Crowded Space Research Articles

How different face mask types affect interpersonal distance perception and threat feeling in social interaction.

Due to the easing of the pandemic, public policies no longer mandated people to wear masks. People can choose to no wear or wear different types of masks based on personal preferences and safety perceptions during daily interaction. Available information about the influence of face mask type on interpersonal distance (IPD) by different aging populations is still lacking. Thus, this study aimed to investigate the face mask type (no wear, cloth, medical and N95 mask) and age group effect of avatars (children, adults and older adults) on IPD perception, threat feeling and physiological skin conductance response under active and passive approaching. One hundred participants with a range from 20 to 35years old were recruited for this study. Twelve avatars (three age groups*four face mask conditions) were created and applied in a virtual reality environment. The results showed that age group, mask type and approach mode had significant effects on IPD and subjective threat feeling. A non-significant effect was found on skin conductance responses. Participants maintained a significantly longer IPD when facing the older adults, followed by adults and then children. In the passive approach condition, people tended to maintain a significantly greater comfort distance than during the active approach. For the mask type effect, people kept a significantly largest and shortest IPD when facing an avatar with no mask or the N95 mask, respectively. A non-significant IPD difference was found between the N95 and medical mask. Additionally, based on the subjective threat feeling, facing an avatar wearing a medical mask generated the lowest threat feeling compared to the others. The findings of this study indicated that wearing medical masks provided a benefit in bringing people closer for interaction during specific situations. Understanding that mask-wearing, especially medical one, brought to shortest IPD when compared to the unmasked condition can be utilized to enhance safety measures in crowded public spaces and health-care settings. This information could guide the development of physical distancing recommendations, taking into account both the type of mask and the age groups involved, to ensure the maintenance of appropriate distances.

A comparative analysis of non-pharmaceutical interventions for preventing the respiratory syncytial virus in 30 European countries.

Respiratory syncytial virus (RSV) is a leading cause of childhood respiratory infections. Non-pharmaceutical interventions (NPIs) can help to reduce RSV transmission and our aim was to provide an overview of recommended NPIs across Europe during the 2022-2023 epidemic season. The webpages of national European public health agencies and ministries were reviewed and the information they provided on RSV prevention was compared with the December 2022 guidelines from the European Centre for Disease Prevention and Control. We examined 30 countries, leading to 21 issued recommendations for RSV prevention through institutional channels, including six that were specific for the 2022-2023 season. The top five recommendations were: hand and respiratory hygiene (95%), avoiding crowded spaces (67%), staying at home when ill (62%), cleaning household items (57%) and limiting contact with sick people (57%). They also included: face masks (33%), ventilating indoor spaces (29%), properly disposing of contaminated material (13%) and keeping siblings home from preschool if there was a newborn infant in the family (10%). There was significant heterogeneity in the NPIs recommended by different countries during RSV epidemics. Ongoing evaluation is essential to optimise the effectiveness of NPIs and adapt to changing RSV patterns.

Entropy and chirality in sphinx tilings

As a toy model of chiral interactions in crowded spaces, we consider sphinx tilings in finite regions of the triangular lattice. The sphinx tiles, hexiamonds composed of six equilateral triangles in the shape of a stylized sphinx, come in left and right enantiomorphs. Regions scaled up from the unit sphinx by an integer factor () require tiles of both chiral forms to produce tilings, including crystalline, quasicrystalline, and fully disordered tilings. For frames up to order 13, we describe methods that permit exact enumeration and computation of partition functions using , , and algorithms. For larger frames, we introduce a Monte Carlo method to sample typical tilings. The key to the latter is the identification of fundamental shapes () that admit multiple tilings and which allow a rejection-free MC simulation. Published by the American Physical Society 2024

Addressing ocean planning challenges in a highly crowded sea space: a case study for the regional sea of Catalonia (Western Mediterranean).

