Prof. Wei Wu | Medicine | Best Researcher Award

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Prof. Wei Wu | Medicine | Best Researcher Award

Prof. Wei Wu | Director | Guangdong Provincial Institute of Public Health Guangdong Provincial Center for Disease Control and Prevention | China

Dr. Wei Wu is a distinguished public health scientist specializing in labor and environmental hygiene, microbial epidemiology, and health data analytics. His research primarily explores the intersection of gut microbiota, metabolic diseases, and public health, employing advanced population cohort and multi-omics data analysis to uncover novel biomarkers and disease risk genes. Dr. Wu’s studies contribute significantly to understanding nutrient–gut flora–host interactions, guiding innovative therapeutic targets for metabolic and infectious diseases. He has also made substantial contributions to vaccine health economics and hygiene assessment in rail transit systems. As a postgraduate supervisor and active member of multiple national scientific committees, Dr. Wu plays a vital role in shaping public health policy and research in China. His impactful scientific output has earned recognition in leading journals, including Nature Medicine and Microbiome. According to Scopus, he has published 35 documents with over 1,577 citations, an h-index of 13, and his work is widely cited across 1,498 documents. On Google Scholar, his publications have similarly received extensive academic engagement, underscoring his global research influence in environmental and preventive health sciences.

Publication Profile

Scopus | ORCID

Featured Publications

He, Y., Wu, W., Zheng, H. M., Li, P., McDonald, D., Sheng, H. F., et al. (2018). Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nature Medicine, 24(10), 1532–1535.

He, Y., Wu, W., Wu, S., Zheng, H. M., Li, P., Sheng, H. F., et al. (2018). Linking gut microbiota, metabolic syndrome and economic status based on a population-level analysis. Microbiome, 6(1), 172.

Guo, X., Yan, M., Huang, D., Chen, S., Zhang, D., Li, Z., Yang, X., & Wu, W. (2023). A large scale 16S ribosomal RNA gene amplicon dataset of hand, foot and mouth patients and healthy individuals. Scientific Data, 10(48), 1–6.

Yan, M., Guo, X., Ji, G., Huang, R., Huang, D., Li, Z., Zhang, D., Chen, S., Cao, R., Yang, X., & Wu, W. (2023). Mechanism-based role of the intestinal microbiota in gestational diabetes mellitus: A systematic review and meta-analysis. Frontiers in Immunology, 13(3), 1–13.

Wang, H., Luo, L., & Wu, W. (2023). SAS statistical analysis and applications from introduction to mastery (2nd ed.). Posts & Telecom Press.

Dr. Maryam Shojaee | Metabolomic | Best Researcher Award

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Dr. Maryam Shojaee | Metabolomic | Best Researcher Award

Dr. Maryam Shojaee | Physcian Resident | Faculty of Medicine Tehran University of Medical Science | Canada

Dr. Maryam Shojaee is a highly dedicated medical researcher and clinician with a strong background in internal medicine, public health, and clinical research. Her professional journey reflects a dynamic blend of medical practice, research coordination, and scholarly contributions to advancing medical science. She has actively participated in research related to neonatology, infectious diseases, trauma, and public health, with special attention to COVID-19 outcomes, spinal cord injury management, and patient safety. Her experience spans clinical data extraction, literature reviews, and the publication of peer-reviewed articles that explore critical issues in medicine, including pulmonary hypertension, inflammatory biomarkers, and healthcare system challenges. Dr. Shojaee’s research work demonstrates a commitment to integrating clinical practice with academic inquiry to improve diagnostic and therapeutic outcomes. Her publications have collectively gained recognition, earning her 50 citations across 50 documents with an h-index of 2 on Scopus, while her Google Scholar profile reflects growing academic visibility and scholarly impact. She has also contributed to educational mentorship, academic discussions, and multidisciplinary collaborations in healthcare and research institutions. Her ongoing focus lies in translational research, clinical epidemiology, and improving patient-centered care through data-driven studies and evidence-based approaches.

Publication Profile

Scopus

Featured Publications 

Kaveh, M., Mahboobipour, A. A., Bitaraf, A., Shojaei, M., & Mohammadpour Ahranjani, B. (2022). A prospective study of neonates with persistent pulmonary hypertension of the newborn: Prevalence, clinical outcomes, and risk factors. Iranian Journal of Neonatology.

