Rare earth metal elements include lanthanide elements as well as scandium and yttrium,totaling seventeen metal elements.Due to the wide application prospects of rare earth metal elements in various fields such as lumi...Rare earth metal elements include lanthanide elements as well as scandium and yttrium,totaling seventeen metal elements.Due to the wide application prospects of rare earth metal elements in various fields such as luminescent materials,magnetic materials,catalytic materials,electronic devices,they have an important strategic position.In the field of electrocatalysis,rare earth metal elements have great potential for development due to their unique 4f electron layer structure,spin orbit coupling,high reactivity,controllable coordination number,and rich optical properties.However,there is currently a lack of systematic reviews on the modification strategies of rare earth metal elements and the latest developments in electrocatalysis.Therefore,in order to stimulate the enthusiasm of researchers,this review focuses on the application progress of rare earth metal element modified metal oxides in multiple fields such as wastewater treatment,hydrogen peroxide synthesis,hydrogen evolution reaction(HER),carbon dioxide reduction reaction(CO_(2)RR),nitrogen reduction reaction(NRR)and machine learning assisted research.In depth analysis of its electrocatalytic mechanism in various application scenarios and key factors affecting electrocatalytic performance.This review is of great significance for further developing high-performance and multifunctional electrocatalysts,and is expected to provide strong support for the development of energy,environment,and chemical industries.展开更多
Accurately predicting the knee point cycle number,marking the onset of accelerated capacity degradation,is the first critical signal for managing lithium-ion battery life cycles in electric vehicles and stationary ene...Accurately predicting the knee point cycle number,marking the onset of accelerated capacity degradation,is the first critical signal for managing lithium-ion battery life cycles in electric vehicles and stationary energy storage systems.However,the inherent electrochemo-mechanical-thermal complexities of battery aging present significant challenges for physics-based models and machine-learning models,often leading to reduced predictive accuracy.Our study developed a comprehensive dataset comprising 20 lithium nickel manganese cobalt oxide(NCM)/graphite cells(0.5-1 C)from our lab and 162 commercial lithium iron phosphate(LFP)/graphite cells(3-6 C)from the public database,with knee point observed between 100 and 1000 cycles.We proposed a new strategy to extract novel features with strong physical context from early-cycle voltage curves,enabling precise knee point predictions across the chemistries without the need for extensive cycling histories.Our model achieved a mean absolute percentage error(MAPE)of 7%for knee point prediction using five selected features.Remarkably,the model yielded 8%MAPE with only one single feature across the initial 200 cycles,and 10%MAPE when applying five features across the initial 50 cycles,spanning different battery chemistries.This work highlights the potential of integrating multi-chemistry datasets with data-driven modeling to forecast aging patterns across diverse battery chemistries,advancing battery longevity and reliability.展开更多
Deep learning(DL),derived from the domain of Artificial Neural Networks(ANN),forms one of the most essential components of modern deep learning algorithms.DL segmentation models rely on layer-by-layer convolution-base...Deep learning(DL),derived from the domain of Artificial Neural Networks(ANN),forms one of the most essential components of modern deep learning algorithms.DL segmentation models rely on layer-by-layer convolution-based feature representation,guided by forward and backward propagation.Acritical aspect of this process is the selection of an appropriate activation function(AF)to ensure robustmodel learning.However,existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning.Most current research on activation function design focuses on classification tasks using natural image datasets such asMNIST,CIFAR-10,and CIFAR-100.To address this gap,this study proposesMed-ReLU,a novel activation function specifically designed for medical image segmentation.Med-ReLU prevents deep learning models fromsuffering dead neurons or vanishing gradient issues.It is a hybrid activation function that combines the properties of ReLU and Softsign.For positive inputs,Med-ReLU adopts the linear behavior of ReLU to avoid vanishing gradients,while for negative inputs,it exhibits the Softsign’s polynomial convergence,ensuring robust training and avoiding inactive neurons across the training set.The training performance and segmentation accuracy ofMed-ReLU have been thoroughly evaluated,demonstrating stable learning behavior and resistance to overfitting.It consistently outperforms state-of-the-art activation functions inmedical image segmentation tasks.Designed as a parameter-free function,Med-ReLU is simple to implement in complex deep learning architectures,and its effectiveness spans various neural network models and anomaly detection scenarios.展开更多
The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big da...The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.展开更多
Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid ...Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).展开更多
The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.C...The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.展开更多
Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO_(2max)is infeas...Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction algorithms.Methods:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)values.We developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging cohorts.Finally,we quantified the associations between measured and predicted VO_(2max)and mortality.Results:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/min.Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,respectively.Incremental quartiles of measured VO_(2max)showed an inverse gradient in mortality risk.Predicted VO_(2max)variables yielded similar effect estimates but were not robust to adjustment.Conclusion:Measured VO_(2max)is a strong predictor of mortality.Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment.Future studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.展开更多
