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
Songpan County is a poverty-stricken county in Tibetan areas of Sichuan Province,it belongs to a concentrated poverty-stricken area in China.Through summarizing the specific conditions of multi-level cooperation and c...Songpan County is a poverty-stricken county in Tibetan areas of Sichuan Province,it belongs to a concentrated poverty-stricken area in China.Through summarizing the specific conditions of multi-level cooperation and complementary advantage poverty alleviation model in Songpan County,this paper analyzed the effect of this model.Through the analysis on the cooperative poverty alleviation model(eastern-western cooperation,provincial targeted assistance,county-wide"four-leading and four-assistance"inner-party assistance,social force"10000 enterprises helping 10000 villages",and"visiting every household and keeping every person busy"),it summarized the implementation and actual results of each level.In addition,it summarized the problems encountered in the implementation process of multi-level cooperation and complementary advantage poverty alleviation model and came up with recommendations.With the aid of this successful multi-level cooperation and complementary advantage poverty alleviation model,Songpan County has achieved a decisive victory in the fight against poverty.At the end of April 2019,the poverty rate in the whole region fell to 0.45%,and it successfully took off the poverty hat.This paper analyzed and refined the specific practice,main achievements,successful experience,implications and reference significance of multi-level cooperation and complementary advantage poverty alleviation model,to provide necessary references for innovation of poverty alleviation model in poverty-stricken Tibetan areas,or even other similar provinces and regions.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
Complementary inverter is the basic unit for logic circuits,but the inverters based on full oxide thin-film transistors(TFTs)are still very limited.The next challenge is to realize complementary inverters using homoge...Complementary inverter is the basic unit for logic circuits,but the inverters based on full oxide thin-film transistors(TFTs)are still very limited.The next challenge is to realize complementary inverters using homogeneous oxide semiconduc-tors.Herein,we propose the design of complementary inverter based on full ZnO TFTs.Li-N dual-doped ZnO(ZnO:(Li,N))acts as the p-type channel and Al-doped ZnO(ZnO:Al)serves as the n-type channel for fabrication of TFTs,and then the complemen-tary inverter is produced with p-and n-type ZnO TFTs.The homogeneous ZnO-based complementary inverter has typical volt-age transfer characteristics with the voltage gain of 13.34 at the supply voltage of 40 V.This work may open the door for the development of oxide complementary inverters for logic circuits.展开更多
As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy o...As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.展开更多
Gait,the unique pattern of how a person walks,has emerged as one of the most promising biometric features in modern intelligent sensing.Unlike fingerprints or facial characteristics,gait can be captured unobtrusively ...Gait,the unique pattern of how a person walks,has emerged as one of the most promising biometric features in modern intelligent sensing.Unlike fingerprints or facial characteristics,gait can be captured unobtrusively and at a distance,without requiring the subject’s awareness or cooperation.This makes it highly suitable for long-range surveillance,forensic investigation,and smart environments where contactless recognition is crucial.Traditional gait-recognition systems rely either on silhouettes,which capture the outer appearance of a person,or on skeletons,which describe the internal structure of human motion.Each modality provides only a partial understanding of gait.Silhouettes emphasize shape and contour but are easily distorted by clothing or carried objects;skeletons describe motion dynamics and limb coordination but lose discriminative details about body shape.This article presents the concept of Complementary Semantic Embedding(CSE),a unified framework that merges silhouette and skeleton information into a comprehensive semantic representation of human walking.By modeling the complementary nature of appearance and structure,the approach achieves more robust and accurate gait recognition even under challenging conditions.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
