Donghan Li | Materials Science | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Donghan Li
Affiliation Shenyang University of Chemical Technology; Liaoning Key Laboratory of Polymer Materials Application Technology
Country China
Scopus ID 57189616692
Documents 48
Citations 596
h-index 14
Subject Area Materials Science
Event Best Paper Awards
Award Best Paper Award

Donghan Li

Donghan Li of Shenyang University of Chemical Technology, China, is recognized with the Best Paper Award for research contributions in materials science, particularly in the development of functional polymeric gels and advanced biomass-derived materials. This profile highlights the researcher’s academic record and the awarded publication titled “From Biomass to Functional Gels: Multiscale Topology Reconstruction and High-Performance Applications.”

Abstract

This article recognizes Donghan Li for receiving the Best Paper Award in recognition of research excellence in materials science. The awarded research, titled “From Biomass to Functional Gels: Multiscale Topology Reconstruction and High-Performance Applications,” addresses the development of functional gels derived from biomass and focuses on multiscale topology reconstruction and high-performance applications. The work represents a research direction connecting biomass resources, functional polymer materials, structural design, and advanced material applications.

Keywords

Biomass-Derived Materials, Functional Gels, Polymer Materials, Multiscale Topology, Topology Reconstruction, High-Performance Gels, Sustainable Materials, Materials Science, Functional Polymer Networks, Advanced Materials.

Introduction

The development of sustainable and high-performance materials has become an important direction in modern materials science. Biomass provides an abundant source of renewable materials that can be transformed into functional polymeric structures and advanced gel systems. Functional gels are particularly important because their three-dimensional networks can be engineered to provide specialized mechanical, chemical, physical, and application-oriented properties.

Research Profile

Donghan Li is affiliated with Shenyang University of Chemical Technology and the Liaoning Key Laboratory of Polymer Materials Application Technology in China. The researcher’s subject area is Materials Science. The available profile records 48 documents, 596 citations, and an h-index of 14. These bibliometric indicators demonstrate an established publication record and significant academic visibility within the research field.

Research Contributions

The awarded publication, “From Biomass to Functional Gels: Multiscale Topology Reconstruction and High-Performance Applications,” contributes to materials science by examining how biomass-derived resources can be transformed into functional gel materials. The research emphasizes multiscale topology reconstruction as an important approach for controlling material architecture and improving functional performance.

Research Impact

The academic profile of Donghan Li records 596 citations across 48 documents, with an h-index of 14. These indicators demonstrate substantial scholarly engagement with the researcher’s published work and establish a strong research presence within materials science.

Award Suitability

The Best Paper Award recognizes research demonstrating academic quality, technical relevance, originality, and meaningful contribution to its respective field. Donghan Li’s awarded research aligns with these objectives through its focus on biomass-derived functional gels, multiscale topology reconstruction, and high-performance material applications.

Conclusion

Donghan Li’s research represents a valuable contribution to the field of Materials Science, particularly in the area of biomass-derived functional polymer materials and advanced gel systems. The awarded publication, “From Biomass to Functional Gels: Multiscale Topology Reconstruction and High-Performance Applications,” highlights the importance of multiscale structural design in developing high-performance functional gels.

External Links

References

  1. Scopus Author Profile: Donghan Li, Author ID 57189616692.
    Scopus. https://www.scopus.com/pages/authors/57189616692
  2. ORCID Research Profile: Donghan Li.
    ORCID: https://orcid.org/0000-0003-1058-9552
  3. Best Paper Awards.
    https://bestpaperawards.com/

Shui Pang | Power electronics | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Shui Pang
Affiliation Tianjin University
Country China
Scopus ID 58018126000
Documents 21
Citations 188
h-index 6
Subject Area Power Electronics
Event Best Paper Awards
Award Best Paper Award

Shui Pang

Shui Pang of Tianjin University, China, is recognized with the Best Paper Award for research contributions in power electronics and wireless power transfer technology. This profile highlights the researcher’s academic record and the awarded publication titled “High efficiency three-dimensional wireless power transfer system using cylindrical transmitting coil.”

