Set Of Experiments Research Articles

The Effects of Short- and Long-Term Ingestion of Plastic Toxin Bisphenol A on Gastrointestinal Transit Time in Rats.

Introduction Exposure to bisphenol A (BPA), a toxic chemical released from plastic, affects various body functions, including reproduction, metabolism, and development. The most common route of exposure to BPA is oral, and the gastrointestinal(GI) tract is, therefore, the first body system to be exposed to BPA. BPA has been well-documented to impair gut contractility in rats, in vitro. It may therefore be hypothesized that BPA may adversely affect GI motility and hence slow down the movement of food, resulting in the increased transit of food bolus in the GI tract. There are no reports so far on the effects of BPA on GI transit time. Objectives The present study was undertaken to examine the impact of exposure to BPA by a single oral dose (termed as short-term ingestion of BPA) and chronic (28-day) oral dose (termed as long-term ingestion of BPA) on the transit time of food bolus in the gut of adult male albino rats. Methods and materials The study was conducted in the Department of Physiology,Institute of MedicalSciences,Banaras Hindu University, Varanasi,Uttar Pradesh, India. In one set of experiments, each animal was fed a food pellet, once (short-term ingestion) containing BPA (2 µg/kg and 50 µg/kg in different groups), and in another set of experiments, each animal was fed a food pellet containing BPA (50 µg/kg/day) for 28 consecutive days (long-term ingestion). Control rats in both sets were fed food pellets without BPA. Subsequently, the gastric transit index (GTI), ileocecal transit index (ICTI), and colonic transit time (CTT) were determined by the standard charcoal marker method. Results One-time ingestion of a food pellet containing BPA caused a significant (p < 0.05) drop in the GTI and ICTI and an increase in the CTT with both doses of BPA (2 and 50 µg/kg). Similarly, after chronic (28-day), oral BPA exposure, a significant decrease in the GTI and ICTT and an increase in CTT were observed. Conclusion Both short-term (one-time) and long-term (28-day) oral exposure to BPA-containing food harmed GI transit. Slow GI transit may lead to metabolic disorders and GI motility disorders, such as constipation.

Individual heat adaptation: Analyzing risk communication, warnings, heat risk perception, and protective behavior in three German cities.

Extreme heat poses severe health threats, as the increased numbers of hospitalizations and fatalities during heat waves show, though little is known about adaptive behavior toward heat. We conducted a household survey on individual perceptions of heat stress and individual heat protection in the summer and autumn of 2019. In total, 1417 people from three medium-sized German cities participated via telephone or online. Based on the Protective Action Decision Model (PADM), which we adapted to heat stress, we analyzed links between risk perception, environmental and demographic factors, perceptions of stakeholders, different heat warning messages, as well as actual and intended adaptive behavior. Overall, the PADM constructs explained around 16% of the variance in protection motivation, 19% in protective response, and 23% in emotion-focused coping. Context factors (i.e., temperature, risk communication, gender, age, and homeownership) were significant predictors of the addressed outcome variables as were psychological factors (i.e., perceived personal vulnerability, response efficacy, response costs, preparedness, and perceived external responsibility). We further explored the effect of different warning messages on situational knowledge and intended behavioral adaptation in an experimental setting. Results showed that respondents felt significantly better informed after receiving a warning with action recommendations and reported more intended specific behaviors. Our research gives insights into individual protective action decision-making processes. Based on our findings, we recommend tailoring risk communication strategies and combining heat warnings with action recommendations whenever possible to increase understanding and individual adaptation.

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications

Object recognition, localization, and tracking play a role of primordial importance in computer vision applications. However, it is still an extremely difficult task, particularly in scenarios where objects are attended to using fast-moving UAVs that need to robustly operate in real time. Typically the performance of these vision-based systems is affected by motion blur and geometric distortions, to name but two issues. Gimbal systems are thus essential to compensate for motion blur and ensure visual streams are stable. In this work, we investigate the advantages of active tracking approaches using a three-degrees-of-freedom (DoF) gimbal system mounted on UAVs. A method that utilizes joint movement and visual information for actively tracking spherical and planar objects in real time is proposed. Tracking methodologies are tested and evaluated in two different realistic Gazebo simulation environments: the first on 3D positional tracking (sphere) and the second on tracking of 6D poses (planar fiducial markers). We show that active object tracking is advantageous for UAV applications, first, by reducing motion blur, caused by fast camera motion and vibrations, and, second, by fixating the object of interest within the center of the field of view and thus reducing re-projection errors due to peripheral distortion. The results demonstrate significant object pose estimation accuracy improvements of active approaches when compared with traditional passive ones. More specifically, a set of experiments suggests that active gimbal tracking can increase the spatial estimation accuracy of known-size moving objects, under conditions of challenging motion patterns and in the presence of image distortion.

