A general method of constructing proxy blind signature is proposed based on multilinear transform. Based on this method, the four proxy blind signature schemes are correspondently generated with four different signatu...A general method of constructing proxy blind signature is proposed based on multilinear transform. Based on this method, the four proxy blind signature schemes are correspondently generated with four different signature equations, and each of them has four forms of variations of signs. Hence there are sixteen signatures in all, and all of them are proxy stronglyblind signature schemes. Furthermore, the two degenerated situations of multi-linear transform are discussed. Their corresponding proxy blind signature schemes are shown, too. But some schemes come from one of these degenerate situations are proxy weakly-blind signature scheme.The security for proposed scheme is analyzed in details. The results indicate that these signature schemes have many good properties such as unforgeability, distinguish-ability of proxy signature,non-repudiation and extensive value of application etc.展开更多
In the present paper, a multi-linearity method is used to address the nonlinear slip control equation for the hydrodynamic analysis of a two-dimensional （2-D） slip gap flow. Numerical analysis of a finite length sli...In the present paper, a multi-linearity method is used to address the nonlinear slip control equation for the hydrodynamic analysis of a two-dimensional （2-D） slip gap flow. Numerical analysis of a finite length slider bearing with wall slip shows that the surface limiting shear stress exerts complicated influences on the hydrodynamic behavior of the gap flow. If the slip occurs at either the stationary surface or the moving surface （especially at the stationary surface）, there is a transition point in the initial limiting shear stress for the proportional coefficient to affect the hydrodynamic load support in two opposite ways： it increases the hydrodynamic load support at higher initial limiting shear stresses, but decreases the hydrodynamic load support at lower initial limiting shear stresses. If the slip occurs at the moving surface only, no fluid pressure is generated in the case of null initial limiting shear stress. If the slip occurs at both the surfaces with the same slip property, the hydrodynamic load support goes off after a critical sliding speed is reached. A small initial limiting shear stress and a small proportionality coefficient always give rise to a low friction drag.展开更多
Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled loc...Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.展开更多
The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Inst...The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.展开更多
The temporal variation of ventilation coefficient was estimated and a simple model for the prediction of urban ventilation coefficient in Changsha was developed. Firstly, Pearson correlation analysis was used to inves...The temporal variation of ventilation coefficient was estimated and a simple model for the prediction of urban ventilation coefficient in Changsha was developed. Firstly, Pearson correlation analysis was used to investigate the relationship between meteorological parameters and mixing layer height during 2005-2009 in Changsha, China. Secondly, the multi-linear regression model between daytime and nighttime was adopted to predict the temporal ventilation coefficient. Thirdly, the validation of the model between the predicted and observed ventilation coefficient in 2010 was conducted. The results showed that ventilation coefficient significantly varied and remained high during daytime, while it stayed relatively constant and low during nighttime. In addition, the diurnal ventilation coefficient was distinctly negatively correlated with PM10 (particle with the diameter less than 10 μm) concentration in Changsha, China. The predicted ventilation coefficient agreed well with the observed values based on the multi-linear regression models during daytime and nighttime. The urban temporal ventilation coefficient could be accurately predicted by some simple meteorological parameters during daytime and nighttime. The ventilation coefficient played an important role in the PM10 concentration level.展开更多
An approach is presented to optimize the surface roughness in high-speed finish milling of 7050- T7451 aeronautical aluminum alloy. In view of this, the multi-linear regression model for surface roughness has been dev...An approach is presented to optimize the surface roughness in high-speed finish milling of 7050- T7451 aeronautical aluminum alloy. In view of this, the multi-linear regression model for surface roughness has been developed in terms of slenderness ratio, cutting speed, radial depth-of-cut and feed per tooth by means of orthogonal experimental design. Variance analyses were applied to check the adequacy of the predictive model and the significances of the independent input parameters. Response contours of surface roughness were generated by using response surface methodology （RSM）. From these contours, it was possible to select an optimum combination of cutting parameters that improves machining efficiency without increasing the surface roughness.展开更多
