Xiaoling Zhou | Computer Vision | Best Scholar Award

Published on by Computer Vision Awards

Best Scholar Award

Xiaoling Zhou
Peking University, China

Xiaoling Zhou
Affiliation Peking University
Country China
Scopus ID 57219746593
Documents 22
Citations 85
h-index 6
Subject Area Computer Vision
Event Global Tech Excellence Awards
ORCID 0000-0002-7305-1779

Xiaoling Zhou is a researcher whose documented scholarly work spans machine learning, graph neural networks, interpretable learning, and human–machine dialogue. Her publications demonstrate an interest in developing computational methods that improve model understanding, robustness, and interaction quality. The profile is considered in the context of the Best Scholar Award and the supplied academic record.

Abstract

Xiaoling Zhou’s documented research profile reflects work across machine learning, graph neural networks, interpretable learning, and human–machine dialogue. Her publications address methodological questions involving sample weighting, graph structure, uncertainty, natural-language interaction, and computational learning systems. The research record includes studies published in established scholarly venues and indexed through academic databases. In particular, her work investigates interpretable weighting mechanisms for learning systems, approaches for reducing ineffective graph edges in Bayesian graph neural network approximations, and methods for making human–machine dialogue more natural through user-choice inference and answer generation. These contributions collectively indicate a research trajectory concerned with improving the reliability, interpretability, adaptability, and practical usefulness of intelligent computational methods.[1][2][3]

Keywords

Computer Vision; Machine Learning; Graph Neural Networks; Bayesian Learning; Interpretable Learning; Sample Weighting; Human–Machine Dialogue; Natural Language Processing; Reverse Question Answering; Artificial Intelligence.

Introduction

Xiaoling Zhou’s research is situated within artificial intelligence and computational learning, with documented studies addressing interpretable learning, graph neural networks, and intelligent dialogue. These areas are connected by a common interest in improving how computational models learn from data, represent information, and respond to users. Her publications provide evidence of this interdisciplinary direction.[1][2][3]

Research Profile

The supplied academic profile records 22 documents, 85 citations, and an h-index of 6, with Computer Vision identified as the principal subject area. Her documented publications also extend into machine learning, graph-based modeling, and dialogue systems. These indicators provide a quantitative and qualitative basis for describing an active computational research profile.[1][2][3]

Research Contributions

Zhou’s documented contributions include an interpretable framework for examining sample weighting, a DropNEdge approach for addressing ineffective graph edges and over-smoothing in graph neural networks, and UCINet and SAGNet methods for user-choice inference and answer generation in dialogue. Together, these studies address learning effectiveness, model structure, uncertainty, and interaction quality.[1][2][3]

Publications

The supplied publication record includes studies appearing in IEEE Transactions on Knowledge and Data Engineering, Lecture Notes in Computer Science, and Knowledge-Based Systems. The works cover interpretable sample weighting, Bayesian graph neural network approximation, and natural human–machine dialogue. Their publication venues and DOI records provide traceable scholarly references for evaluating the research portfolio.[1][2][3]

  • Investigating the Sample Weighting Mechanism Using an Interpretable Weighting Framework — IEEE Transactions on Knowledge and Data Engineering, 36(5), 2041–2055. DOI: 10.1109/TKDE.2023.3316168.[1]
  • Drop “Noise” Edge: An Approximation of the Bayesian GNNs — Pattern Recognition, Lecture Notes in Computer Science, pp. 59–72. DOI: 10.1007/978-3-031-02444-3_5.[2]
  • Increasing naturalness of human–machine dialogue: The users’ choices inference of options in machine-raised questions — Knowledge-Based Systems, 243, 108485. DOI: 10.1016/j.knosys.2022.108485.[3]

Research Impact

The supplied profile reports 85 citations and an h-index of 6 across 22 documents, indicating measurable scholarly visibility. Beyond these metrics, the cited studies address practical and methodological problems in artificial intelligence, including data weighting, graph learning, uncertainty, and dialogue understanding. Their themes support continued relevance to intelligent computational systems and applications.[1][2][3]