Attentional bias to threat: an investigation of psychological predictors beyond trait anxiety

AbstractAttentional bias to threat (ABT) refers to the tendency to more rapidly orient attention to threatening stimuli, as compared to neutral or pleasant stimuli. Issues with the reliability of this phenomenon have been raised and little is currently known about individual differences that may contribute to ABT. This online study aimed to replicate ABT in trait anxiety and identify psychological predictors of ABT using both dot probe and flanker tasks comprising emotional images. Undergraduate students completed various psychological questionnaires, followed by the ABT tasks. Contrary to expectations, faster responses and enhanced accuracy for positive stimuli were found in a low trait anxiety sub-sample (n = 33) in the dot probe task, while overall slower performance among a high trait anxiety sub-sample (n = 34) was found in the flanker task. Higher ratings of state anxiety and stress and lower ratings of calmness were associated with greater flanker ABT scores in the overall sample (N = 111), while higher scores on facets of trait mindfulness and personality were associated with greater dot probe ABT scores. Trait anxiety was not associated with either ABT score. These findings highlight the difficulties replicating ABT in anxious individuals using behavioural tasks and indicate the importance of measuring state and trait psychological experiences when investigating ABT in experimental settings. Further replication of predictors is required.

Solving Large-Scale Unconstrained Optimization Problems with an Efficient Conjugate Gradient Class

The main goal of this paper is to introduce an appropriate conjugate gradient class to solve unconstrained optimization problems. The presented class enjoys the benefits of having three free parameters, its directions are descent, and it can fulfill the Dai–Liao conjugacy condition. Global convergence property of the new class is proved under the weak-Wolfe–Powell line search technique. Numerical efficiency of the proposed class is confirmed in three sets of experiments including 210 test problems and 11 disparate conjugate gradient methods.

Sequential Selection for Minimizing the Variance with Application to Crystallization Experiments

For many crystal-based products (e.g., pharmaceuticals, energy storage), the size uniformity is not only a key quality attribute, but sometimes also an indicator of other attributes such as solid purity. This article proposes a sequential selection approach to find a proper experimental setting that leads to high uniformity, or equivalently, small variance for crystal sizes, from the advanced slug flow reaction crystallization process of a model crystal, called manganese oxalate hydrate. The proposed sequential selection approach contains a Bayesian adaptive method to incorporate new uniformity measurements in each step and two design acquisition functions to improve the selection of the most promising experimental setting in terms of minimizing the variance. We study the performance of the proposed approach through multiple synthetic numerical studies, as well as a case study based on data from slug flow crystallization experiments. Throughout these studies, the proposed approach shows competitive performance in identifying the best experimental setting.

The Contribution of 2D and 3D Geometric Morphometrics to Lithic Taxonomies: Testing Discrete Categories of Backed Flakes from Recurrent Centripetal Core Reduction

