This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides...This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides an in-depth analysis and summary of the effects of biological factors,environmental conditions,agronomic practices,processing methods,and continuous cropping obstacles on the synthesis of P.heterophylla saponins,as well as their underlying mechanisms.Based on identified gaps in the current literature,future research directions and prospects are proposed.The findings of this review offer valuable insights for advancing the understanding of the saponin biosynthesis mechanisms in P.heterophylla and for enhancing its quality.展开更多
Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RI...Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RISs.First,we propose a two-stage channel estimation scheme where RIS phase shifts are tuned to obtain multiple channel soundings.In the first stage,the newtonized orthogonal matching pursuit algorithm extracts the parameters of multiple paths from the received signals.Then,the LOS path and RISreflected paths are identified.In the second stage,the estimated path gains of RIS-reflected paths with different phase shifts are utilized to determine the angle of arrival(AOA)at the RIS by obtaining the angular pseudo spectrum.Consequently,by taking the AP and RISs as reference points,the linear least squares estimator can locate UE with the estimated AOAs.Simulation results show that the proposed algorithm can realize centimeter-level localization accuracy in the discussed scenarios.Moreover,the higher accuracy of pseudo spectrum,a larger number of channel soundings,and a larger number of reference points can realize higher localization accuracy of UE.展开更多
To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-r...To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.展开更多
A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relax...A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relaxation.Distributed optical fiber sensing was used to measure strains across the sample surface by helically wrapping the single-mode fiber around the cylindrical sample.Close agreement was observed between the circumferential strains obtained from the optical fibers and the extensometer.The reconstructed full-field strain contours show strain heterogeneity from the crack closure phase,and the strains in the later deformation phase are dominantly localized within the former high-strain zone.The Gini coefficient was used to quantify the degree of strain localization and shows an initial increase during the crack closure phase,a decrease during the linear elastic phase,and a subsequent increase during the post-yielding phase.This behavior corresponds to a process of initial localization from an imperfect boundary condition,homogenization,and eventual relocalization prior to the macroscopic failure of the sample.The transient strain rate decay during the stress relaxation phase was quantified using the p-value in the“Omori-like"power law function.A higher initial stress at the onset of relaxation results in a lower p-value,indicating a slower strain rate decay.As the sample approaches macroscopic failure,the lowest p-value shifts from the most damaged zone to adjacent areas,suggesting stress redistribution or crack propagation in deformed crystalline rocks under stress relaxation conditions.展开更多
To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-l...To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.展开更多
Electron-electron interactions(EEIs),quantum interference,and the effects of disorder on transport properties are essential topics in condensed matter physics.A series of our characterization work demonstrates that th...Electron-electron interactions(EEIs),quantum interference,and the effects of disorder on transport properties are essential topics in condensed matter physics.A series of our characterization work demonstrates that the morphology of Bi_(2)Te_(3)/MnTe bilayer film mainly depends on the magnetic substrate's growth mode and thickness.We propose that the temperature-dependent quantum interference of the electron wave function caused by disorder drives the transition from weak antilocalization(WAL) to weak localization(WL).Due to spin regulation,WL under low fields originates from the ferromagnetism in MnTe.The quantum interference effect(QIE) model analysis gives the degree of impurity scattering of the electron wave function.The electron wave is scattered by impurities,which causes the Berry phase to change from π to 0,producing a complete WL behavior.The stacked structure provides tunable degrees of freedom,allowing for independent optimization of topological properties and magnetic order through preferential growth orientation of topological insulator(TI) and magnetic layers,respectively.展开更多
The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide...The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.展开更多
Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accu...Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.展开更多
Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)informati...Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)information.This paper proposes a security position verification technique based on Multilateration(MLAT)to detect false signals,ensuring UAV safety and reliable airspace operations.First,the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival(TDOA),Time Sum of Arrival(TSOA),and Angle of Arrival(AOA)information.Then,this estimated position is compared with the ADS-B message to eliminate false UAV signals.Furthermore,a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization.Additionally,an improved Chan-Taylor algorithm is developed,incorporating the Constrained Weighted Least Squares(CWLS)method to initialize UAV position calculations.Finally,a false signal detection method is proposed to distinguish between true and false positioning targets.Numerical simulation results indicate that,at a positioning error threshold of 150 m,the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100%accuracy coverage,significantly enhancing localization precision.And the proposed false signal detection method achieves a detection accuracy rate of at least 90%within a 50-meter error range.展开更多
Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environ...Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant challenge.To address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration.Specifically,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less efficient.Meanwhile,visual texture information complements LiDAR-Visual localization by providing additional contextual details.By incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of localization.This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.展开更多
This work presents a method for the three-dimensional localization of individual shallow NV center in diamond,leveraging the near-field quenching effect of a gold tip.Our experimental setup involves the use of an atom...This work presents a method for the three-dimensional localization of individual shallow NV center in diamond,leveraging the near-field quenching effect of a gold tip.Our experimental setup involves the use of an atomic force microscope to precisely move the gold tip close to the NV center,while simultaneously employing a home-made confocal microscope to monitor the fluorescence of the NV center.This approach allows for lateral super-resolution,achieving a full width at half maximum(FWHM)of 38.0 nm and a location uncertainty of 0.7 nm.Additionally,we show the potential of this method for determining the depth of the NV centers.We also attempt to determine the depth of the NV centers in combination with finite-difference time-domain(FDTD)simulations.Compared to other depth determination methods,this approach allows for simultaneous lateral and longitudinal localization of individual NV centers,and holds promise for facilitating manipulation of the local environment surrounding the NV center.展开更多
Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)applications.This paper explores L...Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)applications.This paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT settings.We comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting methods.Through this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning accuracy.Case studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor applications.Our findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.展开更多
Hydatid disease,caused by the Echinococcus granulosus parasite,is traditionally associated with liver and lung involvement.However,recent years have seen an increase in cases with atypical localizations,such as the ki...Hydatid disease,caused by the Echinococcus granulosus parasite,is traditionally associated with liver and lung involvement.However,recent years have seen an increase in cases with atypical localizations,such as the kidneys,thyroid,soft tissues,and bones.The study by Celik et al presents a series of five clinical cases where hydatid cysts were found in these rare anatomical regions,challenging conventional diagnostic and therapeutic approaches.The paper emphasizes the importance of differential diagnosis,as these cases can mimic other conditions,such as cancer,abscesses,or cysts.Advanced imaging techniques,such as com-puted tomography,magnetic resonance imaging,and ultrasound,play a crucial role in accurate diagnosis and help avoid misdiagnosis.The study demonstrates that early diagnosis and appropriate treatment of echinococosis in atypical localiz-ations are critical for preventing complications like cyst rupture and secondary infections.The use of albendazole and surgical intervention,especially in combi-nation with modern imaging techniques,yields good outcomes in these patients.However,several unanswered questions remain:What are the precise criteria for selecting the optimal treatment method in each case?What is the long-term effect-iveness of different approaches?Do patients with hydatid cysts in atypical lo-cations require additional monitoring and preventive treatment to avoid recu-rrence?Addressing these questions requires further research,and a multidisci-plinary approach involving radiologists,surgeons,and infectious disease spe-cialists is essential to optimize diagnosis and treatment.Early and accurate diagnostic methods based on differential diagnosis play a key role in improving treatment outcomes and reducing morbidity.展开更多
Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable...Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.展开更多
Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is eq...Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is equipped with a GNSS receiver,it can be independent and is readily to be loaded on a flexible platform,such as an unmanned aerial vehicle(UAV).In this paper,we consider using such sensors and timeof-arrival(TOA)techniques to locate a radio signal source,and analyze the performance limit of source localization.Besides the performance analysis,this paper provides the geometric interpretation of the performance limit,which can illustrate how a sensor contributes to the source localization accuracy.The performance analysis and the geometric interpretation together give important insights into how to make better use of GNSS receiver for passive localization.Another contribution is we propose a modified closedform solution for this localization problem.Compared with previous literature,this solution takes both sensor position and synchronization uncertainty into account,and it does not need proper initial guess of source position and is computationally efficient.Our simulation results validate the efficiency of this solution.展开更多
Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing...Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools.The manual forgery localization is often reliant on forensic expertise.In recent times,machine learning(ML)and deep learning(DL)have shown promising results in automating image forgery localization.However,the ML-based method relies on hand-crafted features.Conversely,the DL method automatically extracts shallow spatial features to enhance the accuracy.However,DL-based methods lack the global co-relation of the features due to this performance degradation noticed in several applications.In the proposed study,we designed FLTNet(forgery localization transformer network)with a CNN(convolution neural network)encoder and transformer-based attention.The encoder extracts local high-dimensional features,and the transformer provides the global co-relation of the features.In the decoder,we have exclusively utilized a CNN to upsample the features that generate tampered mask images.Moreover,we evaluated visual and quantitative performance on three standard datasets and comparison with six state-of-the-art methods.The IoU values of the proposed method on CASIA V1,CASIA V2,and CoMoFoD datasets are 0.77,0.82,and 0.84,respectively.In addition,the F1-scores of these three datasets are 0.80,0.84,and 0.86,respectively.Furthermore,the visual results of the proposed method are clean and contain rich information,which can be used for real-time forgery detection.The code used in the study can be accessed through URL:https://github.com/ajit2k5/Forgery-Localization(accessed on 21 January 2025).展开更多
Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highl...Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highly computationally demanding or sensitive to parameters.A deconvolution method, based on modifications of Clean based on Source Coherence(CLEAN-SC), is proposed for coherent sources localization. This method is called Coherence CLEAN-SC(C–CLEAN-SC). C–CLEAN-SC is able to locate coherent and incoherent sources in simulation and experimental cases. It has a high computational efficiency and does not require pre-set parameters.展开更多
BACKGROUND Small-bowel capsule endoscopy(SBCE)is widely used to evaluate obscure gastrointestinal bleeding;however,its interpretation is time-consuming and reader-dependent.Although artificial intelligence(AI)has emer...BACKGROUND Small-bowel capsule endoscopy(SBCE)is widely used to evaluate obscure gastrointestinal bleeding;however,its interpretation is time-consuming and reader-dependent.Although artificial intelligence(AI)has emerged to address these limitations,few models simultaneously perform small-bowel(SB)loca lization and abnormality detection.AIMTo develop an AI model that automatically distinguishes the SB from the stomach and colon and diagnoses SBabnormalities.METHODSWe developed an AI model using 87005 CE images (11925, 33781, and 41299 from the stomach, SB, and colon,respectively) for SB localization and 28405 SBCE images (1337 erosions/ulcers, 126 angiodysplasia, 494 bleeding,and 26448 normal) for abnormality detection. The diagnostic performances of AI-assisted reading and conventionalreading were compared using 32 SBCE videos in patients with suspicious SB bleeding.RESULTSRegarding organ localization, the AI model achieved an area under the receiver operating characteristic curve(AUC) and accuracy exceeding 0.99 and 97%, respectively. For SB abnormality detection, the performance was asfollows: Erosion/ulcer: 99.4% accuracy (AUC, 0.98);angiodysplasia: 99.8% accuracy (AUC, 0.99);bleeding: 99.9%accuracy (AUC, 0.99);normal: 99.3% accuracy (AUC, 0.98). In external validation, AI-assisted reading (8.7 minutes)was significantly faster than conventional reading (53.9 minutes;P < 0.001). The SB localization accuracies (88.6% vs72.7%, P = 0.07) and SB abnormality detection rates (77.3% vs 77.3%, P = 1.00) of the conventional reading and AIassistedreading were comparable.CONCLUSIONOur AI model decreased SBCE reading time and achieved performance comparable to that of experiencedendoscopists, suggesting that AI integration into SBCE reading enables efficient and reliable SB abnormalitydetection.展开更多
