Spreading Behavior and Hot Cracking Mechanism of Single Tracks in High Strength Al–Cu–Mg–Mn Alloy Fabricated by Laser Powder Bed Fusion

Laser powder bed fusion(LPBF)technology is a high-precision metal additive manufacturing(AM)technology.Due to the high specific strength of high strength aluminum alloys,high strength aluminum alloys fabricated by LPBF have broad application prospects in the field of light weighting.However,high strength aluminum alloys have high hot cracking susceptibility.In this study,an analysis of the hot cracking susceptibility as a function of processing parameters is presented for single tracks of LPBF processed(LPBFed)high strength Al–Cu–Mg–Mn alloy.The hot cracking in single tracks of LPBFed Al–Cu–Mg–Mn alloy is solidification cracking based on the experimental observations of microstructure.Combining Rosenthal simulations and spreading behavior of a single droplet,the critical scanning speed of single track with balling phenomenon was obtained.It was found that when the laser power was 200 W,the scanning speed exceeded 440.1 mm/s,the droplet will not be able to spread completely,which is consistent with the experimental result of 500 mm/s.Through the calculation and analysis of the microstructure and the existence time of the molten pool,it was pointed out that the reduction in the liquid phase caused by the high scanning speed,the shortening of the solidification time and the high stress caused by the high-temperature gradient promoted the generation of hot cracking.In summary,this work contains a practical guide to optimize processing parameters of LPBFed Al–Cu–Mg–Mn alloys,which provides a basis for fabricating thin walls and cubic samples without hot cracking.

Determination of diffusion coefficient by image-based fluorescence recovery after photobleaching and single particle tracking system implemented in a single platform

Fluorescence recovery after photobleaching(FRAP)and single particle tracking(SPT)techni-ques determine the diffusion coefficient from average diffusive motion of high-concentration molecules and from trajectories of low-concentration single molecules,respectively.Lateral dif-fusion coefficients measured by FRAP and SPT techniques for the same biomolecule on cell membrane have exhibited inconsistent values across laboratories and platforms with larger dif-fusion coefficient determined by FRAP,but the sources of the inconsistency have not been investigated thoroughly.Here,we designed an image-based FRAP-SPT system and made a direct comparison between FRAP and SPT for diffusion coefficient of submicron particles with known theoretical values derived from Stokes-Einstein equation in aqueous solution.The combined iFRAP-SPT technique allowed us to measure the diffusion coefficient of the same fluorescent particle by utilizing both techniques in a single platform and to scrutinize inherent errors and artifacts of FRAP.Our results reveal that diffusion coefficient overestimated by FRAP is caused by inaccurate estimation of the bleaching spot size and can be corrected by simple image analysis.Our iFRAP-SPT technique can be potentially used for not only cellular membrane dynamics but also for quantitative analysis of the spatiotemporal distribution of the solutes in small scale analytical devices.

SiamDLA: Dynamic Label Assignment for Siamese Visual Tracking

Label assignment refers to determining positive/negative labels foreach sample to supervise the training process. Existing Siamese-based trackersprimarily use fixed label assignment strategies according to human priorknowledge;thus, they can be sensitive to predefined hyperparameters and failto fit the spatial and scale variations of samples. In this study, we first developa novel dynamic label assignment (DLA) module to handle the diverse datadistributions and adaptively distinguish the foreground from the backgroundbased on the statistical characteristics of the target in visual object tracking.The core of DLA module is a two-step selection mechanism. The first stepselects candidate samples according to the Euclidean distance between trainingsamples and ground truth, and the second step selects positive/negativesamples based on the mean and standard deviation of candidate samples.The proposed approach is general-purpose and can be easily integrated intoanchor-based and anchor-free trackers for optimal sample-label matching.According to extensive experimental findings, Siamese-based trackers withDLA modules can refine target locations and outperformbaseline trackers onOTB100, VOT2019, UAV123 and LaSOT. Particularly, DLA-SiamRPN++improves SiamRPN++ by 1% AUC and DLA-SiamCAR improves Siam-CAR by 2.5% AUC on OTB100. Furthermore, hyper-parameters analysisexperiments show that DLA module hardly increases spatio-temporal complexity,the proposed approach maintains the same speed as the originaltracker without additional overhead.

A Real-Time Multi-Vehicle Tracking Framework in Intelligent Vehicular Networks

In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for the time-critical autonomous driving’s requirement.The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario..Our proposed framework is composed of three modules:multi-vehicle detection,multi-vehicle association and miss-detected vehicle tracking.For the first module,we integrate self-attention mechanism into detector of using key point estimation for better detection effect.For the second module,we apply the multi-dimensional information for robustness promotion,including vehicle re-identification(Re-ID)features,historical trajectory information,and spatial position information For the third module,we re-track the miss-detected vehicles with occlusions in the first detection module.Besides,we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up.Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.

