instruments: Some Modelling Approaches." Measurement and Control 29, no. 6 (July 1996): 179–86. http://dx.doi.org/10.1177/002029409602900604.

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Thorpe, Christopher C., and Clifford M. Friend. "Modelling Intelligent Electronic Consumer Products." Journal of Intelligent Material Systems and Structures 10, no. 7 (July 1999): 552–57. http://dx.doi.org/10.1106/beha-5qhj-ua54-vdk9.

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Feray-Beaumont, S., R. Corea, M. T. Tham, and A. J. Morris. "Process modelling for intelligent control." Engineering Applications of Artificial Intelligence 5, no. 6 (November 1992): 483–92. http://dx.doi.org/10.1016/0952-1976(92)90025-f.

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Дисертації з теми "INTELLIGENT MODELLING"

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Li, X. "Intelligent object-oriented modelling." Thesis, Swansea University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.637900.

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Анотація:
Intelligent object-oriented modelling is a technique for organizing heterogeneous knowledge in a uniform way. It is concerned with three aspects of engineering applications: the independence of modelling from simulation, object-oriented paradigm and knowledge-based systems. The thesis investigates these three aspects and addresses their practically usable concepts. A model of engineering modelling is decomposed into a trinity of descriptions: static, dynamic and functional, which are all needed for a complete model presentation. An object-oriented information model is designed using OMT for the purpose of model development along this line. A model development scheme is proposed to formalize the modelling process and a knowledge representation scheme is suggested to classify the modelling process. A co-operation scheme is presented as a unified approach of both these schemes. An application architecture with a multidisciplinary shell on top of an object-oriented kernel is proposed to combine knowledge-based modelling and object-oriented modelling with conventional tools. A prototype system has been implemented in Kappa. The prototype system structure has been considered in the context of both the modelling process and the knowledge functionality. The thesis concludes that intelligent technologies are natural tools for concurrent engineering applications and are not only suited to inductive approaches and qualitative representations but also to deductive methodologies and quantitative descriptions.
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Makriyannis, Elpida. "Intelligent systems for modelling economic policies." Thesis, University College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407455.

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Guillot, Michel 1960. "Process modelling for intelligent machining control." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/13695.

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Theodoridis, Theodoros. "Modelling criminal behaviours for intelligent robot surveillance." Thesis, University of Essex, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.537948.

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Janes, Dominik Sebastian. "Intelligent control system for CFD modelling software." Thesis, University of Greenwich, 2003. http://gala.gre.ac.uk/8618/.

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Анотація:
In this thesis we show that it is possible to create an intelligent agent capable of emulating the human ability to control CFD simulations and provide similar benefits in terms of performance, overall reliability and result accuracy. We initially consider the rule-based approach proposed by other researchers. It is argued that heuristic search is better suited to model the techniques used by human experts. The residual graphs are identified as the most important source of heuristic information relevant to the control decisions. Three different graph features are found to be most important and dedicated algorithms are developed for their extraction. A heuristic evaluation function employing the new extraction algorithms is proposed and implemented in the first version of the heuristic control system (ICS 1.0). The analysis of the test results gives rise to the next version of the system (ICS 2.0). ICS 2.0 employs an additional expert system responsible for dynamic pruning of the search space using the rules obtained by statistical analysis of the initial results. Other features include dedicated goal-driven search plans that help reduce the search space even further. The simulation results and overall improvements are compared with non-controlled runs. We present a detailed analysis of a fire case solution obtained with different control techniques. The effect of the automatic control on the accuracy of the results is explained and discussed. Finally, we provide some indications for further research that promise to provide even greater performance gains.
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Devi, Roshni. "Modelling arithmetic strategies." Thesis, Open University, 1991. http://oro.open.ac.uk/56451/.