Abstract

This article recognizes Shui Pang for receiving the Best Paper Award in recognition of research excellence in power electronics and wireless power transfer technology. The awarded research, titled “High efficiency three-dimensional wireless power transfer system using cylindrical transmitting coil,” focuses on the development of an efficient three-dimensional wireless power transfer system based on a cylindrical transmitting coil. The work represents an important research direction in wireless power transfer, with potential relevance to efficient energy transmission and advanced power-electronic systems.

Keywords

Wireless Power Transfer, Three-Dimensional Wireless Power Transfer, Cylindrical Transmitting Coil, Power Electronics, Wireless Energy Transfer, High-Efficiency Power Transfer, Electromagnetic Coupling, Energy Transmission.

Introduction

Wireless power transfer is an important research area within modern power electronics, enabling electrical energy to be transferred without conventional wired connections. Improvements in transfer efficiency, spatial flexibility, coil configuration, and system design are essential for expanding the practical applications of wireless energy systems. Research into three-dimensional wireless power transfer can contribute to more flexible energy-transfer arrangements by addressing the limitations associated with conventional planar configurations.

Research Profile

Shui Pang is affiliated with Tianjin University, China, and conducts research in the field of Power Electronics. The available research profile records 21 documents, 188 citations, and an h-index of 6. These bibliometric indicators demonstrate an established publication record and measurable academic influence within the researcher’s field.

Research Contributions

The awarded publication, “High efficiency three-dimensional wireless power transfer system using cylindrical transmitting coil,” contributes to wireless power transfer research by investigating a cylindrical transmitting-coil configuration for three-dimensional energy transmission. The study focuses on improving the efficiency and flexibility of wireless power transfer systems through an alternative coil geometry.

Research Impact

The academic profile of Shui Pang records 188 citations across 21 documents, with an h-index of 6. These metrics indicate that the researcher’s publications have received substantial attention within the scientific literature. The awarded research adds to this body of work by addressing high-efficiency three-dimensional wireless power transfer, an area with continuing relevance to modern power-electronic and energy-transfer technologies.

Award Suitability

The Best Paper Award recognizes research that demonstrates academic quality, technical relevance, originality, and meaningful contribution to its respective field. Shui Pang’s research aligns with these objectives through its focus on high-efficiency three-dimensional wireless power transfer and the application of a cylindrical transmitting coil.

Conclusion

Shui Pang’s research represents a valuable contribution to the field of power electronics, particularly in the area of wireless power transfer. The awarded publication, “High efficiency three-dimensional wireless power transfer system using cylindrical transmitting coil,” explores an innovative three-dimensional approach to wireless energy transmission. With 21 documents, 188 citations, and an h-index of 6, the research profile demonstrates meaningful academic activity and impact. The Student Research Paper Award recognizes this contribution and highlights the importance of continued research into efficient and flexible wireless power-transfer technologies.

External Links

References

  1. Scopus Author Profile: Shui Pang, Author ID 58018126000.
    Scopus. https://www.scopus.com/pages/authors/58018126000
  2. ORCID Research Profile: Shui Pang.
    ORCID. https://orcid.org/0000-0002-7477-1436

Fei HE | Engineering | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Fei He
Affiliation Anhui University of Technology
Country China
Subject Area Engineering
Event Best Paper Awards
Research Gate Fei-He-21

Fei He

Fei He of Anhui University of Technology, China, is recognized in connection with the Best Paper Award for research concerning machine vision and its application to steelmaking processes. The associated publication, “Advances in the Application of Machine Vision in Steelmaking Processes,” presents a review of machine-vision technologies used across multiple stages of steel production, including hot metal pretreatment, converter steelmaking, secondary refining, and continuous casting. The article was published in steel research international, volume 97, issue 4, pages 1813–1830, in 2026, and was first published online on 8 November 2025. The article has DOI 10.1002/srin.202500769. [1]

