Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.展开更多
The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces....The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces.The strong convergence result for our method is established under some standard assumptions without any requirement of the knowledge of the Lipschitz constant of the mapping.Several numerical experiments are provided to verify the advantages and efficiency of proposed algorithms.展开更多
A centrifugal pump with a specific speed ns=67 is considered in this study to investigate the impact of blade cutting(at the outlet edge)on the fluid-induced noise,while keeping all the other geometric parameters unch...A centrifugal pump with a specific speed ns=67 is considered in this study to investigate the impact of blade cutting(at the outlet edge)on the fluid-induced noise,while keeping all the other geometric parameters unchanged.The required unsteady numerical calculations are conducted by applying the RNG k-εturbulence model with the volute dipole being used as the sound source.The results indicate that the internal pressure energy of the centrifugal pump essentially depends on the blade passing frequency and its low-frequency harmonic frequency.Moreover,the pressure pulsation distribution directly affects the noise caused by the centrifugal pump.The sound pressure inside and outside the centrifugal pump and the sound power at the blade passing frequency gradually decrease increasing cutting distance of the impeller blades.When the cutting percentage is 1.21%,that is,the clearance ratio between impeller blade and tongue is 8.57%,the comprehensive performance of the centrifugal pump is the best.展开更多
BACKGROUND Normothermic liver machine perfusion(NMP)is a novel technology used to preserve and evaluate the function of liver allografts.AIM To assess NMP utilization in liver transplant(LT)practices.METHODS All adult...BACKGROUND Normothermic liver machine perfusion(NMP)is a novel technology used to preserve and evaluate the function of liver allografts.AIM To assess NMP utilization in liver transplant(LT)practices.METHODS All adult deceased-donor LT recipients between January 2021 and September 2023 in the United States were analyzed.Outcomes including discard rates,survival,preservation time and timing of surgery were compared between two groups:NMP vs non-NMP.RESULTS Between 2021 and 2023,NMP was utilized in 1493(6.3%)of all LTs in the United States.Compared to non-NMP group,NMP group had lower allograft discard rate(6.5%vs 10%,P<0.001),older recipients’age(median:47 vs 42 years,P<0.001),and higher utilization of donors from donation after circulatory death(DCD)(55%vs 11%,P<0.001).NMP group also had longer distances between recipient and donor hospitals(median:156 vs 138 miles,P<0.001),longer preser-vation time(median:12.2 vs 5.8 hours,P<0.001),and more daytime reperfusion(74%vs 55%,P<0.001).Post-transplant survival outcomes were comparable between the two groups.In a subgroup analysis of NMP,recipients in the long preservation time(≥8 hours)group had higher daytime reperfusion(78%vs 55%,P<0.001)and similar post-transplant survival when compared to the short preservation time(<8 hours)group.CONCLUSION The utilization of NMP is associated with lower discard rates and increased DCD organs for LT.NMP allows for prolonging the preservation time and increased occurrence of daytime LT,without any impact on the survival outcomes.展开更多
Photometric redshifts of galaxies obtained by multi-wavelength data are widely used in photometric surveys because of their high efficiency.Although various methods have been developed,template fitting is still adopte...Photometric redshifts of galaxies obtained by multi-wavelength data are widely used in photometric surveys because of their high efficiency.Although various methods have been developed,template fitting is still adopted as one of the most popular approaches.Its accuracy strongly depends on the quality of the spectral energy distribution(SED)templates,which can be calibrated using broadband photometric data from galaxies with known spectroscopic redshifts.Such calibration is expected to improve photometric redshift accuracy,as the calibrated templates will align with observed photometric data more closely.The upcoming China Space Station Telescope(CSST)is one of the Stage IV surveys,which is aiming for high precision cosmological studies.To improve the accuracy of photometric redshift estimation for CSST,we calibrated the CWW+KIN templates using a perturbation algorithm with broadband photometric data from the CSST mock catalog.This calibration used a training set consisting of approximately 4500 galaxies,which is 10%of the total galaxy sample.The outlier fraction and scatter of the photometric redshifts derived from the calibrated templates are 2.55%and 0.036,respectively.Compared to the CWW+KIN templates,these values are reduced by 34%and 23%,respectively.This demonstrates that SED templates calibrated with a small training set can effectively optimize photometric redshift accuracy for future large-scale surveys like CSST,especially with limited spectral training data.展开更多
Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in eac...Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in each sub-network,and apply the degree–degree distance to unify the weight values of connecting edges between different sub-networks, and unify the edges with different meanings in the multilayer network numerically. At this time, the multilayer network is compressed into a single-layer network, also known as the aggregated network. Furthermore, the self-similarity of the multilayer network is represented by analyzing the self-similarity of the aggregate network. The study of self-similarity was conducted on two classical fractal networks and a real-world multilayer network. The results show that multilayer networks exhibit more pronounced self-similarity, and the intensity of self-similarity in multilayer networks can vary with the connection mode of sub-networks.展开更多
