The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We r...The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.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
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 emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespre...The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.展开更多
This article introduces a novel 20 V radiation-hardened high-voltage metal-oxide-semiconductor field-effect transistor(MOSFET)driver with an optimized input circuit and a drain-surrounding-source(DSS)structure.The inp...This article introduces a novel 20 V radiation-hardened high-voltage metal-oxide-semiconductor field-effect transistor(MOSFET)driver with an optimized input circuit and a drain-surrounding-source(DSS)structure.The input circuit of a conventional inverter consists of a thick-gate-oxide n-type MOSFET(NMOS).These conventional drivers can tolerate a total ionizing dose(TID)of up to 100 krad(Si).In contrast,the proposed comparator input circuit uses both a thick-gate-oxide p-type MOSFET(PMOS)and thin-gate-oxide NMOS to offer a high input voltage and higher TID tolerance.Because the thick-gate-oxide PMOS and thin-gate-oxide NMOS collectively provide better TID tolerance than the thick-gate-oxide NMOS,the circuit exhibits enhanced TID tolerance of>300 krad(Si).Simulations and experimental date indicate that the DSS structure reduces the probability of unwanted parasitic bipolar junction transistor activation,yielding a better single-event effect tolerance of over 81.8 MeVcm^(2)mg^(-1).The innovative strategy proposed in this study involves circuit and layout design optimization,and does not require any specialized process flow.Hence,the proposed circuit can be manufactured using common commercial 0.35μm BCD processes.展开更多
As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as inclement weather, is especially ch...As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as inclement weather, is especially challenging for older drivers due to their sensitivity to glare and reduced visibility. As a result, older drivers may adjust their behavior during adverse weather. This paper explores the differential impacts of weather on older drivers with cognitive decline compared to older drivers with normal cognitive function. Data were from a naturalistic driving study of older drivers in Omaha, Nebraska. Driver speed and weather data were extracted and the correlation between speed compliance, road weather conditions, and the cognitive/neurological status of the drivers was examined. Speed compliance was used as the surrogate safety measure since driving at lower speeds can indicate that the driver is challenged by roadway or environmental conditions and can therefore indicate a risk. The percentage of time during a trip when drivers were 16.1 kph under the speed limit was modeled as the dependent variable using beta regression. The variables that resulted in the best fit model were mild cognitive impairment (MCI), age group, traffic density, and weather. Results indicated that the youngest group of older drivers (young-old) spent less time driving at impeding speeds and had the least variability compared to the other two age groups. The middle group of older drivers (middle-old) had the highest amount of time driving at impeding speeds and had more variability than young-old drivers. The oldest group of older drivers (old-old) were the most likely to drive at impeding speeds and had the most variability. In general, older drivers were more likely to drive at impeding speeds during peak hours than during non-peak hours. Additionally, in most cases, older drivers spent less time below the speed limit when the weather was clear than in adverse conditions. Results indicate that older drivers are impacted by weather conditions, and distinct patterns were noted between older drivers who were cognitively impaired compared to drivers with normal cognition.展开更多
Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and...Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and environmental factors influencing its distribution in the semi-arid Karamoja sub-region, Uganda. We surveyed 388 plots (5 m radius) at different altitudes, recording life stages, stem diameters, and regeneration patterns, and analyzed soil samples. Multivariate analyses, including Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA), Non-metric Multidimensional Scaling (NMDS), and Multiple Regression Modeling (MRM), identified key environmental factors affecting its distribution. Findings show that O. lanceolata populations in Moroto, Nakapiripirit, and Amudat districts are severely degraded due to overexploitation. The species is primarily regenerating through coppicing rather than seedlings, with an exploitation intensity of 56.6%. Population densities are low, distribution is irregular, and sustainable harvesting is not viable. Soil properties, particularly Ca2+, N, P, K+, Na+, and organic matter, significantly influence its abundance. Conservation efforts should focus on identifying suitable provenances for genetic preservation and plantation establishment. Areas with at least 9 trees per hectare in Moroto, Nakapiripirit, and Amudat could serve as potential sites for ex-situ plantations. Further research should explore how biotic interactions, genetic diversity, and morphology affect oil yield and quality to support restoration, breeding, and domestication initiatives.展开更多
