Leila Dal Moro | Sustainability Management | Best Researcher Award

Dr. Leila Dal Moro | Sustainability Management | Best Researcher Award

Research professor, Atitus Education College,Brazil

Leila Dal Moro, a prominent figure in academic circles, is recognized for her impactful contributions in various bibliographic citations. 🌐 As MORO, L. D. or MORO, L.D., her work stands testament to her commitment to advancing knowledge. Whether cited as DAL MORO, LEILA or DAL MORO, L., Leila Dal Moro’s name resonates across scholarly publications. 📚 With a versatile representation as Moro, Leila Dal or MORO, Leila dal, her research spans diverse domains, showcasing her expertise. 🏆 Leila’s presence in academic citations underscores her dedication to pushing the boundaries of knowledge and making lasting contributions to her field. 👩‍🔬

Profile

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Academic Graduation

Dr. Leila Dal Moro, a dedicated scholar, earned her PhD in Civil and Environmental Engineering from the University of Passo Fundo (UPF), Brazil, from 2016 to 2020. 🎓 Her dissertation, titled “Rio Grande do Sul Sustainable Development Agenda: Production and Consumption,” showcased her commitment to sustainable practices. As a scholarship holder from CAPES, Brazil, and under the guidance of Advisor Luciana Londero Brandili, Leila actively contributed to the COREDE Production initiative. 🌱 Prior to her PhD, she completed her Master’s degree in Civil and Environmental Engineering, focusing on healthcare waste management. Leila’s educational journey also includes a specialization in Environmental Management and Water Resources and a graduation in Public Management. 👩‍🔬

Professional performance

Dr. Leila Dal Moro, currently engaged in impactful roles, demonstrates a strong institutional bond through her diverse professional journey. 🌐 Since 2023, she has been actively contributing to the academic landscape, holding a position with a functional framework that underscores her expertise. As a CLT employee at Atitus Educação since 2022, Leila serves as a PhD Professor I, dedicating 40 hours weekly to shaping the minds of undergraduate and postgraduate students. 🎓 Her teaching portfolio encompasses subjects like Supply Chain Management, Sustainability Management, and more. With a postdoctoral commitment and complementary training at the Federal University of Mato Grosso do Sul, Leila’s influence extends to organizations like Atitus Educação and Flor de Liz Manipulation Pharmacy. 👩‍🏫

Publications Top Notes

  1. Title: Hazardous elements in urban cemeteries and possible architectural design solutions for a more sustainable environment
    • Authors: Neckel, A., Toscan, P.C., Kujawa, H.A., Moro, L.D., Silva, L.F.O.
    • Journal: Environmental Science and Pollution Research
    • Year: 2023
    • Cited By: 1
  2. Title: Emergent Research Themes on Sustainability in the Beef Cattle Industry in Brazil: An Integrative Literature Review
    • Authors: Casagranda, Y.G., Wiśniewska-Paluszak, J., Paluszak, G., Azevedo, D.B.D., Zhang, D., Moro, L.D.
    • Journal: Sustainability (Switzerland)
    • Year: 2023
    • Cited By: 1
  3. Title: Sustainability in agribusiness: Analysis of environmental changes in agricultural production using spatial geotechnologies
    • Authors: Moro, L.D., Pauli, J., Maculan, L.S., Bodah, B.W., Dornelles, V.D.C.
    • Journal: Environmental Development
    • Year: 2023
    • Cited By: 1
  4. Title: Terrestrial nanoparticle contaminants and geospatial optics using the Sentinel-3B OLCI satellite in the Tinto River estuary region of the Iberian Peninsula
    • Authors: Neckel, A., Oliveira, M.L.S., Maculan, L.S., Moro, L.D., Silva, L.F.O.
    • Journal: Marine Pollution Bulletin
    • Year: 2023
    • Cited By: 1
  5. Title: Barriers and possible drivers for the implementation of sustainability in Brazilian business schools
    • Authors: Guadagnin, A., Pauli, J., Ruffatto, J., Dal Moro, L.
    • Journal: International Journal of Sustainability in Higher Education
    • Year: 2023
  6. Title: Greenwash, show your true colours: how verbal and visual messages influence consumers’ perception?
    • Authors: Basso, K., Pauli, J., Cerutti, P., Dalla Corte, V.F., Dal Moro, L.
    • Journal: International Journal of Environment and Sustainable Development
    • Year: 2023
  7. Title: Brazilian Coal Tailings Projects: Advanced Study of Sustainable Using FIB-SEM and HR-TEM
    • Authors: Oliveira, M.L.S., Pinto, D., Nagel-Hassemer, M.E., Bodah, B.W., Neckel, A.
    • Journal: Sustainability (Switzerland)
    • Year: 2023
    • Cited By: 1
  8. Title: Editorial: the contribution of sustainable production and consumption to a green economy
    • Authors: Brandli, L.L., Salvia, A.L., Moro, L.D.
    • Journal: Discover Sustainability
    • Year: 2022
    • Cited By: 1
  9. Title: Using the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region
    • Authors: Neckel, A., Santosh, M., Bodah, B.W., Almeida Silva, C.C.O.D., Mores, G.D.V.
    • Journal: Sustainability (Switzerland)
    • Year: 2022
    • Cited By: 2

