Lithium iron phosphate batteries have been increasingly utilized in recent years because their higher safety performance can improve the increasing trend of recurring thermal runaway accidents.However,the safety perfo...Lithium iron phosphate batteries have been increasingly utilized in recent years because their higher safety performance can improve the increasing trend of recurring thermal runaway accidents.However,the safety performance and mechanism of high-capacity lithium iron phosphate batteries under internal short-circuit challenges remain to be explored.This work analyzes the thermal runaway evolution of high-capacity LiFePO_(4) batteries under different internal heat transfer modes,which are controlled by different penetration modes.Two penetration cases involving complete penetration and incomplete penetration were detected during the test,and two modes were performed incorporating nails that either remained or were removed after penetration to comprehensively reveal the thermal runaway mechanism.A theoretical model of microcircuits and internal heat conduction is also established.The results indicated three thermal runaway evolution processes for high-capacity batteries,which corresponded to the experimental results of thermal equilibrium,single thermal runaway,and two thermal runaway events.The difference in heat distribution in the three phenomena is determined based on the microstructure and material structure near the pinhole.By controlling the heat dissipation conditions,the time interval between two thermal runaway events can be delayed from 558 to 1417 s,accompanied by a decrease in the concentration of in-situ gas production during the second thermal runaway event.展开更多
The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current re...The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.展开更多
This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset.Initially,a large source dataset(Bandgap dataset)comprising approximately∼7...This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset.Initially,a large source dataset(Bandgap dataset)comprising approximately∼75k compounds is utilized for pretraining,followed by fine-tuning with a smaller Critical Temperature(T_(c))dataset containing∼300 compounds.Comparatively,there is a significant improvement in the performance of the transfer learning model over the traditional deep learning(DL)model in predicting Tc.Subsequently,the transfer learning model is applied to predict the properties of approximately 150k compounds.Predictions are validated computationally using density functional theory(DFT)calculations based on lattice dynamics-related theory.Moreover,to demonstrate the extended predictive capability of the transfer learning model for new materials,a pool of virtual compounds derived from prototype crystal structures from the Materials Project(MP)database is generated.T_(c) predictions are obtained for∼3600 virtual compounds,which underwent screening for electroneutrality and thermodynamic stability.An Extra Trees-based model is trained to utilize E_(hull)values to obtain thermodynamically stable materials,employing a dataset containing Ehull values for approximately 150k materials for training.Materials with Ehull values exceeding 5 meV/atom were filtered out,resulting in a refined list of potential Mg-based superconductors.This study showcases the effectiveness of transfer learning in predicting superconducting properties and highlights its potential for accelerating the discovery of Mg-based materials in the field of superconductivity.展开更多
Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,w...Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.展开更多
Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a ...Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a task susceptible to human error. The application of Deep Transfer Learning (DTL) for the identification of pneumonia through chest X-rays is hindered by a shortage of available images, which has led to less than optimal DTL performance and issues with overfitting. Overfitting is characterized by a model’s learning that is too closely fitted to the training data, reducing its effectiveness on unseen data. The problem of overfitting is especially prevalent in medical image processing due to the high costs and extensive time required for image annotation, as well as the challenge of collecting substantial datasets that also respect patient privacy concerning infectious diseases such as pneumonia. To mitigate these challenges, this paper introduces the use of conditional generative adversarial networks (CGAN) to enrich the pneumonia dataset with 2690 synthesized X-ray images of the minority class, aiming to even out the dataset distribution for improved diagnostic performance. Subsequently, we applied four modified lightweight deep transfer learning models such as Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been fine-tuned and evaluated, demonstrating remarkable detection accuracies of 99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The experimental results validate that the models we have proposed achieve high detection accuracy rates, with the best model reaching up to 99.26% effectiveness, outperforming other models in the diagnosis of pneumonia from X-ray images.展开更多
Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-re...Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels.This study introduces a fast and accurate RT model for the hyperspectral infrared(HIR)sounder based on principal component analysis(PCA)or machine learning(i.e.,neural network,NN).The Geosynchronous Interferometric Infrared Sounder(GIIRS),the first HIR sounder onboard the geostationary Fengyun-4 satellites,is considered to be a candidate example for model development and validation.Our method uses either PCA or NN(PCA/NN)twice for the atmospheric transmittance and radiance,respectively,to reduce the number of independent but similar simulations to accelerate RT simulations;thereby,it is referred to as a multi-domain compression model.The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently.The second PCA/NN is performed in the traditional spectral radiance domain.Meanwhile,a new method is introduced to choose representative variables for the PCA/NN scheme developments.The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference(BTD)less than 0.1 K,and the compressions based on PCA or NN methods result in comparable efficiency and accuracy.Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions.展开更多
Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to ...Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to the temperature difference between the fluids and the surroundings. Heat transfer analysis is very important for the prediction and prevention of deposits in oil and water flowlines, which could impede the flow and give rise to huge financial losses. Therefore, a 3D mathematical model of oil-water Newtonian flow under non-isothermal conditions is established to explore the complex mechanisms of the two-phase oil-water transportation and heat transfer in different flowline inclinations. In this work, a non-isothermal two-phase flow model is first modified and then implemented in the InterFoam solver by introducing the energy equation using OpenFOAM® code. The Low Reynolds Number (LRN) k-ε turbulence model is utilized to resolve the turbulence phenomena within the oil and water mixtures. The flow patterns and the local heat transfer coefficients (HTC) for two-phase oil-water flow at different flowlines inclinations (0°, +4°, +7°) are validated by the experimental literature results and the relative errors are also compared. Global sensitivity analysis is then conducted to determine the effect of the different parameters on the performance of the produced two-phase hydrocarbon systems for effective subsea fluid transportation. Thereafter, HTC and flow patterns for oil-water flows at downward inclinations of 4°, and 7° can be predicted by the models. The velocity distribution, pressure gradient, liquid holdup, and temperature variation at the flowline cross-sections are simulated and analyzed in detail. Consequently, the numerical model can be generally applied to compute the global properties of the fluid and other operating parameters that are beneficial in the management of two-phase oil-water transportation.展开更多
Resonance effects in parallel jointed rocks subject to stress waves are investigated using transfer functions,derived from signals generated through numerical modelling.Resonance is important for a range of engineerin...Resonance effects in parallel jointed rocks subject to stress waves are investigated using transfer functions,derived from signals generated through numerical modelling.Resonance is important for a range of engineering situations as it identifies the frequency of waves which will be favourably transmitted.Two different numerical methods are used for this study,adopting the finite difference method and the combined discrete element-finite difference method.The numerical models are validated by replicating results from previous studies.The two methods are found to behave similarly and show the same resonance effects;one operating at low frequency and the other operating at relatively high frequency.These resonance effects are interpreted in terms of simple physical systems and analytical equations are derived to predict the resonant frequencies of complex rock masses.Low frequency resonance is shown to be generated by a system synonymous with masses between springs,described as spring resonance,with an equal number of resonant frequencies as the number of blocks.High frequency resonance is generated through superposition of multiple reflected waves developing standing waves within intact blocks,described as superposition resonance.While resonance through superposition has previously been identified,resonance based on masses between springs has not been previously identified in jointed rocks.The findings of this study have implications for future analysis of multiple jointed rock masses,showing that a wave travelling through such materials can induce other modes of propagation of waves,i.e.spring resonance.展开更多
The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster ...The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.展开更多
TheMoon is the only celestial body that human beings have visited.The design of the Earth-Moon transfer orbits is a critical issue in lunar exploration missions.In the 21st century,new lunar missions including the con...TheMoon is the only celestial body that human beings have visited.The design of the Earth-Moon transfer orbits is a critical issue in lunar exploration missions.In the 21st century,new lunar missions including the construction of the lunar space station,the permanent lunar base,and the Earth-Moon transportation network have been proposed,requiring low-cost,expansive launch windows and a fixed arrival epoch for any launch date within the launch window.The low-energy and low-thrust transfers are promising strategies to satisfy the demands.This review provides a detailed landscape of Earth-Moon transfer trajectory design processes,from the traditional patched conic to the state-of-the-art low-energy and low-thrust methods.Essential mechanisms of the various utilized dynamic models and the characteristics of the different design methods are discussed in hopes of helping readers grasp thebasic overviewof the current Earth-Moon transfer orbitdesignmethods anda deep academic background is unnecessary for the context understanding.展开更多