Climate change and its resulting effects on seasonality are known to alter a variety of animal behaviors including those related to foraging,phenology,and migration.Although many studies focus on the impacts of phenol...Climate change and its resulting effects on seasonality are known to alter a variety of animal behaviors including those related to foraging,phenology,and migration.Although many studies focus on the impacts of phenological changes on physiology or ftness enhancing behaviors,fewer have investigated the relationship between variation in weather and phenology on risk assessment.Fleeing from predators is an economic decision that incurs costs and benefts.As environmental conditions change,animals may face additional stressors that affect their decision to fee and infuence their ability to effectively assess risk.Flight initiation distance(FID)—the distance at which animals move away from threats—is often used to study risk assessment.FID varies due to both internal and external biotic and physical factors as well as anthropogenic activities.We asked whether variation in weather and phenology is associated with risk-taking in a population of yellow-bellied marmots(Marmota faviventer).As the air temperature increased marmots tolerated closer approaches,suggesting that they either perceived less risk or that their response to a threat was thermally compromised.The effect of temperature was relatively small and was largely dependent upon having a larger range in the full data set that permitted us to detect it.We found no effects of either the date that snow disappeared or July precipitation on marmot FID.As global temperatures continue to rise,rainfall varies more and drought becomes more common,understanding climate-related changes in how animals assess risk should be used to inform population viability models.展开更多
In this paper,we establish a stability estimate for the isoperimetric inequality of horospherically convex domains in hyperbolic plane.This estimate involves a relationship between the Hausdorff distance to a geodesic...In this paper,we establish a stability estimate for the isoperimetric inequality of horospherically convex domains in hyperbolic plane.This estimate involves a relationship between the Hausdorff distance to a geodesic ball and the deficit in the isoperimetric inequality,where the coefficient of the deficit is a universal constant.展开更多
Both acceleration and pseudo-acceleration response spectra play important roles in structural seismic design.However,only one of them is generally provided in most seismic codes.Therefore,many studies have attempted t...Both acceleration and pseudo-acceleration response spectra play important roles in structural seismic design.However,only one of them is generally provided in most seismic codes.Therefore,many studies have attempted to develop conversion models between the acceleration response spectrum(SA)and the pseudo-acceleration response spectrum(PSA).Our previous studies found that the relationship between SA and PSA is affected by magnitude,distance,and site class.Subsequently,we developed an SA/PSA model incorporating these effects.However,this model is suitable for cases with small and moderate magnitudes and its accuracy is not good enough for cases with large magnitudes.This paper aims to develop an efficient SA/PSA model by considering influences of magnitude,distance,and site class,which can be applied to cases not only with small or moderate magnitudes but also with large ones.For this purpose,regression analyses were conducted using 16,660 horizontal seismic records with a wider range of magnitude.The magnitude of these seismic records varies from 4 to 9 and the distances vary from 10 to 200 km.These ground motions were recorded at 338 stations covering four site classes.By comparing them with existing models,it was found that the proposed model shows better accuracy for cases with any magnitudes,distances,and site classes considered in this study.展开更多
Quantitative analysis of colouration is an essential tool for subspecies delimitation but has always posed a challenge in avian taxonomy.In this study on the Chestnut-winged Babbler(Cyanoderma erythropterum)species co...Quantitative analysis of colouration is an essential tool for subspecies delimitation but has always posed a challenge in avian taxonomy.In this study on the Chestnut-winged Babbler(Cyanoderma erythropterum)species complex from tropical Southeast Asia,we made use of colour measurements taken with digital cameras and applied two methodologies—(1)the 75%subspecies rule on quantitative colourimetric variables,and(2)the CIEDE2000 colour distance method to generate phylograms,which has probably never been applied in taxonomy before.Given its large number of described subspecies,many of which have been synonymised in modern taxonomies,the species complex serves as an appropriate model to test subspecies validity.Our data indicate that one synonymised subspecies(C.e.apega),from the islands of Bangka and Belitung,requires re-instalment and recognition,whereas one widely recognised subspecies(C.e.fulviventre),from the Banyak Islands,should be synonymised.Our approach also allowed us to redraw geographic subspecies boundaries.Our work indicates that current subspecies taxonomies of many poorly known tropical species may remain error-ridden and highlights the importance and viability of large-scale taxonomic revisions targeting avian subspecies globally while incorporating quantitative colourimetric approaches.展开更多