Evolution and interaction of plane waves of the multidimensional zero-pressure gas dynamics system leads to the study of the corresponding one dimensional system.In this paper,we study the initial value problem for on...Evolution and interaction of plane waves of the multidimensional zero-pressure gas dynamics system leads to the study of the corresponding one dimensional system.In this paper,we study the initial value problem for one dimensional zero-pressure gas dynamics system.Here the first equation is the Burgers equation and the second one is the continuity equation.We consider the solution with initial data in the space of bounded Borel measures.First we prove a general existence result in the algebra of generalized functions of Colombeau.Then we study in detail special solutions withδ-measures as initial data.We study interaction of waves originating from initial data concentrated on two point sources and interaction with classical shock/rarefaction waves.This gives an understanding of plane-wave interactions in the multidimensional case.We use the vanishing viscosity method in our analysis as this gives the physical solution.展开更多
Next-generation sequencing data are widely utilised for various downstream applications in bioinformatics and numerous techniques have been developed for PCR-deduplication and error-correction to eliminate bias and er...Next-generation sequencing data are widely utilised for various downstream applications in bioinformatics and numerous techniques have been developed for PCR-deduplication and error-correction to eliminate bias and errors introduced during the sequencing.This study first-time provides a joint overview of recent advances in PCR-deduplication and error-correction on short reads.In particular,we utilise UMI-based PCR-deduplication strategies and sequencing data to assess the performance of the solelycomputational PCR-deduplication approaches and investigate how error correction affects the performance of PCR-deduplication.Our survey and comparative analysis reveal that the deduplicated reads generated by the solely-computational PCR-deduplication and error-correction methods exhibit substantial differences and divergence from the sets of reads obtained by the UMI-based deduplication methods.The existing solelycomputational PCR-deduplication and error-correction tools can eliminate some errors but still leave hundreds of thousands of erroneous reads uncorrected.All the error-correction approaches raise thousands or more new sequences after correction which do not have any benefit to the PCRdeduplication process.Based on our findings,we discuss future research directions and make suggestions for improving existing computational approaches to enhance the quality of short-read sequencing data.展开更多
With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as ...With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as data owners,urgently require a secure and fair marketplace to negotiate with the data consumers.In this paper,we introduce a novel federated data acquisition market that consists of a group of local data aggregators(LDAs);a number of data owners;and,one data union to coordinate the data trade with the data consumers.Data consumers offer each data owner an individual price to stimulate participation.The mobile data owners naturally cooperate to gossip about individual prices with each other,which also leads to price fluctuation.It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario.Hence,we propose a data pricing strategy based on mean-field game(MFG)theory to model the data owners’cost considering the price dynamics.We then investigate the interactions among the LDAs by using the distribution of price,namely the mean-field term.A numerical method is used to solve the proposed pricing strategy.The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme.The result further demonstrates that the influential LDAs determine the final price distribution.Last but not least,it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.展开更多
Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surround-ing rock in fron...Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surround-ing rock in front of the tunnel face.In this work,a forward-prediction method for tunnel geology and classification of surrounding rock is developed based on seismic wave velocity layered tomography.In particular,for the problem of strong multi-solution of wave velocity inversion caused by few ray paths in the narrow space of the tunnel,a layered inversion based on regularization is proposed.By reducing the inversion area of each iteration step and applying straight-line interface assumption,the convergence and accuracy of wave velocity inversion are effectively improved.Furthermore,a surrounding rock classification network based on autoencoder is constructed.The mapping relationship between wave velocity and classification of surrounding rock is established with density,Poisson’s ratio and elastic modulus as links.Two numerical examples with geological conditions similar to that in the field tunnel and a field case study in an urban subway tunnel verify the potential of the proposed method for practical application.展开更多
Consciousness is one of the unique features of creatures,and is also the root of biological intelligence.Up to now,all machines and robots havenJt had consciousness.Then,will the artificial intelligence(AI)be consciou...Consciousness is one of the unique features of creatures,and is also the root of biological intelligence.Up to now,all machines and robots havenJt had consciousness.Then,will the artificial intelligence(AI)be conscious?Will robots have real intelligence without consciousness?The most primitive consciousness is the perception and expression of selfexistence.In order to perceive the existence of the concept of‘Ij,a creature must first have a perceivable boundary such as skin to separate‘I’from‘non-1’.For robots,to have the self-awareness,they also need to be wrapped by a similar sensory membrane.Nowadays,as intelligent tools,AI systems should also be regarded as the external extension of human intelligence.These tools are unconscious.The development of AI shows that intelligence can exist without consciousness.When human beings enter into the era of life intelligence from AI,it is not the AI became conscious,but that conscious lives will have strong AI.Therefore,it becomes more necessary to be careful on applying AI to living creatures,even to those lower-level animals with only consciousness.The subversive revolution of such application may produce more careful thinking.展开更多