Formaldehyde（HCHO） is one of the most important intermediate products of atmospheric photochemical reactions in the troposphere, therefore understanding of HCHO sources is essential for effective ozone control measu...Formaldehyde（HCHO） is one of the most important intermediate products of atmospheric photochemical reactions in the troposphere, therefore understanding of HCHO sources is essential for effective ozone control measures. The objective of this work is to distinguish between primary and secondary sources of HCHO. Based on about one month of online measurements in winter in Ziyang,Sichuan, the multi-linear regression analysis of ambient concentrations of HCHO and possible tracers（acetonitrile, propane and peroxyacetyl nitrate） was performed. The results show that during winter in Ziyang, biomass burning contributed an average of 53.2% to ambient HCHO levels, while secondary processes contributed about 30.1%, and vehicular sources accounted for 7.1%.展开更多
In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovative...In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure(DOD), Direction of Arrival(DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the TemporalSpatial Nested Sampling Model(TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom(DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window(TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the highdimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound(CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.展开更多
New exact quasi-periodic and non-periodic solutions for the （2＋ 1）-dimensional nonlinear systems are studied by means of the multi-linear variable separation approach （MLVSA） and the Jacobi elliptic functions wit...New exact quasi-periodic and non-periodic solutions for the （2＋ 1）-dimensional nonlinear systems are studied by means of the multi-linear variable separation approach （MLVSA） and the Jacobi elliptic functions with the space-time-dependent modulus. Though the result is valid for all the MLVSA solvable models, it is explicitly shown for the long-wave and short-wave interaction model.展开更多
With respect to groundwater deterioration from human activities a unique situation of co-disposal of non-engineered Municipal Solid Waste (MSW) dumping and Secondary Wastewater (SWW) disposal on land prevails simultan...With respect to groundwater deterioration from human activities a unique situation of co-disposal of non-engineered Municipal Solid Waste (MSW) dumping and Secondary Wastewater (SWW) disposal on land prevails simultaneously within the same campus at Puducherry in India. Broadly the objective of the study is to apply and compare Artificial Neural Network (ANN) and Multi Linear Regression (MLR) models on groundwater quality applying Canadian Water Quality Index (CWQI). Totally, 1065 water samples from 68 bore wells were collected for two years on monthly basis and tested for 17 physio-chemical and bacteriological parameters. However the study was restricted to the pollution aspects of 10 physio-chemical parameters such as EC, TDS, TH, , Cl-, , Na+, Ca2+, Mg2+ and K+. As there is wide spatial variation (2 to 3 km radius) with ground elevation (more than 45 m) among the bore wells it is appropriate to study the groundwater quality using Multivariate Statistical Analysis and ANN. The selected ten parameters were subjected to Hierarchical Cluster Analysis (HCA) and the clustering procedure generated three well defined clusters. Cluster wise important physio-chemical attributes which were altered by MSW and SWW operations, are statistically assessed. The CWQI was evolved with the objective to deliver a mechanism for interpreting the water quality data for all three clusters. The ANOVA test results viz., F-statistic (F = 134.55) and p-value (p = 0.000 2, low RMSE and MAE values but in Cluster 3 only ANN model fared well. Thus this study will be very useful to decision makers in solving water quality problems.展开更多
Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these...Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these challenges,a novel hybrid model,combining both linear regression and machine learning techniques,is proposed in this study.The hybrid model,MLR-LSTM-FFNN,captures both temporal and non-linear de-pendencies in load data by integrating multi-linear regression(MLR)with long short-term memory(LSTM)networks and feed-forward neural networks(FFNN).Using datasets from Qatar,with 5 min,15 min,30 min,and 1 h time intervals and from Panama City with a 1 h interval,experiments were conducted to thoroughly test the robustness of the model.The results showed that the MLR-LSTM-FFNN hybrid model outperformed the baseline and state-of-the-art hybrid models for each of the datasets,in terms of lower RMSE,MAE,and MAPE values along with a faster training time.This superior performance across different datasets underscores the model’s scal-ability and reliability as an STLF approach,providing a practical solution to energy demand prediction tasks.The improvement in short-term forecasting accuracy provides utilities with a practical tool to optimize demand-side management,reduce operational costs,and enhance grid reliability.展开更多
