Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecol...Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales.Here,we developed a multi-level ecological network(MEN)framework to resolve the tension between urban expansion and ecological integrity.By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism,we established a cross-scale spatial optimization system,which coordinated the regional ecological corridors and local habitat patches.Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions:(1)spatial governance:the primary-level network(peri-urban natural reserves)effectively contained urban sprawl,and the secondary-level network(intra-urban green corridors)mitigated habitat fragmentation and improved the built-environment;(2)scenario robustness:the model maintained an optimal compactness-loose balance in multiple development pathways;(3)landscape metrics:patch fragmentation decreased by 18.25%,and the internal landscape richness improved by 10.66%compared to the scenario without USM optimization.The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability.展开更多
The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated....The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated.By adjusting the furnace cooling time after solution treatment and the aging temperature,Ti6422 alloy samples were developed with a multi-level lamellar microstructure,in-cluding microscaleαcolonies and α_(p) lamellae,as well as nanoscale α_(s) phases.Extending the furnace cooling time after solution treatment at 920℃ for 1 h from 240 to 540 min,followed by aging at 600℃ for 6 h,increased the α_(p) lamella content,reduced the α_(s) phase content,expanded theαcolonies and α_(p) lamellae size,and improved the impact toughness from 22.7 to 53.8 J/cm^(2).Additionally,under the same solution treatment,raising the aging temperature from 500 to 700℃ resulted in a decrease in the α_(s) phase content and a growth in the thickness of the α_(p) lamella and α_(s) phase.The impact toughness increased significantly with these changes.Samples with high α_(p) lamellae content or large α_(s) phase size exhibited high crack initiation and propagation energies.Impact deformation caused severe kinking of the α_(p) lamellae in crack initiation and propagation areas,leading to a uniform and high-density kernel average misorientation(KAM)distribu-tion,enhancing plastic deformation coordination and uniformity.Moreover,the multidirectional arrangement of coarserαcolonies and α_(p) lamellae continuously deflect the crack propagation direction,inhibiting crack propagation.展开更多
The convergence of Artificial Intelligence(AI)and the Internet of Things(IoT)has enabled Artificial Intelligence of Things(AIoT)systems that support intelligent and responsive smart societies,but it also introduces ma...The convergence of Artificial Intelligence(AI)and the Internet of Things(IoT)has enabled Artificial Intelligence of Things(AIoT)systems that support intelligent and responsive smart societies,but it also introduces major security and privacy concerns across domains such as healthcare,transportation,and smart cities.This Systemic Literature Review(SLR)addresses three research questions:identifying major threats and challenges in AIoT ecosystems,reviewing state-of-the-art security and privacy techniques,and evaluating their effectiveness.An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries,including IEEE Xplore,ACM Digital Library,ScienceDirect,SpringerLink,and Wiley Online Library,with a focus on security-and privacy-enhancing techniques such as blockchain,federated learning,and edge AI.The SLR identifies key challenges including data privacy leakage,authentication,cloud dependency,and attack surface expansion,and finds that emerging techniques,while promising,often involve trade-offs related to latency,scalability,and compliance.The study highlights future directions including lightweight cryptography,standardization,and explainable AI to support secure and trustworthy AIoT-enabled smart societies.展开更多
Kidney transplantation(KT)accounts for nearly three-fourths of organ transplants in India,with living donors contributing to 82%of cases.Induction immunosuppression is essential to optimize initial immunosuppression,r...Kidney transplantation(KT)accounts for nearly three-fourths of organ transplants in India,with living donors contributing to 82%of cases.Induction immunosuppression is essential to optimize initial immunosuppression,reduce acute rejections,and enable tailored use of maintenance agents.Rabbit anti-thymocyte globulin(rATG)and interleukin-2 receptor anatagonists(IL-2RA/IL-2RBs)are the most widely used induction therapies.However,data on induction practices across India are limited.To evaluate induction immunosuppression practices across KT centers in India and establish a consensus for different subsets of KT recipients.A nationwide online survey was conducted by the Indian Society of Organ Transplantation(ISOT)among its members(400 KT centers).Responses were analyzed to assess induction practices across diverse donor types,age groups,and immunological risk profiles.Heterogeneity in practices prompted consensus building using a modified Delphi process.Literature review and expert panel discussions(April 2024)were followed by structured voting,and 16 consensus statements were finalized.Of 400 centers approached,254 participated.rATG was the most commonly used induction therapy,followed by IL-2RBs;alemtuzumab was least used.Significant heterogeneity was observed in type,dose,and duration of induction therapy.Consensus recommendations were framed:rATG for high immunological risk recipients and deceased donor KTs;IL-2RB or low-dose rATG for low immunological risk;rituximab in ABOincompatible KTs;and tailoring based on age,diabetes,donor type,infection risk,and affordability.This first ISOT consensus provides 16 India-specific statements on induction therapy in KT.It emphasizes risk-stratified,evidenceinformed,and context-appropriate induction strategies,supporting standardization of care across the country.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
