Aamir Saghir | Statistical Process Control | Best Researcher Award

Dr. Aamir Saghir | Statistical Process Control | Best Researcher Award

Associate professor at Mirpur University of Science and Technology | Pakistan

Dr. Aamir Saghir, Associate Professor at Mirpur University of Science and Technology (MUST), Pakistan, is recognized for his outstanding work in statistical quality control, probability modeling, and industrial data analytics. His research primarily focuses on developing flexible, robust, and Bayesian control charts for process monitoring in both univariate and multivariate environments. With 49 publications, 567 citations, and an h-index of 15 (Scopus), Dr. Saghir’s contributions are well-established in the international scientific community. He has published extensively in high-impact journals, including Computers & Industrial Engineering, Quality and Reliability Engineering International, Communications in Statistics, and Applied and Computational Mathematics. His collaborative research extends globally, involving advanced modeling for reliability analysis, environmental statistics, and the integration of machine learning with statistical monitoring. His co-authored book, “Introduction to Statistical Process Control” (John Wiley & Sons, 2020), serves as a reference for modern control chart design and applications. Dr. Saghir’s scholarly output demonstrates a balance of theoretical innovation and practical relevance, influencing diverse fields such as manufacturing, environmental studies, and data-driven decision-making. Through supervision of postgraduate students and contributions to academic boards, he has fostered research excellence in Pakistan and abroad. His sustained research impact and leadership affirm his suitability for the Best Researcher Award.

Profiles : Scopus | ORCID | Google Scholar

Featured Publications

Saghir, A., Khadim, A., & Lin, Z. (2017). The Maxwell length-biased distribution: Properties and estimation.Journal of Statistical Theory and Practice, 11(1), 26–40. Citation: 24

Saghir, A., Ahmad, L., Aslam, M., & Jun, C. H. (2019). A EWMA control chart based on an auxiliary variable and repetitive sampling for monitoring process location. Communications in Statistics–Simulation and Computation, 48(7), 2034–2045. Citation: 23

Ahmad, I., Abbas, A., Saghir, A., & Fawad, M. (2016). Finding probability distributions for annual daily maximum rainfall in Pakistan using linear moments and variants. Polish Journal of Environmental Studies, 25(3), 925–937. Citation: 23

Saghir, A. (2015). Phase-I design scheme for Xˉ\bar{X}-chart based on posterior distribution. Communications in Statistics–Theory and Methods, 44(3), 644–655. Citation: 22

Hu, X. L., Zhang, S. Y., Zhang, J., & Saghir, A. (2023). Efficient CUSUM control charts for monitoring the multivariate coefficient of variation. Computers & Industrial Engineering, 179, 109159. Citation: 15

Qian Li | Minerals Engineering | Pioneer Researcher Award

Prof. Qian Li | Minerals Engineering | Pioneer Researcher Award

Professor at University of South China | China

Prof. Qian Li, a distinguished scholar in biohydrometallurgy at the University of South China, has made exceptional contributions to understanding microbial processes in mineral engineering, particularly uranium bioleaching and residue stabilization. His research integrates microbiological mechanisms with mineral system engineering to address challenges in uranium extraction and environmental remediation. He has directed numerous national and provincial research projects focused on the behavior of iron/sulfur-oxidizing bacterial consortia, in-situ passivation of uranium residues, and eco-friendly leaching technologies. Prof. Li’s innovative studies on biogenic coatings, microbial oxidation, and nanobubble-assisted leaching have introduced new approaches to sustainable metal recovery and waste control. His extensive publication record exceeds 80 research articles in reputed journals including Journal of Hazardous Materials, Frontiers in Microbiology, and Journal of Cleaner Production, showcasing his interdisciplinary expertise and technical leadership. As documented in his Scopus profile, he has accumulated over 4,651 citations, 289 indexed documents, and an h-index of 39, underscoring his scientific impact and recognition within the international minerals engineering community. Through his pioneering work on microbial-mineral interactions, Prof. Li continues to advance the field toward cleaner and more efficient resource utilization, establishing himself as a leading figure in metallurgical and environmental biotechnology.

Profile : Scopus | ORCID | Google Scholar

Featured Publications

Li, S., Xiao, L., Sun, J., Li, Q., Li, G., Cui, Z., Li, T., & Zhou, X. (2025). Biogenic jarosite coating as an innovative passivator for acidic uranium residue stabilization using Acidithiobacillus ferrooxidans. Journal of Hazardous Materials, 471, 140229. DOI: 10.1016/j.jhazmat.2025.140229

Xiao, L., Li, S., Liu, X., Sun, J., Li, G., Cui, Z., Li, T., & Li, Q. (2024). Linked variations of bioleaching performance, extracellular polymeric substances (EPS) and passivation layer in the uranium bacterial-leaching system. Journal of Radioanalytical and Nuclear Chemistry, 334, 637–651. DOI: 10.1007/s10967-024-09851-6

Li, Q., Liu, X., Ma, J., Sun, J., Li, G., Cui, Z., & Li, T. (2023). Bidirectional effects of sulfur-oxidizer Acidithiobacillus thiooxidans in uranium bioleaching systems with or without sulfur by mixed acidophilic bacteria. Journal of Radioanalytical and Nuclear Chemistry, 332, 1787–1794. DOI: 10.1007/s10967-023-08841-4

Sun, J., Ma, J., Li, Q., Li, G., Shi, W., Yang, Y., Hu, P., & Guo, Z. (2022). Role of Fe/S ratios in the enhancement of uranium bioleaching from a complex uranium ore by Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans consortium. Journal of Central South University, 29(12), 3858–3869. DOI: 10.1007/s11771-022-5216-1

Yang, Y., Li, Q., Li, G., Ma, J., Sun, J., Liu, X., Cui, Z., & Li, T. (2022). Depth-induced deviation of column bioleaching for uranium embedded in granite porphyry by defined mixed acidophilic bacteria. Journal of Radioanalytical and Nuclear Chemistry, 331, 3681–3692. DOI: 10.1007/s10967-022-08418-7

Chen, Z., Li, Q., Yang, Y., Sun, J., Li, G., Liu, X., Shu, S., Li, X., & Liao, H. (2022). Uranium removal from a radioactive contaminated soil by defined bioleaching bacteria. Journal of Radioanalytical and Nuclear Chemistry, 331, 439–449. DOI: 10.1007/s10967-021-08077-0