This research focused on the study of heat and mass transfers in a two-phase stratified turbulent fluid flow in a geothermal pipe with chemical reaction. The derived non-linear partial differential equations governing...This research focused on the study of heat and mass transfers in a two-phase stratified turbulent fluid flow in a geothermal pipe with chemical reaction. The derived non-linear partial differential equations governing the flow were solved using the Finite Difference Method. The effects of various physical parameters on the concentration, skin friction, heat, and mass transfers have been determined. Analysis of the results obtained indicated that the coefficient of skin friction decreased with an increase in Reynolds number and solutal Grasholf number, the rate of heat transfer increased with an increase in Eckert number, Prandtl number, and angle of inclination, and the rate of mass transfer increased with increase in Reynolds number, Chemical reaction parameter and angle of inclination. The findings would be useful to engineers in designing and maintaining geothermal pipelines more effectively.展开更多
This study is to understand the impact of operating conditions, especially initial operation temperature (T<sub>ini</sub>) which is set in a high temperature range, on the temperature profile of the interf...This study is to understand the impact of operating conditions, especially initial operation temperature (T<sub>ini</sub>) which is set in a high temperature range, on the temperature profile of the interface between the polymer electrolyte membrane (PEM) and the catalyst layer at the cathode (i.e., the reaction surface) in a single cell of polymer electrolyte fuel cell (PEFC). A 1D multi-plate heat transfer model based on the temperature data of the separator measured using the thermograph in a power generation experiment was developed to evaluate the reaction surface temperature (T<sub>react</sub>). In addition, to validate the proposed heat transfer model, T<sub>react</sub> obtained from the model was compared with that from the 3D numerical simulation using CFD software COMSOL Multiphysics which solves the continuity equation, Brinkman equation, Maxwell-Stefan equation, Butler-Volmer equation as well as heat transfer equation. As a result, the temperature gap between the results obtained by 1D heat transfer model and those obtained by 3D numerical simulation is below approximately 0.5 K. The simulation results show the change in the molar concentration of O<sub>2</sub> and H<sub>2</sub>O from the inlet to the outlet is more even with the increase in T<sub>ini</sub> due to the lower performance of O<sub>2</sub> reduction reaction. The change in the current density from the inlet to the outlet is more even with the increase in T<sub>ini</sub> and the value of current density is smaller with the increase in T<sub>ini </sub>due to the increase in ohmic over-potential and concentration over-potential. It is revealed that the change in T<sub>react</sub> from the inlet to the outlet is more even with the increase in T<sub>ini</sub> irrespective of heat transfer model. This is because the generated heat from the power generation is lower with the increase in T<sub>ini </sub>due to the lower performance of O<sub>2</sub> reduction reaction.展开更多
Mutations in mitochondrial DNA(mtDNA)are maternally inherited and have the potential to cause severe disorders.Mitochondrial replacement therapies,including spindle,polar body,and pronuclear transfers,are promising st...Mutations in mitochondrial DNA(mtDNA)are maternally inherited and have the potential to cause severe disorders.Mitochondrial replacement therapies,including spindle,polar body,and pronuclear transfers,are promising strategies for preventing the hereditary transmission of mtDNA diseases.While pronuclear transfer has been used to generate mitochondrial replacement mouse models and human embryos,its application in non-human primates has not been previously reported.In this study,we successfully generated four healthy cynomolgus monkeys(Macaca fascicularis)via female pronuclear transfer.These individuals all survived for more than two years and exhibited minimal mtDNA carryover(3.8%–6.7%),as well as relatively stable mtDNA heteroplasmy dynamics during development.The successful establishment of this nonhuman primate model highlights the considerable potential of pronuclear transfer in reducing the risk of inherited mtDNA diseases and provides a valuable preclinical research model for advancing mitochondrial replacement therapies in humans.展开更多
We propose a scheme for realizing high-fidelity topological state transfer via the topological edge states in a onedimensional cavity-magnon system.It is found that the cavity-magnon system can be mapped analytically ...We propose a scheme for realizing high-fidelity topological state transfer via the topological edge states in a onedimensional cavity-magnon system.It is found that the cavity-magnon system can be mapped analytically into the generalized Su-Schrieffer-Heeger model with tunable cavity-magnon coupling.It is shown that the edge state can be served as a quantum channel to realize the photonic and magnonic state transfers by adjusting the coupling strength between adjacent cavity modes.Further,our scheme can realize the quantum state transfer between photonic state and magnonic state by changing the cavity-magnon coupling strength.With the numerical simulation,we quantitatively show that the photonic,magnonic and magnon-to-photon state transfers can be achieved with high fidelity in the cavity-magnon system.Spectacularly,three different types of quantum state transfer schemes can be even transformed into each other in a controllable fashion.The Su-Schrieffer-Heeger model based on the cavity-magnon system provides us a tunable platform to engineer the transport of photon and magnon,which may have potential applications in topological quantum processing.展开更多
The kidney is an important organ of humans to purify the blood.The healthy function of the kidney is always essential to balance the salt,potassium and pH levels in the blood.Recently,the failure of kidneys happens ea...The kidney is an important organ of humans to purify the blood.The healthy function of the kidney is always essential to balance the salt,potassium and pH levels in the blood.Recently,the failure of kidneys happens easily to human beings due to their lifestyle,eating habits and diabetes diseases.Early pre-diction of kidney stones is compulsory for timely treatment.Image processing-based diagnosis approaches provide a greater success rate than other detection approaches.In this work,proposed a kidney stone classification method based on optimized Transfer Learning(TL).The Deep Convolutional Neural Network(DCNN)models of DenseNet169,MobileNetv2 and GoogleNet applied for clas-sification.The combined classification results are processed by ensemble learning to increase classification performance.The hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer(GTO).The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.展开更多
