7,940 research outputs found

    Selection of parameters to predict dew point temperature in arid lands using Grey theory: a case study of Iran

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    Dew point is the temperature at which water vapor in the air condenses into liquid with the same rate it evaporates. Dew point study is important in arid lands with low rainfall, also in other regions with various hydrological and climatological conditions. In this study, the Grey theory is applied for the first time to propose a framework approach to identify the important parameters affecting the prediction of dew point temperature. The ability of Grey theory to estimate and rank the parameters of a problem with missing data and uncertain conditions means that it has a good potential for mentioned application. For this research, 8 parameters are selected using literature review including: global solar radiation on a horizontal surface (H), water vapor pressure (VP), atmospheric pressure (P), sunshine duration (n), minimum air temperature (Tmin), maximum air temperature (Tmax), average air temperature (Tavg), and Relative Humidity (RH). The study is conducted for the city of Abadeh in Iran by using the data pertaining to a 10 year period between 2005 and 2015. The findings show that RH, Tavg, P, Tmax, Tmin, H, n and Vp with the grey possibility degrees of, respectively, 0.534, 0.551, 0.608, 0.622, 0.635, 0.695, 0.697 and 0.712, are the most important and effective parameters in prediction of dew point temperature. The proposed method also prioritizes the studied parameters in the order of their effectiveness on predicted dew point temperature

    A systematic review of empirical methods for modelling sectoral carbon emissions in China

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    © 2019 Elsevier Ltd A number of empirical methods have been developed to study China's sectoral carbon emissions (CSCE). Measuring these emissions is important for climate change mitigation. While several articles have reviewed specific methods, few attempts conduct a systematic analysis of all the major research methods. In total 807 papers were published on CSCE research between 1997 and 2017. The primary source of literature for this analysis was taken from the Web of Science database. Based on a bibliometric analysis using knowledge mapping with the software CiteSpace, the review identified five common families of methods: 1) environmentally-extended input-output analysis (EE-IOA), 2) index decomposition analysis (IDA), 3) econometrics, 4) carbon emission control efficiency evaluation and 5) simulation. The research revealed the main trends in each family of methods and has visualized this research into ten research clusters. In addition, the paper provides a direct comparison of all methods. The research results can help scholars quickly identify and compare different methods for addressing specific research questions

    An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector

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    The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment

    An academic review: applications of data mining techniques in finance industry

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    With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

    Developing long-term energy and carbon emission modelling for the operational activities of ports: A case study of Fremantle Ports

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    The port and maritime industry contributes significantly to global greenhouse gas emissions. As such, there is increasing pressure for ports to decarbonise their operations. Despite the availability of multiple port carbon inventory and emission reduction guidance documents, no published methodologies currently exist for the development of port energy consumption and carbon emission forecasting. To fill this information gap, a methodology was developed through the review and experimentation with established forecasting techniques. The ‘ISCA’ Base Case Approach was adopted as a scaffolding for model development, largely to test the usability of the approach, currently in pilot. The approach consists of a baseline scenario and an ‘actual case’ scenario. A combination of qualitative, quantitative - time series and quantitative - causal modelling techniques were incorporated into the methodology. Linear and non-linear regression analysis curve-fitting techniques were selected as the most appropriate time-series modelling method for long-term energy and emissions projections, with simple linear regression analysis used for causal models. The methodology was tested through its application in a case study for Fremantle Ports. As a result of obligations from the state government to reach net-zero emissions by 2050, Fremantle Ports required the development of long-term energy consumption and carbon emission projections for its internal operations and container terminals to 2050. Using a bottom-up strategy, categorising energy consumption and greenhouse gas emissions by trade type, energy type and facility, the methodology successfully developed long-term energy and emissions projections. As per this modelling, energy consumption at Fremantle Ports is expected to increase 53% under the baseline scenario and 46.5% under the actual case scenario (Figure 1). Despite increases of energy consumption at the port, greenhouse gas emissions are expected to decrease 71% and 74% under the baseline and actual case scenarios, respectively (Figure 2). These drastic emissions reductions are predominantly the result of projected scope 2 emission factor decreases as grid renewable electricity generation capacity increases. The usability of the ISCA Base Case Approach for energy and emissions modelling was found to be adequate, although issues were experienced distinguishing constant and variable energy use. Additionally, it is recommended that a third scenario is incorporated into the approach

