The effects of Sr additions on the microstructure and corrosion performance of a Mg-Al-RE alloy in 3.5 wt.%Na Cl saturated with Mg(OH)_(2)have been investigated.Microstructure examination reveals that the Sr addition ...The effects of Sr additions on the microstructure and corrosion performance of a Mg-Al-RE alloy in 3.5 wt.%Na Cl saturated with Mg(OH)_(2)have been investigated.Microstructure examination reveals that the Sr addition introduces additional intermetallic phases,refines intermetallic networks and dendritic grains,and improves the network continuity.More Al and rare earth elements can be identified in the intermetallics and grain boundaries or inter-dendrite regions under a transmission electron microscope and secondary electron microscope,respectively.On the Sr-containing intermetallic phases and the refined microstructure,the oxide films become more protective,resulting in more corrosion resistant boundary areas and thus dendrite grain grooves.Hence,the presence of large amounts of intermetallics and boundaries can enhance the corrosion performance of the Mg-Al-RE alloy containing Sr.展开更多
Single event effects (SEEs) in a 28-nm system-on-chip (SoC) were assessed using heavy ion irradiations, and susceptibilities in different processor configurations with data accessing patterns were investigated. The pa...Single event effects (SEEs) in a 28-nm system-on-chip (SoC) were assessed using heavy ion irradiations, and susceptibilities in different processor configurations with data accessing patterns were investigated. The patterns included the sole processor (SP) and asymmetric multiprocessing (AMP) patterns with static and dynamic data accessing. Single event upset (SEU) cross sections in static accessing can be more than twice as high as those of the dynamic accessing, and processor configuration pattern is not a critical factor for the SEU cross sections. Cross section interval of upset events was evaluated and the soft error rates in aerospace environment were predicted for the SoC. The tests also indicated that ultra-high linear energy transfer (LET) particle can cause exception currents in the 28-nm SoC, and some even are lower than the normal case.展开更多
A derivative of thiazole（AAT） has been explored as a sensing material for preparation a selective Lu（III） PVC-based membrane sensor.The proposed sensor exhibits a Nernstian response over a wide concentration range...A derivative of thiazole（AAT） has been explored as a sensing material for preparation a selective Lu（III） PVC-based membrane sensor.The proposed sensor exhibits a Nernstian response over a wide concentration range from 1.0×10^（-6） to 1.0×10^（-1） mol/L of Lu（Ⅲ） and the detection limit is 5.7×10^（-7） mol/L.The sensor response is independent of pH of the solution in the range 3.2-8.8 and possesses the advantages of fast response time（～6） and in particular,good selectivity to the lutetium ions with regard to most common metal ions,and especially all lanthanide ions.展开更多
Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consump...Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.展开更多
The improvement on the calculation of anti-Stokes energy transfer rate is studied in the present work. The additional proportion coefficient between Stokes and anti-Stokes light intensities of quantum Raman scattering...The improvement on the calculation of anti-Stokes energy transfer rate is studied in the present work. The additional proportion coefficient between Stokes and anti-Stokes light intensities of quantum Raman scattering theory as compared with the classical Raman theory is introduced to successfully describe the anti-Stokes energy transfer. The theoretical formula for the improvement on the calculation of anti-Stokes energy transfer rate is derived for the first time in this study. The correctness of introducing coefficient exp{△E/kT} from well-known Raman scatter theory is demonstrated also. Moreover, the experimental lifetime measurement in Er0.01YbxY1-0.01-xVO4 crystal is performed to justify the validity of our important improvement in the original phonon-assisted energy transfer theory for the first time.展开更多
For precise and accurate patient dose delivery,the dosimetry system must be calibrated properly according to the recommendations of standard dosimetry protocols such as TG-51 and TRS-398. However, the dosimetry protoc...For precise and accurate patient dose delivery,the dosimetry system must be calibrated properly according to the recommendations of standard dosimetry protocols such as TG-51 and TRS-398. However, the dosimetry protocol followed by a calibration laboratory is usually different from the protocols that are followed by different clinics, which may result in variations in the patient dose.Our prime objective in this study was to investigate the effect of the two protocols on dosimetry measurements.Dose measurements were performed for a Co-60 teletherapy unit and a high-energy Varian linear accelerator with 6 and 15 MV photon and 6, 9, 12, and 15 MeV electron beams, following the recommendations and procedures of the AAPM TG-51 and IAEA TRS-398 dosimetry protocols. The dosimetry systems used for this study were calibrated in a Co-60 radiation beam at the Secondary Standard Dosimetry Laboratory(SSDL) PINSTECH,Pakistan, following the IAEA TRS-398 protocol. The ratio of the measured absorbed doses to water in clinical setting,D_w(TG-51/TRS-398), was 0.999 and 0.997 for 6 and15 MV photon beams,whereas these ratios were 1.013,1.009, 1.003, and 1.000 for 6, 9, 12, and 15 MeV electron beams, respectively. This difference in the absorbed dosesto-water D_w ratio may be attributed mainly due to beam quality(K_Q) and ion recombination correction factor.展开更多
