Mostafa Belal | Green Technology | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Mostafa Belal — Sohag University, Egypt

Mostafa Belal
Affiliation Sohag University
Country Egypt
Scopus ID 57994645800
Documents 4
Citations 95
h-index 3
Subject Area Green Technology
Event Technology Scientists Awards
ORCID 0009-0007-4235-2273

Mostafa Belal is affiliated with Sohag University, Egypt, with research activity in green technology and materials protection. His documented publications address corrosion-resistant polybenzoxazine coatings, surface activation of wood-plastic composites, and protective formulations for mild steel. These works connect materials engineering with durability and environmental considerations in applied research applications. [1][2][3]

Abstract

Mostafa Belal, affiliated with Sohag University in Egypt, conducts research within green technology with emphasis on protective materials and surface engineering. His documented studies examine polybenzoxazine coating precursors, corrosion-resistant formulations, wood-plastic composite surface activation, epoxy adhesion, and protective performance for mild steel. The research combines material synthesis, characterization, surface modification, and corrosion mitigation. These themes indicate an applied approach to developing functional coating systems and improving material durability. The publication record provides a basis for recognizing contributions to materials protection and sustainable engineering applications. This profile summarizes documented themes, publications, impact, and suitability for innovative recognition. [1][2][3] This profile is presented here.

Keywords

Green technology; polybenzoxazine; corrosion protection; protective coatings; surface engineering; wood-plastic composites; epoxy adhesion; mild steel; corrosion inhibition; polymeric materials; material characterization; surface modification; functional coatings; sustainable materials; materials protection. [1][2][3]

Introduction

Mostafa Belal is affiliated with Sohag University, Egypt, with research activity in green technology and materials protection. His documented publications address corrosion-resistant polybenzoxazine coatings, surface activation of wood-plastic composites, and protective formulations for mild steel. These works connect materials engineering with durability and environmental considerations in applied research applications. [1][2][3]

Research Profile

Belal’s research profile centers on materials and surface engineering within green technology. His publications indicate emphasis on polymeric coating systems, adhesion enhancement, corrosion inhibition, and protective performance. Collectively, these topics reflect an applied research approach focused on improving material durability through engineered surface treatments and functional precursor design research overall. [1][2][3]

Research Contributions

The reported contributions involve developing polybenzoxazine-based materials for corrosion protection, designing coating precursors with enhanced performance, and activating wood-plastic composite surfaces to improve epoxy adhesion. The studies also examine protective efficiency toward mild steel, connecting material synthesis, surface modification, coating performance, and corrosion mitigation in applied materials research settings today. [1][2][3]

Publications

Belal’s publications address aspects of material protection research. One study reports a novel polybenzoxazine coating precursor designed through monomer engineering for anti-corrosion performance. Another investigates surface activation of wood-plastic composites for improved epoxy adhesion, while a third examines synthesis, characterization, and protective efficiency of a polybenzoxazine precursor for mild steel. [1][2][3]

Research Impact

The research has clear practical relevance to durable coating systems, corrosion management, and improved adhesion of protective layers on engineered materials. By addressing material protection and surface functionality, the studies contribute knowledge applicable to extending service life and improving coating performance. Their significance is demonstrated through documented research topics overall. [1][2][3]

Award Suitability

The documented research aligns with an Innovative Research Award focused on materials and green technology because it combines material synthesis, surface modification, characterization, and corrosion protection. The publications demonstrate a coherent research direction and practical orientation. Award consideration can be based on originality, technical scope, and relevance of the work. [1][2][3]

Conclusion

Mostafa Belal’s documented work presents a focused research direction in green technology, particularly protective coatings, surface treatment, adhesion, and corrosion inhibition. The cited studies provide evidence of investigation into functional materials and their applications. Together, they establish a research profile connecting material innovation with practical challenges in durability and protection. [1][2][3]

References

  1. Aly, K. I., Amer, A. A., Mahross, M. H., Belal, M. R., Soliman, A. M. M., & Mohamed, M. G. (2023). Construction of novel polybenzoxazine coating precursor exhibiting excellent anti-corrosion performance through monomer design. Heliyon, 9(5), e15976.
    https://doi.org/10.1016/j.heliyon.2023.e15976
  2. Belal, M. R., Naguib, H. M., El-Ghazawy, R. A., Shaker, N. O., Amer, A. A., Soliman, A. M. M., & Kandil, U. F. (2019). Surface activation of wood plastic composites (WPC) for enhanced adhesion with epoxy coating. Materials Performance and Characterization, 8(1), 22–40.
    https://doi.org/10.1520/MPC20180034
  3. Soliman, A. M. M., Aly, K. I., Mohamed, M. G., Amer, A. A., Belal, M. R., & others. (2023). Synthesis, characterization and protective efficiency of novel polybenzoxazine precursor as an anticorrosive coating for mild steel. Scientific Reports, 13, 5581.
    https://doi.org/10.1038/s41598-023-30364-x
  4. Elsevier. (n.d.). Scopus author details: Mostafa Belal, Author ID 57994645800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57994645800

Simeng Ding | Computational Biology | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Simeng Ding

Jilin University, China

Simeng Ding
Affiliation Jilin University
Country China
Documents 2
Subject Area Computational Biology
Event Technology Scientists Awards
ORCID 0009-0006-5489-6975

Simeng Ding is a researcher affiliated with Jilin University whose documented work intersects Computational Biology, molecular enzymology, and sustainable biocatalysis. Her research includes enzyme engineering, computational screening, lactose hydrolysis, and glycoside synthesis. Two identified publications describe GH42 β-galactosidase engineering for whey lactose conversion and glycosidase-catalyzed galactosyl-sn-2-glycerol production. These studies combine computational analysis with biochemical experimentation, including sequence-informed design, molecular-level interpretation, kinetic evaluation, and reaction optimization. The work demonstrates an interdisciplinary approach to developing efficient enzyme-based processes and valorizing biochemical resources. Her publication record provides a basis for consideration under the Best Researcher Award category within a technology-focused research recognition.

Abstract

Simeng Ding is a researcher affiliated with Jilin University whose documented work intersects Computational Biology, molecular enzymology, and sustainable biocatalysis. Her research includes enzyme engineering, computational screening, lactose hydrolysis, and glycoside synthesis. Two identified publications describe GH42 β-galactosidase engineering for whey lactose conversion and glycosidase-catalyzed galactosyl-sn-2-glycerol production. These studies combine computational analysis with biochemical experimentation, including sequence-informed design, molecular-level interpretation, kinetic evaluation, and reaction optimization. The work demonstrates an interdisciplinary approach to developing efficient enzyme-based processes and valorizing biochemical resources. Her publication record provides a basis for consideration under the Best Researcher Award category within a technology-focused research recognition.