We have designed,assembled,and tested a 4-MA,60-ns fast linear transformer driver(LTD),which is the first operating generator featuring multiple LTD modules connected in parallel.The LTD-based accelerator comprises si...We have designed,assembled,and tested a 4-MA,60-ns fast linear transformer driver(LTD),which is the first operating generator featuring multiple LTD modules connected in parallel.The LTD-based accelerator comprises six modules in parallel,each of which has ten-stage cavities stacked in series.The six LTD modules are connected to a water tank of diameter 6 m via a 3-m-long impedance-matched deionized waterinsulated coaxial transmission line.In the water tank,the electrical pulses are transmitted down by six horizontal tri-plate transmission lines.A 2.1-m-diameter two-level vacuum insulator stack is utilized to separate the deionized water region from the vacuum region.In the vacuum,the currents are further transported downstream by a two-level magnetically insulated transmission-line and then converged through four post-hole convolutes.Plasma radiation loads or bremsstrahlung electron beam diodes serve as loads that are expected to generate intense soft X rays or warm X rays.The machine is 3.2 m in height and 22 m in outer diameter,including support systems such as a high-voltage charge supply,magnetic core reset system,trigger system,and support platform for inner stalk installation and maintenance.A total of 1440 individual±100-kV multi-gap spark switches and 2880 individual 100-kV capacitors are employed in the accelerator.A total of 12 fiberoptic laser-controlled trigger generators combining photoconductive and traditional gas spark switch technologies are used to realize the synchronous discharge of the more than 1000 gas switches.At an LTD charge voltage of±85 kV,the accelerator stores an initial energy of about 300 kJ and is expected to deliver a current of 3–5 MA into various loads.To date,the LTD facility has shot into a thick-walled aluminum liner load and a reflex triode load.With a thick-walled aluminum liner of inductance 1.81 nH,a current with peak up to 4.1 MA and rise time(10%–90%)of about 60 ns has been achieved.The current transport efficiency from the insulator stack to the liner load approaches 100%during peak times.The LTD accelerator has been used to drive reflex triode loads generating warm X rays with high energy fluence and large radiation area.It has been demonstrated that this LTD is a promising and high-efficiency prime pulsed power source suitable for use in constructing the next generation of large-scale accelerators with currents of tens of megaamperes.展开更多
When you go somewhere,do you like to be the driver or a passenger?When you are the driver,you are in control.You can go fast or slow.You can pick the route.When and where do you stop?You decide.You enjoy the feeling o...When you go somewhere,do you like to be the driver or a passenger?When you are the driver,you are in control.You can go fast or slow.You can pick the route.When and where do you stop?You decide.You enjoy the feeling of driving.Ifs fun!展开更多
To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortc...To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system,dubbed DriveMe,for identifying drivers on cars.Our novelty aspects are 1⃝Lightweight scheme that depends only on a single sensor data(i.e.,pressure readings)attached to the driver’s seat and belt.2⃝Practical evaluation in which one-class authentication models are trained from only the owner users and tested using data collected from both owners and attackers.3⃝Rapid Authentication to quickly identify drivers’identities using a few pressure samples collected within short durations(1,2,3,5,or 10 s).4⃝Realistic experiments where the sensory data is collected from real experiments rather than computer simulation tools.We conducted real experiments and collected about 13,200 samples and 22,800 samples of belt-only and seat-only datasets from all 12 users under different settings.To evaluate system effectiveness,we implemented extensive evaluation scenarios using four one-class detectors One-Class Support Vector Machine(OCSVM),Local Outlier Factor(LOF),Isolation Forest(IF),and Elliptic Envelope(EE),three dataset types(belt-only,seat-only,and fusion),and four different dataset sizes.Our average experimental results show that the system can authenticate the driver with an F1 score of 93.1%for seat-based data using OCSVM classifier,an F1 score of 98.53%for fusion-based data using LOF classifier,an F1 score of 91.65%for fusion-based data using IF classifier,and an F1 score of 95.79%for fusion-based data using EE classifier.展开更多
