A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan....A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.展开更多
Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles.Despite significant advancements achieved by data-driven methods,diagnosis accuracy remains constr...Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles.Despite significant advancements achieved by data-driven methods,diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data.This paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations.We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehicles.Our analysis comprehensively addresses cell inconsistencies,physical interpretations,and charging uncertainties in real-world applications.This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves.By leveraging inexpensive unlabeled data in a self-supervised approach,our method demonstrates improvements in average root mean square errors of 74.54%and 60.50%in the best and worst cases,respectively,compared to the supervised benchmark.This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in realworld scenarios.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
Advances in radionuclide tracers have allowed for more accurate imaging that reflects the actions of numerous neurotransmitters, energy metabolism utilization, inflammation, and pathological protein accumulation. All ...Advances in radionuclide tracers have allowed for more accurate imaging that reflects the actions of numerous neurotransmitters, energy metabolism utilization, inflammation, and pathological protein accumulation. All of these achievements in molecular brain imaging have broadened our understanding of brain function in Parkinson’s disease(PD).The implementation of molecular imaging has supported more accurate PD diagnosis as well as assessment of therapeutic outcome and disease progression. Moreover, molecular imaging is well suited for the detection of preclinical or prodromal PD cases. Despite these advances, future frontiers of research in this area will focus on using multi-modalities combining positron emission tomography and magnetic resonance imaging along with causal modeling with complex algorithms.展开更多
Purpose: The purpose of the present controlled cross-sectional study was to investigate proximal femur and whole-body bone mineral density(BMD), as well as bone turnover profile, in lifelong trained elderly male footb...Purpose: The purpose of the present controlled cross-sectional study was to investigate proximal femur and whole-body bone mineral density(BMD), as well as bone turnover profile, in lifelong trained elderly male football players and young elite football players compared with untrained age-matched men.Methods: One hundred and forty healthy, non-smoking men participated in the study, including lifelong trained football players(FTE, n = 35)aged 65—80 years, elite football players(FTY, n = 35) aged 18—30 years, as well as untrained age-matched elderly(UE, n = 35) and young(UY,n = 35) men. All participants underwent a regional dual-energy X-ray Absorptiometry(DXA) scan of the proximal femur and a whole-body DXA scan to determine BMD. From a resting blood sample, the bone turnover markers(BTMs) osteocalcin, carboxy-terminal type-1 collagen crosslinks(CTX-1), procollagen type-1 amino-terminal propeptide(P1NP), and sclerostin were measured.Results: FTE had 7.3%—12.9% higher(p < 0.05) BMD of the femoral neck, wards, shaft, and total proximal femur in both legs compared to UE,and 9.3%—9.7% higher(p < 0.05) BMD in femoral trochanter in both legs compared to UY. FTY had 24.3%—37.4% higher(p < 0.001) BMD in all femoral regions and total proximal femur in both legs compared to UY. The whole-body DXA scan confirmed these results, with FTE showing similar whole-body BMD and 7.9% higher(p < 0.05) leg BMD compared to UY, and with FTY having 9.6% higher(p < 0.001) wholebody BMD and 18.2% higher(p < 0.001) leg BMD compared to UY. The plasma concentration of osteocalcin, CTX-1, and P1NP were 29%,53%, and 52% higher(p < 0.01), respectively, in FTY compared to UY.Conclusion: BMD of the proximal femur and whole-body BMD are markedly higher in lifelong trained male football players aged 65—80 years and young elite football players aged 18—30 years compared to age-matched untrained men. Elderly football players even show higher BMD in femoral trochanter and leg BMD than untrained young despite an age difference of 47 years.展开更多
The ability of death-inducing tumor necrosis factor-related apoptosis-inducing ligand （TRAIL） to selectively kill a variety of cancer cells has been largely described, but one of the major concerns with the treatmen...The ability of death-inducing tumor necrosis factor-related apoptosis-inducing ligand （TRAIL） to selectively kill a variety of cancer cells has been largely described, but one of the major concerns with the treatment is the occurrence of drug resistance and possible toxic side effects. Here, we report that TRAIL induces apoptosis in Jurkat and SUPT1 T cell lines and in human T-ALL blasts but not in healthy subject-derived peripheral blood mononuclear cells. In parallel, the treatment with TRAIL and Tyrphostin （AG-490）, a selective Janus kinase 2 inhibitor, produces an evident enhancement of cytotoxicity, characterized by a significant inhibition of Stat3 phosphorylation compared to controls or to TRAIL alone-treated samples, and associated with a dramatic decrease of both clAP-1 and clAP-2 mRNA levels. Downregulation of clAP-1 and cIAP-2 by specific small interference RNAs significantly amplifies TRAIL-reduced cytotoxicity. All together, these findings strongly indicate that clAP-1 and clAP-2 downregulation is a fundamental step in the signaling pathways mediating the combinatorial effect of TRAIL and AG-490 on T cell leukemia. These findings may help to open new routes for the development of less toxic pharmacological strategies in the treatment of patients affected by TRAIL-sensitive leukemias.展开更多