Multiple quadrotors target encirclement is widely used in the intelligent field,as it can effectively monitor and control target behavior.However,it faces the danger of collision,as well as difficulties in localizatio...Multiple quadrotors target encirclement is widely used in the intelligent field,as it can effectively monitor and control target behavior.However,it faces the danger of collision,as well as difficulties in localization and tracking.Therefore,we propose a complete target encirclement method.Firstly,based on Hooke's law,a collision avoidance controller is designed to maintain a safe flying distance among quadrotors.Then,based on the consensus theory,a formation tracking controller is designed to meet the requirements of formation transformation and encirclement tasks,and a stability proof based on Lyapunov was provided.Besides,the target detection is designed based on YOLOv5s,and the target location model is constructed based on the principle of pinhole projection and triangle similarity.Finally,we conducted experiments on the built platform,with 3 reconnaissance quadrotors detecting and localization 3 target vehicles and 7 hunter quadrotors tracking them.The results show that the minimum average error for localization targets with reconnaissance quadrotors can reach 0.1354 m,while the minimum average error for tracking with hunter quadrotors is only 0.2960 m.No quadrotors collision occurred in the whole formation transformation and tracking experiment.In addition,compared with the advanced methods,the proposed method has better performance.展开更多
bHLH transcription factors,widely exist in various plants,and are vital for the growth and development of these plants.Among them,many have been implicated in anthocyanin biosynthesis across various plants.In the pres...bHLH transcription factors,widely exist in various plants,and are vital for the growth and development of these plants.Among them,many have been implicated in anthocyanin biosynthesis across various plants.In the present study,a PdbHLH57 gene,belonging to the bHLH IIIf group,was characterized,which was isolated and cloned from the colored-leaf poplar‘Zhongshancaiyun’(ZSCY).The cDNA sequence of PdbHLH57 was 1887 base pairs,and the protein encoded by PdbHLH57 had 628 amino acids,the isoelectric point and molecular weight of which were 6.26 and 69.75 kDa,respectively.Through bioinformatics analysis,PdbHLH57 has been classified into the IIIf bHLH subgroup,with many members of this subgroup known to participate in anthocyanin biosynthesis.The subcellular localization analysis conducted in the leaf protoplasts of‘ZSCY’revealed that the PdbHLH57 protein is specifically localized in the nucleus.The transcription activation analysis was also conducted,and the results showed that the PdbHLH57 protein had self-transcription activation.To better explore the functions of the PdbHLH57 protein,two parts of this protein(PdbHLH57-1,PdbHLH57-2)were split to detect their transcriptional activation activity.The results indicated that PdbHLH57-1(1-433aa)had self-transcription activation,and PdbHLH57-2(433-628aa)had no transcription activation.The expression of PdbHLH57 peaked in June during different developmental stages in‘ZSCY’,and it was most highly expressed in the phloem among various tissues.These findings offer a basis for understanding the role of PdbHLH57 in colored-leaf poplar.展开更多
基金Supported by Open Fund Project of the Engineering Technology Research Center of Characteristic Medicinal Plants of Fujian(PP202003).
文摘This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides an in-depth analysis and summary of the effects of biological factors,environmental conditions,agronomic practices,processing methods,and continuous cropping obstacles on the synthesis of P.heterophylla saponins,as well as their underlying mechanisms.Based on identified gaps in the current literature,future research directions and prospects are proposed.The findings of this review offer valuable insights for advancing the understanding of the saponin biosynthesis mechanisms in P.heterophylla and for enhancing its quality.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2242022k60004in part by the National Natural Science Foundation of China(NSFC)under Grants 62261160576,624B2036,W2421087,62422105+1 种基金in part by the Young Elite Scientists Sponsorship Program by CAST 2022QNRC001,and the“Zhishan”Scholars Programs of Southeast Universityin part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022,BE2023022-1 and BE2023022-2.
文摘Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RISs.First,we propose a two-stage channel estimation scheme where RIS phase shifts are tuned to obtain multiple channel soundings.In the first stage,the newtonized orthogonal matching pursuit algorithm extracts the parameters of multiple paths from the received signals.Then,the LOS path and RISreflected paths are identified.In the second stage,the estimated path gains of RIS-reflected paths with different phase shifts are utilized to determine the angle of arrival(AOA)at the RIS by obtaining the angular pseudo spectrum.Consequently,by taking the AP and RISs as reference points,the linear least squares estimator can locate UE with the estimated AOAs.Simulation results show that the proposed algorithm can realize centimeter-level localization accuracy in the discussed scenarios.Moreover,the higher accuracy of pseudo spectrum,a larger number of channel soundings,and a larger number of reference points can realize higher localization accuracy of UE.