Award Suitability

Based on the supplied research record, Xiaoling Zhou demonstrates characteristics relevant to a Best Scholar Award, including a sustained publication record, measurable citation activity, and research spanning several connected areas of artificial intelligence. The documented studies show methodological engagement with learning systems and intelligent interaction, providing a reasonable scholarly basis for recognition.[1][2][3]

Conclusion

Xiaoling Zhou’s documented scholarship presents a coherent contribution to artificial intelligence through studies of interpretable learning, graph neural networks, and human–machine dialogue. The combination of publication activity, citation indicators, and technically focused research provides a substantive basis for academic recognition. The record also suggests continued potential for interdisciplinary development in intelligent systems.[1][2][3]

References

  1. Zhou, X., Wu, O., & Li, M. (2024). Investigating the sample weighting mechanism using an interpretable weighting framework. IEEE Transactions on Knowledge and Data Engineering, 36(5), 2041–2055.
    https://ieeexplore.ieee.org/document/10254261
  2. Zhou, X., & Wu, O. (2022). Drop “Noise” Edge: An approximation of the Bayesian GNNs. In Pattern Recognition: 6th Asian Conference, ACPR 2021, Revised Selected Papers (pp. 59–72). Springer.
    https://link.springer.com/chapter/10.1007/978-3-031-02444-3_5
  3. Zhou, X., Wu, O., & Jiang, C. (2022). Increasing naturalness of human–machine dialogue: The users’ choices inference of options in machine-raised questions. Knowledge-Based Systems, 243, 108485. Elsevier.
    https://www.sciencedirect.com/science/article/abs/pii/S0950705122002064
  4. Elsevier. (n.d.). Scopus author details: Xiaoling Zhou, Author ID 57219746593. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57219746593

Tong Zheng | Image Processing and Enhancement | Research Excellence Award

Published on by Computer Vision Awards

Research Excellence Award

Tong Zheng
Affiliation Beijing Technology and Business University
Country China
ORCID
0000-0003-2251-6844
Documents 27
Subject Area Image Processing and Enhancement
Event
Global Tech Excellence Awards

Tong Zheng is a researcher affiliated with Beijing Technology and Business University, China, with scholarly contributions focused on image processing, image enhancement technologies, and computational visual analysis. The researcher has demonstrated academic engagement through peer-reviewed publications indexed in international databases, contributing to the advancement of digital image optimization methodologies and intelligent enhancement systems.[1]

Abstract

This article presents an academic overview of Tong Zheng and the researcher’s contributions to image processing and enhancement research. The profile evaluates scholarly productivity, publication visibility, citation indicators, and thematic contributions in computational imaging systems. The assessment also considers the researcher’s suitability for recognition under the Global Tech Excellence Awards framework based on measurable academic outputs and research relevance in emerging technological applications.[2]

Keywords

  • Image Processing
  • Image Enhancement
  • Computer Vision
  • Digital Imaging
  • Visual Computing
  • Computational Intelligence

Introduction

Image processing and enhancement have become critical research domains within computer science and artificial intelligence due to their broad applications in healthcare imaging, industrial automation, surveillance, and multimedia systems. Researchers working in this field contribute to the development of algorithms capable of improving image quality, extracting meaningful patterns, and supporting intelligent decision-making systems.[3]

Tong Zheng has contributed to this interdisciplinary research area through publications associated with digital image enhancement methodologies and computational visual systems. The researcher’s academic record reflects sustained participation in technological innovation and scholarly dissemination within indexed scientific platforms.[1]

Research Profile

The research profile of Tong Zheng demonstrates involvement in image enhancement, visual analytics, and digital processing technologies. The academic profile includes 27 indexed documents and measurable citation performance indicating growing visibility in computational imaging studies.[1]

The researcher’s publication record indicates interdisciplinary collaboration and technical specialization relevant to contemporary image enhancement applications. These research efforts align with emerging scientific priorities associated with machine intelligence, data interpretation, and adaptive visual systems.[4]

Research Contributions

Tong Zheng has contributed to the advancement of image enhancement algorithms and computational imaging methodologies through research involving image clarity optimization, feature extraction, and intelligent enhancement systems.[5]

The research contributions are relevant to applications requiring precision imaging, pattern recognition, and improved visual interpretation under varying environmental and computational conditions. Such contributions support technological progress in industrial imaging, multimedia analytics, and automated image processing environments.