Keywords

Computational Biology; enzyme engineering; glycosidases; β-galactosidase; whey lactose; transglycosylation; galactosyl-sn-2-glycerol; biocatalysis; molecular enzymology; sustainable biotechnology.

Introduction

Simeng Ding’s research profile at Jilin University is associated with computational biology and enzyme-related research. Her documented publications address glycosidase engineering, lactose hydrolysis, transglycosylation, and computationally informed biocatalysis. These studies connect molecular-level analysis with practical bioprocessing objectives, illustrating an interdisciplinary approach spanning computational and biochemical methods in applied biotechnology research. [1] [2]

Research Profile

Simeng Ding is affiliated with Jilin University, China, and works in a research environment focused on molecular enzymology and engineering. Her publication record includes studies involving glycosidases, enzyme engineering, substrate conversion, and computational analysis. The available record identifies two documents, one citation, and a Computational Biology subject classification. Enzyme technology. [1] [2]

Research Contributions

Ding’s contributions include participation in the design and evaluation of enzyme-based strategies for lactose conversion and galactosylglycerol synthesis. Her research incorporates sequence-informed engineering, computational screening, kinetic analysis, solvent effects, and molecular-level interpretation. Together, these approaches support the development of more effective biocatalytic systems for sustainable biochemical production for industrial applications. [1] [2]

  • Enzyme engineering and computational screening for improved catalytic performance. [1]
  • Biocatalytic conversion of lactose and production of value-added galactosides. [1] [2]
  • Application of kinetic, thermodynamic, and molecular-level approaches to enzyme research. [2]

Publications

The available publication record comprises two research articles. One investigates consensus design and computational screening of GH42 β-galactosidase for improved whey lactose hydrolysis, while the other examines regio-stereoselective galactosyl-sn-2-glycerol synthesis through glycosidase-catalyzed transglycosylation. Both studies demonstrate the application of enzyme engineering and computationally supported biochemical research in contemporary enzyme research. [1] [2]

Research Impact

The reported research has relevance to sustainable biocatalysis, particularly through the conversion of lactose-containing resources and the synthesis of value-added galactosides. The studies demonstrate how enzyme engineering, computational screening, thermodynamic analysis, and reaction optimization can contribute to improved biochemical processes while supporting resource-efficient approaches to biotechnology in sustainable biotechnology research. [1] [2]

Award Suitability

Simeng Ding’s documented research aligns with the Best Researcher Award through its focus on enzyme-related computational biology, biocatalysis, and sustainable biochemical applications. Her involvement in peer-reviewed studies demonstrates research participation across enzyme engineering and reaction optimization. The available evidence supports recognition of emerging scholarly contributions within an interdisciplinary biotechnology context. [1] [2]

Conclusion

Simeng Ding’s research record reflects an interdisciplinary focus connecting computational biology with enzymology and sustainable bioprocessing. Her documented studies address enzyme engineering and glycoside synthesis using computational and experimental approaches. Based on the available publications and research profile, her work represents a relevant contribution to contemporary computationally supported biotechnology research. [1] [2]

References

    1. Ding, S., Li, J., Li, J., Nie, H., Li, Y., Guo, Z., & Gao, R. (2026). Consensus design and in silico screening of GH42 β-galactosidase for enhanced catalytic hydrolysis of whey lactose. Journal of Dairy Science. Advance online publication.
      https://doi.org/10.3168/jds.2026-29005
    2. Lyu, J., Ding, S., Wolff, C. D., Gao, R., & Guo, Z. (2026). Regio-Stereoselective synthesis of galactosyl-sn-2-glycerol by Thermotoga naphthophila glycosidase-catalyzed transglycosylation in a cosolvent-mediated system: Kinetic and thermodynamic insights. ACS Sustainable Chemistry & Engineering, 14(17), 8221–8232.
      https://doi.org/10.1021/acssuschemeng.6c00122
    3. Ding, S. (n.d.). ORCID profile. ORCID.
      https://orcid.org/0009-0006-5489-6975

Sunil Kumar Khare | Green Technology | Outstanding Scientist Award

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Outstanding Scientist Award

Sunil Kumar Khare
University of Petroleum and Energy Studies, India

Sunil Kumar Khare
Affiliation University of Petroleum and Energy Studies
Country India
Scopus ID 26324209600
Documents 18
Citations 214
h-index 7
Subject Area Green Technology
Event Technology Scientists Awards
ORCID 0000-0001-5041-3012

Sunil Kumar Khare is a researcher affiliated with the University of Petroleum and Energy Studies in India whose scholarly profile encompasses green technology and data-driven engineering research. His documented work includes applications of analytics, regression modelling, pipeline network optimization, and geochemical interpretation, demonstrating an interdisciplinary orientation toward technology-enabled scientific problem solving. [1] [2] [3]

Abstract

Sunil Kumar Khare is a researcher at the University of Petroleum and Energy Studies, India, working within the broad domain of Green Technology. His scholarly record includes research involving data analytics, regression modelling, engineering optimization, and geochemical analysis. His publications demonstrate applications of computational methods to energy and geological problems, including geothermal drilling, pipeline configuration, and igneous-province characterization. These studies illustrate an interdisciplinary research profile connecting analytical techniques with practical engineering and environmental contexts. His documented scholarly output and citation record provide evidence of sustained research engagement and academic visibility. [1] [2] [3]

Keywords

Green Technology; Data Analytics; Regression Modelling; Geothermal Wells; Drilling Engineering; Pipeline Network Optimization; Sensitivity Analysis; Geochemistry; Petrogenetics; Igneous Provinces; Energy Technology; Engineering Analytics.