Mild electrocatalytic nitrate reduction reaction(NO_(3)RR),driven by renewable electricity,is regarded as a desirable strategy for green ammonia synthesis and simultaneous removal of nitrogen-containing environmental ...Mild electrocatalytic nitrate reduction reaction(NO_(3)RR),driven by renewable electricity,is regarded as a desirable strategy for green ammonia synthesis and simultaneous removal of nitrogen-containing environmental pollutants.In view of different supply voltages from renewable energy sources,developing costeffective and efficient electrocatalysts with a wide operating potential window is very meaningful for practical application.However,currently reported catalysts usually need to introduce noble metals to synergistically achieve wide-potential selective ammonia synthesis from nitrate.In this work,we present for the first time a dual-transition-metal electrocatalyst(Fe_(3)C-CuO_(x)@NC,x=0,1)with wide-potential-adaptability for highly selective nitrate reduction to ammonia.Such Fe_(3)C-CuO_(x)@NC with spatially separated CuO_(x)and noblemetal-like Fe_(3)C nanoparticles encapsulated with nitrogen-doped graphitized carbon,exhibits outstanding performance in NO_(3)RR with desirable NH_(3)Faraday efficiency of more than 90%over a wide potential ranging from-0.2 V vs.RHE to-0.6 V vs.RHE,comparable to the reported noble metal catalysts.Different from common tandem catalysis,the wide-potential high ammonia selectivity of Fe_(3)C-CuO_(x)@NC is domina ntly ascribed to the complementary enhancement between CuO_(x)and Fe_(3)C,fully supported by results of experiments and density function theory calculations.CuO_(x)exhibit highly intrinsic nitrate reduction to nitrite to compensate for the slow potential determination step(^(*)NO_(3)→^(*)NO_(3)H)of Fe_(3)C,while Fe_(3)C,besides behaving like noble metals to supply adequate active hydrogens,has both good adsorption and reduction abilities for nitrite species to ammonia.Moreover,Fe_(3)C partially stabilizes active Cu^(0)/Cu^(+)sites,and the unique carbon-layer enca psulation structure effectively prevents the agglomeration and corrosion of metal nanoparticles during the electrocatalysis,thus maintaining good cyclic stability.The Zn-NO_(3)^(-)battery assembled with Fe_(3)C-CuO_(x)@NC can reach a high power density of 5.2 mW cm^(-2)at a potential of 1.0 V vs.Zn,with an NH_(3)Faraday efficiency of 92.4%at a current of 8.0 mA,proving its potential practical application.This advance provides unique insights into complementary catalysis mechanisms on multiple metal sites in NO_(3)RR,and offers a reference for the design of other transition metal electrocatalysts matching with renewable electricity.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
Background:The use of complementary and alternative medicine(CAM)is common among individuals with underlying chronic illnesses.Objective:This systematic review and meta-analysis investigated the global prevalence and ...Background:The use of complementary and alternative medicine(CAM)is common among individuals with underlying chronic illnesses.Objective:This systematic review and meta-analysis investigated the global prevalence and patterns of CAM use among individuals with chronic kidney disease(CKD).Search strategy:PubMed,Embase,and Cumulative Index to Nursing and Allied Health Literature Plus were searched from inception until 26th February 2024.Inclusion criteria:Original articles reporting the use of at least one type of CAM among individuals aged above 18 years old and at all stages of CKD or undergoing any form of kidney replacement therapy.Data extraction and analysis:Two independent reviewers performed the literature screening.The data were extracted from the included studies by one reviewer and cross-checked by another.Discrepancies were resolved by discussion and consensus among two reviewers.Primary information included prevalence of CAM use,types of CAM used,reasons for CAM use,factors associated with CAM use,and disclosure to healthcare providers.Meta-analyses were performed to determine the pooled prevalence of CAM use and non-disclosure of CAM using a random effect model.Results:Forty-one studies were included in this systematic review and meta-analysis.The pooled prevalence of CAM use was 43%(95%confidence interval:34%,51%),I2=99.46%.The reasons for CAM use included treatment of underlying comorbidities,complications or symptoms,maintenance of general health,and treatment of CKD.Nutritional approaches were the most common CAM modality,with 412different herbal and dietary supplements reported;psychological and physical approaches included massage therapy,relaxation techniques,and mind–body practices;and other complementary health approaches such as homeopathy,traditional Chinese medicine,and Ayurvedic medicine were also frequently reported.Factors associated with CAM use included sociodemographic characteristics such as older age,female gender,or higher income;disease or therapy factors such as not having diabetes,relying on hemodialysis,or poor adherence to medication;and patient or internal factors such as positive attitude towards CAM and perceived safety of CAM.About 66%(61%,72%)of CAM users did not disclose the use of CAM to their healthcare providers.Conclusion:CAM use is prevalent among individuals with CKD,and healthcare providers should communicate openly and effectively to emphasize the rational use of CAM to avoid potential harm.展开更多