Kheyri, Z., Metanat, S., Hosamirudsari, H., Akbarpour, S., Shojaei, M., Faraji, N., & Mansouri, F. (2022). Neutrophil-to-lymphocyte ratio cut-off point for COVID-19 mortality: A retrospective study. Acta Medica Iranica.

Masoud, S. A., Zahra, G., Habibi, A. R., Zahra, A., Mahdi, S. A., Moein, K., Shojaei, M., & Abbas, R. (2021). Reasons for delayed spinal cord decompression in individuals with traumatic spinal cord injuries in Iran: A qualitative study from the perspective of neurosurgeons. Chinese Journal of Traumatology.

Tabary, M., Ahmadi, S., Amirzade-Iranaq, M. H., Shojaei, M., Asl, M. S., Ghodsi, Z., & Azarhomayoun, A. (2021). The effectiveness of different types of motorcycle helmets: A scoping review. Accident Analysis & Prevention.

Prof. Saad Aljlil | Engineering | Best Researcher Award

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Prof. Saad Aljlil | Engineering | Best Researcher Award

Prof. Saad Aljlil | Chief Researcher | King Abdulaziz City for Science and Technology | Saudi Arabia

Prof. Saad A. Aljlil is a distinguished research professor at the King Abdulaziz City for Science and Technology (KACST), Saudi Arabia, specializing in sustainability, membrane technology, and water and environmental engineering. His research focuses on advanced desalination and water treatment technologies, wastewater purification, membrane synthesis, adsorption processes, and the integration of renewable energy systems for sustainable water management. Prof. Aljlil has made significant contributions to developing ceramic and polymeric membranes, nanocomposite materials, and hybrid desalination systems that enhance water purification efficiency while minimizing environmental impact. His work extends to smart water networks, solar-driven desalination, greywater reuse, and innovative applications of artificial intelligence in membrane distillation and water resource optimization. A highly cited researcher, Prof. Aljlil has achieved 1,079 citations in Scopus across 44 documents with an h-index of 19, and 1,397 citations on Google Scholar with an h-index of 20 and i10-index of 23. His interdisciplinary approach bridges chemical engineering, nanotechnology, and sustainability to address critical challenges in clean water access and environmental preservation.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Ali, A., Macedonio, F., Drioli, E., Aljlil, S. A., & Alharbi, O. A. (2013). Experimental and theoretical evaluation of temperature polarization phenomenon in direct contact membrane distillation. Chemical Engineering Research and Design, 91(10), 1966–1977.

Quist-Jensen, C. A., Macedonio, F., Conidi, C., Cassano, A., Aljlil, S. A., Alharbi, O. A., & Drioli, E. (2016). Direct contact membrane distillation for the concentration of clarified orange juice. Journal of Food Engineering, 187, 37–43.

Fontananova, E., Bahattab, M. A., Aljlil, S. A., Alowairdy, M., Rinaldi, G., Vuono, D., & Drioli, E. (2015). From hydrophobic to hydrophilic polyvinylidenefluoride (PVDF) membranes by gaining new insight into material properties. RSC Advances, 5(69), 56219–56231.

Park, C. H., Tocci, E., Fontananova, E., Bahattab, M. A., Aljlil, S. A., & Drioli, E. (2016). Mixed matrix membranes containing functionalized multiwalled carbon nanotubes: Mesoscale simulation and experimental approach for optimizing dispersion. Journal of Membrane Science, 514, 195–209.

Sedighi, M., Aljlil, S. A., Alsubei, M. D., Ghasemi, M., & Mohammadi, M. (2018). Performance optimisation of microbial fuel cell for wastewater treatment and sustainable clean energy generation using response surface methodology. Alexandria Engineering Journal, 57(4), 4243–4253.

