Yu Xiao | Mathematics | Best Researcher Award

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Assoc Prof Dr. Yu Xiao | Mathematics | Best Researcher Award

Doctoral Supervisor at Harbin Institute of Technology, China.

Yu Xiao, an Associate Professor at Harbin Institute of Technology, holds a Ph.D. in Mathematics and specializes in numerical methods for stochastic differential equations, stability and synchronization theory, and networked control systems. His research focuses on almost sure stability in stochastic systems under periodic intermittent control, employing advanced methodologies like stochastic analysis and multiple Lyapunov functions. Xiao has collaborated extensively with industry experts, resulting in over 40 SCI-indexed publications covering diverse topics. His contributions highlight practical applications of theoretical frameworks, reflecting his significant impact in both academic research and industrial applications.

Professional Profiles:

Education 🎓

Yu Xiao, born in 1978, completed his Ph.D. at the School of Mathematics, Harbin Institute of Technology, in 2011. He holds the position of Associate Professor at the same institution. His research focuses on numerical methods for stochastic differential equations, stability and synchronization theory, and networked control systems. This background highlights his extensive academic training, current professional role, and specific research interests in mathematical methodologies applied to complex systems and control theories.

Research and Innovations

Yu Xiao is actively involved in several research areas, focusing primarily on numerical methods for stochastic differential equations, stability and synchronization theory, and networked control systems. His completed and ongoing research projects include work on periodic intermittent control for almost sure stability of stochastic strict-feedback semi-Markov jump systems, indexed in SCI. He has collaborated extensively with industry experts, contributing to the publication of over 40 SCI papers. Additionally, Yu Xiao has two patents under process and has published two journal articles in prominent scientific databases. He holds membership in the Mathematical Society of Heilongjiang Province, China, demonstrating his commitment to advancing mathematical research and applications.

Contributions

In his recent research, Yu Xiao explores almost sure stability (ASS) for stochastic strict-feedback semi-Markov jump systems (SSSJSs) under periodic intermittent control (PIC). The study systematically designs virtual controllers that culminate in an actual controller, establishing ASS conditions through stochastic analysis and the application of a multiple Lyapunov function method. These conditions are intricately linked to control width and period, effectively reducing conservatism. The research substantiates its findings with compelling simulation examples, showcasing the practical applicability and robustness of the proposed methodologies in enhancing system stability and performance.

Area of Research

Yu Xiao specializes in numerical methods for stochastic differential equations, stability and synchronization theory, and networked control systems. With a Ph.D. from the School of Mathematics at Harbin Institute of Technology and current role as Associate Professor at the same institution, his research spans diverse applications in control theory. His work notably explores almost sure stability in stochastic strict-feedback semi-Markov jump systems under periodic intermittent control, employing innovative methods like stochastic analysis and multiple Lyapunov functions. Xiao’s contributions extend beyond theoretical frameworks, demonstrating practical applications through extensive collaborations with industry experts and a robust publication record in SCI-indexed journals. His dedication to advancing control methodologies underscores his impact in both academic and industrial settings