This study investigates the data requirements of generative artificial intelligence(AI),particularly generative adversarial networks(GANs),for reliable data augmentation in energy applications.Generative AI,though see...This study investigates the data requirements of generative artificial intelligence(AI),particularly generative adversarial networks(GANs),for reliable data augmentation in energy applications.Generative AI,though seen as a solution to data limitations,requires substantial data to learn meaningful distributions—a challenge often overlooked.This study addresses the challenge through synthetic data generation for critical heat flux(CHF)and power grid demand,focusing on renewable and nuclear energy.Two variants of GAN employed are conditional GAN(cGAN)and Wasserstein GAN(wGAN).Our findings include the strong dependency of GAN on data size,with performance declining on smaller datasets and varying performance when generalizing to unseen experiments.Mass flux and heated length significantly influence CHF predictions.wGAN is more robust to feature exclusion,making it suitable for constrained synthetic data generation.In energy demand forecasting,wGAN performed well for solar,wind,and load predictions.Longer lookback hours and larger datasets improved predictions,especially for load power.Seasonal variations posed challenges,with wGAN achieving a relatively high error of Root Mean Squared Error(RMSE)of 0.32 for load power prediction,compared to RMSE of 0.07 under same-season conditions.Feature exclusions impacted cGAN the most,while wGAN showed greater robustness.This study concludes that,while generative AI is effective for data augmentation,it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.展开更多
Although machine learning(ML)has emerged as a powerful tool for rapidly assessing grid contingencies,prior studies have largely considered a static grid topology in their analyses.This limits their application,since t...Although machine learning(ML)has emerged as a powerful tool for rapidly assessing grid contingencies,prior studies have largely considered a static grid topology in their analyses.This limits their application,since they need to be re-trained for every new topology.This paper explores the development of generalizable graph convolutional network(GCN)models by pre-training them across a wide range of grid topologies and contingency types.We found that a GCN model with auto-regressive moving average(ARMA)layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes(VM)and voltage angles(VA).We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines.For pre-training the GCN ARMA model across a variety of topologies,distributed graphics processing unit(GPU)computing afforded us significant training scalability.The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current(DC)approximation.Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory,fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance.In the context of foundational models in ML,this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types.展开更多
文摘The effects of Sr additions on the microstructure and corrosion performance of a Mg-Al-RE alloy in 3.5 wt.%Na Cl saturated with Mg(OH)_(2)have been investigated.Microstructure examination reveals that the Sr addition introduces additional intermetallic phases,refines intermetallic networks and dendritic grains,and improves the network continuity.More Al and rare earth elements can be identified in the intermetallics and grain boundaries or inter-dendrite regions under a transmission electron microscope and secondary electron microscope,respectively.On the Sr-containing intermetallic phases and the refined microstructure,the oxide films become more protective,resulting in more corrosion resistant boundary areas and thus dendrite grain grooves.Hence,the presence of large amounts of intermetallics and boundaries can enhance the corrosion performance of the Mg-Al-RE alloy containing Sr.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11575138,11835006,11690040,and 11690043)the Fund from Innovation Center of Radiation Application(Grant No.KFZC2019050321)+1 种基金the Fund from the Science and Technology on Vacuum Technology and Physics Laboratory,Lanzhou Institute of Physics(Grant No.ZWK1804)the Program of China Scholarships Council(Grant No.201906280343)。
文摘Single event effects (SEEs) in a 28-nm system-on-chip (SoC) were assessed using heavy ion irradiations, and susceptibilities in different processor configurations with data accessing patterns were investigated. The patterns included the sole processor (SP) and asymmetric multiprocessing (AMP) patterns with static and dynamic data accessing. Single event upset (SEU) cross sections in static accessing can be more than twice as high as those of the dynamic accessing, and processor configuration pattern is not a critical factor for the SEU cross sections. Cross section interval of upset events was evaluated and the soft error rates in aerospace environment were predicted for the SoC. The tests also indicated that ultra-high linear energy transfer (LET) particle can cause exception currents in the 28-nm SoC, and some even are lower than the normal case.
基金NFCRS,Nuclear Science&Technology Research Institute(Tehran,Iran)for their financial support
文摘A derivative of thiazole（AAT） has been explored as a sensing material for preparation a selective Lu（III） PVC-based membrane sensor.The proposed sensor exhibits a Nernstian response over a wide concentration range from 1.0×10^（-6） to 1.0×10^（-1） mol/L of Lu（Ⅲ） and the detection limit is 5.7×10^（-7） mol/L.The sensor response is independent of pH of the solution in the range 3.2-8.8 and possesses the advantages of fast response time（～6） and in particular,good selectivity to the lutetium ions with regard to most common metal ions,and especially all lanthanide ions.