Under the current long-term electricity market mechanism,new energy and thermal power face issues such as deviation assessment and compression of generation space.The profitability of market players is limited.Simulta...Under the current long-term electricity market mechanism,new energy and thermal power face issues such as deviation assessment and compression of generation space.The profitability of market players is limited.Simultaneously,the cooperation model among various energy sources will have a direct impact on the alliance’s revenue and the equity of income distribution within the alliance.Therefore,integrating new energy with thermal power units into an integrated multi-energy complementary system to participate in the long-term electricity market holds significant potential.To simulate and evaluate the benefits and internal distribution methods of a multi-energy complementary system participating in long-term market transactions,this paper first constructs a multi-energy complementary system integrated with new energy and thermal power generation units at the same connection point,and participates in the annual bilateral game as a unified market entity to obtain the revenue value under the annual bilateral market.Secondly,based on the entropy weight method,improvements are made to the traditional Shapley value distribution model,and an internal distribution model for multi-energy complementary systems with multiple participants is constructed.Finally,a Markov Decision Process(MDP)evaluation system is constructed for practical case verification.The research results show that the improved Shapley value distribution model achieves higher satisfaction,providing a reasonable allocation scheme for multi-energy complementary cooperation models.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
Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in t...Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in this paper,which can be used to reduce the sidelobe level of multiple waveforms.First,the CAC model is constructed.Then,the waveform design problem is transformed into a nonlinear optimization problem by constructing an objective function using the two indicators of peak-to-sidelobe ratio(PSLR)and integrated sidelobe ratio(ISLR).Finally,genetic algorithm(GA)is used to solve the optimization problem to get the best CAC waveforms.Simulations and experiments are conducted to verify the effectiveness of the proposed method.展开更多
This editorial is based on the network pharmacology and in vivo study by Qin et al,which investigated the potential therapeutic mechanisms of Panax ginseng(P.ginseng)in ulcerative colitis.The key findings from the stu...This editorial is based on the network pharmacology and in vivo study by Qin et al,which investigated the potential therapeutic mechanisms of Panax ginseng(P.ginseng)in ulcerative colitis.The key findings from the study included the identification of the targets of P.ginseng and ulcerative colitis,elucidation of mechanistic pathways,and in vivo validation of the pharmacological activity and therapeutic effect of panaxadiol,a key active component of P.ginseng.This editorial provides additional context regarding the evidence supporting the use of ginseng and other commonly used complementary therapies that clinicians may encounter.We also aim to highlight potential similarities between the mechanisms of popular complementary and conventional medical therapies.展开更多