A Distributed Particle Filter Applied in Single Object Tracking

Visual object-tracking is a fundamental task applied in many applications of computer vision. Particle filter is one of the techniques which has been widely used in object tracking. Due to the virtue of extendability and flexibility on both linear and non-linear environments, various particle filter-based trackers have been proposed in the literature. However, the conventional approach cannot handle very large videos efficiently in the current data intensive information age. In this work, a parallelized particle filter is provided in a distributed framework provided by the Hadoop/Map-Reduce infrastructure to tackle object-tracking tasks. The experiments indicate that the proposed algorithm has a better convergence and accuracy as compared to the traditional particle filter. The computational power and the scalability of the proposed particle filter in single object tracking have been enhanced as well.

Initial refinement of data from video‐based single‐cell tracking

Background: Video recording of cells offers a straightforward way to gainvaluable information from their response to treatments. An indispensable stepin obtaining such information involves tracking individual cells from therecorded data. A subsequent step is reducing such data to represent essentialbiological information. This can help to compare various single‐cell trackingdata yielding a novel source of information. The vast array of potential datasources highlights the significance of methodologies prioritizing simplicity,robustness, transparency, affordability, sensor independence, and freedomfrom reliance on specific software or online services.Methods: The provided data presents single‐cell tracking of clonal (A549)cells as they grow in two‐dimensional (2D) monolayers over 94 hours,spanning several cell cycles. The cells are exposed to three differentconcentrations of yessotoxin (YTX). The data treatments showcase theparametrization of population growth curves, as well as other statisticaldescriptions. These include the temporal development of cell speed in familytrees with and without cell death, correlations between sister cells, single‐cellaverage displacements, and the study of clustering tendencies.Results: Various statistics obtained from single‐cell tracking reveal patternssuitable for data compression and parametrization. These statistics encompassessential aspects such as cell division, movements, and mutual informationbetween sister cells.Conclusion: This work presents practical examples that highlight theabundant potential information within large sets of single‐cell tracking data.Data reduction is crucial in the process of acquiring such information whichcan be relevant for phenotypic drug discovery and therapeutics, extendingbeyond standardized procedures. Conducting meaningful big data analysistypically necessitates a substantial amount of data, which can stem fromstandalone case studies as an initial foundation.

Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction

Visual object tracking is an important issue that has received long-term attention in computer vision.The ability to effectively handle occlusion,especially severe occlusion,is an important aspect of evaluating the performance of object tracking algorithms in long-term tracking,and is of great significance to improving the robustness of object tracking algorithms.However,most object tracking algorithms lack a processing mechanism specifically for occlusion.In the case of occlusion,due to the lack of target information,it is necessary to predict the target position based on the motion trajectory.Kalman filtering and particle filtering can effectively predict the target motion state based on the historical motion information.A single object tracking method,called probabilistic discriminative model prediction(PrDiMP),is based on the spatial attention mechanism in complex scenes and occlusions.In order to improve the performance of PrDiMP,Kalman filtering,particle filtering and linear filtering are introduced.First,for the occlusion situation,Kalman filtering and particle filtering are respectively introduced to predict the object position,thereby replacing the detection result of the original tracking algorithm and stopping recursion of target model.Second,for detection-jump problem of similar objects in complex scenes,a linear filtering window is added.The evaluation results on the three datasets,including GOT-10k,UAV123 and LaSOT,and the visualization results on several videos,show that our algorithms have improved tracking performance under occlusion and the detection-jump is effectively suppressed.

Visualizing the Transport of Porcine Reproductive and Respiratory Syndrome Virus in Live Cells by Quantum Dots-Based Single Virus Tracking

Quantum dots(QDs)-based single particle analysis technique enables real-time tracking of the viral infection in live cells with great sensitivity over a long period of time.The porcine reproductive and respiratory syndrome virus(PRRSV)is a small virus with the virion size of 40–60 nm which causes great economic losses to the swine industry worldwide.A clear understanding of the viral infection mechanism is essential for the development of effective antiviral strategies.In this study,we labeled the PRRSV with QDs using the streptavidin–biotin labeling system and monitored the viral infection process in live cells.Our results indicated that the labeling method had negligible effect on viral infectivity.We also observed that prior to the entry,PRRSV vibrated on the plasma membrane,and entered the cells via endosome mediated cell entry pathway.Viruses moved in a slow–fast–slow oscillatory movement pattern and finally accumulated in a perinuclear region of the cell.Our results also showed that once inside the cell,PRRSV moved along the microtubule,microfilament and vimentin cytoskeletal elements.During the transport process,virus particles also made contacts with non-muscle myosin heavy chainⅡ-A(NMHCⅡ-A),visualized as small spheres in cytoplasm.This study can facilitate the application of QDs in virus infection imaging,especially the smaller-sized viruses and provide some novel and important insights into PRRSV infection mechanism.