Publications

Selected scholarly publications associated with Tong Zheng include contributions related to image enhancement systems, intelligent processing frameworks, and digital imaging technologies indexed in recognized scientific databases.[1]

  • Research involving computational image enhancement and adaptive filtering methodologies.[5]
  • Studies associated with digital image optimization and machine-assisted visual processing.
  • Scholarly contributions indexed through international scientific databases and researcher identity systems.[2]

Research Impact

The research impact associated with Tong Zheng can be observed through indexed publications, citation accumulation, and continued visibility within image processing scholarship. Citation metrics indicate that the researcher’s work has contributed to ongoing scientific discussions within computational imaging disciplines.[1]

The combination of publication productivity and interdisciplinary technical engagement supports the researcher’s growing academic profile within the field of image enhancement and intelligent processing systems.[4]

Award Suitability

Tong Zheng demonstrates characteristics consistent with eligibility for academic recognition under the Global Tech Excellence Awards. The researcher’s contributions to image processing and enhancement technologies reflect active scholarly participation in a technically significant and rapidly evolving scientific domain.

The combination of indexed research output, measurable citation indicators, and institutional affiliation with Beijing Technology and Business University supports the suitability of the researcher for consideration within technology-focused academic recognition programs.[1]

Conclusion

Tong Zheng has established an emerging scholarly presence within the field of image processing and enhancement through indexed publications, citation visibility, and interdisciplinary technological research activities. The academic profile reflects engagement with contemporary computational imaging challenges and demonstrates relevance to ongoing scientific developments in intelligent visual systems.[1]

Based on the available academic indicators and research focus areas, the researcher represents a suitable candidate for recognition within international technology and research excellence initiatives.

References

      1. ORCID. (n.d.). ORCID profile of Tong Zheng.
        https://orcid.org/0000-0003-2251-6844
      2. Semantic segmentation method for sparse point clouds based on straight flow completion and multi-feature fusion.
        https://www.mdpi.com/1424-8220/26/10/3056
      3. Task-driven pruning method for synthetic aperture radar target recognition convolutional neural network model.
        https://www.mdpi.com/1424-8220/25/10/3117
      4. A graph aggregation convolution and attention mechanism based semantic segmentation method for sparse lidar point cloud data.
        https://ieeexplore.ieee.org/document/10343142
      5. Global Tech Excellence Awards. (n.d.). Award evaluation and eligibility framework.
        https://globaltechexcellence.com/

     

Assoc. Prof. Dr. Md. Jakir Hossen | Deep Learning for Computer Vision | Excellence in Research Award

Published on by Computer Vision Awards
After Completing the Registration Process, your Biography will be displayed here.

Register by using your Entry ID: 8442

Register Here

Kun Chen | Machine Learning for Computer Vision | Research Excellence Award

Published on by Computer Vision Awards

Mr. Kun Chen | Machine Learning for Computer Vision | Research Excellence Award

East China Jiaotong University | China

Mr. Kun Chen is a postgraduate researcher at East China Jiaotong University, specializing in machine learning and data mining. His research focuses on clustering analysis and semi-supervised learning, contributing to advancing intelligent data-driven systems. He co-authored the article A Novel Semi-Supervised Clustering Algorithm Based on Ridge Regression with Optimal Scaling, published in Neurocomputing, demonstrating strong analytical and methodological innovation. Despite being in the early stage of his academic career, he shows promising potential through international collaboration and impactful research contributions aimed at improving data interpretation and decision-making across scientific and engineering domains.

Citation Metrics (ORCID)

View ORCID Profile

Featured Publications

Muhammad Bilal | Computer Vision for Robotics and Autonomous Systems | Editorial Board Member

Published on by Computer Vision Awards

Assoc. Prof. Dr. Muhammad Bilal | Computer Vision for Robotics and Autonomous Systems | Editorial Board Member

Gunagzhou Nanfang College | China

Dr. Muhammad Bilal is an Associate Professor at Nanfang College, China, specializing in artificial intelligence, machine learning, and underwater acoustic communication. His research focuses on bio-inspired covert communication, low probability detection systems, and AI-driven signal processing, with applications in marine technology, cybersecurity, and healthcare. He has authored numerous peer-reviewed publications in leading international journals and conferences. Dr. Bilal actively collaborates with global research communities and serves as a reviewer for reputed journals. His work advances secure communication systems and contributes to the development of sustainable and intelligent ocean technologies with broad societal impact.