The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In...The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In the artificial Intelligence domain,machine learning(ML)was developed to make inferences with a degree of accuracy similar to that of humans;however,enormous amounts of data are required.Machine learning enhances the accuracy of the decisions taken by ADAS,by evaluating all the data received from various vehicle sensors.This study summarizes all the critical algorithms used in ADAS technologies and presents the evolution of ADAS technology.Initially,ADAS technology is introduced,along with its evolution,to understand the objectives of developing this technology.Subsequently,the critical algorithms used in ADAS technology,which include face detection,head-pose estimation,gaze estimation,and link detection are discussed.A further discussion follows on the impact of ML on each algorithm in different environments,leading to increased accuracy at the expense of additional computing,to increase efficiency.The aim of this study was to evaluate all the methods with or without ML for each algorithm.展开更多
In recent years,soil acidification has been expanding in many areas of Asia due to increasing reactive nitrogen inputs and industrial activities,which may seriously affect the performance of various ecosystem function...In recent years,soil acidification has been expanding in many areas of Asia due to increasing reactive nitrogen inputs and industrial activities,which may seriously affect the performance of various ecosystem functions.However,the underlying patterns and processes of ecosystem multifunctionality(EMF)are largely unknown at different levels of pH,limiting our understanding of how EMF respond to drivers.This study aims to explore threshold of pH on changes in EMF and differences in the drivers for the changes in EMF on either side of each of the determined pH thresholds.We collected nutrient and environmental databases for raster-level sampling data,totaling 4,000 sampling points.Averaging and cluster-multiple-threshold approach were used to calculate EMF,then quadratic and generalized additive models and Mann-Whitney U were used to determine and test the pH thresholds for changes in EMF,structural equation modellings and variance partitioning analysis were used to explore the main drivers on changes in EMF.The pH threshold for EMF changes in Chinese terrestrial ecosystems is 6.0.When pH<6.0,climate was consistently more important in controlling the variation of EMF than other variables;when pH≥6.0,soil was consistently more important in controlling the variation of EMF than other variables.Specifically,when pH<6.0,mean annual temperature was the main factor in regulating the EMF variation;when pH≥6.0,soil moisture was the main factor in regulating the EMF variation.Our study provides important scientific value for the mechanism of maintaining EMF under global change.For example,with further increases in global nitrogen deposition,leading to increased soil acidification,there are different impacts on EMF in different regions.It may lead to a decrease in EMF in acidic soils and an increase in EMF in alkaline soils.This suggests different management strategies for different regions to maintain EMF stability in the context of future global changes.In the future,more attention should be paid to the biological mechanisms regulating EMF.展开更多
When youre a taxi driver,you never know who youll end up picking up—it might even be a long-lost friend from over 20 years ago!51-year-old Texas resident Danny Blanton was captured in the most funny moment by the das...When youre a taxi driver,you never know who youll end up picking up—it might even be a long-lost friend from over 20 years ago!51-year-old Texas resident Danny Blanton was captured in the most funny moment by the dashboard camera when he realized his passenger was not a stranger.This man was a dear friend from his past!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
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
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
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
