Aytac Aydın | Data Science | Best Researcher Award

Published on by Engineering Scientist

Assoc. Prof. Dr. Aytac Aydın | Data Science
| Best Researcher Award

Karadeniz Technical University | Turkey

Assoc. Prof. Dr. Aytac Aydın Research contributions focus on advancing quality management and performance appraisal within the forest products and wood-based materials industry through the systematic application of statistical analysis and artificial intelligence techniques. The work integrates process optimization, quality control modeling, and ergonomics to enhance productivity, material efficiency, and workplace safety across manufacturing environments. Multiple completed and ongoing research projects have addressed critical challenges in the evaluation of production performance, defect reduction, operational efficiency, and sustainable resource utilization. Innovative methodologies, including multi-criteria decision-making models, predictive analytics, and machine learning algorithms, have been applied to improve decision accuracy in material selection, process planning, and quality inspection. Published studies in SCI-indexed journals contribute valuable data-driven insights to both academia and industry, strengthening standards in forest industrial engineering and promoting evidence-based operational strategies. Research outcomes support environmentally responsible production while increasing economic efficiency in wood-based manufacturing systems. In addition, contributions extend to guiding postgraduate and doctoral-level research focused on quality systems, process improvement, and industrial performance metrics. These efforts collectively strengthen the scientific foundation of forest industry management, drive technological advancement, and support sustainable and intelligent manufacturing practices with a long-term impact on both regional and international forestry-based production sectors.

 Profile: Orcid | Scopus 

Featured Publications

Aydın, A., Temel, B. A., Semercioğlu, İ. N., Başağa, H. B., Toğan, V., & Ağcakoca, E. (2025). Evaluating performance appraisal effects on employee motivation and productivity: Insights from the Turkish construction industry via covariance-based structural equation modeling. Buildings. https://doi.org/10.3390/buildings15224040

Tiryaki, S., Aydın, A., & Ondaral, S. (2025). Process monitoring with individual measurements: A case study in corrugated cardboard industry. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi. https://doi.org/10.17474/artvinofd.1722824

Tiryaki, S., & Aydın, A. (2024). Orta yoğunlukta liflevha üretiminde çekme direncinin iki aşamalı izlenmesi. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi. https://doi.org/10.58816/duzceod.1563540

Aydın, A., & Nemli, G. (2023). Yonga levha endüstrisinde zımparalama sorunlarının ve çözüm önerilerinin belirlenmesine yönelik bir çalışma. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi. https://doi.org/10.58816/duzceod.1394936

Aizhen Ren | Machine learning | Excellence in Innovation Award

Published on by Engineering Scientist

Prof. Aizhen Ren | Machine learning
| Excellence in Innovation Award

College of Science, Inner Mongolia Agricultural University | China

Prof. Aizhen Ren research work centers on machine learning, deep learning, statistical inference, and their applications in economics, finance, and computational biology. A significant portion of the research contributes to the development and mathematical validation of advanced bootstrap techniques, including the speedy double bootstrap method, which enhances the statistical reliability of phylogenetic tree estimation and provides third-order accurate unbiased p-values. These methods have been applied to evolutionary analyses of horse breeds, supporting biological and genomic investigations with high-precision statistical tools. In the financial domain, the research explores machine-learning-based trend prediction models, such as multiscale bootstrap-corrected random forest voting systems used to forecast stock index movement with improved accuracy and inference reliability. Additional work includes the construction of financial risk early-warning models for listed companies using multiple machine learning approaches, reflecting an interdisciplinary blend of statistics, computing, and economics. Contributions also extend to consumption behavior analysis employing regression-based models, as well as deep learning ensemble frameworks integrating empirical mode decomposition and temporal convolutional networks for time-series prediction tasks. The released R package SDBP operationalizes the novel bootstrap methodology, enabling researchers to compute unbiased p-values efficiently. Overall, the research advances methodological innovation and practical applications across data-intensive scientific domains.

 Profile: Orcid

Featured Publications

Ren, A., Duan, Y., & Liu, J. (2025). Multiscale bootstrap correction for random forest voting: A statistical inference approach to stock index trend prediction. Mathematics, 13(22), 3601. https://doi.org/10.3390/math13223601

Ren, A., Ishida, T., & Akiyama, Y. (2020). Mathematical proof of the third order accuracy of the speedy double bootstrap method. Communications in Statistics – Theory and Methods, 49(16), 3950–3964. https://doi.org/10.1080/03610926.2019.1594295

Ren, A., Ishida, T., & Akiyama, Y. (2013). Assessing statistical reliability of phylogenetic trees via a speedy double bootstrap method. Molecular Phylogenetics and Evolution, 67(2), 429–435. https://doi.org/10.1016/j.ympev.2013.02.011