The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate...The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate prediction of SWRCs remains challenging due to the inherently sparse and incomplete nature of site-specific data.This study compiled a comprehensive dataset of SWRCs spanning a wide suction range from various published literature sources.Based on this dataset,multiple machine learning(ML)algorithms were employed to predict SWRCs.The performance of each algorithm was evaluated and ranked using four statistical indicators that quantify simulation accuracy.Feature importance analysis was subsequently conducted to reduce dimensionality by eliminating weakly correlated variables,thereby enhancing both model adaptability and computational efficiency.Following dimensionality reduction,a base learner pool was constructed and integrated through stacked generalization to create a multi-algorithm ensemble model.The proposed stacked model demonstrated robust performance in simulating SWRCs across diverse soil types,using only basic physical properties as inputs,achieving accuracy comparable to or marginally superior to the LightGBM model.The principal advantage of the stacked approach lies in its substantially improved accuracy within high suction ranges,effectively overcoming the limitations observed in LightGBM and enhancing the estimation under these conditions.This study provides valuable insights for researchers evaluating SWRCs through ML algorithms and demonstrates the potential of ensemble techniques in geotechnical prediction tasks.展开更多
Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–di...Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.展开更多
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often fa...In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.展开更多
In mixture experiments,the observed response is determined by the relative proportions of the components,consequently rendering the experimental region a simplex.This paper focuses primarily on the optimal designs of ...In mixture experiments,the observed response is determined by the relative proportions of the components,consequently rendering the experimental region a simplex.This paper focuses primarily on the optimal designs of mixture experiments that involve process variables.Prior research has extensively delved into optimal orthogonal block designs for some classic mixture models with process variables.Based on the framework of general blending models,this paper proposes a class of symmetric linear mixture models,which can be regarded as a generalization of many existing ones.Under the orthogonal blocking conditions,orthogonal block designs are devised through Latin squares in the presence of process variables.TheD-,A-,and E-optimality criteria are utilized to obtain optimal designs at the boundary of the simplex in the case of 3 components.As the values of the exponents change,numerically derived optimal design points are presented to illustrate the pattern of their variations,and to verify the consistency of the results with previous research on some specific symmetric general blending models.展开更多
Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphys...Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.展开更多
To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determ...To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:https://github.com/HuaTThanhIT2327Tqu/datasetv2.展开更多
The construction of new agricultural science has put forward the core requirements of"interdisciplinary integration,service industry demand,and cultivation of composite talents"for the smart agriculture majo...The construction of new agricultural science has put forward the core requirements of"interdisciplinary integration,service industry demand,and cultivation of composite talents"for the smart agriculture major.The"integration of general and specialized education"is the key path to solve the problems of"prominent disciplinary barriers,fragmented knowledge structure,and weak practical ability"in the traditional curriculum system.In this paper,the College of Smart Agriculture from Yulin Normal University is taken as the research object.Based on the characteristics of regional agricultural industry and the positioning of professional education,the prominent problems in the current professional curriculum system of smart agriculture are analyzed,the construction concept of"strong foundation in general education,precise core in professional education,and breaking through boundaries in integrated education"is proposed,and a"three dimensions and four layers"integrated curriculum system framework for general and specialized education is constructed.Moreover,practical exploration is conducted from the aspects of curriculum module design,teaching mode innovation,and guarantee mechanism construction.Practice has shown that this curriculum system effectively enhances students'interdisciplinary application abilities and industry adaptability,and provides a practical sample for the reform of smart agriculture courses in local universities under the background of new agricultural science.展开更多
