Heavy metal pollution poses serious risks to the human health and the natural environment,and there is an urgent need to develop efficient heavy metal removal technologies.The adsorption strategy is one of the most fa...Heavy metal pollution poses serious risks to the human health and the natural environment,and there is an urgent need to develop efficient heavy metal removal technologies.The adsorption strategy is one of the most famous strategies for the capture of heavy metal ions.In recent years,hyper crosslinked polymers(HCPs),a kind of hyper crosslinked porous material prepared by Friedel-Crafts alkylation reaction,have attracted more and more attention because of their advantages of ultra-light framework,wide range of building monomers,easy modification and functionalization.This review focuses on the advances of HCPs in the efficient applications to the removal of heavy metal ions.The fundamentals are presented including physicochemical properties,adsorption mechanism,and preparation strategies.Subsequently,the application and influencing factors of HCPs toward heavy metal ion adsorption are discussed in detail.Furthermore,the opportunities and challenges of HCPs in this promising research field are summarized and anticipated.We are convinced that the advanced HCP-based materials will make further contributions to heavy metal removal in wastewater treatment,further paving the way of advancing researches in this field.展开更多
Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart d...Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.展开更多
0 INTRODUCTION The Yichun Li-Cs-Ta deposit in SE China has received significant attention for several decades,and it represents one of the most typical granitic examples that resemble typical LCT-type mineralization i...0 INTRODUCTION The Yichun Li-Cs-Ta deposit in SE China has received significant attention for several decades,and it represents one of the most typical granitic examples that resemble typical LCT-type mineralization in peraluminous pegmatites.展开更多
Whipple shields as sacrificial bumpers,safeguard the satellites against extremely fast,different-sized projectiles traveling through space in the low earth orbit.Typical Whipple shields comprise a front and rear plate...Whipple shields as sacrificial bumpers,safeguard the satellites against extremely fast,different-sized projectiles traveling through space in the low earth orbit.Typical Whipple shields comprise a front and rear plate,separated by a gap or space.Recent advancements have explored the use of foam,cellular cores,and alternative materials such as ceramics instead of aluminium for the plates.In the current work,the effect of including fluid cores(air/water)sandwiched between the front and rear plates,on the response to hypervelocity impact was explored through a numerical approach.The numerical simulation consisted of hypervelocity impact by a 2 mm diameter,stainless steel projectile,launched at speeds of 3 e9 km/s with a normal impact trajectory towards the Whipple shield.The front and rear bumpers,made of AA6061-T6,were each 1 mm thick.A space of 10 mm was taken between the plates(occupied by fluid).The key metrics analyzed were the perforation characteristics,stages of the debris cloud generation and propagation,energy variations(internal,kinetic and plastic work),temperature variations,and the fragmentation summary.From the computational analysis,employing water-core in Whipple shields could prevent the rear bumper perforation till 6 km/s,lower the peak temperatures at the front bumper perforation zones and debris tip,and generate fewer,larger fragments.展开更多
Super duplex stainless steels(SDSSs)and hyper duplex stainless steels(HDSSs),with more alloying elements content,are more corrosion resistant than the standard grades.Progresses of research works on weldability of SDS...Super duplex stainless steels(SDSSs)and hyper duplex stainless steels(HDSSs),with more alloying elements content,are more corrosion resistant than the standard grades.Progresses of research works on weldability of SDSSs and HDSSs in recent years are reviewed in this paper.If proper heat input is provided,SDSSs and HDSSs can be welded with most fusion welding processes,while tungsten inert gas welding is the most popular process.SDSSs and HDSSs are more prone to secondary phases precipitation than the standard and lean grades,and heat input for SDSSs and HDSSs welding is restricted to a smaller range.Matching filler materials are usually recommended for SDSSs and HDSSs welding,rather than Ni-riched ones for standard and lean grades.Nitrogen addition in shielding gas is always beneficial.Post weld heat treatment with slow cooling rate will be harmful.Hot cracking tendency of SDSSs and HDSSs joints is not high,but sometimes they can suffer from hydrogen induced stress cracking.展开更多
