89 research outputs found
Background data for: "The instantaneous structure of a turbulent wall-bounded flow influenced by freestream turbulence: streamwise evolution"
No full textThis data set contains planar Particle Image Velocimetry measurement fields for the experiments described in the article titled "The instantaneous structure of a turbulent wall-bounded flow influenced by freestream turbulence: streamwise evolution" (doi:10.1017/jfm.2024.1008).
The experiments were conducted in a water channel at the Norwegian University of Science and Technology. The setup includes an active grid to control freestream conditions. To analyze the evolution of the flow, the boundary layer was tested at four different streamwise locations for three grid sequences with freestream turbulence intensities up to 10.9%. Careful preprocessing was implemented to ensure high accuracy and minimal uncertainties.
This work was funded by the Research Council of Norway (see funding information): Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the Research Council of Norway. The granting authority cannot be held responsible for them.</p
Vibration of Bundled Conductors Following Ice Shedding
No full textThe dynamic behavior of bundled conductors following ice shedding from one subconductor is examined numerically using the finite-element method. An existing model of ice shedding from a single conductor is improved by developing a model of spacers which connect subconductors in the span. The resulting system makes it possible to simulate vibrations following ice shedding from one span of an overhead transmission line with twin, triple, or quad bundles. Vibration characteristics are evaluated as the following parameters are varied: thickness of shed ice, distance between adjacent spacers, and number of subconductors in the bundle. Simulation results will provide information on how the amplitude of vibration and the transient dynamic forces change with the application of spacers. The maximum jump height of the ice-shedding cable, the maximum drop of the loaded cable, and the maximum cable tension are approximated as power functions of ice thickness and the distance between adjacent spacers
Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
No full textElectrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers
Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting
No full textDrought stands as a highly perilous natural catastrophe that impacts numerous facets of human existence. Drought data is nonstationary and noisy, posing challenges for accurate forecasting. This study proposes a novel hybrid framework integrating TVF-EMD preprocessing, LASSO feature selection and Ensemble Deep RVFL modeling for improved multistep ahead drought prediction. Using decomposed SPEI values, six machinelearning techniques (Support Vector Regression (SVR), Simple RVFL, Ensemble Deep RVFL, and Recurrent Neural Network (RNN), XGBoost, Random Forest (RF)) were applied to forecast the SPEI 12 12 drought index. The present study involved forecasting drought in two Canadian stations located in the eastern region (Charlottetown in Prince Edward Island and Fredericton in New Brunswick), where agriculture is rainfed and mostly affected by drought. The statistical period of 1980–2022 was considered for analysis. Following the decomposition of drought data with TVF-EMD, lagged data was generated using the TVF-EMD results. Training time was decreased by utilizing the Lasso regression feature selection algorithm to select effective inputs. Various statistical measures, including the root mean square error (RMSE) and correlation coefficient (R), were employed to assess the precision of the models. The research findings indicated that the TVF-ED-RVFL model achieved the highest level of precision in forecasting multistep ahead (1,3,6 and 12) SPEI 12 drought index for both Charlottetown and Fredericton stations. During testing, the TVF-ED-RVFL model predicted 1-month SPEI 12 for Charlottetown (R = 0.9995, RMSE = 0.0352) and Fredericton (R = 0.9974, RMSE = 0.0560). For multistep ahead forecasting, the Rvalues range from 0.9924 for 3-months ahead to 0.9242 for 12-months ahead in Charlottetown and range from 0.9846 for 3-months ahead to 0.8293 for 12-months ahead in Fredericton. By increasing the forecasting horizon, the accuracy of models decreased. The present study’s outcomes can contribute to enhancing water management practices during periods of drought.Natural Sciences and Engineering Research Council of CanadaDepartment of Environment, Energy and Climate ActionGovernment of Prince Edward IslandAtlantic Canada Opportunities Agenc
The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4−2 surface water quality
No full textThe concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4−2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4−2 data recorded at Tange-Takab station for the period 1980–2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4−2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4−2, respectively
Ethnic identity, political identity and ethnic conflict: simulating the effect of congruence between the two identities on ethnic violence and conflict
No full textThis thesis outlines and presents an alternative hypothetical process to the emergence of ethnic conflict. Ethnic conflicts, rather than being dependent upon pre-existing 'ancient hatreds', are instead the result of a congruence between ethnic and political identity which grants individuals the ability to use ethnicity to identify and eliminate political threats. This hypothesis is formed by the examination of three case studies of ethnic conflict: Lebanon, Northern Ireland and Croatia. This hypothesis is then formalised and tested using an agent based simulation in which agent interactions are dependent upon ethnic and political identity and the congruence between the two. As predicted there was a strong positive correlation between how accurately ethnic identity reflected political identity and the level of ethnically motivated violence in the simulation, although the relationship was not linear. Furthermore the effect of a shift in congruence was found to be roughly comparable to the effect of initialising agents with a moderate level of pre-existing ethnic antagonism
SimCC: A novel method to consider both content and citations for computing similarity of scientific papers
