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
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
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
A lightweight composite resonator,consisting of a soft material acoustic black hole(SABH)and multiple vibration absorbers,is embedded in a plate to achieve localization and absorption of low-frequency vibration energy...A lightweight composite resonator,consisting of a soft material acoustic black hole(SABH)and multiple vibration absorbers,is embedded in a plate to achieve localization and absorption of low-frequency vibration energy.The combination of local and global admissible functions for displacement enhances the accuracy of the Ritz method in predicting vibration localization characteristics within the SABH domain.Utilizing soft materials for the SABH can reduce the mass and frequency of the composite resonator.Due to the lack of orthogonality between global vibration modes and localized modes,the low-frequency localized modes induced by the SABH are used to shape the initial global modes,thereby concentrating the global vibration of the plate in the SABH region.Consequently,the absorbers of the composite resonator only need to be a small fraction of the mass of the local SABH to achieve substantial vibration control of the host plate.This vibration localization strategy can significantly reduce the vibration amplitude of the host plate and enhance the effectiveness of lightweight absorbers in vibration reduction.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
In existing image manipulation localization methods,the receptive field of standard convolution is limited,and during feature transfer,it is easy to lose high-frequency information about traces of manipulation.In addi...In existing image manipulation localization methods,the receptive field of standard convolution is limited,and during feature transfer,it is easy to lose high-frequency information about traces of manipulation.In addition,during feature fusion,the use of fixed sampling kernels makes it difficult to focus on local changes in features,leading to limited localization accuracy.This paper proposes an image manipulation localization method based on dual-branch hybrid convolution.First,a dual-branch hybrid convolution module is designed to expand the receptive field of the model to enhance the feature extraction ability of contextual semantic information,while also enabling the model to focus more on the high-frequency detail features of manipulation traces while localizing the manipulated area.Second,a multiscale content-aware feature fusion module is used to dynamically generate adaptive sampling kernels for each position in the feature map,enabling the model to focus more on the details of local features while locating the manipulated area.Experimental results on multiple datasets show that this method not only effectively improves the accuracy of image manipulation localization but also enhances the robustness of the model.展开更多
Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requi...Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method.展开更多
We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and ext...We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and extending this analysis to larger spin chains,we demonstrate that mixed-state entanglement is profoundly shaped by both disorder and temperature.Our results reveal a sharp distinction between many-body localized and ergodic phases,with entanglement vanishing above diferent fnite temperature thresholds.Furthermore,by analyzing non-adjacent spins,we uncover an approximate exponential decay of entanglement with separation.This work advances the understanding of the quantum-to-classical transition by linking the entanglement properties of small subsystems to the broader thermal environment,ofering an explanation for the absence of entanglement in macroscopic systems.These fndings provide critical insights into quantum many-body physics,bridging concepts from thermalization,localization,and quantum information theory.展开更多
A kind of slow deformation wave is produced in the crust under the action of internal and external factors,which plays an important role in the formation and occurrence of earthquakes.In this paper,uniaxial compressio...A kind of slow deformation wave is produced in the crust under the action of internal and external factors,which plays an important role in the formation and occurrence of earthquakes.In this paper,uniaxial compression tests are carried out on red sandstone samples with uniform texture.Displacement controlled loading methods are adopted,and the loading rates are 0.1 mm/min,0.5 mm/min and 1.0 mm/min,respectively.The micro-characterization method of speckle photography and DIC processing technology are adopted.The transfer characteristics of slow deformation and strain localization of red sandstone specimens during loading and deformation are discussed.The results show that the boundary advance velocity is proportional to the slow deformation transfer velocity with the change of position,so it can be considered that the slow deformation transfer velocity is equal to the particle motion transfer velocity.The formation and development of sample strain localization may be determined by the flow channel,nucleation and Luders zone evolution.The formation of the Luders band is related to the maximum value of the flow channel,and as deformation increases,the Luders band merges and develops with the maximum value of the nearby flow channel.By applying different loading rates,the influence of loading rate on the average transfer velocity of slow deformation was obtained;the slow deformation wave during seismic migration has similar characteristics to the deformation transfer in the experiment,therefore the research results have reference significance for further studies on the evolution characteristics of slow deformation waves and seismic migration.展开更多