This study performs an exploratory analysis of current-future sustainability challenges for ocean planning for the regional seas of Catalonia located in the Western Mediterranean (Spain). To address the challenges we develop an Maritime Spatial Planning (MSP)-oriented geodatabase of maritime activities and deploy three spatial models: 1) an analysis of regional contribution to the 30% protection commitment with Biodiversity Strategy 2030; 2) a spatial Maritime Use Conflict (MUC) analysis to address current and future maritime activities interactions and 3) the StressorGenerator QGIS application to locate current and anticipate future sea areas of highest anthropogenic stress. Results show that the i) study area is one of the most protected sea areas in the Mediterranean (44-51% of sea space protected); ii) anthropogenic stressors are highest in 1-4 nautical miles coastal areas, where maritime activities agglomerate, in the Gulf of Roses and Gulf of Saint Jordi. iii) According to the available datasets commercial fishery is causing highest conflict score inside protected areas. Potential new aquaculture sites are causing highest conflict in Internal Waters and the high potential areas for energy cause comparably low to negligible spatial conflicts with other uses. We discuss the added value of performing regional MSP exercises and define five challenges for regional ocean sustainability, namely: Marine protection beyond percentage, offshore wind energy: a new space demand, crowded coastal areas, multi-level governance of the regional sea and MSP knowledge gaps.

A Microfluidic Perfusion Culture Setup to Investigate Cell Migration in 3D Constrictions

AbstractCell migration is a fundamental process underlying the morphological maturation of organs, but also in disease‐related conditions such as cancer. Cells are able to migrate through crowded space and a tight extracellular matrix (ECM). Passing through a constriction, a cell deforms strongly, including its nucleus. Such nuclear deformation can lead to changes in the 3D‐genomic architecture, and putatively, DNA methylation. However, the specific effects of deformation on cells are not well understood. It is highly desired to establish an ex vivo methodology to induce well‐defined cell deformation in complex geometrical constrictions. This study introduces a microfluidic system for the study of migrating cells in precisely controlled geometrical confinement. A procedure for coating, seeding of cerebellar granule cells, and perfusion culture is presented. By leveraging direct laser writing, channels with smooth, anisotropically curved surfaces on the cell‐scale can be fabricated. The system consists of constriction channels with a radius of 2 or 4 µm for the cells to pass through. This corresponds to a compression of the nucleus to 3.5% and 14.2% of its undeformed cross‐sectional area, respectively. The system can be used to investigate the influence of confinement geometry on the migration behavior and transcriptome of various cell types.

Governance by numbers 2.0: policy brokerage as an instrument of global governance in the era of information overload

ABSTRACT The article investigates how and when the two first movers in knowledge-based regulation – the OECD and the World Bank – developed policy brokerage as an instrument of global governance in the education sector. We also examine how their target clientele – national governments – responds to this instrument. Given the surplus of research evidence in today's digital economy, intergovernmental organisations have the challenge of standing out as trusted and credible knowledge brokers in a crowded space. The authors make the case for a comparative research programme – tentatively labeled ‘Governance by Numbers 2.0’ – that is informed by a multidisciplinary (history, political science, interdisciplinary policy studies) interpretive framework and that advances a transnational, relational method of inquiry which draws attention to the global/national nexus.

Ventilation shutdown and the breath-taking violence of infectious disease emergency management in industrial livestock production

Powerful ventilation systems are a crucial technology in industrial livestock production, mitigating the unhealthy ambient conditions that result from great densities of animal bodies, biowastes, and chemical agents, and enabling the rapid production of massive quantities of flesh and eggs in such crowded indoor spaces. But bad air is just one of many biophysical and technoscientific challenges managed in these spaces, chief among them the ever-present risks of infectious disease transmission and evolution that threaten animal health and productivity and pose untold risks for humans. This article examines the intersection of these two central problems, where ventilation systems that are normally used to manage bad air within enclosures have been repurposed in the context of disease outbreaks to quickly and cheaply kill infected populations by hyperthermia. An analysis of this nascent practice, euphemistically termed “ventilation shutdown,” shows how governments and publicly funded scientific institutions have worked with private industry to develop and systematize the use of this and other technologies of mass death to respond to infectious disease emergencies, a dynamic that, we argue, sheds new light on both the precarity and the violence of industrial livestock production.