The overgeneralization of fear is associated with psychiatric disorders and cognitive decline.Recent studies have shown that engram cells in the dorsal dentate gyrus are integrated into functionally heterogeneous ense...The overgeneralization of fear is associated with psychiatric disorders and cognitive decline.Recent studies have shown that engram cells in the dorsal dentate gyrus are integrated into functionally heterogeneous ensembles that are involved in contextual fear memory generalization and discrimination.However,the intracellular signals that promote fear generalization remain to be fully elucidated.In this study,we labeled and manipulated the c-Fos+and Npas4+ensembles in the dorsal dentate gyrus that are activated by contextual fear conditioning using a robust activity marking system.The results showed that increasing the excitability of Fos-dependent robust activity marking by overexpressing NaChBac or decreasing the excitability of Npas4-dependent robust activity marking by overexpressing Kir2.1 promoted fear memory generalization.Furthermore,CRISPR-mediated downregulation of the autophagy-related Atg5 or Atg7 genes in dorsal dentate gyrus neurons inhibited activation of c-Fos,but not Npas4.Knockdown of Atg5 or Atg7 in the Fos-dependent robust activity marking or Npas4-dependent robust activity marking ensemble led to an increase in neuronal excitability and a decrease in spine density in both ensembles.However,Atg7 knockdown in the Fos-dependent robust activity marking ensemble promoted memory generalization,while knockdown of Atg5 or Atg7 in the Npas4-dependent robust activity marking ensemble increased anxiety levels.These results contribute to our understanding of how the varying plasticity of memory engrams is involved in regulating fear memory generalization and anxiety.展开更多
Making full use of the operator ordering method and the integration within ordered products,we obtain the analytical evolution law of a general quadratic state in the amplitude decay channel,and find that it is determ...Making full use of the operator ordering method and the integration within ordered products,we obtain the analytical evolution law of a general quadratic state in the amplitude decay channel,and find that it is determined not only by the decay rate of the amplitude decay channel but also by the coefficients of the initial quadratic state.Further,the quantum statistical properties of the initial quadratic state for amplitude decay are investigated via its average photon number and photon-counting distribution,and its Wigner distribution function evolution is discussed in detail.展开更多
Modern apprenticeship emphasizes strengthening school-enterprise cooperation,deepening the integration of production and education,and promoting the combination of work and study to achieve seamless connection between...Modern apprenticeship emphasizes strengthening school-enterprise cooperation,deepening the integration of production and education,and promoting the combination of work and study to achieve seamless connection between professional education and industrial needs,thereby improving the quality of talent training.This paper analyzes the current problems in the nursing talent training system,elaborates on the significance of cultivating nursing talents based on modern apprenticeship,and proposes new ideas for constructing a modern apprenticeship training model for nursing talents from the perspectives of“integrating general and specialized education,merging virtue and skills,and linking specialization and innovation”.The aim is to further promote the organic combination of nursing talent training goals and post needs,for reference only.展开更多
Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents...Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.展开更多
BACKGROUND Currently,very few studies have examined the analgesic effectiveness and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for ...BACKGROUND Currently,very few studies have examined the analgesic effectiveness and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for inguinal hernia.AIM To investigate the analgesic effect and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for inguinal hernia.METHODS In this retrospective study,94 patients scheduled for laparoscopic minimally invasive surgery for inguinal hernia,admitted to Yiwu Central Hospital between May 2022 and May 2023,were divided into a control group(inhalation combined general anesthesia)and a treatment group(dexmedetomidine-assisted intrave-nous-inhalation combined general anesthesia).Perioperative indicators,analgesic effect,preoperative and postoperative 24-hours blood pressure(BP)and heart rate(HR),stress indicators,immune function levels,and adverse reactions were com-pared between the two groups.RESULTS Baseline data,including age,hernia location,place of residence,weight,monthly income,education level,and underlying diseases,were not significantly different between the two groups,indicating comparability(P>0.05).No significant difference was found in operation time and anesthesia time between the two groups(P>0.05).However,the treatment group exhibited a shorter postoperative urinary catheter removal time and hospital stay than the control group(P<0.05).Preoperatively,no significant differences were found in the visual analog scale(VAS)scores between the two groups(P>0.05).However,at 12,18,and 24 hours postoper-atively,the treatment group had significantly lower VAS scores than the control group(P<0.05).Although no significant differences in preoperative hemodynamic indicators were found between the two groups(P>0.05),both groups experienced some extent of changes in postoperative HR,diastolic BP(DBP),and systolic BP(SBP).Nevertheless,the treatment group showed smaller changes in HR,DBP,and SBP than the control group(P<0.05).Preoperative immune function indicators showed no significant differences between the two groups(P>0.05).However,postoperatively,the treatment group demonstrated higher levels of CD3+,CD4+,and CD4+/CD8+and lower levels of CD8+than the control group(P<0.05).The rates of adverse reactions were 6.38%and 23.40%in the treatment and control groups,respectively,revealing a significant difference(χ2=5.371,P=0.020).CONCLUSION Dexmedetomidine-assisted intravenous-inhalation combined general anesthesia can promote early recovery of patients undergoing laparoscopic minimally invasive surgery for inguinal hernia.It ensures stable blood flow,improves postoperative analgesic effects,reduces postoperative pain intensity,alleviates stress response,improves immune function,facilitates anesthesia recovery,and enhances safety.展开更多