No full textTo compute the similarity of scientific papers, text-based similarity measures, link-based similarity measures, and hybrid methods can be applied. The text-based and link-based similarity measures take into account only a single aspect of scientific papers, content or citations, respectively. The hybrid methods consider both content and citations; however, they do not carefully consider the relation between the content of a pair of papers involved in a citation relationship. In this paper, we propose a novel method, SimCC (similarity based on content and citations), that considers both aspects, content and citations, to compute the similarity of scientific papers. Unlike previous methods, SimCC effectively reflects both content and authority of scientific papers simultaneously in similarity computation by applying a new RA (relevance and authority) weighting scheme. Also, we propose an RA+R weighting scheme to consider the recency of papers and an RA+E weighting scheme to take into account the author expertise of papers in similarity computation. The effectiveness of our proposed method is demonstrated by extensive experiments on a real-world dataset of scientific papers. The results show that our method achieves more than 100% improvement in accuracy in comparison with previous methods. (C) 2015 Elsevier Inc. All rights reserved.This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A1A10054151)
Nonlinear stability analysis of piecewise actuated piezoelectric microstructures
No full textThe main objective of this research is to provide a general nonlinear model of adjustable piezoelectric microwires with the ability to tune the stability conditions. In order to increase the controllability and improve system characteristics, only a part of the substrate is electrostatically actuated and the piezoelectric voltage is also applied. The governing equation of equilibrium (EOE) is derived from the principle of minimum total potential energy. The influences of the surface layer, size dependency, piezoelectricity, and dispersion forces are also included simultaneously. To solve the nonlinear differential equation, a numerical method is implemented and the obtained results are validated with available experimental and numerical results. Afterward, a set of parametric studies is carried out to examine the coupled effects of piezo-voltage, length/position of non-actuated pieces, nonlinear curvature, and molecular forces on the microresonators. It is found that the beam deflection and the pull-in voltage have sensitive-dependence on the system behavior. Furthermore, the beam deflection can increase or decrease with consideration of different positions of non-actuated pieces. This research is expected to fill a gap in the state of the art of the piezoelectric microstructures and present relevant results that are instrumental in the investigation of advanced actuated microdevices
IMMUNO PROTECTIVE, ANTI-DIABETIC AND HISTOCHMICAL-ANTI OXIDATIVE ACTIVITIES OF L-CARNITINE, AND CALF THYMUS EXTRACT IN AGED MALE MICE.
No full textBackground: The mechanisms behind of immunosenescence have remained largely unknown in elderly. Some studies are referred the cause to that, L-carnitine is essential nutrient factor which it is important in transporting of long chain fatty acids to mitochondrial matrix, a process essential for fatty acid oxidation and energy release. The immunobiological properties of a new formulation of the lipid thymus calf extract (CYTOIMMUNE ?) were determined. Methods: In this study, the immunomodulatingeffect of L-carnitine and calf thymus extract were studied in aged male mice. Forty mice were divided into four groups, each group included ten agedmale mice. Group I, each mice was injected intraperitoneal (I.P.) with normal saline for 7 successive days. Group II, each mice was injected I.P. with L-carnitine at dose 200 mg/kg b.wt. for 7 successive days. Group III, each mice was injected I.P. with calf thymus extract at dose 0.5 mg/kg b.wt. for 7 successive days. Group IV, each mice was injected I.P. with L-carnitine at dose 200 mg/kg b.wt. Plus calf thymus extract at dose 0.5 mg/kg b.wt. for 7 successive days. RBCs & WBCs count, PCV, differential leukocytic count, phagocytic activity, phagocytic index, total protein, globulin, albumin, interleukin2, ALT, AST& blood glucose were measured. Moreover, after slaughtering the animals , histological sections were taken from main internal organs (liver, spleen, kidney) to show internal changes of previous tissues and evaluated the protective and antioxidant properties by using the previous experimental preparations (L-carnitine at dose 200 mg/kg -b.wt. , calf thymus extract at dose 0.5 mg/kg b.wt. and combination of them). Results: Theinteraperiotoneal administration of L- carnitine at dose 200 mg/kg b.wt. calf thymus extract at dose 0.5 mg/kg b.wt. and combination between them. L-carnitine at dose 200 mg/kg b.wt.and calf thymus extract at dose 0.5 mg/kg b.wt. for 7 successive days had an improving effect on immune response, glucose level and hepatic marker enzymes as well as improving the histological architecture in the internal tissues (liver, kidney and spleen) and a significant increase in CAT(catalase enzyme), in liver and kidney. These results clearly show the antioxidant and protective property of experimental preparations
Antioxidant, anti-inflammatory, and anti-apoptotic efficacy of pomegranate molasses versus peel extract against sodium nitrate hepatotoxicity in rats
No full textThe current research was performed to compare the efficacy of pomegranate molasses (PM) versus pomegranate peel aqueous extract (PPAE) in ameliorating the hepatotoxicity of sodium nitrate. The phytochemical screenings of PM and PPAE were analyzed using GC/MS. Sixty male rats were randomly assigned to six equal groups and treated for 10 successive weeks. The control group received distilled water orally, the PM and PPAE groups were orally administered PM (0.5 ml/rat) and PPAE (100 mg/kg), respectively, the nitrate group received sodium nitrate (500 ppm) in drinking water, the Nitrate+PM and Nitrate+PPAE groups received PM and PPAE, respectively with nitrate. Sodium nitrate-intoxicated rats showed significant elevations in the activities of serum alanine aminotransferase and aspartate aminotransferase, significant increases in malondialdehyde, nitric oxide, and hydrogen peroxide levels, as well as significant decreases in reduced glutathione content and catalase activity in hepatic tissues. Moreover, sodium nitrate caused histopathological alterations in the liver, along with significant increases in the expressions of caspase-3, Bax, Bax/Bcl-2 ratio, tumor necrosis factor α, and glial fibrillary acidic protein and decrease in B-cell lymphoma-2 expression. Conversely, the concomitant administration of either PM or PPAE with sodium nitrate mitigated the biochemical, histopathological, and histochemical toxic effects induced by sodium nitrate intoxication. Accordingly, pomegranate molasses and peel extract exhibited similar protective effects against sodium nitrate-provoked hepatotoxicity, mainly via their antioxidant, anti-apoptotic, and anti-inflammatory activities
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