The forecasting research literature has developed greatly in recent years as a result of advances in information technology. Financial time-series tasks have made substantial use of machine learning and deep neural ne...The forecasting research literature has developed greatly in recent years as a result of advances in information technology. Financial time-series tasks have made substantial use of machine learning and deep neural networks, but building a prediction model from scratch takes time and computational resources. Transfer learning is growing popular in tackling these constraints of training time and computational resources in several disciplines. This study proposes a hybrid base model for the financial time series prediction employing the recurrent neural network (RNN) and long-short term memory (LSTM) called RNN-LSTM. We used random search to fine-tune the hyperparameters and compared our proposed model to the RNN and LSTM base models and evaluate using the RMSE, MAE, and MAPE metrics. When forecasting Forex currency pairs GBP/USD, USD/ZAR, and AUD/NZD our proposed base model for transfer learning outperforms RNN and LSTM base model with root mean squared errors of 0.007656, 0.165250, and 0.001730 respectively.展开更多
Interbasin water-transfer schemes provide an engineering solution for reconciling the conflict between water demand and availability.In the context of climate change,which brings great uncertainties to water resource ...Interbasin water-transfer schemes provide an engineering solution for reconciling the conflict between water demand and availability.In the context of climate change,which brings great uncertainties to water resource distribution,interbasin water transfer plays an increasingly important role in the global water–food–energy nexus.However,the transfer of water resources simultaneously changes the hydrological regime and the characteristics of local water bodies,affecting biotic communities accordingly.Compared with high economic and technical inputs water-transfer projects require,the environmental and ecological implications of water-transfer schemes have been inadequately addressed.This work selects the largest water-transfer project in China,the South-to-North Water Diversion(SNWD)Project,to critically review its eco-environmental impacts on donor and recipient basins,as well as on regions along the diversion route.The two operated routes of the SNWD Project represent two typical water diversion approaches:The Middle Route uses an excavated canal,while the East Route connects existent river channels.An overview of the eco-environmental implications of these two routes is valuable for the design and optimization of future water-transfer megaprojects.展开更多
The regulation of the burning rate pressure exponent for the ammonium perchlorate/hydroxylterminated polybutadiene/aluminum(AP/HTPB/Al)composite propellants under high pressures is a crucial step for its application i...The regulation of the burning rate pressure exponent for the ammonium perchlorate/hydroxylterminated polybutadiene/aluminum(AP/HTPB/Al)composite propellants under high pressures is a crucial step for its application in high-pressure solid rocket motors.In this work,the combustion characteristics of AP/HTPB/Al composite propellants containing ferrocene-based catalysts were investigated,including the burning rate,thermal behavior,the local heat transfer,and temperature profile in the range of 7-28 MPa.The results showed that the exponent breaks were still observed in the propellants after the addition of positive catalysts(Ce-Fc-MOF),the burning rate inhibitor((Ferrocenylmethyl)trimethylammonium bromide,Fc Br)and the mixture of Fc Br/catocene(GFP).However,the characteristic pressure has increased,and the exponent decreased from 1.14 to 0.66,0.55,and 0.48 when the addition of Ce-FcMOF,Fc Br and Fc Br/GFP in the propellants.In addition,the temperature in the first decomposition stage was increased by 7.50℃ and 11.40℃ for the AP/Fc Br mixture and the AP/Fc Br/GFP mixture,respectively,compared to the pure AP.On the other hand,the temperature in the second decomposition stage decreased by 48.30℃ and 81.70℃ for AP/Fc Br and AP/Fc Br/GFP mixtures,respectively.It was also found that Fc Br might generate ammonia to cover the AP surface.In this case,a reaction between the methyl in Fc Br and perchloric acid caused more ammonia to appear at the AP surface,resulting in the suppression of ammonia desorption.In addition,the coarse AP particles on the quenched surface were of a concave shape relative to the binder matrix under low and high pressures when the catalysts were added.In the process,the decline at the AP/HTPB interface was only exhibited in the propellant with the addition of Ce-Fc-MOF.The ratio of the gas-phase temperature gradient of the propellants containing catalysts was reduced significantly below and above the characteristic pressure,rather than 3.6 times of the difference in the blank propellant.Overall,the obtained results demonstrated that the pressure exponent could be effectively regulated and controlled by adjusting the propellant local heat and mass transfer under high and low pressures.展开更多
基金supported by the National Key R&D Program of China(2021YFB2402001)the China National Postdoctoral Program for Innovative Talents(BX20220286)+1 种基金the China Postdoctoral Science Foundation(2022T150615)supported by the Youth Innovation Promotion Association CAS(Y201768)。