Climate change is an essential topic in climate science,and the accessibility of accurate,high-resolution datasets in recent years has facilitated the extraction of more insights from big-data resources.Nonetheless,cu...Climate change is an essential topic in climate science,and the accessibility of accurate,high-resolution datasets in recent years has facilitated the extraction of more insights from big-data resources.Nonetheless,current research predominantly focuses on mean-value changes and largely overlooks changes in the probability distribution.In this study,a novel method called Wasserstein Stability Analysis(WSA)is developed to identify probability density function(PDF)changes,especially the extreme event shift and nonlinear physical value constraint variation in climate change.WSA is applied to the early 21st century and compared with traditional mean-value trend analysis.The results indicate that despite no significant trend,the equatorial eastern Pacific experienced a decline in hot extremes and an increase in cold extremes,indicating a La Nina-like temperature shift.Further analysis at two Arctic locations suggests sea ice severely restricts the hot extremes of surface air temperature.This impact is diminishing as the sea ice melts.By revealing PDF shifts,WSA emerges as a powerful tool to re-examine climate change dynamics,providing enhanced data-driven insights for understanding climate evolution.展开更多
Ship-bridge collisions happen from time to time globally, and the consequences are often catastrophic. Therefore, this paper pro-poses a new high-pressure water jet interference (HPWJI) method for bridge pier protecti...Ship-bridge collisions happen from time to time globally, and the consequences are often catastrophic. Therefore, this paper pro-poses a new high-pressure water jet interference (HPWJI) method for bridge pier protection against vessel collision. Unliketraditional methods that absorb energy by anti-collision devices to mitigate the impact force of ships on bridges, this methodmainly changes the direction of ship movement by lateral high-pressure water jet impact, so that the ship deviates from the bridgepiers and avoids collision. This paper takes China’s Shawan River as the background and simulates the navigation of a ship(weighing about 2000 t) in the HPWJI method in the ANSYS-FLUENT software. The simulation results show that the HPWJImethod has a significant impact on the direction of the ship’s movement, enabling the ship to deviate from the pier, which istheoretically feasible for preventing bridge-ship collisions. The faster the ship’s speed, the smaller the lateral displacement anddeflection angle of the ship during a certain displacement. When the ship speed is less than 7 m/s, the impact of water flow onthe ship’s trajectory is more significant. Finally, this paper constructs a model formula for the relationship between the lateraldisplacement and speed, and surge displacement of the selected ship. This formula can be used to predict the minimum safedistance of the ship at different speeds.展开更多
This study aims to investigate the minimum required seismic gap distance based on the avoidance of shear failure for reinforced concrete(RC)buildings with potential floor-to-column pounding.Twenty different adjacent m...This study aims to investigate the minimum required seismic gap distance based on the avoidance of shear failure for reinforced concrete(RC)buildings with potential floor-to-column pounding.Twenty different adjacent models reflecting low and mid-rise buildings were created.Dynamic analyses were performed by selecting 11 earthquake record pairs compatible with the Turkish Building Earthquake Code(TBEC-2018).Two different cases were considered to determine the minimum required seismic gap distance.In the first case(named as Case-1),the gap distances between neighboring buildings were determined to avoid collisions during each acceleration record.The required distances calculated from the analyses were compared with the minimum seismic gap requirements of the TBEC-2018.The outcomes indicate that theαcoefficient recommended in TBEC-2018 for adjacent buildings with a potential floor-to-column pounding is sufficient for adjacent buildings with a period ratio of 1 to 1.5.The gap distances in the first case were then reduced by an iterative process to determine the distance at which the shear demand equals the shear strength(named as Case-2).The calculated gap distances to prevent shear failure(Case-2)are approximately 6%to 19%lower than the distances determined for avoidance of pounding(Case-1).展开更多
The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational re...The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational restraints on the proteins or their complexes.The rigid connection of the nitroxide spin label to the protein improves the accuracy and precision of distance measurement.We report a new spin labelling approach by formation of thioester bond between nitroxide(NO)spin label,NOAI(NO spin labels activated by acetylimidazole),and a protein thiol,and this spin labeling method has demonstrated high performance in DEER distance measurement on proteins.The results showed that NOAI has shorter connection to the protein ligation site than 2,2,5,5-tetramethyl-pyrroline-1-oxyl methanethiosulfonate(MTSL)and 3-maleimido-proxyl(M-Prox)in the respective protein conjugate and produces narrower distance distributions for the tested proteins including ubiquitin(Ub),immunoglobulin-binding b1 domain of streptococcal protein G(GB1),and second mitochondria-derived activator of caspases(Smac).The NOAI protein conjugate connected by a thioester bond is resistant to reducing reagent and offers highfidelity DEER distance measurements in cell lysates.展开更多