In order to promote the application of clean energy technology in clothing and promote the integration of industrial development and artificial intelligence wearable technology,this study elaborates the energy applica...In order to promote the application of clean energy technology in clothing and promote the integration of industrial development and artificial intelligence wearable technology,this study elaborates the energy application characteristics of intelligent wearable products at home and abroad and its application in different fields,aiming at the current research status of wearable technology in the field of textile and clothing.The wearable distributed generation technology is classified,and a creative clothing design for detecting climate temperature is designed.Based on the monitoring of body temperature,the changes in clothing pattern color can reflect people’s health and emotional status.At the same time,it can also be applied to the screening of abnormal body temperature during the COVID-19.展开更多
As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalen...As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalent in today’s digital world.In this study,we propose two high-performance R solutions for GWR via Multi-core Parallel(MP)and Compute Unified Device Architecture(CUDA)techniques,respectively GWR-MP and GWR-CUDA.We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models(GWmodel),Multi-scale GWR(MGWR)and Fast GWR(FastGWR).Results showed that all five solutions perform differently across varying sample sizes,with no single solution a clear winner in terms of computational efficiency.Specifically,solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size.For a large sample size,GWR-MP and FastGWR provided coherent solutions on a Personal Computer(PC)with a common multi-core configuration,GWR-MP provided more efficient computing capacity for each core or thread than FastGWR.For cases when the sample size was very large,and for these cases only,GWR-CUDA provided the most efficient solution,but should note its I/O cost with small samples.In summary,GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones,where for certain data-rich GWR studies,they should be preferred.展开更多
The decision-making method of tunnel boring machine(TBM)operating parameters has a significant guiding significance for TBM safe and efficient construction,and it has been one of the TBM tunneling research hotspots.Fo...The decision-making method of tunnel boring machine(TBM)operating parameters has a significant guiding significance for TBM safe and efficient construction,and it has been one of the TBM tunneling research hotspots.For this purpose,this paper introduces an intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization.First,linear cutting tests and numerical simulations are used to investigate the physical rules between different cutting parameters(penetration,cutter spacing,etc.)and rock compressive strength.Second,a dual-driven mapping of rock parameters and TBM operating parameters based on data mining and physical rules of rock breaking is established with high accuracy by combining rock-breaking rules and deep neural networks(DNNs).The decision-making method is established by dual-driven mapping,using the effective rock-breaking capacity and the rated value of mechanical parameters as constraints and the total excavation cost as the optimization objective.The best operational parameters can be obtained by searching for the revolutions per minute and penetration that correspond to the extremum of the constrained objective function.The practicability and effectiveness of the developed decision-making model is verified in the SecondWater Source Channel of Hangzhou,China,resulting in the average penetration rate increasing by 11.3%and the total cost decreasing by 10%.展开更多
This study uses <span style="font-family:Verdana;">an empirical</span><span style="font-family:Verdana;"> analysis to quantify the downstream analysis effects of data pre-processi...This study uses <span style="font-family:Verdana;">an empirical</span><span style="font-family:Verdana;"> analysis to quantify the downstream analysis effects of data pre-processing choices. Bootstrap data simulation is used to measure the bias-variance decomposition of an empirical risk function, mean square error (MSE). Results of the risk function decomposition are used to measure the effects of model development choices on </span><span style="font-family:Verdana;">model</span><span style="font-family:Verdana;"> bias, variance, and irreducible error. Measurements of bias and variance are then applied as diagnostic procedures for model pre-processing and development. Best performing model-normalization-data structure combinations were found to illustrate the downstream analysis effects of these model development choices. </span><span style="font-family:Verdana;">In addition</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;">, results found from simulations were verified and expanded to include additional data characteristics (imbalanced, sparse) by testing on benchmark datasets available from the UCI Machine Learning Library. Normalization results on benchmark data were consistent with those found using simulations, while also illustrating that more complex and/or non-linear models provide better performance on datasets with additional complexities. Finally, applying the findings from simulation experiments to previously tested applications led to equivalent or improved results with less model development overhead and processing time.</span>展开更多
The printed circuit board(PCB)is an indispensable component of electronic products,which deter-mines the quality of these products.With the development and advancement of manufacturing technology,the layout and struct...The printed circuit board(PCB)is an indispensable component of electronic products,which deter-mines the quality of these products.With the development and advancement of manufacturing technology,the layout and structure of PCB are getting complicated.However,there are few effective and accurate PCB defect detection methods.There are high requirements for the accuracy of PCB defect detection in the actual pro-duction environment,so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method(DDMV)and the defect detection by multi-model learning method(DDML).With the purpose of reducing wrong and missing detection,the DDMV and DDML integrate multiple defect detection networks with different fusion strategies.The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets.The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score,and the area under curve value of DDML is also higher than that of any other individual detection model.Furthermore,compared with DDMV,the DDML with an automatic machine learning method achieves the best performance in PCB defect detection,and the Fl-score on the two datasets can reach 99.7%and 95.6%respectively.展开更多