AbstractPaleolithic lithic assemblages are usually dominated by flakes and display a high degree of morphological variability. When analyzing Paleolithic lithic assemblages, it is common to classify flakes into categories based on their morphological and technological features, which are linked to the position of the flake in the reduction sequence and how removals are organized in a given production method. For the analysis of Middle Paleolithic lithic assemblages, two categories of flakes are commonly identified: core–edge flakes and pseudo-Levallois points. A third type, core–edge flakes with a limited back, is also commonly found in the archaeological literature, providing an alternative category whose definition does not match the two previous types but shares many of their morphological and technological features. The present study addresses whether these three flakes constitute discrete categories based on their morphological and technological attributes. 2D and 3D geometric morphometrics are employed on an experimental set composed of the three categories of flakes to quantify morphological variation. Machine learning models and principal components biplots are used to test the discreteness of the categories. The results indicate that geometric morphometrics succeed in capturing the morphological and technological features that characterize each type of product. Pseudo-Levallois points have the highest discreteness of the three technological products, and while some degree of mixture exists between core edge flakes and core edge flakes with a limited back, they are also highly distinguishable. We conclude that the three categories are discrete and can be employed in technological lists of products for the analysis of lithic assemblages and that geometric morphometrics is useful for testing for the validity of categories. When testing these technological categories, we stress the need for well-defined and shared lithic analytical units to correctly identify and interpret the technical steps and decisions made by prehistoric knappers and to properly compare similarities and differences between stone tool assemblages. These are key aspects for current research in which open datasets are becoming more and more common and used to build interpretative techno-cultural models on large geographical scales. Now more than ever, lithic specialists are aware of the need to overcome differences in taxonomies between different school traditions.

The Replacement of Celestine (SrSO4) by Strontianite (SrCO3) in Aqueous Solution Studied In Situ and in Real Time Using Fluid-Cell Raman Spectroscopy

The replacement of celestine (SrSO4) by strontianite (SrCO3) has extensively been studied over the past few decades. It also represents an ideal reaction to in situ study the kinetic and fundamental mechanistic details of mineral replacement reactions in aqueous solutions via fluid-cell Raman spectroscopy. This technique allows us to study the reaction process in real time by continuously analysing the solution or by imaging the solid–liquid interface where the reaction takes place and while the replacement process is ongoing. Two sets of experiments were conducted, the first of which was carried out with celestine powder and an equimolar Na2CO3 solution in a heated fluid cell between 30 and 60 °C. The progress of the reaction was followed by Raman spectroscopic in situ measurements of the solution. Another experiment was performed with a polished cuboid cut from a single celestine crystal and a 1 M Na2CO3 solution in a fluid cell at room temperature (21 °C). In this experiment, the reaction was studied in situ, spatially resolved, and in real time. The results of both types of experiments revealed that the replacement occurs via a coupled dissolution–precipitation mechanism and that the evolution of the solution composition cannot fully be explained by a single rate law derived from a shrinking core model. By applying the model-independent time-to-a-given-fraction method, three kinetic regimes could be identified and the associated activation energies quantified.

Validation of a low-cost continuous renal replacement therapy dialysate fluid controller for experimental purposes

BackgroundContinuous renal replacement therapy (CRRT) support is crucial for critically ill patients and it is underexplored in specific situations. Experimental CRRT offers a means to gain insights into these scenarios, but the prohibitive cost of CRRT machines limits their accessibility. This study aimed to develop and validate a low-cost and precise dialysate controller for experimental CRRT.ResultsOur results demonstrate a commendable level of precision in affluent flow control, with a robust correlation (R2 = 0.99) for continuous flow and a strong correlation (R2 = 0.95) for intermittent flow. Additionally, we observed acceptable agreement with a bias = 3.4 mL (upper limit 95% = 43.9 mL and lower limit 95% = − 37 mL) for continuous flow and bias = − 20.9 mL (upper limit 95% = 54 mL and lower limit 95% = − 95.7 mL) for intermittent flow, in this way, offering a precise CRRT dose for the subjects. Furthermore, we achieved excellent precision in the cumulative ultrafiltration net (UFnet), with a bias = − 2.8 mL (upper limit 95% = 6.5 mL and lower limit 95% = − 12 mL). These results remained consistent even at low affluent flow rates of 8, 12, and 20 mL/min, which are compatible with CRRT doses of 25–30 mL/kg for medium-sized animals. Moreover, the acceptable precision of our findings persisted when the dialysate controller was subjected to high filter dialysate chamber pressure for an extended duration, up to 797 min.ConclusionsThe low-cost dialysate controller developed and tested in this study offers a precise means of regulating CRRT in experimental settings. Its affordability and accuracy render it a valuable instrument for studying CRRT support in unconventional clinical scenarios, particularly in middle-income countries’ experimental ICU laboratories.