An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Techniqu...An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared to the original data and traditional SMOTE. The proposed method (NR-Clustering SMOTE) improves accuracy by 15.34% on the Pima dataset and 20.96% on the Haberman dataset compared to SMOTE-LOF. Compared to Radius-SMOTE, this method increases accuracy by 3.16% on the Pima dataset and 13.24% on the Haberman dataset. Meanwhile, compared to RN-SMOTE, the accuracy improvement reaches 15.56% on the Pima dataset and 19.84% on the Haberman dataset. This research result implies that the proposed method experiences consistent performance improvement compared to traditional SMOTE and its latest variants, such as SMOTE-LOF, Radius-SMOTE, and RN-SMOTE, in solving imbalanced health data with class binaries.展开更多
Trends Traveler Issue6,2025 The Young Traveler The"Grand Tour,"a form of long distance travel that allows young adults to gain insights and broeden their view of the world,began to emerge around the world du...Trends Traveler Issue6,2025 The Young Traveler The"Grand Tour,"a form of long distance travel that allows young adults to gain insights and broeden their view of the world,began to emerge around the world during the Renaissance in Burope and the Tang Dynasty in China.展开更多
The correction of Light Detection and Ranging(LiDAR)intensity data is of great significance for enhancing its application value.However,traditional intensity correction methods based on Terrestrial Laser Scanning(TLS)...The correction of Light Detection and Ranging(LiDAR)intensity data is of great significance for enhancing its application value.However,traditional intensity correction methods based on Terrestrial Laser Scanning(TLS)technology rely on manual site setup to collect intensity training data at different distances and incidence angles,which is noisy and limited in sample quantity,restricting the improvement of model accuracy.To overcome this limitation,this study proposes a fine-grained intensity correction modeling method based on Mobile Laser Scanning(MLS)technology.The method utilizes the continuous scanning characteristics of MLS technology to obtain dense point cloud intensity data at various distances and incidence angles.Then,a fine-grained screening strategy is employed to accurately select distance-intensity and incidence angle-intensity modeling samples.Finally,based on these samples,a high-precision intensity correction model is established through polynomial fitting functions.To verify the effectiveness of the proposed method,comparative experiments were designed,and the MLS modeling method was validated against the traditional TLS modeling method on the same test set.The results show that on Test Set 1,where the distance values vary widely(i.e.,0.1–3 m),the intensity consistency after correction using the MLS modeling method reached 7.692 times the original intensity,while the traditional TLS modeling method only increased to 4.630 times the original intensity.On Test Set 2,where the incidence angle values vary widely(i.e.,0○–80○),the MLS modeling method,although with a relatively smaller advantage,still improved the intensity consistency to 3.937 times the original intensity,slightly better than the TLS modeling method’s 3.413 times.These results demonstrate the significant advantage of the modeling method proposed in this study in enhancing the accuracy of intensity correction models.展开更多
Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of t...Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of the wide range of possible diseases and their effects on the plant and the crop.This study presents an adaptive histogram-based method for solving this problem.Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks.The approach explores colour spaces,namely,Red,Green,and Blue along with Grey.The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes,the colour also changes.The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images.Since the grey image is the average of colour spaces(R,G,and B),it can be considered a reference image.For estimating the distance between grey and colour spaces,the proposed approach uses a Chi-Square distance measure.Further,the method uses an Artificial Neural Network for classification.The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases.The results show that the method outperforms existing techniques in terms of average classification rate.展开更多
Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions f...Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.展开更多
基金co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
基金Supported by NSFC(No.12171062)the Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-JQX0004)+1 种基金the Chongqing Talent Support Program(No.cstc2024ycjh-bgzxm0121)Science and Technology Project of Chongqing Education Committee(No.KJZD-M202300503)。
文摘The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces.The strong convergence result for our method is established under some standard assumptions without any requirement of the knowledge of the Lipschitz constant of the mapping.Several numerical experiments are provided to verify the advantages and efficiency of proposed algorithms.
