Safaa Najah Saud Al-Humairi | Optimization | Innovative Research Award

Published on by Math Scientist

Innovative Research Award

Safaa Najah Saud Al-Humairi
Management and Science University, Malaysia

Safaa Najah Saud Al-Humairi
Affiliation Management and Science University
Country Malaysia
Scopus ID 55851376600
Documents 135
Citations 1,993
h-index 27
Subject Area Optimization
Event Math Scientist Awards
ORCID 0000-0001-5978-7651

Safaa Najah Saud Al-Humairi is a researcher affiliated with Management and Science University in Malaysia whose documented scholarly profile is associated with optimization and computationally supported engineering research. The available bibliographic record lists 135 documents, 1,993 citations, and an h-index of 27. These indicators provide a quantitative context for considering the researcher for an Innovative Research Award.

Abstract

This academic recognition profile examines the research record of Safaa Najah Saud Al-Humairi in the context of optimization-oriented scientific work. Recent publications address photovoltaic panel modelling, curved photovoltaic structures, Internet of Things-enabled environmental monitoring, artificial intelligence in materials research, and bioactive nanomaterials. Collectively, these works illustrate interdisciplinary applications of modelling, optimization, computation, and engineering design.

Keywords

  • Optimization
  • Photovoltaic Modelling
  • Computational Techniques
  • Artificial Intelligence
  • Sustainable Energy

Introduction

Optimization research frequently connects mathematical or computational methods with practical engineering objectives. In this profile, the documented publication portfolio includes studies that apply modelling and design approaches to renewable-energy systems and emerging computational applications. The researcher’s Scopus record supplies an additional bibliometric basis for evaluating scholarly activity. [1]

Research Profile

The profile is characterized by 135 indexed documents, 1,993 citations, and an h-index of 27, as supplied for this recognition article. The subject-area designation is Optimization. These bibliometric measures should be interpreted as indicators of publication and citation activity rather than as standalone measures of research quality. [1]

Research Contributions

Recent work demonstrates attention to sustainable photovoltaic engineering. One 2026 study investigates modelling and optimization of curved photovoltaic panels for high-efficiency solar-energy conversion. [2] Another examines a floating curved photovoltaic panel combined with IoT-enabled weather monitoring for river and lake environments. [3] Additional contributions extend toward artificial intelligence and advanced computational techniques in shape memory alloy research, alongside biomedical nanomaterial applications. [4] [5]

Publications

  • Modelling and optimisation of curved photovoltaic panels for high-efficiency solar energy conversion — Next Sustainability, 2026. [2]
  • Design of a Floating Curved Photovoltaic Panel with IoT-Enabled Weather Monitoring for Rivers and Lakes Environments — Al-Khwarizmi Engineering Journal, 2026. [3]
  • The Role of AI and Advanced Computational Techniques in Transforming Shape Memory Alloy Research — book chapter, 2026. [4]
  • Bioactive Nanomaterials: Revolutionizing Biomedical Science — book chapter, 2026. [5]

Research Impact

The supplied bibliometric record indicates substantial scholarly visibility, with 1,993 citations and an h-index of 27. The publication themes also connect optimization with renewable energy, environmental monitoring, computational engineering, and biomedical research. Such cross-domain activity may support knowledge transfer between computational methods and applied scientific problems. [1]

Award Suitability

The documented research profile provides relevant evidence for consideration for the Innovative Research Award at the Math Scientist Awards. Its suitability is supported by an established publication and citation record and by recent research addressing optimization, sustainable photovoltaic technologies, IoT monitoring, and advanced computational methods. Final award decisions remain subject to the applicable evaluation criteria and review process.

Conclusion

Safaa Najah Saud Al-Humairi’s research profile combines optimization-oriented scholarship with applied investigations in renewable energy, computational engineering, and emerging scientific technologies. The supplied Scopus indicators and recent publications provide a structured basis for academic recognition and demonstrate a research trajectory relevant to interdisciplinary innovation.