BACKGROUND Subcutaneous implantable cardioverter-defibrillator(S-ICD)implantation requires effective anesthesia.General anesthesia(GA)carries risks like hemodynamic instability,while ultrasound-guided intercostal nerv...BACKGROUND Subcutaneous implantable cardioverter-defibrillator(S-ICD)implantation requires effective anesthesia.General anesthesia(GA)carries risks like hemodynamic instability,while ultrasound-guided intercostal nerve block(US-ICNB)may offer better pain control.This study hypothesized US-ICNB is superior in perioperative safety and pain management.AIM To compare perioperative outcomes of GA and US-ICNB in S-ICD implantation.METHODS This retrospective single-center study included 64 patients who received S-ICD implantation between February 2021 and December 2024.They were divided into GA and US-ICNB groups based on anesthesia type.Demographic data,perioperative parameters(operation time,pain scores,analgesic usage),and postoperative outcomes(complications,defibrillation events)were collected and analyzed.Statistical tests were used to compare the two groups.RESULTS This study included 64 patients(20 in the GA group and 44 in the US-ICNB group).Baseline left ventricular ejection fraction was significantly lower in the US-ICNB group(39.20%±12.00%vs 56.20%±11.50%in GA,P<0.001),while American Society of Anesthesiologists scores and comorbidities were comparable.US-ICNB showed superior pain control,with significantly lower numeric rating scale scores at 6-48 hours(P<0.001)and fewer patients requiring analgesics(P=0.02).The US-ICNB group had shorter operation times(P<0.001),total hospital stays(P<0.001),and later first analgesia times(P<0.001).No anesthesia-related complications occurred in either group.CONCLUSION Both anesthetic methods were safe in the short term.However,US-ICNB was superior in reducing operation and hospital stay times and alleviating peri-operative pain.It has high safety in S-ICD implantation and deserves further clinical promotion,though large-scale,multi-center,randomized controlled trials are needed to confirm these findings.展开更多
Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed...Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmenta...Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.展开更多
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,a...To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB.展开更多
The US 2024 general election ended with the Republican Party winning the presidential, House and Senate elections at the same time. In the presidential election, the Republican Party not only won more popular votes in...The US 2024 general election ended with the Republican Party winning the presidential, House and Senate elections at the same time. In the presidential election, the Republican Party not only won more popular votes in over 90% counties than in the 2020 general election, but also won seven highly contested swing States with greater edges. This also marks the first time since 2004 that the Republican Party has won a relative majority of popular votes in the presidential election.展开更多
We present a new two-dimensional(2D)map of total Galactic extinction,Av,across the entire dust half-layer from the Sun to extragalactic space for Galactic latitudes|b|>13°,as well as a three-dimensional(3D)map...We present a new two-dimensional(2D)map of total Galactic extinction,Av,across the entire dust half-layer from the Sun to extragalactic space for Galactic latitudes|b|>13°,as well as a three-dimensional(3D)map of Av within 2 kpc of the Sun.These maps are based on A_(V)and distance estimates derived from a data set,which utilizes Gaia Data Release 3 parallaxes and multi-band photometry for nearly 100 million dwarf stars.We apply our own corrections to account for significant systematics in this data set.Our 2D map achieves an angular resolution of6'.1,while the 3D map offers a transverse resolution of 3.56 pc—corresponding to variable angular resolution depending on distance—and a radial resolution of 50 pc.In constructing these maps,we pay particular attention to the solar neighborhood(within 200 pc)and to high Galactic latitudes.The 3D map predicts Av from the Sun to any extended object within the Galactic dust layer with an accuracy ofσ(A_(V))=0.1 mag.The 2D map provides Av estimates for the entire dust half-layer up to extragalactic distances with an accuracy ofσ(A_(V))=0.07 mag.We provide A_(V)estimates from our maps for various classes of extended celestial objects with angular size primarily in the range of 2′-40′,including 19,809 galaxies and quasars,170 Galactic globular clusters,458 open clusters,and several hundred molecular clouds from two lists.We also present extinction values for 8293 TypeⅠa supernovae.Comparison of our extinction estimates with those from previous maps and literature sources reveals systematic differences,indicating large-scale spatial variations in the extinction law and suggesting that earlier 2D reddening maps based on infrared dust emission tend to underestimate low extinction values.展开更多
基金the National Natural Science Foundation of China(Grant No.42272312)Ningbo Youth Science and Technology Innovation Talent Project(Grant No.2024QL057)the Zhejiang Provincial Xinmiao Talents Program(Grant No.2024R405B093).