Invasive alien plant species(IAPS)pose severe threats to global biodiversity conservation.Effective management of IAPS requires mapping their distribution and identifying the environmental factors that drive their spr...Invasive alien plant species(IAPS)pose severe threats to global biodiversity conservation.Effective management of IAPS requires mapping their distribution and identifying the environmental factors that drive their spread.The Gaoligong Mountains,a renowned biodiversity hotspot in southwestern China,currently face the dual challenges of IAPS invasion and climate change.However,we know little about the distribution patterns,key environmental drivers,and sensitivity of IAPS to future climate change in this region.In this study,we mapped IAPS richness distribution and identified invasion hotspots throughout the Gaoligong Mountains.In addition,we assessed the relative importance of environmental variables in shaping the spatial distribution of IAPS richness and projected potential shifts in IAPS richness under various climate change scenarios.We identified 161 IAPS,primarily concentrated in the low-elevation tropical and subtropical regions along river valleys,forming belt-like invasion hotspots.The key factors shaping IAPS richness included disturbance complexity,elevation,seasonal precipitation,and vegetation types.Notably,IAPS richness significantly declined with increasing elevation and latitude but increased with higher disturbance complexity.Moreover,IAPS were more prevalent in grasslands and shrublands than in forested areas.Ensemble modeling of future climate scenarios predicted that the distribution of IAPS richness would shift to progressively higher elevations.These findings provide valuable insights for managing IAPS in mountainous regions that play a crucial role in global biodiversity conservation.展开更多
Axel Pons,once a Moto2 World Championship competitor,made a big lifestyle change several years ago when he decided to travel the world barefoot.He's the son of Sito Pons,who has been a MotoGP winner twice.At the s...Axel Pons,once a Moto2 World Championship competitor,made a big lifestyle change several years ago when he decided to travel the world barefoot.He's the son of Sito Pons,who has been a MotoGP winner twice.At the start of his motorcycle-racing career,there were high expectations and a lot of pressure on him.In the Moto2 World Championship,he had some good results,with a 16th-place finish in 2016 being his best.But then he took a break from racing and tried fashion modeling.In 2019,he said he wanted to slow down and leave his past behind.But no one expected the kind of change that was coming.展开更多
The Southern Ocean is a critical component in the Earth system by dominating the global heat and anthropogenic carbon uptake and supplying heat to melt the largest ice sheet.Variability and changes in the water masses...The Southern Ocean is a critical component in the Earth system by dominating the global heat and anthropogenic carbon uptake and supplying heat to melt the largest ice sheet.Variability and changes in the water masses of the Southern Ocean are thus important to the global energy and water cycles,carbon cycling,and sea-level change.In this article,we review the recent progress on understanding the variability and changes in the four major water masses in the Southern Ocean,including Subantarctic Mode Water,Antarctic Intermediate Water,Circumpolar Deep Water and Antarctic Bottom Water.Subantarctic Mode Water and Antarctic Intermediate Water show statistically significant strong circumpolar shoaling,warming,and density reductions since 1970s,indicating that signals of global warming have entered the interior ocean.Meanwhile,strong regional variability of Subantarctic Mode Water and Antarctic Intermediate Water responding to surface buoyancy forcing and westerly winds is attracting more attention.Circumpolar Deep Water is an important modulator of heat content and nutrient concentrations on continental shelves around Antarctica and has made significant contributions to the basal melting of Antarctic ice shelves.Since the late 1950s,a long-term freshening trend in Antarctic Bottom Water in the Ross Sea and its downstream region has been observed and is mainly attributed to the accelerated basal melting of ice shelves in West Antarctica.The shrinking of Antarctic Bottom Water in the Weddell Sea during 1992–2020 has also been revealed and is attributed to reduced sea ice production over the southern Weddell continental shelf related to the Interdecadal Pacific Oscillation and the variability in the Amundsen Sea Low.Though significant advances have been achieved,there is an urgent need to enhance and improve both observations and model performances for better understandings and projections of the formation,transformation,and transport of the water masses in the Southern Ocean.展开更多
基金National Key Research and Development Program of China,No.2023YFE0208100,No.2021YFC3000201Natural Science Foundation of Henan Province,No.232300420165。
文摘The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.