Depok has a big opportunity to become an age friendly city (AFC) because it has a large number of posbindu, increased elderly population, it does not have yet the pension security for the elderly people, and it does n...Depok has a big opportunity to become an age friendly city (AFC) because it has a large number of posbindu, increased elderly population, it does not have yet the pension security for the elderly people, and it does not have yet the number of infrastructure and social facilities needed for the elderly. Before Depok City becomes an age friendly city, an assessment of the eight-dimensional indicators of AFC had been carried out by a team of UI researchers from Center of Ageing Studies in collaboration with the Institute for Survey METER in March 2013. One of the main finding was the presence of the three indicators of AFC in Depok which still lack, they are buildings and open space, housing, and civil participation and employment. The purpose of the study was to assess the public’s opinion on the three indicators of AFC that still lack in Depok. The study shows that the majority of people and government as stakeholder assess that Depok is ready to become AFC as long as supported by the government of Depok City with the good coordination with related institutions for the budget and programs prioritizing the interests of the elderly. Moreover, the existence of Komda (Comission Area) of Elderly Depok City can support the establishment of a friendly city towards the elderly. The community prefers to choose the indicators of buildings and open spaces as a top priority for elderly-friendly city rather than chooses the indicators of housing and civil participation and employment for the elderly people. Therefore, hopefully Depok City Government with related institutions can build the building which is elderly-friendly as well as AFC socialization to the government and private sectors in order to achieve rapid implementation of Depok as an AFC.展开更多
The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coup...The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.展开更多
The titanium implant surface plays a crucial role for implant incorporation into bone. A new strategy to improve implant integration in a bone is to develop surface nanocoatings with plant-derived polysaccharides able...The titanium implant surface plays a crucial role for implant incorporation into bone. A new strategy to improve implant integration in a bone is to develop surface nanocoatings with plant-derived polysaccharides able to increase adhesion of bone cells to the implant surface. The aim of the present study was to physically characterize and compare polystyrene and titanium surfaces nanocoated with different Rhamnogalacturonan-Is (RG-I) and to visualize RG-I nanocoatings. RG-Is from potato and apple were coated on aminated surfaces of polystyrene, titianium discs and titanium implants. To characterize, compare and visualize the surface nanocoatings measurements of contact angle measurements and surface roughness with atomic force microscopy, scanning electron microscopy, and confocal microscopy was performed. We found that, both unmodified and enzymatic modified RG-Is influenced surface wettability, without any major effect on surface roughness (Sa, Sdr). Furthermore, we demonstrated that it is possible to visualize the pectin RG-Is molecules and even the nanocoatings on titanium surfaces, which have not been presented before. The comparison between polystyrene and titanium surface showed that the used material affected the physical properties of non-coated and coated surfaces. RG-Is should be considered as a candidate for new materials as organic nanocoatings for biomaterials in order to improve bone healing.展开更多
There is an increasing demand for real-time data-driven fault diagnosis of lithium-ion batteries that can predict battery faults at an early stage to avoid safety issues and improve battery reliability.However,such pr...There is an increasing demand for real-time data-driven fault diagnosis of lithium-ion batteries that can predict battery faults at an early stage to avoid safety issues and improve battery reliability.However,such prediction methods require large amounts of data,generally obtained through experiments or during the operation phase,resulting in substantial economic and time efforts.In this context,generating realistic battery pack data that covers all sensor values a battery management system receives,as well as including fault models,is of particular interest and can mitigate the need to perform extensive laboratory testing.This paper focuses on the systematic development of a data generation platform capable of simulating a large scale of battery packs with random battery faults and generating big data for the following battery fault diagnostics.Initially,the electrical,thermal,and aging modeling of a battery pack is performed.After this,four types of faults,namely hard short circuit,soft short circuit,abnormal internal resistance,and abnormal contact resistance,are modeled using equivalent circuit models.To generate realistic data,both cell-to-cell variations and pack-level variations are considered.Variations included are,for example,the manufacturing quality,temperatures,aging processes,road conditions,state of charge,and fault severity.By combining the battery pack models,fault models,and the different variations through Monte Carlo simulations,a large data set representing different packs with varying levels of inconsistencies is generated.展开更多