Existing image manipulation localization(IML)techniques require large,densely annotated sets of forged images.This requirement greatly increases labeling costs and limits a model’s ability to handle manipulation type...Existing image manipulation localization(IML)techniques require large,densely annotated sets of forged images.This requirement greatly increases labeling costs and limits a model’s ability to handle manipulation types that are novel or absent from the training data.To address these issues,we present CLIP-IML,an IML framework that leverages contrastive language-image pre-training(CLIP).A lightweight feature-reconstruction module transforms CLIP token sequences into spatial tensors,after which a compact feature-pyramid network and a multi-scale fusion decoder work together to capture information from fine to coarse levels.We evaluated CLIP-IML on ten public datasets that cover copy-move,splicing,removal,and artificial intelligence(AI)-generated forgeries.The framework raises the average F1-score by 7.85%relative to the strongest recent baselines and secures either the first-or second-place performance on every dataset.Ablation studies show that CLIP pre-training,higher resolution inputs,and the multi-scale decoder each make complementary contributions.Under six common post-processing perturbations,as well as the compression pipelines used by Facebook,Weibo,and WeChat,the performance decline never exceeds 2.2%,confirming strong practical robustness.Moreover,CLIP-IML requires only a few thousand annotated images for training,which markedly reduces data-collection and labeling effort compared with previous methods.All of these results indicate that CLIP-IML is highly generalizable for image tampering localization across a wide range of tampering scenarios.展开更多
BACKGROUND Hydatid cyst disease,caused by Echinococcus granulosus,primarily affects the liver and lungs,but it can also develop in rare locations such as the kidneys,thyroid,subcutaneous tissues,bones,and the mediasti...BACKGROUND Hydatid cyst disease,caused by Echinococcus granulosus,primarily affects the liver and lungs,but it can also develop in rare locations such as the kidneys,thyroid,subcutaneous tissues,bones,and the mediastinum.These atypical presentations often pose diagnostic challenges,as they can mimic benign and malignant pathologies,leading to potential misdiagnoses and inappropriate treatments.Early and accurate detection of hydatid cysts in uncommon sites is crucial for optimal patient management.CASE SUMMARY This case report series presents five patients with hydatid cysts located in atypical anatomical regions:The kidney,lumbar subcutaneous tissue,gluteal soft tissue,posterior mediastinum,and thyroid gland.The patients exhibited diverse clinical symptoms,including hematuria,palpable masses,localized pain,and chronic cough.Diagnosis was confirmed through a combination of imaging techniquesultrasound,computed tomography,and magnetic resonance imaging-along with serological testing.All cases were managed with antiparasitic therapy(albendazole),and in selected cases,surgical excision was performed to prevent complications such as cyst rupture or secondary infections.Post-treatment follow-up demonstrated complete resolution or stable cystic lesions,with no signs of recurrence.CONCLUSION Recognizing hydatid cysts in atypical locations is essential to avoid misdiagnosis and ensure appropriate treatment strategies.Radiological imaging plays a key role in distinguishing hydatid cysts from other cystic and neoplastic conditions,while serological tests can aid in confirmation,particularly in endemic regions.A multidisciplinary approach,integrating radiology,clinical evaluation,and surgical expertise,is critical for effective diagnosis and management.This report highlights the need for increased awareness of extrapulmonary and extravisceral hydatid disease,emphasizing its significance in differential diagnosis and clinical practice.展开更多
1 Subcellular Organelle Dysfunction and Disease Progression The precise organization of subcellular organelles is important for maintaining cellular homeostasis.Compartmentalization orchestrates metabolic processes,si...1 Subcellular Organelle Dysfunction and Disease Progression The precise organization of subcellular organelles is important for maintaining cellular homeostasis.Compartmentalization orchestrates metabolic processes,signal transductions,and stress responses.Disturbances in organelles,including the nucleus,mitochondria,lysosomes,and endoplasmic reticulum,can lead to widespread intracellular dysfunction and contribute to diverse pathologies.For example,mitochondrial reactive oxygen species(ROS)exacerbate endoplasmic reticulum(ER)stress,as demonstrated in studies linking ROS-mediated mitochondrial dysfunction to apoptosis in neurodegenerative diseases,cancer,and inflammatory diseases[1–4].ER stress has also been implicated in cardiac hypertrophy[5],lung fibrosis[6],liver fibrosis[7],and ulcerative colitis[8].展开更多
Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength...Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.展开更多
Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extracti...Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.展开更多
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 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.
基金supported by the National Natural Science Foundation of China(Grant Nos.12302006,12132002,and 62188101).
