We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)with...We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)within 10 min.Unlike conventional methods,sTED minimizes cell loss and enhances reproducibility,achieving>90%cell viability in mouse tissues and>60%in human tumors,with 1.5×10^(4)-2.5×10^(4)cells/mg yield from mouse liver.Tailored for biopsies and ultrasmall samples,sTED addresses critical standardization challenges in organoid-based research.展开更多
Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhyt...Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhythm of 100-120 times/min for CC.[1,2]Interruptions of the CC must be as short as possible and are related to ventilation,defibrillation and turnover of the rescuers.Most of the automated external defibrillators(AEDs)require interruptions of the CC to perform rhythm analysis.Among the numerous marketed models of AEDs,some provide real-time feedback about the quality of the CC.展开更多
Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape th...Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape the future through intelligent automation.This is a key milestone on the way to achieving Rieter’s vision 2027-the fully automated spinning mill.展开更多
The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and cen...The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and centrifugal honey separation,expose beekeepers to potential bee stings and frequently compromise honeycomb integrity.To address these limitations,we designed an automated honey-harvesting robot capable of autonomous frame extraction and beeswax removal.The robot mainly consists of a mobile mechanism equipped with image recognition for beehive localization,a magnetic adsorption-based beehive frame handling device(60.8 N maximum suction)coupled with a cross-slide mechanism for precise frame manipulation,and a thermal beeswax layer-melting apparatus,with optimal melting parameters(15 m/s airflow at 90℃ for 30 seconds)determined through rigorous thermal flow simulations utilizing FLUENT/Mechanical software.Field experiments demonstrated beehive frames handling success rate exceeding 85%,beeswax layer removal efficacy over 80% and damage of honeycombs below 30%.The experiment results validate the robot's operational reliability and its capacity to automate critical harvesting procedures.This study significantly reduces the labor intensity for beekeepers,effectively eliminates the risk of direct human-bee contact and improves the removal of beeswax layer,thereby catalyzing the modernization of the beekeeping industry.展开更多
The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods...The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.展开更多
To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal draw...To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal drawing and explores automation and intelligent equipment solutions within the framework of the group coal drawing method.Numerical simulations were performed to investigate the impact of the Number of Drawing Openings(NDO)and rounds on top-coal recovery,coal draw-ing efficiency,and Top Coal Loss(TCL)mechanism.Subsequently,considering the recovery and coal drawing efficiency and by introducing the instantaneous gangue content and cumulative gangue content in simulations,the top-coal recovery,gangue content,and coal loss distribution when considering excessive coal drawing were analyzed.This established a foun-dation for determining the optimal NDO and shutdown timing.Finally,the key technical principle and automated control of a shock vibration and hyperspectral fusion recognition device were detailed,and an intelligent coal drawing control method based on this technology was developed.This technology enabled the precise control of the instantaneous gangue content(35%)during coal drawing.The top-coal recovery at the Tashan Mine 8222 working face increased by 14.78%,and the gangue content was controlled at~9%,consistent with the numerical simulation results.Thus,the reliability of the numerical simulation results was confirmed to a certain extent.Meanwhile,the single-group drawing method significantly enhanced the production capacity of the 8222 working face,achieving an annual output of 15 million tons.展开更多
Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-value...Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.展开更多
Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transport...Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transportation capabilities,allowing them tomove in the opposite directionwithout turning around,which helps reduce transportation time.This paper aims at the problem of AGV scheduling and bidirectional conflict-free routing with battery swapping in automated terminals.A bi-level mixed integer programming(MIP)model is proposed,taking into account task assignment,bidirectional conflict-free routing,and battery swapping.The upper model focuses on container task assignment and AGV battery swapping planning,while the lower model ensures conflict-free movement of AGVs.A double-threshold battery swapping strategy is introduced,allowing AGVs to utilize waiting time for loading for battery swapping.An improved differential evolution variable neighborhood search(IDE-VNS)algorithm is developed to solve the bi-level MIP model,aiming to minimize the completion time of all jobs.Experimental results demonstrate that compared to the differential evolution(DE)algorithm and the genetic algorithm(GA),the IDEVNS algorithmreduces fitness values by 44.49% and 45.22%,though it does increase computation time by 56.28% and 62.03%,respectively.Bidirectional transportation reduces the fitness value by an average of 10.97% when the container scale is small.As the container scale increases,the fitness value of bidirectional transportation gradually approaches that of unidirectional transportation.The results further show that the double-threshold battery swapping strategy enhances AGV utilization and reduces the fitness value.展开更多