The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for...The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO_(4)batteries.However,the intricate influence of state-of-charge(SOC),temperature,and battery aging have posed significant challenges for hysteresis modeling,which have not been comprehensively considered in existing studies.This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions,addressing the intricate dependencies on SOC,temperature,and battery aging.First,a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths,temperatures and aging states.Second,the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability.The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information.Third,the conditional matrix,incorporating temperature,health state,and historical paths,is constructed to provide the scenario-specific information for the adversarial network,thereby enhancing the model’s adaptability.Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions,with accuracy improvements of 31.3–48.7%compared to three state-of-the-art models.展开更多
Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we pr...Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization,aiming to augment the datasets used by data-driven models for degradation prediction.We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications.These datasets encompass various chemistries and realistic conditions,including cell-to-cell variations,measurement noise,varying chargedischarge conditions,and capacity recovery.Our results show that it is possible to reduce cell-testing efforts by at least 50%by substituting synthetic data into an existing dataset.This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.展开更多
Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring,control,and design at system scale.Increasing their capabilities involves improving their physical fidelity while maintainin...Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring,control,and design at system scale.Increasing their capabilities involves improving their physical fidelity while maintaining sub-millisecond computational speed.In this work,we introduce machine learning surrogates that learn physical dynamics.Specifically,we benchmark three operator-learning surrogates for the Single Particle Model(SPM):Deep Operator Networks(DeepONets),Fourier Neural Operators(FNOs)and a newly proposed parameter-embedded Fourier Neural Operator(PE-FNO),which conditions each spectral layer on particle radius and solid-phase diffusivity.We extend the comparison to classical machine-learning baselines by including U-Nets.Models are trained on simulated trajectories spanning four current families(constant,triangular,pulse-train,and Gaussian-random-field)and a full range of State-of-Charge(SOC)(0%to 100%).DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads.The basic FNO maintains mesh invariance and keeps concentration errors below 1%,with voltage mean-absolute errors under 1.7mV across all load types.Introducing parameter embedding marginally increases error but enables generalisation to varying radii and diffusivities.PE-FNO executes approximately 200 times faster than a 16-thread SPM solver.Consequently,PE-FNO’s capabilities in inverse tasks are explored in a parameter estimation task with Bayesian optimisation,recovering anode and cathode diffusivities with 1.14%and 8.4%mean absolute percentage error,respectively,and 0.5918 percentage points higher error in comparison with classical methods.These results pave the way for neural operators to meet the accuracy,speed and parametric flexibility demands of real-time battery management,design-of-experiments and large-scale inference.PE-FNO outperforms conventional neural surrogates,offering a practical path towards high-speed and high-fidelity electrochemical digital twins.展开更多
Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits.Here,we propose a data-driven approach with machine learning to classif...Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits.Here,we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data.We extract three classes of features from the raw formation data,considering the statistical aspects,differential analysis,and electro-chemical characteristics.The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms.Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing.The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25℃ and 45℃,respectively.Moreover,the lifetime prediction model is able to predict the battery end-of-life with mean per-centage errors of 6.50% and 5.45%for the batteries aged at 25℃ and 45℃,respectively.This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.展开更多
基金National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.