 

Assist. Prof. Dr. Mehtab Alam | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Mehtab Alam | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Mehtab Alam |Assistant Professor | Delhi University | India

Dr. Mehtab Alam is an accomplished IT professional and academic specializing in Artificial Intelligence (AI), Internet of Things (IoT), Cyber Forensics, and Information Security. His research primarily focuses on developing AI-based smart IoT frameworks for intelligent healthcare systems, with a strong emphasis on predictive modeling, machine learning integration, and cloud-based data analytics. His scholarly contributions demonstrate a multidisciplinary approach combining computer science, data-driven healthcare innovation, and digital transformation. He has explored diverse research areas including smart city technologies, blockchain applications in e-governance, cybersecurity frameworks, and the application of swarm intelligence in network optimization. Dr. Alam has published extensively in reputed international journals and conferences, contributing to advancements in AI-driven sustainable systems and smart healthcare solutions. His works reflect technical depth and practical applicability, addressing modern challenges in digital infrastructure, public health informatics, and secure communication systems. He has authored 15 Scopus-indexed publications, with 30 Scopus citations and an h-index of 4. On Google Scholar, his research has received 256 citations with an h-index of 10 and an i10-index of 11, showcasing his growing academic influence.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Alam, M., Khan, E. R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). The DIABACARE CLOUD: Predicting diabetes using machine learning. Acta Scientiarum Technology, 46(1).

Alam, M., Khan, I. R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). Smart healthcare: Making medicine intelligent. Journal of Propulsion Technology, 44(3).

Alam, M., Khan, R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). AI for sustainable smart city healthcare. China Petroleum Processing and Petrochemical Technology Catalyst Research, 23(2), 2245–2258.

Ansari, A. A., Narain, L., Prasad, S. N., & Alam, M. (2022). Behaviour of motion of infinitesimal variable mass oblate body in the generalized perturbed circular restricted three-body problem. Italian Journal of Pure and Applied Mathematics, 47, 221–239.

Alam, M., Parveen, S. (2021). Shipment delivery and COVID-19: An Indian context. International Journal of Advanced Engineering Research and Science, 8(8), 145–154.

Mr. Jing Zhang | Biomedical Signal Processing | Best Researcher Award

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Mr. Jing Zhang | Biomedical Signal Processing | Best Researcher Award

Mr. Jing Zhang | lecturer | Taiyuan University of Science and Technology | China

Jing Zhang is a dedicated researcher and lecturer at the School of Electronic Information Engineering, Taiyuan University of Science and Technology, China. His research primarily focuses on signal processing, emotion recognition, and video coding and transmission, with a strong interdisciplinary approach bridging neuroscience, artificial intelligence, and communication systems. His innovative work in multimodal neural signal analysis leverages EEG and fNIRS data to explore causal brain connectivity and emotional decoding. By integrating Granger causality with deep learning architectures such as convolutional and graph convolutional networks, as well as attention mechanisms, his research contributes significantly to affective computing and brain–computer interface (BCI) applications. Dr. Zhang has published several high-impact papers in reputed international journals indexed in SCI and Scopus, with over 75 citations and an h-index of 6 on Google Scholar, reflecting the growing influence and recognition of his work in the scientific community. His research outcomes demonstrate both theoretical and practical implications for advancing emotion-aware technologies, neuroadaptive systems, and hybrid video transmission models. His scholarly contributions include publications in prestigious journals such as IEEE Transactions on Circuits and Systems for Video Technology and Frontiers in Neuroscience.

Featured Publications 

Zhang, J., Zhang, X., Chen, G., Huang, L., & Sun, Y. (2022). EEG emotion recognition based on cross-frequency Granger causality feature extraction and fusion in the left and right hemispheres. Frontiers in Neuroscience, 16, 974673.

Zhang, J., Wang, A., Liang, J., Wang, H., Li, S., & Zhang, X. (2018). Distortion estimation-based adaptive power allocation for hybrid digital–analog video transmission. IEEE Transactions on Circuits and Systems for Video Technology, 29(6), 1806–1818.

Zhang, J., Zhang, X., Chen, G., & Zhao, Q. (2022). Granger-causality-based multi-frequency band EEG graph feature extraction and fusion for emotion recognition. Brain Sciences, 12(12), 1649.

Chen, G., Zhang, X., Zhang, J., Li, F., & Duan, S. (2022). A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN. Frontiers in Neurorobotics, 16, 995552.

Li, P., Yang, F., Zhang, J., Guan, Y., Wang, A., & Liang, J. (2020). Synthesis-distortion-aware hybrid digital analog transmission for 3D videos. IEEE Access, 8, 85128–85139.