The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manua...The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.展开更多
Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networ...Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.展开更多
This study analyzed the therapeutic effects of continuous ambulatory peritoneal dialysis(CAPD)and automated peritoneal dialysis(APD)on patients with end-stage renal disease.Fifty patients admitted between January 2024...This study analyzed the therapeutic effects of continuous ambulatory peritoneal dialysis(CAPD)and automated peritoneal dialysis(APD)on patients with end-stage renal disease.Fifty patients admitted between January 2024 and December 2024 were randomly assigned to two groups,with the observation group receiving APD and the reference group receiving CAPD.Renal function indicators,nutritional indicators,mineral metabolism,urine volume,and ultrafiltration volume changes were compared between the two groups.After treatment,the observation group showed lower renal function indicators,higher nutritional indicators,and better mineral metabolism levels compared to the reference group(P<0.05).While there was no significant difference in urine volume between the two groups(P>0.05),the observation group demonstrated superior ultrafiltration volume(P<0.05).These findings suggest that APD offers better clinical outcomes than CAPD by improving renal function,nutritional status,mineral metabolism regulation,and ultrafiltration efficiency in patients with end-stage renal disease.展开更多
Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements...Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.展开更多
During the automated placement process of dry fibers,the positioning and fixation of dry fiber gauze belts are achieved by spraying setting agents.The amount of the setting agent is difficult to control when it is spr...During the automated placement process of dry fibers,the positioning and fixation of dry fiber gauze belts are achieved by spraying setting agents.The amount of the setting agent is difficult to control when it is sprayed manually.Furthermore,it can also affect the permeability of the preform,resin injection and the quality of the vacuum assisted resin infusion(VARI)molding,resulting in a decrease in the mechanical properties of composite materials.This study utilizes dry fiber automated placement equipment and an automated spraying system to manufacture preform structures,followed by VARI process to prepare composite samples with varying setting agent contents.Subsequently,mechanical characterization including interlaminar shear,bending and tensile testing is conducted to investigate the influence of setting agent content on both the manufacturing process and the mechanical properties of composite products.The results show that the interlaminar shear strength,bending strength and tensile strength of the sample gradually decrease with the increase of the content of the setting agent.The optimal setting agent content for automated laying of dry fiber is determined to be 4%-6%,balancing the preformed body’s layup quality and its impact on mechanical properties.Compared with agent-free samples,this range results in reductions of 3% in interlaminar shear strength,9% in bending strength,11% in bending modulus,and 13%-16% in tensile strength.展开更多
The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and ...The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.展开更多
Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal ...Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.展开更多
The recent rapid development of China’s foreign trade has led to the significant increase in waterway transportation and automated container ports. Automated terminals can significantly improve the loading and unload...The recent rapid development of China’s foreign trade has led to the significant increase in waterway transportation and automated container ports. Automated terminals can significantly improve the loading and unloading efficiency of container terminals. These terminals can also increase the port’s transportation volume while ensuring the quality of cargo loading and unloading, which has become an inevitable trend in the future development of ports. However, the continuous growth of the port’s transportation volume has increased the horizontal transportation pressure on the automated terminal, and the problems of route conflicts and road locks faced by automated guided vehicles (AGV) have become increasingly prominent. Accordingly, this work takes Xiamen Yuanhai automated container terminal as an example. This work focuses on analyzing the interference