You are cordially invited to the 40th Topical Meeting of the International Society of Electrochemistry,which will be held from 15 to 17 August 2025 in Changchun,China.Changchun is a City of Science,Education,and Resor...You are cordially invited to the 40th Topical Meeting of the International Society of Electrochemistry,which will be held from 15 to 17 August 2025 in Changchun,China.Changchun is a City of Science,Education,and Resort with a population of more than 9 million.展开更多
The idea of a network society was introduced by Western sociologists at the end of the 20th century after in-depth research was conducted from perspectives such as informationalism.Influenced by these developments,the...The idea of a network society was introduced by Western sociologists at the end of the 20th century after in-depth research was conducted from perspectives such as informationalism.Influenced by these developments,the concept of constructing a network society also emerged in China.Over the past 30 years,China has made significant progress and achievements in constructing a network society,both in terms of its fundamental construction and social development.It is important that these advancements be summarized and reviewed.China’s network society construction can be divided into two relatively independent yet interconnected components,based on their focal points:its foundational infrastructure and its social development.These two components of China’s network society are managed by different departments.China has integrated the fundamental construction of its network society with the social development of its network society,thereby achieving unified planning,collaborative advancement,and coordinated development.This approach aims to harmonize two aspects:building China’s cyberspace strength and contributing to Chinese informatization,thereby advancing Chinese modernization.展开更多
At the Second International Civil Society Solidarity Conference on the Global Development Initiative held in Hohhot,Inner Mongolia,more than 300 Chinese and international representatives from over 50 countries focused...At the Second International Civil Society Solidarity Conference on the Global Development Initiative held in Hohhot,Inner Mongolia,more than 300 Chinese and international representatives from over 50 countries focused on the Global Development Initiative,sharing"Short or individual stories"to illustrate the significance of development.展开更多
What does the impact of migration mean in a social context?This article aims to see migrants by taking their stories into account.Through the individual stories,the readers can see the way in which a bigger picture is...What does the impact of migration mean in a social context?This article aims to see migrants by taking their stories into account.Through the individual stories,the readers can see the way in which a bigger picture is emerged in terms of community,society and nation.By doing so,the authors want to show the way in which migrants integrate into a society,including both migrants having a residence permit and those who are undocumented,as these two groups of people do differ greatly.The cultural clash does mean the inner struggle and the outer struggle that a society has to confront with.展开更多
The Second International Civil Society Solidarity Conference on the Global Development Initiative was held from 9th to 11th September in Hohhot Inner Mongolia.With an outcome-driven orientation,the conference now pres...The Second International Civil Society Solidarity Conference on the Global Development Initiative was held from 9th to 11th September in Hohhot Inner Mongolia.With an outcome-driven orientation,the conference now presents two selected projects.展开更多
With the crisp autumn weather,China's northern frontier region warmly welcomed vips from various parts of the world.From 6th to 11th September,six foreign delegations attending the Second International Civil Soc...With the crisp autumn weather,China's northern frontier region warmly welcomed vips from various parts of the world.From 6th to 11th September,six foreign delegations attending the Second International Civil Society Solidarity Conference on the Global Development Initiative visited Hohhot and the Xilingol League in Inner Mongolia.展开更多
On 10th September,the 2nd International Civil Society Solidarity Conference on the Global Development Initiative kicked off in Hohhot.With the theme of"Building Global Development Consensus and Sharing Developmen...On 10th September,the 2nd International Civil Society Solidarity Conference on the Global Development Initiative kicked off in Hohhot.With the theme of"Building Global Development Consensus and Sharing Development Opportunities for a Prosperous Future",more than 300 Chinese and international participants from more than 50 countries gathered to discuss global development issues.展开更多