Introduction

Green Technology increasingly depends on analytical methods capable of improving resource efficiency, engineering decisions, and environmental understanding. Khare’s research reflects this interdisciplinary direction through studies applying data analytics to geothermal drilling, optimization to pipeline networks, and analytical methods to geological characterization, connecting computational approaches with energy and Earth-science applications. [1] [2] [3]

Research Profile

Khare’s research profile combines engineering analytics, optimization, and geoscientific investigation. His documented publications address prediction of drilling performance, multi-product pipeline configuration, and data-supported interpretation of geochemical and petrogenetic characteristics. Together, these themes indicate a research orientation toward quantitative methods that support complex energy, infrastructure, and geological systems across applied scientific contexts. [1] [2] [3]

Research Contributions

The documented research contributes analytical perspectives to energy and geological engineering problems. Regression modelling is applied to geothermal drilling-rate prediction, optimization frameworks examine pipeline configuration and objective-function sensitivity, while data analytics supports geochemical and petrogenetic interpretation. These contributions demonstrate the practical use of quantitative approaches for complex, multidisciplinary technological investigations. [1] [2] [3]

Publications

Khare’s documented publications cover three complementary areas: predictive analytics for geothermal drilling, optimization of multi-product pipeline networks, and data analytics for geochemical and petrogenetic investigation. These works illustrate the application of quantitative and computational techniques to engineering and Earth-science questions, with relevance to energy systems and technology-oriented research. [1] [2] [3]

Research Impact

The research demonstrates potential practical relevance across geothermal energy, pipeline infrastructure, and geological interpretation. Predictive modelling can support drilling analysis, optimization can inform network configuration decisions, and geochemical analytics can strengthen interpretation of complex geological datasets. The combined portfolio reflects technology-oriented research addressing diverse analytical challenges within energy-related domains. [1] [2] [3]

Award Suitability

Khare’s documented research aligns with the broad objectives of scientific recognition in technology-oriented disciplines. His work combines analytical modelling, engineering optimization, and geoscientific data analysis, while addressing energy and infrastructure applications. The breadth of these themes provides a reasonable basis for consideration under an Outstanding Scientist Award focused on applied technological research. [1] [2] [3]

Conclusion

Sunil Kumar Khare presents a multidisciplinary research profile spanning green technology, energy engineering, optimization, predictive analytics, and geoscience. His documented publications demonstrate the application of quantitative approaches to practical scientific problems. The combination of engineering and Earth-science research provides a substantive foundation for consideration for technology-focused scientific recognition. [1] [2] [3]

References

  1. Khare, S. K., et al. (2025). Data analytics and regression modelling for drilling rate of penetration prediction of geothermal wells. In Advances in Energy and Environmental Engineering. Springer.
    https://doi.org/10.1007/978-981-96-3667-9_11
  2. Khare, S. K., et al. (2024). Optimizing multi-product pipeline network configuration design: A comprehensive framework with objective function sensitivity analysis. Scopus. Publication record: 85184306294.
    https://www.scopus.com/pages/publications/85184306294
  3. Khare, S. K., et al. (2024). Data analytics for geochemical and petrogenetic study of an igneous province: A case study on Andean andesite, South America. Journal of Earth System Science.
    https://doi.org/10.1007/s12040-024-02399-9
  4. Elsevier. (n.d.). Scopus author details: Sunil Kumar Khare, Author ID 26324209600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=26324209600
  5. ORCID. (n.d.). Sunil Kumar Khare: ORCID record. ORCID.
    https://orcid.org/0000-0001-5041-3012

Nikolay Serov | Spectroscopy | Innovative Research Award

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Innovative Research Award

Nikolay Serov — D. S. Rozhdestvensky Optical Society, Russia

Nikolay Serov
Affiliation D. S. Rozhdestvensky Optical Society
Country Russia
Scopus ID 6701668745
Documents 4
Citations 1
h-index 1
Subject Area Spectroscopy
Event Technology Scientists Awards
ORCID 0000-0001-6145-6405

Nikolay Serov is a researcher associated with the D. S. Rozhdestvensky Optical Society whose documented scholarly activity includes work connecting spectroscopy, information models, and the interaction of light with matter. His research profile provides a basis for recognition in innovative research, particularly through conceptual approaches to spectroscopic information and physical systems. [1] [3]

Abstract

Nikolay Serov’s research profile is situated within spectroscopy and information-oriented studies of light–matter interaction. His documented work examines relationships between optical information, absorption spectra, atomic or molecular characteristics, and models for organizing spectroscopic knowledge. A published study proposes an information model connecting photon codes with matter and explores systematic relationships involving electronic terms and ionization potentials. [1] This direction combines spectroscopy with conceptual information modeling and contributes to discussion of how optical measurements may encode structured information about physical systems. His researcher identifiers further support the attribution and traceability of the scholarly record associated with this profile. [2] [3]

Keywords

Spectroscopy; light–matter interaction; information modeling; optical information; absorption spectra; photon codes; electronic terms; ionization potentials; quantum optics; spectroscopic databases. [1]

Introduction

Spectroscopy provides methods for studying matter through its interaction with electromagnetic radiation, while information modeling offers frameworks for organizing complex relationships within scientific observations. Serov’s published research connects these perspectives by examining how spectroscopic characteristics can be interpreted through an information-oriented model of light and matter. [1] His work therefore occupies an interdisciplinary space between optical science, spectroscopy, and theoretical information analysis.

Research Profile

The available research profile identifies Nikolay Serov with the D. S. Rozhdestvensky Optical Society in Russia and the subject area of spectroscopy. Bibliographic identifiers associate the researcher with Scopus author ID 6701668745 and ORCID 0000-0001-6145-6405, supporting consistent identification across scholarly systems. [2] [3]

Research Contributions

A documented contribution is Serov’s information model of the relationship between light and matter, which investigates functional relationships involving photon information-energy codes, absorption spectra, electronic terms, and ionization potentials. [1] The study also considers systematic patterns in molecular and atomic spectroscopic information, presenting a conceptual framework intended to support broader interpretation and organization of spectroscopy data.

Publications

The documented publication record includes the 2021 article “An Information Model of the Relationship of Light to Matter,” published in Automatic Documentation and Mathematical Linguistics, volume 55, issue 3, pages 110–121. [1] The article develops an information-oriented treatment of spectroscopic relationships and provides the principal publication evidence considered in this academic recognition profile.