The international scientific literature presents still incipient results regarding the management of cancer symptom clusters by oncology nursing,especially in pediatric oncology.This is a promising field of investigat...The international scientific literature presents still incipient results regarding the management of cancer symptom clusters by oncology nursing,especially in pediatric oncology.This is a promising field of investigation for clinical nurses and researchers,and when it is subsidized by medium-range theories,they co-rroborate the diagnoses and interventions of nursing in oncology,enhancing the science of nursing care.This minireview article aims to discuss the utilizing the hospital clowns as a complementary therapy,to enhance quality of life and reduce stress and fatigue in pediatric cancer patients.Overall,the evidence presented so far pointed out that complementary therapy might help improve the quality of life of pediatric cancer patients,and that complementary therapy usage should be part of a health comprehensive care model,delivering therapeutic approaches that might enhance the mind-body during a pediatric cancer patients’life span.The results of scientific investigations by nurses,particularly those linked to the basic sciences,play a critical role in advancing personalized care in pediatric integrative oncology.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Songpan County is a poverty-stricken county in Tibetan areas of Sichuan Province,it belongs to a concentrated poverty-stricken area in China.Through summarizing the specific conditions of multi-level cooperation and complementary advantage poverty alleviation model in Songpan County,this paper analyzed the effect of this model.Through the analysis on the cooperative poverty alleviation model(eastern-western cooperation,provincial targeted assistance,county-wide"four-leading and four-assistance"inner-party assistance,social force"10000 enterprises helping 10000 villages",and"visiting every household and keeping every person busy"),it summarized the implementation and actual results of each level.In addition,it summarized the problems encountered in the implementation process of multi-level cooperation and complementary advantage poverty alleviation model and came up with recommendations.With the aid of this successful multi-level cooperation and complementary advantage poverty alleviation model,Songpan County has achieved a decisive victory in the fight against poverty.At the end of April 2019,the poverty rate in the whole region fell to 0.45%,and it successfully took off the poverty hat.This paper analyzed and refined the specific practice,main achievements,successful experience,implications and reference significance of multi-level cooperation and complementary advantage poverty alleviation model,to provide necessary references for innovation of poverty alleviation model in poverty-stricken Tibetan areas,or even other similar provinces and regions.
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.
基金supported by Zhejiang Provincial Natural Science Foundation of China(No.LZ24E020001).
文摘Complementary inverter is the basic unit for logic circuits,but the inverters based on full oxide thin-film transistors(TFTs)are still very limited.The next challenge is to realize complementary inverters using homogeneous oxide semiconduc-tors.Herein,we propose the design of complementary inverter based on full ZnO TFTs.Li-N dual-doped ZnO(ZnO:(Li,N))acts as the p-type channel and Al-doped ZnO(ZnO:Al)serves as the n-type channel for fabrication of TFTs,and then the complemen-tary inverter is produced with p-and n-type ZnO TFTs.The homogeneous ZnO-based complementary inverter has typical volt-age transfer characteristics with the voltage gain of 13.34 at the supply voltage of 40 V.This work may open the door for the development of oxide complementary inverters for logic circuits.
基金funded by the National Key R&D Program of China,grant number 2019YFB1505400.
文摘As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.
文摘Gait,the unique pattern of how a person walks,has emerged as one of the most promising biometric features in modern intelligent sensing.Unlike fingerprints or facial characteristics,gait can be captured unobtrusively and at a distance,without requiring the subject’s awareness or cooperation.This makes it highly suitable for long-range surveillance,forensic investigation,and smart environments where contactless recognition is crucial.Traditional gait-recognition systems rely either on silhouettes,which capture the outer appearance of a person,or on skeletons,which describe the internal structure of human motion.Each modality provides only a partial understanding of gait.Silhouettes emphasize shape and contour but are easily distorted by clothing or carried objects;skeletons describe motion dynamics and limb coordination but lose discriminative details about body shape.This article presents the concept of Complementary Semantic Embedding(CSE),a unified framework that merges silhouette and skeleton information into a comprehensive semantic representation of human walking.By modeling the complementary nature of appearance and structure,the approach achieves more robust and accurate gait recognition even under challenging conditions.
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.