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Анотація:
This thesis examines children's arithmetic strategies and their relation to the concepts of commutativity and associativity. Two complementary methods were used in this research: empirical studies and computational models. Empirical studies were carried out to identify the strategies children used for solving problems like 3 + 4, and 3 + 4 + 7, and the conceptual knowledge associated with them. Their understanding of subtraction problems where the minuend is less than the subtrahend (e.g. 6-8) was also considered. A study with 105 subjects revealed a variety of strategies and information about children's knowledge of commutativity and associativity. Four levels of performance of commutativity were also identified. A longitudinal study was carried out with 12 children in order to obtain details of children's changes in strategy, and to double check the results obtained in the main study. The strategies observed to be used by children over a 20 month period parallel those found in previous studies, which show a general transition to more efficient methods. However, the longitudinal study revealed that development of such arithmetic strategies is a slow process. Furthermore, the studies indicated that knowledge of commutativity is a prerequisite for associativity. Models of the observed strategies have been implemented in the form of production rules in a computer program called PALM. The process of implementation highlighted features of children's problem solving that had not been 'detected during the studies. In addition to models that describe the space of strategies, a model of learning has been implemented for the transition from procedural knowledge of commutativity to that of associativity. The model is capable of generalizing its inbuilt knowledge, for instance, its ability to solve 2-term arithmetic expressions, to allow it to solve more complex problems, such as 3-term arithmetic expressions. A further model has been constructed for learning arithmetic strategies that are more efficient than those already represented in the program. It learns specific rules by adding conditions for efficient problem solving to its previous general rules.
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Merrick, Kathryn Elizabeth. "Modelling motivation for experience-based attention focus in reinforcement learning." Connect to full text, 2007. http://ses.library.usyd.edu.au/handle/2123/1935.

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Анотація:
Thesis (Ph. D.)--University of Sydney, 2007.
Includes graphs, tables. Title from title screen (viewed April 1, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Information Technologies, Faculty of Science. Includes bibliographical references. Also available in print form.
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Sun, Jie. "Modelling and intelligent control of vehicle climatronic systems." Thesis, Nelson Mandela Metropolitan University, 2009. http://hdl.handle.net/10948/1118.

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The modelling and control method of a vehicle climatronic system, based on MATLAB/SIMULINK, is presented. In order to achieve high modelling accuracy, a developed simulation model library is introduced. The modelling approach is described and the developed models are validated with some of experimental data obtained. The models are nonlinear, independent of fluid type and based on thermo-dynamic principles. Analysis of the cooling circuit modelling and empirical real-time control models are created by using Fuzzy logic controller and Stateflow. Both of control input and output are implemented essentially at original vehicle CAN-Bus system. Feasible digital automatic control strategy basic to fuzzy theory, hardware and software solution are given. The simulation experiment is achieved with the Hardware-in-Loop technology. This control methodology is easily operated and worth applying for any further studies or methods.
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Li, Yuefeng, and mikewood@deakin edu au. "Modelling intelligent agents for web-based information gathering." Deakin University. School of Computing and Mathematics, 2000. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051201.151125.

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Анотація:
The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.
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Filipe, Joaquim Belo Lopes. "Normative organisational modelling using intelligent multi-agent systems." Thesis, Staffordshire University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327505.

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Книги з теми "INTELLIGENT MODELLING"

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Miaoulis, Georgios, and Dimitri Plemenos, eds. Intelligent Scene Modelling Information Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92902-4.

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Stýskala, Vítězslav, Dmitrii Kolosov, Václav Snášel, Taalaybek Karakeyev, and Ajith Abraham, eds. Intelligent Systems for Computer Modelling. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27644-1.

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Goel, Nilesh, Shazia Hasan, and V. Kalaichelvi, eds. Modelling, Simulation and Intelligent Computing. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4775-1.

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Simian, Dana, and Laura Florentina Stoica, eds. Modelling and Development of Intelligent Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39237-6.

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5

Kołodziej, Joanna, Samee Ullah Khan, and Tadeusz Burczy´nski, eds. Advances in Intelligent Modelling and Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30154-4.

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Byrski, Aleksander, Zuzana Oplatková, Marco Carvalho, and Marek Kisiel-Dorohinicki, eds. Advances in Intelligent Modelling and Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28888-3.