Abstract

The Best Paper Award recognition highlights research by Fei He and co-authors on the application of machine vision within steelmaking processes. The recognized review examines the development of machine-vision hardware and analytical algorithms, covering the progression from traditional image-processing techniques and machine learning to contemporary deep-learning approaches. It further surveys applications across hot metal pretreatment, converter steelmaking, secondary refining, continuous casting, and related steel-production operations. The publication identifies machine vision as a technological component supporting process optimization, abnormal-condition prediction, product classification and grading, and increased automation in steel manufacturing. [1]

Keywords

Machine Vision, Steelmaking, Artificial Intelligence, Deep Learning, Machine Learning, Image Processing, Intelligent Steelmaking, Converter Steelmaking, Continuous Casting, Secondary Refining, Hot Metal Pretreatment, Industrial Automation, Metallurgical Engineering, Digital Twin.

Introduction

Machine vision has become an increasingly important technology in industrial automation because it enables visual information to be acquired and processed for monitoring, classification, measurement, and decision support. In steelmaking, where production involves high temperatures, complex physical environments, continuous material movement, and demanding quality requirements, machine-vision systems can provide additional sources of information for process monitoring and control. The recognized publication examines the development and application of these systems throughout the steelmaking production chain. [1]

Research Profile

Fei He is affiliated with Anhui University of Technology in China and is associated with research in engineering and metallurgical applications of computational and machine-vision technologies. The recognized publication lists Fei He as a corresponding author and identifies the School of Metallurgical Engineering at Anhui University of Technology as the institutional affiliation. [1]

Research Contributions

The principal contribution of the recognized publication is its systematic overview of machine-vision development in steelmaking. The authors describe the evolution from conventional image-processing methods to machine-learning and deep-learning algorithms and examine how these technologies can be incorporated into industrial steel-production environments. [1]

Publications

The principal publication associated with this recognition is a peer-reviewed review article published in steel research international:

  • Wang, X., He, F., Xu, C., Wu, T., Zhuang, X., Zhang, R., Xie, W., Liu, Y., & Li, H. (2026). Advances in the Application of Machine Vision in Steelmaking Processes. steel research international, 97(4), 1813–1830. DOI: https://doi.org/10.1002/srin.202500769. [1]

Research Impact

The recognized publication contributes to research on intelligent steel manufacturing by consolidating evidence concerning the use of machine vision across different stages of steel production. By connecting visual sensing with machine-learning and deep-learning algorithms, the review provides a structured account of how visual information can support monitoring, prediction, classification, and automation. [1]

Award Suitability

The Best Paper Award recognition is associated with scholarly work demonstrating relevance, methodological organization, research value, and contribution to an academic or professional field. The publication associated with Fei He addresses a clearly defined engineering topic and provides a broad review of machine-vision technologies and their applications within steelmaking. [1]

Conclusion

Fei He is associated with engineering research at Anhui University of Technology, with the recognized publication focusing on machine vision and intelligent steelmaking. The article provides a comprehensive review of the development of machine-vision hardware and algorithms and examines their applications throughout several stages of steel production. [1]

External Links

References

    1. Wang, X., He, F., Xu, C., Wu, T., Zhuang, X., Zhang, R., Xie, W., Liu, Y., & Li, H. (2026). Advances in the Application of Machine Vision in Steelmaking Processes. steel research international, 97(4), 1813–1830. First published online 8 November 2025. DOI: https://doi.org/10.1002/srin.202500769.