The quality of products manufactured or procured by organizations is an important aspect of their survival in the global market. The quality control processes put in place by organizations can be resource-intensive bu...The quality of products manufactured or procured by organizations is an important aspect of their survival in the global market. The quality control processes put in place by organizations can be resource-intensive but substantial savings can be realized by using acceptance sampling in conjunction with batch testing. This paper considers the batch testing model based on the quality control process where batches that test positive are re-tested. The results show that re-testing greatly improves the efficiency over one stage batch testing based on quality control. This is observed using Asymptotic Relative Efficiency (ARE), where for values of </span><i><span style="font-family:Verdana;">p</span></i><span style="font-family:Verdana;"> computed ARE > 1 implying that our estimator has a smaller variance than the one-stage batch testing. Also, it was found that the model is more efficient than the classical two-stage batch testing for relatively high values of proportion.展开更多
Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles.At the same time,the predicate-argument structure in a sentence is important information for semantic r...Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles.At the same time,the predicate-argument structure in a sentence is important information for semantic role labeling task.In this work,we introduce the auxiliary deep neural network model,which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling.Based on the framework of joint learning,part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling.In addition,we introduce the argument recognition layer in the training process of the main task-semantic role labeling,so the argument-related structural information selected by the predicate through the attention mechanism is used to assist the main task.Because the model makes full use of the semantic dependency between part-of-speech and semantic roles and the structural information of predicate-argument,our model achieved the F1 value of 89.0%on the WSJ test set of CoNLL2005,which is superior to existing state-of-the-art model about 0.8%.展开更多
BACKGROUND Alpha-1 antitrypsin deficiency is a rare genetic disease and a leading cause of inherited alterations in plasma protein metabolism(APPM).AIM To understand the prevalence,burden and progression of liver dise...BACKGROUND Alpha-1 antitrypsin deficiency is a rare genetic disease and a leading cause of inherited alterations in plasma protein metabolism(APPM).AIM To understand the prevalence,burden and progression of liver disease in patients with APPM including alpha-1 antitrypsin deficiency.METHODS We conducted a retrospective analysis of anonymized patient-level claims data from a German health insurance provider(AOK PLUS).The APPM cohort comprised patients with APPM(identified using the German Modification of the International Classification of Diseases-10th Revision[ICD-10-GM]code E88.0 between 01/01/2010-30/09/2020)and incident liver disease(ICD-10-GM codes K74,K70.2-3 and K71.7 between 01/01/2012-30/09/2020).The control cohort comprised patients without APPM but with incident liver disease.Outcomes were incidence/prevalence of liver disease in patients with APPM,demographics/baseline characteristics,diagnostic procedures,progression-free survival(PFS),disease progression and mortality.RESULTS Overall,2680 and 26299 patients were included in the APPM(fibrosis,96;cirrhosis,2584)and control(fibrosis,1444;cirrhosis,24855)cohorts,respectively.Per 100000 individuals,annual incidence and prevalence of APPM and liver disease was 10-15 and 36-51,respectively.In the APPM cohort,median survival was 4.7 years[95%confidence interval(CI):3.5-7.0]and 2.5 years(95%CI:2.3-2.8)in patients with fibrosis and cirrhosis,respectively.A higher proportion of patients in the APPM cohort experienced disease progression(92.0%)compared with the control cohort(67.2%).Median PFS was shorter in the APPM cohort(0.9 years,95%CI:0.7-1.1)compared with the control cohort(3.7 years,95%CI:3.6-3.8;P<0.001).Patients with cirrhosis in the control cohort had longer event-free survival for ascites,hepatic encephalopathy,hepatic failure and esophageal/gastric varices than patients with cirrhosis in the APPM cohort(P<0.001).Patients with fibrosis in the control cohort had longer event-free survival for ascites,cirrhosis,hepatic failure and esophageal/gastric varices than patients with fibrosis in the APPM cohort(P<0.001).In the APPM cohort,the most common diagnostic procedures within 12 mo after the first diagnosis of liver disease were imaging procedures(66.3%)and laboratory tests(51.0%).CONCLUSION Among patients with liver disease,those with APPM experience substantial burden and earlier liver disease progression than patients without APPM.展开更多
基金supported by the National Key Research and Development Program of China(No.2023YFC3708005)The Fundamental Research Funds for the Central Universities,Nankai University(No.63241208)supported by the National Natural Science Foundation of China(Nos.21872102 and 22172080)。
文摘Rare earth metal elements include lanthanide elements as well as scandium and yttrium,totaling seventeen metal elements.Due to the wide application prospects of rare earth metal elements in various fields such as luminescent materials,magnetic materials,catalytic materials,electronic devices,they have an important strategic position.In the field of electrocatalysis,rare earth metal elements have great potential for development due to their unique 4f electron layer structure,spin orbit coupling,high reactivity,controllable coordination number,and rich optical properties.However,there is currently a lack of systematic reviews on the modification strategies of rare earth metal elements and the latest developments in electrocatalysis.Therefore,in order to stimulate the enthusiasm of researchers,this review focuses on the application progress of rare earth metal element modified metal oxides in multiple fields such as wastewater treatment,hydrogen peroxide synthesis,hydrogen evolution reaction(HER),carbon dioxide reduction reaction(CO_(2)RR),nitrogen reduction reaction(NRR)and machine learning assisted research.In depth analysis of its electrocatalytic mechanism in various application scenarios and key factors affecting electrocatalytic performance.This review is of great significance for further developing high-performance and multifunctional electrocatalysts,and is expected to provide strong support for the development of energy,environment,and chemical industries.