文摘A lightweight composite resonator,consisting of a soft material acoustic black hole(SABH)and multiple vibration absorbers,is embedded in a plate to achieve localization and absorption of low-frequency vibration energy.The combination of local and global admissible functions for displacement enhances the accuracy of the Ritz method in predicting vibration localization characteristics within the SABH domain.Utilizing soft materials for the SABH can reduce the mass and frequency of the composite resonator.Due to the lack of orthogonality between global vibration modes and localized modes,the low-frequency localized modes induced by the SABH are used to shape the initial global modes,thereby concentrating the global vibration of the plate in the SABH region.Consequently,the absorbers of the composite resonator only need to be a small fraction of the mass of the local SABH to achieve substantial vibration control of the host plate.This vibration localization strategy can significantly reduce the vibration amplitude of the host plate and enhance the effectiveness of lightweight absorbers in vibration reduction.
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National Natural Science Foundation of China(61703363)Shanxi Provincial Basic Research Program(202403021221206)+2 种基金Key Project of Shanxi Provincial Strategic Research on Science and Technology(202304031401011)Funding Project for Scientific Research Innovation Team on Data Mining and Industrial Intelligence Applications(YCXYTD-202402)Yuncheng University Research Project(YQ-2020021)。
文摘In existing image manipulation localization methods,the receptive field of standard convolution is limited,and during feature transfer,it is easy to lose high-frequency information about traces of manipulation.In addition,during feature fusion,the use of fixed sampling kernels makes it difficult to focus on local changes in features,leading to limited localization accuracy.This paper proposes an image manipulation localization method based on dual-branch hybrid convolution.First,a dual-branch hybrid convolution module is designed to expand the receptive field of the model to enhance the feature extraction ability of contextual semantic information,while also enabling the model to focus more on the high-frequency detail features of manipulation traces while localizing the manipulated area.Second,a multiscale content-aware feature fusion module is used to dynamically generate adaptive sampling kernels for each position in the feature map,enabling the model to focus more on the details of local features while locating the manipulated area.Experimental results on multiple datasets show that this method not only effectively improves the accuracy of image manipulation localization but also enhances the robustness of the model.
基金the research result of the 2024 Guangxi Higher Education Undergraduate Teaching Reform Project“OBE-Guided,Digitally Empowered‘Hadoop Big Data Development Technology’Course Ideological and Political Construction Innovation Exploration and Practice”(Project No.:2024JGA396).
文摘Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method.
基金supported by the National Natural Science Foundation of China(Grant Nos.92365202,12475011,and 11921005)the National Key R&D Program of China(Grant No.2024YFA1409002)Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)。
文摘We investigate the mixed-state entanglement between two spins embedded in the XXZ Heisenberg chain under thermal equilibrium.By deriving an analytical expression for the entanglement of two-spin thermal states and extending this analysis to larger spin chains,we demonstrate that mixed-state entanglement is profoundly shaped by both disorder and temperature.Our results reveal a sharp distinction between many-body localized and ergodic phases,with entanglement vanishing above diferent fnite temperature thresholds.Furthermore,by analyzing non-adjacent spins,we uncover an approximate exponential decay of entanglement with separation.This work advances the understanding of the quantum-to-classical transition by linking the entanglement properties of small subsystems to the broader thermal environment,ofering an explanation for the absence of entanglement in macroscopic systems.These fndings provide critical insights into quantum many-body physics,bridging concepts from thermalization,localization,and quantum information theory.
基金Postdoctoral Science Fund(043201027)National Natural Science Key Foundation of China(41831290)National Natural Science Foundation of China(52079068).
文摘A kind of slow deformation wave is produced in the crust under the action of internal and external factors,which plays an important role in the formation and occurrence of earthquakes.In this paper,uniaxial compression tests are carried out on red sandstone samples with uniform texture.Displacement controlled loading methods are adopted,and the loading rates are 0.1 mm/min,0.5 mm/min and 1.0 mm/min,respectively.The micro-characterization method of speckle photography and DIC processing technology are adopted.The transfer characteristics of slow deformation and strain localization of red sandstone specimens during loading and deformation are discussed.The results show that the boundary advance velocity is proportional to the slow deformation transfer velocity with the change of position,so it can be considered that the slow deformation transfer velocity is equal to the particle motion transfer velocity.The formation and development of sample strain localization may be determined by the flow channel,nucleation and Luders zone evolution.The formation of the Luders band is related to the maximum value of the flow channel,and as deformation increases,the Luders band merges and develops with the maximum value of the nearby flow channel.By applying different loading rates,the influence of loading rate on the average transfer velocity of slow deformation was obtained;the slow deformation wave during seismic migration has similar characteristics to the deformation transfer in the experiment,therefore the research results have reference significance for further studies on the evolution characteristics of slow deformation waves and seismic migration.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant No.2023D01C21the National Natural Science Foundation of China under Grant No.62362063.