Citation Metrics (Scopus)

600

400

200

0

Citations
381

Documents
41

h-index
11

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile    View ORCID Profile
     View Google Scholar  Profile
     View ResearchGate Profile

Featured Publications


Biologically inspired covert underwater acoustic communication—A review.

–Physical Communication, 30, 107–114. (2018). Cited By: 55

A frequency hopping pattern inspired bionic underwater acoustic communication.

– Physical Communication, 46, 101288. (2021). Cited By: 43

Irenilza De Alencar Nääs | Object Detection and Recognition | Women Researcher Award

Published on by Computer Vision Awards

Prof. Irenilza De Alencar Nääs | Object Detection and Recognition | Women Researcher Award

Professor | Universidade Paulista | Brazil

Prof. Irenilza de Alencar Nääs is a leading researcher at Universidade Paulista, São Paulo, Brazil, specializing in precision livestock farming, agricultural engineering, and AI-driven animal welfare assessment. She has authored over 339 peer-reviewed publications with more than 3,311 citations h-index 32, reflecting strong international impact and extensive collaboration with more than 400 co-authors worldwide. Her recent work integrates thermography, computer vision (YOLOv8), and machine learning to improve broiler welfare, postharvest quality, and occupational health in agri-food systems. Dr. Nääs’s research significantly advances sustainable agriculture and data-driven decision-making for global food security.

Citation Metrics (Scopus)

4000

3000

2000

1000

0

Citations
3,311

Documents
339

h-index
32

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View ORCID Profile
        View Google Scholar Profile

Featured Publications


Princípios de conforto térmico na produção animal .

– Ícone Editora.. (1989). Cited By : 251

Infrared thermal image for assessing animal health and welfare.

-Journal of Animal Behaviour and Biometeorology. (2014). Cited By: 143

Impact of lameness on broiler well-being.

– Journal of Applied Poultry Research. (2009). Cited By: 116

Real time computer stress monitoring of piglets using vocalization analysis.

– Computers and Electronics in Agriculture. (2025). Cited By: 108

Riadh Harizi | Deep Learning For Computer Vision | Research Excellence Award

Published on by Computer Vision Awards

Dr. Riadh Harizi | Deep Learning For Computer Vision | Research Excellence Award

Teacher | Ecole Nationale d’Ingénieurs de Sfax | Tunisia

Dr. Riadh Harizi is a researcher at the École Nationale d’Ingénieurs de Sfax, Tunisia, with expertise in Machine Learning, Artificial Intelligence, Computer Vision, Deep Learning, and Data Science. He has authored 5 research outputs, receiving 33 citations across 25 citing documents and achieving an h-index of 3. His work spans scene text understanding, reinforcement learning, and AI-driven educational analytics, with publications in Applied Soft Computing, Multimedia Tools and Applications, and leading international conferences. He has collaborated with interdisciplinary teams and contributed an open Latin and Arabic scene character dataset to IEEE Dataport, supporting reproducible research and societal impact in education and intelligent visual systems.

 

Citation Metrics (Scopus)

80

60

40

20

0

Citations
33

Documents
5

h-index
3

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View ORCID Profile
      View Google Scholar Profile

Featured Publications


Deep-learning based end-to-end system for text reading in the wild.

-Multimedia Tools and Applications. (2022) Cited By: 10

SIFT-ResNet synergy for accurate scene word detection in complex scenarios.