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Simian, Dana, and Laura Florentina Stoica, eds. Modelling and Development of Intelligent Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68527-0.

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Simian, Dana, and Laura Florentina Stoica, eds. Modelling and Development of Intelligent Systems. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27034-5.

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Kravets, Alla G., Alexander A. Bolshakov, and Maxim Shcherbakov, eds. Cyber-Physical Systems: Modelling and Intelligent Control. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66077-2.

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Rigatos, Gerasimos G. Modelling and Control for Intelligent Industrial Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17875-7.

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Частини книг з теми "INTELLIGENT MODELLING"

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Weber, R. "Intelligent Switching." In Modelling Future Telecommunications Systems, 144–52. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-2049-8_9.

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Patel, Ravi, Dipankar Deb, Rajeeb Dey, and Valentina E. Balas. "Mathematical Modelling." In Intelligent Systems Reference Library, 11–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18068-3_2.

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3

McCalla, Gordon, Julita Vassileva, Jim Greer, and Susan Bull. "Active Learner Modelling." In Intelligent Tutoring Systems, 53–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45108-0_9.

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Arndt, Markus C., and Gabriele Sadowski. "Thermodynamic Modelling of Hydrogel Systems." In Intelligent Hydrogels, 175–87. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01683-2_14.

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Martins, André C. R. "Modelling Epistemic Systems." In Intelligent Systems Reference Library, 19–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39149-1_3.

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Favorskaya, Margarita N., and Lakhmi C. Jain. "Realistic Tree Modelling." In Intelligent Systems Reference Library, 181–202. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52308-8_6.

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Klostermann, L., and J. A. Kroeze. "Object Oriented IN service modelling." In Intelligent Networks and Intelligence in Networks, 180–88. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35323-4_13.

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Murphy, Maureen, and Michael McTear. "Learner Modelling for Intelligent CALL." In User Modeling, 301–12. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-2670-7_31.

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Zhang, Laobing, and Genserik Reniers. "Intelligent Interaction Modelling: Game Theory." In Advanced Sciences and Technologies for Security Applications, 25–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92618-6_2.

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Barker, Richerd. "Entity Modelling and Intelligent Dictionaries." In Automating Systems Development, 337–45. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1033-4_28.

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Тези доповідей конференцій з теми "INTELLIGENT MODELLING"

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Finn, D. P., J. B. Grimson, and N. M. Harty. "An Intelligent Mathematical Modelling Assistant for Analysis of Physical Systems." In ASME 1992 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/cie1992-0092.

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Abstract This paper describes work in progress aimed at developing an intelligent modelling assistant for the mathematical modelling task associated with engineering analysis. Mathematical modelling precedes detailed numerical analysis and involves formulating and evaluating engineering problems with the objective of proposing a candidate mathematical model that meets desired modelling requirements. The approach taken in this work is based on Chandrasekaran’s proposecritique-modify method which is adapted for modelling. The use of this paradigm is justified by viewing the mathematical modelling process as an activity of successive investigation and refinement of candidate mathematical models. The system architecture is based on exploiting a number of artificial intelligence techniques including model based reasoning, case based reasoning and rule based reasoning. A modelling options case base assists engineers in proposing candidate mathematical models. Engineering 1st principles and formulae are utilised within an artificial intelligence framework to provide a means of evaluating and critiquing the candidate mathematical models. The system is integrated with an existing interactive CAD system. The problem domain covered is application independent but will initially focus on the modelling and analysis of heat transfer problems.
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Frazer, John Hamilton. "Intelligent physical modelling systems." In the fourth international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1709886.1709887.

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Li, X. "Intelligent object-oriented modelling." In IEE Colloquium on Advances in Computer-Aided Control System Design. IEE, 1996. http://dx.doi.org/10.1049/ic:19960424.

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4

Bruzzone, A. G., and M. Massei. "Intelligent Agents for Modelling Country Reconstruction Operation." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.685-052.

