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Kovačević S, Banjac MK, Podunavac-Kuzmanović S, Ajduković J, Salaković B, Rárová L, Đorđević M, Ivanov M. Local QSAR modeling of cytotoxic activity of newly designed androstane 3-oximes towards malignant melanoma cells. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2023.135272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Dursun OO, Toraman S, Aygun H. Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:27539-27559. [PMID: 36383312 PMCID: PMC9666968 DOI: 10.1007/s11356-022-24109-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Aviation emissions originated from the fuel burn have been hot topics by engineers and policy-makers due to their harmful effects on the environment and thereby human health as well as sustainability. In this study, it is tried that several emission indexes (EIs) involving CO, HC and NOx as well as fuel flow of several commercial aircraft engines (CAEs) are predicted using support vector regression (SVR) and long short-term memory (LSTM) approaches for take-off phase. Moreover, exergo-environmental parameters involving exergy efficiency (ExEFF), wasted exergy ratio (WExR) and environmental effect factor (EEF) pertinent to CAEs are computed employing thermodynamics laws. While establishing the models, rated thrust, by-pass ratio, overall pressure ratio and combustion type of the CAEs are utilized as the model inputs. According to the findings of emission modelling, the coefficient of determination (R2) of EI NOx and EI CO of the CAEs is found as 0.929074 and 0.960277 with SVR, whereas their R2 values are elevated to 0.954878 and 0.989283 with LSTM approach, respectively. However, R2 of EI HC is determined lower with 0.632280 (by SVR) and 0.651749 (by LSTM). On the other hand, exergo-environmental parameters for the CAEs are estimated with high correctness at both models. Namely, R2 of ExEFF and EEF regarding the CAEs are computed as 0.991748 and 0.989067 by SVR; however, these are calculated as 0.994785 and 0.992797 by LSTM method. To model these parameters with low error by using significant design variables as model inputs could help in predicting emission and environmental metrics for new engine designs.
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Affiliation(s)
- Omer Osman Dursun
- Department of Aircraft Electric and Electronic, Firat University, 23119 Elazig, Turkey
| | - Suat Toraman
- Department of Air Traffic Control, Firat University, 23119 Elazig, Turkey
| | - Hakan Aygun
- Department of Aircraft Air Frame and Power Plant, Firat University, 23119 Elazig, Turkey
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Lingwal S, Bhatia KK, Singh M. A novel machine learning approach for rice yield estimation. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2062458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Surabhi Lingwal
- Computer Science & Engineering Department, Govind Ballabh Pant Institute of Engineering & Technology, Pauri Garhwal, India
| | - Komal Kumar Bhatia
- Faculty of Informatics and Computing J. C. Bose University of Science & Technology, YMCA, Faridabad, India
| | - Manjeet Singh
- Faculty of Informatics and Computing J. C. Bose University of Science & Technology, YMCA, Faridabad, India
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Kermorvant C, Liquet B, Litt G, Jones JB, Mengersen K, Peterson EE, Hyndman RJ, Leigh C. Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:12803. [PMID: 34886529 PMCID: PMC8657025 DOI: 10.3390/ijerph182312803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/26/2021] [Accepted: 12/02/2021] [Indexed: 11/16/2022]
Abstract
In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.
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Affiliation(s)
- Claire Kermorvant
- Laboratoire de Mathématiques et de Leurs Applications de Pau Fédération MIRA, UMR CNRS 5142, Université de Pau et des Pays de l’Adour, 64600 Anglet, France;
| | - Benoit Liquet
- Laboratoire de Mathématiques et de Leurs Applications de Pau Fédération MIRA, UMR CNRS 5142, Université de Pau et des Pays de l’Adour, 64600 Anglet, France;
- Department of Mathematics and Statistics, Macquarie University, Sydney, NSW 2109, Australia
| | - Guy Litt
- National Ecological Observatory Network, Battelle Boulder, Boulder, CO 80301, USA;
| | - Jeremy B. Jones
- Institute of Arctic Biology and Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK 99775, USA;
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD 4000, Australia;
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia; (E.E.P.); (R.J.H.); (C.L.)
| | - Erin E. Peterson
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia; (E.E.P.); (R.J.H.); (C.L.)