Efficient Generation of Murine Chimeric Antigen Receptor (CAR)-T Cells.

Engineered cell therapies utilizing chimeric antigen receptor (CAR)-T cells have achieved remarkable effectiveness in individuals with hematological malignancies and are presently undergoing development for the treatment of diverse solid tumors. So far, the preliminary evaluation of novel CAR-T cell products has predominantly taken place in xenograft tumor models using immunodeficient mice. This approach is chosen to facilitate the successful engraftment of human CAR-T cells in the experimental setting. However, syngeneic mouse models, in which tumors and CAR-T cells are derived from the same mouse strain, allow evaluation of new CAR technologies in the context of a functional immune system and comprehensive tumor microenvironment (TME). The protocol described here aims to streamline the process of mouse CAR-T cell generation by presenting standardized methods for retroviral transduction and ex vivo T cell culture. The methods described in this protocol can be applied to other CAR constructs beyond the ones used in this study to enable routine evaluation of new CAR technologies in immune-competent systems.

Evaluation of anti-corrosion potential of Datura Metel plant extract on mild steel upon sulphuric acid exposure

ABSTRACT The present study was aimed to investigate the Datura metel plant extract’s anti-corrosion ability on mild steel against sulphuric acid. A comprehensive set of experiments using conventional methodology were employed for asserting the efficacy and velocity of corrosion inhibitory performance. It was found that the effectiveness of inhibition was 85.73%. The results showed that potentiodynamic polarisation causes inhibition regulated anodic responses. Both factors, linear polarisation resistance and corrosion current were improved by the inhibitors. The protective layer on a vulnerable surface has been induced by the phenomenon known as the ‘blanket effect’. The observed outcome may be attributed to the constraints imposed by the experimental methodology. The protective layer composition of mild steel was analysed using FTIR spectroscopy. Additionally, SEM analysis revealed the protective layer was neither smooth nor rough. In summary, the extract derived from Datura metel could be a potential green inhibitor to act as an anti-corrosion agent.

The variability in nociceptive flexion reflex threshold measurement is mostly caused by probabilistic effects of the estimation algorithms: a simulation study

Objectives The nociceptive flexion reflex (NFR) and its threshold are frequently used to investigate spinal nociception in humans. Since this threshold (NFRT) is a probabilistic measure, specific algorithms are used for NFRT estimation based on the stochastic occurrence of reflexes at different stimulus intensities. We used a validated simulation model of the NFR to investigate the amount of NFRT measurement variability induced by different estimation algorithms in a steady setting of reduced external influences. Methods We simulated the behavior of different estimation algorithms in subjects with an artificially steady baseline NFRT variability (standard deviation: 0 mA) or low baseline NFRT variability (standard deviation: 0.156 mA), equaling a quiet experimental setting. The obtained data were analyzed for NFRT measurement variability caused by the algorithms compared to the baseline variability reflecting other physiological influences. Results The standard deviation of the NFRT estimated by the different algorithms ranged between 0.381 and 3.464 mA with 96.8% to 99.6% of the measurement variability attributed to the algorithm used. Out of the investigated algorithms the dynamic staircase algorithm was most precise. Conclusion The NFRT measurement variability observed during quiet and steady experimental sessions is mostly caused by the properties of the estimation algorithms, due to the probabilistic nature of the reflex occurrence. Our results give reference for choosing the optimal estimation algorithm to improve measurement precision.

Abstract 19: Von Willebrand Factor Deficiency Improves Long Term Stroke Outcomes in Mice With Respiratory Infections