文摘A centrifugal pump with a specific speed ns=67 is considered in this study to investigate the impact of blade cutting(at the outlet edge)on the fluid-induced noise,while keeping all the other geometric parameters unchanged.The required unsteady numerical calculations are conducted by applying the RNG k-εturbulence model with the volute dipole being used as the sound source.The results indicate that the internal pressure energy of the centrifugal pump essentially depends on the blade passing frequency and its low-frequency harmonic frequency.Moreover,the pressure pulsation distribution directly affects the noise caused by the centrifugal pump.The sound pressure inside and outside the centrifugal pump and the sound power at the blade passing frequency gradually decrease increasing cutting distance of the impeller blades.When the cutting percentage is 1.21%,that is,the clearance ratio between impeller blade and tongue is 8.57%,the comprehensive performance of the centrifugal pump is the best.
文摘BACKGROUND Normothermic liver machine perfusion(NMP)is a novel technology used to preserve and evaluate the function of liver allografts.AIM To assess NMP utilization in liver transplant(LT)practices.METHODS All adult deceased-donor LT recipients between January 2021 and September 2023 in the United States were analyzed.Outcomes including discard rates,survival,preservation time and timing of surgery were compared between two groups:NMP vs non-NMP.RESULTS Between 2021 and 2023,NMP was utilized in 1493(6.3%)of all LTs in the United States.Compared to non-NMP group,NMP group had lower allograft discard rate(6.5%vs 10%,P<0.001),older recipients’age(median:47 vs 42 years,P<0.001),and higher utilization of donors from donation after circulatory death(DCD)(55%vs 11%,P<0.001).NMP group also had longer distances between recipient and donor hospitals(median:156 vs 138 miles,P<0.001),longer preser-vation time(median:12.2 vs 5.8 hours,P<0.001),and more daytime reperfusion(74%vs 55%,P<0.001).Post-transplant survival outcomes were comparable between the two groups.In a subgroup analysis of NMP,recipients in the long preservation time(≥8 hours)group had higher daytime reperfusion(78%vs 55%,P<0.001)and similar post-transplant survival when compared to the short preservation time(<8 hours)group.CONCLUSION The utilization of NMP is associated with lower discard rates and increased DCD organs for LT.NMP allows for prolonging the preservation time and increased occurrence of daytime LT,without any impact on the survival outcomes.
基金support from the Shanghai Science and Technology Foundation Fund under grant No.20070502400the support from the Innovation Program of Shanghai Municipal Education Commission(grant No.2019-0107-00-02-E00032)+4 种基金the support from National Key R&D Program of China grant Nos.2022YFF0503404,2020SKA0110402the CAS Project for Young Scientists in Basic Research(No.YSBR-092)support from the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning and the Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commissionsupported by the National Natural Science Foundation of China(NSFC,Grant Nos.U1931210,12141302,12173026,and 11933002)China Manned Space Project with grant Nos.CMS-CSST-2021-A01,CMS-CSST-2025-A02,CMS-CSST2025-A03,CMS-CSST-2025-A05 and CMS-CSST-2025-A20。
文摘Photometric redshifts of galaxies obtained by multi-wavelength data are widely used in photometric surveys because of their high efficiency.Although various methods have been developed,template fitting is still adopted as one of the most popular approaches.Its accuracy strongly depends on the quality of the spectral energy distribution(SED)templates,which can be calibrated using broadband photometric data from galaxies with known spectroscopic redshifts.Such calibration is expected to improve photometric redshift accuracy,as the calibrated templates will align with observed photometric data more closely.The upcoming China Space Station Telescope(CSST)is one of the Stage IV surveys,which is aiming for high precision cosmological studies.To improve the accuracy of photometric redshift estimation for CSST,we calibrated the CWW+KIN templates using a perturbation algorithm with broadband photometric data from the CSST mock catalog.This calibration used a training set consisting of approximately 4500 galaxies,which is 10%of the total galaxy sample.The outlier fraction and scatter of the photometric redshifts derived from the calibrated templates are 2.55%and 0.036,respectively.Compared to the CWW+KIN templates,these values are reduced by 34%and 23%,respectively.This demonstrates that SED templates calibrated with a small training set can effectively optimize photometric redshift accuracy for future large-scale surveys like CSST,especially with limited spectral training data.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61763009 and 72172025)。
文摘Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in each sub-network,and apply the degree–degree distance to unify the weight values of connecting edges between different sub-networks, and unify the edges with different meanings in the multilayer network numerically. At this time, the multilayer network is compressed into a single-layer network, also known as the aggregated network. Furthermore, the self-similarity of the multilayer network is represented by analyzing the self-similarity of the aggregate network. The study of self-similarity was conducted on two classical fractal networks and a real-world multilayer network. The results show that multilayer networks exhibit more pronounced self-similarity, and the intensity of self-similarity in multilayer networks can vary with the connection mode of sub-networks.