基金supported by the National Natural Science Foundation of China(Grant No.52339001).
文摘To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.
基金support of her postdoctoral research at the GFZ Helmholtz Centre for Geosciences.P.Pan acknowledges the financial support of the National Natural Science Foundation of China(Grant No.52339001)H.Hofmann and Y.Ji acknowledge the financial support of the Helmholtz Association's Initiative and Networking Fund for the Helmholtz Young Investigator Group ARES(contract number VH-NG-1516).
文摘A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relaxation.Distributed optical fiber sensing was used to measure strains across the sample surface by helically wrapping the single-mode fiber around the cylindrical sample.Close agreement was observed between the circumferential strains obtained from the optical fibers and the extensometer.The reconstructed full-field strain contours show strain heterogeneity from the crack closure phase,and the strains in the later deformation phase are dominantly localized within the former high-strain zone.The Gini coefficient was used to quantify the degree of strain localization and shows an initial increase during the crack closure phase,a decrease during the linear elastic phase,and a subsequent increase during the post-yielding phase.This behavior corresponds to a process of initial localization from an imperfect boundary condition,homogenization,and eventual relocalization prior to the macroscopic failure of the sample.The transient strain rate decay during the stress relaxation phase was quantified using the p-value in the“Omori-like"power law function.A higher initial stress at the onset of relaxation results in a lower p-value,indicating a slower strain rate decay.As the sample approaches macroscopic failure,the lowest p-value shifts from the most damaged zone to adjacent areas,suggesting stress redistribution or crack propagation in deformed crystalline rocks under stress relaxation conditions.
基金supported by Lanzhou Science and Technology Plan Project(No.2023-3-104)Gansu Province Higher Education Industry Support Plan Project(No.2023CYZC-40)Gansu Province Excellent Graduate“Innovation Star”Program(No.2023CXZX-546)。
文摘To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.
基金financially supported by the National Natural Science Foundation of China (Nos.52371204, 52201233,and 52031014)
文摘Electron-electron interactions(EEIs),quantum interference,and the effects of disorder on transport properties are essential topics in condensed matter physics.A series of our characterization work demonstrates that the morphology of Bi_(2)Te_(3)/MnTe bilayer film mainly depends on the magnetic substrate's growth mode and thickness.We propose that the temperature-dependent quantum interference of the electron wave function caused by disorder drives the transition from weak antilocalization(WAL) to weak localization(WL).Due to spin regulation,WL under low fields originates from the ferromagnetism in MnTe.The quantum interference effect(QIE) model analysis gives the degree of impurity scattering of the electron wave function.The electron wave is scattered by impurities,which causes the Berry phase to change from π to 0,producing a complete WL behavior.The stacked structure provides tunable degrees of freedom,allowing for independent optimization of topological properties and magnetic order through preferential growth orientation of topological insulator(TI) and magnetic layers,respectively.
文摘The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.
基金funded by the Youth Fund of the National Natural Science Foundation of China(Grant No.42261070).
文摘Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of the program is constructed, the intra-class and inter-class dependencies in MCDG are extracted by using the relational graph convolutional neural network, and the classifier is used to identify the faulty methods. Then, the GraphSMOTE algorithm is improved to alleviate the impact of class imbalance on classification accuracy. Aiming at the problem of parallel ranking of element suspicious values in traditional SBFL technology, in Phase 2, Doc2Vec is used to learn static features, while spectrum information serves as dynamic features. A RankNet model based on siamese multi-layer perceptron is constructed to score and rank statements in the faulty method. This work conducts experiments on 5 real projects of Defects4J benchmark. Experimental results show that, compared with the traditional SBFL technique and two baseline methods, our approach improves the Top-1 accuracy by 262.86%, 29.59% and 53.01%, respectively, which verifies the effectiveness of Two-RGCNFL. Furthermore, this work verifies the importance of inter-class dependencies through ablation experiments.
