Ahmadreza Khodayari | Industrial and Manufacturing Applications | Excellence in Research

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Mr. Ahmadreza Khodayari | Industrial and Manufacturing Applications | Excellence in Research

PhD Candidate | The University of Adelaide | Australia

Mr. Ahmadreza Khodayari is a mining engineering researcher whose work integrates rock mechanics, blasting science, fracture mechanics, and machine learning to advance precision modelling and optimization in mining operations. With a Published Documents 8 citation index comprising 110 citations, an h-index of 4, and an i10-index of 4, his contributions span experimentally grounded studies, data-driven prediction models and mechanistic simulations that address key challenges in rock breakage and material flow behaviour.His research portfolio includes several completed and ongoing projects focused on blast modelling rock fracture characterization and artificial intelligence applications in geo-materials engineering. Notable works include the calibration of mechanistic blast models using Ernest Henry Mine datasets the development of machine learning models for predicting blast-induced fragment sizes and advanced Blender Physics Engine simulations to assess sublevel caving (SLC) material flow. He has also executed misfire impact analyses on SLC gravity flow supporting safer and more predictable caving performance. Additionally his studies on AI-driven prediction of concrete and rock fracture toughness contribute to bridging traditional fracture mechanics with modern computational intelligence.Ahmadreza’s publications are featured in respected outlets such as Engineering Fracture Mechanics Theoretical and Applied Fracture Mechanics Steel and Composite Structures and the Journal of Mining and Environment. His 2022 work on predicting mixed-mode fracture toughness using extreme gradient boosting and metaheuristic optimization has accumulated significant citations reflecting strong community interest in AI-enhanced fracture modelling. His earlier experimental studies on freeze–thaw effects in Lushan Sandstone provided valuable insights into strength degradation mechanisms in cold-region geomaterials.He collaborates with researchers from the Lebanese French University Imam Khomeini International University and other international institutions strengthening global knowledge exchange in blasting and rock mechanics. His contributions to major conferences including FragBlast MassMin and ARMA demonstrate active engagement with both scientific and industry practitioners.Through a combination of high-fidelity numerical modelling physics-based simulations and advanced data-driven techniques Ahmadreza’s research aims to enhance fragmentation predictability mine productivity and geomechanical safety. His work continues to shape emerging methodologies in intelligent mining systems contributing to more efficient and sustainable resource extraction practices worldwide.

Profiles: Googlescholar | ORCID | ResearchGate 

Featured Publications

1.Fakhri, D., Khodayari, A., Mahmoodzadeh, A., Hosseini, M., Ibrahim, H. H., & Others. (2022). Prediction of mixed-mode I and II effective fracture toughness of several types of concrete using the extreme gradient boosting method and metaheuristic optimization algorithms. Engineering Fracture Mechanics, 276, 108916. Cited By: 39

2.Khodayari, A. R. (2019). Effect of freeze–thaw cycle on strength and rock strength parameters (A Lushan sandstone case study). Journal of Mining and Environment, Cited By: 27

3.Fakhri, D., Mahmoodzadeh, A., Mohammed, A. H., Khodayari, A., Ibrahim, H. H., & Others. (2023). Forecasting failure load of sandstone under different freezing–thawing cycles using Gaussian process regression method and grey wolf optimization algorithm. Theoretical and Applied Fracture Mechanics, 125, 103876. Cited By: 24

4.Hosseini, M., & Khodayari, A. R. (2018). Effects of temperature and confining pressure on mode II fracture toughness of rocks (Case study: Lushan sandstone). Journal of Mining and Environment, 9(2), 379–391. Cited By: 17

5.Khodayari, A., Fakhri, D., Mohammed, A. H., Albaijan, I., Mahmoodzadeh, A., & Others. (2023). The gene expression programming method to generate an equation to estimate fracture toughness of reinforced concrete. Steel and Composite Structures, 48(2), 163–177. Cited By: 3

His research advances intelligent blasting and rock-mass behaviour prediction, enabling safer, more efficient, and data-driven mining practices that strengthen global resource sustainability.