Research Impact

The available bibliographic record reports four documents, one citation, and an h-index of one for the specified Scopus author profile. [2] These indicators provide quantitative context but should be interpreted alongside the substance of the research. The documented publication introduces an information-based perspective on spectroscopy and light–matter relationships, offering a conceptual contribution to this interdisciplinary area. [1]

Award Suitability

Serov’s profile is relevant to the Innovative Research Award because the documented work applies an information-modeling perspective to spectroscopy and the relationship between light and matter. [1] The research demonstrates an interdisciplinary orientation and proposes a structured conceptual approach to spectroscopic information. The award assessment should consider this contribution together with the broader verified publication record and research trajectory. [2] [3]

Conclusion

Nikolay Serov’s documented scholarship presents a distinctive connection between spectroscopy and information modeling, particularly through research on the relationship between light, matter, and spectroscopic information. [1] His indexed researcher identifiers provide a basis for scholarly attribution and profile verification. [2] [3] Within the available evidence, this interdisciplinary research direction provides a reasonable scholarly basis for consideration under the Innovative Research Award.

References

  1. Serov, N. V. (2021). An information model of the relationship of light to matter. Automatic Documentation and Mathematical Linguistics, 55(3), 110–121.
    https://doi.org/10.3103/S0005105521030079
  2. Elsevier. (n.d.). Scopus author details: Nikolay Serov, Author ID 6701668745. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=6701668745
  3. ORCID. (n.d.). Nikolay Serov: ORCID record 0000-0001-6145-6405. ORCID.
    https://orcid.org/0000-0001-6145-6405

Zehra Gulten Yalcın | Renewable Energy | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Zehra Gulten Yalcın — Çankırı Karatekin University, Turkey

Zehra Gulten Yalcın
Affiliation Çankırı Karatekin University
Country Turkey
Scopus ID 6603311969
Documents 8
Citations 60
h-index 5
Subject Area Renewable Energy
Event Technology Scientists Awards
ORCID 0000-0001-5460-289X

Zehra Gülten Yalçın is a researcher affiliated with Çankırı Karatekin University whose scholarly work addresses sustainable engineering, renewable-energy-related processes, industrial waste valorization, corrosion inhibition, polymer composites, and anaerobic digestion. Her recent publications demonstrate an interdisciplinary approach combining experimental investigation, materials characterization, optimization, and data-driven analysis in applied engineering research. [1] [2] [3]

Abstract

Zehra Gülten Yalçın is an engineering researcher at Çankırı Karatekin University whose recent scholarly activities connect sustainable materials, waste utilization, corrosion control, anaerobic digestion, and renewable-energy-oriented engineering. Her publications examine industrial waste in polymer composites, environmentally compatible corrosion inhibition, and optimization of biogas production using experimental and response-surface methodologies. These studies demonstrate an applied research orientation focused on converting industrial and biological waste streams into useful engineering outcomes while improving process performance and sustainability. Her work also incorporates characterization, optimization, and analytical methods to investigate material behavior and energy-related processes. [1] [2] [3]

Keywords

  • Renewable Energy
  • Sustainable Engineering
  • Industrial Waste Valorization
  • Polymer Composites
  • Corrosion Inhibition
  • Anaerobic Digestion
  • Biogas Production
  • Process Optimization

Introduction

Zehra Gülten Yalçın’s research is situated within sustainable chemical and environmental engineering, with particular relevance to waste utilization and energy-related processes. Her recent studies investigate polymer composites containing industrial waste, natural corrosion inhibitors, and anaerobic digestion systems, reflecting practical approaches to resource efficiency, materials performance, and renewable-energy development. [1] [2] [3]

Research Profile

Yalçın’s research profile combines materials engineering, environmental processes, and sustainable energy applications. Her publications indicate experience with experimental methods, material characterization, process optimization, and quantitative analysis. The research addresses practical engineering problems involving industrial waste, corrosion protection, polymeric materials, and biological waste conversion, providing an interdisciplinary foundation for continued work in renewable and sustainable technologies. [1] [2] [3]

Research Contributions

Her contributions include investigation of industrial waste as functional fillers in polyurethane composites, evaluation of Turkish coffee extract as an environmentally oriented corrosion inhibitor, and optimization of anaerobic digestion for biogas generation. Together, these studies connect resource recovery, materials performance, environmental protection, and renewable-energy production through experimentally grounded engineering approaches and quantitative process analysis. [1] [2] [3]

Publications

Yalçın’s recent publication record includes studies spanning sustainable polymer composites, corrosion science, and anaerobic digestion. A 2026 article examined industrial waste incorporation into polyurethane composites and associated mechanical and thermal properties, while a 2025 study investigated Turkish coffee extract for corrosion inhibition. Another 2025 publication examined biogas optimization using response surface methodology. [1] [2] [3]

Research Impact

The practical orientation of Yalçın’s research provides potential value for sustainable manufacturing, environmental protection, and renewable-energy development. Her studies address waste-derived materials, greener corrosion-control strategies, and biological waste conversion into biogas. These themes align with broader efforts to improve resource efficiency and develop engineering solutions that reduce environmental burdens while supporting useful material and energy recovery. [1] [2] [3]

Award Suitability

Yalçın’s documented research activity is relevant to recognition in sustainable and renewable-energy-oriented research because her work integrates waste valorization, environmental engineering, materials development, and biogas production. Her publication portfolio demonstrates a coherent interest in practical sustainability challenges, supported by experimental investigation and analytical methods. These characteristics provide a reasonable academic basis for consideration for the Best Researcher Award. [1] [2] [3]

Conclusion

Zehra Gülten Yalçın’s recent scholarship demonstrates interdisciplinary engagement with sustainable engineering problems involving materials, waste, corrosion, and renewable-energy processes. Her research combines experimental studies with optimization and analytical approaches, contributing to applied knowledge in environmentally relevant engineering fields. The breadth and practical orientation of these publications support continued development within sustainable technology research. [1] [2] [3]

References

  1. Dağ, M., Aydoğmuş, E., Yalçın, Z. G., & Arslanoğlu, H. (2026). Valorization of industrial waste in polymer composites: Enhancing mechanical and thermal properties for insulation applications using machine learning analysis. Polymer Engineering & Science, 66(1), 470–486.
    https://doi.org/10.1002/pen.70230
  2. Hussein, M. Y., Yalçın, Z. G., Yaqoob, G. B., & Dağ, M. (2025). Investigation of the corrosion-inhibition effect of Turkish coffee extract on L-80 carbon steel in 15% HCl: Thermodynamic and surface analyses. Petroleum Science and Technology, 43(25), 3757–3795.
    https://doi.org/10.1080/10916466.2025.2536474
  3. Günay, K., & Yalçın, Z. G. (2025). Maximizing biogas yield in anaerobic digestion: A response surface methodology approach. Black Sea Journal of Engineering and Science, 8(4), 1103–1110.
    https://doi.org/10.34248/bsengineering.1683991
  4. Elsevier. (n.d.). Scopus author details: Zehra Gülten Yalçın, Author ID 6603311969. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=6603311969
  5. ORCID. (n.d.). Zehra Gülten Yalçın: ORCID record 0000-0001-5460-289X. ORCID.
    https://orcid.org/0000-0001-5460-289X