Single virus tracking of Ebola virus entry through lipid rafts in living host cells

Ebola virus(EBOV)is one of the most pathogenic viruses in humans which can cause a lethal hemorrhagic fever.Understanding the cellular entry mechanisms of EBOV can promote the development of new therapeutic strategies to control virus replication and spread.It has been known that EBOV virions bind to factors expressed at the host cell surface.Subsequently,the virions are internalized by a macropinocytosis-like process,followed by being trafficked through early and late endosomes.Recent researches indicate that the entry of EBOV into cells requires integrated and functional lipid rafts.Whilst lipid rafts have been hypothesized to play a role in virus entry,there is a current lack of supporting data.One major technical hurdle is the lack of effective approaches for observing viral entry.To provide evidence on the involvement of lipid rafts in the entry process of EBOV,we generated the fluorescently labeled Ebola virus like particles(VLPs),and utilized single-particle tracking(SPT)to visualize the entry of fluorescent Ebola VLPs in live cells and the interaction of Ebola VLPs with lipid rafts.In this study,we demonstrate the compartmentalization of Ebola VLPs in lipid rafts during entry process,and inform the essential function of lipid rafts for the entry of Ebola virus.As such,our study provides evidence to show that the raft integrity is critical for Ebola virus pathogenesis and that lipid rafts can serve as potential targets for the development of novel therapeutic strategies.

Unveiling Cellular Internalization Dynamics of Single Gold Nanorods by Tracking Their Orientational and Translational Motions

Single nanoparticle tracking(SPT)is a unique and powerful tool to investigate the interaction between nanoparticles and cells,which is of considerable importance for nanotechnology applications in biomedical fields and in-depth understanding of biological activities.However,previous work typically focused on translations of single nanoparticles while they undergo both translational and rotational motions.In this study,we obtained both the translational and rotational dynamics of single gold nanorods during their cellular internalization process using dual-channel polarization microscopy.In particular,the azimuth and polar angles were integrated into a polar coordinate systemto obtain three general orientation distribution patterns,found to have a close relationship with the nanoparticle cellular internalization process and time-dependent alterations.Moreover,the patterns accompanied by trajectories,translational and rotational coefficients,the azimuth and polar angles,and other parameters provided a wealth of knowledge on the nanoparticle cellular internalization dynamics with unprecedented details.We observed that the gold nanorods could initially assume a tip-first quick rotation state with partially restricted orientations,then change to a strongly confined near-vertical insertion state with slight angular fluctuations,and eventually transform into a random and fast rotation state.Our methodology opens up a new avenue for a detailed understanding of biological processes.

United under stress:High-speed transport network emerging at bacterial living edge

Individuals tend to move freely when there is enough room but would act collectively for their survival under external stress.In the case of living cells,for instance,when a drop of low-density flagellated bacterial solution is transferred onto the agar surface,the initially disordered movement of individual bacteria would be replaced with coordinated cell swarming after a lag phase of a few hours.Here,we study how such cooperation is established while overcoming the disorder at the onset of the lag phase with single nanoparticle tracking.Upon the spreading of the droplet,the bacteria in the solution cluster and align near the almost immobilized contact line confining the drop,forming a narrow ring of cells.As individual cells move in and out of the ring continuously,certain flow patterns emerge in the inter-bacterial fluid.We reveal high-speed long-distance unidirectional flows with definite chirality along the outside of the ring,along the inside of the ring and across the ring.We speculate that these flows enable the fast and efficient transport,facilitating the communication and unification of the bacterial community.