References

  1. Elsevier. (n.d.). Scopus author details: Safaa Najah Saud Al-Humairi, Author ID 55851376600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=55851376600
  2. Elsevier. (2026). Modelling and optimisation of curved photovoltaic panels for high-efficiency solar energy conversion. Next Sustainability.
    https://doi.org/10.1016/j.nxsust.2026.100495
  3. Al-Khwarizmi Engineering Journal. (2026). Design of a Floating Curved Photovoltaic Panel with IoT-Enabled Weather Monitoring for Rivers and Lakes Environments.
    https://doi.org/10.22153/kej.2026.03.002
  4. IGI Global. (2026). The Role of AI and Advanced Computational Techniques in Transforming Shape Memory Alloy Research.
    https://doi.org/10.4018/979-8-3373-6127-7.ch006
  5. Springer. (2026). Bioactive Nanomaterials: Revolutionizing Biomedical Science.
    https://doi.org/10.1007/978-981-96-8023-8_5
  6. ORCID. (n.d.). Safaa Najah Saud Al-Humairi, ORCID iD 0000-0001-5978-7651.
    https://orcid.org/0000-0001-5978-7651
  7. Math Scientist Awards. (n.d.). Math Scientist Awards.
    mathscientists.com

Sina Samadi Gharehveran | Optimization | Innovative Research Award

Published on by Math Scientist

Innovative Research Award

Sina Samadi Gharehveran
Tabriz Islamic Art University, Iran

Sina Samadi Gharehveran
Affiliation Tabriz Islamic Art University
Country Iran
Scopus ID 57741285600
Documents 21
Citations 367
h-index 11
Subject Area Optimization
Event Math Scientist Awards
ORCID 0000-0001-5442-2485

Sina Samadi Gharehveran is a researcher affiliated with Tabriz Islamic Art University, Iran, whose documented research profile is associated with optimization and computational approaches to complex engineering and scientific problems. The researcher has 21 indexed documents, 367 citations, and an h-index of 11, providing a measurable basis for academic recognition. The profile is considered in the context of the Math Scientist Awards.

Abstract

Sina Samadi Gharehveran’s research profile reflects scholarly activity in optimization, with applications extending to electric vehicles, adaptive cybersecurity, intelligent agricultural diagnosis, and predictive construction materials research. Available bibliographic indicators document 21 research documents, 367 citations, and an h-index of 11. Recent publications demonstrate the application of multi-objective optimization, reinforcement learning, active vision, and interpretable machine-learning techniques to multidisciplinary problems.

Keywords

  • Optimization
  • Multi-objective optimization
  • Electric vehicles
  • Reinforcement learning
  • Artificial intelligence
  • Predictive modeling

Introduction

Optimization research addresses the systematic selection of solutions under defined objectives and constraints. In contemporary computational research, optimization increasingly intersects with artificial intelligence and machine learning to address complex, data-intensive systems. Gharehveran’s recent publication record illustrates this multidisciplinary orientation, particularly through work involving multi-objective optimization and intelligent computational methods. [1]

Research Profile

The research profile is associated with 21 documents indexed in Scopus, 367 citations, and an h-index of 11. These indicators provide quantitative evidence of research dissemination and citation influence within the indexed scholarly literature. The primary subject area identified for the researcher is Optimization, while the publication portfolio shows applications across engineering, cybersecurity, agriculture, and materials-related computational research. [2]

Research Contributions

A notable research direction concerns the optimization of battery quantity and arrangement in electric vehicles through multi-objective optimization. This work addresses a practical engineering design problem in which competing performance considerations must be evaluated systematically. [3] Other recent work applies graph-aware reinforcement learning to adaptive advanced persistent threat hunting in dynamic enterprise networks, demonstrating the relevance of intelligent optimization strategies to cybersecurity environments. [4]

Publications

  • Optimization of the Number of Batteries and their Arrangement in Electric Vehicles Based on Multi-Objective Optimization. Passive Defense, 2026. [3]
  • Graph-Aware Reinforcement Learning for Adaptive APT Threat Hunting in Dynamic Enterprise Networks. Technologies, 2026. [4]
  • RAVA: a robust recurrent active vision agent for plant disease diagnosis under severe environmental noise. Scientific Reports, 2026. [5]
  • Interpretable Prediction of Geopolymer Concrete Compressive Strength Using DBO–CatBoost and SHAP Analysis. Buildings, 2026. [6]

Research Impact

The combination of 367 citations and an h-index of 11 indicates an established level of scholarly visibility within indexed literature. The range of application domains represented in the recent publications also suggests a research approach that connects optimization with practical computational problems. Citation indicators should, however, be interpreted alongside publication quality, disciplinary norms, collaboration patterns, and the characteristics of individual research fields. [2]

Award Suitability

The available profile provides a reasonable scholarly basis for consideration for an Innovative Research Award. The evidence includes measurable citation impact, an established h-index, and recent research addressing optimization and intelligent computational methods in several application areas. The award assessment should consider the complete publication record and applicable evaluation criteria in addition to bibliometric indicators. [1]

Conclusion

Sina Samadi Gharehveran’s documented research profile combines optimization-focused scholarship with interdisciplinary applications involving electric vehicles, cybersecurity, plant disease diagnosis, and predictive materials research. With 21 documents, 367 citations, and an h-index of 11, the profile demonstrates measurable academic impact. Recent publications further indicate engagement with contemporary computational methods, supporting consideration for recognition through the Innovative Research Award.