Mandara SM | Business | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Mandara SM
Affiliation School of Commerce and Management, SVU
Country India
Subject Area Business
Event Best Paper Awards
ORCID 0009-0006-6234-7618

Mandara SM

Mandara SM of the School of Commerce and Management, Shri Venkateshwara University (SVU), India, is recognized in connection with the Best Paper Award for research examining work–life balance within peripheral and informal labour markets. The recognized publication, “Reconstructing Work–Life Balance in Peripheral Labour Markets: Structural, Sectoral, and Gendered Realities of Informal Employment in Chikmagalur District,” investigates work–life balance across informal employment sectors in Chikmagalur District, Karnataka, with attention to structural, sectoral, occupational, and gender-related conditions. The article was published in Advanced International Journal for Research, Volume 6, Issue 6, in 2025, and is identified with DOI 10.63363/aijfr.2025.v06i06.2270. [1]

Abstract

The recognized research examines work–life balance among workers employed in informal sectors of Chikmagalur District, Karnataka. The study considers five major areas of informal employment—construction, agriculture, textiles, hotels, and department stores—and investigates how working hours, job security, physical strain, income stability, and gendered responsibilities shape workers’ ability to maintain work–life balance. It uses a mixed-method approach incorporating structured surveys and focus group discussions to examine the experiences of informal workers. The reported findings indicate substantial sectoral differences, with construction and agriculture associated with more difficult work–life balance conditions, while textile and hotel employment showed comparatively more balanced outcomes. Gender differences were also reported, with women experiencing additional pressures arising from the combination of paid employment and domestic responsibilities. [1]

Keywords

Work-Life Balance, Informal Labour, Peripheral Labour Markets, Gendered Employment, Informal Economy, Occupational Stress, Labour Welfare, Employment Conditions, Chikmagalur District, India.

Introduction

Work–life balance is an important dimension of employment quality because working conditions influence how individuals allocate time between paid employment, household responsibilities, rest, and other aspects of personal life. In informal labour markets, these relationships may be particularly complex because employment can be characterized by variable working hours, limited employment security, fluctuating income, and comparatively fewer formal workplace protections. The recognized study focuses on these conditions within the context of Chikmagalur District in Karnataka, India. [1]

Research Profile

Mandara SM is affiliated with the School of Commerce and Management at Shri Venkateshwara University and is associated with research in Business and related areas of commerce and management. The supplied recognition information identifies India as the researcher’s country and provides ORCID identifier 0009-0006-6234-7618. Scopus identification and bibliometric values for documents, citations, and h-index were not supplied with the award profile and are therefore not stated as verified metrics on this page.

Research Contributions

The principal contribution of the recognized study is its examination of work–life balance within peripheral and informal labour markets at a district level. Rather than treating informal employment as a homogeneous category, the research compares conditions across construction, agriculture, textiles, hotels, and department stores. This sectoral approach allows differences in work intensity, employment security, income conditions, and scheduling practices to be considered in relation to workers’ reported work–life balance. [1]

The study employs a mixed-method research design combining structured surveys and focus group discussions. This approach permits quantitative assessment of employment-related factors while also incorporating workers’ experiences and perceptions. The publication reports that working hours, job security, physical strain, income stability, and gendered responsibilities are relevant factors in understanding differences in work–life balance among informal workers. [1]

Publications

The principal publication associated with this recognition is the following peer-reviewed journal article:

  • Mandara, S. M., & Sahoo, S. (2025). Reconstructing Work–Life Balance in Peripheral Labour Markets: Structural, Sectoral, and Gendered Realities of Informal Employment in Chikmagalur District. Advanced International Journal for Research, 6(6). DOI: https://doi.org/10.63363/aijfr.2025.v06i06.2270. [1]

Research Impact

The research contributes to the study of informal employment by providing a localized assessment of work–life balance in Chikmagalur District. Its focus on several occupational sectors provides a basis for comparing the employment conditions experienced by workers in different parts of the informal economy. Such sector-specific analysis can help distinguish common labour-market pressures from conditions that are more closely associated with particular forms of employment. [1]