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.
文摘The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.
基金supported by the National Natural Science Foundation of China(U2241221).
文摘This article introduces a novel 20 V radiation-hardened high-voltage metal-oxide-semiconductor field-effect transistor(MOSFET)driver with an optimized input circuit and a drain-surrounding-source(DSS)structure.The input circuit of a conventional inverter consists of a thick-gate-oxide n-type MOSFET(NMOS).These conventional drivers can tolerate a total ionizing dose(TID)of up to 100 krad(Si).In contrast,the proposed comparator input circuit uses both a thick-gate-oxide p-type MOSFET(PMOS)and thin-gate-oxide NMOS to offer a high input voltage and higher TID tolerance.Because the thick-gate-oxide PMOS and thin-gate-oxide NMOS collectively provide better TID tolerance than the thick-gate-oxide NMOS,the circuit exhibits enhanced TID tolerance of>300 krad(Si).Simulations and experimental date indicate that the DSS structure reduces the probability of unwanted parasitic bipolar junction transistor activation,yielding a better single-event effect tolerance of over 81.8 MeVcm^(2)mg^(-1).The innovative strategy proposed in this study involves circuit and layout design optimization,and does not require any specialized process flow.Hence,the proposed circuit can be manufactured using common commercial 0.35μm BCD processes.
文摘As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as inclement weather, is especially challenging for older drivers due to their sensitivity to glare and reduced visibility. As a result, older drivers may adjust their behavior during adverse weather. This paper explores the differential impacts of weather on older drivers with cognitive decline compared to older drivers with normal cognitive function. Data were from a naturalistic driving study of older drivers in Omaha, Nebraska. Driver speed and weather data were extracted and the correlation between speed compliance, road weather conditions, and the cognitive/neurological status of the drivers was examined. Speed compliance was used as the surrogate safety measure since driving at lower speeds can indicate that the driver is challenged by roadway or environmental conditions and can therefore indicate a risk. The percentage of time during a trip when drivers were 16.1 kph under the speed limit was modeled as the dependent variable using beta regression. The variables that resulted in the best fit model were mild cognitive impairment (MCI), age group, traffic density, and weather. Results indicated that the youngest group of older drivers (young-old) spent less time driving at impeding speeds and had the least variability compared to the other two age groups. The middle group of older drivers (middle-old) had the highest amount of time driving at impeding speeds and had more variability than young-old drivers. The oldest group of older drivers (old-old) were the most likely to drive at impeding speeds and had the most variability. In general, older drivers were more likely to drive at impeding speeds during peak hours than during non-peak hours. Additionally, in most cases, older drivers spent less time below the speed limit when the weather was clear than in adverse conditions. Results indicate that older drivers are impacted by weather conditions, and distinct patterns were noted between older drivers who were cognitively impaired compared to drivers with normal cognition.
文摘Osyris lanceolata is heavily and illegally exploited in East Africa for its essential oils, yet little is known about its population status and ecological requirements. This study examined its population structure and environmental factors influencing its distribution in the semi-arid Karamoja sub-region, Uganda. We surveyed 388 plots (5 m radius) at different altitudes, recording life stages, stem diameters, and regeneration patterns, and analyzed soil samples. Multivariate analyses, including Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA), Non-metric Multidimensional Scaling (NMDS), and Multiple Regression Modeling (MRM), identified key environmental factors affecting its distribution. Findings show that O. lanceolata populations in Moroto, Nakapiripirit, and Amudat districts are severely degraded due to overexploitation. The species is primarily regenerating through coppicing rather than seedlings, with an exploitation intensity of 56.6%. Population densities are low, distribution is irregular, and sustainable harvesting is not viable. Soil properties, particularly Ca2+, N, P, K+, Na+, and organic matter, significantly influence its abundance. Conservation efforts should focus on identifying suitable provenances for genetic preservation and plantation establishment. Areas with at least 9 trees per hectare in Moroto, Nakapiripirit, and Amudat could serve as potential sites for ex-situ plantations. Further research should explore how biotic interactions, genetic diversity, and morphology affect oil yield and quality to support restoration, breeding, and domestication initiatives.