Mohamed Edrris | Embedded Systems | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Mohamed Edrris King Saud University, Sudan

Mohamed Edrris
Affiliation King Saud University
Country Sudan
Scopus ID 57205660266
Documents 16
Citations 175
h-index 8
Subject Area Embedded Systems
Event Technology Scientists Awards
ORCID 0009-0002-9577-8853

Mohamed Edrris is a researcher affiliated with King Saud University whose scholarly work intersects embedded systems, sensing technologies, precision agriculture, and controlled-environment crop research. His publication record includes studies on load-cell-based sensing and hydroponic tomato assessment, reflecting an applied orientation toward measurement, automation, and data-supported agricultural engineering research and innovation.[1][2][3]

Abstract

Mohamed Edrris is affiliated with King Saud University and is identified in the supplied profile with Embedded Systems as his subject area. His research record includes sensing, simulation, validation, and agricultural applications. Reported publications address a load-cell-based fertilizer flow sensor and hydroponic tomato responses to salinity using spectral, photosynthetic, yield, and quality measurements. These studies demonstrate an applied research orientation connecting instrumentation, data analysis, and precision agriculture. The supplied Scopus indicators list 16 documents, 175 citations, and an h-index of 8. Collectively, the documented work provides evidence of interdisciplinary technical activity relevant to innovation in sensing and agricultural engineering research.[1][2][3]

Keywords

  • Embedded Systems
  • Sensor Technology
  • Precision Agriculture
  • Agricultural Engineering
  • Hydroponic Agriculture
  • Numerical Simulation
  • Spectral Indices
  • Research Innovation

Introduction

Mohamed Edrris is a researcher affiliated with King Saud University whose scholarly work intersects embedded systems, sensing technologies, precision agriculture, and controlled-environment crop research. His publication record includes studies on load-cell-based sensing and hydroponic tomato assessment, reflecting an applied orientation toward measurement, automation, and data-supported agricultural engineering research and innovation.[1][2][3]

Research Profile

The available research profile identifies Mohamed Edrris with King Saud University and the subject area of Embedded Systems. His Scopus record lists 16 documents, 175 citations, and an h-index of 8. These indicators provide a quantitative context for evaluating his scholarly activity and research visibility across technological domains and applications.[1][2]

Research Contributions

Edrris has contributed to research involving sensor development, numerical simulation, validation, spectral measurements, and plant-performance assessment. His documented work includes data curation, software, and validation responsibilities in hydroponic tomato research, while another publication examines a load-cell-based sensing system for fertilizer flow measurement and monitoring applications for real-world agricultural monitoring systems.[1][2]

Publications

The publication record supplied for this recognition includes studies addressing agricultural sensing and hydroponic tomato production. One 2026 Applied Sciences article develops and validates a load-cell-based mass-flow sensor, while two 2024 and 2025 studies examine salinity, spectral indices, photosynthetic parameters, yield, and fruit quality in hydroponic tomato systems and automation.[1][2][3]

Research Impact

The cited publications demonstrate research relevance to measurement technologies and precision agriculture. The load-cell study combines numerical simulation with laboratory validation for fertilizer-flow sensing, while the tomato studies apply sensing and physiological measurements to characterize salinity responses. Together, these works illustrate interdisciplinary applications of instrumentation and data-driven agricultural assessment in settings.[1][2][3]

Award Suitability

The Innovative Research Award is academically aligned with the documented combination of sensing, simulation, validation, and applied agricultural research. Edrris’s publication contributions provide evidence of technical engagement with measurement systems and experimental analysis, while his Scopus indicators offer context regarding research productivity, citation activity, and scholarly influence within his field.[1][2][3]

Conclusion

Mohamed Edrris presents a research profile connecting embedded systems with practical sensing and agricultural applications. His documented publications address sensor validation and hydroponic crop assessment, supported by measurable Scopus indicators. On the supplied evidence, his work demonstrates a research trajectory appropriate for consideration under an Innovative Research Award within research.[1][2][3]

References

  1. Edrris, M., Al-Gaadi, K., Tola, E., & Gaddal, Y. (2026). Development, numerical simulation and laboratory validation of a load-cell-based mass flow rate measuring sensor for dry fertilizers in seed drills. Applied Sciences, 16(13), 6571.
    https://doi.org/10.3390/app16136571
  2. Al-Gaadi, K. A., Zeyada, A. M., Tola, E., Madugundu, R., Edrris, M. K., & Mahjoop, O. (2025). Use of spectral indices and photosynthetic parameters to evaluate the growth performance of hydroponic tomato at different salinity levels. PLoS ONE, 20(6), e0325839.
    https://doi.org/10.1371/journal.pone.0325839
  3. Al-Gaadi, K. A., Zeyada, A. M., Tola, E., Alhamdan, A. M., Ahmed, K. A. M., Madugundu, R., & Edrris, M. K. (2024). Quantitative and qualitative responses of hydroponic tomato production to different levels of salinity. Phyton-International Journal of Experimental Botany, 93(6), 1311–1323.
    https://doi.org/10.32604/phyton.2024.049535

Jiabo Ding | Simulation | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Jiabo Ding — Chinese Academy of Agricultural Sciences, China

Jiabo Ding
Affiliation Chinese Academy of Agricultural Sciences
Country China
Scopus ID 12804951700
Documents 133
Citations 1,086
h-index 17
Subject Area Simulation
Event Technology Scientists Awards
ORCID 0000-0002-8515-9031

Jiabo Ding is a researcher affiliated with the Chinese Academy of Agricultural Sciences whose documented scholarly work includes studies spanning animal health, infection biology, molecular profiling, and genetic manipulation. His publication record includes research employing proteomic, transcriptomic, and genetic approaches, providing an interdisciplinary basis for evaluating research activity in simulation and related computationally informed scientific domains.[1][2][3]

Abstract

Jiabo Ding, affiliated with the Chinese Academy of Agricultural Sciences, has a documented research profile encompassing animal biosafety, infectious diseases, molecular biology, proteomics, transcriptomics, and genetic manipulation. His recent publications demonstrate participation in multidisciplinary studies using contemporary experimental and analytical approaches. Research addressing feline calicivirus biomarkers, Brucella-associated immune dysregulation, and genetic manipulation of Eimeria illustrates engagement with data-intensive biological investigation. These contributions provide evidence of sustained scholarly activity and collaborative research across veterinary and biomedical science. The available publication record and reported bibliometric indicators provide a basis for recognition under a researcher-focused award framework within Technology Scientists Awards.[1][2][3]

Keywords

Jiabo Ding; Best Researcher Award; Chinese Academy of Agricultural Sciences; Simulation; animal biosafety; veterinary science; infectious disease research; proteomics; transcriptomics; genetic manipulation; Eimeria; Brucella abortus; feline calicivirus; biomedical research.

