Larisa Stepanova | Atomistic simulations | Best Researcher Award

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Dr. Larisa Stepanova | Atomistic simulations | Best Researcher Award

PHD at Lomonosov Moscow State University, Russia

Born on May 21, 1969, in Kuibyshev, Russia, Dr. Stepanova is a distinguished Russian scientist specializing in continuum mechanics and mathematical modeling. He earned his Diploma in Mechanics from Samara State University in 1991, followed by a PhD from Lomonosov Moscow State University in 1994. His academic career includes roles as Assistant Professor, Senior Lecturer, and Associate Professor at Samara State University, where he became a Full Professor in 2011. He holds a Doctorate in Physical and Mathematical Sciences from the Russian Academy of Sciences. His research has been recognized internationally, with fellowships in Sweden, South Africa, and France.

Professional Profiles

Publications:

Coefficients of the williams power expansion of the near crack tip stress field in continuum linear elastic fracture mechanics at the nanoscale, Publication date: 2022.

Methods of perturbation theory and their applications in nonlinear fracture mechanics: From the pioneering studies by Hutchinson, Rice and Rosengren until today, Publication date: 2019.

Stress intensity factors, T-stresses and higher order coefficients of the Williams series expansion and their evaluation through molecular dynamics simulations, Publication date: 2022.

IDENTIFICATION OF STRESS INTENSITY FACTORS, T-STRESSES AND HIGHER-ORDER COEFFICIENTS OF REGULAR TERMS IN THE WILLIAMS SERIES EXPANSION THROUGH MOLECULAR DYNAMICS SIMULATIONS, Publication date: 2023.

Identification of Linear Elastic Fracture Mechanics Parameters Through the Atomistic Simulation Based Analysis, Publication date: 2023.

 

Rachid Masrour | Computational Physics | Excellence in Research

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Prof Dr. Rachid Masrour | Computational Physics | Excellence in Research

PHD at University Habilitation, Morocco

Rachid Masrour, born on December 31, 1978, in Guercif, Morocco, is a distinguished professor at the Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez. He holds a Doctorate in Materials Physics (2006) and a University Habilitation (2013). His career includes roles as a research professor at Cadi Ayyed University and Sidi Mohamed Ben Abdellah University. He has led the Industrial Engineering Department and coordinated the preparatory class stream at the National School of Applied Sciences, Safi. His contributions span teaching, research, and administration, reflecting a commitment to academic excellence. Computational Physics 

Professional Profiles

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Evaluation of Dr. Rachid Masrour for the Best Researcher Award

Strengths

Academic and Professional Qualifications

Full Professor at Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Doctorate in Materials Physics and multiple advanced degrees in physics. Computational Physics 

Research Experience

Extensive experience as a research professor since 2011.
Held significant academic positions including Head of Industrial Engineering Department and Coordinator of preparatory class stream.

Leadership and Administrative Roles

Served on various committees (budget, scientific, pedagogical) demonstrating leadership and involvement in academic governance.
Experience as a department head and coordinator showcases management skills. Computational Physics

Research Focus and Impact

Research spans critical areas in semiconductor processing and solid-state physics.
Contributions to high-impact areas such as materials for energy and optoelectronics, as indicated by publications in well-regarded journals. Computational Physics 

Areas for Improvement

International Collaboration:

While there are collaborations evident in publications, increasing international partnerships and joint research projects could enhance the global impact of his work.

Funding and Grants:

Information on securing research funding and grants is not provided. Strengthening efforts in obtaining competitive funding could further support and expand research activities.

Interdisciplinary Research:

Expanding research to include more interdisciplinary projects could diversify the impact and applicability of his work.

Visibility and Recognition:

Increasing participation in international conferences, seminars, and symposiums could boost visibility and recognition in the global research community.

Education

2022: Full Professor at Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco. 2013: University Habilitation, Faculty of Sciences, Mohamed V University, Rabat, Morocco. March 18, 2006: Doctorate in Materials Physics, Faculty of Sciences Dhar El Mahraz, Fez, Morocco. November 10, 2003: Diploma of Advanced Studies (Master), Faculty of Sciences Ben M’Sik Sidi Othman, Hassan II University, Casablanca, Morocco. 2001: Bachelor in Solid State Physics, Faculty of Sciences Dhar El Mahraz, Fez, Morocco. 1997: Baccalaureate in Experimental Science, High School Hassan Eddakhil, Guercif, Morocco. Computational Physics 

professional experience

Research Professor (2011-2020): National School of Applied Sciences, Cadi Ayyed University, Safi, Morocco. Research Professor (2020-present): Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.