– In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART) . (2024). Cited By: 3

Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

Published on by Computer Vision Awards

Assoc. Prof. Dr. Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

Associate Professor | Shiraz University | Iran

Dr. Mohsen Edalat an accomplished researcher from Shiraz University, Iran, has made notable contributions to the fields of machine learning geospatial modeling and smart agriculture. With an impressive research record comprising 39 scientific publications and over 614 citations Dr. Edalat has demonstrated sustained academic productivity and influence in computational and environmental sciences. His research emphasizes the integration of advanced data-driven algorithms with ecological and agricultural systems to enhance sustainability and decision-making processes.Among his recent works Dr. Edalat has explored diverse applications of machine learning for ecological and agricultural optimization. His 2025 publications include studies on predicting nepetalactone accumulation in Nepeta persica through machine learning and geospatial analysis modeling ecological preferences of Kentucky bluegrass under varying water conditions (Water Switzerland)  and mapping early-season dominant weeds using UAV-based imagery to support precision farming. These investigations reflect his innovative approach to merging remote sensing artificial intelligence and environmental modeling to address complex agroecological challenges.With an h-index of 11 and collaborations with more than 60 co-authors  Dr. Edalat’s work highlights strong interdisciplinary engagement and a commitment to advancing data-driven sustainability. His studies contribute not only to the scientific community but also to practical agricultural applications that promote resource efficiency and ecological resilience. Through his ongoing research Dr. Edalat continues to shape the evolving landscape of smart agriculture and environmental informatics demonstrating the global relevance and societal value of computational intelligence in natural systems.

Profiles:  Scopus | ORCID

Featured Publications

1. Edalat, M., et al. (2025). Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis. Scientific Reports.

2. Edalat, M., et al. (2025). Modeling the ecological preferences and adaptive capacities of Kentucky bluegrass based on water availability using various machine learning algorithms. Water (Switzerland).

3. Edalat, M., et al. (2025). Early season dominant weed mapping in maize field using unmanned aerial vehicle (UAV) imagery: Towards developing prescription map. Smart Agricultural Technology.

Dr. Mohsen Edalat’s research integrates machine learning, geospatial analytics, and agricultural science to enhance crop management and environmental sustainability. His innovative work advances precision agriculture, supporting data-driven decisions that improve resource efficiency, boost food security, and promote sustainable development at a global scale.

Simy Baby | Applications of Computer Vision | Best Researcher Award

Published on by Computer Vision Awards

Mrs. Simy Baby | Applications of Computer Vision | Best Researcher Award

Researcher | National Institute of Technology | India

Mrs. Simy Baby is a pioneering researcher at the National Institute of Technology, Tiruchirappalli, with extensive expertise in machine learning, semantic communication, computer vision, and mmWave radar signal processing. Her research bridges the gap between radar sensing and intelligent communication frameworks, focusing on efficient feature extraction, complex-valued encoding, and task-oriented inference.Her seminal work, “Complex Chromatic Imaging for Enhanced Radar Face Recognition” (Computers and Electrical Engineering,  introduced a novel representation that preserves amplitude and phase information of mmWave radar signals, achieving an exceptional recognition accuracy. Another significant contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication” (IEEE Transactions on Cognitive Communications and Networking ), proposed a CLDA-based encoding framework enhancing feature interpretability and robustness under channel variations. Current investigations include Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, highlighting her commitment to multitask semantic communication systems.Dr. Baby has 21 publications with 20 citations and an h-index of 3.  demonstrating a rapidly growing impact in her field. She is an active member of the Indian Society for Technical Education (ISTE) and contributes to the scientific community through innovative research that combines theory and practical applications. Her work on radar-based recognition, semantic feature transmission, and multi-task inference frameworks holds significant potential for intelligent transportation systems, human activity recognition, and bandwidth-efficient communication technologies.Through her research, Dr. Baby has established herself as a leading figure in advancing radar imaging and semantic communication, providing scalable solutions that merge high-performance computing with real-world societal applications. Her vision continues to shape the future of intelligent sensing and communication systems globally.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Ansal, K. A., Rajan, C. S., Ragamalika, C. S., & Baby, S. M. (2022). A CPW fed monopole antenna for UWB/Ku band applications. Materials Today: Proceedings, 51, 585–590. Cited By : 5

2. Ansal, K. A., Ragamalika, C. S., Rajan, C. S., & Baby, S. M. (2022). A novel ACS fed antenna with comb shaped radiating strip for triple band applications. Materials Today: Proceedings, 51, 332–338. Cited By : 4

