151 research outputs found

    A decision-making framework based on the Fermatean hesitant fuzzy distance measure and TOPSIS

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    A particularly useful assessment tool for evaluating uncertainty and dealing with fuzziness is the Fermatean fuzzy set (FFS), which expands the membership and non-membership degree requirements. Distance measurement has been extensively employed in several fields as an essential approach that may successfully disclose the differences between fuzzy sets. In this article, we discuss various novel distance measures in Fermatean hesitant fuzzy environments as research on distance measures for FFS is in its early stages. These new distance measures include weighted distance measures and ordered weighted distance measures. This justification serves as the foundation for the construction of the generalized Fermatean hesitation fuzzy hybrid weighted distance (DGFHFHWD) scale, as well as the discussion of its weight determination mechanism, associated attributes and special forms. Subsequently, we present a new decision-making approach based on DGFHFHWD and TOPSIS, where the weights are processed by exponential entropy and normal distribution weighting, for the multi-attribute decision-making (MADM) issue with unknown attribute weights. Finally, a numerical example of choosing a logistics transfer station and a comparative study with other approaches based on current operators and FFS distance measurements are used to demonstrate the viability and logic of the suggested method. The findings illustrate the ability of the suggested MADM technique to completely present the decision data, enhance the accuracy of decision outcomes and prevent information loss

    Improved thermodynamic investigation of asphaltene precipitation

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    Asphaltenes are analogous to the “cholesterol” of crude oils, so they may cause significant flow assurance problems to various oil and gas processes and negatively affect the economy of the oil recovery, transportation, and processing by increasing operational expenditures (OPEX). Asphaltenes increase oil viscosity, decrease its market value, and, when they precipitate, cause flow assurance challenges. Understanding asphaltene precipitation and phase behaviour is important to avoid, prevent, and address asphaltene flow assurance challenges. An experimental investigation is time-consuming and requires laboratory expertise with limitations on how many experiments can reasonably be conducted over what range of feasible operating conditions. Furthermore, we need to predict asphaltene and fluid phase behaviour over the full range of operating conditions to avoid flow assurance issues. Therefore, having a thorough knowledge of the phenomenon and applying asphaltene modeling approaches is essential to foresee conditions leading to asphaltene precipitation to treat the phenomenon properly. Despite significant research, asphaltene behaviour in different operating conditions and the application of improved thermodynamic investigations have not been well understood. There is little research on the investigation of the operating conditions and improvement of the thermodynamic models (e.g., application of advanced optimization technique) on asphaltene precipitation. This thesis uses different modeling approaches (e.g., equation of state) to investigate crude oil asphaltene precipitation at operating conditions. Asphaltene phase separation can be triggered by altering the operating conditions, e.g., temperature, composition, and adding n-alkanes. For instance, decreasing temperature from reservoir conditions leads to asphaltene precipitation due to alteration of the solubility of asphaltene in the oil mixture. Moreover, the composition of crude oil is upgraded or downgraded by adding different hydrocarbons at the refinery inlet. Yet, the prediction of asphaltene precipitation and the impact of operating conditions are quite uncertain, and detailed thermodynamic investigations and appropriate techniques for adjusting the models are required. Several research studies have used thermodynamic equations of state (EoS) to model asphaltene precipitation. Recently, advanced EoSs that take into account