Innovative Research Award

Roberto de Moraes
AECOM, United States

Roberto de Moraes is an engineering researcher affiliated with AECOM whose recent scholarly work addresses lunar construction-site screening and geotechnical decision-making. His 2026 publication, co-authored with Chrysothemis Paraskevopoulou, presents an evidence-to-decision framework for interpreting penetration-response observations while maintaining a distinction between measured evidence, mechanical interpretation, uncertainty, and asset-specific verification. The article appeared in Applied Sciences, Volume 16, Issue 19, Article 9648, with DOI 10.3390/app16199648. [1]

Abstract

The featured research develops a mechanics-informed framework for early lunar construction-site screening from penetration-response observations. The approach separates direct observations from conventional mechanical interpretation, preliminary construction ground zoning, uncertainty closure, and asset-specific verification. It recognizes that lunar penetration response may be affected by density, particle morphology, stratigraphy, disturbance, probe configuration, boundaries, and acquisition limitations. Rather than assigning an unsupported numerical historical-stress parameter, the framework uses an auditable evidence ledger and explicit decision gates. Its application to Apollo 16 evidence demonstrates how sparse legacy observations can inform investigation priorities while preserving uncertainty and avoiding unsupported design classifications. [1][2]

Keywords

Lunar regolith; penetration response; construction-site screening; geotechnical site investigation; evidence-to-decision framework; Apollo 16; mechanical-state hypothesis; lunar construction; engineering geology.

Introduction

Lunar construction requires engineering decisions from limited and instrument-dependent ground observations. The lunar surface differs substantially from terrestrial construction environments, making direct transfer of conventional Earth-based assumptions inappropriate without suitable calibration. NASA has identified geotechnical site investigation, topographic mapping, rock removal, grading, and ground-feature formation among relevant capabilities for lunar surface preparation. [3]

The study by de Moraes and Paraskevopoulou addresses this problem by establishing a structured process for interpreting penetration-response evidence. The framework is designed to identify where additional measurements are necessary and to distinguish preliminary screening information from project-specific design parameters. This approach is consistent with the broader scientific understanding that lunar surface conditions are shaped by impact processes, regolith evolution, geological setting, and surface interactions. [2][4]

Research Profile

Roberto de Moraes’s documented research contribution in the supplied publication centers on lunar geotechnical engineering and evidence-based construction-site screening. The work combines penetration observations with geological, stratigraphic, terrain, disturbance, and uncertainty considerations. Its methodological emphasis is on traceability: each interpretation is connected to a stated engineering decision, an uncertainty-closure action, or an independent verification requirement.

The publication also demonstrates an interdisciplinary connection between lunar science, geotechnical engineering, construction planning, and engineering decision analysis. The authors emphasize that penetration response alone should not automatically be converted into density, strength, stiffness, or construction-performance parameters. Instead, these properties require project-specific calibration and verification. [1]

Research Contributions

• Development of an evidence-to-decision framework for preliminary lunar construction-site screening.
• Separation of direct measurements, mechanical interpretation, construction zoning, uncertainty closure, and asset verification.
• Use of Apollo 16 penetration-response evidence as a methodological demonstration rather than as unsupported numerical calibration.
• Emphasis on compatibility checks, uncertainty documentation, alternative explanations, and independent verification before engineering adoption.

Publications

The principal publication associated with this recognition is An Evidence-to-Decision Framework for Lunar Construction-Site Screening from Penetration Response, authored by Roberto de Moraes and Chrysothemis Paraskevopoulou and published in Applied Sciences in 2026. The article is identified as Volume 16, Issue 19, Article 9648 and is available through an open-access publication record. [1]

Research Impact

The research provides a structured method for converting sparse lunar ground-response observations into investigation priorities without overstating the certainty of legacy evidence. Its potential engineering relevance lies in defining where additional soundings, samples, geophysical observations, controlled trials, or asset-level verification may be required. The broader significance is methodological: the framework establishes a transparent boundary between evidence-supported screening and the calibrated parameters required for final engineering design.

Award Suitability

The documented publication provides evidence of research activity in engineering and lunar geotechnical investigation. Its stated methodology addresses a specialized engineering problem through a structured framework linking field-response evidence, uncertainty, construction decisions, and verification. These characteristics provide a factual basis for considering the work within an Innovative Research Award context, subject to the award’s formal evaluation criteria and independent review.

Conclusion

Roberto de Moraes’s documented 2026 research presents an evidence-to-decision approach for lunar construction-site screening based on penetration response. The work emphasizes compatibility, uncertainty, alternative explanations, preliminary zoning, and independent verification rather than unsupported conversion of sparse measurements into design parameters. In this context, the publication represents a focused contribution to engineering research concerning future lunar construction and geotechnical site investigation.

External Links

• Scopus Author Profile
• International Forensic Scientist Awards

References

1. de Moraes, R.; Paraskevopoulou, C. An Evidence-to-Decision Framework for Lunar Construction-Site Screening from Penetration Response. Applied Sciences 2026, 16(19), 9648.
   https://doi.org/10.3390/app16199648
2. Heiken, G.H.; Vaniman, D.T.; French, B.M., eds. Lunar Sourcebook: A User’s Guide to the Moon. Cambridge University Press, 1991.
3. National Aeronautics and Space Administration. Fiscal Year 2024 STTR Phase I Solicitation: Lunar Surface Site Preparation. NASA, 2024.
4. Lucey, P.; Korotev, R.L.; Gillis, J.J.; Taylor, L.A.; Lawrence, D.; Campbell, B.A.; et al. Understanding the Lunar Surface and Space-Moon Interactions. Reviews in Mineralogy and Geochemistry 2006, 60, 83–219.
5. Elsevier. Scopus Author Details: Roberto de Moraes, Author ID 58072897300. Scopus.
   https://www.scopus.com/authid/detail.uri?authorId=58072897300
6. MDPI. Applied Sciences, Volume 16, Issue 19, Article 9648. 2026.
   https://www.mdpi.com/2076-3417/16/19/9648