Award Suitability

The Best Paper Award recognizes scholarly work demonstrating research quality, methodological clarity, relevance, and a meaningful contribution to its field. The publication associated with Mandara SM addresses a clearly defined issue in business and labour research by investigating work–life balance among informal workers in a specific regional labour market. Its examination of structural, sectoral, and gendered dimensions provides a multidimensional framework for understanding the subject. [1]

Conclusion

Mandara SM’s research profile, as presented for the Best Paper Award, reflects scholarly activity in Business and related areas of labour and management research. The recognized publication examines work–life balance among informal workers in Chikmagalur District and considers the structural, sectoral, occupational, and gendered factors that influence employment experiences. [1]

External Links

References

  1. Mandara, S. M., & Sahoo, S. (2025). Reconstructing Work–Life Balance in Peripheral Labour Markets: Structural, Sectoral, and Gendered Realities of Informal Employment in Chikmagalur District. Advanced International Journal for Research, 6(6), published December 3, 2025. DOI: https://doi.org/10.63363/aijfr.2025.v06i06.2270.
  2. Advanced International Journal for Research. (2025). Reconstructing Work–Life Balance in Peripheral Labour Markets: Structural, Sectoral, and Gendered Realities of Informal Employment in Chikmagalur District. Publication record and article information. Publisher Article Page.
  3. Advanced International Journal for Research. (2025). Reconstructing Work–Life Balance in Peripheral Labour Markets: Structural, Sectoral, and Gendered Realities of Informal Employment in Chikmagalur District. Full-text publication record. Publication PDF.

Amr Abdelnasser | Earth and Planetary Sciences | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Abdelnasser Refaey Elwardany
Affiliation Istanbul Technical University
Country Turkey
Scopus ID 57193522232
Documents 29
Citations 301
h-index 9
Subject Area Earth and Planetary Sciences
Event Best Paper Awards
ORCID 0000-0001-5994-7088

Abdelnasser Refaey Elwardany

Abdelnasser Refaey Elwardany of Istanbul Technical University, Turkey, is recognized in connection with the Best Paper Award for research in Earth and Planetary Sciences. This academic profile highlights the researcher’s scholarly record and the featured publication titled “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt.”

The featured research brings together remote sensing, machine learning, geological interpretation, and mineral exploration to investigate gold-mineralized alteration zones in the Fatira Mine Area of Egypt. The work represents an interdisciplinary approach to geological mapping and the identification of alteration patterns associated with mineralization.

Abstract

This article recognizes Abdelnasser Refaey Elwardany in connection with the Best Paper Award for research addressing the application of remote sensing and machine learning to the mapping of gold-mineralized alteration zones. The featured study, “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt,” examines the use of satellite-derived information and computational techniques to support geological interpretation and mineral exploration. The research is situated within the broader field of Earth and Planetary Sciences and demonstrates an interdisciplinary approach to mineral-resource investigation.

Keywords

Remote Sensing, Machine Learning, Gold Mineralization, Alteration Zones, Mineral Exploration, Fatira Mine, Egypt, Geological Mapping, Earth and Planetary Sciences, Satellite Imagery.

Introduction

Remote sensing has become an important tool in geological investigations because satellite observations can provide spatially extensive information for the identification and interpretation of lithological and alteration features. In mineral exploration, remotely sensed spectral information can complement field observations and conventional geological mapping by helping researchers identify spatial patterns that may be associated with mineralized systems.

Research Profile

Abdelnasser Refaey Elwardany is affiliated with Istanbul Technical University in Turkey and is associated with the subject area of Earth and Planetary Sciences. The supplied academic profile records 29 documents, 301 citations, and an h-index of 9. These indicators provide a quantitative overview of the researcher’s indexed scholarly output and citation activity.

The research profile is particularly relevant to geological and planetary sciences because the featured work addresses mineralized alteration, remote sensing, and machine-learning-assisted geological interpretation. The combination of earth-science knowledge with computational analysis reflects the interdisciplinary nature of contemporary mineral exploration research.

