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
In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
BACKGROUND Automated insulin delivery(AID)systems have demonstrated benefits in managing patients with type 2 diabetes(T2D),but data are still limited.Moreover,the efficacy and safety of the AID systems in these patie...BACKGROUND Automated insulin delivery(AID)systems have demonstrated benefits in managing patients with type 2 diabetes(T2D),but data are still limited.Moreover,the efficacy and safety of the AID systems in these patients have been inadequately explored by systematic reviews and meta-analyses.AIM To provide a comprehensive understanding of the optimal use of AID in managing insulin-treated outpatients with T2D.METHODS A systematic search of multiple databases and registries,including MEDLINE,Scopus,Web of Science,Cochrane Library,and ClinicalTrials.gov,was conducted from inception to May 15,2025,to identify studies on AID use for outpatients with T2D.The co-primary outcomes were the change in glycated hemoglobin(HbA1c)and continuous glucose monitoring(CGM)metrics.Statistical analyses were conducted using Review Manager Web software with random-effects models and the inverse variance statistical method.The results were presented as mean differences(MDs)or risk ratios(RRs)with 95%CI.RESULTS A total of 15 studies with 28985 participants were identified,including 6 randomized trials(n=748;3 crossover and 3 parallel-group trials)and 9 single-arm studies.All included randomized trials raised some concerns,and the single-arm studies had serious risks of overall bias.Meta-analysis of randomized trials showed that AID is more effective than the control group in lowering HbA1c(MD:-0.89%,95%CI:-1.32 to-0.46,P<0.0001,I2=82%).Compared to control interventions,AID use was linked to a higher percentage of time in range(MD:19.25%,95%CI:11.43-27.06,P<0.00001,I2=74%)and a lower percentage of time above range>10 mmol/L(MD:-19.48%,95%CI:-27.14 to-11.82,P<0.00001,I2=73%);however,time below range remained similar between the two groups.The mean sensor glucose level was lower in the AID group;however,the coefficient of variation of glucose was the same in both groups.AID use also led to a reduction in insulin dose,but this is not a consistent finding across all study designs.The risks of serious adverse events(AEs)and severe hypoglycemia were similar in both groups;however,AID use raised the risk of device deficiency.Single-arm studies with participants using AID systems also demonstrated reductions in HbA1c(ranging from 0.7%to 2.07%)and improvements in CGM metrics,along with acceptable safety data.CONCLUSION Based on short-term study data,the use of AID systems in outpatients with T2D appears to improve glycemic outcomes and CGM metrics,with no significant AEs.Larger and longer-term randomized controlled trials involving diverse populations,along with a cost-benefit analysis,are needed to guide more informed clinical practice decisions.展开更多
Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.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
BACKGROUND End-stage kidney disease is a growing global health burden with many patients requiring urgent kidney replacement therapy.Urgent-start peritoneal dialysis(PD)has emerged as a viable alternative to hemodialy...BACKGROUND End-stage kidney disease is a growing global health burden with many patients requiring urgent kidney replacement therapy.Urgent-start peritoneal dialysis(PD)has emerged as a viable alternative to hemodialysis particularly in resourcelimited settings.However,concerns remain regarding catheter-related complications associated with early initiation of PD.Automated PD(APD)offers enhanced flexibility and fluid management,but evidence regarding its safety and outcomes in urgent-start scenarios with shortened break-in periods is limited.AIM To evaluate the clinical outcomes and biochemical changes associated with urgent-start APD with a shortened break-in period.METHODS This was a single center,observational study that included 62 patients with endstage kidney disease who required urgent-start dialysis,underwent PD catheter placement,and received APD.Patients were stratified based on catheter opening time(<12 hours vs>12 hours).Catheter-related complications,biochemical parameters,and dialysis efficacy were analyzed.RESULTS The median catheter opening time was 11 h(interquartile range:8-14 hours).No significant differences in catheterrelated complications were observed between groups(P>0.05).Catheter dysfunction,migration,leakage,and replacement occurred in 14.5%,9.7%,12.9%,and 11.3%of patients,respectively.APD led to significant reductions in serum creatinine,blood urea nitrogen,urea,phosphorus,and potassium(P<0.05),alongside correction of metabolic acidosis.No cases of peritonitis or hemoperitoneum were observed.CONCLUSION Urgent-start APD with shortened break-in appears safe with low complication rates and improved biochemical outcomes.展开更多