Best Researcher Award

Oumayma Mabrouk
University of Angers, France

Oumayma Mabrouk is a researcher affiliated with the University of Angers in France whose documented research contribution concerns photovoltaic-system performance diagnosis, renewable-energy monitoring, and data-driven anomaly detection. Her recent article in Solar Energy presents a regime-aware framework for generating robust reference curves for photovoltaic power output under changing meteorological conditions. The publication identifies an approach based on unsupervised learning, statistical reference corridors, and constrained time-series alignment. [1]

Abstract

Mabrouk’s documented publication addresses the challenge of detecting abnormal photovoltaic-system behavior without relying on extensive labeled fault datasets. The study develops reference corridors using median and interquartile-range statistics after identifying representative meteorological regimes through k-means clustering. Limited Dynamic Time Warping is then used to compare daily photovoltaic power profiles with regime-specific reference curves. The reported validation uses receiver operating characteristic analysis and synthetic fault scenarios, with the published study reporting area-under-the-curve values above 0.80. [1]

Keywords

• Photovoltaic systems
• Renewable energy technologies
• Anomaly detection
• Unsupervised learning
• Data-driven diagnosis

Introduction

Reliable monitoring is an important component of photovoltaic-system operation because environmental variability can make it difficult to distinguish normal changes in power production from genuine performance anomalies. The article by Mabrouk and co-authors approaches this problem by separating meteorological regime identification from photovoltaic power anomaly detection. This design seeks to reduce the possibility that changes in weather conditions are interpreted directly as equipment faults. [1]

Research Profile

The available publication record places Mabrouk’s research within renewable energy technologies, with a specific focus on photovoltaic performance monitoring and intelligent diagnostic methods. The 2026 Solar Energy article develops an unsupervised framework that uses environmental variables to identify weather regimes and then constructs statistical reference models for daily photovoltaic power behavior. The research combines machine-learning clustering, robust statistics, and time-series analysis in a single diagnostic workflow. [1]

Research Contributions

The principal contribution is a regime-aware methodology for establishing expected photovoltaic power behavior. Environmental measurements are used to distinguish representative meteorological conditions, while normalized daily power profiles are summarized through median curves and interquartile-range corridors. Limited Dynamic Time Warping provides constrained temporal alignment, and complementary indicators are used for anomaly classification. The study reports that this combination can identify deviations without requiring labeled fault examples. [1]

Publications

The principal documented publication is Robust reference curves generation for photovoltaic systems performance diagnosis using a data-driven approach, authored by Oumayma Mabrouk, Abdérafi Charki, Nizar Chatti, and Xavier Sidambarompoulé. It was published in Solar Energy, volume 317, article 114958, in 2026. The article is indexed in the supplied Scopus profile information and has a reported DOI of 10.1016/j.solener.2026.114958. [1] [2]

Research Impact

The supplied bibliometric record lists one document, one citation, and an h-index of 1. These indicators represent the currently supplied record rather than a comprehensive assessment of research influence. The publication itself addresses a practical problem in photovoltaic operations by combining interpretable statistical reference modeling with unsupervised anomaly detection. Its reported evaluation indicates that the proposed indicators can discriminate injected abnormal behavior from normal operational variation. [1]

Award Suitability

For the Best Researcher Award context, the documented evidence includes a peer-reviewed 2026 journal publication addressing renewable-energy system diagnosis and a methodological contribution combining k-means clustering, robust statistical modeling, and constrained time-series analysis. These documented elements provide a research basis for consideration within an award process focused on renewable energy technologies. Any final award determination remains subject to the applicable evaluation criteria and review procedure of the International Forensic Scientist Awards.

Conclusion

Oumayma Mabrouk’s documented research contribution centers on data-driven photovoltaic performance diagnosis. Her 2026 Solar Energy publication presents a structured approach for modeling expected photovoltaic behavior across meteorological regimes and detecting deviations without labeled fault data. The available bibliometric information records one document, one citation, and an h-index of 1, while the publication provides the principal evidence for assessing her current research profile.

External Links

• Scopus Author Profile
• International Forensic Scientist Awards

References

1. Mabrouk, O., Charki, A., Chatti, N., & Sidambarompoulé, X. (2026). Robust reference curves generation for photovoltaic systems performance diagnosis using a data-driven approach. Solar Energy, 317, 114958.
   https://doi.org/10.1016/j.solener.2026.114958
2. Elsevier. (2026). Robust reference curves generation for photovoltaic systems performance diagnosis using a data-driven approach. Solar Energy, Volume 317, Article 114958.
3. Scopus. (n.d.). Scopus author details: Oumayma Mabrouk, Author ID 60883647200. Elsevier.
   https://www.scopus.com/authid/detail.uri?authorId=60883647200
4. HAL. (2026). Publication record for Robust reference curves generation for photovoltaic systems performance diagnosis using a data-driven approach. Université d’Angers research record.
   https://cv.hal.science/xavier-sidambarompoule
5. Chatti, N. (2026). Selected publications and research outputs: Solar Energy, 2026. University of Angers / LARIS.
   https://perso-laris.univ-angers.fr/~nizar.chatti/