文摘Lithium iron phosphate batteries have been increasingly utilized in recent years because their higher safety performance can improve the increasing trend of recurring thermal runaway accidents.However,the safety performance and mechanism of high-capacity lithium iron phosphate batteries under internal short-circuit challenges remain to be explored.This work analyzes the thermal runaway evolution of high-capacity LiFePO_(4) batteries under different internal heat transfer modes,which are controlled by different penetration modes.Two penetration cases involving complete penetration and incomplete penetration were detected during the test,and two modes were performed incorporating nails that either remained or were removed after penetration to comprehensively reveal the thermal runaway mechanism.A theoretical model of microcircuits and internal heat conduction is also established.The results indicated three thermal runaway evolution processes for high-capacity batteries,which corresponded to the experimental results of thermal equilibrium,single thermal runaway,and two thermal runaway events.The difference in heat distribution in the three phenomena is determined based on the microstructure and material structure near the pinhole.By controlling the heat dissipation conditions,the time interval between two thermal runaway events can be delayed from 558 to 1417 s,accompanied by a decrease in the concentration of in-situ gas production during the second thermal runaway event.
基金supported by the National Social Science Fund of China(23BGL272)。
文摘The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.
文摘This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset.Initially,a large source dataset(Bandgap dataset)comprising approximately∼75k compounds is utilized for pretraining,followed by fine-tuning with a smaller Critical Temperature(T_(c))dataset containing∼300 compounds.Comparatively,there is a significant improvement in the performance of the transfer learning model over the traditional deep learning(DL)model in predicting Tc.Subsequently,the transfer learning model is applied to predict the properties of approximately 150k compounds.Predictions are validated computationally using density functional theory(DFT)calculations based on lattice dynamics-related theory.Moreover,to demonstrate the extended predictive capability of the transfer learning model for new materials,a pool of virtual compounds derived from prototype crystal structures from the Materials Project(MP)database is generated.T_(c) predictions are obtained for∼3600 virtual compounds,which underwent screening for electroneutrality and thermodynamic stability.An Extra Trees-based model is trained to utilize E_(hull)values to obtain thermodynamically stable materials,employing a dataset containing Ehull values for approximately 150k materials for training.Materials with Ehull values exceeding 5 meV/atom were filtered out,resulting in a refined list of potential Mg-based superconductors.This study showcases the effectiveness of transfer learning in predicting superconducting properties and highlights its potential for accelerating the discovery of Mg-based materials in the field of superconductivity.
基金via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.
文摘Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a task susceptible to human error. The application of Deep Transfer Learning (DTL) for the identification of pneumonia through chest X-rays is hindered by a shortage of available images, which has led to less than optimal DTL performance and issues with overfitting. Overfitting is characterized by a model’s learning that is too closely fitted to the training data, reducing its effectiveness on unseen data. The problem of overfitting is especially prevalent in medical image processing due to the high costs and extensive time required for image annotation, as well as the challenge of collecting substantial datasets that also respect patient privacy concerning infectious diseases such as pneumonia. To mitigate these challenges, this paper introduces the use of conditional generative adversarial networks (CGAN) to enrich the pneumonia dataset with 2690 synthesized X-ray images of the minority class, aiming to even out the dataset distribution for improved diagnostic performance. Subsequently, we applied four modified lightweight deep transfer learning models such as Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been fine-tuned and evaluated, demonstrating remarkable detection accuracies of 99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The experimental results validate that the models we have proposed achieve high detection accuracy rates, with the best model reaching up to 99.26% effectiveness, outperforming other models in the diagnosis of pneumonia from X-ray images.
基金supported by the National Natural Science Foundation of China(Grant No.42122038)。
文摘Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels.This study introduces a fast and accurate RT model for the hyperspectral infrared(HIR)sounder based on principal component analysis(PCA)or machine learning(i.e.,neural network,NN).The Geosynchronous Interferometric Infrared Sounder(GIIRS),the first HIR sounder onboard the geostationary Fengyun-4 satellites,is considered to be a candidate example for model development and validation.Our method uses either PCA or NN(PCA/NN)twice for the atmospheric transmittance and radiance,respectively,to reduce the number of independent but similar simulations to accelerate RT simulations;thereby,it is referred to as a multi-domain compression model.The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently.The second PCA/NN is performed in the traditional spectral radiance domain.Meanwhile,a new method is introduced to choose representative variables for the PCA/NN scheme developments.The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference(BTD)less than 0.1 K,and the compressions based on PCA or NN methods result in comparable efficiency and accuracy.Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions.