基金supported by Startup funding from the University of Delaware。
文摘Accurately predicting the knee point cycle number,marking the onset of accelerated capacity degradation,is the first critical signal for managing lithium-ion battery life cycles in electric vehicles and stationary energy storage systems.However,the inherent electrochemo-mechanical-thermal complexities of battery aging present significant challenges for physics-based models and machine-learning models,often leading to reduced predictive accuracy.Our study developed a comprehensive dataset comprising 20 lithium nickel manganese cobalt oxide(NCM)/graphite cells(0.5-1 C)from our lab and 162 commercial lithium iron phosphate(LFP)/graphite cells(3-6 C)from the public database,with knee point observed between 100 and 1000 cycles.We proposed a new strategy to extract novel features with strong physical context from early-cycle voltage curves,enabling precise knee point predictions across the chemistries without the need for extensive cycling histories.Our model achieved a mean absolute percentage error(MAPE)of 7%for knee point prediction using five selected features.Remarkably,the model yielded 8%MAPE with only one single feature across the initial 200 cycles,and 10%MAPE when applying five features across the initial 50 cycles,spanning different battery chemistries.This work highlights the potential of integrating multi-chemistry datasets with data-driven modeling to forecast aging patterns across diverse battery chemistries,advancing battery longevity and reliability.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Deep learning(DL),derived from the domain of Artificial Neural Networks(ANN),forms one of the most essential components of modern deep learning algorithms.DL segmentation models rely on layer-by-layer convolution-based feature representation,guided by forward and backward propagation.Acritical aspect of this process is the selection of an appropriate activation function(AF)to ensure robustmodel learning.However,existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning.Most current research on activation function design focuses on classification tasks using natural image datasets such asMNIST,CIFAR-10,and CIFAR-100.To address this gap,this study proposesMed-ReLU,a novel activation function specifically designed for medical image segmentation.Med-ReLU prevents deep learning models fromsuffering dead neurons or vanishing gradient issues.It is a hybrid activation function that combines the properties of ReLU and Softsign.For positive inputs,Med-ReLU adopts the linear behavior of ReLU to avoid vanishing gradients,while for negative inputs,it exhibits the Softsign’s polynomial convergence,ensuring robust training and avoiding inactive neurons across the training set.The training performance and segmentation accuracy ofMed-ReLU have been thoroughly evaluated,demonstrating stable learning behavior and resistance to overfitting.It consistently outperforms state-of-the-art activation functions inmedical image segmentation tasks.Designed as a parameter-free function,Med-ReLU is simple to implement in complex deep learning architectures,and its effectiveness spans various neural network models and anomaly detection scenarios.
基金supported by two research grants provided by the Karachi Institute of Economics and Technology(KIET)the Big Data Analytics Laboratory at the Insitute of Business Administration(IBAKarachi)。
文摘The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations.
基金the support of the Leverhulme Centre for Wildfires,Environment and Society through the Leverhulme Trust(RC-2018-023)Sibo Cheng,César Quilodran-Casas,and Rossella Arcucci acknowledge the support of the PREMIERE project(EP/T000414/1)+5 种基金the support of EPSRC grant:PURIFY(EP/V000756/1)the Fundamental Research Funds for the Central Universitiesthe support of the SASIP project(353)funded by Schmidt Futures–a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologiesDFG for the Heisenberg Programm Award(JA 1077/4-1)the National Natural Science Foundation of China(61976120)the Natural Science Key Foundat ion of Jiangsu Education Department(21KJA510004)。
文摘Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).
基金partially supported by the Natural Science Foundation of Shanghai(No.23ZR1429300)the Innovation Fund of CNNC(Lingchuang Fund)+1 种基金EP/T000414/1 PREdictive Modeling with QuantIfication of UncERtainty for MultiphasE Systems(PREMIERE)the Leverhulme Centre for Wildfires,Environment,and Society through the Leverhulme Trust(No.RC-2018-023).
文摘The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.