Introduction

Research in contemporary veterinary and biomedical science increasingly integrates experimental biology with computational analysis, molecular profiling, and systems-level interpretation. Jiabo Ding’s documented publications reflect this multidisciplinary environment, addressing infectious disease mechanisms, biomarkers, immune responses, and genetic technologies. These studies demonstrate collaborative engagement with complex biological questions and modern research methodologies.[1][2][3]

Research Profile

Jiabo Ding’s research profile is associated with the Chinese Academy of Agricultural Sciences and encompasses animal biosafety, veterinary infectious diseases, molecular diagnostics, and parasite biology. His recent scholarly contributions include proteomic analysis of feline calicivirus infection, single-cell transcriptomic investigation of Brucella infection, and review of genetic manipulation approaches for Eimeria, demonstrating broad biological research engagement.[1][2][3]

Research Contributions

The documented contributions associated with Jiabo Ding include participation in studies that identify molecular biomarkers, characterize infection-associated immune responses, and assess emerging genetic manipulation technologies. These works employ complementary methodologies, including serum proteomics, single-cell RNA sequencing, flow cytometry, and genetic engineering. Collectively, they contribute evidence toward improved understanding of animal pathogens and disease mechanisms.[1][2][3]

Publications

Selected publications involving Jiabo Ding demonstrate activity across molecular veterinary research and infectious disease biology. The 2026 study on feline calicivirus reported proteomic identification of candidate biomarkers, while research on Brucella abortus applied single-cell transcriptomics to characterize immune dysregulation. A 2025 iScience review examined genetic manipulation advances in the non-model protozoan Eimeria.[1][2][3]

Research Impact

The research record indicates impact through contributions to understanding pathogen biology, host responses, biomarker discovery, and genetic manipulation. The cited studies address practical scientific challenges in veterinary health and infectious disease research. Their use of molecular and single-cell methodologies supports deeper characterization of biological processes and may inform future diagnostic, therapeutic, preventive, or experimental strategies.[1][2][3]

Award Suitability

The available scholarly record supports consideration of Jiabo Ding for a Best Researcher Award based on documented publication activity, multidisciplinary research participation, and contributions to contemporary veterinary and biomedical investigation. His reported profile includes 133 documents, 1,086 citations, and an h-index of 17, while selected publications demonstrate sustained involvement in collaborative, methodologically diverse research.[1][2][3]

Conclusion

Jiabo Ding’s documented research demonstrates sustained engagement with important questions in veterinary science, infectious disease biology, molecular profiling, and genetic technologies. His participation in studies involving proteomics, single-cell transcriptomics, and Eimeria genetic manipulation illustrates methodological breadth. Together with the reported bibliometric indicators, these contributions provide a substantive scholarly basis for researcher recognition.[1][2][3]

References

  1. Xu, C., Liu, H., Gu, H., Wu, D., Tang, X., Liang, L., Hou, S., Ding, J., & Liang, R. (2026). Serum proteomic profiling identifies ACSL4 and S100A2 as novel biomarkers in feline calicivirus infection. International Journal of Molecular Sciences, 27(2), 1047.
    https://pubmed.ncbi.nlm.nih.gov/41596690/
  2. Zhang, G., Shen, Q., Ye, J., Feng, Y., Boireau, P., Fan, X., Lv, L., Li, Y., Xu, X., Cha, H., Shen, C., Zhang, Y., Peng, X., Jiang, H., & Ding, J. (2026). Single-cell transcriptome profiling reveals the immune dysregulation characteristics of mice infected with Brucella abortus. The Journal of Infectious Diseases, 233(1), e55–e66.
    https://pubmed.ncbi.nlm.nih.gov/41074555/
  3. Li, Y., Suo, J., Liang, R., Liang, L., Liu, X., Ding, J., Suo, X., & Tang, X. (2025). Genetic manipulation for the non-model protozoan Eimeria: Advancements, challenges, and future perspective. iScience, 28(3), 112060.
    https://www.sciencedirect.com/science/article/pii/S2589004225003207

Gracia Sanchez Carpena | Machine Learning | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Gracia Sanchez Carpena — University of Murcia, Spain

Gracia Sanchez Carpena
Affiliation University of Murcia
Country Spain
Scopus ID 7202034595
Documents 27
Citations 587
h-index 13
Subject Area Machine Learning
Event Technology Scientists Awards

Gracia Sanchez Carpena is a researcher affiliated with the University of Murcia whose scholarly work is situated within machine learning, evolutionary optimization, feature selection, and ensemble learning. Recent publications address computational strategies for high-dimensional data and integrated optimization of predictive models, feature subsets, and aggregation mechanisms. [1] [2]

Abstract

Gracia Sanchez Carpena’s research profile reflects sustained engagement with machine learning and evolutionary computation, particularly feature selection and ensemble optimization. Her recent scholarly contributions address high-dimensional data through permutation-based multi-objective feature selection and the joint evolutionary optimization of heterogeneous ensembles, learner-specific feature subsets, and aggregation weights. These studies investigate methods that balance predictive performance, computational considerations, and model complexity. The reported work spans classification, regression, and ensemble learning settings, demonstrating an applied research orientation toward scalable optimization strategies. Her publication record and citation indicators provide additional evidence of scholarly activity in machine learning. [1] [2]

Keywords

Machine Learning; Feature Selection; Multi-Objective Evolutionary Algorithms; High-Dimensional Data; Ensemble Learning; Evolutionary Optimization; Regression; Predictive Modeling. [1] [2]