3. Ansal, K. A., Kumar, A. S., & Baby, S. M. (2021). Comparative analysis of CPW fed antenna with different substrate material with varying thickness. Materials Today: Proceedings, 37, 257–264. Cited By : 4

4. Baby, S. M., & Gopi, E. S. (2025). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering, 123, 110198. Cited By : 3

5.Ansal, K. A., Shanmuganatham, T., Baby, S. M., & Joy, A. (2015). Slot coupled microstrip antenna for C and X band application. International Journal of Advanced Research Trends in Engineering and Technology.Cited By : 3

Dr. Simy M. Baby’s research advances the integration of semantic communication and computer vision, enabling high-accuracy radar-based recognition and task-oriented inference. Her work has significant implications for intelligent transportation, human activity monitoring, and bandwidth-efficient communication, driving innovation in both science and industry globally.

Felix Lankester | Face Recognition and Analysis | Research Impact Award

Published on by Computer Vision Awards

Prof. Dr. Felix Lankester | Face Recognition and Analysis | Research Impact Award

Professor | Washington State University | United Kingdom

Dr Felix Lankester is an accomplished veterinary scientist with extensive experience in global health, wildlife conservation, and zoonotic disease research. He earned his PhD from the University of Glasgow, where his research focused on the impact and control of malignant catarrhal fever in Tanzania. He also holds an MSc in Wild Animal Health from the University of London and a Bachelor of Veterinary Science from the University of Liverpool. Dr Lankester serves as a Clinical Associate Professor at the Paul G. Allen School for Global Health, Washington State University, and previously worked as Director of Tanzanian Programs at the Lincoln Park Zoological Society and Country Director for the Pandrillus Foundation in Cameroon. His professional journey also includes roles as Project Director and Head Veterinarian at the Limbe Wildlife Centre, wildlife consultant in Kenya, and veterinary surgeon in the UK and Borneo. His research interests focus on zoonotic disease transmission, particularly rabies and other infectious diseases affecting marginalized communities in East Africa, as well as emerging pathogens with pandemic potential through his leadership in the DEEP VZN project. Dr Lankester has received recognition for his contributions to One Health, disease control, and wildlife health education. His research skills encompass field epidemiology, infectious disease modeling, surveillance design, and interdisciplinary collaboration across human and animal health systems. He continues to mentor young researchers and contribute to the scientific community through publications and international teaching engagements. His work has achieved 2,497 citations by 72 documents and an h-index of 25.

Profiles: Scopus | ORCID

Featured Publications

1.Kibona, T., Buza, J., Shirima, G., Lankester, F., Ngongolo, K., Hughes, E., Cleaveland, S., & Allan, K. J. (2022). The prevalence and determinants of Taenia multiceps infection (cerebral coenurosis) in small ruminants in Africa: A systematic review. Parasitologia.

2.Lankester, F., Kibona, T. J., Allan, K. J., de Glanville, W., Buza, J. J., Katzer, F., Halliday, J. E., Mmbaga, B. T., Wheelhouse, N., Innes, E. A., et al. (2024). Livestock abortion surveillance in Tanzania reveals disease priorities and importance of timely collection of vaginal swab samples for attribution. eLife.

3.Lankester, F., Lugelo, A., Changalucha, J., Anderson, D., Duamor, C. T., Czupryna, A., Lushasi, K., Ferguson, E., Swai, E. S., Nonga, H., et al. (2024). A randomized controlled trial of the effectiveness of a community-based rabies vaccination strategy. Preprint.

4.Kibona, T., Buza, J., Shirima, G., Lankester, F., Nzalawahe, J., Lukambagire, A.-H., Kreppel, K., Hughes, E., Allan, K. J., & Cleaveland, S. (2022). Taenia multiceps in northern Tanzania: An important but preventable disease problem in pastoral and agropastoral farming systems. Parasitologia.

5.Lugelo, A., Hampson, K., Ferguson, E. A., Czupryna, A., Bigambo, M., Duamor, C. T., Kazwala, R., Johnson, P. C. D., & Lankester, F. (2022). Development of dog vaccination strategies to maintain herd immunity against rabies. Viruses.