Mr. Chandan Sheikder | Bio-Robotics | Best Researcher Award

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Mr. Chandan Sheikder | Bio-Robotics | Best Researcher Award

Mr. Chandan Sheikder | Beijing Institute of Technology | China

Mr. Chandan Sheikder is an emerging researcher in the interdisciplinary domains of bio-robotics, animal navigation, medical robotics, and calcium imaging. His current research explores bio-inspired and neuromorphic frameworks for autonomous robot navigation in complex and GPS-denied environments, focusing on sensor fusion, swarm intelligence, and adaptive control algorithms. He integrates mechanical engineering principles with artificial intelligence and neuroscience-inspired mechanisms to develop robust robotic systems capable of navigating dynamic and uncertain conditions. His research extends to assistive and medical robotics, particularly in designing intelligent systems for human–robot interaction and healthcare applications. He has also contributed to aerospace technology research, applying soft computing and physiological sensing techniques to enhance cognitive workload assessment and performance reliability. Mr. Sheikder’s innovative work has resulted in several peer-reviewed publications in high-impact journals such as Nature Reviews Bioengineering, Sensors, and Trends in Biotechnology. His research outputs demonstrate a strong emphasis on hybrid intelligence, ethical AI, and cross-domain applications of robotics in space exploration and medical systems. He holds one pending Chinese patent on bio-inspired fusion navigation frameworks and has received notable academic awards, including the IEEE Best Paper Award. According to Google Scholar, he has 6 citations, an h-index of 1, and an i10-index of 0, reflecting his growing scholarly influence. His Scopus and Google Scholar profiles collectively highlight his promising research trajectory in advancing next-generation robotic autonomy and bio-inspired engineering systems.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Sheikder, C., Zhang, W., Chen, X., Li, F., He, X., Zuo, Z., Tan, X., & Liu, Y. (2025). Bio-inspired navigation systems for robots. Nature Reviews Bioengineering, 1–2.

Sheikder, C., Zhang, W., Chen, X., Li, F., Zuo, Z., & Tan, X. (2025). Marine-inspired multimodal sensor fusion and neuromorphic processing for autonomous navigation in unstructured subaquatic environments. Sensors, 25(216627).

Sheikder, C., Zhang, W., Chen, X., Li, F., He, X., Zuo, Z., Tan, X., & Liu, Y. (2025). A novel adaptive framework interconnects four pillars for tethered robots: Integrating fuzzy logic, genetic algorithms, and neural networks for robust dynamic environment navigation. Robotics and Autonomous Systems.

Sheikder, C., Haque, M. M. (2025). Towards the wearable cardiorespiratory sensors for aerospace applications. Journal of Aviation/Aerospace Education & Research, 34(2), 1–15.

Haque, M. M., Sheikder, C., & Djembong, R. (2023). Retroactive about robotics application with artificial intelligence toward the global pandemic scenario. European Journal of Electrical Engineering and Computer Science, 7(2), 34–43.

Assist. Prof. Dr. Manolis Adamakis | Technologies | Best Researcher Award

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Assist. Prof. Dr. Manolis Adamakis | Technologies | Best Researcher Award

Assist. Prof. Dr. Manolis Adamakis | National and Kapodistrian University of Athens | Greece

Dr. Manolis Adamakis is an accomplished Assistant Professor and Researcher specializing in Physical Education, Physical Activity, Health, and Wellbeing. His scholarly work bridges theoretical and experimental perspectives, with strong expertise in new technologies applied to physical activity and in-depth data analysis using both quantitative and qualitative approaches. His research explores the intersections of physical activity, education, mental health, and digital innovation, contributing significantly to European physical education and public health. Dr. Adamakis is recognized for his leadership in designing, validating, and implementing innovative instruments and methodologies that enhance educational practice and research quality. A highly cited researcher, he has authored 32 documents indexed in Scopus, accumulating 440 citations from 412 sources, and holds an h-index of 10. His Google Scholar record reflects 1,025 citations, an h-index of 16, and an i10-index of 22, highlighting his global academic impact. His collaborative work with international teams has advanced knowledge in teacher education, child motor development, and mental well-being through physical activity. Dr. Adamakis’s commitment to interdisciplinary and evidence-based research underlines his contribution to shaping the future of physical education and health promotion.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

O’Brien, W., Adamakis, M., O’Brien, N., Onofre, M., Martins, J., & Dania, A. (2020). Implications for European physical education teacher education during the COVID-19 pandemic: A cross-institutional SWOT analysis. European Journal of Teacher Education, 43(4), 503–522.

