According to the China Association of Automobile Manufacturers (CAAM),China's auto industry reached record highs in 2025,with production and sales at 34.53 million and 34.4 million vehicles,respectively.This secur...According to the China Association of Automobile Manufacturers (CAAM),China's auto industry reached record highs in 2025,with production and sales at 34.53 million and 34.4 million vehicles,respectively.This secured China's position as the world's largest auto market for the 17th year in a row.展开更多
In this research study,magnesium-aluminum(Mg-Al)bimetallic oxide powders are synthesized via the sol-gel auto combustion method using diethanolamine(DEA)as the fuel.In order to subsequently determine the influence of ...In this research study,magnesium-aluminum(Mg-Al)bimetallic oxide powders are synthesized via the sol-gel auto combustion method using diethanolamine(DEA)as the fuel.In order to subsequently determine the influence of calcination temperatures upon the structure,chemical bonding,morphology,optical properties,and fluorescence properties of the as-synthesized and calcined Mg-Al bimetallic oxide powders,the researcher employed X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FT-IR),scanning electron microscopy(SEM),transmission electron microscopy(TEM),UV–visible diffuse reflectance spectroscopy(UV-DRS),and photoluminescence spectroscopy(PL),respectively.It was apparent on the basis of the XRD and FT-IR analyses that those powders undergoing calcination at temperatures of 500℃,700℃,and 900℃contained the major phase magnesium aluminate(Mg Al_(2)O_(4))spinel with trace magnesium oxide(Mg O)and hydrotalcite(Mg_(6)Al_(2)(CO_(3))(OH)_(16)).When the calcination temperature rose to 1100℃,this resulted in a single phase MgAl_(2)O_(4)while MgO and(Mg_(6)Al_(2)(CO_(3))(OH)_(16))were no longer observed.UV-DRS analysis revealed that in optimized conditions,calcination resulted in better sample absorption and reflection levels when compared to the ultraviolet,visible,and infrared spectra observed in the case of the as-synthesized sample.The bandgap energy(E_(g))for calcined samples was in the range of 2.65 e V to 5.85 e V,in contrast to the value of 4.10 e V for the as-synthesized sample.Analysis of photoluminescence showed that for the as-synthesized samples and those calcined at low temperatures,visible light was emitted only in the violet,blue,and green regions with low intensity,while for samples calcined at higher temperatures,the emissions showed greater intensity and extended to the yellow and orange regions.Multiple defect centers were found in the bandgap which can explain these findings.展开更多
Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmenta...Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmentations are time-consuming and labor-intensive in addition to being subject to inter-observer variability.Many deep learning and automated machine learning(AutoML)frameworks have emerged as a solution to this challenge and show promise in clinical workflows.Objective:This study aims to provide a comprehensive evaluation of existing AutoML frameworks(Auto3DSeg,nnU-Net)against a state-of-the-art non-AutoML framework,the Shifted Window U-Net Transformer(SwinUNETR).Methods:Each framework was trained on the same 122 training images,taken from the Abdominal Multi-Organ Segmentation(AMOS)Grand Challenge.Frameworks were compared using dice similarity coefficient(DSC),surface DSC(sDSC),and 95th percentile Hausdorff distances(HD95)on an additional 72 holdout-validation images.The perceived clinical viability of 30 auto-contoured test cases was assessed by three physicians in a blinded evaluation.Results:Comparisons show significantly better performance by AutoML methods:nnU-Net(average DSC:0.924,average sDSC:0.938,average HD95:4.26,median Likert:4.57),Auto3DSeg(average DSC:0.902,average sDSC:0.919,average HD95:8.76,median Likert:4.49),and SwinUNETR(average DSC:0.837,average sDSC:0.844,average HD95:13.93).AutoML frameworks were quantitatively preferred(13/13 organs at risks[OARs]P<0.05 in DSC and sDSC,12/13 OARs P<0.05 in HD95,comparing Auto3DSeg to SwinUNETR,and all OARs P<0.05 in all metrics comparing SwinUNETR to nnU-Net).Qualitatively,nnU-Net was preferred over Auto3DSeg(P=0.0027).Conclusion:The findings suggest that AutoML frameworks offer a significant advantage in the segmentation of abdominal organs,and underscores the potential of AutoML methods to enhance the efficiency of oncological workflows.展开更多
Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the st...Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.展开更多
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng...On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng Qinghong,chairman of GAC Group.He encouraged GAC to purchase various spare parts in Thailand to enhance Thailand’s position in the global electric vehicle industry supply chain.“Thailand has a favorable business environment,”Zeng responded.“As the location of GAC’s first wholly-owned overseas facility,it was our premier choice.”展开更多
The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 n...The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 new models on display-85 percent of them new energy vehicles-this year’s show spotlighted how the global auto industry is pivoting rapidly towards an era of software-defined and AI-powered mobility.展开更多
文摘According to the China Association of Automobile Manufacturers (CAAM),China's auto industry reached record highs in 2025,with production and sales at 34.53 million and 34.4 million vehicles,respectively.This secured China's position as the world's largest auto market for the 17th year in a row.