The hot deformation behavior of 2707 hyper duplex stainless steel(HDSS)was investigated through a hot compression test at 950℃ to 1,250℃ at strain rates of 0.01 s^(-1) to 10 s^(-1).Observations from the flow stress ...The hot deformation behavior of 2707 hyper duplex stainless steel(HDSS)was investigated through a hot compression test at 950℃ to 1,250℃ at strain rates of 0.01 s^(-1) to 10 s^(-1).Observations from the flow stress curves reveal a balance between work hardening and dynamic recovery at the beginning of the deformation and subsequently demonstrate various softening mechanisms with the increase of strain.At high strain rates,dynamic recovery is the prevailing mechanism,whereas,at medium and low strain rates,dynamic recrystallization becomes dominant.The constitutive equation was constructed,and the deformation activation energy was calculated to be 645.46 kJ·mol^(-1).The hot processing map was drawn based on the dynamic material model at a strain of 0.8.The results indicate that the hot workability of 2707 HDSS decreases due to its high alloying content.The microstructure evolution of 2707 HDSS at 1,050℃ was identified by means of electron backscatter diffraction and transmission electron microscopy.The results demonstrate that the ferrite completes dynamic recrystallization at the strain rate of 1 s^(-1).The softening process of austenite is influenced by ferrite and mainly experiences dynamic recovery.The austenite located at the α/γ phase boundaries tends to undergo dynamic recrystallization.展开更多
This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging...This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.展开更多
Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the futu...Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the future.However,the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets,lack of diversity,and class imbalance.In this work,we explore the possible ways forward to overcome these challenges posed by available datasets.We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art(SOTA)vision-based models and propose the use of generative models for data augmentation,as a future work direction.First,a comparative analysis of two prominent object detection architectures,You Only Look Once version 7(YOLOv7)and YOLOv8 has been carried out using a balanced dataset,where both models have been evaluated across various evaluation metrics including precision,recall,and mean Average Precision(mAP).The results are compared to other recent fire detection models,highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset.Next,a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire,and the effectiveness of the results has been demonstrated by a windowing-based inference approach.The proposed Slicing-Aided Hyper Inference(SAHI)improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold.YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25%compared to the base YOLOv8 model.The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network(DCGAN)and diffusion models like stable diffusion,for data augmentation.展开更多
The controller design and digital simulation for the hyper velocity kinetic energy missile is investigated. A mathematical model of the trajectory deviation from the line of sight was established, the guidance closed ...The controller design and digital simulation for the hyper velocity kinetic energy missile is investigated. A mathematical model of the trajectory deviation from the line of sight was established, the guidance closed loop was compensated with a phase advance lag corrective network, a selecting algorithm of the attitude control motors used to steer the missile's attitude was presented. In the presence of a wide variety of disturbances the results of digital simulation are satisfactory to circular error probability(CEP) being less than 0 5?m. The steering scheme utilizing attitude control motors as actuators to control the attitude of the missile is feasible.展开更多
基金supported by Innovation Platform(Base)and Talent Special Project,Jilin Provincial Science&Technology Department,China(No.20230508033RC)。
文摘Heavy metal pollution poses serious risks to the human health and the natural environment,and there is an urgent need to develop efficient heavy metal removal technologies.The adsorption strategy is one of the most famous strategies for the capture of heavy metal ions.In recent years,hyper crosslinked polymers(HCPs),a kind of hyper crosslinked porous material prepared by Friedel-Crafts alkylation reaction,have attracted more and more attention because of their advantages of ultra-light framework,wide range of building monomers,easy modification and functionalization.This review focuses on the advances of HCPs in the efficient applications to the removal of heavy metal ions.The fundamentals are presented including physicochemical properties,adsorption mechanism,and preparation strategies.Subsequently,the application and influencing factors of HCPs toward heavy metal ion adsorption are discussed in detail.Furthermore,the opportunities and challenges of HCPs in this promising research field are summarized and anticipated.We are convinced that the advanced HCP-based materials will make further contributions to heavy metal removal in wastewater treatment,further paving the way of advancing researches in this field.
基金supported by the Competitive Research Fund of the University of Aizu,Japan(Grant No.P-13).
文摘Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.
基金funded by the National Natural Science Foundation of China(Nos.42403065 and 92462302)the Frontiers Science Center for Deep-time Digital Earth(No.2652023001)the Open Research Project from the State Key Laboratory of Critical Earth Material Cycling and Mineral Deposits,Nanjing University(No.2025-K16).
文摘0 INTRODUCTION The Yichun Li-Cs-Ta deposit in SE China has received significant attention for several decades,and it represents one of the most typical granitic examples that resemble typical LCT-type mineralization in peraluminous pegmatites.