problem of path conflict in its horizontal transportation AGV scheduling. Results show that path conflict, the most prominent interference factor, will cause AGV scheduling to be unable to execute the original plan. Consequently, the disruption management was used to establish a disturbance recovery model, and the Dijkstra algorithm for combining with time windows is adopted to plan a conflict-free path. Based on the comparison with the rescheduling method, the research obtains that the deviation of the transportation path and the deviation degree of the transportation path under the disruption management method are much lower than those of the rescheduling method. The transportation path deviation degree of the disruption management method is only 5.56%. Meanwhile, the deviation degree of the transportation path under the rescheduling method is 44.44%.展开更多
Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machin...Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.展开更多
Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issu...Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.展开更多
BACKGROUND Spontaneous bacterial peritonitis(SBP)is one of the most important complications of patients with liver cirrhosis entailing high morbidity and mortality.Making an accurate early diagnosis of this infection ...BACKGROUND Spontaneous bacterial peritonitis(SBP)is one of the most important complications of patients with liver cirrhosis entailing high morbidity and mortality.Making an accurate early diagnosis of this infection is key in the outcome of these patients.The current definition of SBP is based on studies performed more than 40 years ago using a manual technique to count the number of polymorphs in ascitic fluid(AF).There is a lack of data comparing the traditional cell count method with a current automated cell counter.Moreover,current international guidelines do not mention the type of cell count method to be employed and around half of the centers still rely on the traditional manual method.AIM To compare the accuracy of polymorph count on AF to diagnose SBP between the traditional manual cell count method and a modern automated cell counter against SBP cases fulfilling gold standard criteria:Positive AF culture and signs/symptoms of peritonitis.METHODS Retrospective analysis including two cohorts:Cross-sectional(cohort 1)and case-control(cohort 2),of patients with decompensated cirrhosis and ascites.Both cell count methods were conducted simultaneously.Positive SBP cases had a pathogenic bacteria isolated on AF and signs/symptoms of peritonitis.RESULTS A total of 137 cases with 5 positive-SBP,and 85 cases with 33 positive-SBP were included in cohort 1 and 2,respectively.Positive-SBP cases had worse liver function in both cohorts.The automated method showed higher sensitivity than the manual cell count:80%vs 52%,P=0.02,in cohort 2.Both methods showed very good specificity(>95%).The best cutoff using the automated cell counter was polymorph≥0.2 cells×10^(9)/L(equivalent to 200 cells/mm^(3))in AF as it has the higher sensitivity keeping a good specificity.CONCLUSION The automated cell count method should be preferred over the manual method to diagnose SBP because of its higher sensitivity.SBP definition,using the automated method,as polymorph cell count≥0.2 cells×10^(9)/L in AF would need to be considered in patients admitted with decompensated cirrhosis.展开更多
In-situ consolidation forming of high-performance thermoplastic composites by Automated Fiber Placement(AFP)is of significant interest in aerospace.During the laying process,the heating temperature has a great influen...In-situ consolidation forming of high-performance thermoplastic composites by Automated Fiber Placement(AFP)is of significant interest in aerospace.During the laying process,the heating temperature has a great influence on the quality of the formed components.A threedimensional heat transfer finite element model of Carbon Fiber(CF)/Polyether Ether Ketone(PEEK)heated by Slit Structure Nozzle Hot Gas Torch(SSNHGT)assisted AFP is proposed.The influence of gas flow rate,heat transfer distance,and laying speed on heating temperature is analysed.The results show that the overall temperature increases and then decreases as the gas flow rate increases.With the increase in heat transfer distance and laying speed,the overall temperature decreases.Meanwhile,the gas flow rate has the greatest influence on the temperature of CF/PEEK being heated,followed by the laying speed and finally the heat transfer distance.Furthermore,the model can also be extended to other fiber-reinforced polymer composites formed by hot gas torch assisted AFP,which can guide the optimization of process parameters for subsequent heating temperature control.展开更多
基金supported by the National Natural Science Foundation of China(Nos.32371470 and 82341019)the Department of Science and Technology of Guangdong Province(No.2023B0909020003).
文摘We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)within 10 min.Unlike conventional methods,sTED minimizes cell loss and enhances reproducibility,achieving>90%cell viability in mouse tissues and>60%in human tumors,with 1.5×10^(4)-2.5×10^(4)cells/mg yield from mouse liver.Tailored for biopsies and ultrasmall samples,sTED addresses critical standardization challenges in organoid-based research.