References

  1. Elsevier. (n.d.). Scopus author details: Sina Samadi Gharehveran, Author ID 57741285600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57741285600
  2. Scopus. (n.d.). Author metrics and indexed publication record: Sina Samadi Gharehveran. Elsevier.
  3. Passive Defense. (2026). Optimization of the Number of Batteries and their Arrangement in Electric Vehicles Based on Multi-Objective Optimization.
    https://doi.org/10.47176/pd.2026.1490
  4. MDPI. (2026). Graph-Aware Reinforcement Learning for Adaptive APT Threat Hunting in Dynamic Enterprise Networks. Technologies.
    https://doi.org/10.3390/technologies14100624
  5. Nature Portfolio. (2026). RAVA: a robust recurrent active vision agent for plant disease diagnosis under severe environmental noise. Scientific Reports.
    https://doi.org/10.1038/s41598-026-67160-2
  6. MDPI. (2026). Interpretable Prediction of Geopolymer Concrete Compressive Strength Using DBO–CatBoost and SHAP Analysis. Buildings.
    https://doi.org/10.3390/buildings16163326

Liangliang Sun | Optimization | Best Researcher Award

Published on by Math Scientist

Prof. Liangliang Sun | Optimization | Best Researcher Award

Dean at Northeastern University, China

Prof. Liangliang Sun 🌟 is a distinguished scholar in control theory and intelligent scheduling systems, currently serving at Northeastern University, China 🏫. With a robust academic background including a Ph.D. from both Northeastern University and the University of Connecticut 🎓, he has cultivated deep expertise in steelmaking-continuous casting optimization, energy systems, and industrial automation ⚙️. His prolific research spans over 18 high-impact publications 📚, multiple national-level projects 🎯, and several patented innovations 🔬. Prof. Sun has earned accolades such as the Xingliao Talent Plan 🌟 and has been a two-time champion 🏆 in China’s prestigious Challenge Cup competitions. His contributions extend into teaching, editorial responsibilities 🖋️, and international collaborations 🌐. Known for integrating theory with real-world industrial applications 🔄, he bridges gaps between research and practice seamlessly. As a dynamic thought leader 🚀 in control engineering and smart manufacturing, Prof. Sun continues to inspire innovation and academic excellence across global platforms 🌍.

Professional Profile 

Scopus Profile

🎓 Education

Prof. Liangliang Sun’s academic journey is marked by scholarly excellence and international immersion 🌍. He earned his Bachelor’s and Master’s degrees in Automation and Control Engineering from Northeastern University, China 🇨🇳, laying the groundwork for a solid technical foundation. He later pursued a prestigious dual Ph.D. from Northeastern University and the University of Connecticut 🇺🇸, enriching his expertise through cross-cultural academic synergy. His doctoral research focused on intelligent optimization and industrial system control, setting the stage for impactful innovation. This global academic trajectory reflects a deep commitment to advancing modern engineering frontiers 🔬. With early exposure to cutting-edge research environments, Prof. Sun developed a unique ability to integrate Eastern precision with Western analytical frameworks, a blend that defines his distinctive academic persona 📘.

🏢 Professional Experience

Prof. Sun has cultivated an impressive professional track record in academic and applied engineering settings 🔧. Currently a full professor at Northeastern University’s School of Information Science and Engineering 🏫, he teaches and mentors in systems engineering and automation. His career includes a postdoctoral tenure at the University of Connecticut 🇺🇸, where he deepened his research in smart manufacturing. He has led numerous national research projects as Principal Investigator, collaborating with top-tier steel and energy enterprises 🏭. Beyond academia, he offers expert consultations on intelligent scheduling, production line efficiency, and energy optimization 💡. Prof. Sun also holds leadership roles in editorial boards and technical committees, actively shaping global discourse in control systems and industrial AI 📊. His trajectory exemplifies research-driven engineering leadership ⚙️.