文摘Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to the temperature difference between the fluids and the surroundings. Heat transfer analysis is very important for the prediction and prevention of deposits in oil and water flowlines, which could impede the flow and give rise to huge financial losses. Therefore, a 3D mathematical model of oil-water Newtonian flow under non-isothermal conditions is established to explore the complex mechanisms of the two-phase oil-water transportation and heat transfer in different flowline inclinations. In this work, a non-isothermal two-phase flow model is first modified and then implemented in the InterFoam solver by introducing the energy equation using OpenFOAM® code. The Low Reynolds Number (LRN) k-ε turbulence model is utilized to resolve the turbulence phenomena within the oil and water mixtures. The flow patterns and the local heat transfer coefficients (HTC) for two-phase oil-water flow at different flowlines inclinations (0°, +4°, +7°) are validated by the experimental literature results and the relative errors are also compared. Global sensitivity analysis is then conducted to determine the effect of the different parameters on the performance of the produced two-phase hydrocarbon systems for effective subsea fluid transportation. Thereafter, HTC and flow patterns for oil-water flows at downward inclinations of 4°, and 7° can be predicted by the models. The velocity distribution, pressure gradient, liquid holdup, and temperature variation at the flowline cross-sections are simulated and analyzed in detail. Consequently, the numerical model can be generally applied to compute the global properties of the fluid and other operating parameters that are beneficial in the management of two-phase oil-water transportation.
基金supported by the Engineering and Physical Sciences Research Council(EPSRC)(EP/R513258/1).
文摘Resonance effects in parallel jointed rocks subject to stress waves are investigated using transfer functions,derived from signals generated through numerical modelling.Resonance is important for a range of engineering situations as it identifies the frequency of waves which will be favourably transmitted.Two different numerical methods are used for this study,adopting the finite difference method and the combined discrete element-finite difference method.The numerical models are validated by replicating results from previous studies.The two methods are found to behave similarly and show the same resonance effects;one operating at low frequency and the other operating at relatively high frequency.These resonance effects are interpreted in terms of simple physical systems and analytical equations are derived to predict the resonant frequencies of complex rock masses.Low frequency resonance is shown to be generated by a system synonymous with masses between springs,described as spring resonance,with an equal number of resonant frequencies as the number of blocks.High frequency resonance is generated through superposition of multiple reflected waves developing standing waves within intact blocks,described as superposition resonance.While resonance through superposition has previously been identified,resonance based on masses between springs has not been previously identified in jointed rocks.The findings of this study have implications for future analysis of multiple jointed rock masses,showing that a wave travelling through such materials can induce other modes of propagation of waves,i.e.spring resonance.
基金the National Key R&D Program of China(No.2021YFC2900500).
文摘The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.
基金supported by the National Key Research and Development Program of China(No.2021YFA0717100)the National Natural Science Foundation of China(Nos.12072270 and U2013206).
文摘TheMoon is the only celestial body that human beings have visited.The design of the Earth-Moon transfer orbits is a critical issue in lunar exploration missions.In the 21st century,new lunar missions including the construction of the lunar space station,the permanent lunar base,and the Earth-Moon transportation network have been proposed,requiring low-cost,expansive launch windows and a fixed arrival epoch for any launch date within the launch window.The low-energy and low-thrust transfers are promising strategies to satisfy the demands.This review provides a detailed landscape of Earth-Moon transfer trajectory design processes,from the traditional patched conic to the state-of-the-art low-energy and low-thrust methods.Essential mechanisms of the various utilized dynamic models and the characteristics of the different design methods are discussed in hopes of helping readers grasp thebasic overviewof the current Earth-Moon transfer orbitdesignmethods anda deep academic background is unnecessary for the context understanding.
文摘This research focused on the study of heat and mass transfers in a two-phase stratified turbulent fluid flow in a geothermal pipe with chemical reaction. The derived non-linear partial differential equations governing the flow were solved using the Finite Difference Method. The effects of various physical parameters on the concentration, skin friction, heat, and mass transfers have been determined. Analysis of the results obtained indicated that the coefficient of skin friction decreased with an increase in Reynolds number and solutal Grasholf number, the rate of heat transfer increased with an increase in Eckert number, Prandtl number, and angle of inclination, and the rate of mass transfer increased with increase in Reynolds number, Chemical reaction parameter and angle of inclination. The findings would be useful to engineers in designing and maintaining geothermal pipelines more effectively.