Publications

  1. Almost sure synchronization of stochastic multi-links semi-Markov jump systems via aperiodically intermittent control
    • Authors: Gao, C., Gu, H., Xiao, Y., Guo, B.
    • Journal: Communications in Nonlinear Science and Numerical Simulation, 2024, 135, 108028
  2. Almost sure exponential synchronization analysis of stochastic strict-feedback systems with semi-Markov jump
    • Authors: Gao, C., Zhang, L., Zhang, H., Xiao, Y.
    • Journal: Engineering Applications of Artificial Intelligence, 2024, 133, 108453
  3. Synchronization of multi-link and multi-delayed inertial neural networks with Markov jump via aperiodically intermittent adaptive control
    • Authors: Guo, B., Xiao, Y.
    • Journal: Mathematics and Computers in Simulation, 2024, 219, pp. 435–453
  4. Dynamical analysis of higher-order rogue waves on the various backgrounds for the reverse space–time Fokas–Lenells equation
    • Authors: Song, J.-Y., Xiao, Y., Zhang, C.-P.
    • Journal: Applied Mathematics Letters, 2024, 150, 108971
  5. Aperiodically synchronization of multi-links delayed complex networks with semi-Markov jump and their numerical simulations to single-link robot arms
    • Authors: Gao, C., Guo, B., Xiao, Y., Bao, J.
    • Journal: Neurocomputing, 2024, 575, 127286
    • Citations: 2
  6. Solitonic interactions and explicit solutions for the (2+1) -dimensional nonlocal derivative nonlinear Schrödinger equation
    • Authors: Xiao, Y., Song, J.-Y., Zhang, C.-P.
    • Journal: Nonlinear Dynamics, 2024, 112(5), pp. 3797–3809
  7. Intermittent synchronization for multi-link and multi-delayed large-scale systems with semi-Markov jump and its application of Chua’s circuits
    • Authors: Guo, B., Xiao, Y.
    • Journal: Chaos, Solitons and Fractals, 2023, 174, 113762
    • Citations: 3 (Open access)
  8. Synchronization of Markov Switching Inertial Neural Networks with Mixed Delays under Aperiodically On-Off Adaptive Control
    • Authors: Guo, B., Xiao, Y.
    • Journal: Mathematics, 2023, 11(13), 2906
    • Citations: 3
  9. Soliton solutions and their dynamics of local and nonlocal (2+1)-dimensional Fokas–Lenells equations
    • Authors: Song, J.-Y., Xiao, Y., Bao, J.-C., Tang, H.-C.
    • Journal: Optik, 2023, 273, 170486
    • Citations: 1
  10. Intermittent control for synchronization of hybrid multi-weighted complex networks with reaction-diffusion effects
    • Authors: Guo, B., Xiao, Y.
    • Journal: Mathematical Methods in the Applied Sciences, 2023, 46(1), pp. 1137–1155
    • Citations: 4

 

 

Farhad Samimi Namin | Mathematics | Best Researcher Award

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Assoc Prof Dr. Farhad Samimi Namin | Mathematics |Best Researcher Award

Academic member at University of Zanjan, Iran.

Farhad Samimi Namin is an Assistant Professor at Zanjan University, specializing in mining engineering with a focus on decision theory models for underground mining method selection. He obtained his Ph.D. from Amirkabir University of Technology, Tehran, and has since developed expertise in mine planning, equipment selection, multicriteria decision making, fuzzy logic, and environmental impact assessment. Farhad has authored numerous papers published in prestigious journals and presented at international conferences, showcasing his contributions to advancing mining practices. His teaching portfolio includes courses on underground mining methods and mine design principles, where he emphasizes both theoretical concepts and practical applications. With a commitment to interdisciplinary research and professional development, Farhad continues to play a pivotal role in shaping the future of mining engineering through innovative research and academic leadership.

Professional Profiles:

Education

Farhad Samimi Namin pursued his academic journey in mining engineering, starting with a B.Sc. degree from the International University of Imam Khomeini in Qazvin, Iran. He continued his studies with an M.Sc. in Mining Engineering at Sahid Bahonar University in Kerman, Iran, achieving high academic honors with a GPA of 17.98 out of 20. His educational pursuit culminated in a Ph.D. from Amirkabir University of Technology (Tehran Polytechnic University), Tehran, Iran, where his doctoral research focused on the development of a decision theory-based model for underground mining method selection. His academic achievements reflect a strong foundation in theoretical and applied aspects of mining engineering, preparing him for his subsequent career as an Assistant Professor at the University of Zanjan, Iran.

Professional Experience

Farhad Samimi Namin has garnered extensive professional experience, notably serving as an Assistant Professor in the Mining Engineering Department at Zanjan University, Iran, for over seven years. His academic role includes teaching courses such as Underground Mining Methods, Advanced Underground Excavation Methods, Mine Design Principles, Mine Surveying, Tunneling, Shaft Sinking, Mine Management, Mine Ventilation, and Mine Services. His tenure at Zanjan University has been marked by contributions to both academic instruction and research in the field of mining engineering, particularly focusing on mine planning and the application of Multi-Criteria Decision Making (MADM) techniques in mining contexts.