Study on the infection control performance under interactive cascade ventilation: A focus on a conference room environment

With the rise of respiratory infectious diseases, many scholars focus on finding an appropriate ventilation method to effectively improve the indoor environmental protection effect, especially for crowded spaces, which can cause a widespread spread of the epidemic. Interactive cascade ventilation (ICV), as a recently proposed ventilation method, has presented advantages in thermal comfort, infection control and energy consumption. This study discusses the effect of the outlet/inlet locations on infection control performance under interactive cascade ventilation in a conference room with an experimentally verified model. Ninety-five simulations are carried out by considering changes in the location of the outlet/inlet opening and infection source. Particle removal efficiency (ε), predicted infection risk (Pi) and contaminant dispersion index (CDI) are introduced to evaluate the infection control performance comprehensively. The simulation results show that double-side supply air outlets coupled with double-side return air inlets can reduce Pi by 51.1 % compared with other ventilation strategies investigated in this paper. It indicates that ventilation zoning design is more effective for reducing cross-infection in dense spaces. The findings in this study can efficiently provide new insights for the actual application of ventilation strategies in conference rooms under epidemic normalization.

COVID-19 Testing: Impact of Omicron variants on diagnostic reliability

Rapid antigen testing continues to be recommended across the world for the prevention of transmission of SARS-CoV-2 for anyone with COVID-19 symptoms, recent exposure, those visiting high-risk individuals or those recently in crowded spaces while traveling without wearing a mask. Evidence of decreasing diagnostic reliability of individual tests and potential benefits and harms of non-targeted testing is increasingly becoming a concern across the world. Recent research has found multiple commonly used rapid antigen tests have diagnostic sensitivities below 30%, with sensitivities at or near 0% the first 48 hours of infection, when using polymerase chain reaction (PCR) test positivity as the gold standard. Common rapid antigen test sensitivities are lower in individuals previously infected with COVID-19 or in the setting of the BA.4 and BA.5 omicron subvariants. The changes in the rapid antigen test diagnostic sensitivities is a concern with the rapidly changing Omicron variants entering the population as an effective screening tool. With the decreasing diagnostic reliability, potential detrimental effects and lack of evidence to support continued community-based testing, what is the most up to date advice for institutions and providers regarding testing? In this review, we are critically appraising the current utility of community-based rapid antigen testing for COVID-19.

Optimized charging-station placement and UAV trajectory for enhanced uncertain target detection in intelligent UAV tracking systems

Unmanned Aerial Vehicle (UAV) technology is proposed to improve social safety, provide specialized services, and improve overall well-being in crowded indoor spaces. The deployment of drones in indoor environments can improve emergency response time, offer various wireless services, allow efficient tracking, and improve awareness in crowded scenarios. In this paper, we propose a UAV-based tracking framework that relies on energy-limited UAVs that attempts to determine the appropriate placement of UAV charging stations (CHSs) and design a UAV path planning strategy to effectively carry out detection/tracking tasks of uncertain phenomena. The proposed framework comprises a CHS placement method and a UAV path planning algorithm. The CHS placement method attempts to find the optimal placement of a given number of available CHSs so that the energy consumed by a UAV to reach the nearest CHS is reduced. This, consequently, preserves the UAV’s energy, reducing the time required to return to the CHS and the period of none-tracking during the return time to the CHS. This can extend the tracking mission time and enhance detection performance. Based on the obtained optimal CHS placement, we design a reinforcement learning (RL)–based UAV trajectory algorithm to effectively detect and track a target (event of interest) with unknown behavior. The proposed RL-based UAV trajectory algorithm leverages long-term spatio-temporal behavior knowledge of uncertain targets (i.e., observed and learned events) to improve detection accuracy. Improving the detection of uncertain targets leads to better decision-making, faster responses, and improved security, safety, and efficiency in applications such as surveillance, defense, and search and rescue. The simulation results demonstrate the superior detection accuracy achieved by the proposed framework. Compared to a reference RL-based approach, the proposed algorithm achieves up to 65% higher detection accuracy in symmetric monitored areas and 20% increased accuracy in asymmetric monitored areas.