基金supported by the research project‘‘SafeDaBatt”(03EMF0409A)funded by the German Federal Ministry for Digital and Transport(BMDV)+2 种基金the National Key Research and Development Program of China(2022YFE0102700)the Key Research and Development Program of Shaanxi Province(2023-GHYB-05,2023-YBSF-104)the financial support from the China Scholarship Council(CSC)(202206567008)。
文摘Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles.Despite significant advancements achieved by data-driven methods,diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data.This paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations.We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehicles.Our analysis comprehensively addresses cell inconsistencies,physical interpretations,and charging uncertainties in real-world applications.This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves.By leveraging inexpensive unlabeled data in a self-supervised approach,our method demonstrates improvements in average root mean square errors of 74.54%and 60.50%in the best and worst cases,respectively,compared to the supervised benchmark.This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in realworld scenarios.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金supported by grants from the Natural Science Foundation of China(81571232,81371413,and81671239)the National Basic Research Development Program of China(2016YFC1306500)+1 种基金a Scientific Research Project from Huashan Hospital affiliated to Fudan University(2016QD01)China
文摘Advances in radionuclide tracers have allowed for more accurate imaging that reflects the actions of numerous neurotransmitters, energy metabolism utilization, inflammation, and pathological protein accumulation. All of these achievements in molecular brain imaging have broadened our understanding of brain function in Parkinson’s disease(PD).The implementation of molecular imaging has supported more accurate PD diagnosis as well as assessment of therapeutic outcome and disease progression. Moreover, molecular imaging is well suited for the detection of preclinical or prodromal PD cases. Despite these advances, future frontiers of research in this area will focus on using multi-modalities combining positron emission tomography and magnetic resonance imaging along with causal modeling with complex algorithms.
文摘Purpose: The purpose of the present controlled cross-sectional study was to investigate proximal femur and whole-body bone mineral density(BMD), as well as bone turnover profile, in lifelong trained elderly male football players and young elite football players compared with untrained age-matched men.Methods: One hundred and forty healthy, non-smoking men participated in the study, including lifelong trained football players(FTE, n = 35)aged 65—80 years, elite football players(FTY, n = 35) aged 18—30 years, as well as untrained age-matched elderly(UE, n = 35) and young(UY,n = 35) men. All participants underwent a regional dual-energy X-ray Absorptiometry(DXA) scan of the proximal femur and a whole-body DXA scan to determine BMD. From a resting blood sample, the bone turnover markers(BTMs) osteocalcin, carboxy-terminal type-1 collagen crosslinks(CTX-1), procollagen type-1 amino-terminal propeptide(P1NP), and sclerostin were measured.Results: FTE had 7.3%—12.9% higher(p < 0.05) BMD of the femoral neck, wards, shaft, and total proximal femur in both legs compared to UE,and 9.3%—9.7% higher(p < 0.05) BMD in femoral trochanter in both legs compared to UY. FTY had 24.3%—37.4% higher(p < 0.001) BMD in all femoral regions and total proximal femur in both legs compared to UY. The whole-body DXA scan confirmed these results, with FTE showing similar whole-body BMD and 7.9% higher(p < 0.05) leg BMD compared to UY, and with FTY having 9.6% higher(p < 0.001) wholebody BMD and 18.2% higher(p < 0.001) leg BMD compared to UY. The plasma concentration of osteocalcin, CTX-1, and P1NP were 29%,53%, and 52% higher(p < 0.01), respectively, in FTY compared to UY.Conclusion: BMD of the proximal femur and whole-body BMD are markedly higher in lifelong trained male football players aged 65—80 years and young elite football players aged 18—30 years compared to age-matched untrained men. Elderly football players even show higher BMD in femoral trochanter and leg BMD than untrained young despite an age difference of 47 years.