基金financial support from the National Natural Science Foundation of China(NSFC 22172082)the Fundamental Research Funds for the Central Universities。
文摘Mild electrocatalytic nitrate reduction reaction(NO_(3)RR),driven by renewable electricity,is regarded as a desirable strategy for green ammonia synthesis and simultaneous removal of nitrogen-containing environmental pollutants.In view of different supply voltages from renewable energy sources,developing costeffective and efficient electrocatalysts with a wide operating potential window is very meaningful for practical application.However,currently reported catalysts usually need to introduce noble metals to synergistically achieve wide-potential selective ammonia synthesis from nitrate.In this work,we present for the first time a dual-transition-metal electrocatalyst(Fe_(3)C-CuO_(x)@NC,x=0,1)with wide-potential-adaptability for highly selective nitrate reduction to ammonia.Such Fe_(3)C-CuO_(x)@NC with spatially separated CuO_(x)and noblemetal-like Fe_(3)C nanoparticles encapsulated with nitrogen-doped graphitized carbon,exhibits outstanding performance in NO_(3)RR with desirable NH_(3)Faraday efficiency of more than 90%over a wide potential ranging from-0.2 V vs.RHE to-0.6 V vs.RHE,comparable to the reported noble metal catalysts.Different from common tandem catalysis,the wide-potential high ammonia selectivity of Fe_(3)C-CuO_(x)@NC is domina ntly ascribed to the complementary enhancement between CuO_(x)and Fe_(3)C,fully supported by results of experiments and density function theory calculations.CuO_(x)exhibit highly intrinsic nitrate reduction to nitrite to compensate for the slow potential determination step(^(*)NO_(3)→^(*)NO_(3)H)of Fe_(3)C,while Fe_(3)C,besides behaving like noble metals to supply adequate active hydrogens,has both good adsorption and reduction abilities for nitrite species to ammonia.Moreover,Fe_(3)C partially stabilizes active Cu^(0)/Cu^(+)sites,and the unique carbon-layer enca psulation structure effectively prevents the agglomeration and corrosion of metal nanoparticles during the electrocatalysis,thus maintaining good cyclic stability.The Zn-NO_(3)^(-)battery assembled with Fe_(3)C-CuO_(x)@NC can reach a high power density of 5.2 mW cm^(-2)at a potential of 1.0 V vs.Zn,with an NH_(3)Faraday efficiency of 92.4%at a current of 8.0 mA,proving its potential practical application.This advance provides unique insights into complementary catalysis mechanisms on multiple metal sites in NO_(3)RR,and offers a reference for the design of other transition metal electrocatalysts matching with renewable electricity.
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Background:The use of complementary and alternative medicine(CAM)is common among individuals with underlying chronic illnesses.Objective:This systematic review and meta-analysis investigated the global prevalence and patterns of CAM use among individuals with chronic kidney disease(CKD).Search strategy:PubMed,Embase,and Cumulative Index to Nursing and Allied Health Literature Plus were searched from inception until 26th February 2024.Inclusion criteria:Original articles reporting the use of at least one type of CAM among individuals aged above 18 years old and at all stages of CKD or undergoing any form of kidney replacement therapy.Data extraction and analysis:Two independent reviewers performed the literature screening.The data were extracted from the included studies by one reviewer and cross-checked by another.Discrepancies were resolved by discussion and consensus among two reviewers.Primary information included prevalence of CAM use,types of CAM used,reasons for CAM use,factors associated with CAM use,and disclosure to healthcare providers.Meta-analyses were performed to determine the pooled prevalence of CAM use and non-disclosure of CAM using a random effect model.Results:Forty-one studies were included in this systematic review and meta-analysis.The pooled prevalence of CAM use was 43%(95%confidence interval:34%,51%),I2=99.46%.The reasons for CAM use included treatment of underlying comorbidities,complications or symptoms,maintenance of general health,and treatment of CKD.Nutritional approaches were the most common CAM modality,with 412different herbal and dietary supplements reported;psychological and physical approaches included massage therapy,relaxation techniques,and mind–body practices;and other complementary health approaches such as homeopathy,traditional Chinese medicine,and Ayurvedic medicine were also frequently reported.Factors associated with CAM use included sociodemographic characteristics such as older age,female gender,or higher income;disease or therapy factors such as not having diabetes,relying on hemodialysis,or poor adherence to medication;and patient or internal factors such as positive attitude towards CAM and perceived safety of CAM.About 66%(61%,72%)of CAM users did not disclose the use of CAM to their healthcare providers.Conclusion:CAM use is prevalent among individuals with CKD,and healthcare providers should communicate openly and effectively to emphasize the rational use of CAM to avoid potential harm.
