Yu’e Cha | Pharmaceutical Analysis | Research Excellence Award

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Assoc. Prof. Dr. Yu’e Cha | Pharmaceutical Analysis | Research Excellence Award

National Institute of Environmental Health | China

Assoc. Prof. Dr. Yu’e Cha is a distinguished environmental toxicologist whose academic training and professional achievements reflect a deep commitment to advancing public health through scientific innovation. She began her academic journey in chemistry, earning a Bachelor of Science from the Beijing Institute of Technology, where she cultivated strong analytical and experimental capabilities. Driven by a passion for understanding chemical interactions in environmental and biological systems, she continued her studies with a Master’s Degree in Analytical Chemistry at Capital Normal University. This foundation equipped her with advanced laboratory skills and an appreciation for rigorous scientific methodology, laying the groundwork for her future contributions to environmental health research.Dr. Cha’s professional career spans several prominent research institutions, where she progressively assumed more influential roles. Her early work as a Research Assistant in Applied Chemistry allowed her to gain essential experience in laboratory operations and applied research management. She later advanced to the Chinese Center for Disease Control and Prevention, where she served as a research intern and assistant researcher in the Rural Water Supply Improvement Technology Guidance Center. During this period, she worked on improving rural sanitation systems, enhancing water quality, and supporting national initiatives aimed at reducing environmental health risks among vulnerable populations.Her expertise in environmental toxicology deepened at the National Institute of Environmental Health, where she contributed to the Environmental Toxicology Laboratory. As an assistant researcher and later an associate researcher, she expanded her research scope to include atmospheric particulate exposure, respiratory health risks, toxic components of airborne pollutants, and population-level environmental susceptibility. Her role also extended to national public health preparedness, where she supported epidemic information analysis and early warning activities under the Emergency Office of the National Health Commission. This experience highlighted her ability to integrate scientific research with real-time public health response.Throughout her career, Dr. Cha has participated in and led multiple influential scientific projects supported by national science foundations, health commissions, and international organizations. Her contributions span studies on aging biomarkers, pollutant exposure pathways, respiratory inflammation, particulate matter toxicology, environmental determinants of infectious disease transmission, climate change adaptation strategies, and soil pollution exposure assessments. She has also led international cooperation initiatives aimed at improving environmental sanitation and population health outcomes.Dr. Cha’s scholarly impact is reflected in her academic metrics. Her research is cited 174 times across 154 documents, and she has authored 11 scientific publications. With an h-index of 7, she demonstrates consistent scientific influence and a strong record of meaningful, widely recognized contributions to environmental health science.

Profile: Scopus

Featured Publications

FirstAuthorLastName, A. A., SecondAuthorLastName, B. B., ThirdAuthorLastName, C. C., … (2025). Chemical exposure in females of childbearing age associated with sex hormones: Evidence from an untargeted exposomic approach. Environment International, Year, Article ID or page range.

Mehdi Khashei | Pharmaceutical Analysis | Editorial Board Member

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Prof. Mehdi Khashei | Pharmaceutical Analysis | Editorial Board Member

Isfahan University of Technology | Iran

Prof. Mehdi Khashei is a distinguished scholar in Industrial and Systems Engineering whose extensive academic and research contributions have positioned him as a leading expert in intelligent modeling, hybrid forecasting systems, and data-driven decision-making. He completed his academic journey with a strong mathematical and computational foundation, beginning with a Bachelor’s degree in Applied Mathematics and Computer Science from the University of Isfahan. Building on this analytical background, he pursued advanced studies at the Isfahan University of Technology, earning both his Master’s and Ph.D. in Industrial and Systems Engineering, where he focused on forecasting, analysis of industrial systems, and the integration of artificial intelligence into complex decision environments. His Post-Doctorate research at the same institution further advanced theoretical developments in Modeling Spaces Continuity, reinforcing his expertise in the intersection of systems theory, soft computing, and intelligent analytics.Throughout his academic career, Prof. Khashei has pioneered numerous innovative methodologies in the fields of time series forecasting, intelligent classification, hybrid modeling, and decision science. His research is particularly recognized for introducing discrete learning-based intelligent algorithms, reliability-based hybrid models, and advanced statistical-intelligent approaches for forecasting, classification, and optimization across diverse domains. His work spans energy systems, medicine, chemometrics, financial time series, predictive maintenance, load forecasting, and biomedical decision-making, demonstrating a unique capacity to bridge theory with real-world applications.Prof. Khashei has authored and co-authored an extensive portfolio of high-impact publications across leading international journals. His studies present cutting-edge solutions to challenges in artificial neural networks, fuzzy hybrid models, regression systems, clinical diagnostics, optimization strategies, and intelligent data preprocessing. His contributions have significantly shaped modern approaches to forecasting through hybridization frameworks such as series-parallel models, fuzzy-intelligent seasonal systems, and reliability-based methodologies. With groundbreaking insights into energy classification, breast cancer detection, heart disease diagnosis, and optimal financial turning point detection, his work continues to influence both academic research and applied industry practices.Prof. Mehdi Khashei is widely respected for his scientific rigor, interdisciplinary expertise, and commitment to advancing intelligent systems that enhance decision-making in complex, uncertain environments. His legacy is reflected not only in his prolific scholarly contributions but also in the transformative impact of his methodologies across engineering, computational intelligence, and data-driven industries.

Profile: Google Scholar

Featured Publications

Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for time series forecasting. Expert Systems with Applications, 37(1), 479‑489.

Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664‑2675.

Khashei, M., Bijari, M., & Ardali, G. A. R. (2009). Improvement of auto‑regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing, 72(4‑6), 956‑967.

Khashei, M., Hejazi, S. R., & Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets and Systems, 159(7), 769‑786.

Hajirahimi, Z., & Khashei, M. (2019). Hybrid structures in time series modeling and forecasting: A review. Engineering Applications of Artificial Intelligence, 86, 83‑106.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4), 4344‑4357.

Khashei, M., Hamadani, A. Z., & Bijari, M. (2012). A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Systems with Applications, 39(3), 2606‑2620.

Khashei, M., Bijari, M., & Ardali, G. A. R. (2012). Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Computers & Industrial Engineering, 63(1), 37‑45.

Khashei, M., & Hajirahimi, Z. (2019). A comparative study of series ARIMA/MLP hybrid models for stock price forecasting. Communications in Statistics – Simulation and Computation, 48(9), 2625‑2640.