文摘The ability of death-inducing tumor necrosis factor-related apoptosis-inducing ligand （TRAIL） to selectively kill a variety of cancer cells has been largely described, but one of the major concerns with the treatment is the occurrence of drug resistance and possible toxic side effects. Here, we report that TRAIL induces apoptosis in Jurkat and SUPT1 T cell lines and in human T-ALL blasts but not in healthy subject-derived peripheral blood mononuclear cells. In parallel, the treatment with TRAIL and Tyrphostin （AG-490）, a selective Janus kinase 2 inhibitor, produces an evident enhancement of cytotoxicity, characterized by a significant inhibition of Stat3 phosphorylation compared to controls or to TRAIL alone-treated samples, and associated with a dramatic decrease of both clAP-1 and clAP-2 mRNA levels. Downregulation of clAP-1 and cIAP-2 by specific small interference RNAs significantly amplifies TRAIL-reduced cytotoxicity. All together, these findings strongly indicate that clAP-1 and clAP-2 downregulation is a fundamental step in the signaling pathways mediating the combinatorial effect of TRAIL and AG-490 on T cell leukemia. These findings may help to open new routes for the development of less toxic pharmacological strategies in the treatment of patients affected by TRAIL-sensitive leukemias.
文摘Depok has a big opportunity to become an age friendly city (AFC) because it has a large number of posbindu, increased elderly population, it does not have yet the pension security for the elderly people, and it does not have yet the number of infrastructure and social facilities needed for the elderly. Before Depok City becomes an age friendly city, an assessment of the eight-dimensional indicators of AFC had been carried out by a team of UI researchers from Center of Ageing Studies in collaboration with the Institute for Survey METER in March 2013. One of the main finding was the presence of the three indicators of AFC in Depok which still lack, they are buildings and open space, housing, and civil participation and employment. The purpose of the study was to assess the public’s opinion on the three indicators of AFC that still lack in Depok. The study shows that the majority of people and government as stakeholder assess that Depok is ready to become AFC as long as supported by the government of Depok City with the good coordination with related institutions for the budget and programs prioritizing the interests of the elderly. Moreover, the existence of Komda (Comission Area) of Elderly Depok City can support the establishment of a friendly city towards the elderly. The community prefers to choose the indicators of buildings and open spaces as a top priority for elderly-friendly city rather than chooses the indicators of housing and civil participation and employment for the elderly people. Therefore, hopefully Depok City Government with related institutions can build the building which is elderly-friendly as well as AFC socialization to the government and private sectors in order to achieve rapid implementation of Depok as an AFC.
基金supported by the National Science Fund for Excellent Youth Scholars of China(52222708)the National Natural Science Foundation of China(51977007)。
文摘The safety and durability of lithium-ion batteries under mechanical constraints depend significantly on electrochemical,thermal,and mechanical fields in applications.Characterizing and quantifying the multi-field coupling behaviors requires interdisciplinary efforts.Here,we design experiments under mechanical constraints and introduce an in-situ analytical framework to clarify the complex interaction mechanisms and coupling degrees among multi-physics fields.The proposed analytical framework integrates the parameterization of equivalent models,in-situ mechanical analysis,and quantitative assessment of coupling behavior.The results indicate that the significant impact of pressure on impedance at low temperatures results from the diffusion-controlled step,enhancing kinetics when external pressure,like 180 to 240 k Pa at 10℃,is applied.The diversity in control steps for the electrochemical reaction accounts for the varying impact of pressure on battery performance across different temperatures.The thermal expansion rate suggests that the swelling force varies by less than 1.60%per unit of elevated temperature during the lithiation process.By introducing a composite metric,we quantify the coupling correlation and intensity between characteristic parameters and physical fields,uncovering the highest coupling degree in electrochemical-thermal fields.These results underscore the potential of analytical approaches in revealing the mechanisms of interaction among multi-fields,with the goal of enhancing battery performance and advancing battery management.