A YOLOv5-Based Model for Real-Time Mask Detection in Challenging Environments

This study aims to improve the YOLOV5 (You Only Look Once) model for real-time mask detection in complex scenarios, such as multiple objects, unclear features, and obstructions. The experiment builds upon the initial model by refining its input structure and incorporating the mosaic data augmentation technique. Additionally, it employs Spatial Pyramid Pooling Fusion (SPPF) to amalgamate local and global features at the featherMap level. The detection process is further enhanced by optimizing the overlap computation between detection frames and target frames. This refinement uses the Generalized Intersection over Union (GioU) loss function to enhance target accuracy. The system is built in PyCharm, and the dataset consists of 6005 images with masked and unmasked faces for training. The training set and test set maintain an 8:2 ratio. The enhanced facial feature extraction network efficiently detects mask-wearers in real-time, maintaining high recognition rates in crowded public spaces, supporting real-time virus transmission control in communal areas.The enhanced model achieved an impressive 92.9% recognition accuracy in this experiment, surpassing other detection models, highlighting its high quality and effectiveness.

Potential social facilitation through song in bird communities

The main function of birdsong is to attract mates and defend territories among conspecifics, but little is known about the potential of this behavior in generating interactions among co-existing bird species that signal at the same time. Different hypotheses have been put forward to explain the mechanisms that govern signaling in crowded acoustic spaces, including competition for acoustic space, interspecific territoriality, environmental filters, and acoustic social facilitation. We tested these hypotheses in a forest bird community by studying the behavioral, acoustic, and ecological characteristics of 13 co-occurring species. We used passive recordings to assess the magnitude and the determinants of heterospecific song co-occurrence (i.e., when different species sing at the same time and place), and playback experiments to quantify the propensity of birds to sing if stimulated by heterospecific songs. Birds more frequently sang with species with dissimilar songs. In the breeding season, they also actively sang after the playback of other species, especially if these had dissimilar songs, and occasionally after non-avian unfamiliar playbacks. Heterospecific songs may trigger song utterance because they indicate a predator-free environment, or reduce predation risk through dilution effects, and these benefits may be maximized if there is little acoustic similarity. In line with ecological theory on community assembly mechanisms, these results are compatible with limiting similarity (species with similar songs sing at different times) and suggests social facilitation between phenotypically dissimilar species. These potential biotic interactions through songs may represent a mechanism that leads to bird species coexistence and are worth exploring at the community level.Significance statementIt is generally assumed that bird species avoid acoustic overlap with birds singing similar songs, but there is also evidence of active interspecific territoriality through song displays between pairs of species. Community-wide responses through songs have never been investigated in birds, and to fill this knowledge gap we performed both observations and experiments on multiple coexisting species. Birds tended to sing with species with more dissimilar songs and they were also actively stimulated to sing when the songs of species with dissimilar songs were played back. An interaction between species with dissimilar songs therefore emerged, potentially yielding anti-predatory benefits. Such active behavior, which would have gone unnoticed if observations only were performed, involves multiple species, and therefore may represent a community-wide mechanism of species coexistence.

호텔과 레스토랑 종사원의 혼잡지각이 직무열의 및 고객지향성에 미치는 영향

Purpose: The purpose of this study is to analyze the effect of hotel and restaurant employees' congestion perception on job engagement and customer orientation.
 Method: Simple regression analysis, multiple regression analysis, and correlation analysis were conducted to analyze the relationship between the validity and reliability of each variable.
 Results: In this study, it was found that among hotel and restaurant employees' congestion perception, human congestion perception increases job engagement and can increase customer orientation accordingly, but spatial congestion perception decreases employees' job engagement and consequently can have a negative effect on customer orientation.
 Conclusion: The significance of this study can be found in that it confirmed the effect of crowded service space on hotel and restaurant employees while emphasizing the importance of service space for efficient service provision of employees in service companies.
 Interdisciplinary Research Implications: This study is targeted at hotels and restaurants located in Seoul among the tourism industry, and it is considered that it can be spread to applied research if interdisciplinary research on congestion perception in the leisure sector is carried out in connection with the aspect of physical education in the future.