文摘Whipple shields as sacrificial bumpers,safeguard the satellites against extremely fast,different-sized projectiles traveling through space in the low earth orbit.Typical Whipple shields comprise a front and rear plate,separated by a gap or space.Recent advancements have explored the use of foam,cellular cores,and alternative materials such as ceramics instead of aluminium for the plates.In the current work,the effect of including fluid cores(air/water)sandwiched between the front and rear plates,on the response to hypervelocity impact was explored through a numerical approach.The numerical simulation consisted of hypervelocity impact by a 2 mm diameter,stainless steel projectile,launched at speeds of 3 e9 km/s with a normal impact trajectory towards the Whipple shield.The front and rear bumpers,made of AA6061-T6,were each 1 mm thick.A space of 10 mm was taken between the plates(occupied by fluid).The key metrics analyzed were the perforation characteristics,stages of the debris cloud generation and propagation,energy variations(internal,kinetic and plastic work),temperature variations,and the fragmentation summary.From the computational analysis,employing water-core in Whipple shields could prevent the rear bumper perforation till 6 km/s,lower the peak temperatures at the front bumper perforation zones and debris tip,and generate fewer,larger fragments.
文摘Super duplex stainless steels(SDSSs)and hyper duplex stainless steels(HDSSs),with more alloying elements content,are more corrosion resistant than the standard grades.Progresses of research works on weldability of SDSSs and HDSSs in recent years are reviewed in this paper.If proper heat input is provided,SDSSs and HDSSs can be welded with most fusion welding processes,while tungsten inert gas welding is the most popular process.SDSSs and HDSSs are more prone to secondary phases precipitation than the standard and lean grades,and heat input for SDSSs and HDSSs welding is restricted to a smaller range.Matching filler materials are usually recommended for SDSSs and HDSSs welding,rather than Ni-riched ones for standard and lean grades.Nitrogen addition in shielding gas is always beneficial.Post weld heat treatment with slow cooling rate will be harmful.Hot cracking tendency of SDSSs and HDSSs joints is not high,but sometimes they can suffer from hydrogen induced stress cracking.
基金funded by the Major Science and Technology Program of Luoyang,China(Grant No.2101005A)Provincial and Ministerial Co-construction of Collaborative Innovation Center for Non-ferrous Metal New Materials and Advanced Processing Technology.
文摘The hot deformation behavior of 2707 hyper duplex stainless steel(HDSS)was investigated through a hot compression test at 950℃ to 1,250℃ at strain rates of 0.01 s^(-1) to 10 s^(-1).Observations from the flow stress curves reveal a balance between work hardening and dynamic recovery at the beginning of the deformation and subsequently demonstrate various softening mechanisms with the increase of strain.At high strain rates,dynamic recovery is the prevailing mechanism,whereas,at medium and low strain rates,dynamic recrystallization becomes dominant.The constitutive equation was constructed,and the deformation activation energy was calculated to be 645.46 kJ·mol^(-1).The hot processing map was drawn based on the dynamic material model at a strain of 0.8.The results indicate that the hot workability of 2707 HDSS decreases due to its high alloying content.The microstructure evolution of 2707 HDSS at 1,050℃ was identified by means of electron backscatter diffraction and transmission electron microscopy.The results demonstrate that the ferrite completes dynamic recrystallization at the strain rate of 1 s^(-1).The softening process of austenite is influenced by ferrite and mainly experiences dynamic recovery.The austenite located at the α/γ phase boundaries tends to undergo dynamic recrystallization.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00222536).
文摘This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.
基金supported by a grant from R&D Program Development of Rail-Specific Digital Resource Technology Based on an AI-Enabled Rail Support Platform,grant number PK2401C1,of the Korea Railroad Research Institute.
文摘Fire detection has held stringent importance in computer vision for over half a century.The development of early fire detection strategies is pivotal to the realization of safe and smart cities,inhabitable in the future.However,the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets,lack of diversity,and class imbalance.In this work,we explore the possible ways forward to overcome these challenges posed by available datasets.We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art(SOTA)vision-based models and propose the use of generative models for data augmentation,as a future work direction.First,a comparative analysis of two prominent object detection architectures,You Only Look Once version 7(YOLOv7)and YOLOv8 has been carried out using a balanced dataset,where both models have been evaluated across various evaluation metrics including precision,recall,and mean Average Precision(mAP).The results are compared to other recent fire detection models,highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset.Next,a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire,and the effectiveness of the results has been demonstrated by a windowing-based inference approach.The proposed Slicing-Aided Hyper Inference(SAHI)improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold.YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25%compared to the base YOLOv8 model.The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network(DCGAN)and diffusion models like stable diffusion,for data augmentation.
文摘The controller design and digital simulation for the hyper velocity kinetic energy missile is investigated. A mathematical model of the trajectory deviation from the line of sight was established, the guidance closed loop was compensated with a phase advance lag corrective network, a selecting algorithm of the attitude control motors used to steer the missile's attitude was presented. In the presence of a wide variety of disturbances the results of digital simulation are satisfactory to circular error probability(CEP) being less than 0 5?m. The steering scheme utilizing attitude control motors as actuators to control the attitude of the missile is feasible.