Research Contributions

The featured study contributes to mineral exploration research by examining how remote-sensing data and machine-learning techniques can be combined to identify gold-mineralized alteration zones. The approach provides a framework for extracting geological information from satellite observations and using computational methods to support the interpretation of alteration patterns.

Publications

The featured publication associated with this recognition is “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt.” The publication addresses the application of remote sensing and machine-learning methodologies to geological mapping and mineral exploration in the Fatira Mine Area.

Publication Detail Information
Paper Title Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt
Research Area Earth and Planetary Sciences
Primary Methods Remote Sensing and Machine Learning
Study Location Fatira Mine Area, Egypt
DOI https://doi.org/10.1111/1755-6724.15333

Research Impact

The research has potential significance for geological and mineral exploration because it demonstrates the application of computational techniques to remotely sensed geological information. Mapping alteration zones is an important component of mineral exploration, and remote sensing can provide a means of examining large areas before more detailed field-based investigations are undertaken.

The interdisciplinary character of the work also contributes to the broader development of data-driven Earth sciences. By connecting satellite observations with machine-learning analysis, research of this type can support more systematic approaches to geological interpretation and exploration targeting.

Award Suitability

The Best Paper Award recognizes research that demonstrates scholarly quality, methodological relevance, originality, and meaningful contribution to its field. The featured publication is relevant to these considerations because it addresses a defined geological problem and applies remote sensing and machine-learning techniques to the investigation of gold-mineralized alteration zones.

Conclusion

Abdelnasser Refaey Elwardany’s academic recognition profile highlights research in Earth and Planetary Sciences with a focus on remote sensing, machine learning, geological mapping, and mineral exploration. The featured paper, “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt,” represents an interdisciplinary approach to investigating alteration zones associated with gold mineralization.

The combination of earth-science investigation and computational analysis provides a relevant foundation for research in modern mineral exploration. Together with the supplied publication and bibliometric profile, these characteristics provide an academic basis for consideration within a Best Paper Award framework.

External Links

References

  1. Elwardany, A. R. et al. Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Acta Geologica Sinica – English Edition. DOI: https://doi.org/10.1111/1755-6724.15333 ↩
  2. Scopus Author Profile. Abdelnasser Refaey Elwardany, Author ID 57193522232. Scopus Author Profile ↩
  3. ORCID. Research Profile of Abdelnasser Refaey Elwardany. ORCID iD: 0000-0001-5994-7088. https://orcid.org/0000-0001-5994-7088 ↩

Refaey Elwardany | Earth and Planetary Sciences | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Refaey Elwardany
Affiliation Al-Azhar University
Country Egypt
Scopus ID 57344533900
Documents 18
Citations 312
h-index 8
Subject Area Earth and Planetary Sciences
Event Best Paper Awards
ORCID 0000-0001-6766-6921

Refaey Elwardany

Refaey Elwardany of Al-Azhar University, Egypt, is recognized in connection with the Best Paper Award for research on remote sensing, machine learning, mineral exploration, and geological mapping. His research publication, “Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt,” investigates the integration of multispectral remote sensing, mineralogical analysis, and machine learning for delineating gold-sulfide mineralization in the Fatira mine area. The article was published in Acta Geologica Sinica (English Edition), volume 99, issue 4, pages 1196–1223, in 2025, with DOI 10.1111/1755-6724.15333. [1]

Abstract

The Best Paper Award recognition highlights research by Refaey Elwardany concerning the application of remote sensing and machine learning techniques to gold exploration in the Fatira (Abu Zawal) mine area of Egypt. The study integrates fieldwork and mineralogical analysis with Landsat-8 OLI, ASTER, and Sentinel-2 multispectral imagery to delineate gold-sulfide mineralization and associated hydrothermal alteration. Principal component analysis, independent component analysis, supervised classification, and mineral indices were applied to identify alteration zones and geological features associated with mineralization. The reported Landsat-8 support vector machine classification achieved an accuracy of 88.55% with a Kappa value of 0.86. [1]

Keywords

Remote Sensing, Machine Learning, Gold Exploration, Gold-Sulfide Mineralization, Fatira Gold Mine, Hydrothermal Alteration, Mineralogy, Landsat-8, ASTER, Sentinel-2, Geological Mapping, Egypt.