文摘The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate prediction of SWRCs remains challenging due to the inherently sparse and incomplete nature of site-specific data.This study compiled a comprehensive dataset of SWRCs spanning a wide suction range from various published literature sources.Based on this dataset,multiple machine learning(ML)algorithms were employed to predict SWRCs.The performance of each algorithm was evaluated and ranked using four statistical indicators that quantify simulation accuracy.Feature importance analysis was subsequently conducted to reduce dimensionality by eliminating weakly correlated variables,thereby enhancing both model adaptability and computational efficiency.Following dimensionality reduction,a base learner pool was constructed and integrated through stacked generalization to create a multi-algorithm ensemble model.The proposed stacked model demonstrated robust performance in simulating SWRCs across diverse soil types,using only basic physical properties as inputs,achieving accuracy comparable to or marginally superior to the LightGBM model.The principal advantage of the stacked approach lies in its substantially improved accuracy within high suction ranges,effectively overcoming the limitations observed in LightGBM and enhancing the estimation under these conditions.This study provides valuable insights for researchers evaluating SWRCs through ML algorithms and demonstrates the potential of ensemble techniques in geotechnical prediction tasks.
基金supported by the Shanghai Pilot Program for Basic Research(22T01400100-18)the National Natural Science Foundation of China(22278127 and 12447149)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Postdoctoral Fellowship Program of CPSF(GZB20250159).
文摘Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.
文摘In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.
基金supported by the National Natural Science Foundation of China[grant numbers 12071329,12471246].
文摘In mixture experiments,the observed response is determined by the relative proportions of the components,consequently rendering the experimental region a simplex.This paper focuses primarily on the optimal designs of mixture experiments that involve process variables.Prior research has extensively delved into optimal orthogonal block designs for some classic mixture models with process variables.Based on the framework of general blending models,this paper proposes a class of symmetric linear mixture models,which can be regarded as a generalization of many existing ones.Under the orthogonal blocking conditions,orthogonal block designs are devised through Latin squares in the presence of process variables.TheD-,A-,and E-optimality criteria are utilized to obtain optimal designs at the boundary of the simplex in the case of 3 components.As the values of the exponents change,numerically derived optimal design points are presented to illustrate the pattern of their variations,and to verify the consistency of the results with previous research on some specific symmetric general blending models.
基金National Natural Science Foundation of China(42375153,42105153,42205157)Development of Science and Technology at Chinese Academy of Meteorological Sciences(2023KJ038)。
文摘Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.
文摘To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:https://github.com/HuaTThanhIT2327Tqu/datasetv2.
基金Supported by the Autonomous Region-level Research and Practice Projects for New Engineering,New Medicine,New Agriculture,and New Humanities of Guangxi Department of Education(XNK202409)the Undergraduate Teaching Reform Project of Guangxi Higher Education(2024JGB332+1 种基金2024JGA304)the Guangxi Degree and Graduate Education Reform Project(JGY2025382).
文摘The construction of new agricultural science has put forward the core requirements of"interdisciplinary integration,service industry demand,and cultivation of composite talents"for the smart agriculture major.The"integration of general and specialized education"is the key path to solve the problems of"prominent disciplinary barriers,fragmented knowledge structure,and weak practical ability"in the traditional curriculum system.In this paper,the College of Smart Agriculture from Yulin Normal University is taken as the research object.Based on the characteristics of regional agricultural industry and the positioning of professional education,the prominent problems in the current professional curriculum system of smart agriculture are analyzed,the construction concept of"strong foundation in general education,precise core in professional education,and breaking through boundaries in integrated education"is proposed,and a"three dimensions and four layers"integrated curriculum system framework for general and specialized education is constructed.Moreover,practical exploration is conducted from the aspects of curriculum module design,teaching mode innovation,and guarantee mechanism construction.Practice has shown that this curriculum system effectively enhances students'interdisciplinary application abilities and industry adaptability,and provides a practical sample for the reform of smart agriculture courses in local universities under the background of new agricultural science.
基金the STI2030-Major Projects,Nos.2021ZD0203500(to FW),2021ZD0202100(to XL)the National Natural Science Foundation of China,Nos.32222033(to FW),32330041(to LM)and 82021002(to LM),32171041(to XL)and 32450102(to XL)CAMS Innovation Fund for Medical Sciences,No.2021-I2M-5-009(to LM and XL).