文摘Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.
基金supported by the National Natural Science Foundation of China (Grant No.10674019)
文摘The improvement on the calculation of anti-Stokes energy transfer rate is studied in the present work. The additional proportion coefficient between Stokes and anti-Stokes light intensities of quantum Raman scattering theory as compared with the classical Raman theory is introduced to successfully describe the anti-Stokes energy transfer. The theoretical formula for the improvement on the calculation of anti-Stokes energy transfer rate is derived for the first time in this study. The correctness of introducing coefficient exp{△E/kT} from well-known Raman scatter theory is demonstrated also. Moreover, the experimental lifetime measurement in Er0.01YbxY1-0.01-xVO4 crystal is performed to justify the validity of our important improvement in the original phonon-assisted energy transfer theory for the first time.
文摘For precise and accurate patient dose delivery,the dosimetry system must be calibrated properly according to the recommendations of standard dosimetry protocols such as TG-51 and TRS-398. However, the dosimetry protocol followed by a calibration laboratory is usually different from the protocols that are followed by different clinics, which may result in variations in the patient dose.Our prime objective in this study was to investigate the effect of the two protocols on dosimetry measurements.Dose measurements were performed for a Co-60 teletherapy unit and a high-energy Varian linear accelerator with 6 and 15 MV photon and 6, 9, 12, and 15 MeV electron beams, following the recommendations and procedures of the AAPM TG-51 and IAEA TRS-398 dosimetry protocols. The dosimetry systems used for this study were calibrated in a Co-60 radiation beam at the Secondary Standard Dosimetry Laboratory(SSDL) PINSTECH,Pakistan, following the IAEA TRS-398 protocol. The ratio of the measured absorbed doses to water in clinical setting,D_w(TG-51/TRS-398), was 0.999 and 0.997 for 6 and15 MV photon beams,whereas these ratios were 1.013,1.009, 1.003, and 1.000 for 6, 9, 12, and 15 MeV electron beams, respectively. This difference in the absorbed dosesto-water D_w ratio may be attributed mainly due to beam quality(K_Q) and ion recombination correction factor.
基金supported through Idaho National Laboratory,United States’s Laboratory Directed Research and Development(LDRD)Program Award Number(24A1081-116FP)under Department of Energy Idaho Operations Office contract no.DE-AC07-05ID14517.
文摘This study investigates the data requirements of generative artificial intelligence(AI),particularly generative adversarial networks(GANs),for reliable data augmentation in energy applications.Generative AI,though seen as a solution to data limitations,requires substantial data to learn meaningful distributions—a challenge often overlooked.This study addresses the challenge through synthetic data generation for critical heat flux(CHF)and power grid demand,focusing on renewable and nuclear energy.Two variants of GAN employed are conditional GAN(cGAN)and Wasserstein GAN(wGAN).Our findings include the strong dependency of GAN on data size,with performance declining on smaller datasets and varying performance when generalizing to unseen experiments.Mass flux and heated length significantly influence CHF predictions.wGAN is more robust to feature exclusion,making it suitable for constrained synthetic data generation.In energy demand forecasting,wGAN performed well for solar,wind,and load predictions.Longer lookback hours and larger datasets improved predictions,especially for load power.Seasonal variations posed challenges,with wGAN achieving a relatively high error of Root Mean Squared Error(RMSE)of 0.32 for load power prediction,compared to RMSE of 0.07 under same-season conditions.Feature exclusions impacted cGAN the most,while wGAN showed greater robustness.This study concludes that,while generative AI is effective for data augmentation,it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.
基金supported through the INL Laboratory Directed Research&Development(LDRD)Program under DOE Idaho Operations Office Contract DE-AC07-05ID14517This research made use of the resources of the High-Performance Computing Center at INL,which is supported by the U.S.Department of Energy’s Office of Nuclear Energy and the Nuclear Science User Facilities under contract no.DE-AC07-05ID14517.
文摘Although machine learning(ML)has emerged as a powerful tool for rapidly assessing grid contingencies,prior studies have largely considered a static grid topology in their analyses.This limits their application,since they need to be re-trained for every new topology.This paper explores the development of generalizable graph convolutional network(GCN)models by pre-training them across a wide range of grid topologies and contingency types.We found that a GCN model with auto-regressive moving average(ARMA)layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes(VM)and voltage angles(VA).We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines.For pre-training the GCN ARMA model across a variety of topologies,distributed graphics processing unit(GPU)computing afforded us significant training scalability.The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current(DC)approximation.Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory,fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance.In the context of foundational models in ML,this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types.