Publications:

Authored numerous research papers in reputable journals such as Materials Science in Semiconductor Processing, Ionics, and Arabian Journal for Science and Engineering.
Recent publications indicate ongoing active research and contribution to the field of materials science.

Investigation on the electrical and photocatalytic properties of t-stanagraphene and SnC-graphene, Publication date: 2024.

Impact of boron substitution on the thermoelectric, mechanical stability, electronic and optical properties of InP alloys, Publication date: 2024.

Evaluation of the phonon, optoelectronic, photovoltaic, thermoelectric, photocatalytic and thermodynamic properties of the inorganic perovskite CsPbI2Br using Ab initio, Publication date: 2024.

Structural stability, electronic, magnetic, thermoelectric, optical and thermodynamic properties of CoTiFeGe and Co2Fe0. 25Mn0. 75-xTixGe alloys, Publication date: 2024.

A comprehensive theoretical analysis on structural, electronic, optical, and mechanical properties of Sr2VRuO6 compound, Publication date: 2024.

Magnetic phase diagrams and magnetocaloric effect of alternate layers of nanoborophene like: A Monte Carlo study, Publication date: 2024.

Numerical Simulation of Early Detection of Cancer Cells Using a D-Shaped Fiber-Optic Biosensor Based on Surface Plasmon Resonance, Publication date: 2024.

Investigation of electrochemical, structural, electronic, thermodynamic, and optical properties of LiTi2O4 cathode material for Li-ion battery: an Ab Initio calculations, Publication date: 2024.

Investigation of structural, magnetic, mechanical, electronic and thermal properties of new quaternary Heusler: KMgNZ (Z= O or S), Publication date: 2024.

Magnetic Properties Study of a Core/Shell Cylindrical Structure with RKKY Interaction using Monte Carlo Method, Publication date: 2024.

Conclusion

Dr. Rachid Masrour’s academic qualifications, extensive research experience, and numerous publications in high-impact journals position him as a strong candidate for the Best Researcher Award. His leadership roles and active involvement in academic committees further bolster his credentials. To enhance his candidacy, he could focus on increasing international collaborations, securing more research funding, expanding interdisciplinary research, and improving his visibility in the global research community. Overall, Dr. Masrour demonstrates a commendable blend of research excellence and academic leadership, making him a noteworthy contender for the award. Computational Physics 

Blessing Guembe | Advanced Computing | Best Researcher Award

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Dr. Blessing Guembe | Advanced Computing | Best Researcher Award

PHD at Covenant University, Nigeria

Blessing Guembe is a Research Fellow at the University of Milan, specializing in Explainable AI, Federated Learning, misinformation, privacy, and security. With a Ph.D. in Computer Science from Covenant University, his notable projects include federated learning for asthma prediction and deep learning techniques for malaria diagnosis. He has extensive experience as a senior software developer, having led significant projects at Teesoft Innovations and Reconsoft Nigeria Limited. His technical expertise spans AWS, Azure, Google Cloud, Python, Java, and DevOps, making significant contributions to data analytics and privacy-preserving technologies. Advanced Computing

Professional Profiles

Strengths

Research Contributions:

Explainable AI and Federated Learning: Blessing’s research focuses on cutting-edge areas like explainable AI, federated learning, misinformation, privacy, and security. These topics are highly relevant and impactful in today’s technological landscape.
Publications: Blessing has several notable publications, including reviews on AI-driven cyberattacks and trustworthy machine learning approaches. These publications indicate a strong research output and expertise in cybersecurity and AI.
Projects: Active participation in the KURAMi Project at the University of Milan under Prof. Giovanni Livraga showcases involvement in significant research projects. Advanced Computing