文摘Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhythm of 100-120 times/min for CC.[1,2]Interruptions of the CC must be as short as possible and are related to ventilation,defibrillation and turnover of the rescuers.Most of the automated external defibrillators(AEDs)require interruptions of the CC to perform rhythm analysis.Among the numerous marketed models of AEDs,some provide real-time feedback about the quality of the CC.
文摘Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape the future through intelligent automation.This is a key milestone on the way to achieving Rieter’s vision 2027-the fully automated spinning mill.
基金National Natural Science Foundation of China(31700644)Shandong Province Postdoctoral Innovation Project(SDCX-ZG-202400195)。
文摘The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and centrifugal honey separation,expose beekeepers to potential bee stings and frequently compromise honeycomb integrity.To address these limitations,we designed an automated honey-harvesting robot capable of autonomous frame extraction and beeswax removal.The robot mainly consists of a mobile mechanism equipped with image recognition for beehive localization,a magnetic adsorption-based beehive frame handling device(60.8 N maximum suction)coupled with a cross-slide mechanism for precise frame manipulation,and a thermal beeswax layer-melting apparatus,with optimal melting parameters(15 m/s airflow at 90℃ for 30 seconds)determined through rigorous thermal flow simulations utilizing FLUENT/Mechanical software.Field experiments demonstrated beehive frames handling success rate exceeding 85%,beeswax layer removal efficacy over 80% and damage of honeycombs below 30%.The experiment results validate the robot's operational reliability and its capacity to automate critical harvesting procedures.This study significantly reduces the labor intensity for beekeepers,effectively eliminates the risk of direct human-bee contact and improves the removal of beeswax layer,thereby catalyzing the modernization of the beekeeping industry.
文摘The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.
基金the Fundamental Research Funds for the Central Universities(2023YQTD02)National Key R&D Program of China(2023YFC2907501)。
文摘To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal drawing and explores automation and intelligent equipment solutions within the framework of the group coal drawing method.Numerical simulations were performed to investigate the impact of the Number of Drawing Openings(NDO)and rounds on top-coal recovery,coal draw-ing efficiency,and Top Coal Loss(TCL)mechanism.Subsequently,considering the recovery and coal drawing efficiency and by introducing the instantaneous gangue content and cumulative gangue content in simulations,the top-coal recovery,gangue content,and coal loss distribution when considering excessive coal drawing were analyzed.This established a foun-dation for determining the optimal NDO and shutdown timing.Finally,the key technical principle and automated control of a shock vibration and hyperspectral fusion recognition device were detailed,and an intelligent coal drawing control method based on this technology was developed.This technology enabled the precise control of the instantaneous gangue content(35%)during coal drawing.The top-coal recovery at the Tashan Mine 8222 working face increased by 14.78%,and the gangue content was controlled at~9%,consistent with the numerical simulation results.Thus,the reliability of the numerical simulation results was confirmed to a certain extent.Meanwhile,the single-group drawing method significantly enhanced the production capacity of the 8222 working face,achieving an annual output of 15 million tons.
基金supported by the National Key Project of the Ministry of Science and Technology of China(No.2022YFC3701200)the National Natural Science Foundation of China(No.42090030).
文摘Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.
基金supported by National Natural Science Foundation of China(No.62073212)Shanghai Science and Technology Commission(No.23ZR1426600).
文摘Automated guided vehicles(AGVs)are key equipment in automated container terminals(ACTs),and their operational efficiency can be impacted by conflicts and battery swapping.Additionally,AGVs have bidirectional transportation capabilities,allowing them tomove in the opposite directionwithout turning around,which helps reduce transportation time.This paper aims at the problem of AGV scheduling and bidirectional conflict-free routing with battery swapping in automated terminals.A bi-level mixed integer programming(MIP)model is proposed,taking into account task assignment,bidirectional conflict-free routing,and battery swapping.The upper model focuses on container task assignment and AGV battery swapping planning,while the lower model ensures conflict-free movement of AGVs.A double-threshold battery swapping strategy is introduced,allowing AGVs to utilize waiting time for loading for battery swapping.An improved differential evolution variable neighborhood search(IDE-VNS)algorithm is developed to solve the bi-level MIP model,aiming to minimize the completion time of all jobs.Experimental results demonstrate that compared to the differential evolution(DE)algorithm and the genetic algorithm(GA),the IDEVNS algorithmreduces fitness values by 44.49% and 45.22%,though it does increase computation time by 56.28% and 62.03%,respectively.Bidirectional transportation reduces the fitness value by an average of 10.97% when the container scale is small.As the container scale increases,the fitness value of bidirectional transportation gradually approaches that of unidirectional transportation.The results further show that the double-threshold battery swapping strategy enhances AGV utilization and reduces the fitness value.