Due to the extremely low permeability of shale formations,the combination of horizontal well and volume fracturing has been proposed as an effective technique to improve the production of Dagang continental shale oil ...Due to the extremely low permeability of shale formations,the combination of horizontal well and volume fracturing has been proposed as an effective technique to improve the production of Dagang continental shale oil reservoirs.Based on the flow material balance method(FMB)and straight-line analysis(SLA)method,the stimulated reservoir volume(SRV)and drainage volume are determined to identify the flow regimes of the seepage mechanism of shale oil reservoirs.To resolve the challenges of multi-scaled flow regimes and bottom hole pressure(BHP)variation before and after pumping in shale oil wells,a multi-linear analytical flow model was established to predict the future production and the final expected ultimate recoverable oil(EURo)after fitting the historical production dynamics.Based on the results,it can be concluded that the flow regime of a shale oil well in production can be divided into two stages consisting of linear flow within SRV and composite flow from the un-stimulated area to SRV.The effects of fracturing operation parameters,such as fracturing fluid volume and sand/liquid ratio,on shale oil productivity,are analyzed,and insightful suggestions are drawn for the future development of this pay zone.展开更多
Based on Chameleon Hash and D.Boneh’s one round multi-party key agreement protocol,this paper proposes a multi-designated verifiers signature scheme.In this scheme only the verifiers designated by the signer can inde...Based on Chameleon Hash and D.Boneh’s one round multi-party key agreement protocol,this paper proposes a multi-designated verifiers signature scheme.In this scheme only the verifiers designated by the signer can independently verify the signature.And no one else other than the designated person can be convinced by this signature even if one of the designated verifiers reveals the secret value.The analysis of the proposed scheme shows that it satisfies non-transferability,unforgeability and privacy of the signer’s identity and has to low computational cost.展开更多
基金Supported by the Fundamental Research Program of Commission of Science Technology and Industry for National Defence (No.J1300D004)
文摘A general method of constructing proxy blind signature is proposed based on multilinear transform. Based on this method, the four proxy blind signature schemes are correspondently generated with four different signature equations, and each of them has four forms of variations of signs. Hence there are sixteen signatures in all, and all of them are proxy stronglyblind signature schemes. Furthermore, the two degenerated situations of multi-linear transform are discussed. Their corresponding proxy blind signature schemes are shown, too. But some schemes come from one of these degenerate situations are proxy weakly-blind signature scheme.The security for proposed scheme is analyzed in details. The results indicate that these signature schemes have many good properties such as unforgeability, distinguish-ability of proxy signature,non-repudiation and extensive value of application etc.
基金the National Natural Science Foundation of China(10421002,10332010)the National Basic Research Program of China(2006CB601205)the Science Research Foundation of Liaoning Province(20052178).
文摘In the present paper, a multi-linearity method is used to address the nonlinear slip control equation for the hydrodynamic analysis of a two-dimensional （2-D） slip gap flow. Numerical analysis of a finite length slider bearing with wall slip shows that the surface limiting shear stress exerts complicated influences on the hydrodynamic behavior of the gap flow. If the slip occurs at either the stationary surface or the moving surface （especially at the stationary surface）, there is a transition point in the initial limiting shear stress for the proportional coefficient to affect the hydrodynamic load support in two opposite ways： it increases the hydrodynamic load support at higher initial limiting shear stresses, but decreases the hydrodynamic load support at lower initial limiting shear stresses. If the slip occurs at the moving surface only, no fluid pressure is generated in the case of null initial limiting shear stress. If the slip occurs at both the surfaces with the same slip property, the hydrodynamic load support goes off after a critical sliding speed is reached. A small initial limiting shear stress and a small proportionality coefficient always give rise to a low friction drag.
文摘Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.
基金sponsored by the National Natural Science Foundation of P.R.China(Nos.62102194 and 62102196)Six Talent Peaks Project of Jiangsu Province(No.RJFW-111)Postgraduate Research and Practice Innovation Program of Jiangsu Province(Nos.KYCX23_1087 and KYCX22_1027).
