The interaction between waves and currents in the ocean often complicates the flow field around structures.In this study,a three-dimensional integrated numerical model was established to investigate the seabed respons...The interaction between waves and currents in the ocean often complicates the flow field around structures.In this study,a three-dimensional integrated numerical model was established to investigate the seabed response and liquefaction around a mono-pile under different wave-current interaction angles.In the present model,the Reynolds-averaged Navier-Stokes equations were used to simulate the flow field,and the Biot's poro-elastic theory was adopted to calculate the seabed response caused by crossing wave-current loading.Unlike previous studies,the load on the mono-pile was considered,and the wave-current interaction angle was extended to 180°,which was more in line with practical engineering problems.The numerical results were in a good agreement with the experimental measurements.The results indicated that waves interacted with currents in a large angle could result in a large momentary liquefaction depth of the seabed.The parametric studies proved that the position of the front and two sides of the pile was relatively safer compared with that of the leeside of the pile,and the surface of the seabed downstream of the pile was liable to liquefy.展开更多
Pile foundations have been widely used in offshore engineering.In this study,a three-dimensional numerical model was used to investigate the seabed response around a mono-pile under wave-current loading.Reynolds-avera...Pile foundations have been widely used in offshore engineering.In this study,a three-dimensional numerical model was used to investigate the seabed response around a mono-pile under wave-current loading.Reynolds-averaged Navier-Stokes equations were used to simulate the flow field,and Biot's consolidation equations were used for simulating the response of a porous seabed.The pore water pressure within soil and the effective stress along the depth of the seabed were simulated for various current velocities,with currents traveling either along or against the wave.Results indicate that the current has a significant effect on the effective stress and the pore water pressure distributions,which increases with the current velocity,and that the current traveling against the wave increases the liquefaction depth of the porous seabed.展开更多
Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock marke...Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.展开更多
Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player analytics.Player modeling refers to descriptions of players based on frameworks of data deri...Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player analytics.Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game.Player behavior focuses on dynamic and static information gathered at the time of gameplay.Player experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings.In this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral devices.Long Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer learning.Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems.The homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’engagement.Relevant not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.展开更多
Cerebral Microbleeds(CMBs)are microhemorrhages caused by certain abnormalities of brain vessels.CMBs can be found in people with Traumatic Brain Injury(TBI),Alzheimer’s disease,and in old individuals having a brain i...Cerebral Microbleeds(CMBs)are microhemorrhages caused by certain abnormalities of brain vessels.CMBs can be found in people with Traumatic Brain Injury(TBI),Alzheimer’s disease,and in old individuals having a brain injury.Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke.The CMBs seriously impact individuals’life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life.The existing work report good results but often ignores false-positive’s perspective for this research area.In this paper,an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images(SWI).The proposed framework consists of four main phases(i)making clusters of brain Magnetic Resonance Imaging(MRI)using k-mean classifier(ii)reduce false positives for better classification results(iii)discriminative feature extraction specific to CMBs(iv)classification using a five layers convolutional neural network(CNN).The proposed method is evaluated on a public dataset available for 20 subjects.The proposed system shows an accuracy of 98.9%and a 1.1%false-positive rate value.The results show the superiority of the proposed work as compared to existing states of the art methods.展开更多
Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so...Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so on.SA has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source tools.The current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and classification.The proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible format.Moreover,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments.Furthermore,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.展开更多