基金supported by the Postdoctoral Fellowship Program of CPSF,China(No.GZC20232015)the China Postdoctoral Science Foundation(No.2024M752499)+3 种基金the Postdoctoral Project of Hubei Province,China(No.2024HBBHCXA076)the Wuhan East Lake New Technology Development Zone Open List Project,China(No.2022KJB128)the National Natural Science Foundation of China(No.51875428)the Fundamental Research Funds for the Central Universities,China(No.104972024RSCbs0013)。
文摘The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.
基金the Deanship of Graduate Studies and Scientific Research at Najran University,Saudi Arabia,for their financial support through the Easy Track Research program,grant code(NU/EFP/MRC/13).
文摘Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.
文摘This study analyzed the therapeutic effects of continuous ambulatory peritoneal dialysis(CAPD)and automated peritoneal dialysis(APD)on patients with end-stage renal disease.Fifty patients admitted between January 2024 and December 2024 were randomly assigned to two groups,with the observation group receiving APD and the reference group receiving CAPD.Renal function indicators,nutritional indicators,mineral metabolism,urine volume,and ultrafiltration volume changes were compared between the two groups.After treatment,the observation group showed lower renal function indicators,higher nutritional indicators,and better mineral metabolism levels compared to the reference group(P<0.05).While there was no significant difference in urine volume between the two groups(P>0.05),the observation group demonstrated superior ultrafiltration volume(P<0.05).These findings suggest that APD offers better clinical outcomes than CAPD by improving renal function,nutritional status,mineral metabolism regulation,and ultrafiltration efficiency in patients with end-stage renal disease.
基金Supported by National Key R&D Program of China(Grant No.2022YFB2503203)National Natural Science Foundation of China(Grant No.U1964206).
文摘Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.
基金supported by Jiangsu Provincial Key Research and Development Program(No.BE2023014-4).
文摘During the automated placement process of dry fibers,the positioning and fixation of dry fiber gauze belts are achieved by spraying setting agents.The amount of the setting agent is difficult to control when it is sprayed manually.Furthermore,it can also affect the permeability of the preform,resin injection and the quality of the vacuum assisted resin infusion(VARI)molding,resulting in a decrease in the mechanical properties of composite materials.This study utilizes dry fiber automated placement equipment and an automated spraying system to manufacture preform structures,followed by VARI process to prepare composite samples with varying setting agent contents.Subsequently,mechanical characterization including interlaminar shear,bending and tensile testing is conducted to investigate the influence of setting agent content on both the manufacturing process and the mechanical properties of composite products.The results show that the interlaminar shear strength,bending strength and tensile strength of the sample gradually decrease with the increase of the content of the setting agent.The optimal setting agent content for automated laying of dry fiber is determined to be 4%-6%,balancing the preformed body’s layup quality and its impact on mechanical properties.Compared with agent-free samples,this range results in reductions of 3% in interlaminar shear strength,9% in bending strength,11% in bending modulus,and 13%-16% in tensile strength.
文摘The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.
基金supported by the National Natural Science Foundation of China(51875061)China Scholarship Council(202206050107)。
文摘Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.