文摘Under the current long-term electricity market mechanism,new energy and thermal power face issues such as deviation assessment and compression of generation space.The profitability of market players is limited.Simultaneously,the cooperation model among various energy sources will have a direct impact on the alliance’s revenue and the equity of income distribution within the alliance.Therefore,integrating new energy with thermal power units into an integrated multi-energy complementary system to participate in the long-term electricity market holds significant potential.To simulate and evaluate the benefits and internal distribution methods of a multi-energy complementary system participating in long-term market transactions,this paper first constructs a multi-energy complementary system integrated with new energy and thermal power generation units at the same connection point,and participates in the annual bilateral game as a unified market entity to obtain the revenue value under the annual bilateral market.Secondly,based on the entropy weight method,improvements are made to the traditional Shapley value distribution model,and an internal distribution model for multi-energy complementary systems with multiple participants is constructed.Finally,a Markov Decision Process(MDP)evaluation system is constructed for practical case verification.The research results show that the improved Shapley value distribution model achieves higher satisfaction,providing a reasonable allocation scheme for multi-energy complementary cooperation models.
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.
基金supported by the National Natural Science Foundation of China(62001481,61890542)the Natural Science Foundation of Hunan Province(2021JJ40686).
文摘Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in this paper,which can be used to reduce the sidelobe level of multiple waveforms.First,the CAC model is constructed.Then,the waveform design problem is transformed into a nonlinear optimization problem by constructing an objective function using the two indicators of peak-to-sidelobe ratio(PSLR)and integrated sidelobe ratio(ISLR).Finally,genetic algorithm(GA)is used to solve the optimization problem to get the best CAC waveforms.Simulations and experiments are conducted to verify the effectiveness of the proposed method.
文摘This editorial is based on the network pharmacology and in vivo study by Qin et al,which investigated the potential therapeutic mechanisms of Panax ginseng(P.ginseng)in ulcerative colitis.The key findings from the study included the identification of the targets of P.ginseng and ulcerative colitis,elucidation of mechanistic pathways,and in vivo validation of the pharmacological activity and therapeutic effect of panaxadiol,a key active component of P.ginseng.This editorial provides additional context regarding the evidence supporting the use of ginseng and other commonly used complementary therapies that clinicians may encounter.We also aim to highlight potential similarities between the mechanisms of popular complementary and conventional medical therapies.
基金Supported by the Coordination of Improvement of Higher Education Personnel(CAPES)and National Council for Scientific and Technological Development(CNPq),No.311427/2023-5.
文摘The international scientific literature presents still incipient results regarding the management of cancer symptom clusters by oncology nursing,especially in pediatric oncology.This is a promising field of investigation for clinical nurses and researchers,and when it is subsidized by medium-range theories,they co-rroborate the diagnoses and interventions of nursing in oncology,enhancing the science of nursing care.This minireview article aims to discuss the utilizing the hospital clowns as a complementary therapy,to enhance quality of life and reduce stress and fatigue in pediatric cancer patients.Overall,the evidence presented so far pointed out that complementary therapy might help improve the quality of life of pediatric cancer patients,and that complementary therapy usage should be part of a health comprehensive care model,delivering therapeutic approaches that might enhance the mind-body during a pediatric cancer patients’life span.The results of scientific investigations by nurses,particularly those linked to the basic sciences,play a critical role in advancing personalized care in pediatric integrative oncology.