Research Interest

Farhad Samimi Namin’s research interests primarily revolve around mine planning and design, with a specific focus on mining methods and equipment selection. He specializes in applying Multi-Criteria Decision Making (MADM), fuzzy logic, and environmental impact assessment techniques in the mining industry. His research aims to optimize decision-making processes related to underground mining method selection and to address environmental concerns associated with mining operations. Additionally, he explores practical applications of decision-making techniques in the mining sector, aiming to enhance operational efficiency and sustainability in mineral resource management.

Research Skills

Farhad Samimi Namin is an esteemed figure in mining engineering, currently serving as an Assistant Professor at Zanjan University. He earned his Ph.D. from Amirkabir University of Technology, Tehran, specializing in decision theory models for underground mining method selection. His research interests encompass mine planning, mining equipment selection, multicriteria decision making, fuzzy logic applications, and environmental impact assessment. With a prolific publication record in esteemed journals and presentations at international conferences, Farhad has made significant contributions to advancing mining methodologies. His expertise extends to technical writing, data analysis, and interdisciplinary collaboration, reflecting a comprehensive approach to addressing complex mining challenges. Through his teaching in courses such as underground mining methods and mine design principles, he nurtures the next generation of mining professionals, emphasizing practical applications and theoretical foundations in mining engineering.

Publications

  1. Title: A Review: Applications of Fuzzy Theory in Rock Engineering
    • Authors: Samimi Namin, F.; Rouhani, M.M.
    • Year: 2024
    • Citations: 1
  2. Title: Investigate the potential of using fuzzy similarity in decision making under uncertainty for mining projects
    • Authors: Rouhani, M.M.; Namin, F.S.
    • Year: 2023
    • Citations: 1
  3. Title: Development of a new system for improving blastability by using the Fuzzy Delphi AHP method
    • Authors: Jalali, Z.; Namin, F.S.
    • Year: 2023
    • Citations: 0
  4. Title: Analyzing the effects of natural ventilation caused by excavating the waste pass on the ventilation network of the Anguran mine
    • Authors: Fetri, M.; Shahabi, R.S.; Namin, F.S.; Zeyni, E.E.; Khereshki, M.H.K.
    • Year: 2023
    • Citations: 0
  5. Title: A literature review of Multi Criteria Decision-Making (MCDM) towards mining method selection (MMS)
    • Authors: Namin, F.S.; Ghadi, A.; Saki, F.
    • Year: 2022
    • Citations: 21
  6. Title: 3D computational study of tunnel segment behaviour under loading by TBM thrust jacks
    • Authors: Dastjerdy, B.; Hasanpour, R.; Samimi Namin, F.; Chakeri, H.
    • Year: 2021
    • Citations: 2
  7. Title: Numerical investigation into the effect of stepping on the circumferential joint in the precast tunnel segments under TBM thrust jacks
    • Authors: Karimi, M.; Chakeri, H.; Namin, F.S.; Sharghi, M.; Özçelik, Y.
    • Year: 2020
    • Citations: 6
  8. Title: Uncertainty determination in rock mass classification when using FRMR Software
    • Authors: Namin, F.S.; Rinne, M.; Rafie, M.
    • Year: 2015
    • Citations: 3
  9. Title: Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system
    • Authors: Rafie, M.; Samimi Namin, F.
    • Year: 2015
    • Citations: 84
  10. Title: FMMSIC: A hybrid fuzzy based decision support system for MMS (in order to estimate interrelationships between criteria)
    • Authors: Namin, F.S.; Shahriar, K.; Bascetin, A.; Ghodsypour, S.H.
    • Year: 2012
    • Citations: 28

 

Ayyub Sheikhi | Mathematics | Best Researcher Award

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Assoc Prof Dr. Ayyub Sheikhi | Mathematics | Best Researcher Award

Department of Statistics at Shahid Bahonar University of Kerman, Iran.