文摘The titanium implant surface plays a crucial role for implant incorporation into bone. A new strategy to improve implant integration in a bone is to develop surface nanocoatings with plant-derived polysaccharides able to increase adhesion of bone cells to the implant surface. The aim of the present study was to physically characterize and compare polystyrene and titanium surfaces nanocoated with different Rhamnogalacturonan-Is (RG-I) and to visualize RG-I nanocoatings. RG-Is from potato and apple were coated on aminated surfaces of polystyrene, titianium discs and titanium implants. To characterize, compare and visualize the surface nanocoatings measurements of contact angle measurements and surface roughness with atomic force microscopy, scanning electron microscopy, and confocal microscopy was performed. We found that, both unmodified and enzymatic modified RG-Is influenced surface wettability, without any major effect on surface roughness (Sa, Sdr). Furthermore, we demonstrated that it is possible to visualize the pectin RG-Is molecules and even the nanocoatings on titanium surfaces, which have not been presented before. The comparison between polystyrene and titanium surface showed that the used material affected the physical properties of non-coated and coated surfaces. RG-Is should be considered as a candidate for new materials as organic nanocoatings for biomaterials in order to improve bone healing.
基金funding from the research project“Safe-DaBatt”(03EMF0409A)funded by the German Federal Ministry of Digital and Transport(BMDV).
文摘There is an increasing demand for real-time data-driven fault diagnosis of lithium-ion batteries that can predict battery faults at an early stage to avoid safety issues and improve battery reliability.However,such prediction methods require large amounts of data,generally obtained through experiments or during the operation phase,resulting in substantial economic and time efforts.In this context,generating realistic battery pack data that covers all sensor values a battery management system receives,as well as including fault models,is of particular interest and can mitigate the need to perform extensive laboratory testing.This paper focuses on the systematic development of a data generation platform capable of simulating a large scale of battery packs with random battery faults and generating big data for the following battery fault diagnostics.Initially,the electrical,thermal,and aging modeling of a battery pack is performed.After this,four types of faults,namely hard short circuit,soft short circuit,abnormal internal resistance,and abnormal contact resistance,are modeled using equivalent circuit models.To generate realistic data,both cell-to-cell variations and pack-level variations are considered.Variations included are,for example,the manufacturing quality,temperatures,aging processes,road conditions,state of charge,and fault severity.By combining the battery pack models,fault models,and the different variations through Monte Carlo simulations,a large data set representing different packs with varying levels of inconsistencies is generated.
基金supported by the Natural Science Foundation of China(No.52377221,62172448)the Natural Science Foundation of Hunan Province,China(No.2023JJ30698)+1 种基金Part of the work is supported by the research project“COBALT-P”(16BZF314C)funded by the German Federal Ministry for Economic Affairs and Climate Action(BMWK).Lisen Yan is supported by China Scholarship Council(Grant No.202206370146).