Introduction

High-dimensional machine learning requires effective strategies for identifying informative variables while controlling computational cost and model complexity. Recent research associated with Gracia Sanchez Carpena examines multi-objective evolutionary approaches that address these challenges by combining predictive performance with feature reduction. One study evaluates feature subsets through permutation-based degradation of model performance, extending conventional feature-importance concepts toward subset-level analysis. [1]

Research Profile

The research profile centers on machine learning methodologies involving evolutionary search, feature selection, and ensemble construction. The published work demonstrates particular interest in multi-objective optimization, where competing requirements such as predictive accuracy and structural simplicity are considered simultaneously. This orientation is evident in both high-dimensional feature-selection research and recent work on heterogeneous regression ensembles. [1] [2]

Research Contributions

A notable contribution is the development of permutation-based subset evaluation using a multi-objective evolutionary algorithm for high-dimensional feature selection. The approach evaluates groups of attributes rather than isolated variables and simultaneously considers predictive degradation and subset cardinality. Related work extends evolutionary optimization to heterogeneous ensembles by jointly selecting learners, learner-specific features, and aggregation weights within an integrated framework. [1] [2]

Publications

The publication record includes recent peer-reviewed studies addressing evolutionary machine learning and optimization. The article on permutation-based multi-objective evolutionary feature selection was published in Knowledge and Information Systems in 2026 and investigates high-dimensional classification and regression datasets. A second 2026 publication in Algorithms studies simultaneous optimization of heterogeneous ensembles, feature subsets, and aggregation weights for regression. [1] [2]

Research Impact

The reported research has relevance to machine learning applications where feature dimensionality, predictive accuracy, and model complexity must be balanced. The feature-selection study evaluates its methodology across 27 high-dimensional datasets and reports reductions in feature counts alongside predictive-performance improvements. The ensemble study similarly investigates predictive error and structural sparsity through multi-objective optimization. [1] [2]

Award Suitability

Based on the supplied bibliometric indicators and documented publications, Gracia Sanchez Carpena presents a research profile aligned with an innovative research recognition in machine learning. Her recent work demonstrates methodological development rather than solely application, with evolutionary optimization used to address feature-selection and ensemble-design problems. The combination of 27 documents, 587 citations, and an h-index of 13 further contextualizes the supplied scholarly record. [1] [2]

Conclusion

Gracia Sanchez Carpena’s documented research demonstrates a coherent focus on machine learning, evolutionary optimization, feature selection, and ensemble learning. Her recent publications contribute methods for addressing high-dimensional data and jointly optimizing predictive architectures. The combination of methodological research, peer-reviewed publication, and the supplied bibliometric record provides a reasonable scholarly basis for consideration for an Innovative Research Award. [1] [2]

References

  1. Espinosa, R., Sánchez, G., Palma, J., & Jiménez, F. (2026). Permutation-based multi-objective evolutionary feature selection for high-dimensional data. Knowledge and Information Systems, 68, 116.
    https://doi.org/10.1007/s10115-026-02734-0
  2. Galván, J., Sánchez, G., & Jiménez, F. (2026). Simultaneous multi-objective evolutionary optimization of heterogeneous ensembles, learner-specific feature subsets, and aggregation weights. Algorithms, 19(8), 681.
    https://doi.org/10.3390/a19080681

Wentao Shang | Green Technology | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Wentao Shang
Affiliation Jinan University
Country China
Scopus ID 57604364900
Documents 34
Citations 812
h-index 15
Subject Area Green Technology
Event Technology Scientists Awards
ORCID 0000-0002-5168-7696

Wentao Shang is affiliated with Jinan University, China, and works across membrane science, separation technologies, computational prediction, imaging, and advanced materials. His recent scholarly record includes research on membrane distillation, nanofiltration fouling prediction, and supramolecular materials, providing a multidisciplinary basis for consideration within the field of green technology. [1] [2] [3]

Abstract

Wentao Shang is a researcher at Jinan University whose documented work connects membrane science, green technology, computational modeling, imaging, and advanced materials. His recent publications examine surface patterning for membrane distillation, multimodal convolutional neural networks for dynamic nanofiltration fouling prediction, and solution-sheared supramolecular oligomers with improved thermal-resistant adhesion. These studies demonstrate an interdisciplinary approach combining materials engineering, separation processes, experimental characterization, and data-driven analysis. With 34 documented publications, 812 citations, and an h-index of 15, his profile indicates sustained scholarly activity and measurable research visibility. The breadth and environmental relevance of these themes support consideration for a Best Researcher Award.

Keywords

Keywords: Green Technology, Membrane Distillation, Nanofiltration, Membrane Fouling, Optical Coherence Tomography, Convolutional Neural Networks, Surface Patterning, Advanced Materials, Supramolecular Oligomers, Sustainable Engineering.

Introduction

Wentao Shang’s research profile at Jinan University reflects an interdisciplinary focus connecting membrane processes, nanofiltration, imaging-based analysis, advanced materials, and sustainable engineering. His recent publications address membrane distillation, fouling prediction, and thermally resistant supramolecular materials, indicating a research trajectory relevant to emerging green technology and resource-efficient engineering. [1] [2] [3]

Research Profile

Shang is associated with research spanning membrane science, separation technologies, computational prediction, and functional materials. His publication record includes studies using surface patterning to improve membrane distillation and multimodal convolutional neural networks to model nanofiltration fouling. These themes connect experimental characterization, materials engineering, and data-driven methods for environmental applications. [1] [2]

Research Contributions

Shang’s contributions can be viewed through three complementary areas: engineering membrane surfaces for improved separation performance, applying in-situ optical coherence tomography and multimodal neural networks to characterize fouling dynamics, and investigating supramolecular materials with enhanced thermal and adhesive properties. Together, these studies demonstrate integration of experimental methods, computational analysis, and materials design. [1] [2] [3]

Publications

The documented publications associated with Shang include a 2026 review of surface patterning in membrane distillation, a 2026 Desalination article on multimodal convolutional neural networks for nanofiltration fouling prediction, and a Nature Communications study on solution-sheared supramolecular oligomers. The works collectively cover membrane engineering, machine learning, imaging, adhesion, and advanced materials. [1] [2] [3]

Research Impact

The research has potential relevance to green technology through improved membrane efficiency, fouling management, and durable functional materials. Surface-engineered membranes may support cleaner separation processes, while predictive imaging models can improve understanding of fouling development. Work on thermally resistant adhesives further broadens the profile toward resource-conscious and performance-oriented materials engineering. [1] [2] [3]