Background: Preceding respiratory tract infections (RTI) due to bacteria or virus has been linked to an increasing risk of stroke likely by potentiating an inflammatory immune response, endothelial dysfunction, platelet activation, and coagulopathy. Von Willebrand factor (VWF) plays a crucial role in hemostasis and thrombosis by promoting platelet adhesion to the subendothelial matrix and factor VIII stabilization. VWF activity is regulated by the protease ADAMTS13. Prior population-based studies suggest increased VWF levels and reduced ADAMTS13 activity as independent risk factors for stroke; however, a causal role of the VWF/ADAMTS13 axis in RTI-mediated stroke exacerbation is not investigated yet. Objective: To determine the causal role of the VWF/ADAMTS13 axis in a mouse model of preceding RTI and ischemic stroke. Methods: Wild-type (WT) mice were infected intranasally with sub-lethal doses of S. aureus or vehicle on days 0, 2, and 5. On day 6, infection was measured in the lungs (MRI), brain, and blood. VWF levels and ADTMTS13 activity were quantified by ELISA in the plasma on day 6 post-infection. In another set of experiments, on day 6 post-infection S. aureus -infected WT, Vwf -/- , Adamts13 -/- mice were subjected to transient (30 min) middle cerebral artery ischemia using a filament model. Functional outcomes (modified neurological severity score, rota-rod, corner, novel object recognition, and wire-hanging test) were measured in the same cohort of mice up to 4 weeks. Controls were mock-infected WT mice. Result: We demonstrate that a sustained S. aureus infection localized to lungs in WT mice, increases plasma VWF levels (~2-fold) and reduces ADAMTS13 activity that were associated with larger infarcts and worsened long-term functional outcomes (P&lt;0.05 vs. mock-infected mice). S. aureus infected VWF-deficient mice exhibited reduced infarcts and better long-term functional outcomes (P &lt;0.05 vs. S. aureus infected WT). In contrast, S. aureus infected ADAMTS13-deficient mice exhibited larger infarcts and worse long-term functional outcomes (P &lt;0.05 vs. S. aureus infected WT). Conclusion: In a model of respiratory tract infection during the week preceding stroke, VWF has a causal role in worsening stroke outcomes while ADAMTS13 is protective.

Improving the kinematic accuracy of a collaborative continuum robot by using flexure-hinges

Within various unstructured industrial environments, there is often the requirement to conduct remote engineering tasks, such as sampling the structure for analysis prior to decommissioning. Most existing tools are simply not dexterous enough to fulfil this task, and thus new technology is required. We describe here a simple, lightweight, and water-resistant collaborative dual-arm continuum robot system which can aid in this task. To improve the kinematic accuracy of the system, a class of flexible hinges have been combined with a conventional continuum robot configuration. The thickness and width of said flexible hinges can be adjusted to adapt to various tasks. Kinematic and stiffness models have further been developed, incorporating the influence of these flexible hinges. A set of experiments have been conducted to validate the proposed model and demonstrate the advantages of the platform. It was found that the kinematic accuracy of the continuum robot can be improved by a factor of around 10 with the aid of said hinges.

Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning.

Understanding the environments through interactions has been one of the most important human intellectual activities in mastering unknown systems. Deep reinforcement learning (DRL) has already been known to achieve effective control through human-like exploration and exploitation in many applications. However, the opaque nature of deep neural network (DNN) often hides critical information about feature relevance to control, which is essential for understanding the target systems. In this article, a novel online feature selection framework, namely, the dual-world-based attentive feature selection (D-AFS), is first proposed to identify the contribution of the inputs over the whole control process. Rather than the one world used in most DRL, D-AFS has both the real world and its virtual peer with twisted features. The newly introduced attention-based evaluation (AR) module performs the dynamic mapping from the real world to the virtual world. The existing DRL algorithms, with slight modification, can learn in the dual world. By analyzing the DRL's response in the two worlds, D-AFS can quantitatively identify respective features' importance toward control. A set of experiments is performed on four classical control systems in OpenAI Gym. Results show that D-AFS can generate the same or even better feature combinations than the solutions provided by human experts and seven recent feature selection baselines. In all cases, the selected feature representations are closely correlated with the ones used by underlying system dynamic models.

Pivotal role of myeloid-derived suppressor cells in infection-related tumor growth.