Lopes, L., Santos, R., Coelho-e-Silva, M., Draper, C., Mota, J., Jidovtseff, B., & Adamakis, M. (2021). A narrative review of motor competence in children and adolescents: What we know and what we need to find out. International Journal of Environmental Research and Public Health, 18(1), 18.

Adamakis, M., & Zounhia, K. (2016). The impact of occupational socialization on physical education pre-service teachers’ beliefs about four important curricular outcomes: A cross-sectional study. European Physical Education Review, 22(3), 279–297.

Rocliffe, P., Adamakis, M., O’Keeffe, B. T., Walsh, L., & Bannon, A. (2024). The impact of school physical activity provision on adolescent mental health and well-being: A systematic literature review. Adolescent Research Review, 9(2), 339–364.

Wälti, M., Sallen, J., Adamakis, M., Ennigkeit, F., & Gerlach, E. (2022). Basic motor competencies of 6-to-8-year-old primary school children in 10 European countries: A cross-sectional study. Frontiers in Psychology, 13, 804753.*

Dr. Xiaofeng Liu | Wireless Communication | Best Researcher Award

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Dr. Xiaofeng Liu | Wireless Communication | Best Researcher Award

Dr. Xiaofeng Liu | Lecture | Yancheng Teachers University | China

Dr. Xiaofeng Liu is a dedicated researcher and lecturer in Artificial Intelligence with a strong background in wireless communications, machine learning, and statistical inference. His research primarily focuses on developing advanced algorithms for massive MIMO systems, channel estimation, and machine learning-driven communication models. Dr. Liu has significantly contributed to the integration of statistical learning frameworks in communication system design, particularly through innovations like correlated hybrid message passing and generative diffusion models for channel estimation. His collaborative work with experts from leading research laboratories has produced high-impact publications in IEEE journals, reflecting both theoretical advancement and practical application in intelligent communication systems. His inventive contributions are further evident in several granted Chinese invention patents related to MIMO positioning, channel modeling, and beamspace communications. Dr. Liu’s research achievements are widely recognized, with his publications indexed in Scopus and Google Scholar, accumulating over 135 citations, an h-index of 6, and an i10-index of 5. His scholarly record demonstrates consistent contributions to next-generation wireless communication technologies, bridging the gap between deep learning models and complex signal processing challenges.

Publication Profile

Google Scholar | ORCID

Featured Publications 

Liu, X., Gong, X., & Fu, X. (2025). Activity detection and channel estimation based on correlated hybrid message passing for grant-free massive random access. Entropy.

Fu, X., Gong, X., Liu, X., Sun, R., Shen, Q., & Gao, X. (2025). Beamspace multi-ACB for mMTC in massive MIMO system. IEEE Transactions on Vehicular Technology.

Gong, X., Liu, X., Lu, A. A., Gao, X., Xia, X. G., Wang, C. X., & You, X. (2025). Digital twin of channel: Diffusion model for sensing-assisted statistical channel state information generation. IEEE Transactions on Wireless Communications.

Gong, X., Lu, A. A., Fu, X., Liu, X., Gao, X., & Xia, X. G. (2023). Semisupervised representation contrastive learning for massive MIMO fingerprint positioning. IEEE Internet of Things Journal.

Liu, X., Wang, W., Gong, X., Fu, X., Gao, X., & Xia, X. G. (2023). Structured hybrid message passing based channel estimation for massive MIMO-OFDM systems. IEEE Transactions on Vehicular Technology.