文摘The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.
基金Project(51178466) supported by the National Natural Science Foundation of ChinaProject(FANEDD200545) supported by Foundation for the Author of National Excellent Doctoral Dissertation of ChinaProject(2011JQ006) supported by Fundamental Research Funds of the Central Universities of China
文摘The temporal variation of ventilation coefficient was estimated and a simple model for the prediction of urban ventilation coefficient in Changsha was developed. Firstly, Pearson correlation analysis was used to investigate the relationship between meteorological parameters and mixing layer height during 2005-2009 in Changsha, China. Secondly, the multi-linear regression model between daytime and nighttime was adopted to predict the temporal ventilation coefficient. Thirdly, the validation of the model between the predicted and observed ventilation coefficient in 2010 was conducted. The results showed that ventilation coefficient significantly varied and remained high during daytime, while it stayed relatively constant and low during nighttime. In addition, the diurnal ventilation coefficient was distinctly negatively correlated with PM10 (particle with the diameter less than 10 μm) concentration in Changsha, China. The predicted ventilation coefficient agreed well with the observed values based on the multi-linear regression models during daytime and nighttime. The urban temporal ventilation coefficient could be accurately predicted by some simple meteorological parameters during daytime and nighttime. The ventilation coefficient played an important role in the PM10 concentration level.
基金Sponsored by the National Natural Science Foundation of China (50575126)the Foundation for the Author of National Excellent Doctoral Dis-sertation of China (200231)
文摘An approach is presented to optimize the surface roughness in high-speed finish milling of 7050- T7451 aeronautical aluminum alloy. In view of this, the multi-linear regression model for surface roughness has been developed in terms of slenderness ratio, cutting speed, radial depth-of-cut and feed per tooth by means of orthogonal experimental design. Variance analyses were applied to check the adequacy of the predictive model and the significances of the independent input parameters. Response contours of surface roughness were generated by using response surface methodology （RSM）. From these contours, it was possible to select an optimum combination of cutting parameters that improves machining efficiency without increasing the surface roughness.
文摘Formaldehyde（HCHO） is one of the most important intermediate products of atmospheric photochemical reactions in the troposphere, therefore understanding of HCHO sources is essential for effective ozone control measures. The objective of this work is to distinguish between primary and secondary sources of HCHO. Based on about one month of online measurements in winter in Ziyang,Sichuan, the multi-linear regression analysis of ambient concentrations of HCHO and possible tracers（acetonitrile, propane and peroxyacetyl nitrate） was performed. The results show that during winter in Ziyang, biomass burning contributed an average of 53.2% to ambient HCHO levels, while secondary processes contributed about 30.1%, and vehicular sources accounted for 7.1%.
基金supported in part by the National Natural Science Foundation of China(No.62071476)in part by China Postdoctoral Science Foundation(No.2022M723879)in part by the Science and Technology Innovation Program of Hunan Province,China(No.2021RC3080)。
文摘In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure(DOD), Direction of Arrival(DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the TemporalSpatial Nested Sampling Model(TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom(DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window(TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the highdimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound(CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.
基金The project supported by National Natural Science Foundation of China under Grant Nos. 90203001 and 10475055 Acknowledgment The authors are indebt to the discussions with Dr H.C. Hu
文摘New exact quasi-periodic and non-periodic solutions for the （2＋ 1）-dimensional nonlinear systems are studied by means of the multi-linear variable separation approach （MLVSA） and the Jacobi elliptic functions with the space-time-dependent modulus. Though the result is valid for all the MLVSA solvable models, it is explicitly shown for the long-wave and short-wave interaction model.