Ultra-high-performance seawater sea-sand concrete(UHPSSC)presents a prospective solution to address the natural resource shortage in marine infrastructure construction.To eliminate the corrosion risk of steel fibers a...Ultra-high-performance seawater sea-sand concrete(UHPSSC)presents a prospective solution to address the natural resource shortage in marine infrastructure construction.To eliminate the corrosion risk of steel fibers and broaden the applicability of UHPSSC,this study investigates the mechanical properties and free chloride ion content as well as microstructures of UHPSSC reinforced with superfine stainless wires(SSWs)under natural curing.The results indicate that 1.5%SSWs can remarkably improve the flexural strength and toughness of UHPSSC by 127%and 1724%,respectively,and mitigate the long-term strength degradation of UHPSSC.The strong interfacial bond between SSW and UHPSSC improves the compactness of UHPSSC,thus reducing the growth space for Ca(OH)_(2) crystals and swelling hydration products generated by sulfate and magnesium ions.This can be supported by the observed reduction in the Ca/Si ratio of C–S–H gels,CH crystal orientation index,and porosity.Moreover,through mechanisms such as pull-out,rupture,overlapping network,and internal anchor interface,SSWs effectively prevent microcrack growth and propagation,transforming single long-connected microcracks into multiple-emission microcracks centered on SSW.Additionally,the free chloride ion content of the composites at 28 and 180 d meets the ACI 318-19 standard requirements for concrete exposed to seawater.This compliance is attributed to the chloride immobilization facilitated by Friedel’s salt and C–S–H gels within the interfaces around SSWs and sea-sand.Consequently,SSWs-reinforced UHPSSC exhibits considerable potential for applications in sustainable marine infrastructures,demanding long-term mechanical properties and high durability.展开更多
The self-excited stick-slip oscillations of oilwell drillstrings are attributed to the nonlinear interaction between the drill-bit and the rock formation.Development of more accurate models will lead to improved predi...The self-excited stick-slip oscillations of oilwell drillstrings are attributed to the nonlinear interaction between the drill-bit and the rock formation.Development of more accurate models will lead to improved predictions allowing more potential for successful suppression of the drillstring vibrations,thus reducing damage to the drilling system,prevention of expensive failures and increased output from the oilwell.In this paper,the effect of the transition from static friction to Coulomb friction on modelling of stick-slip phenomenon of oil well drill string is investigated through an analysis of the so called‘decay factor’.Based on a distributed-lumped parameter model(DLPM)of the drilling system,the governing equations of motion for the system are obtained.By using different values of decay factor(low,high and medium),the stick-slip vibrations of the drill string are validated against published data from full-scale drill strings.The results from the simulation show that lowering the decay factor increases the critical speed and thus reduces the propensity for stick slip motion.However,a reduction in the decay factor also has the effect of inducing worse stick-slip motion once the critical speed has been reached.The results indicate the wider impact of both correct modelling of the decay factor,but also the importance of correct characterisation of the mud viscosity and drill/well contact for more accurate selection of drilling parameters in the field.展开更多
基金supported by the Key Project of NSFC-Shangdong Joint Research Funding POW3C(Grant No.U1906230).
文摘The interaction between waves and currents in the ocean often complicates the flow field around structures.In this study,a three-dimensional integrated numerical model was established to investigate the seabed response and liquefaction around a mono-pile under different wave-current interaction angles.In the present model,the Reynolds-averaged Navier-Stokes equations were used to simulate the flow field,and the Biot's poro-elastic theory was adopted to calculate the seabed response caused by crossing wave-current loading.Unlike previous studies,the load on the mono-pile was considered,and the wave-current interaction angle was extended to 180°,which was more in line with practical engineering problems.The numerical results were in a good agreement with the experimental measurements.The results indicated that waves interacted with currents in a large angle could result in a large momentary liquefaction depth of the seabed.The parametric studies proved that the position of the front and two sides of the pile was relatively safer compared with that of the leeside of the pile,and the surface of the seabed downstream of the pile was liable to liquefy.
基金supported by the National Key Research and Development Program of China(Grant No.2017YFC1404200)the National Natural Science Foundation of China(Grant No.51479053)the Marine Renewable Energy Research Project of the State Oceanic Administration(Grant No.GHME2015GC01).
文摘Pile foundations have been widely used in offshore engineering.In this study,a three-dimensional numerical model was used to investigate the seabed response around a mono-pile under wave-current loading.Reynolds-averaged Navier-Stokes equations were used to simulate the flow field,and Biot's consolidation equations were used for simulating the response of a porous seabed.The pore water pressure within soil and the effective stress along the depth of the seabed were simulated for various current velocities,with currents traveling either along or against the wave.Results indicate that the current has a significant effect on the effective stress and the pore water pressure distributions,which increases with the current velocity,and that the current traveling against the wave increases the liquefaction depth of the porous seabed.
基金supported by X-mind Corps program of National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(No.2019H1D8A1105622)the Soonchunhyang University Research Fund.
文摘Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.
基金This study was supported by the BK21 FOUR project(AI-driven Convergence Software Education Research Program)funded by the Ministry of Education,School of Computer Science and Engineering,Kyungpook National University,Korea(4199990214394).This work was also supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea Government(MSIT)under Grant 2017-0-00053(A Technology Development of Artificial Intelligence Doctors for Cardiovascular Disease).