Introduction

Remote sensing has become an important component of modern geological investigation because multispectral satellite observations can support the identification and spatial interpretation of lithological and hydrothermal alteration features. In mineral exploration, the integration of remotely sensed data with field observations, mineralogical information, and computational classification methods can provide a systematic approach for mapping prospective zones. The Fatira mine area, located in the northern Eastern Desert of Egypt, represents a geological setting in which remote sensing and machine learning techniques can contribute to the characterization of gold-sulfide mineralization. [1]

Research Profile

Refaey Elwardany is affiliated with Al-Azhar University in Egypt and is associated with research in Earth and Planetary Sciences. The supplied academic profile records 18 documents, 312 citations, and an h-index of 8. These metrics provide a quantitative representation of the researcher’s publication and citation activity as specified for this recognition profile.

Research Contributions

The principal contribution of the awarded study is the integration of multisource remote sensing data and machine-learning methods for mapping gold-mineralized alteration zones in the Fatira mine area. Fieldwork and mineralogical analysis were combined with satellite imagery from Landsat-8 OLI, ASTER, and Sentinel-2 to identify geological and alteration characteristics associated with gold-sulfide mineralization. [1]

Publications

The principal publication associated with this recognition is the following peer-reviewed journal article:

  • El-Wardany, R., Jiao, J., Zoheir, B., Khedr, L., Kumral, M., Liu, L., Abu El-Leil, I., Orabi, A., Abd El-Salam, L., & Abdelnasser, A. (2025). Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Acta Geologica Sinica (English Edition), 99(4), 1196–1223. https://doi.org/10.1111/1755-6724.15333. [1]

Research Impact

The research contributes to the field of mineral exploration by demonstrating how satellite-based observations can be integrated with machine-learning classification and geological analysis to identify alteration zones associated with gold mineralization. Its methodological framework is relevant to geological mapping and exploration studies in areas where conventional field investigation may benefit from spatially extensive remote sensing data. [1]

Award Suitability

The Best Paper Award is intended to recognize research demonstrating scholarly quality, methodological rigor, originality, and relevance within its field. The publication associated with Refaey Elwardany addresses a defined geological exploration problem through the integration of remote sensing, mineralogical analysis, and machine-learning techniques. Its use of Landsat-8, ASTER, and Sentinel-2 data, together with quantitative classification and alteration mapping, provides a clearly structured methodological contribution. [1]

Conclusion

Refaey Elwardany’s research profile, as presented for the Best Paper Award, reflects scholarly activity in Earth and Planetary Sciences with a particular emphasis on geological mapping, remote sensing, mineral exploration, and machine learning. The recognized publication presents an integrated approach to mapping gold-mineralized alteration zones in the Fatira mine area of Egypt using multispectral satellite imagery, geological observations, mineralogical analysis, and computational classification techniques. [1]

External Links

References

  1. El-Wardany, R., Jiao, J., Zoheir, B., Khedr, L., Kumral, M., Liu, L., Abu El-Leil, I., Orabi, A., Abd El-Salam, L., & Abdelnasser, A. (2025). Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Acta Geologica Sinica (English Edition), 99(4), 1196–1223. DOI: https://doi.org/10.1111/1755-6724.15333.
  2. Wiley Online Library. (2025). Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Acta Geologica Sinica (English Edition). Publisher Article Page.
  3. Istanbul Technical University. (2025). Remote Sensing-based Machine Learning Techniques for Mapping Gold-Mineralized Alteration Zones in the Fatira Mine Area, Egypt. Research publication record. Publication Record.