Ayyub Sheikhi possesses a robust set of research skills honed through years of academic and professional experience. Proficient in both theoretical and applied aspects of statistics and data science, he excels in experimental design, statistical analysis, and modeling techniques. Ayyub demonstrates proficiency in various statistical software packages, including R, and is adept at implementing advanced methodologies such as machine learning algorithms and Bayesian inference. His research skills also extend to data visualization and interpretation, allowing him to effectively communicate complex statistical findings to diverse audiences. Ayyub’s expertise in statistical research methodologies is further augmented by his ability to collaborate across interdisciplinary teams and lead projects from conception to completion. With a keen attention to detail and a commitment to methodological rigor, Ayyub Sheikhi continues to make significant contributions to the advancement of statistical science.

Professional Profiles:

Education

Ayyub Sheikhi pursued his academic journey in statistics and applied mathematics, earning his Ph.D. in Applied Mathematics from Shahid Bahonar University of Kerman, Iran, in 2012. Prior to that, he completed his Master of Science in Statistics at AmirKabir University of Technology, Tehran, Iran, and his Bachelor of Science in Statistics at Shahid Bahonar University of Kerman. His educational background equipped him with a strong foundation in mathematical and statistical principles, which he has since applied in both academic and professional settings, contributing significantly to the field of statistics and data science.

Professional Experience

Ayyub Sheikhi has garnered extensive professional experience in academia, research, and leadership roles. From 2018 to 2020, he served as the Head of the Department of Statistics at Shahid Bahonar University of Kerman, Iran, where he demonstrated his leadership skills in managing departmental affairs and fostering academic excellence. Concurrently, since 2015, he has held the position of Assistant Professor in the Department of Statistics at the same university, contributing to both teaching and research activities. In 2020, he was promoted to the position of Associate Professor, recognizing his scholarly achievements and expertise in the field. Additionally, since 2021, Ayyub has taken on the role of Head of the “Data Mining and Machine Learning” group at the Mahani Mathematical Research Institute, Shahid Bahonar University of Kerman, further showcasing his commitment to advancing statistical research and methodologies. Moreover, he has enriched his professional experience through a research fellowship at the University of Plymouth, UK, during the academic year 2023-2024, broadening his perspective and collaborating with international scholars in the field of statistics and data science.

Research Interest

Ayyub Sheikhi’s research interests encompass a diverse array of topics within the field of statistics and data science. He is particularly interested in Bayesian methods, change-point detection, and dynamic time series analysis, exploring innovative approaches to model complex data structures and uncover temporal patterns. Additionally, he focuses on copula-related techniques for analyzing dependence structures in multivariate data, aiming to develop robust methodologies for financial modeling and risk management. Ayyub also investigates machine learning methods, such as clustering algorithms and support vector machines, with a specific focus on their applications in outlier detection, classification, and regression tasks. Furthermore, he is keen on exploring the intersection of statistics with other disciplines, including economics, medicine, and engineering, to address real-world challenges and advance interdisciplinary research initiatives. Overall, Ayyub’s research interests reflect his dedication to advancing statistical theory and methodology while addressing practical problems across various domains.

Award and Honors

Ayyub Sheikhi, a prominent figure in the field of statistics and data science, has accumulated a wealth of experience and expertise over the years. He has held various academic and research positions, including serving as the Head of the Department of Statistics at Shahid Bahonar University of Kerman, Iran, from 2018 to 2020, and as an Assistant Professor and later an Associate Professor in the same department. Ayyub’s academic journey began with a Ph.D. in Applied Mathematics from Shahid Bahonar University of Kerman, Iran, in 2012, complemented by an M.Sc. in Statistics from AmirKabir University of Technology, Tehran, and a B.Sc. in Statistics from Shahid Bahonar University of Kerman. His scholarly contributions extend beyond academia, as evidenced by his involvement in various visiting appointments, publication of books, development of statistical packages, and leadership in research projects. Ayyub’s commitment to excellence and innovation in statistics continues to drive advancements in the field, positioning him as a respected authority and mentor within the academic community.