基金supported by the National Natural Science Foundation of China(Grant Nos.12027811 and 51790524).
文摘We have designed,assembled,and tested a 4-MA,60-ns fast linear transformer driver(LTD),which is the first operating generator featuring multiple LTD modules connected in parallel.The LTD-based accelerator comprises six modules in parallel,each of which has ten-stage cavities stacked in series.The six LTD modules are connected to a water tank of diameter 6 m via a 3-m-long impedance-matched deionized waterinsulated coaxial transmission line.In the water tank,the electrical pulses are transmitted down by six horizontal tri-plate transmission lines.A 2.1-m-diameter two-level vacuum insulator stack is utilized to separate the deionized water region from the vacuum region.In the vacuum,the currents are further transported downstream by a two-level magnetically insulated transmission-line and then converged through four post-hole convolutes.Plasma radiation loads or bremsstrahlung electron beam diodes serve as loads that are expected to generate intense soft X rays or warm X rays.The machine is 3.2 m in height and 22 m in outer diameter,including support systems such as a high-voltage charge supply,magnetic core reset system,trigger system,and support platform for inner stalk installation and maintenance.A total of 1440 individual±100-kV multi-gap spark switches and 2880 individual 100-kV capacitors are employed in the accelerator.A total of 12 fiberoptic laser-controlled trigger generators combining photoconductive and traditional gas spark switch technologies are used to realize the synchronous discharge of the more than 1000 gas switches.At an LTD charge voltage of±85 kV,the accelerator stores an initial energy of about 300 kJ and is expected to deliver a current of 3–5 MA into various loads.To date,the LTD facility has shot into a thick-walled aluminum liner load and a reflex triode load.With a thick-walled aluminum liner of inductance 1.81 nH,a current with peak up to 4.1 MA and rise time(10%–90%)of about 60 ns has been achieved.The current transport efficiency from the insulator stack to the liner load approaches 100%during peak times.The LTD accelerator has been used to drive reflex triode loads generating warm X rays with high energy fluence and large radiation area.It has been demonstrated that this LTD is a promising and high-efficiency prime pulsed power source suitable for use in constructing the next generation of large-scale accelerators with currents of tens of megaamperes.
文摘When you go somewhere,do you like to be the driver or a passenger?When you are the driver,you are in control.You can go fast or slow.You can pick the route.When and where do you stop?You decide.You enjoy the feeling of driving.Ifs fun!
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(1ITP)(Project Nos.RS-2024-00438551,30%,2022-11220701,30%,2021-0-01816,30%)the National Research Foundation of Korea(NRF)grant funded by the Korean Government(Project No.RS2023-00208460,10%).
文摘To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system,dubbed DriveMe,for identifying drivers on cars.Our novelty aspects are 1⃝Lightweight scheme that depends only on a single sensor data(i.e.,pressure readings)attached to the driver’s seat and belt.2⃝Practical evaluation in which one-class authentication models are trained from only the owner users and tested using data collected from both owners and attackers.3⃝Rapid Authentication to quickly identify drivers’identities using a few pressure samples collected within short durations(1,2,3,5,or 10 s).4⃝Realistic experiments where the sensory data is collected from real experiments rather than computer simulation tools.We conducted real experiments and collected about 13,200 samples and 22,800 samples of belt-only and seat-only datasets from all 12 users under different settings.To evaluate system effectiveness,we implemented extensive evaluation scenarios using four one-class detectors One-Class Support Vector Machine(OCSVM),Local Outlier Factor(LOF),Isolation Forest(IF),and Elliptic Envelope(EE),three dataset types(belt-only,seat-only,and fusion),and four different dataset sizes.Our average experimental results show that the system can authenticate the driver with an F1 score of 93.1%for seat-based data using OCSVM classifier,an F1 score of 98.53%for fusion-based data using LOF classifier,an F1 score of 91.65%for fusion-based data using IF classifier,and an F1 score of 95.79%for fusion-based data using EE classifier.