The Second International Civil Society Solidarity Conference on the Global Development Initiative,organised by the China NGO Network for International Exchanges(CNIE)and the People's Government of Inner Mongolia A...The Second International Civil Society Solidarity Conference on the Global Development Initiative,organised by the China NGO Network for International Exchanges(CNIE)and the People's Government of Inner Mongolia Autonomous Region,was held in Hohhot from 9th to 11th September under the theme of"Building Global Consensus on Development and Sharing Opportunities for a Prosperous Future".展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
The year 2024 marked the 40th anniversary of Advances in Atmospheric Sciences(AAS),as well as the centenary of the Chinese Meteorological Society(CMS).The inaugural issue of AAS was published in 1984,initially being s...The year 2024 marked the 40th anniversary of Advances in Atmospheric Sciences(AAS),as well as the centenary of the Chinese Meteorological Society(CMS).The inaugural issue of AAS was published in 1984,initially being sponsored primarily by Chinese National Committee for the International Association of Meteorological and Atmospheric Sciences(IAMAS)and the Institute of Atmospheric Physics at the Chinese Academy of Sciences.In 2006,Springer became AAS’s international publisher.Then,in 2015,the CMS joined in sponsoring AAS,and in the same year,AAS also became an affiliated journal of the IAMAS.These milestone events helped broaden the reach of AAS,culminating in the journal establishing itself as a truly international journal supporting the advancement of the atmospheric sciences.展开更多
Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective metho...Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
基金National Key Research and Development Program of China,No.2019YFD1101304National Natural Science Foundation of China,No.52278059+1 种基金Natural Science Foundation of Hunan Province of China,No.2024JJ8316Hunan Provincial Innovation Foundation For Postgraduate,No.CX20250634。
文摘Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales.Here,we developed a multi-level ecological network(MEN)framework to resolve the tension between urban expansion and ecological integrity.By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism,we established a cross-scale spatial optimization system,which coordinated the regional ecological corridors and local habitat patches.Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions:(1)spatial governance:the primary-level network(peri-urban natural reserves)effectively contained urban sprawl,and the secondary-level network(intra-urban green corridors)mitigated habitat fragmentation and improved the built-environment;(2)scenario robustness:the model maintained an optimal compactness-loose balance in multiple development pathways;(3)landscape metrics:patch fragmentation decreased by 18.25%,and the internal landscape richness improved by 10.66%compared to the scenario without USM optimization.The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability.
基金supported by the National Natural Science Foundation of China(No.52090041).
文摘The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated.By adjusting the furnace cooling time after solution treatment and the aging temperature,Ti6422 alloy samples were developed with a multi-level lamellar microstructure,in-cluding microscaleαcolonies and α_(p) lamellae,as well as nanoscale α_(s) phases.Extending the furnace cooling time after solution treatment at 920℃ for 1 h from 240 to 540 min,followed by aging at 600℃ for 6 h,increased the α_(p) lamella content,reduced the α_(s) phase content,expanded theαcolonies and α_(p) lamellae size,and improved the impact toughness from 22.7 to 53.8 J/cm^(2).Additionally,under the same solution treatment,raising the aging temperature from 500 to 700℃ resulted in a decrease in the α_(s) phase content and a growth in the thickness of the α_(p) lamella and α_(s) phase.The impact toughness increased significantly with these changes.Samples with high α_(p) lamellae content or large α_(s) phase size exhibited high crack initiation and propagation energies.Impact deformation caused severe kinking of the α_(p) lamellae in crack initiation and propagation areas,leading to a uniform and high-density kernel average misorientation(KAM)distribu-tion,enhancing plastic deformation coordination and uniformity.Moreover,the multidirectional arrangement of coarserαcolonies and α_(p) lamellae continuously deflect the crack propagation direction,inhibiting crack propagation.
文摘The convergence of Artificial Intelligence(AI)and the Internet of Things(IoT)has enabled Artificial Intelligence of Things(AIoT)systems that support intelligent and responsive smart societies,but it also introduces major security and privacy concerns across domains such as healthcare,transportation,and smart cities.This Systemic Literature Review(SLR)addresses three research questions:identifying major threats and challenges in AIoT ecosystems,reviewing state-of-the-art security and privacy techniques,and evaluating their effectiveness.An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries,including IEEE Xplore,ACM Digital Library,ScienceDirect,SpringerLink,and Wiley Online Library,with a focus on security-and privacy-enhancing techniques such as blockchain,federated learning,and edge AI.The SLR identifies key challenges including data privacy leakage,authentication,cloud dependency,and attack surface expansion,and finds that emerging techniques,while promising,often involve trade-offs related to latency,scalability,and compliance.The study highlights future directions including lightweight cryptography,standardization,and explainable AI to support secure and trustworthy AIoT-enabled smart societies.