基金supported in part by the Intramural Research Program of the National Institute on Agingsupported by the National Cancer Institute(K01 CA234317)+1 种基金the San Diego State University/UC San Diego Comprehensive Cancer Center Partnership(U54 CA132384 and U54 CA132379)the Alzheimer's Disease Resource Center for Minority Aging Research at the University of California San Diego(P30 AG059299)。
文摘Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction algorithms.Methods:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)values.We developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging cohorts.Finally,we quantified the associations between measured and predicted VO_(2max)and mortality.Results:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/min.Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,respectively.Incremental quartiles of measured VO_(2max)showed an inverse gradient in mortality risk.Predicted VO_(2max)variables yielded similar effect estimates but were not robust to adjustment.Conclusion:Measured VO_(2max)is a strong predictor of mortality.Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment.Future studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.
基金supported by the TIFR-CAM Doctoral Fellowshipthe NISER Postdoctoral Fellowship (through the project “Basic research in physics and multidisciplinary sciences” with identification # RIN4001) during the preparation of this papersupported by the Raja Ramanna Fellowship
文摘Evolution and interaction of plane waves of the multidimensional zero-pressure gas dynamics system leads to the study of the corresponding one dimensional system.In this paper,we study the initial value problem for one dimensional zero-pressure gas dynamics system.Here the first equation is the Burgers equation and the second one is the continuity equation.We consider the solution with initial data in the space of bounded Borel measures.First we prove a general existence result in the algebra of generalized functions of Colombeau.Then we study in detail special solutions withδ-measures as initial data.We study interaction of waves originating from initial data concentrated on two point sources and interaction with classical shock/rarefaction waves.This gives an understanding of plane-wave interactions in the multidimensional case.We use the vanishing viscosity method in our analysis as this gives the physical solution.
文摘Next-generation sequencing data are widely utilised for various downstream applications in bioinformatics and numerous techniques have been developed for PCR-deduplication and error-correction to eliminate bias and errors introduced during the sequencing.This study first-time provides a joint overview of recent advances in PCR-deduplication and error-correction on short reads.In particular,we utilise UMI-based PCR-deduplication strategies and sequencing data to assess the performance of the solelycomputational PCR-deduplication approaches and investigate how error correction affects the performance of PCR-deduplication.Our survey and comparative analysis reveal that the deduplicated reads generated by the solely-computational PCR-deduplication and error-correction methods exhibit substantial differences and divergence from the sets of reads obtained by the UMI-based deduplication methods.The existing solelycomputational PCR-deduplication and error-correction tools can eliminate some errors but still leave hundreds of thousands of erroneous reads uncorrected.All the error-correction approaches raise thousands or more new sequences after correction which do not have any benefit to the PCRdeduplication process.Based on our findings,we discuss future research directions and make suggestions for improving existing computational approaches to enhance the quality of short-read sequencing data.
基金supported within the project TRACE-V2Xfunding from the European Union’s HORIZON-MSCA-2022-SE-01-01 under grant agreement(101131204).
文摘With the increasing global mobile data traffic and daily user engagement,technologies,such as mobile crowdsensing,benefit hugely from the constant data flows from smartphone and IoT owners.However,the device users,as data owners,urgently require a secure and fair marketplace to negotiate with the data consumers.In this paper,we introduce a novel federated data acquisition market that consists of a group of local data aggregators(LDAs);a number of data owners;and,one data union to coordinate the data trade with the data consumers.Data consumers offer each data owner an individual price to stimulate participation.The mobile data owners naturally cooperate to gossip about individual prices with each other,which also leads to price fluctuation.It is challenging to analyse the interactions among the data owners and the data consumers using traditional game theory due to the complex price dynamics in a large-scale heterogeneous data acquisition scenario.Hence,we propose a data pricing strategy based on mean-field game(MFG)theory to model the data owners’cost considering the price dynamics.We then investigate the interactions among the LDAs by using the distribution of price,namely the mean-field term.A numerical method is used to solve the proposed pricing strategy.The evaluations demonstrate that the proposed pricing strategy efficiently allows the data owners from multiple LDAs to reach an equilibrium on data quantity to sell regarding the current individual price scheme.The result further demonstrates that the influential LDAs determine the final price distribution.Last but not least,it shows that cooperation among mobile data owners leads to optimal social welfare even with the additional cost of information exchange.
基金The research work described herein was funded by the National Natural Science Foundation of China(Grant No.51922067)The Key Research and Development Plan of Shandong Province of China(Grant No.2020ZLYS01)Taishan Scholars Program of Shan-dong Province of China(Grant No.tsqn201909003).
文摘Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surround-ing rock in front of the tunnel face.In this work,a forward-prediction method for tunnel geology and classification of surrounding rock is developed based on seismic wave velocity layered tomography.In particular,for the problem of strong multi-solution of wave velocity inversion caused by few ray paths in the narrow space of the tunnel,a layered inversion based on regularization is proposed.By reducing the inversion area of each iteration step and applying straight-line interface assumption,the convergence and accuracy of wave velocity inversion are effectively improved.Furthermore,a surrounding rock classification network based on autoencoder is constructed.The mapping relationship between wave velocity and classification of surrounding rock is established with density,Poisson’s ratio and elastic modulus as links.Two numerical examples with geological conditions similar to that in the field tunnel and a field case study in an urban subway tunnel verify the potential of the proposed method for practical application.