文摘The recent rapid development of China’s foreign trade has led to the significant increase in waterway transportation and automated container ports. Automated terminals can significantly improve the loading and unloading efficiency of container terminals. These terminals can also increase the port’s transportation volume while ensuring the quality of cargo loading and unloading, which has become an inevitable trend in the future development of ports. However, the continuous growth of the port’s transportation volume has increased the horizontal transportation pressure on the automated terminal, and the problems of route conflicts and road locks faced by automated guided vehicles (AGV) have become increasingly prominent. Accordingly, this work takes Xiamen Yuanhai automated container terminal as an example. This work focuses on analyzing the interference problem of path conflict in its horizontal transportation AGV scheduling. Results show that path conflict, the most prominent interference factor, will cause AGV scheduling to be unable to execute the original plan. Consequently, the disruption management was used to establish a disturbance recovery model, and the Dijkstra algorithm for combining with time windows is adopted to plan a conflict-free path. Based on the comparison with the rescheduling method, the research obtains that the deviation of the transportation path and the deviation degree of the transportation path under the disruption management method are much lower than those of the rescheduling method. The transportation path deviation degree of the disruption management method is only 5.56%. Meanwhile, the deviation degree of the transportation path under the rescheduling method is 44.44%.
基金supported by the State Administration of Science,Technology and Industry for National Defence,PRC(KJSP2020020303)the National Institute of Natural Hazards,Ministry of Emergency Management of China(ZDJ2021-12)。
文摘Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.
基金supported in part by the National Natural Science Foundation of China (61973219,U21A2019,61873058)the Hainan Province Science and Technology Special Fund (ZDYF2022SHFZ105)。
文摘Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
文摘BACKGROUND Spontaneous bacterial peritonitis(SBP)is one of the most important complications of patients with liver cirrhosis entailing high morbidity and mortality.Making an accurate early diagnosis of this infection is key in the outcome of these patients.The current definition of SBP is based on studies performed more than 40 years ago using a manual technique to count the number of polymorphs in ascitic fluid(AF).There is a lack of data comparing the traditional cell count method with a current automated cell counter.Moreover,current international guidelines do not mention the type of cell count method to be employed and around half of the centers still rely on the traditional manual method.AIM To compare the accuracy of polymorph count on AF to diagnose SBP between the traditional manual cell count method and a modern automated cell counter against SBP cases fulfilling gold standard criteria:Positive AF culture and signs/symptoms of peritonitis.METHODS Retrospective analysis including two cohorts:Cross-sectional(cohort 1)and case-control(cohort 2),of patients with decompensated cirrhosis and ascites.Both cell count methods were conducted simultaneously.Positive SBP cases had a pathogenic bacteria isolated on AF and signs/symptoms of peritonitis.RESULTS A total of 137 cases with 5 positive-SBP,and 85 cases with 33 positive-SBP were included in cohort 1 and 2,respectively.Positive-SBP cases had worse liver function in both cohorts.The automated method showed higher sensitivity than the manual cell count:80%vs 52%,P=0.02,in cohort 2.Both methods showed very good specificity(>95%).The best cutoff using the automated cell counter was polymorph≥0.2 cells×10^(9)/L(equivalent to 200 cells/mm^(3))in AF as it has the higher sensitivity keeping a good specificity.CONCLUSION The automated cell count method should be preferred over the manual method to diagnose SBP because of its higher sensitivity.SBP definition,using the automated method,as polymorph cell count≥0.2 cells×10^(9)/L in AF would need to be considered in patients admitted with decompensated cirrhosis.
基金co-supported by the National Natural Science Foundation of China(No.52205460)the Heilongjiang Provincial Natural Science Foundation of China(No.LH2023E041)the China Scholarship Council(CSC)to study abroad at the Nanyang Technological University.
文摘In-situ consolidation forming of high-performance thermoplastic composites by Automated Fiber Placement(AFP)is of significant interest in aerospace.During the laying process,the heating temperature has a great influence on the quality of the formed components.A threedimensional heat transfer finite element model of Carbon Fiber(CF)/Polyether Ether Ketone(PEEK)heated by Slit Structure Nozzle Hot Gas Torch(SSNHGT)assisted AFP is proposed.The influence of gas flow rate,heat transfer distance,and laying speed on heating temperature is analysed.The results show that the overall temperature increases and then decreases as the gas flow rate increases.With the increase in heat transfer distance and laying speed,the overall temperature decreases.Meanwhile,the gas flow rate has the greatest influence on the temperature of CF/PEEK being heated,followed by the laying speed and finally the heat transfer distance.Furthermore,the model can also be extended to other fiber-reinforced polymer composites formed by hot gas torch assisted AFP,which can guide the optimization of process parameters for subsequent heating temperature control.