文摘The advanced driver assistance system(ADAS)primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision,which leads to fewer fatal accidents and ensures higher safety.In the artificial Intelligence domain,machine learning(ML)was developed to make inferences with a degree of accuracy similar to that of humans;however,enormous amounts of data are required.Machine learning enhances the accuracy of the decisions taken by ADAS,by evaluating all the data received from various vehicle sensors.This study summarizes all the critical algorithms used in ADAS technologies and presents the evolution of ADAS technology.Initially,ADAS technology is introduced,along with its evolution,to understand the objectives of developing this technology.Subsequently,the critical algorithms used in ADAS technology,which include face detection,head-pose estimation,gaze estimation,and link detection are discussed.A further discussion follows on the impact of ML on each algorithm in different environments,leading to increased accuracy at the expense of additional computing,to increase efficiency.The aim of this study was to evaluate all the methods with or without ML for each algorithm.
基金This work was supported by the Tianshan Programme of Excellence(2022TSYCCX0001)the National Key Program for Basic Research and Development(973 Program)(2012CB417101)。
文摘In recent years,soil acidification has been expanding in many areas of Asia due to increasing reactive nitrogen inputs and industrial activities,which may seriously affect the performance of various ecosystem functions.However,the underlying patterns and processes of ecosystem multifunctionality(EMF)are largely unknown at different levels of pH,limiting our understanding of how EMF respond to drivers.This study aims to explore threshold of pH on changes in EMF and differences in the drivers for the changes in EMF on either side of each of the determined pH thresholds.We collected nutrient and environmental databases for raster-level sampling data,totaling 4,000 sampling points.Averaging and cluster-multiple-threshold approach were used to calculate EMF,then quadratic and generalized additive models and Mann-Whitney U were used to determine and test the pH thresholds for changes in EMF,structural equation modellings and variance partitioning analysis were used to explore the main drivers on changes in EMF.The pH threshold for EMF changes in Chinese terrestrial ecosystems is 6.0.When pH<6.0,climate was consistently more important in controlling the variation of EMF than other variables;when pH≥6.0,soil was consistently more important in controlling the variation of EMF than other variables.Specifically,when pH<6.0,mean annual temperature was the main factor in regulating the EMF variation;when pH≥6.0,soil moisture was the main factor in regulating the EMF variation.Our study provides important scientific value for the mechanism of maintaining EMF under global change.For example,with further increases in global nitrogen deposition,leading to increased soil acidification,there are different impacts on EMF in different regions.It may lead to a decrease in EMF in acidic soils and an increase in EMF in alkaline soils.This suggests different management strategies for different regions to maintain EMF stability in the context of future global changes.In the future,more attention should be paid to the biological mechanisms regulating EMF.
文摘When youre a taxi driver,you never know who youll end up picking up—it might even be a long-lost friend from over 20 years ago!51-year-old Texas resident Danny Blanton was captured in the most funny moment by the dashboard camera when he realized his passenger was not a stranger.This man was a dear friend from his past!
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.
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.
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 Second Tibetan Plateau Scientific Expedition and Research(STEP)Program(Grant No.2019QZKK0502)National Natural Science Foundation of China(Grant No.U23A20160)Shanghai Science and Technology Innovation Action Plan(Grant No.23015810100).