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Dani, Ajay R., and Ved P. Gulati. "Intelligent Reputation Tracking System for Electronic Marketplace." In Applied Simulation and Modelling. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.773-030.

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Magnenat-Thalmann, Nadia. "Modelling socially intelligent virtual humans." In the 8th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1670252.1670254.

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7

Thangarajah, John, Lin Padgham, and Sebastian Sardina. "Modelling situations in intelligent agents." In the fifth international joint conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1160633.1160819.

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Harris, C. J. "Intelligent modelling, estimation and fusion." In IEE Colloquium Update on Developments in Intelligent Control. IEE, 1998. http://dx.doi.org/10.1049/ic:19981028.

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9

Storan Clarke, Rebekah. "Propensity Modelling for Intelligent Content." In UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3099023.3099029.

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10

Wright, D. T. "Qualitative modelling for intelligent control." In International Conference on Control '94. IEE, 1994. http://dx.doi.org/10.1049/cp:19940171.

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Звіти організацій з теми "INTELLIGENT MODELLING"

1

Anderson, John R., C. F. Boyle, Albert T. Corbett, and Matthew W. Lewis. Cognitive Modelling and Intelligent Tutoring. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada165981.

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2

Lock, X. Ge, and N. Prywes. An Intelligent Mathematical Modelling System - Mathmodel. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada207807.

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3

Chen, Yan, Christopher Nwagboso, and Panagiotis Georgakis. Modelling Integrated Safety Systems With Collision Avoidance and Intelligent Speed Adaptation. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0260.

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4

Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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5

de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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6

de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Анотація:
Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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7

Perdigão, Rui A. P. New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211021.

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Анотація:
Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of quotidian life. The socio-natural coevolution in climate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between cosmos and chaos. Subjacent to the conceptual and operational interdisciplinarity of that challenge, lies the simple formal elegance of a lingua franca for communication with Nature. This emerges from the innermost mathematical core of the Physics of Coevolutionary Complex Systems, articulating the wealth of insights and flavours from frontier natural, social and technical sciences in a coherent, integrated manner. Communicating thus with Nature, we equip ourselves with formal tools to better appreciate and discern complexity, by deciphering a synergistic codex underlying its emergence and dynamics. Thereby opening new pathways to see the “invisible” and predict the “unpredictable” – including relative to emergent non-recurrent phenomena such as irreversible transformations and extreme geophysical events in a changing climate. Frontier advances will be shared pertaining a dynamic that translates not only the formal, aesthetical and functional beauty of the Physics of Coevolutionary Complex Systems, but also enables and capacitates the analysis, modelling and decision support in crucial matters for the environment and society. By taking our emerging Physics in an optic of operational empowerment, some of our pioneering advances will be addressed such as the intelligence system Earth System Dynamic Intelligence and the Meteoceanics QITES Constellation, at the interface between frontier non-linear dynamics and emerging quantum technologies, to take the pulse of our planet, including in the detection and early warning of extreme geophysical events from Space.
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8

Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).
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9

Semerikov, Serhiy, Viacheslav Osadchyi, and Olena Kuzminska. Proceedings of the 1st Symposium on Advances in Educational Technology - Volume 2: AET. SciTePress, 2022. http://dx.doi.org/10.31812/123456789/7011.