the association of hydrogen bonding has become popular. For example, Cubic Plus Association (CPA) has shown promising results in modeling asphaltene precipitation. There is uncertainty in using EoSs, e.g., tuning the adjustable parameters. Hence, there is a need to systematically study how to adjust the tunable parameters to predict asphaltene precipitation using advanced EoS. The objective of this research is to investigate and improve the performance of EoS modeling of asphaltene precipitation. For this purpose, first, a comprehensive literature review was conducted to address asphaltene precipitation from different standpoints. While a comprehensive literature review to study asphaltene precipitation and deposition was missing in the literature, the focus of this research is to provide an overview of the nature and physical properties of asphaltenes, experimental and thermodynamic/simulation tools investigations, operating/fluid/reservoir impact, inhibition/treatment, and economic analysis of flow assurance. The literature review findings highlighted two main gaps in asphaltene thermodynamic modeling; 1) only gradient-based optimization techniques have been used to tune the EoS parameters, and 2) the effect of heteroatoms in asphaltene precipitation has not been considered. Therefore, the two other objectives of this thesis are tailored to address the gaps. In order to address the fact that only gradient-based methods have been used to tune the parameters, we used a global optimization approach instead of gradient-based optimization to relate and correlate hydrogen bonding to the binary interaction parameters of the Cubic Plus Association (CPA) EoS model. While the application of advanced optimization methods and a systematic sensitivity analysis of operational conditions/BIPs were missing in the literature, the focus of this section is to consider the association of hydrogen bonding in asphaltene precipitation while developing correlations for binary interactions (BIs) using global optimization. The advantages of using global optimization are to avoid entrapment in local minima while optimizing the parameters of the EoS and to improve the correlation/prediction capability of the EoS by finding the best fit of the adjustable parameters. The CPA EoS is validated by predicting unseen data, comparing with cubic EoSs, i.e., SRK and PR, using different oil characterization, e.g., SARA analysis, and drawing an analogy between scaling equation and CPA. Application of the proposed technique significantly improved the performance of the CPA EoS in modeling asphaltene precipitation (average deviation of less than 0.067 for correlation and prediction). The relative importance analysis revealed that the composition of the mixture (dilution ratio) is the most influential factor contributing to the asphaltene precipitation (other factors are temperature and carbon number of the diluents). The effect of polar forces due to the presence of heteroatoms on asphaltene phase behaviour is investigated using a Cubic Plus Polar EoS (CPP). To the best of our knowledge, we have not found any literature focused on polar heteroatom forces in asphaltene thermodynamic modeling. In this novel work, we demonstrate how a single term that accounts for polarity can be added to the extension of the cubic EoS and be effectively applied to calculate asphaltene precipitation. Further, a simplified oil characterization method is adapted to reduce the number of adjustable parameters (binary interactions) and reduce the need for experimental measurements. A global optimization approach and molecular dynamic (MD) simulation have also been used to increase the reliability of the optimization and reduce the number of adjustable parameters for polar forces. This section of the research finds that the CPP approach using global optimization to tune parameters of the EoS is the most reliable approach, followed by CPP EoS using MD to find dipole moment for the aryl-linked core asphaltene structure (average R2 for both modes are above 0.98). The improved thermodynamic approaches (global optimization and including the effect of heteroatoms) introduced in this research can be used by other researchers to increase the efficiency of the asphaltene thermodynamic modeling