Masoumeh Moslemi | Agricultural and Biological Sciences | Best Paper Award

Published on by Best Paper Awards

Best Paper Award

Masoumeh Moslemi
Affiliation Halal Research Center of Islamic Republic of Iran
Country Iran
Documents 42
Citations 499
h-index 11
Subject Area Agricultural and Biological Sciences
Event Best Paper Awards

Masoumeh Moslemi

Masoumeh Moslemi of the Halal Research Center of the Islamic Republic of Iran is recognized for valuable contributions to food safety, environmental health, and agricultural sciences. This article highlights the academic profile, scientific achievements, and Best Paper Award recognition for the publication titled “Health Risk Assessment of Heavy Metals in Black Tea Infusion by Monte Carlo Simulation.”

Abstract

This article recognizes Masoumeh Moslemi for receiving the Best Paper Award in recognition of outstanding research on food safety and public health. The awarded study evaluates the health risks associated with heavy metal contamination in black tea infusion using Monte Carlo simulation. The research provides a comprehensive probabilistic assessment of consumer exposure and offers valuable insights for improving food quality monitoring, regulatory standards, and public health protection.

Keywords

Black Tea, Heavy Metals, Monte Carlo Simulation, Health Risk Assessment, Food Safety, Environmental Health, Agricultural Sciences.

Introduction

Food safety remains a global public health priority, particularly regarding contaminants such as heavy metals that may accumulate in commonly consumed beverages. Black tea is one of the world’s most widely consumed drinks, making continuous monitoring of contaminant levels essential. Advanced statistical techniques such as Monte Carlo simulation provide more reliable estimates of population exposure and health risks, enabling researchers and policymakers to develop evidence-based food safety regulations.

Research Profile

Masoumeh Moslemi has authored 42 scientific publications that have received 499 citations, achieving an h-index of 11. Her research focuses primarily on food safety, environmental contaminants, health risk assessment, toxicology, and agricultural sciences. Through interdisciplinary research, she has contributed to improving scientific understanding of contaminant exposure and consumer health protection.

Research Contributions

The awarded publication introduces a probabilistic framework using Monte Carlo simulation to evaluate heavy metal exposure from black tea consumption. The research improves conventional deterministic assessment methods by accounting for uncertainty and variability within the population. Its findings provide valuable information for food safety authorities, researchers, and policymakers responsible for minimizing dietary exposure to hazardous contaminants.

Research Impact

This research has strengthened scientific knowledge in agricultural and biological sciences by supporting evidence-based food safety evaluation. The publication has contributed to improved risk assessment methodologies and has influenced ongoing research concerning contaminant monitoring, dietary exposure, and environmental health. Citation metrics further demonstrate its academic influence and relevance within the international scientific community.

Award Suitability

The Best Paper Award recognizes publications demonstrating scientific excellence, innovation, originality, and meaningful societal impact. Masoumeh Moslemi’s research fulfills these objectives by providing a rigorous analytical approach for evaluating heavy metal exposure in food products, contributing to safer food systems and informed public health decision-making.

Conclusion

Masoumeh Moslemi has made significant contributions to food safety and environmental health research through innovative applications of quantitative risk assessment. The awarded publication demonstrates scientific excellence by integrating Monte Carlo simulation with health risk analysis, reinforcing the importance of evidence-based approaches for protecting consumer health and advancing agricultural sciences.

External Links

References

    1. Elsevier. (n.d.). Scopus Author Details: Masoumeh Moslemi, Author ID 57189761475. Scopus.
      https://www.scopus.com/pages/authors/57189761475
    2. ORCID. (n.d.). Research Profile of Masoumeh Moslemi.
      https://orcid.org/0000-0002-7120-3329