文摘Existing image manipulation localization(IML)techniques require large,densely annotated sets of forged images.This requirement greatly increases labeling costs and limits a model’s ability to handle manipulation types that are novel or absent from the training data.To address these issues,we present CLIP-IML,an IML framework that leverages contrastive language-image pre-training(CLIP).A lightweight feature-reconstruction module transforms CLIP token sequences into spatial tensors,after which a compact feature-pyramid network and a multi-scale fusion decoder work together to capture information from fine to coarse levels.We evaluated CLIP-IML on ten public datasets that cover copy-move,splicing,removal,and artificial intelligence(AI)-generated forgeries.The framework raises the average F1-score by 7.85%relative to the strongest recent baselines and secures either the first-or second-place performance on every dataset.Ablation studies show that CLIP pre-training,higher resolution inputs,and the multi-scale decoder each make complementary contributions.Under six common post-processing perturbations,as well as the compression pipelines used by Facebook,Weibo,and WeChat,the performance decline never exceeds 2.2%,confirming strong practical robustness.Moreover,CLIP-IML requires only a few thousand annotated images for training,which markedly reduces data-collection and labeling effort compared with previous methods.All of these results indicate that CLIP-IML is highly generalizable for image tampering localization across a wide range of tampering scenarios.
文摘BACKGROUND Hydatid cyst disease,caused by Echinococcus granulosus,primarily affects the liver and lungs,but it can also develop in rare locations such as the kidneys,thyroid,subcutaneous tissues,bones,and the mediastinum.These atypical presentations often pose diagnostic challenges,as they can mimic benign and malignant pathologies,leading to potential misdiagnoses and inappropriate treatments.Early and accurate detection of hydatid cysts in uncommon sites is crucial for optimal patient management.CASE SUMMARY This case report series presents five patients with hydatid cysts located in atypical anatomical regions:The kidney,lumbar subcutaneous tissue,gluteal soft tissue,posterior mediastinum,and thyroid gland.The patients exhibited diverse clinical symptoms,including hematuria,palpable masses,localized pain,and chronic cough.Diagnosis was confirmed through a combination of imaging techniquesultrasound,computed tomography,and magnetic resonance imaging-along with serological testing.All cases were managed with antiparasitic therapy(albendazole),and in selected cases,surgical excision was performed to prevent complications such as cyst rupture or secondary infections.Post-treatment follow-up demonstrated complete resolution or stable cystic lesions,with no signs of recurrence.CONCLUSION Recognizing hydatid cysts in atypical locations is essential to avoid misdiagnosis and ensure appropriate treatment strategies.Radiological imaging plays a key role in distinguishing hydatid cysts from other cystic and neoplastic conditions,while serological tests can aid in confirmation,particularly in endemic regions.A multidisciplinary approach,integrating radiology,clinical evaluation,and surgical expertise,is critical for effective diagnosis and management.This report highlights the need for increased awareness of extrapulmonary and extravisceral hydatid disease,emphasizing its significance in differential diagnosis and clinical practice.
基金funded by the National Natural Science Foundation of China(No.12272246)(YZ)partially funded by ARO(Army Research Office)(W911NF2310189)a grant from NSF(NSF 2324052)of the USA(BMF).
文摘1 Subcellular Organelle Dysfunction and Disease Progression The precise organization of subcellular organelles is important for maintaining cellular homeostasis.Compartmentalization orchestrates metabolic processes,signal transductions,and stress responses.Disturbances in organelles,including the nucleus,mitochondria,lysosomes,and endoplasmic reticulum,can lead to widespread intracellular dysfunction and contribute to diverse pathologies.For example,mitochondrial reactive oxygen species(ROS)exacerbate endoplasmic reticulum(ER)stress,as demonstrated in studies linking ROS-mediated mitochondrial dysfunction to apoptosis in neurodegenerative diseases,cancer,and inflammatory diseases[1–4].ER stress has also been implicated in cardiac hypertrophy[5],lung fibrosis[6],liver fibrosis[7],and ulcerative colitis[8].
基金supported by James M.Cox Foundation,National Institute on Deafness and Other Communication Disorders(grant no.1R21DC021029-01A1)Cox Enterprises Inc.,National Institute of Child Health and Human Development(grant no.AWD-006196-G1)Thrasher Research Fund Early Career Award Program.
文摘Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)+2 种基金the Open Research Fund of National Mobile Communications Research Laboratory Southeast University(No.2023D07)the Outstanding Youth Program of Natural Science Foundation of Heilongjiang Province(No.YQ2020F012)the Funda-mental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.