文摘The overgeneralization of fear is associated with psychiatric disorders and cognitive decline.Recent studies have shown that engram cells in the dorsal dentate gyrus are integrated into functionally heterogeneous ensembles that are involved in contextual fear memory generalization and discrimination.However,the intracellular signals that promote fear generalization remain to be fully elucidated.In this study,we labeled and manipulated the c-Fos+and Npas4+ensembles in the dorsal dentate gyrus that are activated by contextual fear conditioning using a robust activity marking system.The results showed that increasing the excitability of Fos-dependent robust activity marking by overexpressing NaChBac or decreasing the excitability of Npas4-dependent robust activity marking by overexpressing Kir2.1 promoted fear memory generalization.Furthermore,CRISPR-mediated downregulation of the autophagy-related Atg5 or Atg7 genes in dorsal dentate gyrus neurons inhibited activation of c-Fos,but not Npas4.Knockdown of Atg5 or Atg7 in the Fos-dependent robust activity marking or Npas4-dependent robust activity marking ensemble led to an increase in neuronal excitability and a decrease in spine density in both ensembles.However,Atg7 knockdown in the Fos-dependent robust activity marking ensemble promoted memory generalization,while knockdown of Atg5 or Atg7 in the Npas4-dependent robust activity marking ensemble increased anxiety levels.These results contribute to our understanding of how the varying plasticity of memory engrams is involved in regulating fear memory generalization and anxiety.
文摘Making full use of the operator ordering method and the integration within ordered products,we obtain the analytical evolution law of a general quadratic state in the amplitude decay channel,and find that it is determined not only by the decay rate of the amplitude decay channel but also by the coefficients of the initial quadratic state.Further,the quantum statistical properties of the initial quadratic state for amplitude decay are investigated via its average photon number and photon-counting distribution,and its Wigner distribution function evolution is discussed in detail.
基金2025 Shanghai Secondary Vocational School Curriculum and Teaching Reform Research Project(Project No.:2025K-03)。
文摘Modern apprenticeship emphasizes strengthening school-enterprise cooperation,deepening the integration of production and education,and promoting the combination of work and study to achieve seamless connection between professional education and industrial needs,thereby improving the quality of talent training.This paper analyzes the current problems in the nursing talent training system,elaborates on the significance of cultivating nursing talents based on modern apprenticeship,and proposes new ideas for constructing a modern apprenticeship training model for nursing talents from the perspectives of“integrating general and specialized education,merging virtue and skills,and linking specialization and innovation”.The aim is to further promote the organic combination of nursing talent training goals and post needs,for reference only.
基金Project (No. 60805001) partially supported by the National NaturalScience Foundation of China
文摘Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.
文摘BACKGROUND Currently,very few studies have examined the analgesic effectiveness and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for inguinal hernia.AIM To investigate the analgesic effect and safety of dexmedetomidine-assisted intravenous-inhalation combined general anesthesia in laparoscopic minimally invasive surgery for inguinal hernia.METHODS In this retrospective study,94 patients scheduled for laparoscopic minimally invasive surgery for inguinal hernia,admitted to Yiwu Central Hospital between May 2022 and May 2023,were divided into a control group(inhalation combined general anesthesia)and a treatment group(dexmedetomidine-assisted intrave-nous-inhalation combined general anesthesia).Perioperative indicators,analgesic effect,preoperative and postoperative 24-hours blood pressure(BP)and heart rate(HR),stress indicators,immune function levels,and adverse reactions were com-pared between the two groups.RESULTS Baseline data,including age,hernia location,place of residence,weight,monthly income,education level,and underlying diseases,were not significantly different between the two groups,indicating comparability(P>0.05).No significant difference was found in operation time and anesthesia time between the two groups(P>0.05).However,the treatment group exhibited a shorter postoperative urinary catheter removal time and hospital stay than the control group(P<0.05).Preoperatively,no significant differences were found in the visual analog scale(VAS)scores between the two groups(P>0.05).However,at 12,18,and 24 hours postoper-atively,the treatment group had significantly lower VAS scores than the control group(P<0.05).Although no significant differences in preoperative hemodynamic indicators were found between the two groups(P>0.05),both groups experienced some extent of changes in postoperative HR,diastolic BP(DBP),and systolic BP(SBP).Nevertheless,the treatment group showed smaller changes in HR,DBP,and SBP than the control group(P<0.05).Preoperative immune function indicators showed no significant differences between the two groups(P>0.05).However,postoperatively,the treatment group demonstrated higher levels of CD3+,CD4+,and CD4+/CD8+and lower levels of CD8+than the control group(P<0.05).The rates of adverse reactions were 6.38%and 23.40%in the treatment and control groups,respectively,revealing a significant difference(χ2=5.371,P=0.020).CONCLUSION Dexmedetomidine-assisted intravenous-inhalation combined general anesthesia can promote early recovery of patients undergoing laparoscopic minimally invasive surgery for inguinal hernia.It ensures stable blood flow,improves postoperative analgesic effects,reduces postoperative pain intensity,alleviates stress response,improves immune function,facilitates anesthesia recovery,and enhances safety.