SiamCPN:Visual tracking with the Siamese center-prediction network

Object detection is widely used in object tracking;anchor-free object tracking provides an end-to-end single-object-tracking approach.In this study,we propose a new anchor-free network,the Siamese center-prediction network(SiamCPN).Given the presence of referenced object features in the initial frame,we directly predict the center point and size of the object in subsequent frames in a Siamese-structure network without the need for perframe post-processing operations.Unlike other anchor-free tracking approaches that are based on semantic segmentation and achieve anchor-free tracking by pixel-level prediction,SiamCPN directly obtains all information required for tracking,greatly simplifying the model.A center-prediction sub-network is applied to multiple stages of the backbone to adaptively learn from the experience of different branches of the Siamese net.The model can accurately predict object location,implement appropriate corrections,and regress the size of the target bounding box.Compared to other leading Siamese networks,SiamCPN is simpler,faster,and more efficient as it uses fewer hyperparameters.Experiments demonstrate that our method outperforms other leading Siamese networks on GOT-10K and UAV123 benchmarks,and is comparable to other excellent trackers on LaSOT,VOT2016,and OTB-100 while improving inference speed 1.5 to 2 times.

3D Single Object Tracking with Multi-View Unsupervised Center Uncertainty Learning

Center point localization is a major factor affecting the performance of 3D single object tracking.Point clouds themselves are a set of discrete points on the local surface of an object,and there is also a lot of noise in the labeling.Therefore,directly regressing the center coordinates is not very reasonable.Existing methods usually use volumetric-based,point-based,and view-based methods,with a relatively single modality.In addition,the sampling strategies commonly used usually result in the loss of object information,and holistic and detailed information is beneficial for object localization.To address these challenges,we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker(MUT).MUT models the potential uncertainty of center coordinates localization using an unsupervised manner,allowing the model to learn the true distribution.By projecting point clouds,MUT can obtain multi-view depth map features,realize efficient knowledge transfer from 2D to 3D,and provide another modality information for the tracker.We also propose a former attraction probability sampling strategy that preserves object information.By using both holistic and detailed descriptors of point clouds,the tracker can have a more comprehensive understanding of the tracking environment.Experimental results show that the proposed MUT network outperforms the baseline models on the KITTI dataset by 0.8%and 0.6%in precision and success rate,respectively,and on the NuScenes dataset by 1.4%,and 6.1%in precision and success rate,respectively.The code is made available at https://github.com/abchears/MUT.git.

Reveal heterogeneous motion states in single nanoparticle trajectory using its own history

Single particle tracking(SPT)has long been utilized for investigation of complex system dynamics such as nanoparticle-cell interaction,however,the analysis of individual particle motions is always a difficult issue.Existing methods treat each data point or fragment on the recorded trajectory as an isolated"atom"and determine their relationship based on externally predefined models or physical states,which inevitably lead to oversimplification of the associated spatiotemporal complexity.Herein,inspired by the historical analysis in social science,we propose a modeless preprocessing framework for SPT analysis based on the"history"of the particle.This new strategy consists of 3 steps:(1)assign a"history"to each data point and construct successive overlapped historical vectors;(2)perform unsupervised clustering in the vector space to find their relative differences;(3)project differences back to the trajectory by coloring each point accordingly for visualization.As a result,the inner heterogeneity of the particle motion self-emerges as a colored trajectory,exhibiting a global picture of the local state transitions and providing valuable information for further model-based analysis.Since the complexity issues at various spatiotemporal scales have attracted increasing attention,and individual objects such as single molecules,cells,vehicles and even stars in the universe could all be treated as"single particles",this presuppositionless data preprocessing approach could help the investigations of many complex systems in fundamental research.

In-situ alloyed CoCrFeMnNi high entropy alloy:Microstructural development in laser powder bed fusion

In-situ alloying has the potential to combine the compositional flexibility of high entropy alloys(HEAs)and the advanced forming capability of laser powder bed fusion(LPBF).This study fundamentally investigated the elemental homogenisation and grain development in the in-situ alloying process of CoCrFeMnNi HEA,by analysing the basic units,i.e.,tracks and layers,and introducing Mn as an alloying element to the base Co Cr Fe Ni HEA.Different modelling methods were employed to predict meltpool dimensions,and the results indicated the dependence of the modelling on practical meltpool modes.Delimitation of elemental distribution was found in keyhole meltpools since an intensive flow was generated due to recoil pressure.The homogeneity of in-situ alloyed Mn in single tracks was insufficient whether operated in conduction mode or keyhole mode,which required remelting from adjacent tracks and following layers to promote homogenisation significantly.The preferred orientation in single tracks along scanning directions changed from<001>to<101>as the scanning speed increased,although the cross-sections were similar in size with identical linear energy density.Such preference can be inherited during the printing process and lead to different textures in three-layer samples.It was also observed that applying hatch spacing smaller than a half meltpool width could coarsen the grains in a layer.The results from this study provide structure-parameter correlations for future microstructural tailoring and manipulation.