基金financial supported from the Thailand Research Fund,Office of the Higher Education Commission(Grant number MRG6280220)。
文摘In this research study,magnesium-aluminum(Mg-Al)bimetallic oxide powders are synthesized via the sol-gel auto combustion method using diethanolamine(DEA)as the fuel.In order to subsequently determine the influence of calcination temperatures upon the structure,chemical bonding,morphology,optical properties,and fluorescence properties of the as-synthesized and calcined Mg-Al bimetallic oxide powders,the researcher employed X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FT-IR),scanning electron microscopy(SEM),transmission electron microscopy(TEM),UV–visible diffuse reflectance spectroscopy(UV-DRS),and photoluminescence spectroscopy(PL),respectively.It was apparent on the basis of the XRD and FT-IR analyses that those powders undergoing calcination at temperatures of 500℃,700℃,and 900℃contained the major phase magnesium aluminate(Mg Al_(2)O_(4))spinel with trace magnesium oxide(Mg O)and hydrotalcite(Mg_(6)Al_(2)(CO_(3))(OH)_(16)).When the calcination temperature rose to 1100℃,this resulted in a single phase MgAl_(2)O_(4)while MgO and(Mg_(6)Al_(2)(CO_(3))(OH)_(16))were no longer observed.UV-DRS analysis revealed that in optimized conditions,calcination resulted in better sample absorption and reflection levels when compared to the ultraviolet,visible,and infrared spectra observed in the case of the as-synthesized sample.The bandgap energy(E_(g))for calcined samples was in the range of 2.65 e V to 5.85 e V,in contrast to the value of 4.10 e V for the as-synthesized sample.Analysis of photoluminescence showed that for the as-synthesized samples and those calcined at low temperatures,visible light was emitted only in the violet,blue,and green regions with low intensity,while for samples calcined at higher temperatures,the emissions showed greater intensity and extended to the yellow and orange regions.Multiple defect centers were found in the bandgap which can explain these findings.
基金funding from the University of Alabama at Birmingham,the National Institutions of Health/National Cancer Institute Award(LRP0000018407)National Center for Advancing Translational Sciences(5KL2TR003097-05).
文摘Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmentations are time-consuming and labor-intensive in addition to being subject to inter-observer variability.Many deep learning and automated machine learning(AutoML)frameworks have emerged as a solution to this challenge and show promise in clinical workflows.Objective:This study aims to provide a comprehensive evaluation of existing AutoML frameworks(Auto3DSeg,nnU-Net)against a state-of-the-art non-AutoML framework,the Shifted Window U-Net Transformer(SwinUNETR).Methods:Each framework was trained on the same 122 training images,taken from the Abdominal Multi-Organ Segmentation(AMOS)Grand Challenge.Frameworks were compared using dice similarity coefficient(DSC),surface DSC(sDSC),and 95th percentile Hausdorff distances(HD95)on an additional 72 holdout-validation images.The perceived clinical viability of 30 auto-contoured test cases was assessed by three physicians in a blinded evaluation.Results:Comparisons show significantly better performance by AutoML methods:nnU-Net(average DSC:0.924,average sDSC:0.938,average HD95:4.26,median Likert:4.57),Auto3DSeg(average DSC:0.902,average sDSC:0.919,average HD95:8.76,median Likert:4.49),and SwinUNETR(average DSC:0.837,average sDSC:0.844,average HD95:13.93).AutoML frameworks were quantitatively preferred(13/13 organs at risks[OARs]P<0.05 in DSC and sDSC,12/13 OARs P<0.05 in HD95,comparing Auto3DSeg to SwinUNETR,and all OARs P<0.05 in all metrics comparing SwinUNETR to nnU-Net).Qualitatively,nnU-Net was preferred over Auto3DSeg(P=0.0027).Conclusion:The findings suggest that AutoML frameworks offer a significant advantage in the segmentation of abdominal organs,and underscores the potential of AutoML methods to enhance the efficiency of oncological workflows.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R319),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publicationResearchers Supporting Project Number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
文摘On July 17,2024,Chinese electric vehicle manufacturer GAC International opened a smart factory in Rayong Province,Thailand.The next day,then Prime Minister of Thailand Saita Thawaixin met with a delegation led by Zeng Qinghong,chairman of GAC Group.He encouraged GAC to purchase various spare parts in Thailand to enhance Thailand’s position in the global electric vehicle industry supply chain.“Thailand has a favorable business environment,”Zeng responded.“As the location of GAC’s first wholly-owned overseas facility,it was our premier choice.”
文摘The 2025 Shanghai Auto Show reaffirmed its role as one of the world’s most influential automotive industry events,offering a panoramic view of the future shaped by intelligent and electrified vehicles.With over 200 new models on display-85 percent of them new energy vehicles-this year’s show spotlighted how the global auto industry is pivoting rapidly towards an era of software-defined and AI-powered mobility.