基金supported by the National Natural Science Foundation of China(Nos.U2441250,62301380,and 62231027)Natural Science Basic Research Program of Shaanxi,China(2024JC-JCQN-63)+3 种基金the Key Research and Development Program of Shaanxi,China(No.2023-YBGY-249)the Guangxi Key Research and Development Program,China(No.2022AB46002)the China Postdoctoral Science Foundation(No.2022M722504 and 2024T170696)the Innovation Capability Support Program of Shaanxi,China(No.2024RS-CXTD-01).
文摘Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)information.This paper proposes a security position verification technique based on Multilateration(MLAT)to detect false signals,ensuring UAV safety and reliable airspace operations.First,the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival(TDOA),Time Sum of Arrival(TSOA),and Angle of Arrival(AOA)information.Then,this estimated position is compared with the ADS-B message to eliminate false UAV signals.Furthermore,a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization.Additionally,an improved Chan-Taylor algorithm is developed,incorporating the Constrained Weighted Least Squares(CWLS)method to initialize UAV position calculations.Finally,a false signal detection method is proposed to distinguish between true and false positioning targets.Numerical simulation results indicate that,at a positioning error threshold of 150 m,the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100%accuracy coverage,significantly enhancing localization precision.And the proposed false signal detection method achieves a detection accuracy rate of at least 90%within a 50-meter error range.
基金supported by the project“GEF9874:Strengthening Coordinated Approaches to Reduce Invasive Alien Species(lAS)Threats to Globally Significant Agrobiodiversity and Agroecosystems in China”funding from the Excellent Talent Training Funding Project in Dongcheng District,Beijing,with project number 2024-dchrcpyzz-9.
文摘Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant challenge.To address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration.Specifically,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less efficient.Meanwhile,visual texture information complements LiDAR-Visual localization by providing additional contextual details.By incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of localization.This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.
基金supported by the National Natural Science Foundation of China(T2325023,92265204,12104447)the National Key R&D Program of China(2023YFF0718400)+1 种基金the Innovation Program for Quantum Science and Technology(2021ZD0302200)the Fundamental Research Funds for the Central Universities。
文摘This work presents a method for the three-dimensional localization of individual shallow NV center in diamond,leveraging the near-field quenching effect of a gold tip.Our experimental setup involves the use of an atomic force microscope to precisely move the gold tip close to the NV center,while simultaneously employing a home-made confocal microscope to monitor the fluorescence of the NV center.This approach allows for lateral super-resolution,achieving a full width at half maximum(FWHM)of 38.0 nm and a location uncertainty of 0.7 nm.Additionally,we show the potential of this method for determining the depth of the NV centers.We also attempt to determine the depth of the NV centers in combination with finite-difference time-domain(FDTD)simulations.Compared to other depth determination methods,this approach allows for simultaneous lateral and longitudinal localization of individual NV centers,and holds promise for facilitating manipulation of the local environment surrounding the NV center.
基金supported by the Natural Science Foundation of Zhejiang Province under grant no.LGF22F010006the Humanities and Social Science Research Project of Ministry of Education of China under grant no.22YJAZH016.
文摘Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)applications.This paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT settings.We comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting methods.Through this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning accuracy.Case studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor applications.Our findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.
文摘Hydatid disease,caused by the Echinococcus granulosus parasite,is traditionally associated with liver and lung involvement.However,recent years have seen an increase in cases with atypical localizations,such as the kidneys,thyroid,soft tissues,and bones.The study by Celik et al presents a series of five clinical cases where hydatid cysts were found in these rare anatomical regions,challenging conventional diagnostic and therapeutic approaches.The paper emphasizes the importance of differential diagnosis,as these cases can mimic other conditions,such as cancer,abscesses,or cysts.Advanced imaging techniques,such as com-puted tomography,magnetic resonance imaging,and ultrasound,play a crucial role in accurate diagnosis and help avoid misdiagnosis.The study demonstrates that early diagnosis and appropriate treatment of echinococosis in atypical localiz-ations are critical for preventing complications like cyst rupture and secondary infections.The use of albendazole and surgical intervention,especially in combi-nation with modern imaging techniques,yields good outcomes in these patients.However,several unanswered questions remain:What are the precise criteria for selecting the optimal treatment method in each case?What is the long-term effect-iveness of different approaches?Do patients with hydatid cysts in atypical lo-cations require additional monitoring and preventive treatment to avoid recu-rrence?Addressing these questions requires further research,and a multidisci-plinary approach involving radiologists,surgeons,and infectious disease spe-cialists is essential to optimize diagnosis and treatment.Early and accurate diagnostic methods based on differential diagnosis play a key role in improving treatment outcomes and reducing morbidity.