基金supported by the University of Ottawa and a NSERC Discovery grant(DGECR-2019-00289,RGPIN-2019-05000)the National Geographic Society,the University of California Los Angeles(Faculty Senate and Division of Life Sciences),an RMBL research fellowship,and the U.S.National Science Foundation(NSF IDBR-0754247 and DEB-1119660 and 1557130 to D.T.B.,as well as DBI 0242960,07211346,1226713,and 1755522 to RMBL).
文摘Climate change and its resulting effects on seasonality are known to alter a variety of animal behaviors including those related to foraging,phenology,and migration.Although many studies focus on the impacts of phenological changes on physiology or ftness enhancing behaviors,fewer have investigated the relationship between variation in weather and phenology on risk assessment.Fleeing from predators is an economic decision that incurs costs and benefts.As environmental conditions change,animals may face additional stressors that affect their decision to fee and infuence their ability to effectively assess risk.Flight initiation distance(FID)—the distance at which animals move away from threats—is often used to study risk assessment.FID varies due to both internal and external biotic and physical factors as well as anthropogenic activities.We asked whether variation in weather and phenology is associated with risk-taking in a population of yellow-bellied marmots(Marmota faviventer).As the air temperature increased marmots tolerated closer approaches,suggesting that they either perceived less risk or that their response to a threat was thermally compromised.The effect of temperature was relatively small and was largely dependent upon having a larger range in the full data set that permitted us to detect it.We found no effects of either the date that snow disappeared or July precipitation on marmot FID.As global temperatures continue to rise,rainfall varies more and drought becomes more common,understanding climate-related changes in how animals assess risk should be used to inform population viability models.
文摘In this paper,we establish a stability estimate for the isoperimetric inequality of horospherically convex domains in hyperbolic plane.This estimate involves a relationship between the Hausdorff distance to a geodesic ball and the deficit in the isoperimetric inequality,where the coefficient of the deficit is a universal constant.
基金National Natural Science Foundation of China under Grant No.52278135。
文摘Both acceleration and pseudo-acceleration response spectra play important roles in structural seismic design.However,only one of them is generally provided in most seismic codes.Therefore,many studies have attempted to develop conversion models between the acceleration response spectrum(SA)and the pseudo-acceleration response spectrum(PSA).Our previous studies found that the relationship between SA and PSA is affected by magnitude,distance,and site class.Subsequently,we developed an SA/PSA model incorporating these effects.However,this model is suitable for cases with small and moderate magnitudes and its accuracy is not good enough for cases with large magnitudes.This paper aims to develop an efficient SA/PSA model by considering influences of magnitude,distance,and site class,which can be applied to cases not only with small or moderate magnitudes but also with large ones.For this purpose,regression analyses were conducted using 16,660 horizontal seismic records with a wider range of magnitude.The magnitude of these seismic records varies from 4 to 9 and the distances vary from 10 to 200 km.These ground motions were recorded at 338 stations covering four site classes.By comparing them with existing models,it was found that the proposed model shows better accuracy for cases with any magnitudes,distances,and site classes considered in this study.