文摘The hysteresis effect represents the difference in open circuit voltage(OCV)between the charge and discharge processes of batteries.An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of LiFePO_(4)batteries.However,the intricate influence of state-of-charge(SOC),temperature,and battery aging have posed significant challenges for hysteresis modeling,which have not been comprehensively considered in existing studies.This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions,addressing the intricate dependencies on SOC,temperature,and battery aging.First,a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths,temperatures and aging states.Second,the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability.The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information.Third,the conditional matrix,incorporating temperature,health state,and historical paths,is constructed to provide the scenario-specific information for the adversarial network,thereby enhancing the model’s adaptability.Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions,with accuracy improvements of 31.3–48.7%compared to three state-of-the-art models.
基金funding from the research project"SPEED"(03XP0585)funded by the German Federal Ministry of Education and Research(BMBF).
文摘Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data.However,generating such data,whether in the laboratory or the field,is time-and resource-intensive.Here,we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need for invasive battery characterization,aiming to augment the datasets used by data-driven models for degradation prediction.We validate our method by evaluating the performance of both shallow and deep learning models using diverse datasets from laboratory and field applications.These datasets encompass various chemistries and realistic conditions,including cell-to-cell variations,measurement noise,varying chargedischarge conditions,and capacity recovery.Our results show that it is possible to reduce cell-testing efforts by at least 50%by substituting synthetic data into an existing dataset.This paper highlights the effectiveness of our synthetic data augmentation method in supplementing existing methodologies in battery health prognostics while dramatically reducing the expenditure of time and resources on battery aging experiments.
基金funding from the project“SPEED”(03XP0585)funded by the German Federal Ministry of ResearchTech-nology and Space(BMFTR)and the project“ADMirABLE”(03ETE053E)funded by the German Federal Ministry for Economic Affairs and Energy(BMWE)support of Shell Research UK Ltd.for the Ph.D.studentship of Amir Ali Panahi and the EPSRC Faraday Institution Multi-Scale Modelling Project(FIRG084).
文摘Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring,control,and design at system scale.Increasing their capabilities involves improving their physical fidelity while maintaining sub-millisecond computational speed.In this work,we introduce machine learning surrogates that learn physical dynamics.Specifically,we benchmark three operator-learning surrogates for the Single Particle Model(SPM):Deep Operator Networks(DeepONets),Fourier Neural Operators(FNOs)and a newly proposed parameter-embedded Fourier Neural Operator(PE-FNO),which conditions each spectral layer on particle radius and solid-phase diffusivity.We extend the comparison to classical machine-learning baselines by including U-Nets.Models are trained on simulated trajectories spanning four current families(constant,triangular,pulse-train,and Gaussian-random-field)and a full range of State-of-Charge(SOC)(0%to 100%).DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads.The basic FNO maintains mesh invariance and keeps concentration errors below 1%,with voltage mean-absolute errors under 1.7mV across all load types.Introducing parameter embedding marginally increases error but enables generalisation to varying radii and diffusivities.PE-FNO executes approximately 200 times faster than a 16-thread SPM solver.Consequently,PE-FNO’s capabilities in inverse tasks are explored in a parameter estimation task with Bayesian optimisation,recovering anode and cathode diffusivities with 1.14%and 8.4%mean absolute percentage error,respectively,and 0.5918 percentage points higher error in comparison with classical methods.These results pave the way for neural operators to meet the accuracy,speed and parametric flexibility demands of real-time battery management,design-of-experiments and large-scale inference.PE-FNO outperforms conventional neural surrogates,offering a practical path towards high-speed and high-fidelity electrochemical digital twins.
基金funding from the research project“SPEED”(03XP0585)funded by the German Federal Ministry of Education and Research(BMBF)Part of the work was done within the research project"Model2life"(03XP0334),funded by the German Federal Ministry of Education and Research(BMBF).
文摘Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits.Here,we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data.We extract three classes of features from the raw formation data,considering the statistical aspects,differential analysis,and electro-chemical characteristics.The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms.Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing.The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25℃ and 45℃,respectively.Moreover,the lifetime prediction model is able to predict the battery end-of-life with mean per-centage errors of 6.50% and 5.45%for the batteries aged at 25℃ and 45℃,respectively.This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.