Professional Experience

Research Fellow: Current role at the University of Milan, working on privacy and data protection in social media, demonstrates a focus on real-world applications of research.
Research Assistant: Experience at Covenant University, working on federated learning for medical applications and homomorphic encryption, highlights the practical and impactful nature of Blessing’s work.
Software Development: Extensive experience as a senior software developer at Teesoft Innovations and Reconsoft Nigeria Limited, leading large-scale projects and mentoring junior developers. This indicates strong leadership and technical skills. Advanced Computing

Education

Doctor of Philosophy in Computer Science: Obtained from Covenant University in 2023, reflecting advanced knowledge and research capabilities in the field. Advanced Computing

Technical Skills

Core Competence: Proficiency in various programming languages, cloud platforms, and data science tools (AWS, Azure, Google Cloud Platform, Python, Java, Machine Learning, Deep Learning, XAI, Federated Learning, Privacy-Preserving techniques).
Interdisciplinary Skills: Ability to integrate AI, cybersecurity, and data privacy into research and development projects.

Areas for Improvement

Diversity of Publications:

While the existing publications are strong, increasing the diversity in terms of co-authors and exploring different journals and conferences could enhance visibility and impact. Advanced Computing

Collaborations:

Engaging in more international collaborations and interdisciplinary projects could broaden the scope and application of Blessing’s research.

Grants and Funding:

Securing more research grants and funding would not only support Blessing’s work but also demonstrate the ability to attract resources for innovative projects.

Community Engagement:

Increasing involvement in academic and professional communities, such as presenting at conferences, participating in workshops, and contributing to professional societies, could further establish Blessing as a thought leader in the field.

Publications

  1. Federated Bayesian Optimization XGBoost Model for Cyberattack Detection in Internet of Medical Things, Publication date: 2024.
  3. A Deep Learning Approach For Cassava Leaf Disease Diagnosis, Publication date: 2023.
  4. Explainable artificial intelligence, the fourth pillar of zero trust security, Publication date: 2022.
  5. Machine Learning Techniques for Automatic Long Text Examination in Open and Distance Learning, Publication date: 2022.
  6. The Emerging Threat of Ai-driven Cyber Attacks: A, Publication date: 2022.
  7. Multivariate and Univariate Anomaly Detection in Machine Learning: A Bibliometric Analysis, Publication date: 2022.
  8. Trustworthy Machine Learning Approaches for Cyberattack Detection: A Review, Publication date: 2022.
  9. A machine learning prediction of automatic text based assessment for open and distance learning: a review, Publication date: 2021.
  10. The emerging threat of ai-driven cyber attacks: A review, Publication date: 2022.

 

Conclusion

Blessing Guembe is a strong candidate for the Best Researcher Award due to their significant contributions to explainable AI, federated learning, and cybersecurity. The combination of advanced research, impactful publications, extensive professional experience, and robust technical skills positions Blessing as an influential researcher in the field. Advanced Computing

By enhancing the diversity of publications, seeking more collaborations, securing additional grants, and engaging with the academic community, Blessing could further solidify their standing and potential for future contributions. Overall, Blessing’s profile is highly suitable for consideration for the Best Researcher Award.

 

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S.K. Geetha | Density Functional Theory | Excellence in Research

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Dr. S.K. Geetha | Density Functional Theory | Excellence in Research

PHD at Anna University, India

Dr. S.K. Geetha is an Associate Professor in the PG & Research Department of Physics at Government Arts College for Men (A), affiliated with Madras University in Chennai. With 28 years of undergraduate and 15 years of postgraduate teaching experience, she has a robust background in physics education. Dr. Geetha holds a Ph.D. from Anna University and has extensive research experience, including Ph.D. guideship with one awarded and four pursuing students. Her accolades include the Best Teacher Award (2005-2006) and several certificates of appreciation for her contributions to academic excellence and college administration. Density Functional Theory

Professional Profiles

Education

Ph.D. in Physics: Anna University, Chennai M.Phil. in Physics: MCC Tambaram M.Sc. in Physics: QMC, Chennai

Research Experience in Physics

General Research: 27 years Ph.D. Guideship (since 2015): 1 Awarded, 4 pursuing Density Functional Theory

Achievements

Best Teacher Award: 2005-2006 at Meenakshi College of Engineering, Chennai Certificate of Appreciation: For producing 92% results at Rajiv Gandhi College of Engineering Administrative Committee Membership: Inducted at Rajiv Gandhi College of Engineering for dedication and hard work Certificate of Honour: Awarded for services rendered to the college during 1992-1993 Density Functional Theory