Research Skills

Dr. Sheikhi Ayyub possesses a breadth of research skills spanning statistical and mathematical domains, underpinned by a dedication to practical problem-solving. His expertise encompasses statistical inference, where he adeptly employs hypothesis testing, estimation, and model fitting to derive meaningful insights from data. Proficient in data analysis techniques, Dr. Ayyub excels in uncovering patterns and trends within complex datasets, thereby facilitating informed decision-making processes. Moreover, he demonstrates a keen understanding of machine learning algorithms, employing them for tasks ranging from classification and regression to clustering and anomaly detection, thereby enhancing predictive modeling capabilities. Additionally, Dr. Ayyub showcases proficiency in time series analysis, employing various techniques to analyze temporal data, identify patterns, and forecast future values effectively. Furthermore, his expertise extends to Bayesian statistics, copula models, and robust statistical methods, further enriching his analytical toolkit. Proficient in utilizing statistical software packages like R, Python, and MATLAB, Dr. Ayyub applies these skills across interdisciplinary research endeavors, contributing to advancements in fields ranging from healthcare and economics to engineering and finance.

Publications

  1. A two-stage Bridge estimator for regression models with endogeneity based on control function method
    • Authors: Bahador, F., Sheikhi, A., Arabpour, A.
    • Year: 2024
    • Journal: Computational Statistics
    • Volume: 39
    • Issue: 3
    • Pages: 1351–1370
    • Citations: 0
  2. Convex weak concordance measures and their constructions
    • Authors: Mesiar, R., Kolesárová, A., Sheikhi, A., Shvydka, S.
    • Year: 2024
    • Journal: Fuzzy Sets and Systems
    • Volume: 478
    • Pages: 108841
    • Citations: 1
  3. Multivariate Asymmetric Distributions of Copula Related Random Variables
    • Authors: Sheikhi, A., Arad, F., Mesiar, R.
    • Year: 2023
    • Journal: Austrian Journal of Statistics
    • Volume: 52
    • Issue: 4
    • Pages: 1–14
    • Citations: 0
  4. A dimension reduction in neural network using copula matrix
    • Authors: Sheikhi, A., Mesiar, R., Holeňa, M.
    • Year: 2023
    • Journal: International Journal of General Systems
    • Volume: 52
    • Issue: 2
    • Pages: 131–146
    • Citations: 3
  5. Convex concordance measures
    • Authors: Mesiar, R., Kolesárová, A., Sheikhi, A.
    • Year: 2022
    • Journal: Fuzzy Sets and Systems
    • Volume: 441
    • Pages: 366–377
    • Citations: 2
  6. Copula-based Berkson measurement error models
    • Authors: Ziaei, A.R., Zare, K., Sheikhi, A.
    • Year: 2022
    • Journal: Iranian Journal of Fuzzy Systems
    • Volume: 19
    • Issue: 4
    • Pages: 165–175
    • Citations: 0
  7. A heteroscedasticity diagnostic of a regression analysis with copula dependent random variables
    • Authors: Sheikhi, A., Arad, F., Mesiar, R.
    • Year: 2022
    • Journal: Brazilian Journal of Probability and Statistics
    • Volume: 36
    • Issue: 2
    • Pages: 408–419
    • Citations: 0
  8. ON ASYMMETRIC DISTRIBUTIONS OF COPULA RELATED RANDOM VARIABLES WHICH INCLUDES THE SKEW-NORMAL ONES
    • Authors: Sheikhi, A., Arad, F., Mesiar, R.
    • Year: 2022
    • Journal: Kybernetika
    • Volume: 58
    • Issue: 6
    • Pages: 984–995
    • Citations: 1
  9. Asymmetric distributions based on the t-copula
    • Authors: Arad, F., Sheikhi, A.
    • Year: 2022
    • Conference: 9th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2022
    • Citations: 0
  10. On a generalization of the test of endogeneity in a two stage least squares estimation
    • Authors: Sheikhi, A., Bahador, F., Arashi, M.
    • Year: 2022
    • Journal: Journal of Applied Statistics
    • Volume: 49
    • Issue: 3
    • Pages: 709–721
    • Citations: 11