Award Suitability

The Best Researcher Award profile is supported by a combination of publication activity, citation indicators, interdisciplinary research themes, and alignment with green technology. The reported record of 34 documents, 812 citations, and an h-index of 15 provides quantitative evidence of scholarly visibility, while recent publications demonstrate continuing research activity. [1] [2] [3]

Conclusion

Wentao Shang presents a research profile combining membrane technology, computational modeling, imaging, and advanced materials. His recent work addresses practical challenges in separation efficiency, fouling prediction, and material durability. The combination of documented scholarly output and green-technology relevance provides a reasonable academic basis for consideration under the Best Researcher Award. [1] [2] [3]

References

  1. Zhang, C., Lin, Y., Lu, G., Yuan, B., Chen, P., Farid, M. U., Lee, V. P. H., Shang, W., Li, W., & An, A. K. (2026). Surface patterning in membrane distillation: Fabrication, mechanism, and performance enhancement. Separation and Purification Technology, 394(Part 3), Article 137561.
    https://www.sciencedirect.com/science/article/abs/pii/S1383586626008270
  2. Shang, W., Zeng, Y., Xiao, F., Wu, M., Wang, Y., Yang, Z., He, J., & Sun, F. (2026). A multimodal convolutional neural network trained by in-situ OCT characterization for dynamic structural prediction of nanofiltration fouling. Desalination, 639, Article 120676.
    https://www.sciencedirect.com/science/article/pii/S0011916426008325
  3. Lu, G., Ma, R., Zhao, Y., Wang, D., Shang, W., Chen, H., Khan, S. A., Li, M., & Saiz, E. (2025). Solution-sheared supramolecular oligomers with enhanced thermal resistance in interfacial adhesion and bulk cohesion. Nature Communications, 16, 7754.
    https://www.nature.com/articles/s41467-025-63123-9
  4. Elsevier. (n.d.). Scopus author details: Wentao Shang, Author ID 57604364900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57604364900
  5. ORCID. (n.d.). Wentao Shang, ORCID 0000-0002-5168-7696. ORCID.
    https://orcid.org/0000-0002-5168-7696

Yuanyao Miao | Technology | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award: Yuanyao Miao

Yuanyao Miao is affiliated with Xi’an University of Architecture and Technology, China, and is engaged in technology-oriented research involving structural dynamics and engineering assessment. His scholarly record includes research on masonry pagodas exposed to combined vibration, inclination, and material effects, connecting computational analysis with practical preservation challenges in structural engineering. [1] [2]

Yuanyao Miao
Affiliation Xi’an University of Architecture and Technology
Country China
Scopus ID 36773518800
Documents 15
Citations 177
h-index 6
Subject Area Technology
Event Technology Scientists Awards

Abstract

Yuanyao Miao is a researcher affiliated with Xi’an University of Architecture and Technology, China, with scholarly activity in technology and structural engineering. His indexed record includes 15 documents, 177 citations, and an h-index of 6. A 2026 publication examines masonry pagodas under coupled train, human, soil, inclination, and material effects. Using measurements, theoretical analysis, and three-dimensional finite element simulation, the study evaluates dynamic response, damage development, and fatigue life. Its findings support technology-based assessment and conservation of historic masonry structures exposed to complex vibration environments, providing evidence of interdisciplinary engineering research and practical relevance to resilient structural preservation. [1] [2]

Keywords

Relevant keywords for this recognition profile include structural dynamics, masonry pagodas, vibration analysis, soil–structure interaction, finite element analysis, fatigue life prediction, structural preservation, engineering technology, heritage conservation, numerical simulation, multi-source vibration, material deterioration, dynamic response, resilient structures, applied engineering, technology research, computational mechanics, infrastructure assessment, historic structures, and scholarly research. [1]

Introduction

Yuanyao Miao is a researcher affiliated with Xi’an University of Architecture and Technology, China, whose indexed work addresses technology-related engineering problems. His publication record includes research on the dynamic behavior and service-life assessment of masonry pagodas under combined environmental and human-induced effects, demonstrating engagement with applied structural technology. [1] [2]

Research Profile

Miao’s Scopus-indexed profile reports 15 documents, 177 citations, and an h-index of 6, indicating a sustained research record with measurable scholarly visibility. His work connects structural dynamics, numerical simulation, vibration analysis, material deterioration, and life prediction, placing his research within an interdisciplinary technology-oriented framework relevant to resilient engineering. [1] [2]

Research Contributions

Miao contributed to research examining masonry pagodas under multi-factor coupled effects, integrating in-situ measurements, theoretical analysis, and numerical simulation. The study considers train-induced vibration, human-induced loading, structural inclination, and material degradation together, offering a comprehensive basis for evaluating dynamic response, damage development, and fatigue life under realistic service conditions. [1]

Publications

Miao is a coauthor of “Dynamic response and life prediction of masonry pagodas under multi-factor effects,” published in the Journal of Vibration and Shock in 2026. The article investigates the Giant Wild Goose Pagoda and reports dynamic behavior, stress, plastic strain, and fatigue life estimates under service conditions. [1]

Research Impact

The reported research provides technical evidence for assessing historic masonry structures exposed to complex vibration environments. Its combination of finite element modeling, soil–structure interaction, multi-source excitation, and fatigue-life analysis can support condition assessment and conservation planning. The study contributes an applied technology perspective to structural preservation and risk-informed maintenance. [1]

Award Suitability

Miao’s documented publication activity, citation record, and focused contribution to structural technology provide a reasonable basis for consideration for the Best Researcher Award. His research demonstrates methodological integration and practical relevance, while the indexed bibliographic record supplies traceable evidence that can support an objective assessment of scholarly achievement. [1] [2]

Conclusion

Yuanyao Miao presents a research profile centered on applied technology, structural dynamics, and preservation-oriented engineering analysis. His documented scholarly metrics and contribution to research on complex vibration effects provide evidence of academic activity and relevance. Based on available indexed information, his work aligns with the award’s recognition purpose. [1] [2]

References

  1. Ma, J., Miao, Y., Ren, R., Lu, W., Liu, R., Qian, C., & Li, D. (2026). Dynamic response and life prediction of masonry pagodas under multi-factor coupled effects. Journal of Vibration and Shock, 45(15), 35–46.
    https://jvs.sjtu.edu.cn/EN/Y2026/V45/I15/35
  2. Elsevier. (n.d.). Scopus author details: Yuanyao Miao, Author ID 36773518800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=36773518800