文摘Kidney transplantation(KT)accounts for nearly three-fourths of organ transplants in India,with living donors contributing to 82%of cases.Induction immunosuppression is essential to optimize initial immunosuppression,reduce acute rejections,and enable tailored use of maintenance agents.Rabbit anti-thymocyte globulin(rATG)and interleukin-2 receptor anatagonists(IL-2RA/IL-2RBs)are the most widely used induction therapies.However,data on induction practices across India are limited.To evaluate induction immunosuppression practices across KT centers in India and establish a consensus for different subsets of KT recipients.A nationwide online survey was conducted by the Indian Society of Organ Transplantation(ISOT)among its members(400 KT centers).Responses were analyzed to assess induction practices across diverse donor types,age groups,and immunological risk profiles.Heterogeneity in practices prompted consensus building using a modified Delphi process.Literature review and expert panel discussions(April 2024)were followed by structured voting,and 16 consensus statements were finalized.Of 400 centers approached,254 participated.rATG was the most commonly used induction therapy,followed by IL-2RBs;alemtuzumab was least used.Significant heterogeneity was observed in type,dose,and duration of induction therapy.Consensus recommendations were framed:rATG for high immunological risk recipients and deceased donor KTs;IL-2RB or low-dose rATG for low immunological risk;rituximab in ABOincompatible KTs;and tailoring based on age,diabetes,donor type,infection risk,and affordability.This first ISOT consensus provides 16 India-specific statements on induction therapy in KT.It emphasizes risk-stratified,evidenceinformed,and context-appropriate induction strategies,supporting standardization of care across the country.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
文摘You are cordially invited to the 40th Topical Meeting of the International Society of Electrochemistry,which will be held from 15 to 17 August 2025 in Changchun,China.Changchun is a City of Science,Education,and Resort with a population of more than 9 million.
基金“Research on Social Change and Network Society Planning in the Internet of Everything Era”(ID:21BSH005),a project under the National Social Science Fund of China
文摘The idea of a network society was introduced by Western sociologists at the end of the 20th century after in-depth research was conducted from perspectives such as informationalism.Influenced by these developments,the concept of constructing a network society also emerged in China.Over the past 30 years,China has made significant progress and achievements in constructing a network society,both in terms of its fundamental construction and social development.It is important that these advancements be summarized and reviewed.China’s network society construction can be divided into two relatively independent yet interconnected components,based on their focal points:its foundational infrastructure and its social development.These two components of China’s network society are managed by different departments.China has integrated the fundamental construction of its network society with the social development of its network society,thereby achieving unified planning,collaborative advancement,and coordinated development.This approach aims to harmonize two aspects:building China’s cyberspace strength and contributing to Chinese informatization,thereby advancing Chinese modernization.
文摘At the Second International Civil Society Solidarity Conference on the Global Development Initiative held in Hohhot,Inner Mongolia,more than 300 Chinese and international representatives from over 50 countries focused on the Global Development Initiative,sharing"Short or individual stories"to illustrate the significance of development.
文摘What does the impact of migration mean in a social context?This article aims to see migrants by taking their stories into account.Through the individual stories,the readers can see the way in which a bigger picture is emerged in terms of community,society and nation.By doing so,the authors want to show the way in which migrants integrate into a society,including both migrants having a residence permit and those who are undocumented,as these two groups of people do differ greatly.The cultural clash does mean the inner struggle and the outer struggle that a society has to confront with.
文摘The Second International Civil Society Solidarity Conference on the Global Development Initiative was held from 9th to 11th September in Hohhot Inner Mongolia.With an outcome-driven orientation,the conference now presents two selected projects.
文摘With the crisp autumn weather,China's northern frontier region warmly welcomed vips from various parts of the world.From 6th to 11th September,six foreign delegations attending the Second International Civil Society Solidarity Conference on the Global Development Initiative visited Hohhot and the Xilingol League in Inner Mongolia.
文摘On 10th September,the 2nd International Civil Society Solidarity Conference on the Global Development Initiative kicked off in Hohhot.With the theme of"Building Global Development Consensus and Sharing Development Opportunities for a Prosperous Future",more than 300 Chinese and international participants from more than 50 countries gathered to discuss global development issues.
文摘The Second International Civil Society Solidarity Conference on the Global Development Initiative,organised by the China NGO Network for International Exchanges(CNIE)and the People's Government of Inner Mongolia Autonomous Region,was held in Hohhot from 9th to 11th September under the theme of"Building Global Consensus on Development and Sharing Opportunities for a Prosperous Future".
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
文摘The year 2024 marked the 40th anniversary of Advances in Atmospheric Sciences(AAS),as well as the centenary of the Chinese Meteorological Society(CMS).The inaugural issue of AAS was published in 1984,initially being sponsored primarily by Chinese National Committee for the International Association of Meteorological and Atmospheric Sciences(IAMAS)and the Institute of Atmospheric Physics at the Chinese Academy of Sciences.In 2006,Springer became AAS’s international publisher.Then,in 2015,the CMS joined in sponsoring AAS,and in the same year,AAS also became an affiliated journal of the IAMAS.These milestone events helped broaden the reach of AAS,culminating in the journal establishing itself as a truly international journal supporting the advancement of the atmospheric sciences.
基金supported by the Fund of Key Laboratory of Biomedical Engineering of Hainan Province(No.BME20240001)the STI2030-Major Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