基金supported by a Singapore National Research Foundation(NRF)Investigatorship(NRF-NRFI07-2021-0008)。
文摘Quantitative analysis of colouration is an essential tool for subspecies delimitation but has always posed a challenge in avian taxonomy.In this study on the Chestnut-winged Babbler(Cyanoderma erythropterum)species complex from tropical Southeast Asia,we made use of colour measurements taken with digital cameras and applied two methodologies—(1)the 75%subspecies rule on quantitative colourimetric variables,and(2)the CIEDE2000 colour distance method to generate phylograms,which has probably never been applied in taxonomy before.Given its large number of described subspecies,many of which have been synonymised in modern taxonomies,the species complex serves as an appropriate model to test subspecies validity.Our data indicate that one synonymised subspecies(C.e.apega),from the islands of Bangka and Belitung,requires re-instalment and recognition,whereas one widely recognised subspecies(C.e.fulviventre),from the Banyak Islands,should be synonymised.Our approach also allowed us to redraw geographic subspecies boundaries.Our work indicates that current subspecies taxonomies of many poorly known tropical species may remain error-ridden and highlights the importance and viability of large-scale taxonomic revisions targeting avian subspecies globally while incorporating quantitative colourimetric approaches.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFC3000904)the National Natural Science Foundation of China(42005039)the Science and Technology Development Fund of CAMS(Grant No.2024KJ013)。
文摘Climate change is an essential topic in climate science,and the accessibility of accurate,high-resolution datasets in recent years has facilitated the extraction of more insights from big-data resources.Nonetheless,current research predominantly focuses on mean-value changes and largely overlooks changes in the probability distribution.In this study,a novel method called Wasserstein Stability Analysis(WSA)is developed to identify probability density function(PDF)changes,especially the extreme event shift and nonlinear physical value constraint variation in climate change.WSA is applied to the early 21st century and compared with traditional mean-value trend analysis.The results indicate that despite no significant trend,the equatorial eastern Pacific experienced a decline in hot extremes and an increase in cold extremes,indicating a La Nina-like temperature shift.Further analysis at two Arctic locations suggests sea ice severely restricts the hot extremes of surface air temperature.This impact is diminishing as the sea ice melts.By revealing PDF shifts,WSA emerges as a powerful tool to re-examine climate change dynamics,providing enhanced data-driven insights for understanding climate evolution.
基金supported by the National Natural Science Foundation of China (Grant No.11802347)Guangdong Basic and Applied Basic Research Foundation (Grant Nos.2018A030310334 and 2023A1515012482)Guangzhou Basic and Applied Basic Research Foundation (Grant No.2023A04J1618).
文摘Ship-bridge collisions happen from time to time globally, and the consequences are often catastrophic. Therefore, this paper pro-poses a new high-pressure water jet interference (HPWJI) method for bridge pier protection against vessel collision. Unliketraditional methods that absorb energy by anti-collision devices to mitigate the impact force of ships on bridges, this methodmainly changes the direction of ship movement by lateral high-pressure water jet impact, so that the ship deviates from the bridgepiers and avoids collision. This paper takes China’s Shawan River as the background and simulates the navigation of a ship(weighing about 2000 t) in the HPWJI method in the ANSYS-FLUENT software. The simulation results show that the HPWJImethod has a significant impact on the direction of the ship’s movement, enabling the ship to deviate from the pier, which istheoretically feasible for preventing bridge-ship collisions. The faster the ship’s speed, the smaller the lateral displacement anddeflection angle of the ship during a certain displacement. When the ship speed is less than 7 m/s, the impact of water flow onthe ship’s trajectory is more significant. Finally, this paper constructs a model formula for the relationship between the lateraldisplacement and speed, and surge displacement of the selected ship. This formula can be used to predict the minimum safedistance of the ship at different speeds.
文摘This study aims to investigate the minimum required seismic gap distance based on the avoidance of shear failure for reinforced concrete(RC)buildings with potential floor-to-column pounding.Twenty different adjacent models reflecting low and mid-rise buildings were created.Dynamic analyses were performed by selecting 11 earthquake record pairs compatible with the Turkish Building Earthquake Code(TBEC-2018).Two different cases were considered to determine the minimum required seismic gap distance.In the first case(named as Case-1),the gap distances between neighboring buildings were determined to avoid collisions during each acceleration record.The required distances calculated from the analyses were compared with the minimum seismic gap requirements of the TBEC-2018.The outcomes indicate that theαcoefficient recommended in TBEC-2018 for adjacent buildings with a potential floor-to-column pounding is sufficient for adjacent buildings with a period ratio of 1 to 1.5.The gap distances in the first case were then reduced by an iterative process to determine the distance at which the shear demand equals the shear strength(named as Case-2).The calculated gap distances to prevent shear failure(Case-2)are approximately 6%to 19%lower than the distances determined for avoidance of pounding(Case-1).
基金supported by National Natural Science Foundation of China(22161142018,21991081,22177056,and 22174074)the Ministry of Science and Technology of China(2021YFA1600304).