文摘BACKGROUND Subcutaneous implantable cardioverter-defibrillator(S-ICD)implantation requires effective anesthesia.General anesthesia(GA)carries risks like hemodynamic instability,while ultrasound-guided intercostal nerve block(US-ICNB)may offer better pain control.This study hypothesized US-ICNB is superior in perioperative safety and pain management.AIM To compare perioperative outcomes of GA and US-ICNB in S-ICD implantation.METHODS This retrospective single-center study included 64 patients who received S-ICD implantation between February 2021 and December 2024.They were divided into GA and US-ICNB groups based on anesthesia type.Demographic data,perioperative parameters(operation time,pain scores,analgesic usage),and postoperative outcomes(complications,defibrillation events)were collected and analyzed.Statistical tests were used to compare the two groups.RESULTS This study included 64 patients(20 in the GA group and 44 in the US-ICNB group).Baseline left ventricular ejection fraction was significantly lower in the US-ICNB group(39.20%±12.00%vs 56.20%±11.50%in GA,P<0.001),while American Society of Anesthesiologists scores and comorbidities were comparable.US-ICNB showed superior pain control,with significantly lower numeric rating scale scores at 6-48 hours(P<0.001)and fewer patients requiring analgesics(P=0.02).The US-ICNB group had shorter operation times(P<0.001),total hospital stays(P<0.001),and later first analgesia times(P<0.001).No anesthesia-related complications occurred in either group.CONCLUSION Both anesthetic methods were safe in the short term.However,US-ICNB was superior in reducing operation and hospital stay times and alleviating peri-operative pain.It has high safety in S-ICD implantation and deserves further clinical promotion,though large-scale,multi-center,randomized controlled trials are needed to confirm these findings.
文摘Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金supported by National Natural Science Foundation of China(61673186 and 61871196)Beijing Normal University Education Reform Project(jx2024040)Guangdong Undergraduate Universities Teaching Quality and Reform Project(jx2024309).
文摘Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
文摘To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB.
文摘The US 2024 general election ended with the Republican Party winning the presidential, House and Senate elections at the same time. In the presidential election, the Republican Party not only won more popular votes in over 90% counties than in the 2020 general election, but also won seven highly contested swing States with greater edges. This also marks the first time since 2004 that the Republican Party has won a relative majority of popular votes in the presidential election.
基金financial support from the Russian Science Foundation(grant No.20-72-10052)。
文摘We present a new two-dimensional(2D)map of total Galactic extinction,Av,across the entire dust half-layer from the Sun to extragalactic space for Galactic latitudes|b|>13°,as well as a three-dimensional(3D)map of Av within 2 kpc of the Sun.These maps are based on A_(V)and distance estimates derived from a data set,which utilizes Gaia Data Release 3 parallaxes and multi-band photometry for nearly 100 million dwarf stars.We apply our own corrections to account for significant systematics in this data set.Our 2D map achieves an angular resolution of6'.1,while the 3D map offers a transverse resolution of 3.56 pc—corresponding to variable angular resolution depending on distance—and a radial resolution of 50 pc.In constructing these maps,we pay particular attention to the solar neighborhood(within 200 pc)and to high Galactic latitudes.The 3D map predicts Av from the Sun to any extended object within the Galactic dust layer with an accuracy ofσ(A_(V))=0.1 mag.The 2D map provides Av estimates for the entire dust half-layer up to extragalactic distances with an accuracy ofσ(A_(V))=0.07 mag.We provide A_(V)estimates from our maps for various classes of extended celestial objects with angular size primarily in the range of 2′-40′,including 19,809 galaxies and quasars,170 Galactic globular clusters,458 open clusters,and several hundred molecular clouds from two lists.We also present extinction values for 8293 TypeⅠa supernovae.Comparison of our extinction estimates with those from previous maps and literature sources reveals systematic differences,indicating large-scale spatial variations in the extinction law and suggesting that earlier 2D reddening maps based on infrared dust emission tend to underestimate low extinction values.