Research Focus

Dr. S.K. Geetha’s research primarily focuses on the field of crystallography, specifically in the modification and improvement of crystal properties through doping with metal ions. Her work includes extensive computational and experimental investigations into the structural, vibrational, and optical properties of various compounds. Dr. Geetha has contributed significantly to understanding nucleation kinetics, solvent effects, and non-covalent interactions in crystal growth. Her research extends to practical applications in photoelectrochemical water splitting, with a strong emphasis on nanomaterials like bismuth tungstate. She also explores the therapeutic effects and characterization of plant extracts, showcasing her diverse expertise in applied physics and materials science. Density Functional Theory

Publications

  1. Synthesis, experimental and theoretical spectroscopic electronic elucidation along with biological assessment and molecular docking studies on 2-(3-(1, 4-diazepan-1-yl) propyl, Publication date: 2024.
  2. Comprehensive Evaluation of Nanosized Bismuth Tungstate (Bi2WO6) as Photoanodes for Photoelectrochemical Water Splitting Performance, Publication date: 2024.
  3. Comprehensive Evaluation of Nanosized Bismuth Tungstate (Bi2WO6) as Photoanodes for Photoelectrochemical Water Splitting Performance, Publication date: 2024.
  4. pH controlled synthesis and photoelectrochemical kinetics of bismuth molybdate (Bi2MoO6) nanoplates as photoanodes for solar-driven water splitting, Publication date: 2023.
  5. Therapeutic Effect and Characterisation of Ethanol and Acetone Extracts of Aegle marmeloes, Publication date: 2023.
  6. Computational examination on monomeric, dimeric, trimeric structural and vibrational interactions, AIM, Hirshfeld, IGM and oxygenated solvent effect on optical properties for … Publication date: 2022.
  7. Vibrational spectra and Wavefunction investigation for antidepressant drug of Amoxapine based on quantum computational studies, Publication date: 2021.
  8. Theoretical investigation on influence of protic and aprotic solvents effect and structural (Monomer, Dimer), Van-der Waals and Hirshfeld surface analysis for clonidine molecule, Publication date: 2021.
  9. Probing solvent effect and strong and weak interactions in 2-Nitrophenyl-hydrazine using independent gradient model and Hirshfeld from wave function calculation, Publication date: 2021.
  10. Extension of modified classical theory to nucleation of NKAP from aqueous solutions, Publication date: 2014.
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Sajjad Ali | Computational Methods | Best Researcher Award

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Dr. Sajjad Ali | Computational Methods | Best Researcher Award

PHD at Abdul Wali Khan University Mardan, Pakistan

Dr. Sajjad Ali is a Lecturer in Mathematics at Shaheed Benazir Bhutto University Sheringal, Dir (Upper), Pakistan. He received his Ph.D. in Mathematics from Abdul Wali Khan University Mardan in 2019. With over a decade of teaching experience, Dr. Ali specializes in Bio-Mathematics, Fractional Differential Equations, and Advanced Homotopy Methods. He supervises M.Phil and Ph.D. students and is involved in departmental administration. His research interests include mathematical modeling and computational methods. Dr. Ali is proficient in MATLAB, MS Office, Excel, and LaTeX, and he is fluent in Urdu, English, Pashto, and Khohar.

Professional Profiles

Education

Ph.D. in Mathematics (2015 – 2019) Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan M.Phil in Mathematics (2009 – 2012) Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan Bachelor of Education (B.Ed) (2007 – 2008) Allama Iqbal Open University, Islamabad, Pakistan Master of Science in Mathematics (2005 – 2007) University of Malakand Chakdara, Khyber Pakhtunkhwa, Pakistan Bachelor of Science in Mathematics (2002 – 2004) Government Degree College Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan F.Sc. Pre-Engineering (2000 – 2002) Government Degree College Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan Metric in Science (1999 – 2000) Government School No.2 Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan.