1 Introduction Dissecting the dynamics of cell statesiscrucial for understanding various biological processes,such as tissue development and tumor drug responses.Recent advancements in single-cell lineage tracing(scLT...1 Introduction Dissecting the dynamics of cell statesiscrucial for understanding various biological processes,such as tissue development and tumor drug responses.Recent advancements in single-cell lineage tracing(scLT)technologies provide effective ways to track single-cell lineages through heritable cellular barcodes,while simultaneously detecting the molecular states of cells by sequencing[1].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
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
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
Large language model-based agent systems are emerging as transformative technologies in chemical process simulation, enhancing efficiency, accuracy, and decision-making. By automating data analysis across structured a...Large language model-based agent systems are emerging as transformative technologies in chemical process simulation, enhancing efficiency, accuracy, and decision-making. By automating data analysis across structured and unstructured sources—including process parameters, experimental results, simulation data, and textual specifications—these systems address longstanding challenges such as manual parameter tuning, subjective expert reliance, and the gap between theoretical models and industrial application. This paper reviews the key barriers to broader adoption of large language model-based agent systems, including unstable software interfaces, limited dynamic modeling accuracy, and difficulties in multimodal data integration, which hinder scalable deployment. We then survey recent progress in domain-specific foundation models, model interpretability techniques, and industrial-grade validation platforms. Building on these insights, we propose a technical framework centered on three pillars: multimodal task perception, autonomous planning, and knowledge-driven iterative optimization. This framework supports adaptive reasoning and robust execution in complex simulation environments. Finally, we outline a next-generation intelligent paradigm where natural language-driven agent workflows unify high-level strategic intent with automated task execution. The paper concludes by identifying future research directions to enhance robustness, adaptability, and safety, paving the way for practical integration of large language model based agent systems into industrial-scale chemical process simulation.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
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supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
文摘BACKGROUND Automated insulin delivery(AID)systems have demonstrated benefits in managing patients with type 2 diabetes(T2D),but data are still limited.Moreover,the efficacy and safety of the AID systems in these patients have been inadequately explored by systematic reviews and meta-analyses.AIM To provide a comprehensive understanding of the optimal use of AID in managing insulin-treated outpatients with T2D.METHODS A systematic search of multiple databases and registries,including MEDLINE,Scopus,Web of Science,Cochrane Library,and ClinicalTrials.gov,was conducted from inception to May 15,2025,to identify studies on AID use for outpatients with T2D.The co-primary outcomes were the change in glycated hemoglobin(HbA1c)and continuous glucose monitoring(CGM)metrics.Statistical analyses were conducted using Review Manager Web software with random-effects models and the inverse variance statistical method.The results were presented as mean differences(MDs)or risk ratios(RRs)with 95%CI.RESULTS A total of 15 studies with 28985 participants were identified,including 6 randomized trials(n=748;3 crossover and 3 parallel-group trials)and 9 single-arm studies.All included randomized trials raised some concerns,and the single-arm studies had serious risks of overall bias.Meta-analysis of randomized trials showed that AID is more effective than the control group in lowering HbA1c(MD:-0.89%,95%CI:-1.32 to-0.46,P<0.0001,I2=82%).Compared to control interventions,AID use was linked to a higher percentage of time in range(MD:19.25%,95%CI:11.43-27.06,P<0.00001,I2=74%)and a lower percentage of time above range>10 mmol/L(MD:-19.48%,95%CI:-27.14 to-11.82,P<0.00001,I2=73%);however,time below range remained similar between the two groups.The mean sensor glucose level was lower in the AID group;however,the coefficient of variation of glucose was the same in both groups.AID use also led to a reduction in insulin dose,but this is not a consistent finding across all study designs.The risks of serious adverse events(AEs)and severe hypoglycemia were similar in both groups;however,AID use raised the risk of device deficiency.Single-arm studies with participants using AID systems also demonstrated reductions in HbA1c(ranging from 0.7%to 2.07%)and improvements in CGM metrics,along with acceptable safety data.CONCLUSION Based on short-term study data,the use of AID systems in outpatients with T2D appears to improve glycemic outcomes and CGM metrics,with no significant AEs.Larger and longer-term randomized controlled trials involving diverse populations,along with a cost-benefit analysis,are needed to guide more informed clinical practice decisions.