文摘The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational restraints on the proteins or their complexes.The rigid connection of the nitroxide spin label to the protein improves the accuracy and precision of distance measurement.We report a new spin labelling approach by formation of thioester bond between nitroxide(NO)spin label,NOAI(NO spin labels activated by acetylimidazole),and a protein thiol,and this spin labeling method has demonstrated high performance in DEER distance measurement on proteins.The results showed that NOAI has shorter connection to the protein ligation site than 2,2,5,5-tetramethyl-pyrroline-1-oxyl methanethiosulfonate(MTSL)and 3-maleimido-proxyl(M-Prox)in the respective protein conjugate and produces narrower distance distributions for the tested proteins including ubiquitin(Ub),immunoglobulin-binding b1 domain of streptococcal protein G(GB1),and second mitochondria-derived activator of caspases(Smac).The NOAI protein conjugate connected by a thioester bond is resistant to reducing reagent and offers highfidelity DEER distance measurements in cell lysates.
基金funded by Universitas Negeri Malang,contract number 4.4.841/UN32.14.1/LT/2024.
文摘An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared to the original data and traditional SMOTE. The proposed method (NR-Clustering SMOTE) improves accuracy by 15.34% on the Pima dataset and 20.96% on the Haberman dataset compared to SMOTE-LOF. Compared to Radius-SMOTE, this method increases accuracy by 3.16% on the Pima dataset and 13.24% on the Haberman dataset. Meanwhile, compared to RN-SMOTE, the accuracy improvement reaches 15.56% on the Pima dataset and 19.84% on the Haberman dataset. This research result implies that the proposed method experiences consistent performance improvement compared to traditional SMOTE and its latest variants, such as SMOTE-LOF, Radius-SMOTE, and RN-SMOTE, in solving imbalanced health data with class binaries.
文摘Trends Traveler Issue6,2025 The Young Traveler The"Grand Tour,"a form of long distance travel that allows young adults to gain insights and broeden their view of the world,began to emerge around the world during the Renaissance in Burope and the Tang Dynasty in China.
基金supported in part by the National Natural Science Foundation of China under grant number 31901239funded by Researchers Supporting Project Number(RSPD2025R947),King Saud University,Riyadh,Saudi Arabia.
文摘The correction of Light Detection and Ranging(LiDAR)intensity data is of great significance for enhancing its application value.However,traditional intensity correction methods based on Terrestrial Laser Scanning(TLS)technology rely on manual site setup to collect intensity training data at different distances and incidence angles,which is noisy and limited in sample quantity,restricting the improvement of model accuracy.To overcome this limitation,this study proposes a fine-grained intensity correction modeling method based on Mobile Laser Scanning(MLS)technology.The method utilizes the continuous scanning characteristics of MLS technology to obtain dense point cloud intensity data at various distances and incidence angles.Then,a fine-grained screening strategy is employed to accurately select distance-intensity and incidence angle-intensity modeling samples.Finally,based on these samples,a high-precision intensity correction model is established through polynomial fitting functions.To verify the effectiveness of the proposed method,comparative experiments were designed,and the MLS modeling method was validated against the traditional TLS modeling method on the same test set.The results show that on Test Set 1,where the distance values vary widely(i.e.,0.1–3 m),the intensity consistency after correction using the MLS modeling method reached 7.692 times the original intensity,while the traditional TLS modeling method only increased to 4.630 times the original intensity.On Test Set 2,where the incidence angle values vary widely(i.e.,0○–80○),the MLS modeling method,although with a relatively smaller advantage,still improved the intensity consistency to 3.937 times the original intensity,slightly better than the TLS modeling method’s 3.413 times.These results demonstrate the significant advantage of the modeling method proposed in this study in enhancing the accuracy of intensity correction models.
文摘Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for farmers.In the specific case of pomegranates,this is challenging because of the wide range of possible diseases and their effects on the plant and the crop.This study presents an adaptive histogram-based method for solving this problem.Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks.The approach explores colour spaces,namely,Red,Green,and Blue along with Grey.The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes,the colour also changes.The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images.Since the grey image is the average of colour spaces(R,G,and B),it can be considered a reference image.For estimating the distance between grey and colour spaces,the proposed approach uses a Chi-Square distance measure.Further,the method uses an Artificial Neural Network for classification.The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases.The results show that the method outperforms existing techniques in terms of average classification rate.
基金funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R348),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.