文摘Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player analytics.Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game.Player behavior focuses on dynamic and static information gathered at the time of gameplay.Player experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings.In this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral devices.Long Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer learning.Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems.The homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’engagement.Relevant not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.
基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2019R1F1A1058715).
文摘Cerebral Microbleeds(CMBs)are microhemorrhages caused by certain abnormalities of brain vessels.CMBs can be found in people with Traumatic Brain Injury(TBI),Alzheimer’s disease,and in old individuals having a brain injury.Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke.The CMBs seriously impact individuals’life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life.The existing work report good results but often ignores false-positive’s perspective for this research area.In this paper,an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images(SWI).The proposed framework consists of four main phases(i)making clusters of brain Magnetic Resonance Imaging(MRI)using k-mean classifier(ii)reduce false positives for better classification results(iii)discriminative feature extraction specific to CMBs(iv)classification using a five layers convolutional neural network(CNN).The proposed method is evaluated on a public dataset available for 20 subjects.The proposed system shows an accuracy of 98.9%and a 1.1%false-positive rate value.The results show the superiority of the proposed work as compared to existing states of the art methods.
基金We acknowledge Taif University for Supporting this study through Taif University Researchers Supporting Project number(TURSP-2020/173)Taif University,Taif,Saudi Arabia.
文摘Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so on.SA has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source tools.The current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and classification.The proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible format.Moreover,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments.Furthermore,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.
基金supported by the National Natural Science Foundation of China(Grant Nos.52178188 and 52308236)the Natural Science Joint Foundation of Liaoning Province(Grant No.2023-BSBA-077)+1 种基金the Provincial-Municipal Joint Fund(Youth Fund)of Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515110437)the Major Science and Technology Research Project of the China Building Materials Federation(Grant No.2023JBGS10-02).
文摘Ultra-high-performance seawater sea-sand concrete(UHPSSC)presents a prospective solution to address the natural resource shortage in marine infrastructure construction.To eliminate the corrosion risk of steel fibers and broaden the applicability of UHPSSC,this study investigates the mechanical properties and free chloride ion content as well as microstructures of UHPSSC reinforced with superfine stainless wires(SSWs)under natural curing.The results indicate that 1.5%SSWs can remarkably improve the flexural strength and toughness of UHPSSC by 127%and 1724%,respectively,and mitigate the long-term strength degradation of UHPSSC.The strong interfacial bond between SSW and UHPSSC improves the compactness of UHPSSC,thus reducing the growth space for Ca(OH)_(2) crystals and swelling hydration products generated by sulfate and magnesium ions.This can be supported by the observed reduction in the Ca/Si ratio of C–S–H gels,CH crystal orientation index,and porosity.Moreover,through mechanisms such as pull-out,rupture,overlapping network,and internal anchor interface,SSWs effectively prevent microcrack growth and propagation,transforming single long-connected microcracks into multiple-emission microcracks centered on SSW.Additionally,the free chloride ion content of the composites at 28 and 180 d meets the ACI 318-19 standard requirements for concrete exposed to seawater.This compliance is attributed to the chloride immobilization facilitated by Friedel’s salt and C–S–H gels within the interfaces around SSWs and sea-sand.Consequently,SSWs-reinforced UHPSSC exhibits considerable potential for applications in sustainable marine infrastructures,demanding long-term mechanical properties and high durability.
文摘The self-excited stick-slip oscillations of oilwell drillstrings are attributed to the nonlinear interaction between the drill-bit and the rock formation.Development of more accurate models will lead to improved predictions allowing more potential for successful suppression of the drillstring vibrations,thus reducing damage to the drilling system,prevention of expensive failures and increased output from the oilwell.In this paper,the effect of the transition from static friction to Coulomb friction on modelling of stick-slip phenomenon of oil well drill string is investigated through an analysis of the so called‘decay factor’.Based on a distributed-lumped parameter model(DLPM)of the drilling system,the governing equations of motion for the system are obtained.By using different values of decay factor(low,high and medium),the stick-slip vibrations of the drill string are validated against published data from full-scale drill strings.The results from the simulation show that lowering the decay factor increases the critical speed and thus reduces the propensity for stick slip motion.However,a reduction in the decay factor also has the effect of inducing worse stick-slip motion once the critical speed has been reached.The results indicate the wider impact of both correct modelling of the decay factor,but also the importance of correct characterisation of the mud viscosity and drill/well contact for more accurate selection of drilling parameters in the field.