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
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.
文摘BACKGROUND End-stage kidney disease is a growing global health burden with many patients requiring urgent kidney replacement therapy.Urgent-start peritoneal dialysis(PD)has emerged as a viable alternative to hemodialysis particularly in resourcelimited settings.However,concerns remain regarding catheter-related complications associated with early initiation of PD.Automated PD(APD)offers enhanced flexibility and fluid management,but evidence regarding its safety and outcomes in urgent-start scenarios with shortened break-in periods is limited.AIM To evaluate the clinical outcomes and biochemical changes associated with urgent-start APD with a shortened break-in period.METHODS This was a single center,observational study that included 62 patients with endstage kidney disease who required urgent-start dialysis,underwent PD catheter placement,and received APD.Patients were stratified based on catheter opening time(<12 hours vs>12 hours).Catheter-related complications,biochemical parameters,and dialysis efficacy were analyzed.RESULTS The median catheter opening time was 11 h(interquartile range:8-14 hours).No significant differences in catheterrelated complications were observed between groups(P>0.05).Catheter dysfunction,migration,leakage,and replacement occurred in 14.5%,9.7%,12.9%,and 11.3%of patients,respectively.APD led to significant reductions in serum creatinine,blood urea nitrogen,urea,phosphorus,and potassium(P<0.05),alongside correction of metabolic acidosis.No cases of peritonitis or hemoperitoneum were observed.CONCLUSION Urgent-start APD with shortened break-in appears safe with low complication rates and improved biochemical outcomes.
基金supported by the National Key Research and Development Program of China(Nos.2020YFA0712403 and 2021YFF1200901)the National Natural Science Foundation of China(NSFC)(Grant Nos.62133006 and 92268104)+1 种基金the Tsinghua University Initiative Scientific Research Program(No.20221080076)the China Postdoctoral Science Foundation(No.2022M721839).
文摘1 Introduction Dissecting the dynamics of cell statesiscrucial for understanding various biological processes,such as tissue development and tumor drug responses.Recent advancements in single-cell lineage tracing(scLT)technologies provide effective ways to track single-cell lineages through heritable cellular barcodes,while simultaneously detecting the molecular states of cells by sequencing[1].
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.
文摘Large language model-based agent systems are emerging as transformative technologies in chemical process simulation, enhancing efficiency, accuracy, and decision-making. By automating data analysis across structured and unstructured sources—including process parameters, experimental results, simulation data, and textual specifications—these systems address longstanding challenges such as manual parameter tuning, subjective expert reliance, and the gap between theoretical models and industrial application. This paper reviews the key barriers to broader adoption of large language model-based agent systems, including unstable software interfaces, limited dynamic modeling accuracy, and difficulties in multimodal data integration, which hinder scalable deployment. We then survey recent progress in domain-specific foundation models, model interpretability techniques, and industrial-grade validation platforms. Building on these insights, we propose a technical framework centered on three pillars: multimodal task perception, autonomous planning, and knowledge-driven iterative optimization. This framework supports adaptive reasoning and robust execution in complex simulation environments. Finally, we outline a next-generation intelligent paradigm where natural language-driven agent workflows unify high-level strategic intent with automated task execution. The paper concludes by identifying future research directions to enhance robustness, adaptability, and safety, paving the way for practical integration of large language model based agent systems into industrial-scale chemical process simulation.
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.