In this study, we investigated infection-related tumor growth, focusing on myeloid-derived suppressor cells (MDSCs) in clinical and experimental settings. In the clinical study, a total 109 patients who underwent gastrectomy or esophagectomy were included. Blood samples were collected from a preoperative time point through 3 months after surgery, and MDSCs were analyzed using flow cytometry. In animal experiments, peritonitis model mice were created by CLP method. We investigated the number of splenic MDSCs in these mice using flow cytometry. Malignant melanoma cells (B16F10) were inoculated on the back of the mice, and tumor growth was monitored. We compared the level of MDSC infiltration around the tumor and the migration ability between CLP and sham-operated mice-derived MDSCs. Finally, we focused on PD-L1+ MDSCs to examine the effectiveness of anti-PD-L1 antibodies on tumor growth in CLP mice. In patients with postoperative infectious complication, MDSC number was found to remain elevated 3 months after surgery, when the inflammatory responses were normalized. CLP mice showed increased numbers of MDSCs, and following inoculation with B16F10 cells, this higher number of MDSCs was associated with significant tumor growth. CLP-mice-derived MDSCs had higher levels of accumulation around the tumor and had more enhanced migration ability. Finally, CLP mice had increased numbers of PD-L1+ MDSCs and showed more effective inhibition of tumor growth by anti-PD-L1 antibodies compared to sham-operated mice. Long-lasting enhanced MDSCs associated with infection may contribute to infection-related tumor progression.

An Effective Video Transformer With Synchronized Spatiotemporal and Spatial Self-Attention for Action Recognition.

Convolutional neural networks (CNNs) have come to dominate vision-based deep neural network structures in both image and video models over the past decade. However, convolution-free vision Transformers (ViTs) have recently outperformed CNN-based models in image recognition. Despite this progress, building and designing video Transformers have not yet obtained the same attention in research as image-based Transformers. While there have been attempts to build video Transformers by adapting image-based Transformers for video understanding, these Transformers still lack efficiency due to the large gap between CNN-based models and Transformers regarding the number of parameters and the training settings. In this work, we propose three techniques to improve video understanding with video Transformers. First, to derive better spatiotemporal feature representation, we propose a new spatiotemporal attention scheme, termed synchronized spatiotemporal and spatial attention (SSTSA), which derives the spatiotemporal features with temporal and spatial multiheaded self-attention (MSA) modules. It also preserves the best spatial attention by another spatial self-attention module in parallel, thereby resulting in an effective Transformer encoder. Second, a motion spotlighting module is proposed to embed the short-term motion of the consecutive input frames to the regular RGB input, which is then processed with a single-stream video Transformer. Third, a simple intraclass frame interlacing method of the input clips is proposed that serves as an effective video augmentation method. Finally, our proposed techniques have been evaluated and validated with a set of extensive experiments in this study. Our video Transformer outperforms its previous counterparts on two well-known datasets, Kinetics400 and Something-Something-v2.

Sensitivity analysis of multipactor susceptibility zone to variations in secondary electron yield values

We present a sensitivity analysis of the multipactor susceptibility zones to variations in the secondary electron yield (SEY) of materials, specifically focusing on the first and second unity crossover points of SEYs. In conducting this research, we leveraged our semi-analytic approach, which allows for the rapid prediction of the full multipactor zones with enhanced accuracy. Using this approach, we unveil several unique features of multipactor susceptibility zones, including the infinite extension of different-order multipactor zones and the overlap between them. Building upon this prediction capability, our results complement previous findings on the same topic and reveal that the multipactor zones depend not only on the first crossover point but also on the second crossover point of SEY, which this latter predominantly impacts multipactor susceptibility zones for low SEY materials. To validate our predictions, we present two distinct sets of multipactor experiments, providing empirical support for our results.

POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

A Semantic Tree-Based Fast-Moving Object Trajectory Tracking Algorithm for Table Tennis

Table tennis is a popular sport around the world. A key technology in table tennis education and analysis system is reconstructing the trajectory of the fast-moving ball from videos. Typically the table tennis ball is too small and barely visible in the video, making it difficult to be recognized directly by detection models like YOLO. However, table tennis balls usually has obvious motion features, which are usually not found in similar false targets. It inspired the authors to first find all candidate targets and then use the motion features of table tennis ball to select them out. In this article, the authors propose a tree-based algorithm named T-FORT to track the ball and reconstruct its trajectory. Specifically, they consider all the possible objects in a tree-framework, and identify the real target by integrating visual features and moving patterns. The authors conduct a set of experiments on three datasets to evaluate the effectiveness and performance of the proposed algorithm. The experimental results show that the proposed method is more precise than existing algorithms, and is robust in various scenarios.