🔍 Research Interest

Prof. Sun’s research orbits around intelligent optimization, industrial control, and smart scheduling algorithms 🚀. He explores dynamic production scheduling in steelmaking, aiming to enhance process integration, reduce emissions, and boost system efficiency 🔄. His work leverages artificial intelligence, deep learning, and energy modeling to address complex industrial challenges. With a passion for merging theory with real-world applicability 🛠️, his investigations span multi-objective optimization, cyber-physical systems, and data-driven control frameworks. He has also ventured into hydrogen-rich energy systems and low-carbon manufacturing pathways 🌱. His scientific vision aligns seamlessly with the evolving demands of Industry 4.0 and sustainable engineering 🌐. Through over 18 peer-reviewed papers, Prof. Sun has contributed significantly to reshaping production intelligence and process automation across diverse industrial landscapes 📚.

🏅 Awards and Honors

Prof. Liangliang Sun has garnered prestigious awards that reflect his academic leadership and innovation excellence 🏆. He’s a dual Champion of the National Challenge Cup, a rare feat highlighting his creativity and technical mastery early on 🧠. He is a selected recipient of the Xingliao Talent Plan, the “Hundred and Ten Thousand Talents Project”, and the Young Top Talent in Liaoning Province 🌟. Recognized nationally and provincially, he exemplifies the profile of a high-impact scholar pushing disciplinary boundaries 🚧. His awarded projects from the National Natural Science Foundation of China (NSFC) underline the national trust in his research potential 🧪. These honors demonstrate his sustained excellence in advancing automation, energy intelligence, and industrial digitization. Each accolade fortifies his status as a distinguished researcher 💼.

🧩 Conclusion

Prof. Liangliang Sun represents a remarkable blend of intellectual rigor, innovation, and practical relevance 🎯. With interdisciplinary strength across automation, AI, and energy systems, he bridges theoretical research with industrial transformation 🌐. His impactful publications, pioneering projects, and patented inventions underscore a career built on thoughtful innovation and global relevance 💡. He leads with purpose, engages in collaborative discovery, and mentors the next generation of scientific leaders 👨‍🏫. Prof. Sun’s contributions enhance the technological landscape and redefine the future of smart manufacturing and sustainable engineering 🔋. As a thought leader, he continues to shape high-impact solutions for complex industrial ecosystems, leaving a lasting imprint on global engineering science 🌟. His career is a testament to vision, resilience, and relentless pursuit of excellence 🔝.

Publications Top Notes

📘 Title: A learning-enhanced ant colony optimization algorithm for integrated planning and scheduling in hot rolling production lines under uncertainty
Authors: S. Jiang, L. He, L. Cao, L. Sun, G. Peng
Year: 2025
Citations: 1
Source: Swarm and Evolutionary Computation

📘 Title: A Self-adaptive two stage iterative greedy algorithm based job scales for energy-efficient distributed permutation flowshop scheduling problem
Authors: Y. Yu, Q. Zhong, L. Sun, X. Jing, Z. Wang
Year: 2025
Source: Swarm and Evolutionary Computation

📘 Title: Research on steelmaking-continuous casting cast batch planning based on an improved surrogate absolute-value Lagrangian relaxation framework
Authors: C. Li, L. Sun
Year: 2025
Source: International Journal of Automation and Control

📘 Title: An Online Learning-Based mACO Approach for Hot Rolling Scheduling Problems Involving Dynamic Order Arrivals
Authors: S. Jiang, Q. Liu, L. Cao, L. Sun
Year: 2025
Source: IEEE Transactions on Automation Science and Engineering

📘 Title: A robust optimization approach for steeling-continuous casting charge batch planning with uncertain slab weight
Authors: C. Li, L. Sun
Year: 2024
Citations: 1
Source: Journal of Process Control

📘 Title: Optimal control of three-dimensional unsteady partial differential equations with convection term in continuous casting
Authors: Y. Yu, Y. Wang, X. Pang, L. Sun
Year: 2024
Citations: 1
Source: Computers and Mathematics with Applications

📘 Title: Optimal Scheduling Works for Two Employees with Ordered Criteria
Authors: N.M. Matsveichuk, Y.N. Sotskov, L. Sun
Year: 2024
Source: WSEAS Transactions on Business and Economics