Mr. Ibra Fall | Computational Fluid Dynamics | Best Researcher Award

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Mr. Ibra Fall | Computational Fluid Dynamics | Best Researcher Award

Mr. Ibra Fall | PhD | National Research Center of Pumps | China

Dr. Fall Ibra is a Senegalese researcher specializing in Power Engineering, Thermo-Physics, and Computational Fluid Dynamics (CFD). His research encompasses applied fluid mechanics, multiphase flow theory, hydraulic design, and numerical simulation of gas–liquid two-phase flow systems. His work focuses on exploring the hydrodynamic mechanisms, energy conversion efficiency, and entropy generation in rotodynamic multiphase pumps and related systems. With a strong background in CFD, population balance modeling, and machine learning applications in fluid dynamics, he integrates computational modeling with experimental data to analyze flow structures, cavitation phenomena, and energy performance under complex multiphase conditions. His contributions extend to deep-sea oil and gas transport, hydraulic stability of pumping systems, and advanced turbulence modeling for gas–liquid interactions. Dr. Ibra has published extensively in high-impact journals such as Physics of Fluids, Alexandria Engineering Journal, Chaos, Solitons and Fractals, and Engineering Applications of Computational Fluid Mechanics. He has also presented his work at international symposiums on cavitation and multiphase flow. According to Scopus, Dr. Fall Ibra has authored 10 documents, cited by 49 other publications, with an h-index of 4. His Google Scholar profile similarly reflects a growing citation record and international research visibility.

Publication Profile

Scopus

Featured Publications

Falla, I., Geng, L., Gao, Y., Appiah, D., Ali, A., & Zhang, D. (2025). Effect of bubble coalescence and breakup on entropy generation in rotodynamic multiphase flow pumps. Physics of Fluids.

Falla, I., Geng, L., Gao, Y., Appiah, D., Ali, A., & Zhang, D. (2025). Numerical investigation of CFD-PBM coupled air–water flow in pipes under varying flow regimes. Alexandria Engineering Journal.

Shah, F., Falla, I., & Zhang, D. (2025). Experimental and CFD evaluation of bubble diameter and turbulence model influence on nonlinear flow dynamics. Chaos, Solitons and Fractals.

Ali, A., Yuan, J., Si, Q., & Falla, I. (2024). Comprehensive analysis of unsteady two-phase flow patterns in multiphase flow models. Engineering Applications of Computational Fluid Mechanics.

Gao, Y., Geng, L., Verdin, P. G., Falla, I., Zhang, R., Tian, Z., & Zhang, D. (2023). Modeling of dual-factor drag correction for bubbly flow under elevated pressures. Chemical Engineering and Technology.

Dr. Fernanda Diaz | Complementary Agriculture | Best Researcher Award

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Dr. Fernanda Diaz | Complementary Agriculture | Best Researcher Award

Dr. Fernanda Diaz | University of Guanajuato | Mexico

Fernanda Díaz Sánchez is a dedicated researcher in agribusiness and natural resource management, focusing on sustainable agricultural systems, rural innovation, and agri-environmental development. With an academic background in agribusiness and a master’s in innovation for natural resource management, she integrates ecological, social, and economic perspectives to enhance rural productivity and biodiversity conservation. Her research emphasizes sustainable models for germplasm production, agricultural diversification, and conservation practices in semi-arid and rural regions of Mexico. Fernanda has made significant contributions to understanding the ecological and socioeconomic dimensions of agricultural resilience, with her work being recognized through awards and academic success stories that highlight her innovative approaches to rural development. Her studies have been published in reputable journals such as Agro-Disculgation and Agroproductivity, reflecting her commitment to advancing agricultural sustainability through interdisciplinary collaboration. Fernanda actively participates in national and international conferences, presenting research on agri-food competitiveness, rural business models, and ecosystem-based management strategies. Her scholarly impact continues to grow, with publications indexed in Scopus and Google Scholar, supported by citations that underline the relevance of her work in sustainable agriculture and agribusiness management. She currently holds an h-index of 3 on Scopus and 5 on Google Scholar, demonstrating early-career influence in her field.

Publication Profile

ORCID

Featured Publication 

Díaz Sánchez, F., Cadena Iñiguez, J., Ruiz Vera, V. M., Barrera Guzmán, L. A., Cadena Zamudio, J. D., & Silos Espino, H. (2024). Conservation and production of Opuntia spp. under a complementary agricultural scheme in the Mexican highlands. Agro-Disculgation, 4(5).