Funa Zhou | Fault diagnosis | Best Researcher Award

Prof Dr. Funa Zhou | Fault diagnosis | Best Researcher Award

Professor, School of Logistic Engineering, Shanghai Maritime University, China

Professor Zhou Funa, born in April 1978, is a distinguished academic figure and Doctoral Supervisor at the School of Logistics Engineering, Shanghai Maritime University, China. 🎓 With expertise in logistics, his contributions to education and research have left an indelible mark. 🌐 Professor Zhou can be reached at zhoufn@shmtu.edu.cn or contacted at +86 15800531379 and 021 38282619. 📚 His commitment to shaping the next generation of professionals in the field is evident, making him a valuable asset to the academic community. 🌟 Professor Zhou’s dedication to the School of Logistics Engineering reflects in both his teaching and mentorship. 🚢

Profile

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Research performance

Professor Zhou Funa has exhibited exceptional leadership by chairing 12 projects, showcasing his commitment to advancing research and innovation in the field of logistics engineering. 🚀 Among these, his current project, funded by the National Natural Science Foundation of China (NSFC), focuses on Modular Recursive Federated Learning for Fault Diagnosis and Prediction of Port Machines. His previous accomplishments include completing projects like Deep Feature Extraction Based Fault Diagnosis and Life Prediction, A Data-driven Approach for Multimodal Fault Diagnosis, and Data-driven Multimodal Fault Diagnosis and Predictive Maintenance. 🌐 Professor Zhou’s extensive project portfolio underscores his dedication to advancing knowledge and solving real-world challenges in logistics engineering. 🌟

Awards and honors

Professor Zhou Funa’s remarkable contributions have garnered several prestigious awards and recognitions, underscoring his excellence in the field of logistics engineering. 🏆 In 2023, he received the Second Prize of Natural Science from the Chinese Association of Automation, highlighting his cutting-edge research. Previous accolades include the Second Prize of Scientific and Technological Progress in Henan Province (2017) and multiple awards from the Henan Provincial Natural Science Academic Award, including a first prize in 2011 and two second prizes in 2013. 🌟 His diverse achievements encompass leadership roles, teaching excellence, and recognition as an outstanding Communist Party member, solidifying his status as a distinguished figure in academia. 👨‍🏫

Academic and Adjunct

Professor Zhou Funa stands as a recognized authority in the field of logistics engineering, contributing expertise as an Expert for the National Natural Science Foundation of China and a Young Expert for the Chinese Society of Automation. 🌐 His commitment to advancing knowledge extends to serving as a meticulous Reviewer for prestigious journals like the Journal of Automation and Electronics Journal. As a dedicated professional, he actively participates in various committees, including the Technical Process Failure Diagnosis and Safety Committee and the Intelligent Automation Committee of the Chinese Society of Automation. 🏅 Professor Zhou’s multifaceted engagement, from national foundations to local societies, reflects his commitment to the advancement of automation and technological innovation. 👨‍🔧

Publications Top Notes

 

  1. Title: “Attention Gate Guided Multiscale Recursive Fusion Strategy for Deep Neural Network-based Fault Diagnosis”
    • Authors: Zhang, Z., Zhou, F., Karimi, H.R., … Wen, C., Wang, T.
    • Journal: Engineering Applications of Artificial Intelligence
    • Publication Year: 2023
    • Volume: 126
    • Cited By: 3 (2023)
  2. Title: “An Active Federated Method Driven by Inter-Client Informativeness Variability of Labeled Data”
    • Authors: Zhou, F., Wang, C., Hu, X., Wang, C., Wang, T.
    • Journal: Signal, Image and Video Processing
    • Publication Year: 2023
    • Volume: 17(8)
    • Pages: 3973–3982
    • Cited By: 1 (2023)
  3. Title: “Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism”
    • Authors: Liu, S., Zhou, F., Tang, S., … Wang, C., Wang, T.
    • Journal: Entropy
    • Publication Year: 2023
    • Volume: 25(10)
    • Pages: 1470
    • Year:  2023
  4. Title: “A Personalized Federated Learning-based Fault Diagnosis Method for Data Suffering from Network Attacks”
    • Authors: Zhang, Z., Zhou, F., Zhang, C., … Hu, X., Wang, T.
    • Journal: Applied Intelligence
    • Publication Year: 2023
    • Volume: 53(19)
    • Pages: 22834–22849
    • Cited By: 3 (2023)
  5. Title: “A Multiscale Recursive Attention Gate Federation Method for Multiple Working Conditions Fault Diagnosis”
    • Authors: Zhang, Z., Zhou, F., Wang, C., … Hu, X., Wang, T.
    • Journal: Entropy
    • Publication Year: 2023
    • Volume: 25(8)
    • Pages: 1165
    • Year:  2023
  6. Title: “Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree”
    • Authors: Zhou, F., Yang, Y., Wang, C., Hu, X.
    • Journal: Entropy
    • Publication Year: 2023
    • Volume: 25(4)
    • Pages: 606
    • Cited By: 2 (2023)
  7. Title: “Multi-Scale Recursive Semi-Supervised Deep Learning Fault Diagnosis Method with Attention Gate”
    • Authors: Tang, S., Wang, C., Zhou, F., Hu, X., Wang, T.
    • Journal: Machines
    • Publication Year: 2023
    • Volume: 11(2)
    • Pages: 153
    • Cited By: 1 (2023)
  8. Title: “Trend Feature Consistency Guided Deep Learning Method for Minor Fault Diagnosis”
    • Authors: Jia, P., Wang, C., Zhou, F., Hu, X.
    • Journal: Entropy
    • Publication Year: 2023
    • Volume: 25(2)
    • Pages: 242
    • Cited By: 3 (2023)