文摘Invasive alien plant species(IAPS)pose severe threats to global biodiversity conservation.Effective management of IAPS requires mapping their distribution and identifying the environmental factors that drive their spread.The Gaoligong Mountains,a renowned biodiversity hotspot in southwestern China,currently face the dual challenges of IAPS invasion and climate change.However,we know little about the distribution patterns,key environmental drivers,and sensitivity of IAPS to future climate change in this region.In this study,we mapped IAPS richness distribution and identified invasion hotspots throughout the Gaoligong Mountains.In addition,we assessed the relative importance of environmental variables in shaping the spatial distribution of IAPS richness and projected potential shifts in IAPS richness under various climate change scenarios.We identified 161 IAPS,primarily concentrated in the low-elevation tropical and subtropical regions along river valleys,forming belt-like invasion hotspots.The key factors shaping IAPS richness included disturbance complexity,elevation,seasonal precipitation,and vegetation types.Notably,IAPS richness significantly declined with increasing elevation and latitude but increased with higher disturbance complexity.Moreover,IAPS were more prevalent in grasslands and shrublands than in forested areas.Ensemble modeling of future climate scenarios predicted that the distribution of IAPS richness would shift to progressively higher elevations.These findings provide valuable insights for managing IAPS in mountainous regions that play a crucial role in global biodiversity conservation.
文摘Axel Pons,once a Moto2 World Championship competitor,made a big lifestyle change several years ago when he decided to travel the world barefoot.He's the son of Sito Pons,who has been a MotoGP winner twice.At the start of his motorcycle-racing career,there were high expectations and a lot of pressure on him.In the Moto2 World Championship,he had some good results,with a 16th-place finish in 2016 being his best.But then he took a break from racing and tried fashion modeling.In 2019,he said he wanted to slow down and leave his past behind.But no one expected the kind of change that was coming.
基金The Independent Research Foundation of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract Nos SML2023SP201 and SML2021SP306the Natural Science Foundation of Guangdong Province of China under contract No.2024A1515012717+5 种基金the Initial Research Foundation of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract Nos 313021004,313022009,and 313022001the National Natural Science Foundation of China under contract No.41706225the National Key R&D Program of China under contract No.2018YFA0605701the Impact and Response of Antarctic Seas to Climate Change under contract No.IRASCC 1-02-01Bthe Shenlan Program funded by Shanghai Jiao Tong University under contract No.SL2020MS021the fund from Shanghai Frontiers Science Center of Polar Research.
文摘The Southern Ocean is a critical component in the Earth system by dominating the global heat and anthropogenic carbon uptake and supplying heat to melt the largest ice sheet.Variability and changes in the water masses of the Southern Ocean are thus important to the global energy and water cycles,carbon cycling,and sea-level change.In this article,we review the recent progress on understanding the variability and changes in the four major water masses in the Southern Ocean,including Subantarctic Mode Water,Antarctic Intermediate Water,Circumpolar Deep Water and Antarctic Bottom Water.Subantarctic Mode Water and Antarctic Intermediate Water show statistically significant strong circumpolar shoaling,warming,and density reductions since 1970s,indicating that signals of global warming have entered the interior ocean.Meanwhile,strong regional variability of Subantarctic Mode Water and Antarctic Intermediate Water responding to surface buoyancy forcing and westerly winds is attracting more attention.Circumpolar Deep Water is an important modulator of heat content and nutrient concentrations on continental shelves around Antarctica and has made significant contributions to the basal melting of Antarctic ice shelves.Since the late 1950s,a long-term freshening trend in Antarctic Bottom Water in the Ross Sea and its downstream region has been observed and is mainly attributed to the accelerated basal melting of ice shelves in West Antarctica.The shrinking of Antarctic Bottom Water in the Weddell Sea during 1992–2020 has also been revealed and is attributed to reduced sea ice production over the southern Weddell continental shelf related to the Interdecadal Pacific Oscillation and the variability in the Amundsen Sea Low.Though significant advances have been achieved,there is an urgent need to enhance and improve both observations and model performances for better understandings and projections of the formation,transformation,and transport of the water masses in the Southern Ocean.