文摘This study is to understand the impact of operating conditions, especially initial operation temperature (T<sub>ini</sub>) which is set in a high temperature range, on the temperature profile of the interface between the polymer electrolyte membrane (PEM) and the catalyst layer at the cathode (i.e., the reaction surface) in a single cell of polymer electrolyte fuel cell (PEFC). A 1D multi-plate heat transfer model based on the temperature data of the separator measured using the thermograph in a power generation experiment was developed to evaluate the reaction surface temperature (T<sub>react</sub>). In addition, to validate the proposed heat transfer model, T<sub>react</sub> obtained from the model was compared with that from the 3D numerical simulation using CFD software COMSOL Multiphysics which solves the continuity equation, Brinkman equation, Maxwell-Stefan equation, Butler-Volmer equation as well as heat transfer equation. As a result, the temperature gap between the results obtained by 1D heat transfer model and those obtained by 3D numerical simulation is below approximately 0.5 K. The simulation results show the change in the molar concentration of O<sub>2</sub> and H<sub>2</sub>O from the inlet to the outlet is more even with the increase in T<sub>ini</sub> due to the lower performance of O<sub>2</sub> reduction reaction. The change in the current density from the inlet to the outlet is more even with the increase in T<sub>ini</sub> and the value of current density is smaller with the increase in T<sub>ini </sub>due to the increase in ohmic over-potential and concentration over-potential. It is revealed that the change in T<sub>react</sub> from the inlet to the outlet is more even with the increase in T<sub>ini</sub> irrespective of heat transfer model. This is because the generated heat from the power generation is lower with the increase in T<sub>ini </sub>due to the lower performance of O<sub>2</sub> reduction reaction.
基金supported by the National Natural Science Foundation of China (82021001,31825018)National Key Research and Development Program of China (2022YFF0710901)+3 种基金Shanghai Municipal Science and Technology Major Project (2018SHZDZX05)Strategic Priority Research Program of the Chinese Academy of Sciences (XDB32060100)Biological Resources Program of Chinese Academy of Sciences (KFJ-BRP-005)National Science and Technology Innovation 2030 Major Program 2021ZD0200900。
文摘Mutations in mitochondrial DNA(mtDNA)are maternally inherited and have the potential to cause severe disorders.Mitochondrial replacement therapies,including spindle,polar body,and pronuclear transfers,are promising strategies for preventing the hereditary transmission of mtDNA diseases.While pronuclear transfer has been used to generate mitochondrial replacement mouse models and human embryos,its application in non-human primates has not been previously reported.In this study,we successfully generated four healthy cynomolgus monkeys(Macaca fascicularis)via female pronuclear transfer.These individuals all survived for more than two years and exhibited minimal mtDNA carryover(3.8%–6.7%),as well as relatively stable mtDNA heteroplasmy dynamics during development.The successful establishment of this nonhuman primate model highlights the considerable potential of pronuclear transfer in reducing the risk of inherited mtDNA diseases and provides a valuable preclinical research model for advancing mitochondrial replacement therapies in humans.
基金supported by the National Natural Science Foundation of China(Grant Nos.11874190,61835013,and 12047501)the Supercomputing Center of Lanzhou University。
文摘We propose a scheme for realizing high-fidelity topological state transfer via the topological edge states in a onedimensional cavity-magnon system.It is found that the cavity-magnon system can be mapped analytically into the generalized Su-Schrieffer-Heeger model with tunable cavity-magnon coupling.It is shown that the edge state can be served as a quantum channel to realize the photonic and magnonic state transfers by adjusting the coupling strength between adjacent cavity modes.Further,our scheme can realize the quantum state transfer between photonic state and magnonic state by changing the cavity-magnon coupling strength.With the numerical simulation,we quantitatively show that the photonic,magnonic and magnon-to-photon state transfers can be achieved with high fidelity in the cavity-magnon system.Spectacularly,three different types of quantum state transfer schemes can be even transformed into each other in a controllable fashion.The Su-Schrieffer-Heeger model based on the cavity-magnon system provides us a tunable platform to engineer the transport of photon and magnon,which may have potential applications in topological quantum processing.