文摘Consciousness is one of the unique features of creatures,and is also the root of biological intelligence.Up to now,all machines and robots havenJt had consciousness.Then,will the artificial intelligence(AI)be conscious?Will robots have real intelligence without consciousness?The most primitive consciousness is the perception and expression of selfexistence.In order to perceive the existence of the concept of‘Ij,a creature must first have a perceivable boundary such as skin to separate‘I’from‘non-1’.For robots,to have the self-awareness,they also need to be wrapped by a similar sensory membrane.Nowadays,as intelligent tools,AI systems should also be regarded as the external extension of human intelligence.These tools are unconscious.The development of AI shows that intelligence can exist without consciousness.When human beings enter into the era of life intelligence from AI,it is not the AI became conscious,but that conscious lives will have strong AI.Therefore,it becomes more necessary to be careful on applying AI to living creatures,even to those lower-level animals with only consciousness.The subversive revolution of such application may produce more careful thinking.
文摘In order to promote the application of clean energy technology in clothing and promote the integration of industrial development and artificial intelligence wearable technology,this study elaborates the energy application characteristics of intelligent wearable products at home and abroad and its application in different fields,aiming at the current research status of wearable technology in the field of textile and clothing.The wearable distributed generation technology is classified,and a creative clothing design for detecting climate temperature is designed.Based on the monitoring of body temperature,the changes in clothing pattern color can reflect people’s health and emotional status.At the same time,it can also be applied to the screening of abnormal body temperature during the COVID-19.
基金supported by National Key Research and Development Program of China[grant num-ber 2021YFB3900904]the National Natural Science Foundation of China[grant numbers 42071368,U2033216,41871287].
文摘As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalent in today’s digital world.In this study,we propose two high-performance R solutions for GWR via Multi-core Parallel(MP)and Compute Unified Device Architecture(CUDA)techniques,respectively GWR-MP and GWR-CUDA.We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models(GWmodel),Multi-scale GWR(MGWR)and Fast GWR(FastGWR).Results showed that all five solutions perform differently across varying sample sizes,with no single solution a clear winner in terms of computational efficiency.Specifically,solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size.For a large sample size,GWR-MP and FastGWR provided coherent solutions on a Personal Computer(PC)with a common multi-core configuration,GWR-MP provided more efficient computing capacity for each core or thread than FastGWR.For cases when the sample size was very large,and for these cases only,GWR-CUDA provided the most efficient solution,but should note its I/O cost with small samples.In summary,GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones,where for certain data-rich GWR studies,they should be preferred.
基金supported by the National Natural Science Foundation of China(Grant No.52021005)Outstanding Youth Foundation of Shandong Province of China(Grant No.ZR2021JQ22)Taishan Scholars Program of Shandong Province of China(Grant No.tsqn201909003)。
文摘The decision-making method of tunnel boring machine(TBM)operating parameters has a significant guiding significance for TBM safe and efficient construction,and it has been one of the TBM tunneling research hotspots.For this purpose,this paper introduces an intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization.First,linear cutting tests and numerical simulations are used to investigate the physical rules between different cutting parameters(penetration,cutter spacing,etc.)and rock compressive strength.Second,a dual-driven mapping of rock parameters and TBM operating parameters based on data mining and physical rules of rock breaking is established with high accuracy by combining rock-breaking rules and deep neural networks(DNNs).The decision-making method is established by dual-driven mapping,using the effective rock-breaking capacity and the rated value of mechanical parameters as constraints and the total excavation cost as the optimization objective.The best operational parameters can be obtained by searching for the revolutions per minute and penetration that correspond to the extremum of the constrained objective function.The practicability and effectiveness of the developed decision-making model is verified in the SecondWater Source Channel of Hangzhou,China,resulting in the average penetration rate increasing by 11.3%and the total cost decreasing by 10%.
文摘This study uses <span style="font-family:Verdana;">an empirical</span><span style="font-family:Verdana;"> analysis to quantify the downstream analysis effects of data pre-processing choices. Bootstrap data simulation is used to measure the bias-variance decomposition of an empirical risk function, mean square error (MSE). Results of the risk function decomposition are used to measure the effects of model development choices on </span><span style="font-family:Verdana;">model</span><span style="font-family:Verdana;"> bias, variance, and irreducible error. Measurements of bias and variance are then applied as diagnostic procedures for model pre-processing and development. Best performing model-normalization-data structure combinations were found to illustrate the downstream analysis effects of these model development choices. </span><span style="font-family:Verdana;">In addition</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;">, results found from simulations were verified and expanded to include additional data characteristics (imbalanced, sparse) by testing on benchmark datasets available from the UCI Machine Learning Library. Normalization results on benchmark data were consistent with those found using simulations, while also illustrating that more complex and/or non-linear models provide better performance on datasets with additional complexities. Finally, applying the findings from simulation experiments to previously tested applications led to equivalent or improved results with less model development overhead and processing time.</span>
基金the Natural Science Foundation of Shanghai(No.20ZR1420400)the State Key Program of National Natural Science Foundation of China(No.61936001)。
文摘The printed circuit board(PCB)is an indispensable component of electronic products,which deter-mines the quality of these products.With the development and advancement of manufacturing technology,the layout and structure of PCB are getting complicated.However,there are few effective and accurate PCB defect detection methods.There are high requirements for the accuracy of PCB defect detection in the actual pro-duction environment,so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method(DDMV)and the defect detection by multi-model learning method(DDML).With the purpose of reducing wrong and missing detection,the DDMV and DDML integrate multiple defect detection networks with different fusion strategies.The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets.The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score,and the area under curve value of DDML is also higher than that of any other individual detection model.Furthermore,compared with DDMV,the DDML with an automatic machine learning method achieves the best performance in PCB defect detection,and the Fl-score on the two datasets can reach 99.7%and 95.6%respectively.