Machine learning-driven SERS fingerprinting of disintegrated viral components for rapid detection of SARS-CoV-2 in environmental dust

Surveillance of airborne viruses in crowded indoor spaces is crucial for managing outbreaks, as highlighted by the SARS-CoV-2 pandemic. However, the rapid and on-site detection of fast-mutating viruses, such as SARS-CoV-2, in complex environmental backgrounds remains challenging. Our study introduces a machine learning (ML)-driven surface-enhanced Raman spectroscopy (SERS) approach for detecting viruses within environmental dust matrices. By decomposing intact virions into individual structural components via a Raman-background-free lysis protocol and concentrating them into nanogap SERS hotspots, we significantly enhance the SERS signal intensity and fingerprint information density from viral structural components. Utilizing Principal Component Analysis (PCA), we establish a robust connection between the SERS data of these structural components and their biological sequences, laying a solid foundation for virus detection through SERS. Furthermore, we demonstrate reliable quantitative detection of SARS-CoV-2 using identified SARS-CoV-2 peaks at concentrations down to 102 pfu/ml through Gaussian Process Regression (GPR) and a digital SERS methodology. Finally, applying a Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) algorithm, we identify SARS-CoV-2, influenza A virus, and Zika virus within an environmental dust background with over 86% accuracy. Therefore, our ML-driven SERS approach holds promise for rapid environmental virus monitoring to manage future outbreaks.

Detection of SARS-CoV-2 in aerosols in long term care facilities and other indoor spaces with known COVID-19 outbreaks

Coronavirus outbreaks are likely to occur in crowded and congregate indoor spaces, and their effects are most severe in vulnerable long term care facilities (LTCFs) residents. Public health officers benefit from tools that allow them to control COVID-19 outbreaks in vulnerable settings such as LTCFs, but which could be translated in the future to control other known and future virus outbreaks. This study aims to develop and test a methodology based on detection of SARS-CoV-2 in aerosol samples collected with personal pumps that could be easily implemented by public health officers. The proposed methodology was used to investigate the levels of SARS-CoV-2 in aerosol in indoor settings, mainly focusing on LTCFs, suffering COVID-19 outbreaks, or in the presence of known COVID-19 cases, and targeting the initial days after diagnosis.Aerosol samples (N = 18) were collected between November 2020 and March 2022 in Castelló (Spain) from LTCFs, merchant ships and a private home with recently infected COVID-19 cases. Sampling was performed for 24-h, onto 47 mm polytetrafluoroethylene (PTFE) and quartz filters, connected to personal pumps at 2 and 4 L/min respectively. RNA from filters was extracted and SARS-CoV-2 was determined by detection of regions N1 and N2 of the nucleocapsid gene alongside the E gene using RT-PCR technique.SARS-CoV-2 genetic material was detected in 87.5% samples. Concentrations ranged ND-19,525 gc/m3 (gene E). No genetic traces were detected in rooms from contacts that were isolated as a preventative measure. Very high levels were also measured at locations with poor ventilation. Aerosol measurement conducted with the proposed methodology provided useful information to public health officers and contributed to manage and control 12 different COVID-19 outbreaks.SARS-CoV-2 was detected in aerosol samples collected during outbreaks in congregate spaces. Indoor aerosol sampling is a useful tool in the early detection and management of COVID-19 outbreaks and supports epidemiological investigations.

The niche and value creation mechanism of the innovation ecosystem of crowd innovation space: a multi-case study

ABSTRACT Twelve crowd innovation spaces (from China) are selected as case samples, and the niche distribution model of the crowd innovation space’s innovation ecosystem is constructed based on structural hole theory. Further, this research reveals the interactive relationship and value creation mechanism among the innovation ecosystem's components from the value co-creation theory perspective. The research draws the following conclusions from three aspects: (1) The niche of the components of the crowd innovation space ecosystem is characterised by multiple levels, which can be divided into the core layer, convergence layer, and support layer. (2) In the innovation ecosystem, crowd innovation space is at the convergence layer. By filling structural holes, the innovation ecosystem can reach the ‘no holes’ state. Thus, it can promote the connection of the components of the innovation ecosystem to form a network of innovation resources such as knowledge and technology; (3) The value creation in the crowd innovation space’s innovation ecosystem is two-dimensional. Based on whether there is direct interaction or not, value creation can be divided into value co-creation and value auxiliary-creation. Relationship embeddedness and value co-creation are two important mechanisms of value creation in the innovation ecosystem of crowd innovation space.