文摘The kidney is an important organ of humans to purify the blood.The healthy function of the kidney is always essential to balance the salt,potassium and pH levels in the blood.Recently,the failure of kidneys happens easily to human beings due to their lifestyle,eating habits and diabetes diseases.Early pre-diction of kidney stones is compulsory for timely treatment.Image processing-based diagnosis approaches provide a greater success rate than other detection approaches.In this work,proposed a kidney stone classification method based on optimized Transfer Learning(TL).The Deep Convolutional Neural Network(DCNN)models of DenseNet169,MobileNetv2 and GoogleNet applied for clas-sification.The combined classification results are processed by ensemble learning to increase classification performance.The hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer(GTO).The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.
文摘The forecasting research literature has developed greatly in recent years as a result of advances in information technology. Financial time-series tasks have made substantial use of machine learning and deep neural networks, but building a prediction model from scratch takes time and computational resources. Transfer learning is growing popular in tackling these constraints of training time and computational resources in several disciplines. This study proposes a hybrid base model for the financial time series prediction employing the recurrent neural network (RNN) and long-short term memory (LSTM) called RNN-LSTM. We used random search to fine-tune the hyperparameters and compared our proposed model to the RNN and LSTM base models and evaluate using the RMSE, MAE, and MAPE metrics. When forecasting Forex currency pairs GBP/USD, USD/ZAR, and AUD/NZD our proposed base model for transfer learning outperforms RNN and LSTM base model with root mean squared errors of 0.007656, 0.165250, and 0.001730 respectively.
基金supported by National Key Science and Technology Program of China(2022YFC3203802)National Natural Science Foundation of China(52121006,92047303,and 51879165)+1 种基金supported by the Xplorer Prizesupported by the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(2021QNRC001)。
文摘Interbasin water-transfer schemes provide an engineering solution for reconciling the conflict between water demand and availability.In the context of climate change,which brings great uncertainties to water resource distribution,interbasin water transfer plays an increasingly important role in the global water–food–energy nexus.However,the transfer of water resources simultaneously changes the hydrological regime and the characteristics of local water bodies,affecting biotic communities accordingly.Compared with high economic and technical inputs water-transfer projects require,the environmental and ecological implications of water-transfer schemes have been inadequately addressed.This work selects the largest water-transfer project in China,the South-to-North Water Diversion(SNWD)Project,to critically review its eco-environmental impacts on donor and recipient basins,as well as on regions along the diversion route.The two operated routes of the SNWD Project represent two typical water diversion approaches:The Middle Route uses an excavated canal,while the East Route connects existent river channels.An overview of the eco-environmental implications of these two routes is valuable for the design and optimization of future water-transfer megaprojects.
基金the support of the National Natural Science Foundation of China grant number 51776175。
文摘The regulation of the burning rate pressure exponent for the ammonium perchlorate/hydroxylterminated polybutadiene/aluminum(AP/HTPB/Al)composite propellants under high pressures is a crucial step for its application in high-pressure solid rocket motors.In this work,the combustion characteristics of AP/HTPB/Al composite propellants containing ferrocene-based catalysts were investigated,including the burning rate,thermal behavior,the local heat transfer,and temperature profile in the range of 7-28 MPa.The results showed that the exponent breaks were still observed in the propellants after the addition of positive catalysts(Ce-Fc-MOF),the burning rate inhibitor((Ferrocenylmethyl)trimethylammonium bromide,Fc Br)and the mixture of Fc Br/catocene(GFP).However,the characteristic pressure has increased,and the exponent decreased from 1.14 to 0.66,0.55,and 0.48 when the addition of Ce-FcMOF,Fc Br and Fc Br/GFP in the propellants.In addition,the temperature in the first decomposition stage was increased by 7.50℃ and 11.40℃ for the AP/Fc Br mixture and the AP/Fc Br/GFP mixture,respectively,compared to the pure AP.On the other hand,the temperature in the second decomposition stage decreased by 48.30℃ and 81.70℃ for AP/Fc Br and AP/Fc Br/GFP mixtures,respectively.It was also found that Fc Br might generate ammonia to cover the AP surface.In this case,a reaction between the methyl in Fc Br and perchloric acid caused more ammonia to appear at the AP surface,resulting in the suppression of ammonia desorption.In addition,the coarse AP particles on the quenched surface were of a concave shape relative to the binder matrix under low and high pressures when the catalysts were added.In the process,the decline at the AP/HTPB interface was only exhibited in the propellant with the addition of Ce-Fc-MOF.The ratio of the gas-phase temperature gradient of the propellants containing catalysts was reduced significantly below and above the characteristic pressure,rather than 3.6 times of the difference in the blank propellant.Overall,the obtained results demonstrated that the pressure exponent could be effectively regulated and controlled by adjusting the propellant local heat and mass transfer under high and low pressures.