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Анотація:
Symposium on Advances in Educational Technology (AET) is a peer-reviewed international conference focusing on research advances and applications of combined use of computer hardware, software, and educational theory and practice to facilitate learning. Today, AET is the premier interdisciplinary forum for learning scientists, academicians, researchers, professionals, policymakers, postgraduate students, and practitioners to present their latest research results, ideas, developments, and applications. AET topics of interest are: • Artificial intelligence in education • Augmented reality in education • Cloud-based learning environments • Cloud technologies for mathematics learning • Cloud technologies for informatics learning • Computer simulation in science and mathematics learning • ICT in primary and secondary education • ICT in higher education • Learning environments • Learning technology • Professional training in the digital space • Massive open online courses • Methodology of informatization in education • Modelling systems in education • Psychological safety in the digital educational environment • Soft skills development • STEM education • Virtualization of learning This volume represents the proceedings of the Symposium on Advances in Educational Technology, held in Kyiv, Ukraine, on November 12-13, 2020. It comprises 110 contributed papers that were carefully peer-reviewed and selected from 282 submissions. Each submission was reviewed by at least 3, and on the average 3.1, program committee members. The accepted papers present a state-of-the-art overview of successful cases and provide guidelines for future research. We are thankful to all the authors who submitted papers and the delegates for their participation and their interest in AET as a platform to share their ideas and innovation. Also, we are also thankful to all the program committee members for providing continuous guidance and efforts taken by peer reviewers contributed to improve the quality of papers provided constructive critical comments, improvements and corrections to the authors are gratefully appreciated for their contribution to the success of the workshop. Moreover, we would like to thank the developers of HotCRP, who made it possible for us to use the resources of this excellent and comprehensive conference management system, from the call of papers and inviting reviewers, to handling paper submissions, communicating with the authors, and creating the volume of the workshop proceedings.
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10

Davies, Will. Improving the engagement of UK armed forces overseas. Royal Institute of International Affairs, January 2022. http://dx.doi.org/10.55317/9781784135010.

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Анотація:
The UK government’s Integrated Review of security, defence, development and foreign policy, published in March 2021 alongside a supporting defence command paper, set a new course for UK national security and highlighted opportunities for an innovative approach to international engagement activity. The Integrated Review focused principally on the state threats posed by China’s increasing power and by competitors – including Russia – armed with nuclear, conventional and hybrid capabilities. It also stressed the continuing risks to global security and resilience due to conflict and instability in weakened and failed states. These threats have the potential to increase poverty and inequality, violent extremism, climate degradation and the forced displacement of people, while presenting authoritarian competitors with opportunities to enhance their geopolitical influence. There are moral, security and economic motives to foster durable peace in conflict-prone and weakened regions through a peacebuilding approach that promotes good governance, addresses the root causes of conflict and prevents violence, while denying opportunities to state competitors. The recent withdrawal from Afghanistan serves to emphasize the complexities and potential pitfalls associated with intervention operations in complex, unstable regions. Success in the future will require the full, sustained and coordinated integration of national, allied and regional levers of power underpinned by a sophisticated understanding of the operating environment. The UK armed forces, with their considerable resources and global network, will contribute to this effort through ‘persistent engagement’. This is a new approach to overseas operations below the threshold of conflict, designed as a pre-emptive complement to warfighting. To achieve this, the UK Ministry of Defence (MOD) must develop a capability that can operate effectively in weak, unstable and complex regions prone to violent conflict and crises, not least in the regions on the eastern and southern flanks of the Euro-Atlantic area. The first step must be the development of a cohort of military personnel with enhanced, tailored levels of knowledge, skills and experience. Engagement roles must be filled by operators with specialist knowledge, skills and experience forged beyond the mainstream discipline of combat and warfighting. Only then will individuals develop a genuinely sophisticated understanding of complex, politically driven and sensitive operating environments and be able to infuse the design and delivery of international activities with practical wisdom and insight. Engagement personnel need to be equipped with: An inherent understanding of the human and political dimensions of conflict, the underlying drivers such as inequality and scarcity, and the exacerbating factors such as climate change and migration; - A grounding in social sciences and conflict modelling in order to understand complex human terrain; - Regional expertise enabled by language skills, cultural intelligence and human networks; - Familiarity with a diverse range of partners, allies and local actors and their approaches; - Expertise in building partner capacity and applying defence capabilities to deliver stability and peace; - A grasp of emerging artificial intelligence technology as a tool to understand human terrain; - Reach and insight developed through ‘knowledge networks’ of external experts in academia, think-tanks and NGOs. Successful change will be dependent on strong and overt advocacy by the MOD’s senior leadership and a revised set of personnel policies and procedures for this cohort’s selection, education, training, career management, incentivization, sustainability and support.
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