Facilitating cell segmentation with the projection-enhancement network

Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures. However, limitations in microscopy systems or efforts to prevent phototoxicity commonly require recording sub-optimally sampled data that greatly reduces the utility of such 3D data, especially in crowded sample space with significant axial overlap between objects. In such regimes, 2D segmentations are both more reliable for cell morphology and easier to annotate. In this work, we propose the projection enhancement network (PEN), a novel convolutional module which processes the sub-sampled 3D data and produces a 2D RGB semantic compression, and is trained in conjunction with an instance segmentation network of choice to produce 2D segmentations. Our approach combines augmentation to increase cell density using a low-density cell image dataset to train PEN, and curated datasets to evaluate PEN. We show that with PEN, the learned semantic representation in CellPose encodes depth and greatly improves segmentation performance in comparison to maximum intensity projection images as input, but does not similarly aid segmentation in region-based networks like Mask-RCNN. Finally, we dissect the segmentation strength against cell density of PEN with CellPose on disseminated cells from side-by-side spheroids. We present PEN as a data-driven solution to form compressed representations of 3D data that improve 2D segmentations from instance segmentation networks.

Real‐time 3D multi‐pedestrian detection and tracking using 3D LiDAR point cloud for mobile robot

AbstractMobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian‐rich spaces. This study proposes real‐time, accurate, three‐dimensional (3D) multi‐pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected‐component algorithm. The multi‐pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.

A roadmap for the future of crowd safety research and practice: Introducing the Swiss Cheese Model of Crowd Safety and the imperative of a Vision Zero target

Crowds can be subject to intrinsic and extrinsic sources of risk, and previous records have shown that, in the absence of adequate safety measures, these sources of risk can jeopardise human lives. To mitigate these risks, we propose that implementation of multiple layers of safety measures for crowds—what we label The Swiss Cheese Model of Crowd Safety—should become the norm for crowd safety practice. Such system incorporates a multitude of safety protection layers including regulations and policymaking, planning and risk assessment, operational control, community preparedness, and incident response. The underlying premise of such model is that when one (or multiple) layer(s) of safety protection fail(s), the other layer(s) can still prevent an accident. In practice, such model requires a more effective implementation of technology, which can enable provision of real-time data, improved communication and coordination, and efficient incident response. Moreover, implementation of this model necessitates more attention to the overlooked role of public education, awareness raising, and promoting crowd safety culture at broad community levels, as one of last lines of defence against catastrophic outcomes for crowds. Widespread safety culture and awareness has the potential to empower individuals with the knowledge and skills that can prevent such outcomes or mitigate their impacts, when all other (exogenous) layers of protection (such as planning and operational control) fail. This requires safety campaigns and development of widespread educational programs. We conclude that, there is no panacea solution to the crowd safety problem, but a holistic multi-layered safety system that utilises active participation of all potential stakeholders can significantly reduce the likelihood of disastrous accidents. At a global level, we need to target a Vision Zero of Crowd Safety, i.e., set a global initiative of bringing deaths and severe injuries in crowded spaces to zero by a set year.

An Intelligent Grazing Development Strategy for Unmanned Animal Husbandry in China

Grazing is the most important and lowest cost means of livestock breeding. Because of the sharp contradiction between the grassland ecosystem and livestock, the grassland ecosystem has tended to degrade in past decades in China; therefore, the ecological balance of the grassland has been seriously damaged. The implementation of grazing prohibition, rotational grazing and the development of a large-scale breeding industry have not only ensured the supply of animal husbandry products, but also promoted the restoration of the grassland ecosystem. For the large-scale breeding industry, the animal welfare of livestock cannot be guaranteed due to the narrow and crowded space, thus, the production of the breeding industry usually has lower competitiveness than grazing. Disorderly grazing leads to grassland ecological crises; however, intelligent grazing can not only ensure animal welfare, but also fully improve the competitiveness of livestock husbandry products. Under the development of urbanization, the workforce engaged in grazing and breeding in pastoral areas is gradually lost. Intelligent grazing breeding methods need to be developed and popularized. This paper focuses on intelligent grazing, reviews grass remote sensing and aerial seeding, wearable monitoring equipment of livestock, UAV monitoring and intelligent grazing robots, and summarizes the development of intelligent grazing elements, exploring the new development direction of automatic grazing management with the grazing robot at this stage.