    Three Decades of Fuzzy AHP: A Bibliometric Analysis

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    [EN] For decades, Fuzzy Sets Theory (FST) has been consistently developed, and its use has spread across multiple disciplines. In this process of knowledge transfer, fuzzy applications have experienced great diffusion. Among them, Fuzzy Analytic Hierarchy Process (fuzzy AHP) is one of the most widely used methodologies today. This study performs a systematic review following the PRISMA statement and addresses a bibliometric analysis of all articles published on fuzzy AHP in journals indexed in Web of Science, specifically in Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI). The analyzed database includes 2086 articles published between 1994 and 2022. The results show the thematic clusters, the evolution of the academic conversation and the main collaboration networks. The main contribution of this article is to clarify the research agenda on fuzzy AHP. The results of the study allow academics to detect publication opportunities. In addition, the evidence found allows researchers and academics setting the field¿s agenda to advise the editors of high-impact journals on gaps and new research trends.Castello-Sirvent, F.; Meneses-Eraso, C.; Alonso-Gómez, J.; Peris-Ortiz, M. (2022). Three Decades of Fuzzy AHP: A Bibliometric Analysis. Axioms. 11(10):1-34. https://doi.org/10.3390/axioms11100525134111

    Future of Carbon Capture: Materials and Strategies.

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    Emissions of greenhouse gases into the atmosphere represent a long-term social and environmental challenge. Fossil fuels, which are the main source of these emissions, will likely continue to be used in energy production and transportation for the foreseeable future. In order to mitigate these emissions and prevent the worst potential effects of climate change, carbon capture technologies will need to achieve widespread use across various industries. To inform further development of next-generation carbon capture systems, two potential technologies were explored. The first technology, flexible metal-organic frameworks, represent alternative materials for carbon capture. A group of flexible frameworks known as elastic layer-structured metal organic frameworks (ELMs) were chosen as a representative class. These crystalline materials have exotic “gated” isotherms which show abrupt reversible transitions from nonporous structures to porous structures through cooperative adsorption of guest molecules between layer planes. These unique materials show potential for selective CO2 capture combined with energy efficient adsorbent regeneration. Two aspects of CO2 capture using ELMs were investigated in detail. First, the ability of ELMs to maintain their structure and capture performance in the presence of unwanted trace species present in flue gas streams, such as NOx, SOx, and water vapor, was analyzed using both experimental and computational techniques. It was found that ELMs can be tailored for robust performance through careful choice of framework components, such as metal ion or counter ion substitution. Second, the breakthrough performance of ELMs was explored using a combination of experimental breakthrough curves and theoretical treatment. ELMs show a “stepped” breakthrough curve not seen in rigid adsorbents. These “stepped” curves are representative of the breakthrough curves of flexible frameworks and pose a potential hurdle to their use in carbon capture applications. The second technology, mobile carbon capture, represents an alternative strategy for mitigating emissions from the transportation sector. Using a combination of techniques, the potential costs and design trade-offs associated with implementing a mobile carbon capture scheme were explored. It was found that mobile carbon capture could greatly reduce transportation emissions while being cheaper to implement than competing direct air capture schemes, which suffer from significant thermodynamic penalties.PhDEnvironmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133406/1/sotfranc_1.pd

    Переклад у галузі електроенергетики. Методичні рекомендації до практичних занять з дисципліни для студентів спеціальності 7.030507 «Переклад» напряму підготовки 035 «Філологія»

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    Методичні матеріали призначено для самостійної роботи студентів спеціальності 7.030507 “Переклад” напряму підготовки 035 «Філологія» для організації практичних занять із дисципліни «Переклад у галузі електроенергетики». Рекомендації орієнтовано на вдосконалення навичок перекладу науково-технічних текстів

    Переклад у галузі електроенергетики. Методичні рекомендації до практичних занять з дисципліни для студентів спеціальності 7.030507 «Переклад» напряму підготовки 035 «Філологія»

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    Методичні матеріали призначено для самостійної роботи студентів спеціальності 7.030507 “Переклад” напряму підготовки 035 «Філологія» для організації практичних занять із дисципліни «Переклад у галузі електроенергетики». Рекомендації орієнтовано на вдосконалення навичок перекладу науково-технічних текстів
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