- Peterson Consulting, Brisbane, QLD 4000, Australia
| | - Rob J. Hyndman
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia; (E.E.P.); (R.J.H.); (C.L.)
- Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia
| | - Catherine Leigh
- ARC Centre of Excellence for Mathematics and Statistical Frontiers, Melbourne, VIC 3000, Australia; (E.E.P.); (R.J.H.); (C.L.)
- Biosciences and Food Technology Discipline, School of Science, RMIT University, Bundoora, VIC 3083, Australia
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Case Study: Reconstruction of Runoff Series of Hydrological Stations in the Nakdong River, Korea. WATER 2020. [DOI: 10.3390/w12123461] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Reliable runoff series is sine qua non for flood or drought analysis as well as for water resources management and planning. Since observed hydrological measurement such as runoff can sometimes show abnormalities, data quality control is necessary. Generally, the data of adjacent hydrological stations are used. However, difficulties are frequently encountered when runoff series of the adjacent stations have different flow characteristics. For instance, when the correlation between the up- and downstream locations in which the stations are located is used as the main criterion for quality control, difficulties can occur. Therefore, this study aims to suggest a method to reconstruct an abnormal daily runoff series in the Nakdong River, Korea. The variational mode decomposition (VMD) technique is applied to the runoff series of the three target stations: Goryeong County (Goryeong bridge) and Hapcheon County (Yulji bridge and Jeogpo bridge). These runoff series are also divided into several intrinsic mode functions (IMFs) that are governed by basin runoff and disturbed flow caused by the hydraulic structure. The decomposition results based on VMD show that the runoff components in a particular station that is influenced by hydraulic structures could be reconstructed using adjacent stations, but the residual mode could not. The runoff reconstruction model using an artificial neural network (ANN), the two “divided” modes, and the residual component is established and applied to the runoff series for the target station (Yulji bridge in Hapcheon County). The reconstructed series from the model show relatively good results, with R2 = 0.92 and RMSE = 99.3 in the validation year (2019). Abnormal runoff series for 2012 to 2013 at the Yulji bridge station in Hapcheon County are also reconstructed. Using the suggested method, a well-matched result with the observations for the period from 2014 onwards is produced and a reconstructed abnormal series is obtained.
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Abstract
Supervisory Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. Ideally, they must run without interruption, but in practice, some data may be lost due to a sensor failure or a communication breakdown. When it happens, given the nature of these failures, information is lost in bursts, that is, sets of consecutive samples. When this occurs, it is necessary to fill out the gaps of the historical data with a reliable data completion method. This paper presents an ad hoc method to complete the data lost by a SCADA system in case of long bursts. The data correspond to levels of drinking water tanks of a Water Network company which present fluctuation patterns on a daily and a weekly scale. In this work, a new tensorization process and a novel completion algorithm mainly based on two tensor decompositions are presented. Statistical tests are realised, which consist of applying the data reconstruction algorithms, by deliberately removing bursts of data in verified historical databases, to be able to evaluate the real effectiveness of the tested methods. For this application, the presented approach outperforms the other techniques found in the literature.
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Abstract
This work contributes to the techniques used for SCADA (Supervisory Control and Data Acquisition) system data completion in databases containing historical water sensor signals from a water supplier company. Our approach addresses the data restoration problem in two stages. In the first stage, we treat one-dimensional signals by estimating missing data through the combination of two linear predictor filters, one working forwards and one backwards. In the second stage, the data are tensorized to take advantage of the underlying structures at five minute, one day, and one week intervals. Subsequently, a low-range approximation of the tensor is constructed to correct the first stage of the data restoration. This technique requires an offset compensation to guarantee the continuity of the signal at the two ends of the burst. To check the effectiveness of the proposed method, we performed statistical tests by deleting bursts of known sizes in a complete tensor and contrasting different strategies in terms of their performance. For the type of data used, the results show that the proposed data completion approach outperforms other methods, the difference becoming more evident as the size of the bursts of missing data grows.
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Application of a Hybrid Interpolation Method Based on Support Vector Machine in the Precipitation Spatial Interpolation of Basins. WATER 2017. [DOI: 10.3390/w9100760] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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