基金funded by the Ministry of Science and Technology,Taiwan,under grant number MOST 114-2224-E-A49-002was received by En-Cheng Liou.
文摘Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.
基金supported by the National Natural Science Foundation of China(Grant No.61973181)Tsinghua University Initiative Scientific Research Program(Grant No.2018Z05JZY004).
文摘Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is equipped with a GNSS receiver,it can be independent and is readily to be loaded on a flexible platform,such as an unmanned aerial vehicle(UAV).In this paper,we consider using such sensors and timeof-arrival(TOA)techniques to locate a radio signal source,and analyze the performance limit of source localization.Besides the performance analysis,this paper provides the geometric interpretation of the performance limit,which can illustrate how a sensor contributes to the source localization accuracy.The performance analysis and the geometric interpretation together give important insights into how to make better use of GNSS receiver for passive localization.Another contribution is we propose a modified closedform solution for this localization problem.Compared with previous literature,this solution takes both sensor position and synchronization uncertainty into account,and it does not need proper initial guess of source position and is computationally efficient.Our simulation results validate the efficiency of this solution.
文摘Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools.The manual forgery localization is often reliant on forensic expertise.In recent times,machine learning(ML)and deep learning(DL)have shown promising results in automating image forgery localization.However,the ML-based method relies on hand-crafted features.Conversely,the DL method automatically extracts shallow spatial features to enhance the accuracy.However,DL-based methods lack the global co-relation of the features due to this performance degradation noticed in several applications.In the proposed study,we designed FLTNet(forgery localization transformer network)with a CNN(convolution neural network)encoder and transformer-based attention.The encoder extracts local high-dimensional features,and the transformer provides the global co-relation of the features.In the decoder,we have exclusively utilized a CNN to upsample the features that generate tampered mask images.Moreover,we evaluated visual and quantitative performance on three standard datasets and comparison with six state-of-the-art methods.The IoU values of the proposed method on CASIA V1,CASIA V2,and CoMoFoD datasets are 0.77,0.82,and 0.84,respectively.In addition,the F1-scores of these three datasets are 0.80,0.84,and 0.86,respectively.Furthermore,the visual results of the proposed method are clean and contain rich information,which can be used for real-time forgery detection.The code used in the study can be accessed through URL:https://github.com/ajit2k5/Forgery-Localization(accessed on 21 January 2025).
基金supported by the National Science and Technology Major Project of China (No. 2017-II-003–0015)。
文摘Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highly computationally demanding or sensitive to parameters.A deconvolution method, based on modifications of Clean based on Source Coherence(CLEAN-SC), is proposed for coherent sources localization. This method is called Coherence CLEAN-SC(C–CLEAN-SC). C–CLEAN-SC is able to locate coherent and incoherent sources in simulation and experimental cases. It has a high computational efficiency and does not require pre-set parameters.
基金Supported by The Bio and Medical Technology Development Program of the National Research Foundation,No.NRF-2022R1C1C1010643.