文摘The quality of products manufactured or procured by organizations is an important aspect of their survival in the global market. The quality control processes put in place by organizations can be resource-intensive but substantial savings can be realized by using acceptance sampling in conjunction with batch testing. This paper considers the batch testing model based on the quality control process where batches that test positive are re-tested. The results show that re-testing greatly improves the efficiency over one stage batch testing based on quality control. This is observed using Asymptotic Relative Efficiency (ARE), where for values of </span><i><span style="font-family:Verdana;">p</span></i><span style="font-family:Verdana;"> computed ARE > 1 implying that our estimator has a smaller variance than the one-stage batch testing. Also, it was found that the model is more efficient than the classical two-stage batch testing for relatively high values of proportion.
基金The work of this article is supported by Key Scientific Research Projects of Colleges and Universities in Henan Province(Grant No.20A520007)National Natural Science Foundation of China(Grant No.61402149).
文摘Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles.At the same time,the predicate-argument structure in a sentence is important information for semantic role labeling task.In this work,we introduce the auxiliary deep neural network model,which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling.Based on the framework of joint learning,part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling.In addition,we introduce the argument recognition layer in the training process of the main task-semantic role labeling,so the argument-related structural information selected by the predicate through the attention mechanism is used to assist the main task.Because the model makes full use of the semantic dependency between part-of-speech and semantic roles and the structural information of predicate-argument,our model achieved the F1 value of 89.0%on the WSJ test set of CoNLL2005,which is superior to existing state-of-the-art model about 0.8%.
文摘BACKGROUND Alpha-1 antitrypsin deficiency is a rare genetic disease and a leading cause of inherited alterations in plasma protein metabolism(APPM).AIM To understand the prevalence,burden and progression of liver disease in patients with APPM including alpha-1 antitrypsin deficiency.METHODS We conducted a retrospective analysis of anonymized patient-level claims data from a German health insurance provider(AOK PLUS).The APPM cohort comprised patients with APPM(identified using the German Modification of the International Classification of Diseases-10th Revision[ICD-10-GM]code E88.0 between 01/01/2010-30/09/2020)and incident liver disease(ICD-10-GM codes K74,K70.2-3 and K71.7 between 01/01/2012-30/09/2020).The control cohort comprised patients without APPM but with incident liver disease.Outcomes were incidence/prevalence of liver disease in patients with APPM,demographics/baseline characteristics,diagnostic procedures,progression-free survival(PFS),disease progression and mortality.RESULTS Overall,2680 and 26299 patients were included in the APPM(fibrosis,96;cirrhosis,2584)and control(fibrosis,1444;cirrhosis,24855)cohorts,respectively.Per 100000 individuals,annual incidence and prevalence of APPM and liver disease was 10-15 and 36-51,respectively.In the APPM cohort,median survival was 4.7 years[95%confidence interval(CI):3.5-7.0]and 2.5 years(95%CI:2.3-2.8)in patients with fibrosis and cirrhosis,respectively.A higher proportion of patients in the APPM cohort experienced disease progression(92.0%)compared with the control cohort(67.2%).Median PFS was shorter in the APPM cohort(0.9 years,95%CI:0.7-1.1)compared with the control cohort(3.7 years,95%CI:3.6-3.8;P<0.001).Patients with cirrhosis in the control cohort had longer event-free survival for ascites,hepatic encephalopathy,hepatic failure and esophageal/gastric varices than patients with cirrhosis in the APPM cohort(P<0.001).Patients with fibrosis in the control cohort had longer event-free survival for ascites,cirrhosis,hepatic failure and esophageal/gastric varices than patients with fibrosis in the APPM cohort(P<0.001).In the APPM cohort,the most common diagnostic procedures within 12 mo after the first diagnosis of liver disease were imaging procedures(66.3%)and laboratory tests(51.0%).CONCLUSION Among patients with liver disease,those with APPM experience substantial burden and earlier liver disease progression than patients without APPM.