文摘With respect to groundwater deterioration from human activities a unique situation of co-disposal of non-engineered Municipal Solid Waste (MSW) dumping and Secondary Wastewater (SWW) disposal on land prevails simultaneously within the same campus at Puducherry in India. Broadly the objective of the study is to apply and compare Artificial Neural Network (ANN) and Multi Linear Regression (MLR) models on groundwater quality applying Canadian Water Quality Index (CWQI). Totally, 1065 water samples from 68 bore wells were collected for two years on monthly basis and tested for 17 physio-chemical and bacteriological parameters. However the study was restricted to the pollution aspects of 10 physio-chemical parameters such as EC, TDS, TH, , Cl-, , Na+, Ca2+, Mg2+ and K+. As there is wide spatial variation (2 to 3 km radius) with ground elevation (more than 45 m) among the bore wells it is appropriate to study the groundwater quality using Multivariate Statistical Analysis and ANN. The selected ten parameters were subjected to Hierarchical Cluster Analysis (HCA) and the clustering procedure generated three well defined clusters. Cluster wise important physio-chemical attributes which were altered by MSW and SWW operations, are statistically assessed. The CWQI was evolved with the objective to deliver a mechanism for interpreting the water quality data for all three clusters. The ANOVA test results viz., F-statistic (F = 134.55) and p-value (p = 0.000 2, low RMSE and MAE values but in Cluster 3 only ANN model fared well. Thus this study will be very useful to decision makers in solving water quality problems.
基金support from the Qatar National Research Fund through grant AICC05-0508-230001(Solar Trade(ST):An Equitable and Efficient Blockchain-Enabled Renewable Energy Ecosystem-“Oppor-tunities for Fintech to Scale up Green Finance for Clean Energy”)and from Qatar Environment and Energy Research Institute is gratefully acknowledged.
文摘Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these challenges,a novel hybrid model,combining both linear regression and machine learning techniques,is proposed in this study.The hybrid model,MLR-LSTM-FFNN,captures both temporal and non-linear de-pendencies in load data by integrating multi-linear regression(MLR)with long short-term memory(LSTM)networks and feed-forward neural networks(FFNN).Using datasets from Qatar,with 5 min,15 min,30 min,and 1 h time intervals and from Panama City with a 1 h interval,experiments were conducted to thoroughly test the robustness of the model.The results showed that the MLR-LSTM-FFNN hybrid model outperformed the baseline and state-of-the-art hybrid models for each of the datasets,in terms of lower RMSE,MAE,and MAPE values along with a faster training time.This superior performance across different datasets underscores the model’s scal-ability and reliability as an STLF approach,providing a practical solution to energy demand prediction tasks.The improvement in short-term forecasting accuracy provides utilities with a practical tool to optimize demand-side management,reduce operational costs,and enhance grid reliability.
基金supported by Beijing Municipal Natural Science Foundation (No.2214077)。
文摘Due to the extremely low permeability of shale formations,the combination of horizontal well and volume fracturing has been proposed as an effective technique to improve the production of Dagang continental shale oil reservoirs.Based on the flow material balance method(FMB)and straight-line analysis(SLA)method,the stimulated reservoir volume(SRV)and drainage volume are determined to identify the flow regimes of the seepage mechanism of shale oil reservoirs.To resolve the challenges of multi-scaled flow regimes and bottom hole pressure(BHP)variation before and after pumping in shale oil wells,a multi-linear analytical flow model was established to predict the future production and the final expected ultimate recoverable oil(EURo)after fitting the historical production dynamics.Based on the results,it can be concluded that the flow regime of a shale oil well in production can be divided into two stages consisting of linear flow within SRV and composite flow from the un-stimulated area to SRV.The effects of fracturing operation parameters,such as fracturing fluid volume and sand/liquid ratio,on shale oil productivity,are analyzed,and insightful suggestions are drawn for the future development of this pay zone.
基金supported by the National Basic Research Program of China (No.2007CB311203)the National Natural Science Foundation of China (Grant No.90604022).
文摘Based on Chameleon Hash and D.Boneh’s one round multi-party key agreement protocol,this paper proposes a multi-designated verifiers signature scheme.In this scheme only the verifiers designated by the signer can independently verify the signature.And no one else other than the designated person can be convinced by this signature even if one of the designated verifiers reveals the secret value.The analysis of the proposed scheme shows that it satisfies non-transferability,unforgeability and privacy of the signer’s identity and has to low computational cost.