    Estimating Penetration Rate of Excavation Machine Using Geotechnical Parameters and Neural Networks in Tabriz Metro

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    In this study, the penetration rate of the excavation machine in Tabriz Metro Line 2 using geotechnical parameters and neural networks is estimated. For this purpose, through comprehensive analysis, including borehole drilling, field and laboratory tests, and consideration of similar projects, the geotechnical parameters for soil and rock layers have been determined. Preprocessing data techniques, such as normalization, have been applied to address challenges such as noise and bias in raw data. Also, neural networks with varying architectures were evaluated using mean square error and correlation coefficient as evaluation metrics. The architecture (1-12-8) of this research demonstrates superior performance with a mean square error of 1.630 and a correlation coefficient of 0.932. This shows a strong relationship between predicted and actual penetration rate values. The findings of this research highlight the effectiveness of neural networks in estimating the penetration rate. Accurate estimations of the non-linear penetration rate were achieved by employing a single-layer neural network with multiple neurons using appropriate transfer functions. Overall, this research contributes to the understanding of geotechnical considerations for urban train routes and demonstrates the accuracy of neural networks for penetration rate estimation. These insights have implications for the design and engineering of similar projects

    The co-evolution of networked terrorism and information technology

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    This thesis describes for the first time the mechanism by which high-performing terrorist networks leverage new iterations of information technology and the two interact in a mutually propulsive manner. Using process tracing as its methodology and complexity theory as its ontology, it identifies both terrorism and information technology as complex adaptive systems, a key characteristic of whose make-up is that they co-evolve in pursuit of augmented performance. It identifies this co-evolutionary mechanism as a classic information system that computes the additional scale with which the new technology imbues its terrorist partner, in other words, the force multiplier effect it enables. The thesis tests the mechanism’s theoretical application rigorously in three case studies spanning a period of more than a quarter of a century: Hezbollah and its migration from terrestrial to satellite broadcasting, Al Qaeda and its leveraging of the internet, and Islamic State and its rapid adoption of social media. It employs the NATO Allied Joint Doctrine for Intelligence Procedures estimative probability standard to link its assessment of causal inference directly to the data. Following the logic of complexity theory, it contends that a more twenty-first century interpretation of the key insight of RAND researchers in 1972 would be not that ‘terrorism evolves’ but that it co-evolves, and that co-evolution too is arguably the first logical explanation of the much-vaunted ‘symbiotic relationship’ between terrorists and the media that has been at the heart of the sub-discipline of terrorism studies for 50 years. It maintains that an understanding of terrorism based on co-evolution belatedly explains the newness of much-debated ‘new terrorism’. Looking forward, it follows the trajectory of terrorism driven by information technology and examines the degree to which the gradual symbiosis between biological and digital information, and the acknowledgment of human beings as reprogrammable information systems, is transforming the threat landscape

    ATHENA Research Book, Volume 2

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    ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of OrlĂ©ans, University of Siegen, Hellenic Mediterranean University, NiccolĂČ Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-SkƂodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers

    Tech Imaginations

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    Prof. Dr. Jens Schröter, Christoph Borbach, Max Kanderske und Prof. Dr. Benjamin Beil sind Herausgeber der Reihe. Die Herausgeber*innen der einzelnen Hefte sind renommierte Wissenschaftler*innen aus dem In- und Ausland.Technologies and especially media technologies are pervasive in modern societies. But even more omnipresent are the imaginaries of modern technologies – what technologies are thought to be capable of or what effects they are supposed to have. These imaginations reveal a lot of the political and ideological self-descriptions of societies, hence the (techno-)imaginary also functions as a kind of epistemic tool. Concepts of the imaginary therefore have experienced an increasing attention in cultural theory and the social sciences in recent years. In particular, work from political philosophy, but also approaches from science and technology studies (STS) or communication and media studies are worth mentioning here. The term "techno-imagination", coined by VilĂ©m Flusser in the early 1990s, refers to the close interconnection of (digital) media and imaginations, whose coupling can not only be understood as a driver of future technology via fictional discourses (e.g. science fiction), but much more fundamentally also as a constitutive element of society and sociality itself, as Castoriadis has argued. In the first part of the issue several theoretical contributions add new aspects to the discussion of socio-technical imaginaries, while in the second part a workshop held in January 2022 at the CAIS in Bochum is documented, in which the case of the imaginaries of “Future Internets” was discussed

    A Flexible Combination Forecast Method for Modeling Agricultural Commodity Prices: A Case Study Iran’s ‎Livestock and Poultry Meat Market

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    In recent years, the fluctuation in agricultural commodity prices in Iran is increased and thus, accurate forecasting of price change is necessary. In this article, a flexible combined method in modeling monthly prices of beef, lamb and chicken from April 2001 to March 2021, was proposed. In this new method, three different approaches namely simple averaging, discounted and shrinkage methods were effectively used to combine the forecasting outputs of three hybrid methods (MLPANN-GA, MLPANN-PSO and MLPANN-ICA) together. In implementation stage of hybrid methods, based on test and error method, the optimal MLPANN structure was found with 3/2/4–6–1 architectures and the controlling parameters are carefully assigned. The results obtained from three hybrid methods indicate that, based on the RMSE statistical index, the MLPANN-ICA method performs the best when forecasting prices for beef, lamb, and chicken. The outputs of three combination approaches show that the shrinkage method, with a parameter value of K=0.25, achieves the highest prediction accuracy when forecasting prices for these three meats. In summary, the proposed method outperforms the other three hybrid methods overall

    University of Windsor Undergraduate Calendar 2022 Winter

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1017/thumbnail.jp
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