Wolfgang Härdle | Industrial and Manufacturing Applications | Outstanding Contribution Award

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Prof. Dr. Wolfgang Härdle | Industrial and Manufacturing Applications | Outstanding Contribution Award

Humboldt-Universität zu Berlin | IDA Inst Digital Assets | Germany

Prof. Wolfgang Karl Härdle, Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin, is an internationally recognized leader in modern statistics, digital finance, machine learning, and smart data analytics. With an exceptional body of work spanning more than three decades, he has shaped the global landscape of statistical science through groundbreaking contributions to nonparametric statistics, multivariate analysis, econometrics, and quantitative finance. His academic influence is reflected in an outstanding scholarly output of 994 documents which have collectively amassed over 48,217 citations, supported by a remarkable h-index of 93 and i10-index of 311.A pioneer of applied nonparametric regression Prof. Härdle’s seminal works such as Applied Nonparametric Regression Applied Multivariate Statistical Analysis and Nonparametric and Semiparametric Models remain foundational references used across statistics econometrics  and data science. His highly cited research on smoothing techniques bandwidth selection average derivatives and optimal smoothing rules has advanced the theoretical and practical understanding of regression modeling. Additionally his contributions to wavelets financial econometrics copula theory tail-risk modeling and network risk analysis have had significant implications for financial stability risk assessment and decision analytics.Prof. Härdle has collaborated extensively with leading scholars worldwide producing influential publications that continue to guide contemporary methodological innovations. His interdisciplinary reach includes co-authoring major handbooks such as the Springer Handbook of Computational Statistics and the Handbook of Data Visualization which broaden access to advanced analytical methodologies for global researchers and practitioners.Beyond scholarly impact his work plays a vital societal role by strengthening statistical foundations for digital finance  high-dimensional modeling and smart data solutions helping institutions and industries make informed data-driven decisions. Through his research leadership mentorship and high-impact publications Prof. Härdle continues to advance statistical science and shape the future of data-centric research worldwide.

Profile:  Googlescholar

Featured Publications

1.Härdle, W. (1990). Applied nonparametric regression. Cambridge University Press. Cited By: 6559

2.Härdle, W., & Simar, L. (2007). Applied multivariate statistical analysis. Springer Berlin Heidelberg.Cited By: 3465

3.Härdle, W., Werwatz, A., Müller, M., & Sperlich, S. (2004). Nonparametric and semiparametric models. Springer Berlin Heidelberg.Cited By: 2006

4.Härdle, W., & Mammen, E. (1993). Comparing nonparametric versus parametric regression fits. The Annals of Statistics, 21(4), 1926–1947.Cited By: 1558

5.Härdle, W. (2012). Smoothing techniques: With implementation in S. Springer Science & Business Media.Cited By: 1529

Prof. Wolfgang Karl Härdle’s pioneering contributions in nonparametric statistics, digital finance, and machine learning have transformed data-driven decision-making across science, industry, and global financial systems. His methods for robust modeling, risk analytics, and smart data solutions empower researchers, policymakers, and institutions to navigate complex, high-dimensional data with greater accuracy, transparency, and resilience. He envisions a future where advanced statistical intelligence drives safer financial ecosystems and more equitable, evidence-based innovation worldwide.

Industrial and Manufacturing Applications

Published on by Computer Vision Awards

Introduction of Industrial and Manufacturing Applications

Industrial and Manufacturing Applications research in the realm of computer vision is pivotal for enhancing productivity, quality control, and efficiency across various manufacturing processes. Leveraging computer vision technologies, this field seeks to optimize manufacturing operations, reduce defects, and ensure consistent product quality in industries ranging from automotive and electronics to pharmaceuticals and food production.

Subtopics in Industrial and Manufacturing Applications:

  1. Quality Inspection and Defect Detection: Researchers develop computer vision systems to inspect and identify defects, deviations, or anomalies in manufacturing processes, ensuring products meet stringent quality standards.
  2. Robotic Vision and Automation: The integration of computer vision with industrial robots for tasks such as pick-and-place, assembly, and material handling, optimizing production workflows and reducing labor costs.
  3. Process Monitoring and Control: Implementing computer vision for real-time monitoring of manufacturing processes, allowing for immediate adjustments to maintain product consistency and reduce wastage.
  4. 3D Vision for Metrology: The application of 3D vision techniques for precision measurement and metrology in industries where accurate dimensional control is critical, such as aerospace and automotive manufacturing.
  5. Safety and Compliance: Research addressing worker safety by using computer vision for monitoring and ensuring compliance with safety regulations in industrial settings, particularly in hazardous environments.

Industrial and Manufacturing Applications research harnesses the power of computer vision to enhance the quality, efficiency, and safety of manufacturing operations, contributing to advancements in various industries. These subtopics represent key areas where researchers are driving innovation.

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