文摘BACKGROUND Small-bowel capsule endoscopy(SBCE)is widely used to evaluate obscure gastrointestinal bleeding;however,its interpretation is time-consuming and reader-dependent.Although artificial intelligence(AI)has emerged to address these limitations,few models simultaneously perform small-bowel(SB)loca lization and abnormality detection.AIMTo develop an AI model that automatically distinguishes the SB from the stomach and colon and diagnoses SBabnormalities.METHODSWe developed an AI model using 87005 CE images (11925, 33781, and 41299 from the stomach, SB, and colon,respectively) for SB localization and 28405 SBCE images (1337 erosions/ulcers, 126 angiodysplasia, 494 bleeding,and 26448 normal) for abnormality detection. The diagnostic performances of AI-assisted reading and conventionalreading were compared using 32 SBCE videos in patients with suspicious SB bleeding.RESULTSRegarding organ localization, the AI model achieved an area under the receiver operating characteristic curve(AUC) and accuracy exceeding 0.99 and 97%, respectively. For SB abnormality detection, the performance was asfollows: Erosion/ulcer: 99.4% accuracy (AUC, 0.98);angiodysplasia: 99.8% accuracy (AUC, 0.99);bleeding: 99.9%accuracy (AUC, 0.99);normal: 99.3% accuracy (AUC, 0.98). In external validation, AI-assisted reading (8.7 minutes)was significantly faster than conventional reading (53.9 minutes;P < 0.001). The SB localization accuracies (88.6% vs72.7%, P = 0.07) and SB abnormality detection rates (77.3% vs 77.3%, P = 1.00) of the conventional reading and AIassistedreading were comparable.CONCLUSIONOur AI model decreased SBCE reading time and achieved performance comparable to that of experiencedendoscopists, suggesting that AI integration into SBCE reading enables efficient and reliable SB abnormalitydetection.
基金the National Natural Science Foundation of China(Grant Nos.62303348 and 62173242)the Aeronautical Science Foundation of China(Grant No.2024M071048002)the National Science Fund for Distinguished Young Scholars(Grant No.62225308)to provide fund for conducting experiments.
文摘Multiple quadrotors target encirclement is widely used in the intelligent field,as it can effectively monitor and control target behavior.However,it faces the danger of collision,as well as difficulties in localization and tracking.Therefore,we propose a complete target encirclement method.Firstly,based on Hooke's law,a collision avoidance controller is designed to maintain a safe flying distance among quadrotors.Then,based on the consensus theory,a formation tracking controller is designed to meet the requirements of formation transformation and encirclement tasks,and a stability proof based on Lyapunov was provided.Besides,the target detection is designed based on YOLOv5s,and the target location model is constructed based on the principle of pinhole projection and triangle similarity.Finally,we conducted experiments on the built platform,with 3 reconnaissance quadrotors detecting and localization 3 target vehicles and 7 hunter quadrotors tracking them.The results show that the minimum average error for localization targets with reconnaissance quadrotors can reach 0.1354 m,while the minimum average error for tracking with hunter quadrotors is only 0.2960 m.No quadrotors collision occurred in the whole formation transformation and tracking experiment.In addition,compared with the advanced methods,the proposed method has better performance.
基金supported by the Natural Science Foundation of Jiangsu Province,China(BK20242007)the Natural Science Foundation of China(32271916)the Jiangsu Agricultural Science and Technology Innovation Fund[CX(24)3048].
文摘bHLH transcription factors,widely exist in various plants,and are vital for the growth and development of these plants.Among them,many have been implicated in anthocyanin biosynthesis across various plants.In the present study,a PdbHLH57 gene,belonging to the bHLH IIIf group,was characterized,which was isolated and cloned from the colored-leaf poplar‘Zhongshancaiyun’(ZSCY).The cDNA sequence of PdbHLH57 was 1887 base pairs,and the protein encoded by PdbHLH57 had 628 amino acids,the isoelectric point and molecular weight of which were 6.26 and 69.75 kDa,respectively.Through bioinformatics analysis,PdbHLH57 has been classified into the IIIf bHLH subgroup,with many members of this subgroup known to participate in anthocyanin biosynthesis.The subcellular localization analysis conducted in the leaf protoplasts of‘ZSCY’revealed that the PdbHLH57 protein is specifically localized in the nucleus.The transcription activation analysis was also conducted,and the results showed that the PdbHLH57 protein had self-transcription activation.To better explore the functions of the PdbHLH57 protein,two parts of this protein(PdbHLH57-1,PdbHLH57-2)were split to detect their transcriptional activation activity.The results indicated that PdbHLH57-1(1-433aa)had self-transcription activation,and PdbHLH57-2(433-628aa)had no transcription activation.The expression of PdbHLH57 peaked in June during different developmental stages in‘ZSCY’,and it was most highly expressed in the phloem among various tissues.These findings offer a basis for understanding the role of PdbHLH57 in colored-leaf poplar.