Skills

Research in Mathematics Teaching and Administrative Skills Software MATLAB MS Office Excel LaTeX

Work History

Shaheed Benazir Bhutto University Sheringal, Dir (Upper), Pakistan Lecturer in Mathematics (2011 – Present) Teaching Ph.D./M.Phil courses in Bio-Mathematics, Fractional Differential Equations, and Advanced Homotopy Methods Teaching BS courses in Partial Differential Equations and Ordinary Differential Equations Supervising M.Phil and Ph.D. research students M.Phil/Ph.D. Coordinator in the Department of Mathematics Member of the Departmental Admission Committee Warden of Ihsan Boys Hostel Tameer e Seerat Degree College, Mardan Campus, Pakistan Lecturer in Mathematics (2010 – 2011) Taught graduate-level Mathematics Hostel Warden Farabi Degree College, Peshawar, Pakistan Lecturer in Mathematics (2009 – 2010) Taught graduate-level Mathematics Government Degree College, Tangi, Charsadda, Pakistan Lecturer in Mathematics (2007 – 2009) Taught graduate-level Mathematics

Interests

Mathematical modeling, Computational methods

Awards

Top Position Holder in Master of Mathematics at University of Malakand (2007) Awardee as Lecturer in Mathematics through the Higher Education Commission Pakistan (2008)

Research Focuse

Dr. Sajjad Ali’s research focuses on the numerical treatment and computational solutions of fractional order differential equations, with applications in reaction-diffusion systems, biological population models, and ion-acoustic waves. His work includes developing iterative and stable methods for solving boundary value problems of nonlinear fractional differential equations. Dr. Ali has collaborated extensively with international researchers, contributing to journals such as Chaos, Solitons & Fractals and the Journal of Advanced Research. His studies also explore the stability analysis and exact solutions of complex mathematical models, emphasizing fractional calculus and its applications in various scientific and engineering problems.

Publications

  1. Nonlinear coupling of upper-hybrid waves with lower-hybrid waves in a degenerate dense plasma, Publication date: 2021.
  2. Unstable mode of ion-acoustic waves with two temperature q-nonextensive distributed electrons, Publication date: 2021.
  3. Computation of solution to fractional order partial reaction diffusion equations, Publication date: 2020.
  4. On stable iterative solutions for a class of boundary value problem of nonlinear fractional order differential equations, Publication date: 2019.
  5. Computation of iterative solutions along with stability analysis to a coupled system of fractional order differential equations, Publication date: 2019.
  6. Approximate solutions to nonlinear fractional order partial differential equations arising in ion-acoustic waves, Publication date: 2019.
  7. Stable monotone iterative solutions to a class of bound-ary value problems of nonlinear fractional order differential equations, Publication date: 2019.
  8. Monotone iterative technique and Ulam-Hyers stability analysis for nonlinear fractional order differential equations with integral boundary value conditions, Publication date: 2019.
  9. Optimum solutions of space fractional order diffusion equation, Publication date: 2018.
  10. On Approximate solutions of fractional Order partial differential equations, Publication date: 2018.
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Computational Particle Physics

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Introduction to Computational Particle Physics:

Computational Particle Physics represents a vital branch of scientific research at the intersection of particle physics, computer science, and data analysis. It involves the use of advanced computational techniques and high-performance computing to simulate, model, and analyze the behavior of subatomic particles, their interactions, and the outcomes of high-energy experiments. Computational methods are essential for interpreting the vast amount of data produced by particle accelerators and for making precise predictions within the framework of particle physics theories.

Monte Carlo Simulations:

Explore the use of Monte Carlo methods to simulate particle interactions, detector responses, and event generation, crucial for understanding experimental data and developing analysis strategies.

Lattice Quantum Chromodynamics (QCD):

Investigate lattice QCD simulations, which use a discretized spacetime lattice to study the behavior of quarks and gluons within the strong nuclear force, enabling the calculation of hadron properties and masses.

Event Reconstruction and Data Analysis:

Delve into the development of algorithms and software tools for event reconstruction and data analysis in particle physics experiments, including techniques for particle identification and background rejection.

Machine Learning and Artificial Intelligence:

Focus on the integration of machine learning and artificial intelligence techniques for particle physics data analysis, feature extraction, and pattern recognition, aiding in the discovery of new particles and phenomena.

Grid and Cloud Computing:

Examine the use of distributed computing environments, such as grid computing and cloud computing, to handle the immense computational demands of particle physics simulations and data processing.

 

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